Split (1)

train · 521k rows

text stringlengths 254 1.16M
--- title: Plasmodium falciparum adapts its investment into replication versus transmission according to the host environment authors: - Abdirahman I Abdi - Fiona Achcar - Lauriane Sollelis - João Luiz Silva-Filho - Kioko Mwikali - Michelle Muthui - Shaban Mwangi - Hannah W Kimingi - Benedict Orindi - Cheryl Andisi Kivisi - Manon Alkema - Amrita Chandrasekar - Peter C Bull - Philip Bejon - Katarzyna Modrzynska - Teun Bousema - Matthias Marti journal: eLife year: 2023 pmcid: PMC10059685 doi: 10.7554/eLife.85140 license: CC BY 4.0 --- # Plasmodium falciparum adapts its investment into replication versus transmission according to the host environment ## Abstract The malaria parasite life cycle includes asexual replication in human blood, with a proportion of parasites differentiating to gametocytes required for transmission to mosquitoes. Commitment to differentiate into gametocytes, which is marked by activation of the parasite transcription factor ap2-g, is known to be influenced by host factors but a comprehensive model remains uncertain. Here, we analyze data from 828 children in Kilifi, Kenya with severe, uncomplicated, and asymptomatic malaria infection over 18 years of falling malaria transmission. We examine markers of host immunity and metabolism, and markers of parasite growth and transmission investment. We find that inflammatory responses associated with reduced plasma lysophosphatidylcholine levels are associated with markers of increased investment in parasite sexual reproduction (i.e. transmission investment) and reduced growth (i.e. asexual replication). This association becomes stronger with falling transmission and suggests that parasites can rapidly respond to the within-host environment, which in turn is subject to changing transmission. ## Introduction Malaria remains one of the world’s major public health problems. In 2021, an estimated 619,000 deaths and 247 million cases were reported (WHO, 2022). Around $70\%$ of deaths are in African children under five years of age and are caused by a single parasite species, Plasmodium falciparum (WHO, 2022). P. falciparum has a complex life cycle, involving obligatory transmission through a mosquito vector and asexual replication within erythrocytes of the human host. Between-host transmission requires the formation of gametocytes from asexual blood stage forms, as gametocytes are the only parasite stage to progress the cycle in the mosquito. A series of recent studies has demonstrated that commitment to gametocyte formation (i.e. stage conversion) is epigenetically regulated and occurs via activation of the transcription factor, AP2-G that in turn induces transcription of the first set of gametocyte genes (Kafsack et al., 2014; Sinha et al., 2014). The parasites that do not convert into gametocytes continue to replicate asexually, contributing to within-host parasite population growth (i.e. parasite burden) and determining P. falciparum infection outcome that ranges from asymptomatic infections to severe complications and death (Marsh and Snow, 1999; Langhorne et al., 2008; White, 2018). Cytoadhesion of infected erythrocytes (IE) to receptors on microvascular endothelium of deep tissues reduces the rate of parasite elimination in the spleen (Rowe et al., 2009; Turner et al., 2013), thus supporting the within-host expansion of the parasite population (i.e. parasite burden). As a side effect of this parasite survival strategy, cytoadhesion reduces the diameter of the vascular lumen, thus impairing perfusion and contributing to severe malaria pathology (Silamut et al., 1999; Taylor et al., 2004; Hanson et al., 2012). P. falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the var multi-gene family, plays a critical role in both pathogenesis (through cytoadhesion) (Smith et al., 1995; Su et al., 1995) and establishment of chronic infection (through variant switching and immune evasion) (Recker et al., 2004; Scherf et al., 2008). Both var gene transcription and stage conversion (and hence ap2-g transcription) are subject to within-host environmental pressures such as immunity (Rono et al., 2018), febrile temperature (Oakley et al., 2007; Rawat et al., 2021), nutritional stress (Carter and Miller, 1979), and drugs (Buckling et al., 1999), perhaps via a common epigenetic regulation mechanism (Coleman et al., 2014). For example, in vitro studies revealed that stage conversion can be induced by nutritional depletion such as spent culture media (Carter and Miller, 1979; Williams, 1999) and depletion of Lysophosphatidylcholine (LPC) (Brancucci et al., 2017). Recent work from Kenya and Sudan provides some evidence that parasites in low relative to high transmission settings invest more in sexual commitment and less in replication and vice versa (Rono et al., 2018). Altogether these studies suggest that the parasite can sense and rapidly adapt to its environment in vitro and in vivo. A family of protein deacetylases called sirtuins is known to link environmental cues to various cellular processes via metabolic regulation (Li, 2013; Bosch-Presegué and Vaquero, 2014; Vasquez et al., 2017). They do this through epigenetic control of gene expression (Vasquez et al., 2017) and post-translational modification of protein function (Vasquez et al., 2017; Zhu et al., 2012). The P. falciparum genome contains two sirtuins (Pfsir2a/b) which have been linked to the control of var gene transcription (Tonkin et al., 2009; Merrick et al., 2012), and their expression is influenced by febrile temperature (Oakley et al., 2007) and low transmission intensity (Rono et al., 2018; O’Meara et al., 2008; Mogeni et al., 2016). Here, we investigated the interplay between parasite and host environmental factors governing parasite investment in reproduction (to maximize between-host transmission) versus replication (to ensure within-host persistence) in vivo. We analyzed samples and clinical data collected from children in Kilifi county, Kenya, over changing malaria transmission intensity between 1994 and 2014. We quantified parasite transcripts for ap2-g, PfSir2a, and var genes, as well as PfHRP2 protein levels (for parasite biomass) and levels of host inflammatory markers and lipid metabolites. We then integrated these host and parasite-derived parameters to interrogate their dynamics and interactions in the context of changing transmission intensity and immunity. ## A clinical malaria patient cohort across changing transmission periods in Kilifi, Kenya The study included samples and clinical data collected from 828 children from Kilifi county, Kenya, over 18 years of changing malaria transmission (O’Meara et al., 2008; Mogeni et al., 2016). The study period encompassed three defined transmission phases: pre-decline (1990–2002), decline (2003–2008), and post-decline (2009–2014) (Figure 1A). During the study period, a total of 26,564 malaria admissions were recorded at Kilifi county hospital (Figure 1A and B). While the number of parasite-positive admissions decreased, the mean patient age at admission increased over time (Marsh and Snow, 1999; White, 2018; Njuguna et al., 2019; Figure 1A). For our study, 552 of the admissions were pragmatically selected to ensure adequate sampling of the transmission periods and clinical phenotypes (Figure 1C). 150 patients presented with moderate malaria and 402 with one or a combination of the severe malaria syndromes: impaired consciousness (IC), respiratory distress (RD), and severe malaria anemia (SMA) (Marsh et al., 1995; Figure 1D). 223 samples from children presenting with mild malaria at outpatient clinics and 53 asymptomatic children from a longitudinal malaria cohort study were added to cover the full range of the possible outcomes of malaria infection (Figure 1B–E), bringing the total number included in this study to 828 children. The characteristics of participants and clinical parameters are summarized in Supplementary file 1. Figure 1.: A clinical malaria patient cohort during changing the transmission in Kilifi, Kenya.(A) Total malaria admissions and patient age of the parent cohort. Number of patients per year (gray histogram, left axis). The solid blue line is the average patient age in the parent cohort, the dashed line is the average patient age in this study (both right axis). (B) Schematic of sample selection for this study. (C) Clinical presentation of patients selected for this study. Left: all patients, middle: admissions only, right: subset selected for luminex and lipidomics analysis. sma = severe malarial anemia, ic = impaired consiousness, rd = respiratory distress. (D) Number of patients in this study with different clinical presentations (402 severe cases initially selected). (E) Overview of the data available for each patient of the study, after excluding samples with Pfsir2a and ap2-g transcript transcription units greater or equal to 32 as described in the methods. Each row is one patient, organized by patient category (left axis) and transmission period (right axis). ## Dynamics of parasite parameters across transmission period and clinical phenotype First, we analyzed the dynamics of parasite parameters across transmission periods and clinical outcomes. For this purpose, we measured both total parasite biomass based on PfHRP2 levels (Dondorp et al., 2005) and peripheral parasitemia based on parasite counts from blood smears. Total parasite biomass (PfHRP2) but not peripheral parasitemia decreased with declining transmission (Figure 2A). This decrease was significant in the patients presenting with mild malaria at outpatient clinics which is a more homogenous clinical subgroup as compared to admissions consisting of a range of clinical phenotypes (Figure 1C–D). Figure 2.: Dynamics of parasite parameters across transmission periods.(A) Peripheral parasitemia (smear, left), total parasite biomass (PfHRP2, middle), and patient age (right) across patients. Number of patients: Asymptomatics: decline: n=29, postdecline: n=21; Outpatients: predecline: n=35, decline: n=94, postdecline: n=70; Admissions: predecline: n=185, decline: n=175, postdecline: n=164. (B) ap2-g transcript levels (left) and Pfsir2a levels (right) across patients. (C) Spearman’s correlation between Pfs16 and ap2-g (blue), or PfSir2a transcription (red) across patients (corrected for transmission). The lines fitted are linear regressions for visualization only. (D) Spearman’s correlation between Pfsir2a and ap2-g transcription (blue), or PfHRP2 levels (red) across patients (corrected for transmission). The lines fitted are linear regressions for visualization only. (E) ap2-g and Pfsir2a transcription (corrected for transmission) stratified by patient temperature. "<37.5": n=122, ">=37.5 & <39": n=228, ">=39": n=196. (F) Linear regression showing the association of var gene transcription levels with Pfsir2a levels (adjusted for transmission). 95% confidence intervals are shown. n=723 for all but c2 n=577. The color indicates whether the relationship is statistically significant (with Benjamini & Hochberg multiple tests correction). Positive correlations are in red, and negative in blue. gpA1, gpA2, and dc13 represent group A var gene transcripts. dc8-1 to dc8-4 represent DC8 var gene transcripts while gpb1 and gpc2 represent group B and C var genes. In the above graphs C-F (and all subsequent figures), asymptomatics were excluded in analyses involving the transmission period since they are not represented in the pre-decline period. All pairwise statistical tests indicated in the graphs are Wilcoxon tests corrected for multiple testing (Benjamini & Hochberg, =FDR < 0.05, =<0.01, =<0.001, and **=<0.0001). Parasite samples were subjected to qRT-PCR analysis to quantify transcription of ap2-g, Pfsir2a, and var gene subgroups relative to two housekeeping genes (fructose biphosphate aldolase and seryl tRNA synthetase) (Salanti et al., 2003; Rottmann et al., 2006; Lavstsen et al., 2012; Abdi et al., 2016). In line with recent findings (Rono et al., 2018), ap2-g transcription increased significantly with declining malaria transmission (Figure 2B, left). In the subsequent analysis, only clinical cases were considered. Asymptomatic patients were excluded except for analysis of clinical phenotype since asymptomatic sampling was limited to the decline and post-decline periods (Figure 1C), and, therefore, data could not be corrected for transmission. Further analysis demonstrated that ap2-g transcription is highly significantly correlated with transcription levels of the gametocyte marker Pfs16 (Figure 2C). This association validates ap2-g as a proxy for both, stage conversion and gametocyte levels. Pfsir2a transcription followed the same trend across transmission periods (Figure 2B, right) and was positively associated with ap2-g transcription (Figure 2D). Pfsir2a and ap2-g transcription also showed a positive association with fever (Figure 2E), suggesting that both factors are sensitive to changes in the host inflammatory response. Pfsir2a but not ap2-g transcription also showed a significant negative association with PfHRP2 (Figure 2D). Given this unexpected observation, we investigated the well-established associations between Pfsir2a transcription and var gene transcription patterns (Merrick et al., 2012; Abdi et al., 2016). Pfsir2a transcription showed a positive association with global upregulation of var gene transcription, particularly with subgroup B (Figure 2F). Likewise, transcription of group B var subgroup, Pfsir2a, and ap2-g transcription followed a similar pattern in relation to clinical phenotypes (Figure 2—figure supplement 1). To ensure that there is no systematic bias in the qRT-PCR data, we also tested the association of Pfs16 expression against var gene subgroups. Pfs16 expression showed a significant positive correlation with group B (here termed gpb1) and C (gpc2) var genes, while no correlation was observed with the rest of the var subgroups (Figure 3—figure supplement 1A). Altogether these data suggest co-regulation of ap2-g and Pfsir2a and a negative association between Pfsir2a and PfHRP2, likely through host factors that are changing with the declining transmission. ## ap2-g and Pfsir2a transcription is associated with a distinct host inflammation profile We hypothesized that the observed variation in ap2-g and Pfsir2a levels across the transmission period and clinical phenotype is due to underlying differences in the host inflammatory response. To test this hypothesis, we quantified 34 inflammatory markers (Huang et al., 2017) with Luminex xMAP technology in the plasma of the 523 patients from the outpatient and admissions groups (Figure 1B). These patients were selected from the original set of 828 to ensure adequate representation of the transmission periods and clinical phenotypes (including fever), as summarized in Figure 1C and E. For this analysis, all associations were corrected for patient age and PfHRP2 levels as possible confounders. The markers MCP-1, IL-10, IL-6, and IL-1ra were significantly positively correlated with ap2-g and Pfsir2a transcription (Figure 3A and Figure 3—figure supplement 1B). To cluster the inflammatory markers based on their correlation within the dataset, we used exploratory factor analysis and retained five factors with eigenvalues above 1 (Figure 3—figure supplement 2). Factor loadings structured the inflammation markers into five profiles with distinct inflammatory states (Figure 3B). F1 consists of a mixture of inflammatory markers that support effector Th1/Th2/Th9/Th17 responses (i.e. hyperinflammatory state), F2 represents a Th2 response, F3 represents markers that support follicular helper T cell development and Th17 (Dong, 2021; Chao et al., 2023), F4 represents markers of immune paralysis/tissue-injury linked to response to cellular/tissue injury (Kumar et al., 2014), and F5 represents the inflammasome/Th1 response (Weiss et al., 2018). F4 showed a significant positive association with ap2-g and Pfsir2a transcription and fever (Figure 3C). In contrast, F5 showed a negative association with ap2-g and fever while F1 was positively associated with fever (Figure 3C). In parallel with the observed decrease in PfHRP2 levels (Figure 2A), F1 and F5 significantly declined with falling transmission (Figure 3D). Figure 3.: ap2-g and Pfsir2a transcription levels are associated with the host inflammation profile.(A) Association of inflammatory markers with ap2-g and Pfsir2a transcripts, tested using transmission period, age, and PfHRP2 adjusted linear regression (p-values adjusted for multiple testing using Benjamini & Hochberg multiple tests correction). Plotted is the regression coefficient (estimate) and 95% CI. Above and below zero indicate statistically significant positive (red) and negative associations (blue), respectively. n=523 (B) Principal exploratory factor analysis. The figure shows the inflammatory marker loadings on the five factors (F1–F5) identified to have eigenvalue above 1. (C) Linear regression between inflammatory factors. (F1–F5) and ap2-g and Pfsir2a transcription and patient temperature (adjusted for transmission, PfHRP2, and age). Plotted is the coefficient between the factor and the parameter (estimate) and 95% CI. Number of Patients: ap2-g and Pfsir2a: n=523, Temperature: n=496. The association is significant if the correlation FDR <0.05, in which case the positive associations are marked in red and the negative ones in blue. (D) Inflammatory factors stratified by transmission period. Pairwise tests are Wilcoxon tests (Benjamini & Hochberg, =FDR < 0.05, =<0.01, =<0.001, and **=<0.0001). Number of patients: predecline: n=131, decline: n=180, postdecline: n=212. The data support our hypothesis and suggest that the host inflammatory response changes with the falling transmission. Of note, the observed negative association between Pfsir2a transcription and PfHRP2 levels appears to be independent of the measured cytokine levels (Figure 3—figure supplement 3) and is hence likely the result of parasite intrinsic regulation of replication. ## Plasma phospholipids link variation in the host inflammatory profile to ap2-g and Pfsir2a transcription We have previously demonstrated in vitro that the serum phospholipid LPC serves as a substrate for parasite membrane biosynthesis during asexual replication, and as an environmental factor sensed by the parasite that triggers stage conversion (Brancucci et al., 2017). Plasma LPC is mainly derived from the turnover of phosphatidylcholine (PC) via phospholipase A2, while in the presence of Acyl-CoA the enzyme LPC acyltransferase (LPCAT) can drive the reaction in the other direction (Law et al., 2019; Amunugama et al., 2021). LPC is an inflammatory mediator that boosts type 1 immune response to eliminate pathogens (Huang et al., 1999; Qin et al., 2014). LPC turnover to PC can be triggered by inflammatory responses aimed to repair and restore tissue homeostasis rather than eliminate infection (Law et al., 2019). Here, we performed an unbiased lipidomics analysis of plasma from a representative subset of the outpatient and admission patients (Figure 1 and Figure 4—figure supplement 1) to explore whether the host inflammatory profile modifies the plasma lipid profile and consequently ap2-g and Pfsir2a transcription levels in vivo. We examined associations between the host inflammatory factors (F1-F5) and the plasma lipidome data. Again, these associations were corrected for transmission period, patient age, and PfHRP2 levels. 24 lipid species dominated by phospholipids, showed significant association with the inflammatory factors at a false discovery rate below 0.05 (Figure 4A). Like the observed associations with ap2-g and Pfsir2a transcription, cytokines in the F4 and F5 factors showed reciprocal associations with various LPC species and PC (Figure 4A): F5 showed a significant positive association with one LPC species and negative associations with PC, respectively, while F4 showed a significant negative association with three LPC species (Figure 4A, Supplementary file 2). The positive association of LPC with the F5 inflammatory factor is consistent with previous findings that identified LPC as an immunomodulator that can enhance IFN-γ production and the activation of the inflammasome (Huang et al., 1999; Qin et al., 2014), which results in increased levels of cytokines such as IL-18 (Weiss et al., 2018) and is necessary for eliminating parasites. Depletion of LPC is also associated with elevated markers of tissue injury (F4), perhaps following uncontrolled parasite growth or maladaptive inflammation. In summary, the association of inflammatory factors with lipids identified LPC, PC, and PE species as the most significant ones (Figure 4A), in line with their known immunomodulatory role. Importantly, we observed the same pattern in a controlled human infection model where parasite densities were allowed to rise to microscopic levels, both after sporozoite, and blood-stage infection (Figure 4B and Figure 4—figure supplement 2; Alkema et al., 2021; Alkema et al., 2022). Next, we examined the main lipid species associated with the five inflammatory factors with respect to ap2-g and Pfsir2a transcription. Indeed, LPC species showed a negative association with both ap2-g and Pfsir2a transcription levels (Figure 4C–F). The association was only significant in our data when inflammation is highest (and LPC level lowest), which is at low transmission (i.e. post decline). In contrast, LPC levels are highest and vary little across patients at high transmission (pre-decline). Figure 4.: Plasma Lysophosphatidylcholine (LPC) links host inflammation to ap2-g and Pfsir2a transcription.(A) Heatmap of the linear regression coefficients between lipids and inflammatory factors (F1-F5, adjusted for transmission period and corrected for multiple testing). Shown are all lipids that are significantly associated (positive or negative) with factors F1-F5, clustered using R hclust (distance = Euclidean, method = centroid) and that have been manually identified and filtered for peak quality (isotopes and fragments weres also filtered out). (B) Shown are the lipids with significant differences (student’s t-test corrected for multiple testing) between pre- and post-treatment in the controlled human malaria infections (CHMI) for either infection route (blood or sporozoite infection). Plotted is the fold-change post-treatment vs pre-treatment. On the left is indicated whether the lipid is significantly increased (red) or decreased (blue) in either route of infection. (C) Linear regression between the lipids from A and ap2-g or Pfsir2a transcription levels. Plotted is the coefficient and 95% CI. Blue indicates statistically significant negative correlations, while red indicates statically significant positive correlations (FDR <0.05). (D) Distribution of ap2-g transcript levels (left) and LysoPC (16:0) intensity (right) across patients and stratified by transmission period. Data are corrected for age and PfHRP2. (E) Correlation between LPC (16:0) (top) and ap2-g (top), or Pfsir2a (bottom) transcription (Spearman’s correlations corrected for multiple testing). (F) Correlations between identified LPCs and ap2-g transcription by transmission period (Spearman’s correlations corrected for multiple testing). Note that the predecline period is not plotted separately in panels C-E due to insufficient sample numbers for the statistical analysis. Number of patients: predecline: n=22, decline: n=53, postdecline: n=160. Altogether, these data provide in vivo evidence for the previously observed link between LPC depletion and ap2-g activation and strongly suggest that LPC is both, a key immune modulator and a metabolite whose level is sensed by the parasite. Importantly, the key relationships described in Figures 2—4 were independently significant in a structural equation model that examined how host immunity modifies the host-parasite interaction, the within-host environment, and parasite investment in transmission or replication (Supplementary file 3). ## Discussion Malaria parasites must adapt to changing environmental conditions across the life cycle in the mammalian and mosquito hosts. Similarly, changing conditions across seasons and transmission settings require both within- and between-host adaptation to optimize survival in the human host versus transmission to the next host. First, a recent transcriptomic study from Kenya and Sudan suggested that parasites in low transmission settings (where within-host competition is low) invest more in gametocyte production compared to high transmission settings (where within-host competition is high) (Rono et al., 2018). Second, a longitudinal study from Senegal demonstrated that human-to-mosquito transmission efficiency (and gametocyte density) increases when parasite prevalence in the human population decreases, suggesting that parasites can adapt to changes in the environment (Churcher et al., 2015). However, the within-host mechanisms driving parasite adaptation to the prevailing environment remain unclear. Here, we analyzed parasite and host signatures in the plasma from a large malaria patient cohort over 18 years of declining malaria transmission in Kenya. This investigation allowed us to define some of the within-host environmental factors that change with transmission intensity and consequently influence the parasite’s decision to invest in reproduction versus replication. A major strength of our study is that observations are from a single site and are thus plausibly reflective of transmission-related changes in parasite investments, rather than differences between geographically distinct parasite populations. We show that high transmission is associated with a host immune response that promotes parasite killing without compromising the intrinsic replicative ability of the individual parasite. In contrast, low transmission is associated with a host immune response that increases within-host stressors (fever, nutrient depletion), which trigger higher parasite investment into transmission (see also the model in Figure 5). Importantly, the observed associations between the parasite parameters ap2-g, Pfsir2a, and host inflammation remain significant if corrected for transmission, but they are strongest at low transmission (i.e. post decline period) when inflammation and the risk of damaging the host are highest. We anticipate that the proposed model could be tested in controlled human malaria infections with malaria naïve and semi-immune participants as a proxy for low vs high transmission settings. Figure 5.: Proposed model on a within-host adaptation of the parasite to changing environments.The model is based on the interaction between the different host and parasite parameters described in this study. It proposes that declining transmission reduces host immunity, resulting in an inflammatory response associated with increased host stressors (including reduced Lysophosphatidylcholine (LPC) availability, and fever) and susceptibility to clinical symptoms/damage. The altered host response modifies the parasite response during infection, resulting in increased investment in transmission (as indicated by the elevated ap2-g levels) and reduced replication (as indicated by elevated Pfsir2a levels and reduced parasite burden/Plasmodium falciparum histidine-rich protein 2 (PfHRP2) levels). At a systemic level, inflammation can influence the within-host environment and modulate parasite investment in replication versus reproduction by altering the levels of environmental stressors (e.g. oxidative, thermal, or nutritional stress). Consistent with this hypothesis, we show that a pro-inflammatory response mediated by IFN-γ/IL-18 (F5 in our analysis) promoting pathogen killing (Weiss et al., 2018; Hoffman et al., 1997; Kearney et al., 2013) is negatively associated with ap2-g and Pfsir2a transcription. In contrast, inflammatory markers that increase within-host environmental stress (e.g. fever) or reflect the extent of host tissue injury and are secreted to heal and restore homeostasis rather than kill pathogens (F4) (Shapouri-Moghaddam et al., 2018; Tan et al., 2021) are positively associated with ap2-g and Pfsir2a transcription. At a metabolic level, we previously demonstrated that LPC depletion induces ap2-g transcription and, therefore, gametocyte production in vitro (Brancucci et al., 2017). A recent study has provided the first indications of a possible association between LPC and ap2-g levels in a small malaria patient cohort (Usui et al., 2019). Here, we reveal that LPC levels are negatively associated with ap2-g transcription in patient plasma, thus providing direct evidence for our in vitro findings (Brancucci et al., 2017) across a large malaria patient cohort. LPC is an immune effector molecule promoting macrophage polarization to M1 phenotype that induces the secretion of various cytokines such as IFN-γ and IL-1 family (i.e. IL-18) (Huang et al., 1999; Qin et al., 2014) through activation of the inflammasome in endothelial cells and peripheral blood mononuclear cells (PBMCs). Furthermore, LPC is the main component of the oxidized form of LDL (oxLDL) that induces inflammasome-mediated trained immunity in human monocytes (Law et al., 2019; Amunugama et al., 2021), resulting in increased responsiveness to LPS re-stimulation. Indeed, we demonstrate that LPC levels are positively associated with IFN-γ/IL-18 levels (Factor 5). These observations are in line with recent data from experimentally infected macaques and malaria patients, where LPC levels decreased in the acute phase compared to the pre-challenge and the chronic phase (Cordy et al., 2019). LPC is also a nutritional resource required by the parasite for replication (Brancucci et al., 2017) and hence scarcity is expected to promote reproduction, as gametocytes require less nutritional resource and, therefore, provide a better adaptation strategy. Surprisingly, we also identified a link between Pfsir2a transcription, host inflammatory response, and parasite biomass (PfHRP2). PfSir2a belongs to the evolutionarily conserved family of sirtuins that act as environmental sensors to regulate various cellular processes (Li, 2013; Vasquez et al., 2017; Palacios et al., 2009). In P. falciparum, PfSir2a and PfSir2b paralogues cooperate to regulate virulence gene transcription including var genes (Tonkin et al., 2009; Duraisingh et al., 2005). In vitro data have also demonstrated that increased PfSir2a levels are associated with reduced parasite replication (i.e. lower merozoite numbers) (Mancio-Silva et al., 2013). We hypothesize that the observed upregulation of Pfsir2a transcription in response to inflammation is part of an orchestrated stress response linking replication and antigenic variation (via Pfsir2a) to reproduction and transmission (via ap2-g), perhaps through a shared epigenetic control mechanism (Coleman et al., 2014). It is well known that host tolerance to malaria infection reduces with falling transmission (Borrmann et al., 2011; Dollat et al., 2019), as shown by the declining threshold of parasite biomass (PfHRP2) required for clinical malaria. This suggests that parasites have more pronounced harmful consequences on the infected host (i.e. clinical symptoms) in low compared to high transmission settings, perhaps due to increasing host age (Sorci et al., 2021). Under this scenario, we propose that parasites experience increased within-host stress to which they respond through increased ap2-g transcription (to increase reproduction, hence transmission) and increased Pfsir2a transcription (to affect stress coping mechanisms at the expense of replication, hence the negative association with PfHRP2) – as part of a self-preservation strategy in the face of imminent risk of host death. In summary, we propose a model where the falling host immunity with declining transmission modifies the predominant host immune response, and consequently, the within-host environment (e.g. LPC availability, fever), resulting in increased investment in transmission (i.e. higher ap2-g transcription) and limiting replication (i.e. higher Pfsir2a transcription). Our findings provide critical information to accurately model parasite population dynamics. They suggest that parasite populations in elimination scenarios may increase their transmission potential. Understanding how malaria parasites adapt to their environment, for example by increasing investment in transmission stages at low endemicity, is highly relevant for public health. Not only would this affect the timelines for successful elimination, but it would also form an important argument for the deployment of gametocytocidal drugs once transmission has been successfully reduced. ## Study design and participants Ethical approval was granted by the Scientific Ethics Review Unit of the Kenya Medical Research Institute under the protocol; KEMRI/SERU/3149, and informed consent was obtained from the parents/guardian of the children. The study was conducted in Kilifi county which is a malaria-endemic region along the Kenyan coast. Over the last three decades, Kilifi has experienced changes in the pattern of malaria transmission and clinical presentation spectrum (O’Meara et al., 2008; Mogeni et al., 2016; Njuguna et al., 2019). The study included (i) children admitted with malaria at Kilifi county hospital (KCH) between 1994–2012 and recruited as part of the hospital admission surveillance system, (ii) children presenting with mild malaria at an outpatient clinic, and (iii) asymptomatic children which were part of a longitudinal malaria surveillance cohort which were sampled during annual cross-section bleed in 2007 and 2010. Clinical data, parasite isolates, and plasma samples collected from the children were used to conduct the study. The selection of sub-samples for quantifying inflammatory markers and lipids was informed by the availability of fever data and resources. ## Clinical definitions Admission to malaria was defined as all hospitalized children with malaria parasitemia. The severe malaria syndromes: severe malarial anemia (SMA), impaired consciousness (IC), and respiratory distress (RD) were defined as hemoglobin <5 g/dl, Blantyre coma score (BCS) <5, and deep breathing, respectively (Marsh et al., 1995). Malaria admissions that did not present with either of the severe malaria syndromes were defined as moderate malaria. Mild malaria was defined as stable children presenting at the outpatient clinic with peripheral parasitemia, and asymptomatic as those with positive malaria (Giemsa smear) but without fever or any other sign(s) of illness. The combination of mild and moderate was referred to as uncomplicated. ## Controlled infection cohort Malaria naïve volunteers were infected by either bite from five P. falciparum 3D7–infected mosquitoes ($$n = 12$$) or by intravenous injection with approximately 2800 P. falciparum 3D7–infected erythrocytes ($$n = 12$$); treatment with piperaquine was provided at a parasite density of 5000 /mL or on day eight following blood-stage exposure, respectively (Alkema et al., 2022). ## Parasite parameters Thick and thin blood films were stained with Giemsa and examined for P. falciparum parasites according to standard methods. Data were presented as the number of infected RBCs per 500, 200, or 100 RBCs counted. This data was then used to calculate parasitemia per µl of blood using the formula described in ‘2096-OMS-GMP-SOP-09–20160222_v2.indd (who.int).’ Briefly, parasites/µl=number of parasitized RBCs × number of RBCs per µl /number of RBCs counted or the number of parasites counted × number of WBCs per µl/number of WBCs counted. Where data on the actual number of RBCs or number of WBCs per µl of blood was not available, 5 million RBCs and 8000 WBCs per µl of blood were assumed. ## PfHRP2 ELISA P. falciparum histidine-rich protein 2 (PfHRP2) was quantified in the malaria acute plasma samples using ELISA as outlined. Nunc MaxiSorp flat-bottom 96-well plates (ThermoFisher Scientific) were coated with 100 µl/well of the primary/capture antibody [Mouse anti-PfHRP2 monoclonal antibody (MPFM-55A; MyBioscience)] in 1 × phosphate buffered saline (PBS) at a titrated final concentration of 0.9 µg/ml (stock = 8.53 mg/ml; dilution = 1:10,000) and incubated overnight at 4 °C. On the following day, the plates were washed thrice with 1 × PBS/$0.05\%$ Tween-20 (Sigma-Aldrich) using a BioTek ELx405 Select washer (BioTek Instruments, USA) and blotted on absorbent paper to remove residual buffer. These plates were then blocked with 200 µl/well of 1 × PBS/$3\%$ Marvel skimmed milk (Premier Foods; Thame, Oxford) and incubated for 2 hr at room temperature (RT) on a shaker at 500 rpm. The plates were then washed thrice as before. After the final wash, plasma samples and standards were then added at 100 ul/well and in duplicates. The samples and standards (PfHRP2 Recombinant protein; MBS232321, MyBioscience) had been appropriately diluted in 1 × PBS/$2\%$ bovine serum albumin (BSA). The samples and standards were incubated for 2 hr at RT on a shaker at 500 rpm followed by three washes with 1 × PBS/$0.05\%$ Tween-20 and blotted dry as before. This was followed by the addition of a 100 µl/well of the secondary/detection antibody [Mouse anti-PfHRP2 HRP-conjugated antibody (MPFG-55P; MyBioscience) diluted in 1 × PBS/$2\%$ BSA and at a final titrated concentration of 0.2 µg/ml (stock = 1 mg/ml; dilution = 1:5000)]. The plates were then incubated for 1 hr at RT on a shaker at 500 rpm, washed thrice as before, and dried on absorbent paper towels. o-Phenylenediamine dihydrochloride (OPD) (ThermoFisher Scientific) substrate was then added at 100 µl/well and incubated for 15 min for color development. The reaction was stopped with 50 µl/well of 2 M sulphuric acid (H2SO4) and optical densities (OD) read at 490 nm with a BioTek Synergy4 reader (BioTek Instruments, USA). ## Parasite transcript quantification using quantitative RT-PCR RNA was obtained from TRIzol reagent (Invitrogen, catalog number 15596026) preserved P. falciparum positive venous blood samples obtained from the children recruited in the study. RNA was extracted by Chloroform method (Bull et al., 2005) and cDNA was synthesized using a Superscript III kit (Invitrogen, catalog number 18091050) following the manufacturer’s protocol. Parasite gene transcription analysis was carried out through quantitative real-time PCR as described below. Real-time PCR data was obtained as described (Dondorp et al., 2005; Borrmann et al., 2011; Dollat et al., 2019). Four primer pairs targeting DC8 (named dc8-1, dc8-2, dc8-3, dc8-4), one primer pair targeting DC13 (dc13), and two primer pairs targeting the majority of group A var genes (gpA1 and gpA2) were used in real-time PCR analysis as described (Abdi et al., 2016). We also used two primer pairs, b1, and c2, targeting group B and C var genes, respectively (Rottmann et al., 2006). Primer pairs targeting Pfsir2a, and ap2-g were also used (Abdi et al., 2016). Two housekeeping genes, Seryl tRNA synthetase and Fructose bisphosphate aldolase (Salanti et al., 2003; Lavstsen et al., 2012; Abdi et al., 2016) were used for relative quantification of the expressed var genes, Pfsir2a, and ap2-g. The PCR reaction and cycling conditions were carried out as described (Lavstsen et al., 2012; Abdi et al., 2016) using the Applied Biosystems 7500 Real-time PCR system. We set the cycle threshold (Ct) at 0.025. Controls with no template were included at the end of each batch of 22 samples per primer pair and the melt curves were analyzed for non-specific amplification. The var gene ‘transcript quantity’ was determined relative to the mean transcript of the two housekeeping genes, Sery tRNA synthetase and Fructose biphosphate aldolase as described (Lavstsen et al., 2012). For each test primer, the ∆Ct was calculated relative to the average Ct of the two housekeeping genes which were then transformed to arbitrary transcript unit (Tus) using the formula (Tus = 2(5-∆ct)) as described (Lavstsen et al., 2012). We assigned a zero Tus value if a reaction did not result in detectable amplification after 40 cycles of amplification, that is, if the Ct value was undetermined. ## Measurement of cytokine levels in the plasma samples The selection for this subset was primarily informed by the availability of fever data but the transmission period and clinical phenotype were also considered. However, there were more children with a fever data recorded in the post-decline period than in pre-decline and decline periods which biased the sampling toward the post-decline period. The plasma samples were analyzed using ProcartaPlex Human Cytokine & Chemokine Panel 1 A(34plex) [Invitrogen/ThermoFisher Scientific; catalog # EPX340-12167-901; Lot:188561049] following the manufacturer’s instructions. The following 34 cytokines were measured: GM-CSF, IFN-α, IFN-γ, IL-1α, IL-1β, IL-1RA, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p70), IL-13, IL-15, IL-17A, IL-18, IL-21, IL-22, IL-23, IL-27, IL-31, IP-10 (CXCL10), MCP-1 (CCL2), MIP-1α (CCL3), MIP-1β (CCL4), TNF-α, TNF-β, Eotaxin/CCL11, RANTES, GRO-a, and SDF-1a. Briefly, 50 µl of magnetic beads mix was added into each plate well and the 96-well plate was securely placed on a hand-held magnetic plate washer for 2 min for the beads to settle. The liquid was then removed by carefully inverting the plate over a waste container while still on the magnet and lightly blotted on absorbent paper towels. The beads were then washed by adding 150 µl of 1 × wash buffer, left to settle for 2 min, and the liquid removed as before followed by blotting. This was followed by adding 25 µl of Universal Assay Buffer per well and then 25 µl of plasma samples and standards into appropriate wells or 25 µl of Universal Assay Buffer in blank wells. The plate was covered and shaken on a plate shaker at 500 rpm for 30 min at room temperature followed by an overnight incubation at 4 °C. After the overnight incubation, the plate was shaken on a plate shaker at 500 rpm for 30 min at room temperature and the beads were then washed twice while on a magnetic plate holder as outlined above. The beads were then incubated in the dark with 25 µl of detection antibody mixture on a plate shaker at 500 rpm for 30 min at room temperature followed by two washes as before. A 50 µl of Streptavidin-Phycoerythrin (SAPE) solution was then added per well and similarly incubated for 30 min on a plate shaker at 500 rpm and at room temperature followed by two washes. After the final wash, the beads were resuspended in 120 µl of Reading Buffer per well, and incubated for 5 min on a plate shaker at 500 rpm before running on a MAGPIX reader running on MAGPIX xPOTENT 4.2 software (Luminex Corporation). The instrument was set to count 100 beads for each analyte. The analyte concentrations were calculated (via Milliplex Analyst v5.1 [VigeneTech]) from the median fluorescence intensity (MdFI) expressed in pg/mL using the standard curves of each cytokine. ## Lipidomics analysis Patient temperature, ap2-g transcription level, and disease type were used to subset samples for lipidomics. This resulted in five groups from severe disease categories and matching mild cases (Figure 4—figure supplement 1). Plasma samples were preserved at –80 °C until extraction with the chloroform/methanol method. 25 µL of plasma was extracted with 1 mL of the extraction solvent chloroform/methanol/water (1:3:1 ratio), the tubes were rocked for 10 min at 4 °C and centrifuged for 3 min at 13,000 g. Supernatant was collected and stored at –80 °C in glass tubes until analysis. Sample vials were placed in the autosampler tray in random order and kept at 5 °C. Separation was performed using a Dionex UltiMate 3000 RSLC system (Thermo Scientific, Hemel Hempstead) by injection of 10 μl sample onto a silica gel column (150 mm × 3 mm × 3 μm; HiChrom, Reading, UK) used in hydrophilic interaction chromatography (HILIC) mode held at 30°C (Zheng et al., 2010). Two solvents were used: solvent A [$20\%$ isopropyl alcohol (IPA) in acetonitrile] and solvent B [$20\%$ IPA in ammonium formate (20 mM)]. Elution was achieved using the following gradient at 0.3 ml/min: 0–1 min $8\%$ B, 5 min $9\%$ B, 10 min $20\%$ B, 16 min $25\%$ B, 23 min $35\%$ B, and 26–40 min $8\%$ B. Detection of lipids were performed in a Thermo Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific Inc, Hemel Hempstead, UK) in polarity switching mode. The instrument was calibrated according to the manufacturer’s specifications to give an rms mass error <1 ppm. The following electrospray ionization settings were used: source voltage, ±4.30 kV; capillary temp, 325 °C; sheath gas flow, 40 arbitrary units (AU); auxiliary gas flow, 5 AU; sweep gas flow, 1 AU. All LC-MS spectra were recorded in the range of 100–1200 at 120,000 resolutions (FWHM at m/z 500). The preprocessed lipidomics data was tested using transmission period, PfHRP2, and age-adjusted linear regression with any of the five factors. All m/z with a significant false discovery rate with any of the factors were then manually checked for peak quality and identified masses on mass and retention time (Reis et al., 2013). The remaining identified lipids were then checked for the relationship with ap2-g and Pfsir2a transcription levels (linear regression adjusted for transmission period, HRP, and age, see methods above). The CHMI lipidomics data was analyzed the same way but the peaks retained were those significantly different pre and post-treatment in either type of infection (student’s t-tests corrected for multiple testing). ## Data preprocessing The raw data were converted to mzML files using proteowizard (v 3.0.9706 [2016-5-12]). These files were then analyzed using R (v 4.2.1) libraries xcms (v 3.14.1) and mzmatch 2 (v 1.0–4) for peak picking, alignment, filtering, and annotations (Chong and Xia, 2018; Scheltema et al., 2011; Smith et al., 2006). Batch correction was applied as in (https://www.mdpi.com/2218-$\frac{1989}{10}$/$\frac{6}{241}$/htm), and the data was then checked using PCA calculated using the R function prcomp (see Figure 4—figure supplement 1). Data was then range normalized and logged transformed using MetaboanalystR (v3.1.0). The CHMI lipidomics data was analyzed the same way but did not require batch correction as the samples were run in one batch. ## Statistical analysis Data analysis was performed using R (v4.2.1) except for the structural equation modeling which was done in Mplus8. We normalized non-normally distributed variables by log transformation. qRT-PCR: Zeros in qRT-PCR values were replaced by 0.001 (the value before log transformation as the smallest measured value is about 0.0017). The median transcript units from qRT-PCR were calculated as follows: DC8 median from four primer pairs used (DC8-1, DC8-2, DC8-3, and DC8-4) and group A median from three primer pairs (gpA1, gpA2, and dc13). Samples for which ap2-g or pfsir2a arbitrary transcript unit was greater or equal to 32 (that is the transcript quantity of the reference genes based on the formula (Tus = 2(5-∆ct)) Zheng et al., 2010) were deemed unreliable and excluded from the analysis that went into generating Figures 2—4. Comparison between the two groups was done using a two-sided Wilcoxon test. All correlations were conducted using Spearman’s rank correlation coefficient test. All forest plots were done using linear regressions adjusted for transmission period, PfHRP2, and age of the patient (see figure legends) using R function lm. All multiple test corrections were done using Benjamini & Hochberg multiple tests (using R function p.adjust). ## Principal factor analysis A measurement model (i.e. factor analytic model) was fitted to summarize the 34 analytes into fewer variables called factors. An exploratory factor analysis (EFA) was performed to explore the factor structure underlying the 34 analytes. Factors were retained based on the Kaiser’s ‘eigenvalue rule’ of retaining eigenvalues larger than 1. In addition, we also considered the scree plot, parallel analysis, fit statistics, and interpretability of the model/factors. This analysis resulted in the cytokine data being reduced to five factors. This analysis was done using the R ‘psych’ library (v 2.1.9) available at https://CRAN.R-project.org/package=psych. The 34 analytes were individually linearly regressed to ap2-g or Pfsir2 transcript levels with the transmission, PfHRP2, and age correction (model: analyte ~transmission + PfHRP2 +age). Then each factor was analyzed the same way. ## Figures All heatmaps were done using the R library pheatmap (v 1.0.12) available at https://CRAN.R-project.org/package=pheatmap, and all other plots using the R libraries ggplot2 (v 3.3.5) and ggpubr (v 0.4.0) available at https://CRAN.R-project.org/package=ggpubr. ## Funding Information This paper was supported by the following grants: ## Data availability Raw data and script for all the analyses in this manuscript are available at https://doi.org/10.7910/DVN/BXXVRY. mzML mass spectrometry files are available at MetaboLights at https://www.ebi.ac.uk/metabolights/editor/study/MTBLS5130. The following datasets were generated: AbdiAI AchcarF SollelisL Silva-FilhoJL MwikaliK MuthuiM MwangiS KimingiHW OrindiB KivisiCA AlkemaM ChandrasekarA BullPC BejonP ModrzynskaK BousemaT MartiM 2023Plasmodium falciparum adapts its investment into replicationversustransmission according to the host environmentEBIMTBLS5130 AbdiAI AchcarF SollelisL Silva-FilhoJL MwikaliK MuthuiM MwangiS KimingiHW OrindiB KivisiCA AlkemaM ChandrasekarA BullPC BejonP ModrzynskaK BousemaT MartiM 2023Replication Data for: Plasmodium falciparum adapts investment into replication versus reproduction transmission according to host environmentHarvard Dataverse10.7910/DVN/BXXVRY ## References 1. Abdi AI, Warimwe GM, Muthui MK, Kivisi CA, Kiragu EW, Fegan GW, Bull PC. Global selection of. Scientific Reports (2016) 6. DOI: 10.1038/srep19882 2. Alkema M, Reuling IJ, de Jong GM, Lanke K, Coffeng LE, van Gemert GJ, van de Vegte-Bolmer M, de Mast Q, van Crevel R, Ivinson K, Ockenhouse CF, McCarthy JS, Sauerwein R, Collins KA, Bousema T. A randomized clinical trial to compare. The Journal of Infectious Diseases (2021) 224 1257-1265. DOI: 10.1093/infdis/jiaa157 3. Alkema M, Yap XZ, de Jong GM, Reuling IJ, de Mast Q, van Crevel R, Ockenhouse CF, Collins KA, Bousema T, McCall MBB, Sauerwein RW. Controlled human malaria infections by mosquito bites induce more severe clinical symptoms than asexual blood-stage challenge infections. EBioMedicine (2022) 77. DOI: 10.1016/j.ebiom.2022.103919 4. Amunugama K, Pike DP, Ford DA. The lipid biology of sepsis. Journal of Lipid Research (2021) 62. DOI: 10.1016/j.jlr.2021.100090 5. Borrmann S, Sasi P, Mwai L, Bashraheil M, Abdallah A, Muriithi S, Frühauf H, Schaub B, Pfeil J, Peshu J, Hanpithakpong W, Rippert A, Juma E, Tsofa B, Mosobo M, Lowe B, Osier F, Fegan G, Lindegårdh N, Nzila A, Peshu N, Mackinnon M, Marsh K. Declining responsiveness of. PLOS ONE (2011) 6. DOI: 10.1371/journal.pone.0026005 6. Bosch-Presegué L, Vaquero A. Sirtuins in stress response: guardians of the genome. Oncogene (2014) 33 3764-3775. DOI: 10.1038/onc.2013.344 7. Brancucci NMB, Gerdt JP, Wang C, De Niz M, Philip N, Adapa SR, Zhang M, Hitz E, Niederwieser I, Boltryk SD, Laffitte MC, Clark MA, Grüring C, Ravel D, Blancke Soares A, Demas A, Bopp S, Rubio-Ruiz B, Conejo-Garcia A, Wirth DF, Gendaszewska-Darmach E, Duraisingh MT, Adams JH, Voss TS, Waters AP, Jiang RHY, Clardy J, Marti M. Lysophosphatidylcholine regulates sexual stage differentiation in the human malaria parasite. Cell (2017) 171 1532-1544. DOI: 10.1016/j.cell.2017.10.020 8. Buckling A, Ranford-Cartwright LC, Miles A, Read AF. Chloroquine increases. Parasitology (1999) 118 (Pt 4) 339-346. DOI: 10.1017/s0031182099003960 9. Bull PC, Berriman M, Kyes S, Quail MA, Hall N, Kortok MM, Marsh K, Newbold CI. PLOS Pathogens (2005) 1. DOI: 10.1371/journal.ppat.0010026 10. Carter R, Miller LH. Evidence for environmental modulation of gametocytogenesis in. Bulletin of the World Health Organization (1979) 57 Suppl 1 37-52. PMID: 397008 11. Chao YY, Puhach A, Frieser D, Arunkumar M, Lehner L, Seeholzer T, Garcia-Lopez A, van der Wal M, Fibi-Smetana S, Dietschmann A, Sommermann T, Ćiković T, Taher L, Gresnigt MS, Vastert SJ, van Wijk F, Panagiotou G, Krappmann D, Groß O, Zielinski CE. Human th17 cells engage gasdermin E pores to release IL-1α on NLRP3 inflammasome activation. Nature Immunology (2023) 24 295-308. DOI: 10.1038/s41590-022-01386-w 12. Chong J, Xia J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics (2018) 34 4313-4314. DOI: 10.1093/bioinformatics/bty528 13. Churcher TS, Trape JF, Cohuet A. Human-to-mosquito transmission efficiency increases as malaria is controlled. Nature Communications (2015) 6. DOI: 10.1038/ncomms7054 14. Coleman BI, Skillman KM, Jiang RHY, Childs LM, Altenhofen LM, Ganter M, Leung Y, Goldowitz I, Kafsack BFC, Marti M, Llinás M, Buckee CO, Duraisingh MT. A. Cell Host & Microbe (2014) 16 177-186. DOI: 10.1016/j.chom.2014.06.014 15. Cordy RJ, Patrapuvich R, Lili LN, Cabrera-Mora M, Chien JT, Tharp GK, Khadka M, Meyer EV, Lapp SA, Joyner CJ, Garcia A, Banton S, Tran V, Luvira V, Rungin S, Saeseu T, Rachaphaew N, Pakala SB, DeBarry JD, Kissinger JC, Ortlund EA, Bosinger SE, Barnwell JW, Jones DP, Uppal K, Li S, Sattabongkot J, Moreno A, Galinski MR. Distinct amino acid and lipid perturbations characterize acute versus chronic malaria. JCI Insight (2019) 4. DOI: 10.1172/jci.insight.125156 16. Dollat M, Talla C, Sokhna C, Diene Sarr F, Trape JF, Richard V. Measuring malaria morbidity in an area of seasonal transmission: pyrogenic parasitemia thresholds based on a 20-year follow-up study. PLOS ONE (2019) 14. DOI: 10.1371/journal.pone.0217903 17. Dondorp AM, Desakorn V, Pongtavornpinyo W, Sahassananda D, Silamut K, Chotivanich K, Newton PN, Pitisuttithum P, Smithyman AM, White NJ, Day NPJ. Estimation of the total parasite biomass in acute falciparum malaria from plasma pfhrp2. PLOS Medicine (2005) 2. DOI: 10.1371/journal.pmed.0020204 18. Dong C. Cytokine regulation and function in T cells. Annual Review of Immunology (2021) 39 51-76. DOI: 10.1146/annurev-immunol-061020-053702 19. Duraisingh MT, Voss TS, Marty AJ, Duffy MF, Good RT, Thompson JK, Freitas-Junior LH, Scherf A, Crabb BS, Cowman AF. Heterochromatin silencing and locus repositioning linked to regulation of virulence genes in. Cell (2005) 121 13-24. DOI: 10.1016/j.cell.2005.01.036 20. Hanson J, Lam SWK, Mahanta KC, Pattnaik R, Alam S, Mohanty S, Hasan MU, Hossain A, Charunwatthana P, Chotivanich K, Maude RJ, Kingston H, Day NP, Mishra S, White NJ, Dondorp AM. Relative contributions of macrovascular and microvascular dysfunction to disease severity in falciparum malaria. The Journal of Infectious Diseases (2012) 206 571-579. DOI: 10.1093/infdis/jis400 21. Hoffman SL, Crutcher JM, Puri SK, Ansari AA, Villinger F, Franke ED, Singh PP, Finkelman F, Gately MK, Dutta GP, Sedegah M. Sterile protection of monkeys against malaria after administration of interleukin-12. Nature Medicine (1997) 3 80-83. DOI: 10.1038/nm0197-80 22. Huang YH, Schäfer-Elinder L, Wu R, Claesson HE, Frostegård J. Lysophosphatidylcholine (LPC) induces proinflammatory cytokines by a platelet-activating factor (PAF) receptor-dependent mechanism. Clinical and Experimental Immunology (1999) 116 326-331. DOI: 10.1046/j.1365-2249.1999.00871.x 23. Huang DTN, Lu CY, Chi YH, Li WL, Chang LY, Lai MJ, Chen JS, Hsu WM, Huang LM. Adaptation of influenza A (H7N9) virus in primary human airway epithelial cells. Scientific Reports (2017) 7. DOI: 10.1038/s41598-017-10749-5 24. Kafsack BFC, Rovira-Graells N, Clark TG, Bancells C, Crowley VM, Campino SG, Williams AE, Drought LG, Kwiatkowski DP, Baker DA, Cortés A, Llinás M. A transcriptional switch underlies commitment to sexual development in malaria parasites. Nature (2014) 507 248-252. DOI: 10.1038/nature12920 25. Kearney S, Delgado C, Lenz LL. Differential effects of type I and II interferons on myeloid cells and resistance to intracellular bacterial infections. Immunologic Research (2013) 55 187-200. DOI: 10.1007/s12026-012-8362-y 26. Kumar M, Seeger W, Voswinckel R. Senescence-associated secretory phenotype and its possible role in chronic obstructive pulmonary disease. American Journal of Respiratory Cell and Molecular Biology (2014) 51 323-333. DOI: 10.1165/rcmb.2013-0382PS 27. Langhorne J, Ndungu FM, Sponaas AM, Marsh K. Immunity to malaria: more questions than answers. Nature Immunology (2008) 9 725-732. DOI: 10.1038/ni.f.205 28. Lavstsen T, Turner L, Saguti F, Magistrado P, Rask TS, Jespersen JS, Wang CW, Berger SS, Baraka V, Marquard AM, Seguin-Orlando A, Willerslev E, Gilbert MTP, Lusingu J, Theander TG. PNAS (2012) 109 E1791-E1800. DOI: 10.1073/pnas.1120455109 29. Law SH, Chan ML, Marathe GK, Parveen F, Chen CH, Ke LY. An updated review of lysophosphatidylcholine metabolism in human diseases. International Journal of Molecular Sciences (2019) 20. DOI: 10.3390/ijms20051149 30. Li X. Sirt1 and energy metabolism. Acta Biochimica et Biophysica Sinica (2013) 45 51-60. DOI: 10.1093/abbs/gms108 31. Mancio-Silva L, Lopez-Rubio JJ, Claes A, Scherf A. Sir2a regulates rdna transcription and multiplication rate in the human malaria parasite. Nature Communications (2013) 4. DOI: 10.1038/ncomms2539 32. Marsh K, Forster D, Waruiru C, Mwangi I, Winstanley M, Marsh V, Newton C, Winstanley P, Warn P, Peshu N. Indicators of life-threatening malaria in african children. The New England Journal of Medicine (1995) 332 1399-1404. DOI: 10.1056/NEJM199505253322102 33. Marsh K, Snow RW. Malaria transmission and morbidity. Parassitologia (1999) 41 241-246. PMID: 10697862 34. Merrick CJ, Huttenhower C, Buckee C, Amambua-Ngwa A, Gomez-Escobar N, Walther M, Conway DJ, Duraisingh MT. Epigenetic dysregulation of virulence gene expression in severe. The Journal of Infectious Diseases (2012) 205 1593-1600. DOI: 10.1093/infdis/jis239 35. Mogeni P, Williams TN, Fegan G, Nyundo C, Bauni E, Mwai K, Omedo I, Njuguna P, Newton CR, Osier F, Berkley JA, Hammitt LL, Lowe B, Mwambingu G, Awuondo K, Mturi N, Peshu N, Snow RW, Noor A, Marsh K, Bejon P. Age, spatial, and temporal variations in hospital admissions with malaria in kilifi county, kenya: a 25-year longitudinal observational study. PLOS Medicine (2016) 13. DOI: 10.1371/journal.pmed.1002047 36. Njuguna P, Maitland K, Nyaguara A, Mwanga D, Mogeni P, Mturi N, Mohammed S, Mwambingu G, Ngetsa C, Awuondo K, Lowe B, Adetifa I, Scott JAG, Williams TN, Atkinson S, Osier F, Snow RW, Marsh K, Tsofa B, Peshu N, Hamaluba M, Berkley JA, Newton CRJ, Fondo J, Omar A, Bejon P. Observational study: 27 years of severe malaria surveillance in kilifi, kenya. BMC Medicine (2019) 17. DOI: 10.1186/s12916-019-1359-9 37. Oakley MSM, Kumar S, Anantharaman V, Zheng H, Mahajan B, Haynes JD, Moch JK, Fairhurst R, McCutchan TF, Aravind L. Molecular factors and biochemical pathways induced by febrile temperature in intraerythrocytic. Infection and Immunity (2007) 75 2012-2025. DOI: 10.1128/IAI.01236-06 38. O’Meara WP, Bejon P, Mwangi TW, Okiro EA, Peshu N, Snow RW, Newton C, Marsh K. Effect of a fall in malaria transmission on morbidity and mortality in kilifi, kenya. Lancet (2008) 372 1555-1562. DOI: 10.1016/S0140-6736(08)61655-4 39. Palacios OM, Carmona JJ, Michan S, Chen KY, Manabe Y, Ward JL, Goodyear LJ, Tong Q. Diet and exercise signals regulate SIRT3 and activate AMPK and PGC-1alpha in skeletal muscle. Aging (2009) 1 771-783. DOI: 10.18632/aging.100075 40. Qin X, Qiu C, Zhao L. Lysophosphatidylcholine perpetuates macrophage polarization toward classically activated phenotype in inflammation. Cellular Immunology (2014) 289 185-190. DOI: 10.1016/j.cellimm.2014.04.010 41. Rawat M, Srivastava A, Johri S, Gupta I, Karmodiya K. Single-cell RNA sequencing reveals cellular heterogeneity and stage transition under temperature stress in synchronized. Microbiology Spectrum (2021) 9. DOI: 10.1128/Spectrum.00008-21 42. Recker M, Nee S, Bull PC, Kinyanjui S, Marsh K, Newbold C, Gupta S. Transient cross-reactive immune responses can orchestrate antigenic variation in malaria. Nature (2004) 429 555-558. DOI: 10.1038/nature02486 43. Reis A, Rudnitskaya A, Blackburn GJ, Fauzi NM, Pitt AR, Spickett CM. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL. Journal of Lipid Research (2013) 54 1812-1824. DOI: 10.1194/jlr.M034330 44. Rono MK, Nyonda MA, Simam JJ, Ngoi JM, Mok S, Kortok MM, Abdullah AS, Elfaki MM, Waitumbi JN, El-Hassan IM, Marsh K, Bozdech Z, Mackinnon MJ. Adaptation of. Nature Ecology & Evolution (2018) 2 377-387. DOI: 10.1038/s41559-017-0419-9 45. Rottmann M, Lavstsen T, Mugasa JP, Kaestli M, Jensen ATR, Müller D, Theander T, Beck HP. Differential expression of var gene groups is associated with morbidity caused by. Infection and Immunity (2006) 74 3904-3911. DOI: 10.1128/IAI.02073-05 46. Rowe JA, Claessens A, Corrigan RA, Arman M. Adhesion of. Expert Reviews in Molecular Medicine (2009) 11. DOI: 10.1017/S1462399409001082 47. Salanti A, Staalsoe T, Lavstsen T, Jensen ATR, Sowa MPK, Arnot DE, Hviid L, Theander TG. Selective upregulation of a single distinctly structured var gene in chondroitin sulphate A-adhering. Molecular Microbiology (2003) 49 179-191. DOI: 10.1046/j.1365-2958.2003.03570.x 48. Scheltema RA, Jankevics A, Jansen RC, Swertz MA, Breitling R. PeakML/mzmatch: a file format, java library, R library, and tool-chain for mass spectrometry data analysis. Analytical Chemistry (2011) 83 2786-2793. DOI: 10.1021/ac2000994 49. Scherf A, Lopez-Rubio JJ, Riviere L. Antigenic variation in. Annual Review of Microbiology (2008) 62 445-470. DOI: 10.1146/annurev.micro.61.080706.093134 50. Shapouri-Moghaddam A, Mohammadian S, Vazini H, Taghadosi M, Esmaeili SA, Mardani F, Seifi B, Mohammadi A, Afshari JT, Sahebkar A. Macrophage plasticity, polarization, and function in health and disease. Journal of Cellular Physiology (2018) 233 6425-6440. DOI: 10.1002/jcp.26429 51. Silamut K, Phu NH, Whitty C, Turner GD, Louwrier K, Mai NT, Simpson JA, Hien TT, White NJ. A quantitative analysis of the microvascular sequestration of malaria parasites in the human brain. The American Journal of Pathology (1999) 155 395-410. DOI: 10.1016/S0002-9440(10)65136-X 52. Sinha A, Hughes KR, Modrzynska KK, Otto TD, Pfander C, Dickens NJ, Religa AA, Bushell E, Graham AL, Cameron R, Kafsack BFC, Williams AE, Llinas M, Berriman M, Billker O, Waters AP. A cascade of DNA-binding proteins for sexual commitment and development in plasmodium. Nature (2014) 507 253-257. DOI: 10.1038/nature12970 53. Smith JD, Chitnis CE, Craig AG, Roberts DJ, Hudson-Taylor DE, Peterson DS, Pinches R, Newbold CI, Miller LH. Switches in expression of. Cell (1995) 82 101-110. DOI: 10.1016/0092-8674(95)90056-x 54. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry (2006) 78 779-787. DOI: 10.1021/ac051437y 55. Sorci G, Léchenault-Bergerot C, Faivre B. Age reduces resistance and tolerance in malaria-infected mice. Infection, Genetics and Evolution (2021) 88. DOI: 10.1016/j.meegid.2020.104698 56. Su XZ, Heatwole VM, Wertheimer SP, Guinet F, Herrfeldt JA, Peterson DS, Ravetch JA, Wellems TE. The large diverse gene family var encodes proteins involved in cytoadherence and antigenic variation of. Cell (1995) 82 89-100. DOI: 10.1016/0092-8674(95)90055-1 57. Tan JL, Lash B, Karami R, Nayer B, Lu YZ, Piotto C, Julier Z, Martino MM. Restoration of the healing microenvironment in diabetic wounds with matrix-binding IL-1 receptor antagonist. Communications Biology (2021) 4. DOI: 10.1038/s42003-021-01913-9 58. Taylor TE, Fu WJ, Carr RA, Whitten RO, Mueller JS, Fosiko NG, Lewallen S, Liomba NG, Molyneux ME. Differentiating the pathologies of cerebral malaria by postmortem parasite counts. Nature Medicine (2004) 10 143-145. DOI: 10.1038/nm986 59. Tonkin CJ, Carret CK, Duraisingh MT, Voss TS, Ralph SA, Hommel M, Duffy MF, Silva L da, Scherf A, Ivens A, Speed TP, Beeson JG, Cowman AF. Sir2 paralogues cooperate to regulate virulence genes and antigenic variation in. PLOS Biology (2009) 7. DOI: 10.1371/journal.pbio.1000084 60. Turner L, Lavstsen T, Berger SS, Wang CW, Petersen JEV, Avril M, Brazier AJ, Freeth J, Jespersen JS, Nielsen MA, Magistrado P, Lusingu J, Smith JD, Higgins MK, Theander TG. Severe malaria is associated with parasite binding to endothelial protein C receptor. Nature (2013) 498 502-505. DOI: 10.1038/nature12216 61. Usui M, Prajapati SK, Ayanful-Torgby R, Acquah FK, Cudjoe E, Kakaney C, Amponsah JA, Obboh EK, Reddy DK, Barbeau MC, Simons LM, Czesny B, Raiciulescu S, Olsen C, Abuaku BK, Amoah LE, Williamson KC. Publisher correction:. Nature Communications (2019) 10. DOI: 10.1038/s41467-019-10805-w 62. Vasquez MC, Beam M, Blackwell S, Zuzow MJ, Tomanek L. Sirtuins regulate proteomic responses near thermal tolerance limits in the blue mussels mytilus galloprovincialis and. The Journal of Experimental Biology (2017) 220 4515-4534. DOI: 10.1242/jeb.160325 63. Weiss ES, Girard-Guyonvarc’h C, Holzinger D, de Jesus AA, Tariq Z, Picarsic J, Schiffrin EJ, Foell D, Grom AA, Ammann S, Ehl S, Hoshino T, Goldbach-Mansky R, Gabay C, Canna SW. Interleukin-18 diagnostically distinguishes and pathogenically promotes human and murine macrophage activation syndrome. Blood (2018) 131 1442-1455. DOI: 10.1182/blood-2017-12-820852 64. White NJ. Anaemia and malaria. Malaria Journal (2018) 17. DOI: 10.1186/s12936-018-2509-9 65. WHO 2022World malaria ReportWHO. World malaria Report (2022) 66. Williams JL. Stimulation of. The American Journal of Tropical Medicine and Hygiene (1999) 60 7-13. DOI: 10.4269/ajtmh.1999.60.7 67. Zheng L, T’Kind R, Decuypere S, von Freyend SJ, Coombs GH, Watson DG. Profiling of lipids in leishmania donovani using hydrophilic interaction chromatography in combination with fourier transform mass spectrometry. Rapid Communications in Mass Spectrometry (2010) 24 2074-2082. DOI: 10.1002/rcm.4618 68. Zhu AY, Zhou Y, Khan S, Deitsch KW, Hao Q, Lin H. ACS Chemical Biology (2012) 7 155-159. DOI: 10.1021/cb200230x
--- title: Diagnostic Value of the Combined Measurement of Serum HCY and NRG4 in Type 2 Diabetes Mellitus with Early Complicating Diabetic Nephropathy authors: - Sheng Ding - Yi Yang - Yuming Zheng - Jinling Xu - Yangyang Cheng - Wei Wei - Fuding Yu - Li Li - Menglan Li - Mengjie Wang - Zhongjing Wang - Guangda Xiang journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10059699 doi: 10.3390/jpm13030556 license: CC BY 4.0 --- # Diagnostic Value of the Combined Measurement of Serum HCY and NRG4 in Type 2 Diabetes Mellitus with Early Complicating Diabetic Nephropathy ## Abstract Purpose: This study aimed to investigate the value of combined detection of HCY and NRG4 in the diagnosis of early diabetic kidney disease (DKD) and to explore the association between the ratio of HCY/NRG4 and DKD. Methods: A total of 140 diabetic patients and 43 healthy people were prospectively enrolled. The plasma HCY level, NRG4 level and HCY/NRG4 of them were measured to compare their differences and analyze the correlation with DKD. The independent influencing factors of patients with DKD were screened, and the nomograph of DKD occurrence was constructed. Results: The levels of HCY and HCY/NRG4 in diabetic patients were significantly increased, while the level of NRG4 was significantly decreased ($p \leq 0.01$). The AUCs of HCY/NRG4 predicted for DKD were 0.961. HCY/NRG4 and the course of DM were independent risk factors for DKD. A predictive nomograph of DKD was constructed, and decision curve analysis (DCA) showed good clinical application value. HCY/NRG4 was positively correlated with Scr, UACR, TG, UA, BUN, TCHOL and LDL and negatively correlated with eGFR and HDL ($p \leq 0.05$). Conclusions: The level of HCY and NRG4 is closely related to the severity of DM, and combined detection of HCY/NRG4 can identify patients with DKD at an early stage. ## 1. Introduction According to the statistical report of the World Health Organization, in the past 34 years, the number of people living with diabetes has reached 314 million, and due to the increasing incidence and mortality of diabetes, this disease is expected to become the seventh leading cause of death by 2030 [1]. There are many types of diabetes, of which type 2 diabetes mellitus (T2DM) is an increasing health problem worldwide due to the body’s inability to produce insulin or use insulin from the pancreas [2], and insulin resistance is a major underlying pathophysiology of T2DM. T2DM is a chronic, noninfectious, multisystem disease in which chronic exposure to hyperglycemia affects the microvascular and macrovascular systems, leading to diabetic nephropathy (DN), retinopathy, neuropathy and cardiovascular disease, with significant implications for quality of life and overall life expectancy [3,4]. The main characteristics of DN are persistent proteinuria, elevated creatinine levels and decreased glomerular filtration rate. Pathologically, podocyte injury, glomerulosclerosis, glomerular basement membrane thickening, interstitial fibrosis and tubular atrophy mainly contribute to the development of DN [5]. Podocyte insulin resistance is a main cause of podocyte injury, playing a crucial role in contributing to albuminuria in early DN [6]. DN is an important cause of increased morbidity and mortality in diabetic patients [7]. A meta-analysis found that the prevalence of DN was relatively high in Chinese patients with T2DM, with a total co-morbidity of $21.8\%$, and there were regional and gender differences. These data indicated that the risk and burden of DN to society increased year by year [8]. DN is the main cause of end-stage renal disease, and strict management of modifiable risk factors is essential to prevent and delay renal decline. The new markers may help in the early diagnosis of this common and serious complication, but further research is needed to clarify their effectiveness [9]. Studies have shown that DN is a chronic inflammatory disease mediated by a range of cytokines, including interleukin-6, interleukin-8, homocysteine (HCY) and cystatin C (Cys C). Among them, HCY is believed to be related to microvascular complications in diabetic patients [9,10,11]. Studies have found that HCY is closely related to the occurrence and development of DN. Wang et al. [ 12] showed that serum HCY and Cys C levels were consistent with the occurrence and progression of DN, and serum HCY and Cys C were sensitive biomarkers for detecting early DN and monitoring its progression. Ye et al. [ 13] found that serum HCY level in the DN group was higher than that in the T2DM group, which was associated with kidney damage, and could be used as a potential serological indicator for the early diagnosis of DN. In addition, a meta-analysis suggested that serum HCY was a promising biomarker of DN [14]. Therefore, HCY may be involved in the occurrence and progression of DN, which can be used for the early prediction of DN. Neuregulin 4 (NRG4) is a novel adipokine released from brown adipose tissue. It plays a key role in regulating overall body energy balance, glycolipid metabolism and reducing chronic inflammation [15,16]. It has been reported that decreased NRG4 levels are closely associated with type 2 diabetes, obesity, hyperglycemia, oxidative stress and inflammation. Yan et al. [ 17] measured plasma NRG4 levels in T2DM patients by ELISA and found that the level of NRG4 was negatively correlated with most of the metabolic syndrome analysis (MetS) indicators, and the decrease of plasma NRG4 level may be related to oxidative stress, inflammation and dyslipidemia, which may be related to the occurrence of MetS in T2DM patients. A cross-sectional study of T2DM patients without diabetic peripheral neuropathy (DPN) showed significantly lower levels of circulating NRG4 than that of the control group, and the level of circulating NRG4 was further reduced in T2DM patients with DPN. The level of circulating NRG4 decreased gradually with the increase in screening for abnormal DPN. NRG4 may be a novel adipokine associated with inflammation, oxidative stress and long-term glycemic control in patients with T2DM [18]. Kocak et al. [ 19] found that NRG4 decreased 1.9 times in patients with diabetic microvascular complications compared with the normal group. In patients with diabetes, NRG4 levels may be a good predictor of early detection of one or more diabetic microvascular complications. The above studies indicate that NRG4 may be a potential predictor of DN. However, at present, to improve the early prediction and detection level of diabetic nephropathy, it is still necessary to further study new non-invasive diagnostic markers. The prediction of a single indicator often has limitations, and feasible measures for diagnosing DN before advanced renal insufficiency are considered to be of clinical significance. Therefore, the purpose of this study was to evaluate serum HCY and NRG4 levels in T2DM patients with DN and to explore the predictive value of serum HCY combined with NRG4 levels in the early detection of type 2 diabetic nephropathy. ## 2.1. Selection of Patients and the Research Design Inclusion criteria: ① Patients with type 2 diabetes; ② Han population; ③ Age over 18 years old; ④ Body mass index (BMI) is between 18.5–30 kg/m2. Exclusion criteria: ① Patients with type 1 diabetes or other special types of diabetes; ② Non-diabetes nephropathy patients with chronic kidney disease; ③ Patients with glomerular filtration rate (eGFR) less than 10 mL/min/1.73 m2 requiring dialysis; ④ Complicated with acute complications of diabetes, such as diabetic ketoacidosis and diabetic lactic acidosis; ⑤ Recently, cerebral infarction, acute myocardial infarction and peripheral vascular obstructive disease were combined; ⑥ In recent 6 months, he has used drugs to delay the development of diabetic nephropathy, such as DPP4 inhibitor, GLP-1 receptor agonist, SGLT-2 inhibitor hypoglycemic drugs, ACEI, ARBs antihypertensive drugs; ⑦ Complicated with acute infection, tumor or liver disease; ⑧ Pregnancy status. At the same time, healthy subjects matched with age were selected as the control group. A total of 140 diabetes patients in the Central Hospital of Wuhan were prospectively enrolled, including 55 type 2 diabetes patients (DM group) and 85 diabetic kidney disease patients (DKD group); 43 healthy people (NC group) in the same period were selected as the control group. This study was approved by the Medical Ethics Committee of the Central Hospital of Wuhan (NO.2021[11]-01), and all subjects signed the informed consent form. ## 2.2. Data Collection and Definition The subjects’ basic information (gender, age, medical history, medication history, BMI, blood pressure, waist circumference, etc.) was recorded; urinary albumin/creatinine ratio (UACR), blood lipids including Low-Density Lipoprotein (LDL), cholesterol (TCHOL), High-Density Lipoprotein (HDL), triglyceride (TG), serum creatinine (Scr), blood urea nitrogen (BUN), blood uric acid (UA), blood glucose, glycosylated hemoglobin (HbA1C), glomerular filtration rate (eGFR) and inflammatory factors were determined. Insulin or C-peptide (calculation of HOMA-IR and HOMA-β), plasma HCY and NRG4 levels were measured by ELISA. DM or DKD is defined as meeting the 2018 ADA diagnostic criteria for type 2 diabetes and diabetic kidney disease [20]. Albuminuria was defined as UACR greater than 30 mg/g on 2 or more consecutive 3 urine tests in the last 6 months. ## 2.3. Serum HCY and Neuregulin-4 Measurement Serum HCY (AUSA Co., Ltd., Shenzhen, China) and NRG4 (Zell Bio GmbH, Lonsee, Germany) were measured using an enzyme-linked immunosorbent assay (ELISA) according to the provided instructions. The sensitivity of the kit was 4 μmol/L and 0.02 ng/mL, respectively. The standard curve was developed by a linear range of the standard for each cytokine and used for the calculation of the concentrations. The intra- and inter-assay variations were <$10\%$. ## 2.4. Statistical Analysis SAS9.4 (SAS Institute Inc., Cary, NC, USA) software was used for statistical analysis. The measurement data were described by mean ± standard deviation. Two independent samples t-test was used for comparison between the two groups, and one-way ANOVA was used for comparison between multiple groups. Counting data were expressed by examples (%) and compared between groups by χ2 inspection. Logistic regression was used to analyze the relationship between clinical characteristics, laboratory test results and DKD. Then, analyze and evaluate the predictive ability of HCY/NRG4 to patients’ DKD by drawing the ROC. To further test the accuracy of the histogram in predicting disease occurrence, a calibration curve was generated, and the observations were compared with the predictions. In addition, Pearson correlation was used to analyze the correlation between laboratory test indicators and HCY/NRG4. $p \leq 0.05$ was considered statistically significant. ## 3.1. Clinical Data Characteristics of Patients A total of 140 patients with diabetes were prospectively included in this study; 55 patients with diabetes, 85 patients with DKD and 43 healthy controls were selected at the same time. The UACR, HCY, HCY/NRG4, FBG, HbA1C, BUN, Scr, LDL, TG and UA levels in DM and DKD groups were higher than those in the NC group ($p \leq 0.05$ or $p \leq 0.01$). NRG4 and eGFR were lower than those in the NC group ($p \leq 0.05$ or $p \leq 0.01$) (Table 1). Compared with the healthy control group, the level of HCY and HCY/NRG4 in DM and DKD groups were significantly increased, while the level of NRG4 was significantly decreased, with statistical significance ($p \leq 0.01$) (Figure 1A–C). ## 3.2. The Predictive Efficacy of HCY, NRG4 and HCY/NRG4 in Predicting DKD The prediction specificity and sensitivity of NRG4 for DKD were 0.745 and 0.941, and the prediction specificity and sensitivity of HCY for DKD were 0.909 and 0.824. The specificity and sensitivity of HCY/NRG4 for predicting DKD were 0.927 and 0.929. The AUCs predicted for DKD were NRG4 (0.91, $95\%$CI: 0.859, 0.961), HCY (0.885, $95\%$CI: 0.822, 0.948), and HCY/NRG4 (0.961, $95\%$CI: 0.928, 0.994), respectively (Figure 1D). ## 3.3. Analysis of Multiple Factors Affecting DKD Occurrence Multivariate logistic regression analysis was performed on all the factors affecting the occurrence of DKD in patients, and the results showed that HCY/NRG4 (HR: 1.870, CI: 1.496–2.573, $p \leq 0.001$); course of DM (HR: 1.015, CI: 1.004–1.029, $$p \leq 0.012$$) was an independent factor influencing the occurrence of DKD in patients (Table 2). ## 3.4. Construction and Clinical Value of Predictive Nomogram Based on the multivariable logistic regression results of DKD, we finally selected HCY/NRG4 and the course of DM as two valuable factors to establish the prediction model (Figure 2A). In our cohort, the calibration curve of predicting patients’ DKD nomogram shows that there is good consistency between prediction and observation, and the calibration curve of the nomogram has no deviation from the reference line, with high reliability (Figure 2B). Decision curve analysis (DCA) is a novel strategy for evaluating alternative predictive treatment methods and has advantages over the Area Under the Receiver Operating Characteristic Curve (AUROC) in clinical value evaluation. The DCA curves for the developed nomogram in the cohorts are presented in Figure 2C. The DCA of the nomogram has higher net benefits, indicating that it had better clinical outcome values. ## 3.5. Pearson Correlation Analysis between Serum HCY/NRG4 Level and Other Indicators Pearson correlation analysis showed that HCY/NRG4 was positively correlated with Scr, UACR, TG, UA, BUN, TCHOL and LDL ($p \leq 0.05$), negatively correlated with eGFR and HDL ($p \leq 0.05$ or $p \leq 0.01$) (Figure 3). ## 4. Discussion A total of 140 diabetic patients were included in this study. Compared with the healthy control group, the levels of HCY and HCY/NRG4 in DM and DKD groups were significantly increased, while the levels of NRG4 were significantly decreased. The predictive specificity and sensitivity of NRG4 for DKD were 0.745 and 0.941, and that of HCY for DKD were 0.909 and 0.824, respectively. The specificity and sensitivity of DKD predicted by HCY/NRG4 were 0.927 and 0.929, respectively. Logistic regression analysis of all factors influencing the development of DKD in patients showed that HCY/NRG4 and the course of DM were independent factors influencing the development of DKD in patients. These two valuable factors were selected to establish a nomogram, and the results of DCA showed that the nomogram had better clinical prediction value. Early prediction of T2DM with diabetic nephropathy has also been reported. Shoukry et al. [ 21] found that urinary monocyte chemotactic protein-1 (MCP-1) and vitamin D-binding protein(VDBP) levels in diabetic patients were significantly higher than those in the normal group, and the ROC curve analysis of urinary MCP-1 and urinary VDBP levels showed high sensitivity and specificity for the early diagnosis and detection of DN. The optimal cut-off point of uMCP-1 for predicting DN was 110 pg/mg (AUC = 0.987). However, the optimal cut-off point of uVDBP for predicting DN was 550 ng/mg (AUC = 0.947). Urinary MCP-1 and urinary VDBP levels may be considered as novel potential diagnostic biomarkers for the early detection of diabetic nephropathy. Lee et al. [ 22], in a clinically based cross-sectional study of 320 patients with type 2 diabetes who underwent staging of diabetic nephropathy and evaluated the prognosis of type 2 diabetic nephropathy based on serum creatinine and cystatin C (CysC), found that serum CysC seemed to predict prognosis more accurately than serum creatinine; CysC-based GFR may be more valuable than creatinine-based GFR in predicting the stage of microalbuminuria. A meta-analysis found that CysC predicted DN with sensitivity and specificity of 0.88 and 0.85, the positive predictive value of DN was 7.04, and the area under the ROC curve was 0.9549, which could be considered an early predictor of DN [23]. The AUCs predicted for DKD were HCY/NRG4 (0.961), CysC (0.9549), urinary MCP-1 (0.987) and VDBP (0.947), showing early diagnostic value for DN of these biomarkers. Inflammatory biomarkers, such as TNF-α and IL-1β, also play a predictive role in DN [24]. Previous studies have confirmed that the levels of IL-1β and TNF-α produced by macrophages cultured in the glomerular basement membrane of diabetic rats are significantly higher than those produced by macrophages cultured in the basement membrane of normal non-diabetic rats, indicating that these pro-inflammatory cytokines can be involved in the development of DN [25]. Clinical studies have shown a direct and significant relationship between urinary protein excretion and serum TNF-α in patients with normal renal function and diabetes, and the fact that urinary TNF-α excretion increases significantly with DN progression strongly supports the prospect of using this cytokine as a biomarker for predicting DN [26,27]. Although these biomarkers have a role to play in the assessment of diabetic nephropathy, the current data still rule out most biomarkers for routine clinical use. However, the trajectory of research on novel biomarkers of DN should be a sustained effort to validate them through high-quality and large-scale longitudinal studies and subsequently develop DN biomarkers capable of reliably predicting and evaluating them [28]. In recent years, serum HCY and NRG4 have increased in studies on T2DM and DN, which may be potential biomarkers for the early prediction of DN [29]. In a study on the risk of circulating homocysteine and DKD in the population, it was found that for every 5 μmol/L increase in blood homocysteine concentration, the odds ratio of DKD to diabetes was 3.86. In logistic regression analysis, hypertension, homocysteine and triglycerides were significantly associated with an increased risk of DKD. It is suggested that there is a causal relationship between increased circulating homocysteine concentration and increased risk of DKD [30]. In addition, recent data show that the expression of NRG4 is significantly down-regulated in mice and human obesity. NRG4 may enhance the activity of brown adipose tissue, increase the expression of thermogenic markers, reduce the expression of lipogenic/adipogenic genes, aggravate the browning of white adipose tissue, promote the oxidation and ketogenesis of liver fat, induce neurite growth and enhance the blood vessels of adipose tissue, to prevent obesity and related metabolic complications [31]. Ye et al. [ 13] found that the sensitivity, specificity and AUC of serum HCY level in the diagnosis of DN were $84.31\%$, $74.55\%$ and 0.85, showing high sensitivity, specificity and AUC. In our study, HCY showed higher specificity and sensitivity in predicting DKD with more data samples. A prospective observational study similar to our findings found that baseline levels of HCY in patients with DN were significantly elevated and correlated with disease severity, supporting plasma HCY as an independent risk factor for DN and an early predictor of DN progression in patients with type 2 diabetes [32]. Some studies have found that the level of circulating NRG4 in patients with metabolic syndrome is lower than that in the healthy control group, and the concentration of NRG4 is negatively correlated with the risk of developing metabolic syndrome, and NRG4 concentration may be a protective factor [33]. However, studies have found no correlation between the circulating NRG4 level and the prevalence of diabetic nephropathy and diabetic retinopathy [34]. In our study, through the analysis of large sample data, it was found that NRG4 was closely related to the incidence of HCY and DN, and the combination of NRG and HCY had excellent predictive efficacy in predicting early DN of T2DM. However, this study still has some limitations. First, our enrolled samples are not enough, and we lack an internal validation cohort for verification. Second, as this study is a single-center study, further multi-center prospective clinical studies are needed to prove the clinical validity of this model. Third, only clinicopathological features were included in the variable analysis, and molecular pathologic features should be included to further improve the nomogram prediction. However, our study is also very important clinically. We used the serum HCY/NRG4 ratio to predict T2DM with early DN, and the AUC curve area reached 0.96, suggesting that this new indicator can be considered as an important factor of T2DM with early DN. Our study developed an auxiliary model to predict the onset of T2DM with early DN, but this auxiliary model should be used with caution based on the overall situation of the patient. ## 5. Conclusions In summary, the levels of HCY and NRG4 were closely related to the severity of DKD in T2DM patients with early DKD. Combined HCY/NRG4 detection can detect the occurrence of DKD in diabetic patients at an early stage. Further study of the mechanism of how NRG4 plays a protective role in patients with diabetic nephropathy would be necessary for the future. ## References 1. Diabetes. (2021) 2. Dekamin A., Wahab M.I.M., Keshavjee K., Guergachi A.. High cardiovascular disease risk-associated with the incidence of Type 2 diabetes among prediabetics. Eur. J. Intern. Med. (2022) 106 56-62. DOI: 10.1016/j.ejim.2022.09.005 3. Faselis C., Katsimardou A., Imprialos K., Deligkaris P., Kallistratos M., Dimitriadis K.. Microvascular Complications of Type 2 Diabetes Mellitus. Curr. Vasc. Pharmacol. (2020) 18 117-124. DOI: 10.2174/1570161117666190502103733 4. Viigimaa M., Sachinidis A., Toumpourleka M., Koutsampasopoulos K., Alliksoo S., Titma T.. Macrovascular Complications of Type 2 Diabetes Mellitus. Curr. Vasc. Pharmacol. (2020) 18 110-116. DOI: 10.2174/1570161117666190405165151 5. Jiang A., Song A., Zhang C.. Modes of podocyte death in diabetic kidney disease: An update. J. Nephrol. (2022) 35 1571-1584. DOI: 10.1007/s40620-022-01269-1 6. Lu J., Chen P.P., Zhang J.X., Li X.Q., Wang G.H., Yuan B.Y., Huang S.J., Liu X.Q., Jiang T.T., Wang M.Y.. GPR43 deficiency protects against podocyte insulin resistance in diabetic nephropathy through the restoration of AMPKα activity. Theranostics (2021) 11 4728-4742. DOI: 10.7150/thno.56598 7. Cooper M.E.. Pathogenesis, prevention, and treatment of diabetic nephropathy. Lancet (1998) 352 213-219. DOI: 10.1016/S0140-6736(98)01346-4 8. Zhang X.X., Kong J., Yun K.. Prevalence of Diabetic Nephropathy among Patients with Type 2 Diabetes Mellitus in China: A Meta-Analysis of Observational Studies. J. Diabetes Res. (2020) 2020 2315607. DOI: 10.1155/2020/2315607 9. Tziomalos K., Athyros V.G.. Diabetic Nephropathy: New Risk Factors and Improvements in Diagnosis. Rev. Diabet Stud. (2015) 12 110-118. DOI: 10.1900/RDS.2015.12.110 10. Lazzerini P.E., Capecchi P.L., Selvi E., Lorenzini S., Bisogno S., Galeazzi M., Pasini F.L.. Hyperhomocysteinemia: A cardiovascular risk factor in autoimmune diseases?. Lupus (2007) 16 852-862. DOI: 10.1177/0961203307084176 11. Karabag T., Kaya A., Temizhan A., Koç F., Yavuz S., Cam S.. The influence of homocysteine levels on endothelial function and their relation with microvascular complications in T2DM patients without macrovascular disease. Acta Diabetol. (2007) 44 69-75. DOI: 10.1007/s00592-007-0245-7 12. Wang T., Wang Q., Wang Z., Xiao Z., Liu L.. Diagnostic value of the combined measurement of serum hcy, serum cys C, and urinary microalbumin in type 2 diabetes mellitus with early complicating diabetic nephropathy. ISRN Endocrinol. (2013) 2013 407452. DOI: 10.1155/2013/407452 13. Ye B., Zhu X., Zeng Z., Ji X., Ji M.. Clinical significance of serum homocysteine as a biomarker for early diagnosis of diabetic nephropathy in type 2 diabetes mellitus patients. Pteridines (2021) 32 11-16. DOI: 10.1515/pteridines-2020-0025 14. Zhou X., Shi A., Zhou X.. A meta-analysis of serum Hcy in diagnosis of diabetic nephropathy. Pteridines (2020) 31 1-8. DOI: 10.1515/pteridines-2020-0001 15. Pfeifer A.. NRG4: An endocrine link between brown adipose tissue and liver. Cell Metab. (2015) 21 13-14. DOI: 10.1016/j.cmet.2014.12.008 16. Wang G.X., Zhao X.Y., Lin J.D.. The brown fat secretome: Metabolic functions beyond thermogenesis. Trends Endocrinol. Metab. (2015) 26 231-237. DOI: 10.1016/j.tem.2015.03.002 17. Yan P., Xu Y., Wan Q., Feng J., Li H., Yang J., Zhong H., Zhang Z.. Plasma Neuregulin 4 Levels Are Associated with Metabolic Syndrome in Patients Newly Diagnosed with Type 2 Diabetes Mellitus. Dis. Markers (2018) 2018 6974191. DOI: 10.1155/2018/6974191 18. Yan P., Xu Y., Zhang Z., Gao C., Zhu J., Li H., Wan Q.. Decreased plasma neuregulin 4 levels are associated with peripheral neuropathy in Chinese patients with newly diagnosed type 2 diabetes: A cross-sectional study. Cytokine (2019) 113 356-364. DOI: 10.1016/j.cyto.2018.10.007 19. Kocak M.Z., Aktas G., Atak B.M., Duman T.T., Yis O.M., Erkus E., Savli H.. Is Neuregulin-4 a predictive marker of microvascular complications in type 2 diabetes mellitus?. Eur. J. Clin. Investig. (2020) 50 e13206. DOI: 10.1111/eci.13206 20. Li Y., Teng D., Shi X., Qin G., Qin Y., Quan H., Shi B., Sun H., Ba J., Chen B.. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: National cross sectional study. BMJ (2020) 369 m997. DOI: 10.1136/bmj.m997 21. Shoukry A., Bdeer S.e.l.-A., El-Sokkary R.H.. Urinary monocyte chemoattractant protein-1 and vitamin D-binding protein as biomarkers for early detection of diabetic nephropathy in type 2 diabetes mellitus. Mol. Cell. Biochem. (2015) 408 25-35. DOI: 10.1007/s11010-015-2479-y 22. Lee B.W., Ihm S.H., Choi M.G., Yoo H.J.. The comparison of cystatin C and creatinine as an accurate serum marker in the prediction of type 2 diabetic nephropathy. Diabetes Res. Clin. Pract. (2007) 78 428-434. DOI: 10.1016/j.diabres.2007.06.015 23. Zhou B., Zou H., Xu G.. Clinical Utility of Serum Cystatin C in Predicting Diabetic Nephropathy Among Patients with Diabetes Mellitus: A Meta-Analysis. Kidney Blood Press Res. (2016) 41 919-928. DOI: 10.1159/000452593 24. Navarro J.F., Mora C.. Diabetes, inflammation, proinflammatory cytokines, and diabetic nephropathy. Sci. World J. (2006) 6 908-917. DOI: 10.1100/tsw.2006.179 25. Hasegawa G., Nakano K., Sawada M., Uno K., Shibayama Y., Ienaga K., Kondo M.. Possible role of tumor necrosis factor and interleukin-1 in the development of diabetic nephropathy. Kidney Int. (1991) 40 1007-1012. DOI: 10.1038/ki.1991.308 26. Navarro J.F., Mora C., Maca M., Garca J.. Inflammatory parameters are independently associated with urinary albumin in type 2 diabetes mellitus. Am. J. Kidney Dis. (2003) 42 53-61. DOI: 10.1016/S0272-6386(03)00408-6 27. Kalantarinia K., Awad A.S., Siragy H.M.. Urinary and renal interstitial concentrations of TNF-alpha increase prior to the rise in albuminuria in diabetic rats. Kidney Int. (2003) 64 1208-1213. DOI: 10.1046/j.1523-1755.2003.00237.x 28. Uwaezuoke S.N.. The role of novel biomarkers in predicting diabetic nephropathy: A review. Int. J. Nephrol. Renovasc. Dis. (2017) 10 221-231. DOI: 10.2147/IJNRD.S143186 29. Mao S., Xiang W., Huang S., Zhang A.. Association between homocysteine status and the risk of nephropathy in type 2 diabetes mellitus. Clin. Chim. Acta (2014) 431 206-210. DOI: 10.1016/j.cca.2014.02.007 30. Ma L., Liu Q., Jiang Y., Zhao H., Zhao T., Cao Y., Li P., Niu W.. Genetically elevated circulating homocysteine concentrations increase the risk of diabetic kidney disease in Chinese diabetic patients. J. Cell. Mol. Med. (2019) 23 2794-2800. DOI: 10.1111/jcmm.14187 31. Tutunchi H., Ostadrahimi A., Hosseinzadeh-Attar M.J., Miryan M., Mobasseri M., Ebrahimi-Mameghani M.. A systematic review of the association of neuregulin 4, a brown fat-enriched secreted factor, with obesity and related metabolic disturbances. Obes. Rev. (2020) 21 e12952. DOI: 10.1111/obr.12952 32. Wang H., Cui K., Xu K., Xu S.. Association between plasma homocysteine and progression of early nephropathy in type 2 diabetic patients. Int. J. Clin. Exp. Med. (2015) 8 11174-11180. PMID: 26379920 33. Cai C., Lin M., Xu Y., Li X., Yang S., Zhang H.. Association of circulating neuregulin 4 with metabolic syndrome in obese adults: A cross-sectional study. BMC Med. (2016) 14. DOI: 10.1186/s12916-016-0703-6 34. Yan P., Zhang Z., Miao Y., Xu Y., Zhu J., Wan Q.. Changes of circulating neuregulin 4 and its relationship with 25-hydroxy vitamin D and other diabetic vascular complications in patients with diabetic peripheral neuropathy. Diabetol. Metab. Syndr. (2020) 12 42. DOI: 10.1186/s13098-020-00550-2
--- title: 'Atherosclerotic Cardiovascular Disease in Inflammatory Bowel Disease: The Role of Chronic Inflammation and Platelet Aggregation' authors: - Sofija I. Lugonja - Ivana L. Pantic - Tamara M. Milovanovic - Vesna M. Grbovic - Bojana M. Djokovic - Željko D. Todorovic - Stefan M. Simovic - Raša H. Medovic - Nebojsa D. Zdravkovic - Natasa D. Zdravkovic journal: Medicina year: 2023 pmcid: PMC10059701 doi: 10.3390/medicina59030554 license: CC BY 4.0 --- # Atherosclerotic Cardiovascular Disease in Inflammatory Bowel Disease: The Role of Chronic Inflammation and Platelet Aggregation ## Abstract Background and Objectives: Atherosclerosis is one of inflammatory bowel disease’s most significant cardiovascular manifestations. This research aimed to examine the relationship between biochemical, haemostatic, and immune parameters of atherosclerosis and ulcerative colitis patients and its relationship to platelet aggregation. Materials and Methods: A clinical, observational cross-sectional study was performed, during which the tested parameters were compared in the experimental and control groups. The patients were divided into four groups. The first group had 25 patients who had ulcerative colitis and atherosclerosis. The second group included 39 patients with ulcerative colitis without atherosclerosis. The third group comprised 31 patients suffering from atherosclerosis without ulcerative colitis, and the fourth group comprised 25 healthy subjects. Results: In our study, we registered statistically higher levels of inflammatory markers like SE, CRP, Le, fecal calprotectin, TNF-α, and IL-6, as well as the higher value of thrombocytes and thrombocyte aggregation in the group of patients with ulcerative colitis compared to the control group. Lower levels of total cholesterol and LDL were also recorded in patients with ulcerative colitis and atherosclerosis and ulcerative colitis without atherosclerosis compared to healthy control. Triglyceride and remnant cholesterol were higher in patients with ulcerative colitis and atherosclerosis when compared to patients with ulcerative colitis and healthy control but lower than in patients with atherosclerosis only. Conclusions: Several inflammatory markers and platelet aggregation could be good discrimination markers for subjects with ulcerative colitis with the highest risk of atherosclerosis. ## 1. Introduction Inflammatory bowel diseases are chronic idiopathic gastrointestinal tract diseases, primarily Crohn’s disease and ulcerative colitis, with 5–$15\%$ of patients presenting as indeterminate colitis [1,2]. Ulcerative colitis is a chronic immune-mediated inflammation that can affect the mucosa of any part of the colon, with a tendency to spread from the rectum proximally in continuity [3,4,5]. Ulcerative colitis is characterized by periods of relapse and remission. The typical clinical presentation includes bloody diarrhea with or without mucus, abdominal pain, rectal urgency, tenesmus, weight loss, and asthenia [6,7]. Inflammatory bowel diseases (IBD) can give a wide range of extraintestinal manifestations: hepatobiliary, genitourinary, musculoskeletal, respiratory, ophthalmic, skin, and cardiovascular [8,9]. One of the IBD’s most significant cardiovascular manifestations is atherosclerosis, the most common and important cause of coronary, cerebral, and peripheral artery diseases and the aorta. It is a pathological process that most often affects the tunica intima of the arteries, causing later changes in the tunica media and tunica adventitia [10,11]. Possible mechanisms involved in the increased risk of cardiovascular disease in patients with IBD include increased levels of inflammatory cytokines and oxidative stress, altered platelet function, hypercoagulability, endothelial dysfunction, and changes in gut microbiota [12]. Moreover, microbial translocation, defined as the migration of bacteria or their products from the gut to the extraintestinal space and eventually to the systemic circulation, might be promoted by increased intestinal permeability induced by disruption of intestinal epithelial barrier function, intestinal bacterial overgrowth, and changes in the composition of bacterial microbes in the gut, all conditions that could promote and perpetuate systemic inflammation [13,14]. Overall, IBD affects more than 6.8 million patients worldwide, and several metaanalyses, including up to 27 studies, showed an independent association between IBD and atherosclerotic cardiovascular disease (ASCVD) [15,16]. Chronic inflammation and endothelial dysfunction are the two most important factors of atherogenesis [17,18]. Several mechanisms maintain chronic inflammation. A disturbed intestinal barrier in IBD allows the products of luminal microorganisms (lipopolysaccharides and other endotoxins) to enter the bloodstream. Lipopolysaccharides induce the expression of proinflammatory cytokines and affect the oxidation of low-density cholesterol and the activation of macrophages, contributing to endothelial dysfunction, foam cell formation, and, consequently, atherosclerosis. Metabolism of lipids by gut microbiota can also affect atherosclerosis [19,20]. Intestinal microbiota contributes to atherosclerosis by increasing the trimethylamine N-oxide level and inducing Toll-like receptor expression 2 and 4 [17,18]. In addition to structural and functional vascular alterations induced by chronic systemic inflammation, dyslipidemia, and accelerated development of atherosclerosis contribute to arterial thromboembolism [21,22,23,24,25,26,27,28,29,30,31]. Patients with ulcerative colitis have altered lipid profiles. Although the exact mechanism behind this is unknown, it is thought to be due to chronic inflammation and/or malabsorption [32]. CRP, TNF-α, vascular endothelial growth factor, and IL-6 participate in atherogenesis development and the pathogenesis of inflammatory bowel diseases. Their elevated serum levels in patients with ulcerative colitis contribute to the accelerated process of atherogenesis [21]. The overlap of the pathogenetic mechanisms of ulcerative colitis and atherosclerosis is also reflected in the elevated value of calprotectin, an acute reactant phase of inflammation. Calprotectin binds to Toll-like receptor 4 (TLR4), which mediates inflammation and atherosclerosis [25]. Disturbed platelet function is recognized in the pathogenesis of clinical complications of atherosclerosis. Aggregation (Ag) and activation of platelets play a crucial role in myocardial infarction, unstable angina pectoris, and stroke [33]. Moreover, elevated proinflammatory cytokines in patients with IBD, such as TNF-α and IL-1, can induce changes in endothelial cells, monocytes, macrophages, and platelets, such as upregulation of tissue factor, which binds plasma factor VIIa, resulting in procoagulant activity [34,35,36]. In addition, in patients with IBD, decreased levels of protein C and protein S, increased plasma levels of PAI-1, and reduced plasma levels of thrombin-activatable fibrinolysis inhibitor (TAFI) were found, indicating the imbalance of fibrinolysis in IBD [35,37]. In patients with IBD, absorption of nutrients, including folate and vitamin B12, is impaired [38,39,40,41,42,43,44,45]. Literature data also confirm a reduced concentration of vitamin B6 and elevated homocysteine in these patients [46]. It is known that a high level of homocysteine is a risk factor for thrombosis [47,48,49]. Folic acid and vitamin B12 play an essential role in the metabolic reactions of homocysteine [50,51]. The demethylation of methionine produces homocysteine, and the lack of vitamin B complex is the leading cause of hyperhomocysteinemia in patients with IBD [46]. Among the B complex vitamins, pyridoxine deficiency is a significant risk factor for hyperhomocysteinemia in IBD [52]. Therefore, the main goal of this research was to examine the relationship between biochemical, haemostatic and immune parameters of atherosclerosis and ulcerative colitis patients and its relationship to platelet aggregation. ## 2.1. Patients and Settings A clinical, observational, cross-sectional study was performed at the Djordje Joanović General Hospital, Zrenjanin, University Clinical Center Kragujevac, Center for Gastroenterohepatology and the Faculty of Medical Sciences, University of Kragujevac. All research procedures were made to the Principle of Good Clinical Practice, and ethical approvals were obtained from relevant ethics committees. A total of 120 patients were included in the trial. The patients were divided into four groups. The first group had 25 patients who had ulcerative colitis and atherosclerosis. The second group included 39 patients with ulcerative colitis without atherosclerosis. The third group consisted of 31 patients suffering from atherosclerosis without ulcerative colitis, and the fourth group consisted of 25 subjects as healthy control, without ulcerative colitis and atherosclerosis. ## 2.2. Inclusion and Exclusion Criteria The presence of the following inclusion and exclusion criteria had to be met to participate in the study (depending on the assigned group). 1. Inclusion criteria for experimental groups (ulcerative colitis and atherosclerosis, ulcerative colitis only and atherosclerosis only groups). (a) A diagnosis of ulcerative colitis based on the endoscopic examination of the colon and the pathohistological findings of the biopsies taken during the endoscopic examination of the colon, and following the criteria of the Third European Evidence-Based Consensus on Diagnosis and Management of Ulcerative Colitis from 2017 [53], and/or (b) an established diagnosis of atherosclerosis based on laboratory, clinical, and ultrasound parameters measured on carotid blood vessels. 2. Inclusion criteria for the control group include (a) normal findings on the endoscopic examination of the colon and negative laboratory and ultrasound parameters of atherosclerosis. 3. Signed voluntary consent to participate in the study (for all groups). The exclusion criteria were the following. (a) Respondents under 18, pregnant women, nursing mothers and persons with limited legal responsibility and reduced cognitive abilities; (b) respondents who took vitamin supplements in the previous 6 months; (c) subjects with other conditions or diseases that can cause vitamin deficiency (daily alcohol intake above 35 g, strict vegetarians, history of cancer, previous gastrectomy); (d) respondents who take or have taken in the previous six months medications that could affect the status of vitamin B and homocysteine (proton pump inhibitors, oral contraceptives, metformin, phenytoin, theophylline); (e) subjects with chronic and malignant diseases and/or therapy that may affect the investigated parameters including antilipidemic, antiaggregation, immunosuppressive, immunomodulatory, and corticosteroid therapy; and (f) infection and infectious syndromes two months before and during research. ## 2.3. Biochemical Parameters and Platelet Aggregability The complete blood count, biochemical analyses, and stool specimen analysis were determined in the Central Biochemical Laboratory of the University Clinical Center Kragujevac and the General Hospital Djordje Joanović laboratory Zrenjanin by using enzymatic methods on a Roche Cobas 6000 (c501module) analyzer (Roche Diagnostics, Basel Switzerland) and colourimetric assay by using commercially available kits, respectively. Serum concentrations of homocysteine were determined with high-performance liquid chromatography. Heparinized whole blood samples were used to assess platelet aggregability by using the impedance aggregometry method with a multiplate analyzer (Dynabyte, Munchen, Germany). Omega-3 PUFA’s antiplatelet impact was evaluated in two different ways. The first method involved taking precise measurements of platelet aggregability following the addition of agonists such as adenosine phosphate (ADP test) and arachidonate (ASPI test), with higher results indicating increased residual platelet aggregation and decreased antiplatelet effect of supplementation. When a patient did not take a glycoprotein IIb/IIIa antagonist, basal platelet aggregability was measured by using the thrombin receptor-activating protein (TRAP) test, which was used to evaluate the impact of inhibitors of glycoprotein IIb/IIIa receptors on the platelet aggregability. ## 2.4. Diagnosis of Atherosclerosis The Acuson 128XP ultrasonography (Siemens, Germany) with 5 MHz or 7 MHz linear-array transducers were used for carotid duplex ultrasound and color Doppler flow imaging by a single skilled sonographer. Subjects were examined in supine positions with their necks extended and their heads turned 45 degrees to the left or right. The first proximal centimetre of the internal carotid arteries in three separate projections (anterior, lateral, and posterior), as well as the last distal centimetre of the right and left common carotid artery and the bifurcation, were all scanned by using ultrasound technology. Measurement of the increased intima-media thickness was performed as a valid marker of atherosclerosis. The atherogenic index of plasma was calculated as the logarithm of triglycerides (TGL)/high-density lipoprotein (HDL) ratio, the atherogenic index was calculated as low-density lipoprotein (LDL)/high-density lipoprotein ratio, and the coronary risk index was calculated as total cholesterol/HDL ratio [54,55]. ## 2.5. Measurement of Cytokines in the Serum The separated serum of patients participating in the research was frozen at −20 °C until analysis. The concentration of cytokines involved in the pathogenesis of ulcerative colitis and atherosclerosis (TNF-α, IL-6) was measured by the ELISA method according to the established protocol of the manufacturer (R&D Systems, Minneapolis, MN, USA). ## 2.6. Statistical Analysis Numeric variables are shown as mean ± standard deviation (SD) or median (IQR). The data distribution was examined using the Shapiro–Wilk test or Kolmogorov–Smirnov test. A statistically significant difference between the four groups was determined by a Kruskal–Wallis or one-way analysis of variance (ANOVA) test, depending on the normality of the distribution of the examined parameter. Post hoc (Mann–Whitney U or Tukey Test) tests were conducted to determine which specific groups statistically significant difference occurred. During the post hoc tests, Bonferroni’s alpha value was corrected ($\frac{0.05}{6}$ = 0.008). The ROC curve method was used, and the statistical analysis reliability level was determined by determining the sensitivity and specificity of the test. Statistics were deemed to be significant at values of $p \leq 0.05.$ The statistical analysis was conducted by using SPSS version 20.0. ## 3. Results A total of 120 patients were included in this study, 68 ($56.7\%$) men and 52 ($43.3\%$) women. The average age of the patients with ulcerative colitis and atherosclerosis was 68.76 ± 8.90 years, while the average age of the patients with ulcerative colitis was only 38.08 ± 9.84 years. The average age of the patients with atherosclerosis only was 62.10 ± 9.89 years, and the average age of the healthy controls was 39.52 ± 9.88 years old. When compared to healthy controls, patients with ulcerative colitis and atherosclerosis, patients with ulcerative colitis without atherosclerosis and patients with atherosclerosis without ulcerative colitis had higher levels of SE, CRP, Ag PLT ADP, Ag PLT ASPI, Ag PLT TRAP, leukocytes, platelets, faecal calprotectin, TNF-α, and IL6 (Table 1). No significant difference was found between any groups regarding the parameters of vitamin B6, folic acid levels, coronary risk, atherogenic, and atherogenic index of plasma and TIBC values. A significant difference in the values of erythrocyte sedimentation rate ($$p \leq 0.008$$), Ag PLT ASPI value ($$p \leq 0.004$$), and Ag PLT TRAP value ($$p \leq 0.001$$) was observed between patients with ulcerative colitis and atherosclerosis and patients with ulcerative colitis, with higher levels in patients with both ulcerative colitis and atherosclerosis. Patients with ulcerative colitis and atherosclerosis had higher levels of erythrocyte sedimentation rate ($$p \leq 0.000$$), CRP ($$p \leq 0.000$$), Ag PLT ADP ($$p \leq 0.000$$), Ag PLT ASPI ($$p \leq 0.000$$), Ag PLT TRAP ($$p \leq 0.000$$), leukocyte ($$p \leq 0.000$$), platelet count ($$p \leq 0.001$$), and fecal calprotectin values ($$p \leq 0.000$$) when compared to the patients with atherosclerosis only. Values of vitamin B12 ($$p \leq 0.000$$), triglycerides ($$p \leq 0.000$$), ferritin ($$p \leq 0.001$$), and transferrin ($$p \leq 0.000$$) were significantly higher in patients with atherosclerosis only. Significantly higher levels of erythrocyte sedimentation rate ($$p \leq 0.000$$), CRP ($$p \leq 0.000$$), HDL ($$p \leq 0.000$$), transferrin saturation ($$p \leq 0.000$$), Ag PLT ADP ($$p \leq 0.000$$), Ag PLT ASPI ($$p \leq 0.000$$), Ag PLT TRAP ($$p \leq 0.000$$), leukocyte ($$p \leq 0.000$$), platelet count ($$p \leq 0.000$$), IL-6 ($$p \leq 0.000$$) and TNF-α values ($$p \leq 0.000$$) were observed in patients with ulcerative colitis and atherosclerosis when compared to healthy controls. In comparison, higher levels of vitamin B12 ($$p \leq 0.002$$) and serum iron values ($$p \leq 0.000$$) were observed in healthy patients. When patients with ulcerative colitis only and atherosclerosis only were compared, values of CRP ($$p \leq 0.000$$), transferrin saturation ($$p \leq 0.000$$), Ag PLT ADP ($$p \leq 0.000$$), leukocyte ($$p \leq 0.001$$) and fecal calprotectin ($$p \leq 0.000$$) were significantly higher in patients with ulcerative colitis only. Patients with atherosclerosis only had higher levels of vitamin B12 ($$p \leq 0.004$$), HDL ($$p \leq 0.004$$), cholesterol ($$p \leq 0.001$$), triglyceride ($$p \leq 0.000$$), remnant cholesterol ($$p \leq 0.000$$), and serum iron values ($$p \leq 0.001$$). Patients with ulcerative colitis only had higher levels of erythrocyte sedimentation rate ($$p \leq 0.000$$), CRP ($$p \leq 0.000$$), triglyceride ($$p \leq 0.000$$), Ag PLT ADP ($$p \leq 0.000$$), leukocyte ($$p \leq 0.001$$), fecal calprotectin ($$p \leq 0.000$$), IL-6 ($$p \leq 0.000$$), and TNF-α values ($$p \leq 0.000$$) than healthy controls. Higher levels of vitamin B12 ($$p \leq 0.004$$), HDL ($$p \leq 0.004$$), cholesterol ($$p \leq 0.001$$), serum iron ($$p \leq 0.001$$), and transferrin saturation ($$p \leq 0.000$$) were observed in healthy controls. Significantly higher values of erythrocyte sedimentation rate ($$p \leq 0.001$$), CRP ($$p \leq 0.000$$), LDL ($$p \leq 0.002$$), triglyceride ($$p \leq 0.000$$), remnant cholesterol ($$p \leq 0.001$$), ferritin ($$p \leq 0.000$$), Ag PLT ASPI ($$p \leq 0.001$$), IL-6 ($$p \leq 0.000$$), and TNF-α values ($$p \leq 0.000$$) were observed in patients with atherosclerosis only when compared to healthy controls. A one-way ANOVA test was used to analyze the variables shown in Table 2. The groups were compared to determine between which groups there was a statistically significant difference in the observed variables. No statistically significant difference in serum homocysteine values was shown between the examined groups. After the ANOVA test showed a significant difference between the four groups in values of non-HDL and UIBC (Table 2), the post-hoc Tukey test revealed that significantly higher levels of non-HDL in patients with atherosclerosis only, when compared to patients with ulcerative colitis only ($$p \leq 0.013$$). Levels of UIBC were significantly lower in patients with ulcerative colitis and atherosclerosis when compared to the patients with atherosclerosis only ($$p \leq 0.044$$) (Table 2). The receiver operating characteristic (ROC) curve analysis showed that Ag PLT TRAP has the highest sensitivity and specificity in assessing the risk of developing atherosclerosis (area under the curve (AUC)) = 0.753, sensitivity $85.3\%$, specificity $70.8\%$) (Figure 1A–F). ## 4. Discussion The connection of atherosclerotic parameters as predictors of cardiovascular risk in patients with ulcerative colitis is explained by inflammation, which represents the pathophysiological basis of both conditions. Inflammation plays a strong role in the pathogenesis of the atherosclerotic cardiovascular disease (ASCVD). Although many serological markers of inflammation exist today, no marker alone seems to predict or identify disease activity in ulcerative colitis [56]. Our study shows higher levels of SE, CRP, Ag PLT ASPI, Ag PLT TRAP, Ag PLT ADP, Le, PLT, FCP, TNF-α, and IL-6 in patients with ulcerative colitis, when compared to the healthy controls, as well as lower levels of vitamins B12, B6, serum Fe, and transferrin saturation. Several large studies have confirmed an increased risk of ASCVD, especially myocardial infarction, in those patients with elevated CRP and hs-CRP values [56,57]. On the other hand, different CRP levels correlate with the clinical and endoscopic activity of ulcerative colitis [58,59]. Determining these serum markers in daily clinical practice could assess the activity and dynamics of ulcerative colitis disease and the risk of ASCVD. In our study, the highest CRP values were in patients with ulcerative colitis and patients with ulcerative colitis and atherosclerosis, which was expected because CRP is a positive reactant of acute inflammation. A significant difference was also registered between patients with ulcerative colitis and patients with atherosclerosis compared to healthy patients, confirming that CRP is a good marker of chronic inflammation. Vitamin B12 deficiency occurs in $5\%$, and folic acid deficiency is reported in 30–$40\%$ of ulcerative colitis patients [60]. Vitamin B12 and folate deficiency can contribute to hyperhomocysteinemia, a risk factor for thrombosis [42,43,44,47,48,49]. Literature data confirm that patients with IBD are at a higher risk of hyperhomocysteinemia [50,51]. Vitamin B deficiency, specifically vitamin B6, is a significant risk factor for hyperhomocysteinemia in patients with IBD [46,52]. Our research revealed no deficiency of vitamins B12, B6, folic acid, or hyperhomocysteinemia in any of the studied groups. Numerous studies have analyzed lipid profiles in patients with ulcerative colitis, and results show significantly lower lipid concentrations in the blood than those without IBD. Despite these results, it was shown that early signs of ASCVD are still detected in patients with ulcerative colitis, including increased carotid artery thickness, elevated levels of homocysteine, and hs-CRP [56]. In our study, lower levels of total cholesterol and LDL were recorded in patients with ulcerative colitis and those with ulcerative colitis and atherosclerosis, similar to the study’s results. Some studies favour triglycerides and remnant cholesterol as significant risk factors for atherosclerosis and ASCVD [61,62,63,64]. In our study, despite the lower triglyceride levels registered in patients with ulcerative colitis, the levels of triglycerides were higher in patients with ulcerative colitis and atherosclerosis. Additionally, patients with ulcerative colitis and atherosclerosis had higher remnant cholesterol and triglyceride values when compared to patients with ulcerative colitis. Analyzing the atherosclerosis index, which was obtained by calculating based on the quotient of lipid values in the examined groups, we noticed that the atherogenic index and coronary risk index were the highest in patients with atherosclerosis, which was expected and then in patients with ulcerative colitis. The coronary risk index was the highest in patients with ulcerative colitis and the lowest in patients with atherosclerosis. Although different average values of the atherosclerosis index were registered, no statistically significant difference was recorded when comparing the groups. Iron deficiency is registered in 60–$80\%$ of patients with IBD. Hypoferremia is the cause of microcytic anemia, which can also overlap with anemia due to chronic illness in these patients. In conditions in which biochemical and clinical signs of inflammation are absent in the patient, iron deficiency should be suspected when the serum ferritin level is lower than 30 μg/L [65,66]. Our research recorded no serum ferritin level lower than 30 μg/L. Higher serum ferritin values in patients with atherosclerosis and patients with ulcerative colitis than those with ulcerative colitis and atherosclerosis can be explained by low-grade chronic inflammation. In patients with ulcerative colitis and atherosclerosis, it can be observed that the ferritin is lower, and consequent microcytic anemia is in agreement with other literature data. Leukocytosis, as a consequence of inflammation, is common in patients with atherosclerosis and those with ulcerative colitis [67,68]. In our research, leukocytosis was not recorded. However, leukocyte values were higher in patients with ulcerative colitis and atherosclerosis and patients with ulcerative colitis than in the other two groups, which was expected due to chronic inflammation. Ulcerative colitis is also associated with thrombocytosis. The high platelet count is likely due to increased thrombopoiesis, which is induced by higher plasma levels of thrombopoietin and IL-6 [69,70,71] or is caused by iron deficiency [72]. Some studies describe a correlation between high platelet counts and atherosclerosis [73,74,75]. In our research, higher values of platelets were registered in patients with ulcerative colitis and atherosclerosis and patients with ulcerative colitis, which coincides with the results of the mentioned studies. In addition to the value of platelets in patients with ulcerative colitis and atherosclerosis patients, perhaps even more important is the aggregation of platelets. The highest values of platelet aggregation—Ag PLT ADP, Ag PLT ASPI, and Ag PLT TRAP—were registered in patients with ulcerative colitis and atherosclerosis, and a statistically significant difference was registered in Ag PLT ASPI, Ag PLT ADP, and Ag PLT TRAP. The results are expected and potentially indicate a greater tendency for thrombosis in patients with ulcerative colitis and atherosclerosis as a result of increased platelet aggregation. Fecal calprotectin in patients with ulcerative colitis has great clinical significance in monitoring disease activity [76,77]. Our research obtained results consistent with other research and clinical presentation. Namely, elevated values of fecal calprotectin were registered in patients with ulcerative colitis (with or without atherosclerosis), while in patients with atherosclerosis only, the value of fecal calprotectin was normal. In healthy control, the value of fecal calprotectin was not determined. Our research included determining cytokine values with a significant and proven role in atherosclerosis and ulcerative colitis pathogenesis. Our results showed increased values of TNF-α and IL6 in patients with ulcerative colitis and atherosclerosis, ulcerative colitis only, and atherosclerosis only, considering that chronic inflammation is present in the aforementioned investigated groups [78,79,80,81]. The studied groups’ average blood pressure (BP) values were also analyzed. The highest blood pressure values were recorded in patients with atherosclerosis (with or without ulcerative colitis). An elevated blood pressure value was not recorded in patients with ulcerative colitis and healthy controls. The obtained results were expected and simply can be interpreted by the presence of atherosclerosis, which is also the most crucial pathophysiological mechanism underlying hypertension. According to our results, Ag PLT TRAP showed the highest sensitivity and specificity between all analysed serum markers, which allows discrimination of subjects with ulcerative colitis with the highest risk of developing atherosclerosis. The limitations of this study are the small number of patients included in the research and, therefore, limited analysis. Regardless, this research provides insight into the possible mechanisms of the connection between ulcerative colitis and atherosclerosis, one of the most common cardiovascular manifestations. Moreover, in this study, there was no follow-up of patients that would provide temporal insight into the relationship between markers of inflammation, platelet aggregability, and outcomes in patients with ulcerative colitis and atherosclerosis. This study provides insights into possible mechanisms of the connection between ulcerative colitis and atherosclerosis as one of the most common manifestations, as well as the role of inflammation and platelet aggregation. In our study, the levels of inflammatory markers were markedly elevated in patients with both ulcerative colitis and atherosclerosis when compared to patients with ulcerative colitis only, confirming the hypothesis that inflammation is a crucial mechanism of accelerated atherosclerosis in patients with ulcerative colitis. Further studies are needed to examine all possible mechanisms and associations. ## References 1. Fakhoury M., Al-Salami H., Negrulj R., Mooranian A.. Inflammatory Bowel Disease: Clinical Aspects and Treatments. J. Inflamm. Res. (2014) 7 113. DOI: 10.2147/JIR.S65979 2. Venkateswaran N., Weismiller S., Clarke K.. Indeterminate Colitis—Update on Treatment Options. J. Inflamm. Res. (2021) 14 6383-6395. DOI: 10.2147/JIR.S268262 3. Feuerstein J.D., Moss A.C., Farraye F.A.. Ulcerative Colitis. Mayo Clin. Proc. (2019) 94 1357-1373. DOI: 10.1016/j.mayocp.2019.01.018 4. Maaser C., Sturm A., Vavricka S.R., Kucharzik T., Fiorino G., Annese V., Calabrese E., Baumgart D.C., Bettenworth D., Borralho Nunes P.. ECCO-ESGAR Guideline for Diagnostic Assessment in IBD Part 1: Initial Diagnosis, Monitoring of Known IBD, Detection of Complications. J. Crohn’s Colitis (2019) 13 144-164K. DOI: 10.1093/ecco-jcc/jjy113 5. Sairenji T., Collins K.L., Evans D.V.. An Update on Inflammatory Bowel Disease. Prim. Care Clin. Off. Pract. (2017) 44 673-692. DOI: 10.1016/j.pop.2017.07.010 6. Gajendran M., Loganathan P., Jimenez G., Catinella A.P., Ng N., Umapathy C., Ziade N., Hashash J.G.. A Comprehensive Review and Update on Ulcerative Colitis. Dis. A Mon. (2019) 65 100851. DOI: 10.1016/j.disamonth.2019.02.004 7. Nóbrega V.G., Silva I.N.D.N., Brito B.S., Silva J., Silva M.C.M.D., Santana G.O.. The Onset of Clinical Manifestations in Inflammatory Bowel Disease Patients. Arq. De Gastroenterol. (2018) 55 290-295. DOI: 10.1590/s0004-2803.201800000-73 8. Olpin J.D., Sjoberg B.P., Stilwill S.E., Jensen L.E., Rezvani M., Shaaban A.M.. Beyond the Bowel: Extraintestinal Manifestations of Inflammatory Bowel Disease. RadioGraphics (2017) 37 1135-1160. DOI: 10.1148/rg.2017160121 9. Karmiris K., Avgerinos A., Tavernaraki A., Zeglinas C., Karatzas P., Koukouratos T., Oikonomou K.A., Kostas A., Zampeli E., Papadopoulos V.. Prevalence and Characteristics of Extra-Intestinal Manifestations in a Large Cohort of Greek Patients with Inflammatory Bowel Disease. J. Crohn’s Colitis (2016) 10 429-436. DOI: 10.1093/ecco-jcc/jjv232 10. Faxon D.P., Fuster V., Libby P., Beckman J.A., Hiatt W.R., Thompson R.W., Topper J.N., Annex B.H., Rundback J.H., Fabunmi R.P.. Atherosclerotic Vascular Disease Conference. Circulation (2004) 109 2617-2625. DOI: 10.1161/01.CIR.0000128520.37674.EF 11. Libby P., Ridker P.M., Hansson G.K.. Progress and Challenges in Translating the Biology of Atherosclerosis. Nature (2011) 473 317-325. DOI: 10.1038/nature10146 12. Wu H., Hu T., Hao H., Hill M.A., Xu C., Liu Z.. Inflammatory Bowel Disease and Cardiovascular Diseases: A Concise Review. Eur. Heart J. Open (2022) 2 oeab029. DOI: 10.1093/ehjopen/oeab029 13. Oliva A., Aversano L., De Angelis M., Mascellino M.T., Miele M.C., Morelli S., Battaglia R., Iera J., Bruno G., Corazziari E.S.. Persistent Systemic Microbial Translocation, Inflammation, and Intestinal Damage during Clostridioides Difficile Infection. Open Forum Infect. Dis. (2019) 7 ofz507. DOI: 10.1093/ofid/ofz507 14. Oliva A., Miele M.C., Di Timoteo F., De Angelis M., Mauro V., Aronica R., Al Ismail D., Ceccarelli G., Pinacchio C., d’Ettorre G.. Persistent Systemic Microbial Translocation and Intestinal Damage during Coronavirus Disease-19. Front. Immunol. (2021) 12 708149. DOI: 10.3389/fimmu.2021.708149 15. Alatab S., Sepanlou S.G., Ikuta K., Vahedi H., Bisignano C., Safiri S., Sadeghi A., Nixon M.R., Abdoli A., Abolhassani H.. The Global, Regional, and National Burden of Inflammatory Bowel Disease in 195 Countries and Territories, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol. Hepatol. (2020) 5 17-30. DOI: 10.1016/S2468-1253(19)30333-4 16. Sun H.-H., Tian F.. Inflammatory Bowel Disease and Cardiovascular Disease Incidence and Mortality: A Meta-Analysis. Eur. J. Prev. Cardiol. (2018) 25 1623-1631. DOI: 10.1177/2047487318792952 17. Shen X., Li L., Sun Z., Zang G., Zhang L., Shao C., Wang Z.. Gut Microbiota and Atherosclerosis—Focusing on the Plaque Stability. Front. Cardiovasc. Med. (2021) 8 668532. DOI: 10.3389/fcvm.2021.668532 18. van den Munckhof I.C.L., Kurilshikov A., ter Horst R., Riksen N.P., Joosten L.A.B., Zhernakova A., Fu J., Keating S.T., Netea M.G., de Graaf J.. Role of Gut Microbiota in Chronic Low-Grade Inflammation as Potential Driver for Atherosclerotic Cardiovascular Disease: A Systematic Review of Human Studies. Obes. Rev. (2018) 19 1719-1734. DOI: 10.1111/obr.12750 19. Vourakis M., Mayer G., Rousseau G.. The Role of Gut Microbiota on Cholesterol Metabolism in Atherosclerosis. Int. J. Mol. Sci. (2021) 22. DOI: 10.3390/ijms22158074 20. Duttaroy A.K.. Role of Gut Microbiota and Their Metabolites on Atherosclerosis, Hypertension and Human Blood Platelet Function: A Review. Nutrients (2021) 13. DOI: 10.3390/nu13010144 21. Reiss A.B., Siegart N.M., De Leon J.. Interleukin-6 in atherosclerosis: Atherogenic or atheroprotective. Clin. Lipidol. (2017) 12 14-23. DOI: 10.1080/17584299.2017.1319787 22. Yarur A.J., Deshpande A.R., Pechman D.M., Tamariz L., Abreu M.T., Sussman D.A.. Inflammatory Bowel Disease Is Associated with an Increased Incidence of Cardiovascular Events. Am. J. Gastroenterol. (2011) 106 741-747. DOI: 10.1038/ajg.2011.63 23. Manichanh C., Borruel N., Casellas F., Guarner F.. The Gut Microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol. (2012) 9 599-608. DOI: 10.1038/nrgastro.2012.152 24. Tang W.H.W., Li D.Y., Hazen S.L.. Dietary Metabolism, the Gut Microbiome, and Heart Failure. Nat. Rev. Cardiol. (2018) 16 137-154. DOI: 10.1038/s41569-018-0108-7 25. Kruzliak P., Novák J., Novák M., Fodor G.J.. Role of Calprotectin in Cardiometabolic Diseases. Cytokine Growth Factor Rev. (2014) 25 67-75. DOI: 10.1016/j.cytogfr.2014.01.005 26. Tan V.P., Chung A., Yan B.P., Gibson P.R.. Venous and Arterial Disease in Inflammatory Bowel Disease. J. Gastroenterol. Hepatol. (2013) 28 1095-1113. DOI: 10.1111/jgh.12260 27. Zezos P.. Inflammatory Bowel Disease and Thromboembolism. World J. Gastroenterol. (2014) 20 13863. DOI: 10.3748/wjg.v20.i38.13863 28. Harper J.W., Zisman T.L.. Interaction of Obesity and Inflammatory Bowel Disease. World J. Gastroenterol. (2016) 22 7868-7881. DOI: 10.3748/wjg.v22.i35.7868 29. Kamperidis N., Kamperidis V., Zegkos T., Kostourou I., Nikolaidou O., Arebi N., Karvounis H.. Atherosclerosis and Inflammatory Bowel Disease—Shared Pathogenesis and Implications for Treatment. Angiology (2020) 72 303-314. DOI: 10.1177/0003319720974552 30. Zanoli L., Rastelli S., Granata A., Inserra G., Empana J.-P., Boutouyrie P., Laurent S., Castellino P.. Arterial Stiffness in Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis. J. Hypertens. (2016) 34 822-829. DOI: 10.1097/HJH.0000000000000867 31. Scaldaferri F.. Haemostatic System in Inflammatory Bowel Diseases: New Players in Gut Inflammation. World J. Gastroenterol. (2011) 17 594. DOI: 10.3748/wjg.v17.i5.594 32. Rungoe C., Nyboe Andersen N., Jess T.. Inflammatory Bowel Disease and Risk of Coronary Heart Disease. Trends Cardiovasc. Med. (2015) 25 699-704. DOI: 10.1016/j.tcm.2015.03.010 33. Wang L., Tang C.. Targeting Platelet in Atherosclerosis Plaque Formation: Current Knowledge and Future Perspectives. Int. J. Mol. Sci. (2020) 21. DOI: 10.3390/ijms21249760 34. Kirchhofer D., Tschopp T.B., Hadváry P., Baumgartner H.R.. Endothelial Cells Stimulated with Tumor Necrosis Factor-Alpha Express Varying Amounts of Tissue Factor Resulting in Inhomogenous Fibrin Deposition in a Native Blood Flow System. Effects of Thrombin Inhibitors. J. Clin. Investig. (1994) 93 2073-2083. DOI: 10.1172/JCI117202 35. Stadnicki A., Stadnicka I.. Venous and Arterial Thromboembolism in Patients with Inflammatory Bowel Diseases. World J. Gastroenterol. (2021) 27 6757-6774. DOI: 10.3748/wjg.v27.i40.6757 36. Butenas S., Orfeo T., Mann K.G.. Tissue Factor in Coagulation: Which? Where? When?. Arterioscler. Thromb. Vasc. Biol. (2009) 29 1989-1996. DOI: 10.1161/ATVBAHA.108.177402 37. Koutroubakis I.E., Sfiridaki A., Tsiolakidou G., Coucoutsi C., Theodoropoulou A., Kouroumalis E.A.. Plasma Thrombin-Activatable Fibrinolysis Inhibitor and Plasminogen Activator Inhibitor-1 Levels in Inflammatory Bowel Disease. Eur. J. Gastroenterol. Hepatol. (2008) 20 912-916. DOI: 10.1097/MEG.0b013e3282faa759 38. Leddin D., Tamim H., Levy A.R.. Is Folate Involved in the Pathogenesis of Inflammatory Bowel Disease?. Med. Hypotheses (2013) 81 940-941. DOI: 10.1016/j.mehy.2013.08.025 39. Weisshof R., Chermesh I.. Micronutrient Deficiencies in Inflammatory Bowel Disease. Curr. Opin. Clin. Nutr. Metab. Care (2015) 18 576-581. DOI: 10.1097/MCO.0000000000000226 40. Massironi S., Rossi R.E., Cavalcoli F.A., Della Valle S., Fraquelli M., Conte D.. Nutritional Deficiencies in Inflammatory Bowel Disease: Therapeutic Approaches. Clin. Nutr. (2013) 32 904-910. DOI: 10.1016/j.clnu.2013.03.020 41. Battat R., Kopylov U., Byer J., Sewitch M.J., Rahme E., Nedjar H., Zelikovic E., Dionne S., Bessissow T., Afif W.. Vitamin B12 Deficiency in Inflammatory Bowel Disease. Eur. J. Gastroenterol. Hepatol. (2017) 29 1361-1367. DOI: 10.1097/MEG.0000000000000970 42. Owczarek D.. Diet and Nutritional Factors in Inflammatory Bowel Diseases. World J. Gastroenterol. (2016) 22 895. DOI: 10.3748/wjg.v22.i3.895 43. Ratajczak A.E., Szymczak-Tomczak A., Rychter A.M., Zawada A., Dobrowolska A., Krela-Kaźmierczak I.. Does Folic Acid Protect Patients with Inflammatory Bowel Disease from Complications?. Nutrients (2021) 13. DOI: 10.3390/nu13114036 44. Pan Y., Liu Y., Guo H., Jabir M.S., Liu X., Cui W., Li D.. Associations between Folate and Vitamin B12 Levels and Inflammatory Bowel Disease: A Meta-Analysis. Nutrients (2017) 9. DOI: 10.3390/nu9040382 45. Jayawardena D., Dudeja P.K.. Micronutrient Deficiency in Inflammatory Bowel Diseases: Cause or Effect?. Cell. Mol. Gastroenterol. Hepatol. (2020) 9 707-708. DOI: 10.1016/j.jcmgh.2019.12.009 46. Erzin Y., Uzun H., Celik A.F., Aydin S., Dirican A., Uzunismail H.. Hyperhomocysteinemia in Inflammatory Bowel Disease Patients without Past Intestinal Resections: Correlations with Cobalamin, Pyridoxine, Folate Concentrations, Acute Phase Reactants, Disease Activity, and Prior Thromboembolic Complications. J. Clin. Gastroenterol. (2008) 42 481-486. DOI: 10.1097/MCG.0b013e318046eab0 47. Vasilopoulos S., Saiean K., Emmons J., Berger W.L., Abu-Hajir M., Seetharam B., Binion D.G.. Terminal Ileum Resection Is Associated with Higher Plasma Homocysteine Levels in Crohn’s Disease. J. Clin. Gastroenterol. (2001) 33 132-136. DOI: 10.1097/00004836-200108000-00008 48. Chowers Y., Sela B.A., Holland R., Fidder H., Simoni F.B., Bar-Meir S.. Increased Levels of Homocysteine in Patients with Crohn’s Disease Are Related to Folate Levels. Am. J. Gastroenterol. (2000) 95 3498-3502. DOI: 10.1111/j.1572-0241.2000.03367.x 49. Oussalah A., Guéant J.-L., Peyrin-Biroulet L.. Meta-Analysis: Hyperhomocysteinaemia in Inflammatory Bowel Diseases. Aliment. Pharmacol. Ther. (2011) 34 1173-1184. DOI: 10.1111/j.1365-2036.2011.04864.x 50. Stipanuk M.H.. SULFUR AMINO ACID METABOLISM: Pathways for Production and Removal of Homocysteine and Cysteine. Annu. Rev. Nutr. (2004) 24 539-577. DOI: 10.1146/annurev.nutr.24.012003.132418 51. Hwang C., Ross V., Mahadevan U.. Micronutrient Deficiencies in Inflammatory Bowel Disease: From a to Zinc. Inflamm. Bowel Dis. (2012) 18 1961-1981. DOI: 10.1002/ibd.22906 52. Vagianos K., Bector S., McConnell J., Bernstein C.N.. Nutrition Assessment of Patients with Inflammatory Bowel Disease. JPEN. J. Parenter. Enter. Nutr. (2007) 31 311-319. DOI: 10.1177/0148607107031004311 53. Magro F., Gionchetti P., Eliakim R., Ardizzone S., Armuzzi A., Barreiro-de Acosta M., Burisch J., Gecse K.B., Hart A.L., Hindryckx P.. Third European Evidence-Based Consensus on Diagnosis and Management of Ulcerative Colitis. Part 1: Definitions, Diagnosis, Extra-Intestinal Manifestations, Pregnancy, Cancer Surveillance, Surgery, and Ileo-Anal Pouch Disorders. J. Crohn’s Colitis (2017) 11 649-670. DOI: 10.1093/ecco-jcc/jjx008 54. Kim S.H., Cho Y.K., Kim Y.-J., Jung C.H., Lee W.J., Park J.-Y., Huh J.H., Kang J.G., Lee S.J., Ihm S.-H.. Association of the Atherogenic Index of Plasma with Cardiovascular Risk beyond the Traditional Risk Factors: A Nationwide Population-Based Cohort Study. Cardiovasc. Diabetol. (2022) 21 81. DOI: 10.1186/s12933-022-01522-8 55. Kazemi T., Hajihosseini M., Moossavi M., Hemmati M., Ziaee M.. Cardiovascular Risk Factors and Atherogenic Indices in an Iranian Population: Birjand East of Iran. Clin. Med. Insights Cardiol. (2018) 12 117954681875928. DOI: 10.1177/1179546818759286 56. Bigeh A., Sanchez A., Maestas C., Gulati M.. Inflammatory Bowel Disease and the Risk for Cardiovascular Disease: Does All Inflammation Lead to Heart Disease?. Trends Cardiovasc. Med. (2019) 30 463-469. DOI: 10.1016/j.tcm.2019.10.001 57. Grundy S.M., Stone N.J., Bailey A.L., Beam C., Birtcher K.K., Blumenthal R.S., Braun L.T., de Ferranti S., Faiella-Tommasino J., Forman D.E.. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol. Circulation (2018) 139 e1082-e1143. DOI: 10.1161/cir.0000000000000625 58. Croft A., Lord A., Radford-Smith G.. Markers of Systemic Inflammation in Acute Attacks of Ulcerative Colitis: What Level of C-Reactive Protein Constitutes Severe Colitis?. J. Crohn’s Colitis (2022) 16 1089-1096. DOI: 10.1093/ecco-jcc/jjac014 59. Bakkaloglu O.K., Eskazan T., Celik S., Kurt E.A., Hatemi I., Erzin Y., Celik A.F.. Can We Predict Mucosal Remission in Ulcerative Colitis More Precisely with a Redefined Cutoff Level of C-Reactive Protein?. Color. Dis. Off. J. Assoc. Coloproctology Great Br. Irel. (2022) 24 77-84. DOI: 10.1111/codi.15940 60. Guagnozzi D., Lucendo A.J.. Anemia in Inflammatory Bowel Disease: A Neglected Issue with Relevant Effects. World J. Gastroenterol. WJG (2014) 20 3542-3551. DOI: 10.3748/wjg.v20.i13.3542 61. Castañer O., Pintó X., Subirana I., Amor A.J., Ros E., Hernáez Á., Martínez-González M.Á., Corella D., Salas-Salvadó J., Estruch R.. Remnant Cholesterol, Not LDL Cholesterol, Is Associated with Incident Cardiovascular Disease. J. Am. Coll. Cardiol. (2020) 76 2712-2724. DOI: 10.1016/j.jacc.2020.10.008 62. Nordestgaard B.G.. Triglyceride-Rich Lipoproteins and Atherosclerotic Cardiovascular Disease. Circ. Res. (2016) 118 547-563. DOI: 10.1161/CIRCRESAHA.115.306249 63. Sandesara P.B., Virani S.S., Fazio S., Shapiro M.D.. The Forgotten Lipids: Triglycerides, Remnant Cholesterol, and Atherosclerotic Cardiovascular Disease Risk. Endocr. Rev. (2018) 40 537-557. DOI: 10.1210/er.2018-00184 64. Jepsen A.-M.K., Langsted A., Varbo A., Bang L.E., Kamstrup P.R., Nordestgaard B.G.. Increased Remnant Cholesterol Explains Part of Residual Risk of All-Cause Mortality in 5414 Patients with Ischemic Heart Disease. Clin. Chem. (2016) 62 593-604. DOI: 10.1373/clinchem.2015.253757 65. Stein J., Dignass A.U.. Management of Iron Deficiency Anemia in Inflammatory Bowel Disease—A Practical Approach. Ann. Gastroenterol. (2013) 26 104-113. PMID: 24714874 66. Dignass A.U., Gasche C., Bettenworth D., Birgegård G., Danese S., Gisbert J.P., Gomollon F., Iqbal T., Katsanos K., Koutroubakis I.. European Consensus on the Diagnosis and Management of Iron Deficiency and Anaemia in Inflammatory Bowel Diseases. J. Crohn’s Colitis (2015) 9 211-222. DOI: 10.1093/ecco-jcc/jju009 67. Cioffi M.. Laboratory Markers in Ulcerative Colitis: Current Insights and Future Advances. World J. Gastrointest. Pathophysiol. (2015) 6 13. DOI: 10.4291/wjgp.v6.i1.13 68. Madjid M., Awan I., Willerson J.T., Casscells S.W.. Leukocyte Count and Coronary Heart Disease. J. Am. Coll. Cardiol. (2004) 44 1945-1956. DOI: 10.1016/j.jacc.2004.07.056 69. Chen Z., Lu Y., Wu J., Zhang H.. Clinical Significance of Blood Platelets and Mean Platelet Volume in Patients with Ulcerative Colitis. J. Int. Med. Res. (2021) 49 3000605211009715. DOI: 10.1177/03000605211009715 70. Giannotta M., Tapete G., Emmi G., Silvestri E., Milla M.. Thrombosis in Inflammatory Bowel Diseases: What’s the Link?. Thromb. J. (2015) 13 14. DOI: 10.1186/s12959-015-0044-2 71. Heits F., Stahl M., Ludwig D., Stange E.F., Jelkmann W.. Elevated Serum Thrombopoietin and Interleukin-6 Concentrations in Thrombocytosis Associated with Inflammatory Bowel Disease. J. Interferon Cytokine Res. (1999) 19 757-760. DOI: 10.1089/107999099313604 72. Evstatiev R., Bukaty A., Jimenez K., Kulnigg-Dabsch S., Surman L., Schmid W., Eferl R., Lippert K., Scheiber-Mojdehkar B., Michael Kvasnicka H.. Iron Deficiency Alters Megakaryopoiesis and Platelet Phenotype Independent of Thrombopoietin. Am. J. Hematol. (2014) 89 524-529. DOI: 10.1002/ajh.23682 73. Bath P.M., Missouris C.G., Buckenham T., MacGregor G.A.. Increased Platelet Volume and Platelet Mass in Patients with Atherosclerotic Renal Artery Stenosis. Clin. Sci. (1994) 87 253-257. DOI: 10.1042/cs0870253 74. Liu K., Xu J., Tao L., Yang K., Sun Y., Guo X.. Platelet Counts Are Associated with Arterial Stiffness in Chinese Han Population: A Longitudinal Study. BMC Cardiovasc. Disord. (2020) 20. DOI: 10.1186/s12872-020-01634-7 75. Lee M.K.S., Kraakman M.J., Dragoljevic D., Hanssen N.M.J., Flynn M.C., Al-Sharea A., Sreejit G., Bertuzzo-Veiga C., Cooney O.D., Baig F.. Apoptotic Ablation of Platelets Reduces Atherosclerosis in Mice with Diabetes. Arterioscler. Thromb. Vasc. Biol. (2021) 41 1167-1178. DOI: 10.1161/ATVBAHA.120.315369 76. Khaki-Khatibi F., Qujeq D., Kashifard M., Moein S., Maniati M., Vaghari-Tabari M.. Calprotectin in Inflammatory Bowel Disease. Clin. Chim. Acta (2020) 510 556-565. DOI: 10.1016/j.cca.2020.08.025 77. Xiang J.-Y., Ouyang Q., Li G.-D., Xiao N.-P.. Clinical Value of Fecal Calprotectin in Determining Disease Activity of Ulcerative Colitis. World J. Gastroenterol. (2008) 14 53. DOI: 10.3748/wjg.14.53 78. Ridker P.M., Everett B.M., Thuren T., MacFadyen J.G., Chang W.H., Ballantyne C., Fonseca F., Nicolau J., Koenig W., Anker S.D.. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N. Engl. J. Med. (2017) 377 1119-1131. DOI: 10.1056/NEJMoa1707914 79. Kirii H., Niwa T., Yamada Y., Wada H., Saito K., Iwakura Y., Asano M., Moriwaki H., Seishima M.. Lack of Interleukin-1β Decreases the Severity of Atherosclerosis in ApoE-Deficient Mice. Arterioscler. Thromb. Vasc. Biol. (2003) 23 656-660. DOI: 10.1161/01.ATV.0000064374.15232.C3 80. Shimokawa H., Ito A., Fukumoto Y., Kadokami T., Nakaike R., Sakata M., Takayanagi T., Egashira K., Takeshita A.. Chronic Treatment with Interleukin-1 Beta Induces Coronary Intimal Lesions and Vasospastic Responses in Pigs in Vivo. The Role of Platelet-Derived Growth Factor. J. Clin. Investig. (1996) 97 769-776. DOI: 10.1172/JCI118476 81. Duewell P., Kono H., Rayner K.J., Sirois C.M., Vladimer G., Bauernfeind F.G., Abela G.S., Franchi L., Nuñez G., Schnurr M.. NLRP3 Inflamasomes Are Required for Atherogenesis and Activated by Cholesterol Crystals That Form Early in Disease. Nature (2010) 464 1357-1361. DOI: 10.1038/nature08938
--- title: Early Administration of Vancomycin Inhibits Pulmonary Embolism by Remodeling Gut Microbiota authors: - Zhengyan Zhang - Huiling Chen - Jiating Huang - Shilong Zhang - Zhanming Li - Chaoyue Kong - Yuqin Mao - Bing Han journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10059710 doi: 10.3390/jpm13030537 license: CC BY 4.0 --- # Early Administration of Vancomycin Inhibits Pulmonary Embolism by Remodeling Gut Microbiota ## Abstract Pulmonary embolism (PE) is a common and potentially fatal condition in the emergency department, and early identification of modifiable risk factors for prevention and management is highly desirable. Although gut dysbiosis is associated with a high incidence of venous thromboembolism, the role and mechanisms of the gut microbiome in the pathogenesis of venous thromboembolism, especially PE, remain unexplored. Here, we attempted to elucidate the benefits of the gut microbiome in the pathogenesis of PE using multiple antibiotics and fecal microbiota transplantation (FMT) for early intervention in a classical mouse model of PE. The results showed that early administration of various antibiotics (except ampicillin) could inhibit pulmonary thrombosis to a certain extent and reduced mortality in young and old mice with PE. Among them, vancomycin has the best inhibitory effect on PE. With the help of gut microbiota sequencing analysis, we found that antibiotic treatment can reshape the gut microbiota; especially vancomycin can significantly improve the gut microbiota structure in PE mice. Furthermore, FMT could transfer vancomycin-modified gut microbes into mice and inhibit the pathogenesis of PE, possibly due to increased intestinal colonization by Parasutterella. These data elucidate the underlying molecular mechanism by which early administration of vancomycin can remodel the gut microbiota to suppress PE, providing new clues for clinical optimization and development of PE prevention strategies. ## 1. Introduction Deep vein thrombosis and pulmonary embolism (PE) constitute venous thromboembolism [1]. Venous thromboembolism is a major global burden with approximately 10 million cases per year, therefore representing the third leading vascular disease after acute myocardial infarction and stroke [2]. Venous thromboembolism is a frequently recurring chronic disease that is associated with death, major bleeding associated with anticoagulation, and long-term disability. In the United States, the annual medical costs of venous thromboembolism are estimated at USD 7–10 billion [3]. Incidence is steadily rising due to an aging population and increased prevalence of comorbidities associated with venous thromboembolism, such as obesity, heart failure, and cancer, as well as increased sensitivity and widespread use of imaging tests to detect venous thromboembolism [4]. The average annual incidence rate increases exponentially with age, reaching as high as one case per hundred people over the age of 80 [5]. Beginning at age 45, the lifetime risk of developing venous thromboembolism is $8\%$ [6]. In the International Cooperative Pulmonary Embolism Registry, the primary outcome—all-cause mortality rate at 3 months—associated with acute pulmonary embolism was $17\%$. The registry had no exclusion criteria and consecutively enrolled 2454 patients from 52 hospitals in seven countries in Europe and North America. PE was considered to be the cause of death in $45\%$ of patients [7]. Sequelae of venous thromboembolism are also associated with severe disability and include post-thrombotic syndrome, which develops in 20–$50\%$ of patients with deep vein thrombosis, and chronic thromboembolic pulmonary hypertension, which complicates 0.1–$4.0\%$ of pulmonary embolisms [8]. Post-thrombotic syndrome can lead to chronic lower leg swelling, which can lead to trophic disorders including venous ulcers. Chronic thromboembolic pulmonary hypertension is defined as a mean pulmonary arterial pressure greater than 25 mm Hg for 6 months after the diagnosis of pulmonary embolism. It causes dyspnea and even sudden cardiac death with rest and exertion [9]. Deaths from PE usually occur within weeks after the diagnosis is made. The short-term mortality rate of PE varies widely and ranges from less than $2\%$ in many patients with non-massive PE to more than $95\%$ in patients who experience cardiorespiratory arrest [10,11]. Hence, given the associated morbidity and mortality, there is a strong need to identify modifiable risk factors to prevent thrombotic events and, by extension, adverse long-term outcomes. The development of clinical thrombosis could be attributed to a combination of vessel wall damage, altered blood flow, and abnormal composition of the blood [12,13]. There is growing evidence that inflammation is an important risk factor for PE. Inflammation activates endothelial cells, platelets, and leukocytes to initiate coagulation. Activated leukocytes are a major source of procoagulant tissue factor-positive particles that may stimulate thrombus formation and growth. Neutrophil extracellular traps (NETs) composed of DNA, histones, and antimicrobial proteins provide erythrocytes, platelets, and procoagulant molecules with the ability to promote thrombosis [14]. Activation of the coagulation cascade can trigger the immune system, and inflammation is further associated with dysbiosis, increased intestinal permeability, and production of specific metabolites. With the development and progress of artificial intelligence, the combination of machine learning and electronic medical record (EHR) may reduce the risk of adverse outcomes by identifying previously unknown interventions. Arghya Datta et al. designed a machine learning model to determine the factors affecting the risk of hospital-acquired venous thromboembolism (HA-VTE) [15]. In addition to showing drug-drug interactions, machine learning sometimes also helps to remodel gut microbiome [16]. The gut microbiome plays a critical role in various inflammatory conditions (ranging from obesity and cardiovascular disease to autoimmune phenomena), and its modulation is a potential treatment option for these conditions [17,18,19]. Perturbations of the gut microbiome from various environmental or genetic factors can lead to activation of inflammatory pathways in vascular endothelial cells, platelets, and innate immune cells resulting in release of various coagulation proteins leading to a prothrombotic state [20,21,22]. The role of the gut microbiome in the pathogenesis of thromboembolism has not been fully elucidated. Many treatment options to modulate gut dysbiosis, such as fecal microbial transplantation (FMT), probiotics, and selective antibiotics, are currently being explored, but reports on efficacy are mixed [23]. In order to clarify the effect and mechanism of gut microbiota in the treatment of PE, here we used two models of broad-spectrum antibiotics and FMT treatment to study and found that vancomycin could significantly alter the gut dysbiosis in PE mice, reduced pathogen abundance, enhanced beneficial symbiotic anaerobic bacteria, and significantly reduced mortality in mice with PE. This study clarified the effect of broad-spectrum antibiotics in the treatment of PE, clarified the potential mechanism of vancomycin in the treatment of PE, and provided new clues for clinical optimization and development of prevention and treatment strategies for PE. ## 2. Materials and Methods Mice. Male C57BL/6 mice (3 months and 14 months old) were purchased from Charles River Laboratory (Hangzhou, China) and maintained under pathogen-free (SPF) conditions in a temperature-controlled colony chamber (light/dark cycle for 12 h). Mice were fed the rodent chow and water ad libitum. After a week of adaptation, mice were used for the study. The animal study protocol was approved by the Ethics Committee of the Department of Experimental Animal Science, Fudan University. Antibiotic treatments. For assessing the treatment potential of antibiotics, mice were divided into six groups: Saline, Ampicillin (200 mg/kg), Vancomycin (100 mg/kg), Metronidazole (200 mg/kg), Neomycin (200 mg/kg), and AVMN ((ampicillin (200 mg/kg), vancomycin (100 mg/kg), metronidazole (200 mg/kg), and neomycin (200 mg/kg)). The antibiotics were dissolved in saline, and then each group of mice was given the corresponding antibiotics dissolved in saline by gavage for 10 days (200 uL/mice). Pulmonary Embolism Mouse Model. Mice were anesthetized with isoflurane. Then, the left side of the internal jugular vein was completely exposed by cutting the skin of the neck, and 15 IU/30 g thrombin was injected into the jugular vein. Then, the mortality rate was recorded in all groups within 30 min of the injection (the time of respiratory arrest lasted for at least 2 min). Dead mice caused by massive bleeding events were discarded. The left lung lobe was then removed, fixed in $10\%$ formalin, and embedded in paraffin. Sections were stained with hematoxylin–eosin. Under the microscope, three fields (X10 objective, X10 ocular) were chosen at random in each section (one section per mouse). The thrombus area of lung tissue was evaluated in each field which was performed blind to groups [24,25]. Microbial DNA extraction and 16S rRNA gene sequencing. After the treatment of antibiotics and FMT, mouse fecal samples were collected and stored at −80 °C until processing. The genomic DNA of feces was extracted by DNA Extraction Kit (TIANGEN, Beijing, China). The Microbial 16S rDNA V3–V4 region was amplified by PCR. According to the previously published protocols and primers, the PCR amplification was performed in triplicate using the barcoded universal bacterial primers in a Gene Amp PCR-System 9700 (Applied Biosystems, Foster City, CA, USA). The PCR amplification products were sequenced using an Illumina HiSeq platform (Illumina MiSeq, SanDiego, CA, USA) [26]. Microbiome bioinformatic analysis. Gut microbiota α- and β-diversity analysis utilized QIIME and R. Differential abundance at the genus level was identified using R package DESeq2. To further analyze differentially abundant taxa responsible for the classification between two groups, an unsupervised RandomForest classification analysis was performed with the R package randomForest using 1000 trees as well as default settings. Metagenome functional content prediction was performed using PICRUSt (Version 1.1.1) [27]. LEfSe was used for linear discriminant analysis [28]. Fecal microbiota transplantation. For the transplant material preparation, feces were collected from the donor mice. Fecal samples weighing 80–100 mg (3–5 fresh feces pellets) were homogenized in 5 mL of PBS and then centrifuged at 8000 rpm (4 °C). The supernatant was discarded. This process was repeated 3 times to remove the impact of residual antibiotics in the bacterial suspension from donors [29,30]. Lastly, 1 mL of PBS was added to resuspend. The resulting homogenate was filtered by the 100 μm filter and used as the transplant material. Before FMT, recipient mice were treated for five consecutive days with 200 μL of an antibiotic cocktail by oral gavage to remove their own flora. The antibiotic cocktail contained ampicillin (200 mg/kg), vancomycin (100 mg/kg), metronidazole (200 mg/kg), and neomycin (200 mg/kg). Thereafter, recipient mice were given 200 μL of the fresh microbiota suspension by oral gavage three times a week for two weeks. All animal experiments were approved by the Ethical Committee of Minhang hospital, Fudan University. Statistics. Data analyses were performed with either GraphPad Prism 8 (GraphPad, San Diego, CA, USA) or R (http://www.R-project.org/, accessed on 3 May 2019). Data were expressed as the mean ± standard error of mean (SEM). Survival difference was assessed by Kaplan–Meier survival curves. Unpaired two-tailed Student’s t-test and Log-rank tests were performed as indicated. $p \leq 0.05$ was considered to indicate a statistically significant difference. ## 3. Results Vancomycin treatment reduces mortality in mice with pulmonary embolism. In order to explore the effect and mechanism of gut microbiota in the treatment of pulmonary embolism (PE), we treated mice by gavage with broad-spectrum antibiotics commonly used in clinical treatment to remodel the microbiota and then established an acute pulmonary embolism (APE) mouse model to observe the effect of different treatments on APE. First, three-month-old male mice were weighed and randomly divided into six groups, including control (NC; Sterile saline) ($$n = 8$$), vancomycin (Van; 100 mg/kg) ($$n = 8$$), ampicillin (Amp; 200 mg/kg) ($$n = 8$$), metronidazole (Met;200 mg/kg) ($$n = 8$$), neomycin (Neo; 200 mg/kg) ($$n = 8$$)), and the antibiotic cocktail group (AVMN; (ampicillin (200 mg/kg), vancomycin (100 mg/kg), metronidazole (200 mg/kg), and neomycin (200 mg/kg)) ($$n = 9$$). To remodel the gut microbiota in vivo, mice were continuously treated with the corresponding antibiotics by gavage for ten days (200 uL/mice). The body weight of the mice was examined to be unchanged before and after antibiotic treatment, thus ruling out the toxic effects of antibiotics on the mice. Mice were then injected with thrombin (15 IU/30 g) intravenously to induce APE. The results showed that within 5 min after thrombin injection, all the mice in the NC group died. Compared with the NC group, other antibiotic treatment groups except the ampicillin group had inhibitory effects on the death of mice caused by APE. Among them, vancomycin treatment can significantly reduce the mortality of APE mice, can prolong the survival of mice ($40\%$ within 5 min, $50\%$ within 30 min), and has the best inhibitory effect on mortality caused by APE (Figure 1A,B). Next, we sacrificed the mice 30 min after thrombin injection and performed H&E staining on the lung tissue of the mice to observe the effect of antibiotic treatment on pulmonary thrombus in each group. Consistent with the mortality results, except for the NC and ampicillin-treated mice, which had extensive pulmonary thrombus areas, the other antibiotic-treated groups had reduced pulmonary thrombus; especially the vancomycin-treated mice had the least pulmonary thrombus area (Figure 1C,D). Since the incidence of pulmonary embolism in the population increases with age, we further observed the effect of different antibiotic treatments on pulmonary embolism in 14-month-old older mice (equivalent to 45 years in humans). The treatment scheme of antibiotics is the same as that of three-month-old mice. The results showed that, similar to the human population, acute pulmonary embolism developed more rapidly and more severely in older mice than in younger mice. Within 5 min after thrombin injection (15 IU/30 g), all mice in the NC group died (NC, $$n = 5$$); vancomycin could significantly reduce the mortality of APE mice to $40\%$ (Van, $$n = 5$$), while other antibiotic-treated groups had a slight protective effect on APE mice (the lethality was $80\%$), and this phenotype remained unchanged until 30 min (Neo, $$n = 5$$;Amp, $$n = 5$$; Met, $$n = 5$$; AVMN, $$n = 10$$) (Figure 1E,F). Correspondingly, the area of thrombus formation in the lung tissue of the antibiotic-treated mice was less than that of the NC group, and the pulmonary thrombus in the vancomycin-treated group was the least (Figure 1G,H). Antibiotic treatment reshapes gut microbiota in mice. The results in Figure 1 suggest that different antibiotic treatments can reduce pulmonary thrombosis and mortality in APE mice, most likely by altering the gut microbiota in mice. To assess how these antibiotics affect gut microbial community structure, we analyzed the alpha and beta diversity of gut microbiota across groups and compared microbial diversity within and between communities. First, fresh feces of three-month-old mice after antibiotic treatment were collected (NC, $$n = 6$$; Van, $$n = 6$$; Amp, $$n = 7$$; Neo, $$n = 5$$; Met, $$n = 5$$; AVMN, $$n = 5$$); bacterial DNA was extracted; and 16S rRNA gene sequencing was used for classification and analysis. The alpha diversity of the gut microbiota of mice in each group was analyzed and compared by calculating observed species (Figure 2A), Chao1 richness (Figure 2B), and Shannon diversity (Figure 2C). The results suggested that different antibiotic treatments resulted in different diversity and richness of gut microbiota in mice. Next, using the Bray–Curtis dissimilarity to identify differential clusters in principal coordinate analysis (PCoA), the beta diversity of the gut microbiota was assessed, and it was found that the clusters of gut microbiota were clearly separated across groups, and most importantly, vancomycin-treated group was significantly different from the other groups (Figure 2D). To further investigate the similarity between different samples, we constructed a hierarchical clustering tree at the OTU level using the unweighted UniFrac distances (left) and the component proportion of the bacterial phylum in each group (right), in which mice treated with the same antibiotic were grouped together, and the distances were also closest (Figure 2E). Our findings indicate that antibiotic treatment changes the structure of the gut microbial community in mice. The use of antibiotics can reshape the gut microbiome of mice due to their unique antimicrobial spectrum. Vancomycin treatment improves the taxonomic composition of the gut microbiota in mice. To further identify candidate effector microorganisms that may contribute to the differences in APE mortality across groups, we next focused on differences in gut microbiota composition between the vancomycin-treated group and each of the other antibiotic-treated groups. To this end, we first compared enrichment of OTUs among all groups, which revealed enrichment for specific bacterial communities in each group at the phylum level and genus level (Figure 3A,B). To further investigate these findings, we conducted high dimensional class comparisons using linear discriminant analysis of effect size (LEfSe) that detected marked differences in the predominance of bacterial communities among all groups (Figure 3C,D): The Van group exhibited an enrichment of Deltaproteobacteria (class level). The NC group was dominated by Verrucomicrobia (class level). Clostridia and Bacteroidia (class level) were significantly enriched in the Amp group. The Met group had a predominance of Bacilli; the Neo was dominated by Erysipelotrichia (class level); and AVMN had an enrichment of Gammaproteobacteria at class level (Figure 3C,D). We then explored whether the gut microbiome could be separated according to the comparative heatmap of OTU abundance at genus level (Figure 3E). Logistic regression and LASSO were used to screen the genus features. Differential segregation of different taxonomic communities can be seen according to antibiotic treatment of APE mice. Compared with the other five groups, the Van group showed enrichment in Parasutterella, Bilophila, Enterobacter, Proteus, Providencia, and Morganella (genus level), while the relative abundance of Roseburia, Bacteroides, and Muribaculum (genus level) was significantly lower (Figure 3E). Vancomycin-induced gut microbiota changes protect against pulmonary embolism. Based on the analysis of the microbial communities in each group after antibiotic treatment in Figure 3, we next focused on the nine microbial communities that were more variable in the vancomycin-treated group compared to the other groups at the genus level. Through further verification, it was found that the six genera of Parasutterella, Bilophila, Enterobacter, Proteus, Providencia, and Morganella in the vancomycin treatment group were more abundant (Figure 4A), while the other three genera Bacteroides, Muribaculum, and Roseburia were less than in the NC group (Figure 4B). To further explore the relationship between gut microbiota and pulmonary embolism, we focused on the association of five bacterial genera significantly altered in the vancomycin group with prognosis in APE mice. After the establishment of the APE model, the relative abundance (genus level) of Parasutterella, Bilophila, and Enterobacter in surviving mice ($$n = 7$$) after different antibiotic treatments was slightly higher than those in dead mice ($$n = 27$$), whereas the levels of Bacteroides and Muribaculum were slightly lower in surviving mice than in dead mice (Figure 4C). We classified APE mice into high and low categories based on the average relative abundance of these five bacterial genera (Parasutterella, Bilophila, Enterobacter, Bacteroides, and Muribaculum) in surviving mice following antibiotic treatment of APE. Kaplan–Meier was used to analyze the correlation between the relative abundance of these five bacterial genera and the survival of APE mice by log-rank test. We found that APE mice with high relative abundance of Parasutterella had higher survival rates than mice with low Parasutterella abundance (high, $$n = 7$$; low, $$n = 27$$; $$p \leq 0.046$$). APE mice with higher relative abundance of Enterobacter had better prognosis (high, $$n = 10$$; low, $$n = 24$$; $$p \leq 0.031$$). In addition, APE mice with high Bilophila content had a slightly higher survival rate (high, $$n = 9$$; low, $$n = 25$$; $$p \leq 0.385$$). Conversely, APE mice with lower relative abundances of Bacteroides or Muribaculum had better prognosis (Bacteroides, high, $$n = 20$$; low, $$n = 14$$; $$p \leq 0.042$$; Muribaculum, high, $$n = 19$$; low, $$n = 15$$; $$p \leq 0.016$$) (Figure 4D). Transfer of vancomycin-improved gut microbiota to mice via FMT inhibits PE. The above results highly suggest that vancomycin treatment can prevent pulmonary embolism by modulating the gut microbiota in mice. Next, we used the fecal microbial transplantation (FMT) method to further verify this conclusion. FMT is the transplantation of a donor’s fecal samples by either oral or rectal delivery to restore gut microbiota homeostasis. Currently, the role of FMT in metabolic syndrome has been studied in animal models and humans with favorable results [23,31]. FMT has been reported to be a safe and effective treatment option for recurrent C. difcile infection with remission rates of over $80\%$ compared to antibiotics alone [32,33]. Before FMT, recipient mice were treated for five consecutive days with 200 μL of an antibiotic cocktail by oral gavage to remove their own flora as “receptors” mice. The antibiotic cocktail contained ampicillin (200 mg/kg), vancomycin (100 mg/kg), metronidazole (200 mg/kg), and neomycin (200 mg/kg). The feces of the mice in the NC group and the vancomycin-treated group were collected as donors, and the fecal microorganisms were transplanted into the “microbiota-deficient mice” (receptors). Thereafter, recipient mice were given 200 μL of the fresh microbiota suspension by oral gavage three times a week for two weeks. At the same time, the mice were gavaged with normal saline as a control group. Then, APE modeling was performed, and the specific experimental design was shown in Figure 5A. The recipient mice were checked for no change in body weight before and after FMT, thereby excluding the toxic side effects of FMT in mice. Thrombin was administered intravenously to recipient mice to induce APE. Within 5 min, both groups of recipient mice that were gavaged with normal saline (Saline group, $$n = 10$$) and feces in the NC group (R-NC group, $$n = 7$$) developed APE, resulting in massive death (mortality > $80\%$). However, the recipient mice transplanted with vancomycin-treated feces (R-Van group, $$n = 11$$) had significantly reduced APE-induced death ($50\%$ mortality) (Figure 5C), and survival rate was also prolonged to some extent (Figure 5D). Consistently, mice were sacrificed 30 min after thrombin injection, and H&E staining of mouse lung tissue showed that the R-Van group had less pulmonary thrombus, and the area of pulmonary thrombus was significantly smaller than the other two groups (Figure 5E,F). These results suggest that transfer of vancomycin-improved gut microbiota to mice via FMT also inhibits pulmonary embolism, similar to vancomycin treatment alone. Vancomycin may inhibit pulmonary embolism by increasing Parasutterella. Taken together, since FMT was used to transfer vancomycin-improved gut microbiota to recipient mice, and vancomycin alone was used, both models inhibited mortality in mice with pulmonary embolism. Then, these two models are likely to regulate similar gut microbiota and exert their efficacy through the same mechanism. Therefore, we further analyzed the gut microbiota of FMT-treated recipient mice to try to find the key strains that vancomycin inhibits PE. Using the Bray–Curtis dissimilarity to identify differential clusters in principal coordinates analysis (PCoA) to assess beta diversity of gut microbiota, the results are consistent with Figure 2D, with clear separation of animal clusters between R-NC and R-Van groups. Next, high-dimensional class comparisons were performed using linear discriminant analysis of effect size (LEfSe), which detected significant differences in bacterial community dominance among groups and enriched bacteria between the R-NC and R-Van groups. Communities were significantly different (Figure 6B). Further, Logistic regression and LASSO were used to screen genus features, and the gut microbiome was analyzed according to the genus-level OTU abundance comparison heatmap, which could see differential segregation of taxonomic communities between the R-NC and R-Van groups (Figure 6C). Based on our results above, treatment with vancomycin alone mainly caused five significant changes in bacterial genera that were strongly associated with mortality in APE mice (Figure 4). So, next we focus on the changes in these five bacteria. First, Enterobacter was not found in the differential microbial communities of the two groups of recipient mice, suggesting that this bacterial genus likely did not colonize the recipient mice efficiently by FMT. In addition, Bacteroides were slightly elevated in the R-Van group compared with the R-NC group, which was different from the changes in APE mice treated with vancomycin alone, indicating that Bacteroides are not the key bacteria for vancomycin-inhibited pulmonary embolism. Importantly, Parasutterella was significantly increased in the R-Van group compared with the R-NC group (Figure 6D), which is consistent with the above changes in APE mice treated with vancomycin alone. The relative abundance of these three bacterial genera was closely related to the survival rate of APE mice (Figure 6E). In particular, the relatively high relative abundance of Parasutterella in the recipient mice of the R-Van group explained the high survival rate of APE mice, suggesting that Parasutterella may be the key bacteria for vancomycin to inhibit PE. ## 4. Discussion Pulmonary embolism (PE) is a common and potentially lethal condition in the emergency department requiring early and accurate management, and given its high morbidity and mortality, identification of modifiable risk factors to prevent PE is highly desirable. The pathogenesis of PE is complex and occurs from the additive effects of genetic and environmental risk factors [34], of which inflammation is an important risk factor for PE. Inflammation not only initiates coagulation, but also leads to consumptive coagulopathy and increases in proinflammatory cytokines, chemokines, and various leukocyte subtypes [35,36]. Inflammatory states of several diseases, such as obesity, sepsis/infection, inflammatory bowel disease (IBD), and intestinal failure (IF), have been reported to be associated with a high incidence of intestinal dysbiosis and venous thromboembolism [37,38,39,40,41,42]. However, the molecular mechanisms of the gut microbiome in the pathogenesis of venous thromboembolism, especially PE, remain largely unexplored. Using a mouse model of PE, we performed early intervention using multiple broad-spectrum antibiotics and FMT for the first time to clarify the benefits of the gut microbiome in the pathogenesis of PE. The results show that the early administration of various antibiotics (except ampicillin) in young and old mice can inhibit pulmonary thrombosis to a certain extent, reduce the mortality rate of PE, and prolong the survival time of mice. Among them, vancomycin has the best effect on inhibiting the pathogenesis of pulmonary embolism. With the help of gut microbiota sequencing analysis, we found that antibiotic treatment can reshape the intestinal microbiota of mice; especially vancomycin can significantly improve the intestinal microbiota structure of mice with PE. Further studies found that FMT could transfer vancomycin-improved gut microbes into mice to exert anti-pulmonary embolism effects, possibly due to increased intestinal colonization of Parasutterella. These data demonstrate the importance of early and timely use of vancomycin to inhibit the pathogenesis of pulmonary embolism, elucidate the potential molecular mechanism of vancomycin inhibiting pulmonary embolism by remodeling gut microbes, and also provide new clues for clinical optimization and development of PE prevention strategies. Important risk factors of thrombosis include cancer, surgery, inflammation, bed restraint, major trauma, long journeys, pregnancy, oral contraceptives, previous venous thromboembolism, and bacterial infections [43]. Cancer is an independent and major risk factor for venous thromboembolism (VTE) [44], including deep vein thrombosis (DVT) and pulmonary embolism (PE). Of all first VTE events, $20\%$ to $30\%$ are malignancy-associated, and VTE is the second leading cause of death in patients with malignancy [45]. The main reason is that measures such as direct tumor invasion, radiotherapy and chemotherapy, or central venous catheterization will directly damage the blood vessel wall to activate the coagulation system, resulting in an abnormal increase in platelets, placing the body in a hypercoagulable state and abnormal function of the fibrinolytic system, thereby promoting thrombosis [46,47]. Once VTE occurs in cancer patients, the difficulty of treatment increases; the survival period is shortened; and the consequences are serious. Therefore, early diagnosis and prevention are particularly important. In the process of cancer treatment, patients are susceptible to infection by pathogenic microorganisms due to low immunity and organ failure. It is generally prevented and treated with antibiotics. It is well known that the role of the gut microbiome in various aspects of human health is increasingly recognized as having a major impact on host physiology. The human body is colonized by trillions of resident microorganisms consisting of a large number of commensal obligate anaerobic bacteria and potentially pathogenic bacteria [48]. Dysbiosis is defined as a microbial imbalance that typically manifests as a decrease in microbial diversity: for example, a decrease in commensal anaerobic gut bacteria and an overgrowth of pathobionts such as the bacterial family Enterobacteriaceae (ENTERO) [49]. Antibiotics can eliminate pathogenic bacteria as well as beneficial bacteria, which can lead to microbial imbalances affecting local and systemic pathophysiological processes. In this paper, we explored for the first time the efficacy of early use of various commonly used antibiotics to inhibit pulmonary embolism, which may provide new clues for the use of antibiotics in clinical cancer patients and the treatment of pulmonary embolism. There are still many unidentified factors that need to be further verified in the future, such as the timing of antibiotic use, the impact of baseline gut microbiota on efficacy, and the possibility of combined FMT therapy. Here, either vancomycin alone or FMT to transfer vancomycin-improved gut microbiota into recipient mice significantly suppressed mortality in mice with PE. Prescribing vancomycin or FMT in patients at VTE risk for its prevention seems impossible in a near future. We found that the flora had an impact on the blood coagulation function of mice through FMT experiment. Transferring vancomycin-treated gut microbiota into recipient mice significantly suppressed mortality in mice with PE. Are there certain flora that are beneficial to the prevention of patients at VTE risk? Further studies found that the gut microbiota analysis of both models suggested that vancomycin may inhibit PE mainly by regulating the colonization of Parasutterella in the gut and increasing its abundance. Parasutterella has been recognized for about 10 years as a genus of Betaproteobacteria, which has been defined as a member of the healthy fecal core microbiome in the human gastrointestinal tract [50]. Based on sequences reported in the Ribosomal Database Project (RDP), members of the genus Parasutterella have also been found in a variety of host species, including mouse, rat, dog, pig, chicken, turkey, and calf [51]. Interestingly, Parasutterella was recently reported to use succinic acid as a fermentative end-product, and its production of succinic acid was even greater than that of Bacteroides fragilis, one of the well-identified succinate producers [52]. Succinate, as one of the key intermediate metabolites produced by gut microbiota, plays an important role in cross-feeding metabolic pathways [53]. Studies have shown that Parasutterella is transmitted between mothers and vaginally born infants, with a gradual increase in relative abundance up to 12 months of age, suggesting that Parasutterella is one of the early colonizers in the neonatal gut and increases in response to dietary change and host development [54]. Using a germ-free mouse model study of neonatal microbiota reconstitution, bacterial-derived succinate was found to promote the colonization of strict anaerobic bacteria, Clostridiales, to protect mice from infection [55]. As one of the early colonizers, as well as a succinate-producing symbiotic bacteria, Parasutterella may play a role in microbial interactions and infection resistance, especially early in life. Furthermore, there is increasing evidence that the relative abundance of Parasutterella is associated with different host health outcomes, such as inflammatory bowel disease, obesity, diabetes, and fatty liver disease. An inverse correlation between Parasutterella abundance and high-fat diet (HFD)-induced metabolic phenotypes, including hypothalamic inflammation, has been observed in multiple animal models and human studies [56,57,58]. Furthermore, patients with Clostridium difficile infection (CDI) showed a significant increase in the abundance of Proteobacteria in the gut; however, within the phylum of Proteobacteria, Parasutterella in CDI patients and asymptomatic carriers was significantly lower than in healthy controls [59]. To our knowledge, our observations are the first to show that Parasutterella abundance increases with vancomycin administration and is inversely associated with the pulmonary embolism phenotype. Our study provides clues for expanding new biological effects of Parasutterella in inhibiting PE. In conclusion, we demonstrated the effects of various antibiotics in a mouse model of PE for the first time and found that early administration of antibiotics could modulate the mouse gut microbiome, thereby inhibiting pulmonary thrombosis and reducing PE mortality to a certain extent. Among them, vancomycin has the best efficacy in inhibiting PE. These data highlight a key cross-talk between gut microbiota and pulmonary thrombosis during antibiotic treatment. Furthermore, with the aid of gut microbiota sequencing and FMT approaches, we found that vancomycin may inhibit PE by improving gut microbiome composition mainly by increasing the gut colonization of Parasutterella. These data elucidate the underlying molecular mechanism by which vancomycin inhibits the pathogenesis of PE and provide new clues for clinical optimization and development of PE prevention strategies. ## References 1. Goldhaber S.Z., Bounameaux H.. Pulmonary embolism and deep vein thrombosis. Lancet (2012) 379 1835-1846. DOI: 10.1016/S0140-6736(11)61904-1 2. Raskob G.E., Angchaisuksiri P., Blanco A.N., Buller H., Gallus A., Hunt B.J., Hylek E.M., Kakkar A., Konstantinides S.V., McCumber M.. Day, Thrombosis: A major contributor to global disease burden. Arterioscler. Thromb. Vasc. Biol. (2014) 34 2363-2371. DOI: 10.1161/ATVBAHA.114.304488 3. Grosse S.D., Nelson R.E., Nyarko K.A., Richardson L.C., Raskob G.E.. The economic burden of incident venous thromboembolism in the United States: A review of estimated attributable healthcare costs. Thromb. Res. (2016) 137 3-10. DOI: 10.1016/j.thromres.2015.11.033 4. Huang W., Goldberg R.J., Anderson F.A., Kiefe C.I., Spencer F.A.. Secular trends in occurrence of acute venous thromboembolism: The Worcester VTE study (1985–2009). Am. J. Med. (2014) 127 829-839.e825. DOI: 10.1016/j.amjmed.2014.03.041 5. Heit J.A.. Epidemiology of venous thromboembolism. Nat. Rev. Cardiol. (2015) 12 464-474. DOI: 10.1038/nrcardio.2015.83 6. Bell E.J., Lutsey P.L., Basu S., Cushman M., Heckbert S.R., Lloyd-Jones D.M., Folsom A.R.. Lifetime Risk of Venous Thromboembolism in Two Cohort Studies. Am. J. Med. (2016) 129. DOI: 10.1016/j.amjmed.2015.10.014 7. Goldhaber S.Z., Visani L., De Rosa M.. Acute pulmonary embolism: Clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet (1999) 353 1386-1389. DOI: 10.1016/S0140-6736(98)07534-5 8. Di Nisio M., van Es N., Buller H.R.. Deep vein thrombosis and pulmonary embolism. Lancet (2016) 388 3060-3073. DOI: 10.1016/S0140-6736(16)30514-1 9. Piazza G., Goldhaber S.Z.. Chronic thromboembolic pulmonary hypertension. N. Engl. J. Med. (2011) 364 351-360. DOI: 10.1056/NEJMra0910203 10. Simonneau G., Sors H., Charbonnier B., Page Y., Laaban J., Azarian R., Laurent M., Hirsch J., Ferrari E., Bosson J.. A comparison of low-molecular-weight heparin with unfractionated heparin for acute pulmonary embolism. N. Engl. J. Med. (1997) 337 663-669. DOI: 10.1056/NEJM199709043371002 11. Büller H.R., Davidson B.L., Decousus H., Gallus A., Gent M., Piovella F., Prins M.H., Raskob G., Berg-Segers A.E.M.V.D., Cariou R.. Subcutaneous Fondaparinux versus Intravenous Unfractionated Heparin in the Initial Treatment of Pulmonary Embolism. N. Engl. J. Med. (2003) 349 1695-1702. PMID: 14585937 12. Franchini M., Martinelli I., Mannucci P.M.. Uncertain thrombophilia markers. Thromb. Haemost. (2016) 115 25-30. DOI: 10.1160/TH15-06-0478 13. Ballard R.B., Marques M.B.. Pathology consultation on the laboratory evaluation of thrombophilia: When, how, and why. Am. J. Clin. Pathol. (2012) 137 553-560. DOI: 10.1309/AJCP5SQT3ZKYQFBM 14. D’Atri L.P., Schattner M.A.. Platelet toll-like receptors in thromboinflammation. Front. Biosci. (2017) 22 1867-1883 15. Datta A., Matlock M.K., Le Dang N., Moulin T., Woeltje K.F., Yanik E.L., Swamidass S.J.. ‘Black Box’ to ‘Conversational’ Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism. IEEE J. Biomed. Health Inform. (2021) 25 2204-2214. DOI: 10.1109/JBHI.2020.3033405 16. Datta A., Flynn N.R., Barnette D.A., Woeltje K.F., Miller G.P., Swamidass S.J.. Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort. PLOS Comput. Biol. (2021) 17. DOI: 10.1371/journal.pcbi.1009053 17. Spor A., Koren O., Ley R.. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. (2011) 9 279-290. DOI: 10.1038/nrmicro2540 18. Aron-Wisnewsky J., Clément K.. The gut microbiome, diet, and links to cardiometabolic and chronic disorders. Nat. Rev. Nephrol. (2016) 12 169-181. DOI: 10.1038/nrneph.2015.191 19. Fan D., Coughlin L.A., Neubauer M.A., Kim J., Kim M.S., Zhan X., Simms-Waldrip T.R., Xie Y., Hooper L.V., Koh A.Y.. Activation of HIF-1alpha and LL-37 by commensal bacteria inhibits Candida albicans colonization. Nat. Med. (2015) 21 808-814. DOI: 10.1038/nm.3871 20. Andonegui G., Kerfoot S.M., McNagny K., Ebbert K.V.J., Patel K.D., Kubes P.. Platelets express functional Toll-like receptor-4. Blood (2005) 106 2417-2423. DOI: 10.1182/blood-2005-03-0916 21. Zhu W., Gregory J.C., Org E., Buffa J.A., Gupta N., Wang Z., Li L., Fu X., Wu Y., Mehrabian M.. Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk. Cell (2016) 165 111-124. DOI: 10.1016/j.cell.2016.02.011 22. Jäckel S., Kiouptsi K., Lillich M., Hendrikx T., Khandagale A., Kollar B., Hörmann N., Reiss C., Subramaniam S., Wilms E.. Gut microbiota regulate hepatic von Willebrand factor synthesis and arterial thrombus formation via Toll-like receptor-2. Blood (2017) 130 542-553. DOI: 10.1182/blood-2016-11-754416 23. de Groot P.F., Frissen M.N., de Clercq N.C., Nieuwdorp M.. Fecal microbiota transplantation in metabolic syndrome: History, present and future. Gut Microbes (2017) 8 253-267. DOI: 10.1080/19490976.2017.1293224 24. Xu J., Zhang Y., Xu J., Liu G., Di C., Zhao X., Li X., Li Y., Pang N., Yang C.. Engineered Nanoplatelets for Targeted Delivery of Plasminogen Activators to Reverse Thrombus in Multiple Mouse Thrombosis Models. Adv. Mater. (2020) 32 e1905145. DOI: 10.1002/adma.201905145 25. EWeiss E.J., Hamilton J.R., Lease K.E., Coughlin S.R.. Protection against thrombosis in mice lacking PAR3. Blood (2002) 100 3240-3244. PMID: 12384423 26. Chen H.L., Zeng Y.B., Zhang Z.Y., Kong C.Y., Zhang S.L., Li Z.M., Huang J.T., Xu Y.Y., Mao Y.Q., Cai P.R.. Gut and Cutaneous Microbiome Featuring Abundance of Lactobacillus reuteri Protected Against Psoriasis-Like Inflammation in Mice. J. Inflamm. Res. (2021) 14 6175-6190. DOI: 10.2147/JIR.S337031 27. Zhang S.-L., Han B., Mao Y.-Q., Zhang Z.-Y., Li Z.-M., Kong C.-Y., Wu Y., Chen G.-Q., Wang L.-S.. Gut Microbes (2022) 14 2046246. DOI: 10.1080/19490976.2022.2046246 28. Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W.S., Huttenhower C.. Metagenomic biomarker discovery and explanation. Genome Biol. (2011) 12 R60. DOI: 10.1186/gb-2011-12-6-r60 29. Pushalkar S., Hundeyin M., Daley D., Zambirinis C.P., Kurz E., Mishra A., Mohan N., Aykut B., Usyk M., Torres L.E.. The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov. (2018) 8 403-416. DOI: 10.1158/2159-8290.CD-17-1134 30. Tanoue T., Morita S., Plichta D.R., Skelly A.N., Suda W., Sugiura Y., Narushima S., Vlamakis H., Motoo I., Sugita K.. A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature (2019) 565 600-605. DOI: 10.1038/s41586-019-0878-z 31. Vrieze A., Van Nood E., Holleman F., Salojärvi J., Kootte R.S., Bartelsman J.F., Dallinga-Thie G.M., Ackermans M.T., Serlie M.J., Oozeer R.. Transfer of Intestinal Microbiota from Lean Donors Increases Insulin Sensitivity in Individuals With Metabolic Syndrome. Gastroenterology (2012) 143 913-916.e7. DOI: 10.1053/j.gastro.2012.06.031 32. Leffler D.A., Lamont J.T.. Clostridium difficile infection. N. Engl. J. Med. (2015) 372 1539-1548. DOI: 10.1056/NEJMra1403772 33. Van Nood E., Vrieze A., Nieuwdorp M., Fuentes S., Zoetendal E.G., De Vos W.M., Visser C.E., Kuijper E.J., Bartelsman J.F.W.M., Tijssen J.G.P.. Duodenal Infusion of Donor Feces for Recurrent Clostridium difficile. N. Engl. J. Med. (2013) 368 407-415. DOI: 10.1056/NEJMoa1205037 34. Cushman M.. Epidemiology and Risk Factors for Venous Thrombosis. Semin. Hematol. (2007) 44 62-69. DOI: 10.1053/j.seminhematol.2007.02.004 35. Saghazadeh A., Rezaei N.. Inflammation as a cause of venous thromboembolism. Crit. Rev. Oncol. Hematol. (2016) 99 272-285. DOI: 10.1016/j.critrevonc.2016.01.007 36. Lentz S.R.. Thrombosis in the setting of obesity or inflammatory bowel disease. Blood (2016) 128 2388-2394. DOI: 10.1182/blood-2016-05-716720 37. Kappelman M.D., Horvath-Puho E., Sandler R.S., Rubin D.T., Ullman T.A., Pedersen L., Baron J.A., Sorensen H.T.. Thromboembolic risk among Danish children and adults with inflammatory bowel diseases: A population-based nationwide study. Gut (2011) 60 937-943. DOI: 10.1136/gut.2010.228585 38. Karlsson F.H., Fak F., Nookaew I., Tremaroli V., Fagerberg B., Petranovic D., Backhed F., Nielsen J.. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. (2012) 3 1245. DOI: 10.1038/ncomms2266 39. Ley R.E., Bäckhed F., Turnbaugh P., Lozupone C.A., Knight R.D., Gordon J.I.. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. USA (2005) 102 11070-11075. DOI: 10.1073/pnas.0504978102 40. Gonzalez-Hernandez J., Daoud Y., Styers J., Journeycake J.M., Channabasappa N., Piper H.G.. Central venous thrombosis in children with intestinal failure on long-term parenteral nutrition. J. Pediatr. Surg. (2016) 51 790-793. DOI: 10.1016/j.jpedsurg.2016.02.024 41. Kaplan D., Casper T.C., Elliott C.G., Men S., Pendleton R.C., Kraiss L.W., Weyrich A.S., Grissom C.K., Zimmerman G.A., Rondina M.T.. VTE Incidence and Risk Factors in Patients with Severe Sepsis and Septic Shock. Chest (2015) 148 1224-1230. DOI: 10.1378/chest.15-0287 42. Miehsler W., Reinisch W., Valic E., Osterode W., Tillinger W., Feichtenschlager T., Grisar J., Machold K., Scholz S., Vogelsang H.. Is inflammatory bowel disease an independent and disease specific risk factor for thromboembolism?. Gut (2004) 53 542-548. DOI: 10.1136/gut.2003.025411 43. Lichota A., Gwozdzinski K., Szewczyk E.M.. Microbial Modulation of Coagulation Disorders in Venous Thromboembolism. J. Inflamm. Res. (2020) 13 387-400. DOI: 10.2147/JIR.S258839 44. Nickel K.F., Ronquist G., Langer F., Labberton L., Fuchs T.A., Bokemeyer C., Sauter G., Graefen M., Mackman N., Stavrou E.X.. The polyphosphate–factor XII pathway drives coagulation in prostate cancer-associated thrombosis. Blood (2015) 126 1379-1389. DOI: 10.1182/blood-2015-01-622811 45. Timp J.F., Braekkan S.K., Versteeg H., Cannegieter S.C.. Epidemiology of cancer-associated venous thrombosis. Blood (2013) 122 1712-1723. DOI: 10.1182/blood-2013-04-460121 46. Prandoni P., Falanga A., Piccioli A.. Cancer and venous thromboembolism. Lancet Oncol. (2005) 6 401-410. DOI: 10.1016/S1470-2045(05)70207-2 47. Font C., Farrús B., Vidal L., Caralt T.M., Visa L., Mellado B., Tàssies D., Monteagudo J., Reverter J.C., Gascon P.. Incidental versus symptomatic venous thrombosis in cancer: A prospective observational study of 340 consecutive patients. Ann. Oncol. (2011) 22 2101-2106. DOI: 10.1093/annonc/mdq720 48. Fouhy F., Ross R.P., Fitzgerald G.F., Stanton C., Cotter P.D.. Composition of the early intestinal microbiota: Knowledge, knowledge gaps and the use of high-throughput sequencing to address these gaps. Gut Microbes (2012) 3 203-220. DOI: 10.4161/gmic.20169 49. McDonald D., Ackermann G., Khailova L., Baird C., Heyland D., Kozar R., Lemieux M., Derenski K., King J., Vis-Kampen C.. Extreme Dysbiosis of the Microbiome in Critical Illness. Msphere (2016) 1 e00199-16. DOI: 10.1128/mSphere.00199-16 50. Willing B.P., Dicksved J., Halfvarson J., Andersson A.F., Lucio M., Zheng Z., Järnerot G., Tysk C., Jansson J.K., Engstrand L.. A Pyrosequencing Study in Twins Shows That Gastrointestinal Microbial Profiles Vary With Inflammatory Bowel Disease Phenotypes. Gastroenterology (2010) 139 1844-1854e1. DOI: 10.1053/j.gastro.2010.08.049 51. Morotomi M., Nagai F., Watanabe Y.. Int. J. Syst. Evol. Microbiol. (2011) 61 637-643. DOI: 10.1099/ijs.0.023556-0 52. Ju T., Kong J.Y., Stothard P., Willing B.P.. Defining the role of Parasutterella, a previously uncharacterized member of the core gut microbiota. ISME J. (2019) 13 1520-1534. DOI: 10.1038/s41396-019-0364-5 53. Fischbach M.A., Sonnenburg J.L.. Eating for Two: How Metabolism Establishes Interspecies Interactions in the Gut. Cell Host Microbe (2011) 10 336-347. DOI: 10.1016/j.chom.2011.10.002 54. Backhed F., Roswall J., Peng Y., Feng Q., Jia H., Kovatcheva-Datchary P., Li Y., Xia Y., Xie H., Zhong H.. Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. Cell Host. Microbe (2015) 17 852. DOI: 10.1016/j.chom.2015.05.012 55. Kim G.Y., Sakamoto K., Seo S.U., Pickard J.M., Gillilland M.G., Pudlo N.A., Hoostal M., Li X., Wang T.D., Feehley T.. Neonatal acquisition of Clostridia species protects against colonization by bacterial pathogens. Science (2017) 356 315-319. DOI: 10.1126/science.aag2029 56. Kreutzer C., Peters S., Schulte D.M., Fangmann D., Türk K., Wolff S., van Eimeren T., Ahrens M., Beckmann J., Schafmayer C.. Hypothalamic Inflammation in Human Obesity Is Mediated by Environmental and Genetic Factors. Diabetes (2017) 66 2407-2415. DOI: 10.2337/db17-0067 57. Zhang C., Zhang M., Pang X., Zhao Y., Wang L., Zhao L.. Structural resilience of the gut microbiota in adult mice under high-fat dietary perturbations. ISME J. (2012) 6 1848-1857. DOI: 10.1038/ismej.2012.27 58. Danneskiold-Samsøe N.B., Andersen D., Radulescu I.D., Normann-Hansen A., Brejnrod A., Kragh M., Madsen T., Nielsen C., Josefsen K., Fretté X.. A safflower oil based high-fat/high-sucrose diet modulates the gut microbiota and liver phospholipid profiles associated with early glucose intolerance in the absence of tissue inflammation. Mol. Nutr. Food Res. (2017) 61 1600528. DOI: 10.1002/mnfr.201600528 59. Zhang L., Dong D., Jiang C., Li Z., Wang X., Peng Y.. Insight into alteration of gut microbiota in Clostridium difficile infection and asymptomatic C. difficile colonization. Anaerobe (2015) 34 1-7. DOI: 10.1016/j.anaerobe.2015.03.008
--- title: 'Oral Microbiota in Children and Adolescents with Type 1 Diabetes Mellitus: Novel Insights into the Pathogenesis of Dental and Periodontal Disease' authors: - Maria Carelli - Alice Maguolo - Chiara Zusi - Francesca Olivieri - Federica Emiliani - Gelinda De Grandi - Ilaria Unali - Nicoletta Zerman - Caterina Signoretto - Claudio Maffeis journal: Microorganisms year: 2023 pmcid: PMC10059713 doi: 10.3390/microorganisms11030668 license: CC BY 4.0 --- # Oral Microbiota in Children and Adolescents with Type 1 Diabetes Mellitus: Novel Insights into the Pathogenesis of Dental and Periodontal Disease ## Abstract The oral microbiota can be influenced by multiple factors, but only a few studies have focused on the role of glycemic control in determining early alterations of oral microbiota and their association with pathogenesis of both periodontitis and caries. The aim of this study is to evaluate the interplay between bacteria composition, oral hygiene, and glycemic control in a cohort of children with T1D. A total of 89 T1D children were enrolled ($62\%$ males, mean age: 12.6 ± 2.2 years). Physical and clinical characteristics, glucometabolic parameters, insulin treatment, and oral hygiene habits data were collected. Microbiological analysis was performed from saliva samples. A high prevalence of cariogenic and periodontopathogens bacteria in our cohort was detected. In particular, in all subjects Actinomyces spp., Aggregatibacter actinomycetemcomitans, Prevotella intermedia, and Lactobacillus spp. were isolated. S. mutans was found in about half of the analyzed sample ($49.4\%$), in particular in patients with imbalance values of glycemic control. Moreover, a higher presence of both S. mutans and Veillonella spp. was detected in subjects with poorer glycemic control, in terms of HbA1c, %TIR and %TAR, even adjusting for age, sex, and hygiene habits as covariates. Virtuous oral hygiene habits, such as frequency of toothbrush changes and professional oral hygiene, negatively correlated with the simultaneous presence of Tannerella forsythia, Treponema denticola, and Porphyromonas gingivalis, red complex bacteria. Our study shows it is crucial to pay attention to glycemic control and regular oral hygiene to prevent the establishment of an oral microbiota predisposing to dental and periodontal pathology in subjects with T1D since childhood. ## 1. Introduction Type 1 diabetes (T1D) is a complex autoimmune disease caused by the destruction of pancreatic beta cells that leads to both acute and chronic complications [1]. An inadequate glycemic control is a major risk factor for the development of chronic complications, including cardiovascular disease, peripheral vascular disease, retinopathy, nephropathy, and neuropathy [2,3,4,5]. Periodontal disease (PD) is classified as the “sixth complication” of diabetes [6]. This chronic inflammation of periodontal tissues is characterized by the progressive destruction of the supporting structures of the teeth induced by a state of dysbiosis promoting host inflammatory response [7,8]. According to the new classification of PD, although there is sufficient evidence to believe that PD observed in the context of systemic disease that severely impair the immune response should be considered a periodontal manifestation of systemic disease, there is currently insufficient evidence to sustain that PD observed in poorly controlled diabetes is characterized by a unique pathophysiology [9]. The incidence of PD in patients with T1D is higher than in the healthy population and is significantly associated with a longer duration of diabetes and poor glycemic control [10,11]. Indeed, the latter together with changes in host response and differences in the composition of the oral microbiota are suggested as determinants of increased susceptibility of T1D patients to the development of PD and caries [12]. Two meta-analyses have recently confirmed the association between diabetes and periodontal disease, indicating a positive, bidirectional association between these two disorders [13,14]. On one hand, diabetes increases the risk and severity of inflammatory PD; on the other, periodontitis can trigger inflammatory host immune responses locally and systemically, affecting glucometabolic control in patients with T1D [15,16,17]. This relationship impacts on early onset of gingival disease and increased periodontal disease even in children and adolescents with T1D [10,11]. Different combinations of bacterial species are involved in periodontitis, such as the concomitant presence of Aggregatibacter actinomycetemcomitans and Prevotella intermedia in saliva [18]. In addition, the “red complex”, consisting of three strictly anaerobic bacteria, i.e., Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola, is associated with severe forms of periodontal disease [19,20]. Moreover, a relationship between T1D and an increased risk of dental caries has been suggested. This link is influenced by diabetes-induced changes in saliva composition and levels of glycemic control [21,22]. As regards microbial composition, Streptococcus mutans and Lactobacillus are the most cariogenic bacteria because of their ability to survive in an acidic environment and form biofilm [23]. A molecular study found that the simultaneous presence of Veillonella spp. and Streptococcus spp. can promote the development and progression of dental caries [24,25]. In the general population, oral microbiome is suspected to be affected by several variables including host genetics, geography, age, cohabitation, and familial relationship. In particular, the salivary microbiota of youths, aged 3 to 18 years, is still maturing [26,27], while, as they age, the composition of the oral microbiome changes and periodontal pathogens increase in abundance, leading to increased susceptibility to oral disease [28]. Generally, children and adolescents have an oral microbiota characterized by bacteria that are protective against periodontal disease and caries, whereas people > 50 years of age show changes in the microenvironment that lead to an increase in certain bacterial species that predispose to periodontitis. For example, the study by Rodenburg et al. showed that the prevalence of periodontopathogens such as Porphyromonas gingivalis in individuals with periodontitis increases with age [29]. Proper education of the patient with T1D in hygiene maintenance with professional oral hygiene sessions with special tools could positively affect the maintenance of oral and dental health by preventing oral dysbiosis [30]. However, the oral microbiota composition may be influenced by additional multiple modifiable factors: dietary habits, oral hygiene, and use of drugs or antibiotics. Currently, few studies have focused on the role of glycemic control, analyzing different CGM metrics, in determining alterations in the oral microbiota of subjects with T1D, and, to date, its association with pathogenesis of both periodontitis and caries remains controversial [31,32,33]. A few data are available in children and adolescents with T1D. Clarifying the relationship between the oral microbiota and the dental and metabolic health of T1D individuals from an early age is crucial in order to develop novel, effective, preventative, and therapeutic strategies. Therefore, the aim of this study was to assess the presence of cariogenic and periodontopathogenic bacteria through saliva sample analysis and evaluate their potential roles in the interplay with oral hygiene and glycemic control in a cohort of children and adolescents with T1D. ## 2.1. Study Population Eighty-nine children and adolescents with T1D (age: 12.6 ± 2.2 years, 55 boys) were consecutively recruited at the Regional Center for Pediatric Diabetes of the University Hospital, Verona (Italy) during a follow-up visit between December 2020 and February 2022. Inclusion criteria were diagnosis of T1D for at least one year, confirmed by positivity of at least two diabetes-associated autoantibodies (GADA, ZnT8A, IAA or IA–2A), and an age between 9 and 15 years. Exclusion criteria were chronic diseases other than T1D requiring pharmacotherapy, presence of other related genetic diseases, intake of drugs that alter salivary secretion, fixed orthodontic appliances, use of antibiotics or probiotics three months prior to inclusion, use of probiotic-containing food and medical conditions believed to affect oral and gut microbiota. Written informed consent to participate in the study was obtained from the parents/guardians of the children and adolescents. The Ethical Committee of the University Hospital of Verona approved the study, in accordance with the World Medical Association Declaration of Helsinki (approval number: Prog. 2722CESC, Prot n.29192, $\frac{25}{05}$/2020). ## 2.2. Clinical Data Collection Clinical and demographic parameters were recorded at enrollment: age, gender, age of onset, and duration of T1D and anthropometric measurements (i.e., body height, body weight, pubertal status determined using Tanner stages I–V [34], according to standard procedures, as previously reported [34,35]. Body mass index (BMI) was calculated using the formula: body weight (kg)/body height (m2), values were then standardized (BMI z-score) calculating age and sex-specific BMI percentiles according to World Health Organization (WHO) child growth standards [36]. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by a physician three times on the left arm with the subject sitting, using a manual sphygmomanometer and a cuff of appropriate size [37]. Other clinical data such as daily insulin dosages (total, basal) and type of treatment (multiple daily insulin injections or continuous subcutaneous insulin infusion) were also recorded. Moreover, a questionnaire on oral hygiene habits was administered the same day as the visit and saliva sample collection. The two-page self-completion questionnaire was developed following a scoping review of the literature. The following topics were covered in the questionnaire: frequency of professional and daily oral hygiene, oral health advice received, current dental care, and oral hygiene behavior. ## 2.3. Glucometabolic Parameters Glucometabolic control parameters (glycated hemoglobin (HbA1c) and continuous glucose monitoring (CGM) metrics of glycemic control and glucose variability) were collected. HbA1c was measured using the high-performance liquid chromatography technique and standardized to the normal range established by the DCCT (4.0–$6.0\%$, 20–42 mmol/mol). Intermittently scanned continuous glucose monitoring device (isCGM, Abbott FreeStyle Libre® Glucose Monitoring System, Abbott Diabetes Care, Alameda, CA, USA) or real-time CGM device (rtCGM, Dexcom G5® CGM System or Dexcom G6® CGM System, Dexcom, San Diego, CA, USA)-derived data were recorded. For each participant, several metrics of glycemic control and variability have been computed separately for the full 12-week period of data collection immediately before the enrollment visit with HbA1c measurement and saliva collection. In particular, the following metrics were calculated: (a) glucose management indicator (GMI); (b) percentage of time below range [<70 mg/dL (TBR)]; (c) percentage of time in target range [70–180 mg/dL (TIR)]; (d) percentage of the time above range [>180 mg/dL (TAR)]; (e) coefficient of variation (CV). The following cut-offs were used according to the international consensus on use of CGM: TIR (using $70\%$ as cut-off), TAR (using $25\%$ as cut-off), CV (using $36\%$ as cut-off) [38]. As regards HbA1c and GMI, we used a less-stringent goal (using $7.5\%$ as cut-off) to classify patients as having good or poor glycemic control, as optimal glycemic control is often more difficult to achieve in the pediatric setting, and 7.5 represented exactly the median HbA1c of our population. Hence, an HbA1c value of <$7.5\%$ (58 mmol/mol) was taken as indicator of good glycemic control while a value of ≥$7.5\%$ (58 mmol/mol) was considered to be indicator of poor control. In order to ensure an adequate amount of data, participants were included in the analysis if at least $80\%$ of expected CGM readings were available for each patient. ## 2.4. Microbiological Analysis At the baseline visit a sample of 4 mL of saliva was collected while fasting for at least 8h and before performing daily oral hygiene. Samples were sent within 24 h at the Microbiology section of the Department Diagnostic and Public Health of the University of Verona and subjected to nucleic acid extraction. On saliva, bacterial culture-based analysis was also performed. Several culture media were selected for the detection and isolation of the species of interest. In particular, Blood agar (Blood Agar Base Oxoid™) and Chocolate Agar (Chocolate Agar Base Oxoid™) have been selected as enriched media for overall oral bacterial microflora; Sabouraud agar (Oxoid™ Prepared Sabouraud Dextrose Agar) was used to isolate fungi and yeasts; Mannitol Salt agar (MSA Base Oxoid™) was employed for the growth of presumptive pathogenic staphylococci. Mitis Salivarius Agar (NutriSelect® Plus) was used for the isolation of oral streptococci. Mitis salivarius sucrose bacitracin (MSB), obtained by adding 0.2 units/mL bacitracin and by increasing the sucrose concentration to $20\%$ starting from Mitis Salivarius Agar, was employed for the selective isolation of Streptococcus mutans. The plates were then incubated at 37 °C for 48 h in an anaerobic condition, except Sabouraud plates that require aerobic conditions. To detect the bacterial load (colony-forming units (CFU)/mL) a serial dilution method was performed for each saliva sample. Nucleic acids were extracted using the QIAamp DNA Microbiome Kit (Qiagen, Milan, Italy) following the manufacturer instructions and all DNA samples were suspended in 50 μL of elution buffer. Concentrations of extracted DNAs were assessed using the Qubit 2.0 fluorometer (Invitrogen, Thermo Fisher Scientific, Darmstadt, Germany). Briefly, 10 µL of extracted genomic DNA were mixed with 190 µL of a Qubit working solution (Qubit High Sensitivity Assay, Invitrogen, Thermo Fisher Scientific, Darmstadt, Germany) according to the manufacturer’s protocol. Extracted DNA was stored at −20 °C until further use. Presence of pathogens’ DNA in the samples was assessed through PCR. Two different multiplex PCRs were performed to identify the presence of: (a) P. gingivalis, P. intermedia, and A. actinomycetemcomitans [39], and (b) T. forsythia, T. denticola, and A. naeslundii. Additionally, a single PCR was performed to identify Actinomyces spp., S. mutans, Veillonella spp., and Lactobacillus spp., as previously reported [40]. PCR reactions were performed using the 5Prime Hot Master Mix (Quantabio, Beverly, MA, USA) according to manufacturer’s instructions. Briefly, PCR reactions mixture were composed of 8 μL of 5 PRIME HotMaster Mix (2.5x), 100 nM of each primer, 50 ng of template, and Ultrapure DNase/RNase-free distilled water (Thermo Fisher Scientific, Waltham, MA, USA) to reach a final volume of 20 μL. Primers and PCR conditions are reported in Supplementary Table S1. ## 2.5. Statistical Methods Data are presented as arithmetic mean with the relative standard deviation (SD), the medians and interquartile range [IQR], or as an absolute and relative frequency. Normal distribution of variables was assessed via the Kolmogorov–Smirnov test. Skewed variables were log-transformed unless deviations from the Gaussian distribution could not be corrected via transformation. Differences between patients stratified by sex and glycemic parameters were assessed via Student’s t-test, for Gaussian variables, and via the Mann–Whitney test, for skewed variables. A chi-square test was applied to detect differences in categorical variables. Correlations between variables were calculated by using Spearman’s rho. The relation between the presence of cariogenic bacteria (S. mutans and Veillonella spp.) and glycemic metabolic control parameters (i.e., % of HbA1c, TIR, TAR, GMI) was assessed using linear logistic regression analysis. Sex, age, bleeding during brushing, and professional hygiene frequency were used as covariates. Covariates included in multivariate regression models were selected as potential confounding factors based on their plausibility. Significance level for all tests was set at $p \leq 0.05.$ All analyses were performed in R environment, STATA, and IBM SPSS Statistics 26 statistical package (SPSS, Chicago, IL, USA). ## 3. Results A population of 89 children and adolescents with T1D (55 males, $61.8\%$) with a mean age of 12.6 ± 2.2 years was recruited. Table 1 showed the main anthropometric and metabolic characteristics and bacterial populations identified in the study sample stratified by sex and glucose control (i.e., HbA1c). Subjects’ characteristics are described according to glucometabolic control parameters (i.e., GMI, %TIR and %TAR) in Supplementary Tables S2–S4 are reported. The bacterial distribution in our population is represented in Figure 1. Actinomyces spp., A. actinomycetemcomitans, P. intermedia, and Lactobacillus spp. were found in all investigated samples, while $93.3\%$ of the subjects were colonized by Veillonella spp. A. naeslundii, T. denticola, and T. forsythia were identified in $47.2\%$, $36.0\%$, and $33.7\%$ of the cohort, respectively. S. mutans was found in about half of the analyzed sample ($49.4\%$). Its distribution, according to glycemic control, in terms of TIR < $70\%$, TAR > $25\%$, and HbA1c > $7.5\%$, showed a significantly higher percentage (all $p \leq 0.03$). The difference by GMI was at the limit of statistical significance ($$p \leq 0.052$$). Similarly, the concomitant presence of Veillonella spp. and S. mutans, both known as cariogenic pathogens, was higher in subjects with poor glycemic control (all glycemic metrics but GMI). No differences in CFU/mL counts were found according to the glucose control parameters (Table 1 and Supplementary Tables S2–S4). Respondents’ use of professional dental care and oral hygiene behaviors are shown in Table 2. Slightly less than $40\%$ of the children and adolescents ($38.7\%$) reported bleeding episodes, an early sign of gingivitis, during brushing. According to oral hygiene habits, only $51.4\%$ of subjects underwent professional oral hygiene at least once a year, while approximately $65\%$ replaced toothbrushes or brush heads every two to three months. The simultaneous presence of T. forsythia, T. denticola, and P. gingivalis (i.e., the red complex) negatively correlated with virtuous oral health habits such as frequency of dental visits and professional oral hygiene (rho = −0.314; $$p \leq 0.006$$ and rho = −0.263; $$p \leq 0.023$$, respectively). In addition, S. mutans correlates with the above poor oral hygiene practices as supported by molecular and culture-based analysis (rho = −0.280; $$p \leq 0.021$$ and rho = −0. −0.301; $$p \leq 0.01$$, respectively). The regression analysis confirmed that S. mutans and Veillonella spp. were associated with poor glycemic control: the combination of these two bacteria was associated with higher HbA1c (OR = 3.83, $95\%$CI (1.26;11.65)), higher TAR (OR = 6.48, $95\%$ CI (1.6;26.2)), lower TIR (OR = 0.21, $95\%$ CI (0.06;0.71)), and higher GMI (OR = 4.53, $95\%$ CI (1.25;15.15)), adjusting for age, sex, bleeding during brushing, and professional hygiene frequency as covariates (all $p \leq 0.02$) (Table 3). ## 4. Discussion Our study, in accordance with the literature [10,11], concurs in emphasizing the high prevalence of cariogenic and periodontopathogenic in children and adolescents with T1D and that their oral microbiota is characterized by the presence of Actinomyces spp., Lactobacillus spp., A. actinomycetemcomitans, and P. intermedia. In addition, in a high percentage of subjects, other opportunistic bacterial species associated with dental and periodontal disease have been found, including Veillonella spp., S. mutans, A. naeslundii, T. denticola, and T. forsythia, indicating evident oral microbiota dysbiosis. As regards pathogens belonging to the red complex (T. forsythia, T. denticola and P. gingivalis), known to be associated with chronic and severe PD, $80\%$ of the subjects had at least one of them and $30\%$ showed the co-presence of T. forsythia and T. denticola, despite the young age of the participants [41]. Similar frequencies were found in a study on adult patients with T1D and PD in which the combination of these two periodontal pathogens correlated with poor glycemic control [42,43]. Nonetheless, in our study, levels of these periodontopathogens did not differ according to glycemic control. However, the simultaneous presence of red complex periodontopathogens negatively correlated with virtuous oral hygiene habits such as frequency of toothbrush changes and professional oral hygiene, pointing out that these modifiable factors are fundamental determinants in the prevention of dysbiosis associated with the risk of PD [44]. The detection of A. actinomycetemcomitans in whole analyzed samples is in accordance with previous studies reporting a high presence of this microorganism in subjects with both PD and T1D or T2D [45,46,47]. Evidence clearly underlines its etiological role in localized aggressive juvenile PD [48]. S. mutans, along with colonization by Lactobacillus, is considered one of the key elements in the early development of caries and an important factor in the predisposition of future caries risk [49]. S. mutans leads to an alteration of the local environment by forming an acid and exopolysaccharide-rich milieu, thus creating a favorable niche for the growth of other acidogenic and aciduric species [50]. In a study by Gross et al., the presence of Veillonella spp. was associated with the future development of caries in a population of healthy children, suggesting a key role of this bacteria as an early indicator of a caries-predisposing oral environment [51]. In our study, Lactobacillus and Veillonella spp. have been found in $100\%$ and $93.3\%$ of subjects enrolled, respectively, suggesting that an early alteration in the composition of oral microbiota could increase the risk of developing caries in children and adolescents with T1D. Despite some controversial studies [21,22,52], most recent evidence supported the presence of higher levels of cariogenic bacteria (i.e., S. mutans, Veillonella spp. and Lactobacillus) in patients with diabetes, particularly in subjects with poor glycemic control, supporting an association between poor glycaemic control and dysbiosis status of the oral microbiota, which is associated with a higher risk of oral and dental diseases [21,22,53,54]. Accordingly, in our study children and adolescents with poor glycemic control had a significantly higher presence of cariogenic bacteria (i.e., S. mutans) than peers with a good metabolic control. Presence of S. mutans and Veillonella spp was significantly associated with indicators of suboptimal glycemic control, such as higher HbA1c and TAR, and lower TIR. Although our results support the idea that an adequate metabolic control may decelerate the proliferation of pathogenic oral bacteria, it remains unclear whether the association is causative or reactive and whether an intervention to manipulate the oral microbiota could be of clinical utility for improving diabetes control [55]. The positive correlation between diabetes and pro-cariogenic bacteria may also be related to environmental factors and behavioral aspects, particularly diet. Subjects with T1D have generally higher frequency of food intake than non-diabetic peers and more frequent use of simple and complex sugar intake to deal with hypoglycaemia episodes [56]. A diet rich in fermentable carbohydrates exposes these patients to prolonged acidic conditions. Furthermore, subjects with T1D have reduced salivary flow, increased viscosity, and reduced buffering and antimicrobial capacity of saliva [57,58]. All these factors favor the selection and proliferation of acidogenic and acid-tolerant bacterial species responsible for caries development. Brushing habits and frequent dental visits are considered the main methods to prevent oral diseases as early as during childhood, including gingivitis and dental caries. Our results indicate that the habit of brushing teeth after hypoglycaemia correction with simple sugars is infrequent ($22\%$) and, in any case, this never occurs after overnight hypoglycaemia corrections. Our study could provide significant insights into the interaction between glycemic control, oral hygiene habits, and oral microbiota composition in subjects with T1D. Although the underlying mechanism and the mutual interaction between oral microbiota composition and diabetes and glycaemic control need to be further investigated, the presence of periodontopathogens and cariogenic bacteria is certainly an early indicator of dental and periodontal disease risk that is associated with glycemic control and oral hygiene habits. Thus, oral health education and early diagnosis and treatment of PD should be recommended to T1D subjects as early as during childhood. In this regard, young patients with diabetes and their families should be educated on proper oral hygiene care to counteract the accumulation of bacterial biofilm for the prevention of caries and PD, also given the long-term impact on metabolic control. Appropriate patient education in hygiene maintenance combined with professional oral hygiene sessions with specific devices could positively influence the oral health of subjects, and in the case of T1D subjects, also have positive impacts on metabolic health in the short and long term. Subjecting patients to a monitoring protocol and professional oral hygiene sessions could be the key to the success in preventing oral complications [30]. Patients should be monitored regularly to assess the dental and periodontal health status, and sessions of professional oral hygiene should be scheduled periodically, particularly in patients with a tendency to accumulate plaque and tartar and with early signs of PD, such as gingivitis [59,60]. Probiotics and antibacterial substances for local and/or systemic use have been the subject of intense recent investigations [61] and may provide clinical benefits in the nonsurgical treatment of periodontal disease and prevention of complications, although these systems should be further studied and analyzed [62]. The use of paraprobiotics formulations resulted in a significant reduction in most clinical indices evaluated in comparison with conventional chlorhexidine treatments in adult patients with PD and significantly reduced, after 6 months of use, the percentage of pathological bacteria, including the “red complex” ones [63]. Their immunomodulatory role and their ability to maintain or restore the balance of the oral flora appear to be promising in the prevention and therapy of oral dysbiosis and may have long-term effects not only on the course of oral pathology, but also on metabolic control and the risk of complications. The limitations of this study include: [1] the small sample size and qualitative microbial analysis; [2] the absence of healthy controls to compare prevalence of periodontopathogens; [3] the cross-sectional study design that is not suitable for inferring causality; and [4] the absence of oral health evaluation by a dental hygienist. The strengths of our study are: [1] the young age of the sample which, lacking evident disease processes, allows for the detection of early changes in the composition of the oral microbiota and identification of potential targets for prevention and intervention; and [2] the use of CGM data recorded for a 12-week period that allows a detailed assessment of glucose metabolism. ## 5. Conclusions Our study shows that children and adolescents with T1D have a characteristic composition of the oral microbiota with a high prevalence of cariogenic and periodontopathogens bacteria from an early age. The presence of cariogenic bacteria and periodontopathogens are associated with glycemic control parameters and oral hygiene habits. Therefore, it is crucial to pay attention to glycemic control and regular daily and professional oral hygiene to prevent the establishment of an oral microbiota predisposing to dental and periodontal pathology in subjects with T1D since a pediatric age. Given the bidirectional relationships between oral and metabolic health, prevention and treatment of dental and periodontal health should be part of the multidisciplinary care of T1D. ## References 1. Atkinson M.A., Eisenbarth G.S., Michels A.W.. Type 1 diabetes. Lancet (2014) 383 69-82. DOI: 10.1016/S0140-6736(13)60591-7 2. Boulton A.J., Vileikyte L., Ragnarson-Tennvall G., Apelqvist J.. The global burden of diabetic foot disease. Lancet (2005) 366 1719-1724. DOI: 10.1016/S0140-6736(05)67698-2 3. Coresh J., Astor B.C., Greene T., Eknoyan G., Levey A.S.. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third national health and nutrition examination survey. Am. J. Kidney Dis. (2003) 41 1-12. DOI: 10.1053/ajkd.2003.50007 4. Yau J.W.Y., Rogers S.L., Kawasaki R., Lamoureux E.L., Kowalski J.W., Bek T., Chen S.-J., Dekker J.M., Fletcher A., Grauslund J.. Global Prevalence and Major Risk Factors of Diabetic Retinopathy. Diabetes Care (2012) 35 556-564. DOI: 10.2337/dc11-1909 5. Einarson T.R., Acs A., Ludwig C., Panton U.H.. Prevalence of cardiovascular disease in type 2 diabetes: A systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc. Diabetol. (2018) 17 83. DOI: 10.1186/s12933-018-0728-6 6. Löe H.. Periodontal Disease: The sixth complication of diabetes mellitus. Diabetes Care (1993) 16 329-334. DOI: 10.2337/diacare.16.1.329 7. Pihlstrom B.L., Michalowicz B.S., Johnson N.W.. Periodontal diseases. Lancet (2005) 366 1809-1820. DOI: 10.1016/S0140-6736(05)67728-8 8. Tonetti M.S., Greenwell H., Kornman K.S.. Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. J. Periodontol. (2018) 89 S159-S172. DOI: 10.1002/JPER.18-0006 9. Caton J.G., Armitage G., Berglundh T., Chapple I.L., Jepsen S., Kornman K.S., Mealey B.L., Papapanou P.N., Sanz M., Tonetti M.S.. A new classification scheme for periodontal and peri-implant diseases and conditions—Introduction and key changes from the 1999 classification. J. Periodontol. (2018) 89 S1-S8. DOI: 10.1002/JPER.18-0157 10. Al-Khabbaz A.K., Al-Shammari K.F., Hasan A., Abdul-Rasoul M.. Periodontal Health of Children with Type 1 Diabetes Mellitus in Kuwait: A Case-Control Study. Med. Princ. Pract. (2012) 22 144-149. DOI: 10.1159/000342624 11. Carneiro V.L., Fraiz F.C., Ferreira F.D.M., Pintarelli T.P., Oliveira A.C.B., Boguszewski M.C.D.S.. The influence of glycemic control on the oral health of children and adolescents with diabetes mellitus type 1. Arq. Bras. Endocrinol. Metabol. (2015) 59 535-540. DOI: 10.1590/2359-3997000000117 12. Salvi G.E., Carollo-Bittel B., Lang N.P.. Effects of diabetes mellitus on periodontal and peri-implant conditions: Update on associations and risks. J. Clin. Periodontol. (2008) 35 398-409. DOI: 10.1111/j.1600-051X.2008.01282.x 13. Stöhr J., Barbaresko J., Neuenschwander M., Schlesinger S.. Bidirectional association between periodontal disease and diabetes mellitus: A systematic review and meta-analysis of cohort studies. Sci. Rep. (2021) 11 13686. DOI: 10.1038/s41598-021-93062-6 14. Nascimento G.G., Leite F.R.M., Vestergaard P., Scheutz F., López R.. Does diabetes increase the risk of periodontitis? A systematic review and meta-regression analysis of longitudinal prospective studies. Acta Diabetol. (2018) 55 653-667. DOI: 10.1007/s00592-018-1120-4 15. Taylor J.J., Preshaw P.M., Lalla E.. A review of the evidence for pathogenic mechanisms that may link periodontitis and diabetes. J. Periodontol. (2013) 84 S113-S134. DOI: 10.1111/jcpe.12059 16. Pradhan S., Goel K.. Interrelationship between diabetes and periodontitis: A review. J. Nepal Med. Assoc. (2011) 51 144-153. DOI: 10.31729/jnma.41 17. Tetè G., D’orto B., Ferrante L., Polizzi E., Cattoni F.. Role of mast cells in oral inflammation. J. Biol. Regul. Homeost Agents. (2021) 35 65-70. DOI: 10.23812/21-4supp1-6 18. Paju S., Pussinen P., Suominen-Taipale L., Hyvönen M., Knuuttila M., Könönen E.. Detection of Multiple Pathogenic Species in Saliva Is Associated with Periodontal Infection in Adults. J. Clin. Microbiol. (2009) 47 235-238. DOI: 10.1128/JCM.01824-08 19. Mineoka T., Awano S., Rikimaru T., Kurata H., Yoshida A., Ansai T., Takehara T.. Site-Specific Development of Periodontal Disease Is Associated with Increased Levels of. J. Periodontol. (2008) 79 670-676. DOI: 10.1902/jop.2008.070398 20. Deng Z.-L., Szafrański S.P., Jarek M., Bhuju S., Wagner-Döbler I.. Dysbiosis in chronic periodontitis: Key microbial players and interactions with the human host. Sci. Rep. (2017) 7 3703. DOI: 10.1038/s41598-017-03804-8 21. Siudikiene J., Machiulskiene V., Nyvad B., Tenovuo J., Nedzelskiene I.. Dental Caries Increments and Related Factors in Children with Type 1 Diabetes Mellitus. Caries Res. (2008) 42 354-362. DOI: 10.1159/000151582 22. El-Tekeya M., El Tantawi M., Fetouh H., Mowafy E., Khedr N.A.. Caries risk indicators in children with type 1 diabetes mellitus in relation to metabolic control. Dent. Traumatol. (2012) 34 510-516 23. Ferizi L., Dragidella F., Spahiu L., Begzati A., Kotori V.. The Influence of Type 1 Diabetes Mellitus on Dental Caries and Salivary Composition. Int. J. Dent. (2018) 2018 5780916. DOI: 10.1155/2018/5780916 24. Becker M.R., Paster B.J., Leys E.J., Moeschberger M.L., Kenyon S.G., Galvin J.L., Boches S.K., Dewhirst F.E., Griffen A.L.. Molecular Analysis of Bacterial Species Associated with Childhood Caries. J. Clin. Microbiol. (2002) 40 1001-1009. DOI: 10.1128/JCM.40.3.1001-1009.2002 25. Mashima I., Nakazawa F.. Interaction between Streptococcus spp. and Veillonella tobetsuensis in the Early Stages of Oral Biofilm Formation. J. Bacteriol. (2015) 197 2104-2111. DOI: 10.1128/JB.02512-14 26. Liu S., Wang Y., Zhao L., Sun X., Feng Q.. Microbiome succession with increasing age in three oral sites. Aging (2020) 12 7874-7907. DOI: 10.18632/aging.103108 27. Crielaard W., Zaura E., Schuller A.A., Huse S.M., Montijn R.C., Keijser B.J.F.. Exploring the oral microbiota of children at various developmental stages of their dentition in the relation to their oral health. BMC Med. Genom. (2011) 4. DOI: 10.1186/1755-8794-4-22 28. Burcham Z.M., Garneau N.L., Comstock S.S., Tucker R.M., Knight R., Metcalf J.L., Miranda A., Reinhart B., Meyers D., Woltkamp D.. Patterns of Oral Microbiota Diversity in Adults and Children: A Crowdsourced Population Study. Sci. Rep. (2020) 10 2133. DOI: 10.1038/s41598-020-59016-0 29. Rodenburg J.P., Winkelhoff A.J., Winkel E.G., Goene R.J., Abbas F., Graaff J.. Occurrence of Bacteroides gingivalis, Bacteroides intermedius and Actinobacillus actinomycetemcomitans in severe periodontitis in relation to age and treatment history. J. Clin. Periodontol. (1990) 17 392-399. DOI: 10.1111/j.1600-051X.1990.tb00036.x 30. Cattoni F., Tetè G., D’orto B., Bergamaschi A., Polizzi E., Gastaldi G.. Comparison of hygiene levels in metal-ceramic and stratified zirconia in prosthetic rehabilitation on teeth and implants: A retrospective clinical study of a three-year follow-up. J. Biol. Regul. Homeost. Agents (2021) 35 41-49. DOI: 10.23812/21-4SUPP1-4 31. Yang Y., Liu S., Wang Y., Wang Z., Ding W., Sun X., He K., Feng Q., Zhang X.. Changes of saliva microbiota in the onset and after the treatment of diabetes in patients with periodontitis. Aging (2020) 12 13090-13114. DOI: 10.18632/aging.103399 32. de Groot P.F., Belzer C., Aydin Ö., Levin E., Levels J.H., Aalvink S., Boot F., Holleman F., van Raalte D.H., Scheithauer T.P.. Distinct fecal and oral microbiota composition in human type 1 diabetes, an observational study. PLoS ONE (2017) 12. DOI: 10.1371/journal.pone.0188475 33. Jensen E.D., Selway C.A., Allen G., Bednarz J., Weyrich L.S., Gue S., Peña A.S., Couper J.. Early markers of periodontal disease and altered oral microbiota are associated with glycemic control in children with type 1 diabetes. Pediatr. Diabetes (2020) 22 474-481. DOI: 10.1111/pedi.13170 34. Garn S.M.. Growth at adolescence. By J. M. Tanner. Pp. vii + 212. Blackwell Scientific Publications, Oxford. Publisher simultaneously by Charles C Thomas and the Ryerson Press. 1955. Am. J. Phys. Anthropol. (1956) 14 120-122. DOI: 10.1002/ajpa.1330140125 35. Maguolo A., Rioda M., Zusi C., Emiliani F., Olivieri F., Piona C., Marigliano M., Orsi S., Morandi A., Maffeis C.. Cardiovascular risk factors in children and adolescents with type 1 diabetes mellitus: The role of insulin resistance and associated genetic variants. Horm. Res. Paediatr. (2022). DOI: 10.1159/000527520 36. De Onis M., Onyango A.W., Borghi E., Siyam A., Nishida C., Siekmann J.. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ. (2007) 85 660-667. DOI: 10.2471/BLT.07.043497 37. Flynn J.T., Kaelber D.C., Baker-Smith C.M., Blowey D., Carroll A.E., Daniels S.R., De Ferranti S.D., Dionne J.M., Falkner B., Flinn S.K.. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics (2017) 140 e20171904. DOI: 10.1542/peds.2017-1904 38. Danne T., Nimri R., Battelino T., Bergenstal R.M., Close K.L., DeVries J.H., Garg S., Heinemann L., Hirsch I., Amiel S.A.. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care (2017) 40 1631-1640. DOI: 10.2337/dc17-1600 39. Garcia L., Tercero J.C., Legido B., Ramos J.A., Alemany J., Sanz M.. Rapid detection of Actinobacillus actinomycetemcomitans, Prevotella intermedia and Porphyromona gingivalis by multiplex PCR. J. Periodontal Res. (1998) 33 59-64. DOI: 10.1111/j.1600-0765.1998.tb02292.x 40. Pardo A., Signoriello A., Signoretto C., Messina E., Carelli M., Tessari M., De Manna N.D., Rossetti C., Albanese M., Lombardo G.. Detection of Periodontal Pathogens in Oral Samples and Cardiac Specimens in Patients Undergoing Aortic Valve Replacement: A Pilot Study. J. Clin. Med. (2021) 10. DOI: 10.3390/jcm10173874 41. Sedghi L.M., Bacino M., Kapila Y.L.. Periodontal Disease: The Good, The Bad, and The Unknown. Front. Cell. Infect. Microbiol. (2021) 11 1210. DOI: 10.3389/fcimb.2021.766944 42. Longo P.L., Dabdoub S., Kumar P., Artese H.P.C., Dib S.A., Romito G.A., Mayer M.P.A.. Glycaemic status affects the subgingival microbiome of diabetic patients. J. Clin. Periodontol. (2018) 45 932-940. DOI: 10.1111/jcpe.12908 43. Schara R., Skaleric E., Seme K., Skaleric U.. Prevalence of periodontal pathogens and metabolic control of type 1 diabetes patients. J. Int. Acad. Periodontol. (2013) 15 29-34. PMID: 23413629 44. Genco R.J., Borgnakke W.S.. Risk factors for periodontal disease. Periodontology 2000 (2013) 62 59-94. DOI: 10.1111/j.1600-0757.2012.00457.x 45. Akherati M., Shafaei E., Salehiniya H., Abbaszadeh H.. Comparison of the frequency of periodontal pathogenic species of diabetics and non-diabetics and its relation to periodontitis severity, glycemic control and body mass index. Clin. Exp. Dent. Res. (2021) 7 1080-1088. DOI: 10.1002/cre2.453 46. Chakraborty P., Chowdhury R., Bhakta A., Mukhopahyay P., Ghosh S.. Microbiology of periodontal disease in adolescents with Type 1 diabetes. Diabetes Metab. Syndr. Clin. Res. Rev. (2021) 15 102333. DOI: 10.1016/j.dsx.2021.102333 47. Castrillon C.A., Hincapie J.P., Yepes F.L., Roldan N., Moreno S.M., Contreras A., Botero J.E.. Occurrence of red complex microorganisms and. J. Investig. Clin. Dent. (2013) 6 25-31. DOI: 10.1111/jicd.12051 48. Raja M.. Aggregatibacter Actinomycetemcomitans—A Tooth Killer?. J. Clin. Diagn. Res. (2014) 8 ZE13-ZE16. DOI: 10.7860/JCDR/2014/9845.4766 49. Mitrakul K., Asvanund Y., Vongsavan K.. Prevalence of Five Biofilm-Related Oral Streptococci Species from Plaque. J. Clin. Pediatr. Dent. (2011) 36 161-166. DOI: 10.17796/jcpd.36.2.d7r750u227j85813 50. Lemos J.A., Palmer S.R., Zeng L., Wen Z.T., Kajfasz J.K., Freires I.A., Abranches J., Brady L.J.. The Biology of Streptococcus mutans. Microbiol. Spectr. (2019) 7 7. DOI: 10.1128/microbiolspec.GPP3-0051-2018 51. Gross E.L., Beall C., Kutsch S.R., Firestone N.D., Leys E.J., Griffen A.L.. Beyond Streptococcus mutans: Dental Caries Onset Linked to Multiple Species by 16S rRNA Community Analysis. PLoS ONE (2012) 7. DOI: 10.1371/journal.pone.0047722 52. Thorstensson H.. Periodontal disease in adult insulin-dependent diabetics. Swed. Dent. J. Suppl. (1995) 107 1-68. PMID: 7638766 53. Dusková J., Broukal Z.. Compensation criteria of basal disease in the prevention and treatment of periodontal disease in patients with diabetes mellitus. Prakt. Zubn. Lek. (1991) 39 51-54. PMID: 1886879 54. Lai S., Cagetti M.G., Cocco F., Cossellu D., Meloni G., Campus G., Lingström P.. Evaluation of the difference in caries experience in diabetic and non-diabetic children—A case control study. PLoS ONE (2017) 12. DOI: 10.1371/journal.pone.0188451 55. Yuan X., Wu J., Chen R., Chen Z., Su Z., Ni J., Zhang M., Sun C., Zhang F., Liu Y.. Characterization of the oral microbiome of children with type 1 diabetes in the acute and chronic phases. J. Oral Microbiol. (2022) 14 2094048. DOI: 10.1080/20002297.2022.2094048 56. Nansel T.R., Haynie D., Lipsky L., Laffel L.M., Mehta S.N.. Multiple Indicators of Poor Diet Quality in Children and Adolescents with Type 1 Diabetes Are Associated with Higher Body Mass Index Percentile but not Glycemic Control. J. Acad. Nutr. Diet. (2012) 112 1728-1735. DOI: 10.1016/j.jand.2012.08.029 57. Zalewska A., Knaś M., Kuźmiuk A., Waszkiewicz N., Niczyporuk M., Waszkiel D., Zwierz K.. Salivary innate defense system in type 1 diabetes mellitus in children with mixed and permanent dentition. Acta Odontol. Scand. (2013) 71 1493-1500. DOI: 10.3109/00016357.2013.773071 58. Moreira A., Passos I., Sampaio F., Soares M., Oliveira R.. Flow rate, pH and calcium concentration of saliva of children and adolescents with type 1 diabetes mellitus. Braz. J. Med. Biol. Res. (2009) 42 707-711. DOI: 10.1590/S0100-879X2009005000006 59. Giuca M.R., Pasini M., Giuca G., Caruso S., Necozione S., Gatto R.. Investigation of periodontal status in type 1 diabetic adolescents. Eur. J. Paediatr. Dent. (2015) 16 319. PMID: 26637258 60. Sas B.. Anti-discoloration system: A new chlorhexidine mouthwash. J. Biol. Regul. Homeost. Agents (2021) 35 113-118. DOI: 10.23812/21-4SUPP1-10 61. Chugh P., Dutt R., Sharma A., Bhagat N., Dhar M.S.. A critical appraisal of the effects of probiotics on oral health. J. Funct. Foods (2020) 70 103985. DOI: 10.1016/j.jff.2020.103985 62. Butera A., Maiorani C., Gallo S., Pascadopoli M., Venugopal A., Marya A., Scribante A.. Evaluation of Adjuvant Systems in Non-Surgical Peri-Implant Treatment: A Literature Review. Healthcare (2022) 10. DOI: 10.3390/healthcare10050886 63. Butera A., Gallo S., Pascadopoli M., Maiorani C., Milone A., Alovisi M., Scribante A.. Paraprobiotics in Non-Surgical Periodontal Therapy: Clinical and Microbiological Aspects in a 6-Month Follow-Up Domiciliary Protocol for Oral Hygiene. Microorganisms (2022) 10. DOI: 10.3390/microorganisms10020337 64. Xia T., Baumgartner J.C.. Occurrence of Actinomyces in Infections of Endodontic Origin. J. Endod. (2003) 29 549-552. DOI: 10.1097/00004770-200309000-00001 65. Mashima I., Theodorea C.F., Thaweboon B., Thaweboon S., Nakazawa F.. Identification of Veillonella species in the tongue biofilm by using a novel one-step polymerase chain reaction method. PLoS ONE (2016) 11. DOI: 10.1371/journal.pone.0157516 66. Nakano K., Inaba H., Nomura R., Nemoto H., Takeda M., Yoshioka H., Matsue H., Takahashi T., Taniguchi K., Amano A.. Detection of cariogenic Streptococcus mutans in extirpated heart valve and atheromatous plaque specimens. J. Clin. Microbiol. (2006) 44 3313-3317. DOI: 10.1128/JCM.00377-06 67. Byun R., Nadkarni M.A., Chhour K.L., Martin F.E., Jacques N.A., Hunter N.. Quantitative analysis of diverse Lactobacillus species present in advanced dental caries. J. Clin. Microbiol. (2004) 42 3128-3136. DOI: 10.1128/JCM.42.7.3128-3136.2004
--- title: 'Celiac Disease and the Gluten Free Diet during the COVID-19 Pandemic: Experiences of Children and Parents' authors: - Johanna M. Kreutz - Laura Heynen - Lisanne Arayess - Anita C. E. Vreugdenhil journal: Medicina year: 2023 pmcid: PMC10059717 doi: 10.3390/medicina59030425 license: CC BY 4.0 --- # Celiac Disease and the Gluten Free Diet during the COVID-19 Pandemic: Experiences of Children and Parents ## Abstract The COVID-19 pandemic perturbed the everyday life of children and those with chronic illnesses, along with the lives of their families. Patients with celiac disease (CD) follow a strict gluten-free diet (GFD), and gluten ingestion is associated with negative health outcomes. The aim of this study was to investigate the experiences of children with CD and their families concerning their GFD, symptoms and CD management during the first period of the COVID-19 pandemic. A cross-sectional questionnaire-based study was performed including 37 Dutch pediatric patients with CD, along with their parents. The majority reported good compliance to the GFD and stated that the diet was easier to follow during the pandemic, mainly due to eating more meals in the home. Some discovered a greater variety of GF products by utilizing online shopping, potentially increasing the financial burden of the GFD. Concerning general dietary habits, $21.6\%$ reported a healthier eating pattern, in contrast to $37.8\%$ and $10.8\%$ who consumed more unhealthy snacks and fewer fruits and vegetables, respectively, than normal during the pandemic. The natural experiment of the COVID-19 pandemic provides valuable information regarding the management of pediatric CD. Education on healthy dietary patterns is important, especially for children with restrictive diets, and the findings of this study show that there is room for improvement in this respect, regardless of the current pandemic. ## 1. Introduction The SARS-CoV-2 (COVID-19) outbreak and the national mitigation measures have temporarily led to a radical change in daily life. The infection itself, as well as the effects of the pandemic, could possibly have an even stronger impact on patients with chronic illness, along with their families. Celiac disease (CD) is a common chronic autoimmune disease that often manifests in childhood. The prevalence in the Netherlands is estimated to be $1\%$. Currently, a strict gluten-free diet (GFD) is the only treatment, which typically leads to complete symptom resolution. Gluten ingestion can have a number of adverse effects in patients with CD, including a flare-up of the auto immune reaction, with associated complaints and negative short and long term consequences. An additional challenge for patients with CD is ensuring a diet that is not only gluten-free, but also of high nutritional quality. Several government-issued rules and regulations were put in place in the Netherlands during the COVID-19 pandemic. These measures were introduced step-wise from March 2020 onward [1,2,3]. Consequently, altered behavior in everyday life could be observed, such as hoarding of groceries, as well as remote work and education. Dutch citizens were encouraged to practice social distancing by staying at home as much as possible. Sport clubs, restaurants, museums, and theaters, as well as schools and daycare centers, were closed [4]. Altered grocery shopping behavior led to shortages of common supermarket items such as flour, bread, and soap [5]. A shortage of certain food items, as well as limited access to food outside the home, might have affected eating habits and dietary patterns [6,7,8]. Initial investigations suggested that dietary patterns in the general population were unfavorably influenced by the lockdown period [9]. This was also found in a Dutch child cohort of the COLC study investigating the lifestyle and well-being of children since the beginning of the COVID-19 pandemic. The COLC study included 189 children in the Netherlands between the ages of 4 and 18 years. A subgroup of families participating in a qualitative study via semi structured interviews showed an overall unhealthier lifestyle and a decline in well-being since the start of the pandemic [10]. Additional analysis of quantitative data from the COLC cohort confirmed these findings (unpublished data from the COLC study: ClinicalTrials.gov Identifier: NCT04411511, accessed on 19 February 2023). It is unclear whether diet was also unfavorably impacted in children with CD due to the lockdown. Further, experiences with the GFD during the pandemic concerning issues such as gluten avoidance or product availability have not been examined. Due to the COVID-19 pandemic, medical care was drastically reduced, leading to postponement of hospital visits or diagnostic procedures [11]. Among other societies, the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition advised the postponing of medical care of patients with CD when possible, noting that this could lead to anxiety or other adverse outcomes in patients [12]. Consequentially, telehealth options were made available on a larger scale, which appears to be a potentially positive option for this patient population [13,14,15]. Apart from expert opinions, there is limited empirical data available on the impact of the pandemic and its protective measures on children with underlying diseases and specific dietary restrictions. The primary goal of this study was to investigate the experiences of children with CD and their families during the COVID-19 pandemic in regards to adherence to, and quality of, the (gluten-free) diet, along with access to and availability of gluten-free products. Secondly, the impact of the national measures instituted by the Dutch government, as well as possible changes in societal behavior, on patients with CD was investigated. ## 2. Materials and Methods The study consisted of a cross-sectional questionnaire sent out to children with CD and their families during the lock-down measures of the COVID-19 pandemic in the Netherlands. Questionnaires were sent out at the end of June 2020. Responses were included until the beginning of September 2020. The study population included patients, 0–18 years old, of the Maastricht University Medical Centre (MUMC+) who were diagnosed with CD according to the ESPGHAN (European Society for Pediatric Gastroenterology, Hepatology, and Nutrition) guidelines [16]. Patients that were not diagnosed according to the ESPGHAN guidelines were excluded. After providing informed consent, parents of children under the age of 12 years, or the children themselves, if they were aged 12 years and older, completed the questionnaires at home. The questionnaire included baseline questions regarding the period prior to the COVID-19 pandemic, starting in February 2020 in the Netherlands, and questions concerning the period in which several measures were taken to limit the spread of the virus (for the translated questionnaires, see Supplementary Material S1). The questionnaire included five main domains. The first domain involved questions about symptoms related to CD activity or COVID-19 infection (see Supplementary Table S1). Patients reported on symptoms related to either CD activity, a possible infection with COVID-19, or both (concordant symptoms, such as diarrhea).The second domain was comprised of questions regarding the management of the GFD in general, as well as during the pandemic. The third domain contained questions on dietary habits in general, prior to and during the pandemic. In the fourth domain, patients and their parents were asked to what extend they followed the government-issued measures as a response to the pandemic. The fifth domain contained questions regarding perceived obstacles or possible benefits concerning CD, its management, or the CD-related care patients received during the pandemic. The questionnaire comprised multiple choice questions and open questions, with space for free text responses. The results were mainly descriptive and are presented as such in this manuscript. Statistical analysis was executed using SPSS version 25 (SPSS incorporated, Chicago, IL, USA). The local institutional review board Medisch Ethische Toetsingscommissie (METC) of the Maastricht UMC+ approved the study, which was performed based on the Declaration of Helsinki. During the pandemic, the board of directors of Maastricht UMC+ adopted a policy to inform patients and ask their consent for COVID-19 research purposes. ## 3. Results A digital questionnaire was sent to 104 pediatric patients with CD and an identical paper version was sent by regular mail. In total, $36\%$ (37 children) responded with a completed questionnaire, $67.6\%$ of whom were female ($$n = 25$$). The mean age was 10.4 years (range 3 to 18 years). The median duration of diagnosis prior to participation was 52.5 months (ranging from 6 months to 16 years). There were seven patients who had followed a GFD for less than two years, while the others had received a more long-term diagnoses. Patients answered 11 questions regarding their management of the GFD before the COVID-19 pandemic (see Supplementary Material S1). All 37 patients reported following the GFD strictly and taking measures to prevent unintentional gluten ingestion, such as discussing the diet when eating out of the home, storing gluten-free products separately from gluten containing food items, and only using gluten-free medicine products. A total of $94.6\%$ ($$n = 35$$) of the children reported not eating products containing wheat starch, $64.9\%$ ($$n = 24$$) of the children did not eat food labeled ‘prepared in an environment where gluten is processed,’ and half of the children ($$n = 19$$) did not eat food labeled ‘may contain traces of gluten or wheat’. When asked about their experiences with the GFD during the COVID-19 pandemic, 6 patients ($16.2\%$) stated that they ingested gluten during this period, 1 of which did so intentionally, whereas the other 5 patients reported unintentional gluten ingestion (see Table 1). The majority of the patients, ($$n = 31$$; $83.7\%$) where either certain or quite sure that they did not ingest gluten during this period. Patients were asked whether they developed new symptoms or experienced an increase in symptoms which could be related to either a COVID-19 infection, CD activity, or both (see Table 2; for the list of symptoms, see Supplementary Table S1). A total of $57\%$ ($$n = 21$$) of the children reported new complaints or an increase in existing complaints during the COVID-19 pandemic (see Table 2). The common cold ($$n = 9$$), coughing ($$n = 6$$), fever ($$n = 5$$), sneezing ($$n = 6$$), and fatigue ($$n = 5$$) were the most prevalent symptoms among these patients. Only two patients underwent a nose and throat swab for PCR testing to determine whether an active COVID-19 infection was present, both with negative results. A total of 14 patients reported that one or more of their family members developed complaints that could be attributed to COVID-19. For this reason, 9 family members underwent a nose and throat swab, only 1 of which was positive for the mother of a patient. At the time the questionnaire was sent out (from June 1st onward), anyone with mild symptoms could be tested through the municipal health services in the Netherlands. Before 1 June 2020, testing was more limited due to limited resources [3]. Concerning the availability of GF products during the COVID-19 pandemic, $67.6\%$ of patients stated that there were enough products present at all times; $21.6\%$ stated that they could not purchase the gluten-free products they would usually buy, but that enough gluten-free alternatives were available to maintain and ensure a GFD; and $10.8\%$ reported a scarcity in gluten-free products during the pandemic. In response to the question regarding whether patients took specific precautions during the COVID-19 pandemic to ensure that their GFD was not comprised, $54.1\%$ stated that they did indeed do so. These measures consisted of hoarding gluten-free products, ordering online groceries, and having products shipped from abroad. Patients that started buying gluten-free products online stated that as a side-effect, they discovered a new array of products they did not know about before, and which they would not have discovered if not for the COVID-19 pandemic. Interestingly, $21.6\%$ of the participants reported that they started eating healthier during the pandemic than during a normal school week ($21.6\%$) (See Table 3). Healthy snacks were eaten more often in $29.7\%$ of patients and $18.9\%$ of patients stated that they ate more fruit and vegetables compared to the amount consumed during a normal school week. Patients also reported that buying groceries online was easier than going out to a store to buy gluten-free products ($10.8\%$). Further, staying at home made it easier to adhere to the GFD, as reported by $32.4\%$ of patients. One parent stated: “because we were at home more often, the eating environment was ‘clean’ which led to less contamination with gluten.” A significant ($$p \leq 0.01$$) increase in the number of times patients or their parents cooked at home was also observed (see Table 4). No significant differences were observed in other dietary habits, such as the number of fruits and vegetables eaten daily, water and soda intake, snacking behavior, or ordering take-away. A total of 16 out of 37 children achieved the daily norm for fruit intake of 1.5 pieces of fruit per day before the COVID-19 pandemic, and 17 children achieved this norm during COVID-19. In contrast to these effects, 13 patients ($35.1\%$) reported a negative impact of the lockdown on their dietary behavior (see Table 3). This entailed eating more unhealthy snacks while staying at home and the scarcity of gluten-free products. Five participants ($13.5\%$) reported that they would like to have had financial support during the COVID-19 pandemic, due to the increase in expenses of the GFD. One mother stated: “Since a lot of gluten-free products were sold out, we had to buy more expensive products than we would have normally done.” Patients and their parents were asked whether they wanted more or different support from their health care providers during the COVID-19 pandemic. This was not the case for all 37 patients. One parent reported that a consultation with the dietitian concerning the GFD was converted to a video-call instead of an in-person visit to the hospital, which they perceived as more convenient. ## 4. Discussion This cross-sectional questionnaire-based study explores the experiences of a small group of Dutch children, diagnosed with CD according to the ESPGHAN guidelines, along with their families, regarding the GFD and their disease in general during the first period of the COVID-19 pandemic. Their experiences can be utilized as learning points for the management of CD in children in general. This group of patients exhibited a generally good to high compliance to the diet before the COVID-19 pandemic. However, during the COVID-19 pandemic, an effect on eating behavior could be observed in these children. Only a very limited amount of gluten ingestion was reported. This is in contrast with an anonymous survey conducted in the United States of America, where significantly more intentional gluten intake was reported by patients with CD, as well as an impactful drop in the availability of gluten free products [17]. In contrast, in the current study, a majority of patients surprisingly felt it was easier to follow the GFD under the conditions of the lock-down, in which children consumed all meals at home. This indicates that eating outside of the home is perceived by patients as an important risk factor for gluten contamination [18]. In addition, the results revealed areas with room for improvement with regard to healthy product choices and the convenience of buying gluten-free products, which were discovered by patients due to the COVID-19 pandemic. Overall, this study suggests that pediatric patients with CD, along with their families, appear to be moderately affected by the COVID-19 pandemic with regards to diet and patient care. The pandemic and especially its effects on the everyday life of children has been identified as a potential risk factor for unhealthy dietary patterns [10,11,15] (unpublished quantitative data from the COLC study: ClinicalTrials.gov Identifier: NCT04411511). Therefore, a main focus of this study was on the management of the GFD and general dietary habits during and prior to the COVID-19 pandemic. Interestingly, about $\frac{1}{5}$th of patients stated that their eating patterns were healthier during the COVID-19 pandemic than before. This was attributed to having more time to cook at home. This is in contrast to findings of our research group regarding a general population of healthy children in the Netherlands (unpublished data from the COLC study). Here, a large group reported more unhealthy eating patterns during the lockdown. Among the group of children with CD, $29.7\%$ of the patients reported eating more healthy snacks, whereas only half of this percentage reported doing so in the COLC-cohort. Besides an increase in healthy snack consumption in a subgroup of children, in the current study, $37.8\%$ of the patients reported eating unhealthy snacks more often. This is in line with the COLC-study cohort, where approximately one in three participants reported eating more unhealthy snacks. This emphasizes the need to promote a healthy lifestyle for all children, especially in a lockdown period. Notably, less than half of the patients in the study reported achieving the recommended daily amount of fruit intake, prior to as well as during the pandemic. Healthy eating becomes of even greater importance in patients with a restrictive diet, such as the GFD, as it often inherently of lower nutritional quality [19]. Doctors and dieticians should therefore emphasize this during their education of patients with CD and find new strategies to encourage favorable dietary patterns in this population. The pandemic affected grocery shopping behavior, prompting shifts such as the hoarding of products and limiting visits to supermarkets, which affected the availability of products. About half of the patients and their families took special measures to ensure that there were sufficient gluten-free products at home. Furthermore, a substantial number of patients reported that gluten-free products were indeed less available during that time, which is a potential risk factor for decreased compliance. This could possibly lead to more stress and worry in families with patients with CD or other chronic diseases that require a special diet. This should therefore receive more attention in the event of future lock-down periods. Interestingly, as a positive outcome of the first lockdown period, some patients reported that they became more aware of a wider assortment of products due to more online grocery shopping. Due to the scarcity of products and in order to limit visits to the supermarket, they explored online resources for gluten-free products. Ideally, a health crisis should not have been necessary to make patients aware of these resources. Dieticians and other health care providers could make patients more aware of the online resources for gluten-free products. Practical information regarding how to acquire affordable, high quality gluten-free products does not seem to be readily available to all families with patients with CD, although this is an important aspect of the everyday lives of patients with CD, as well as their families. However, the scarcity of products, as well as online grocery shopping, led to a perceived higher financial burden of the GFD during the pandemic, as reported by participants and their parents. Consequentially, parents stated that they would have liked to receive financial support during the pandemic to compensate for this. This finding should be taken into account, for example, in the form of governmental or health care insurance support, either financially or in form of resources, for patients with special dietary requirements [7]. In Jordan for example, registered patients with CD received gluten-free flour during the COVID-19 lock down [20]. With regards to patient care, it should be noted that this questionnaire was filled out by patients with a known CD diagnosis. They did not perceive disadvantages of their care during the COVID-19 pandemic. One patient even reporting that telehealth consultations with the dietitian were more convenient than in-person visits to the hospital. The emergence of telehealth as a consequence of the pandemic should not be strictly reserved for new health crises in the future. Chronic diseases such as CD could benefit from telehealth in general clinical practice. The resources for this form of patient care have rapidly grown due to the COVID-19 crisis, but obstacles and pitfalls still need further attention. Reducing visits to the hospital could result in reducing the disease burden of CD in the pediatric CD population. Earlier studies explored the emergence of telehealth in adult CD, with positive results, especially for young adults, and introduced best practice recommendations for introducing telehealth in pediatric gastroenterology, showing its possible benefits [13,21]. Patients that were newly diagnosed with CD during the pandemic or shortly thereafter were not included in this study. It would be worthwhile to examine how patients perceived the care they received or did not receive during the lock-down period, and how possibly delaying healthcare visits or diagnostics affected this group. None of the 37 participating patients suffered from a proven COVID-19 infection. This cohort is too small to draw any definite conclusions regarding the prevalence of COVID-19 in patients with CD. However, these findings appear to be in line with other reports that did not find an increased risk for COVID-19 infection in patients with CD [22,23]. The current study population was very small, with an accompanying limitation that the compliance to the GFD was very high prior to the pandemic. This might indicate that not all results can be extrapolated to a larger pediatric CD population. On the other hand, the changes that were reported by these families are more likely to be related to the COVID-19 pandemic rather than other factors, as they appeared to have stable dietary habits with a good compliance to the GFD prior to the pandemic [24]. Under lockdown conditions, this possible inclusion bias may have affected how families coped with the dietary challenges of the GFD. As a result of the novelty of the situation, the questionnaire used was not validated. However, several characteristic of the study population were fairly representative of the pediatric CD patient population in the Netherlands. It included a greater percentage of girls; the mean age was 10 years, with a range from 3 up to 18 years. The experience with the GFD and CD diagnosis had a wide range. A further strength of the study was that it only included patients with a confirmed CD diagnosis according to the ESPGHAN guidelines. ## 5. Conclusions In conclusion, this study creates new insight into the experiences of a small group of children with CD and their families during the first period of the current health crisis. In general, adherence to the diet appeared to be more feasible during the COVID-19 lockdown period, due to the controlled situation of eating at home. Further, patients reported that the circumstances led to more online grocery shopping which could increase the diversity of products available for patients. Although most children with CD in this cohort appeared to follow a healthier diet, attention should be given to unhealthy food habits that could have developed in patients with CD during this crisis. The natural experiment of the COVID-19 pandemic provides us with valuable information about the effects of the outbreak, as well as learning points for the management of pediatric CD in general. ## References 1. Corona-Overzicht 15 Maart: De Dag Dat Nederland Verder Op Slot Ging 2. Lockdown Tot 20 Mei, Basisscholen 11 Mei Open En Geen Evenementen in Zomer 3. Archief COVID-19 Updates 2020: Update 22 July t/m 28 July 2020 4. Rijksoverheid; Aanvullende Maatregelen 23 Maart. 2020 5. Stop Hoarding: “We Have Enough” Says Dutch PM 6. Laguna L., Fiszman S., Puerta P., Chaya C., Tarrega A.. The impact of COVID-19 lockdown on food priorities. Results from a preliminary study using social media and an online survey with Spanish consumers. Food Qual. Prefer. (2020.0) 86 104028. DOI: 10.1016/j.foodqual.2020.104028 7. Brown E., Das R., Brewer A.G., Martinez E., Bilaver L.A., Gupta R.S.. Food Insecure and Allergic in a Pandemic: A Vulnerable Population. J. Allergy Clin. Immunol. Pract. (2020.0) 8 2149-2151. DOI: 10.1016/j.jaip.2020.04.038 8. Pal R., Yadav U., Verma A., Bhadada S.K.. Awareness regarding COVID-19 and problems being faced by young adults with type 1 diabetes mellitus amid nationwide lockdown in India: A qualitative interview study. Prim. Care Diabetes (2020.0) 15 10-15. DOI: 10.1016/j.pcd.2020.07.001 9. Luo Y., Chen L., Xu F., Gao X., Han D., Na L.. Investigation on knowledge, attitudes, and practices about food safety and nutrition in the Chinese during the epidemic of corona virus disease 2019. Public Health Nutr. (2021.0) 24 267-274. DOI: 10.1017/S1368980020002797 10. van de Pas K.G.H., Hesselink M.L., Schlechtriem R., Arayess L., Kreutz J.M., Vreugdenhil A.C.E.. The Impact of the COVID-19 Pandemic on Lifestyle and Wellbeing of Children, Adolescents and Their Parents: A Qualitative Study. Children (2022.0) 9. DOI: 10.3390/children9121929 11. Douglas M., Katikireddi S.V., Taulbut M., McKee M., McCartney G.. Mitigating the wider health effects of covid-19 pandemic response. BMJ (2020.0) 369 m1557. DOI: 10.1136/bmj.m1557 12. Walsh C.M., Fishman D.S., Lerner D.G., Endoscopy N., Procedures C.. Pediatric Endoscopy in the Era of Coronavirus Disease 2019: A North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition Position Paper. J. Pediatr. Gastroenterol. Nutr. (2020.0) 70 741-750. DOI: 10.1097/MPG.0000000000002750 13. Berg E.A., Picoraro J.A., Miller S.D., Srinath A., Franciosi J.P., Hayes C.E., Farrell P.R., Cole C.R., LeLeiko N.S.. COVID-19-A Guide to Rapid Implementation of Telehealth Services: A Playbook for the Pediatric Gastroenterologist. J. Pediatr. Gastroenterol. Nutr. (2020.0) 70 734-740. DOI: 10.1097/MPG.0000000000002749 14. Ines Pinto-Sanchez M., Lebwohl B.. Delivery of care remotely through telemedicine in celiac disease: Thinking beyond COVID-19: Commentary on: “COVID-19 pandemic perception in adults with celiac disease: An impulse to implement the use of telemedicine” by Siniscalchi M et al. Dig Liv Dis 2020; 52: 1071-5. Dig. Liver Dis. Off. J. Ital. Soc. Gastroenterol. Ital. Assoc. Study Liver (2020.0) 52 1069-1070. DOI: 10.1016/j.dld.2020.05.022 15. Mehta P., Stahl M.G., Germone M.M., Nagle S., Guigli R., Thomas J., Shull M., Liu E.. Telehealth and Nutrition Support During the COVID-19 Pandemic. Kompass Nutr. Diet. (2021.0) 1 110-112. DOI: 10.1159/000519959 16. Husby S., Koletzko S., Korponay-Szabo I.R., Mearin M.L., Phillips A., Shamir R., Troncone R., Giersiepen K., Branski D., Catassi C.. European Society for Pediatric Gastroenterology, Hepatology, and Nutrition guidelines for the diagnosis of coeliac disease. J. Pediatr. Gastroenterol. Nutr. (2012.0) 54 136-160. DOI: 10.1097/MPG.0b013e31821a23d0 17. Du N., Mehrotra I., Weisbrod V., Regis S., Silvester J.A.. Survey-Based Study on Food Insecurity During COVID-19 for Households with Children on a Prescribed Gluten-Free Diet. Am. J. Gastroenterol. (2022.0) 117 931-934. DOI: 10.14309/ajg.0000000000001778 18. MacCulloch K., Rashid M.. Factors affecting adherence to a gluten-free diet in children with celiac disease. Paediatr. Child Health (2014.0) 19 305-309. DOI: 10.1093/pch/19.6.305 19. Kreutz J.M., Adriaanse M.P.M., van der Ploeg E.M.C., Vreugdenhil A.C.E.. Narrative Review: Nutrient Deficiencies in Adults and Children with Treated and Untreated Celiac Disease. Nutrients (2020.0) 12. DOI: 10.3390/nu12020500 20. Altamimi E.. Effect of COVID-19 Pandemic and Lockdown on Children With Gastrointestinal Disorders. Gastroenterol. Res. (2020.0) 13 125-128. DOI: 10.14740/gr1290 21. Siniscalchi M., Zingone F., Savarino E.V., D’Odorico A., Ciacci C.. COVID-19 pandemic perception in adults with celiac disease: An impulse to implement the use of telemedicine. Dig. Liver Dis. (2020.0) 52 1071-1075. DOI: 10.1016/j.dld.2020.05.014 22. Emmi G., Bettiol A., Mattioli I., Silvestri E., Di Scala G., Urban M.L., Vaglio A., Prisco D.. SARS-CoV-2 infection among patients with systemic autoimmune diseases. Autoimmun. Rev. (2020.0) 19 102575. DOI: 10.1016/j.autrev.2020.102575 23. Zingone F., D’Odorico A., Lorenzon G., Marsilio I., Farinati F., Savarino E.V.. Risk of COVID-19 in celiac disease patients. Autoimmun. Rev. (2020.0) 19 102639. DOI: 10.1016/j.autrev.2020.102639 24. Hopman E.G., le Cessie S., von Blomberg B.M., Mearin M.L.. Nutritional management of the gluten-free diet in young people with celiac disease in The Netherlands. J. Pediatr. Gastroenterol. Nutr. (2006.0) 43 102-108. DOI: 10.1097/01.mpg.0000228102.89454.eb
--- title: Association of HTTLPR, BDNF, and FTO Genetic Variants with Completed Suicide in Slovakia authors: - Aneta Bednarova - Viera Habalova - Silvia Farkasova Iannaccone - Ivan Tkac - Dominika Jarcuskova - Michaela Krivosova - Matteo Marcatili - Natasa Hlavacova journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10059737 doi: 10.3390/jpm13030501 license: CC BY 4.0 --- # Association of HTTLPR, BDNF, and FTO Genetic Variants with Completed Suicide in Slovakia ## Abstract Since suicide and suicidal behavior are considered highly heritable phenotypes, the identification of genetic markers that can predict suicide risk is a clinically important topic. Several genes studied for possible associations between genetic polymorphisms and suicidal behaviors had mostly inconsistent and contradictory findings. The aim of this case-control study was to evaluate the associations between completed suicide and polymorphisms in genes BDNF (rs6265, rs962369), SLC6A4 (5-HTTLPR), and FTO (rs9939609) in relation to sex and BMI. We genotyped 119 completed suicide victims and 137 control subjects that were age, sex, and ethnicity matched. A significant association with completed suicide was found for BDNF rs962369. This variant could play a role in completed suicide, as individuals with the CC genotype were more often found among suicides than in control subjects. After sex stratification, the association remained significant only in males. A nominally significant association between the gene variant and BMI was observed for BDNF rs962369 under the overdominant model. Heterozygotes with the TC genotype showed a lower average BMI than homozygotes with TT or CC genotypes. FTO polymorphism (rs9939609) did not affect BMI in the group of Slovak suicide completers, but our findings follow an inverse association between BMI and completed suicide. ## 1. Introduction Suicide is the cause of more than 700,000 deaths every year [1]. Although the data from 2019 showed that the suicide mortality rate had a decreasing trend (from 19.65 to 10.50 per population of 100,000) in Europe between the years 2000–2019, the COVID-19 pandemic could have affected the current situation, and the most recent data are yet to come [2,3]. In Slovakia, the suicide mortality rate was lately reported to be 9.31 per 100,000 inhabitants, being higher with increasing age. The prevalence of completed suicide is almost seven-fold higher in Slovak men than women [4,5]. Suicidal behavior is characterized as suicide ideation, suicide planning and attempts, and suicide itself [6]. The contributing factors are categorized as distal (genetic predisposition, personality, early life trauma, neurobiological disturbances) and proximal, such as psychiatric disorder, physical disorder, and acute psychosocial crisis [7]. Indeed, suicide and suicidal behavior are considered highly heritable phenotypes [8,9]. However, the specific genes involved and underlying mechanisms together with the role of epigenetics are yet to be elucidated. So far, genetic association studies have discovered various candidate genes potentially involved in suicide susceptibility, and among the most studied have been genes affecting the serotoninergic system, other neurotransmitters from the monoaminergic system, or neurotrophic factors [10]. Since the discovery of brain-derived neurotrophic factor (BDNF), it has been widely studied in relation to various neuropsychiatric disorders [11]. The essential role of BDNF in neuronal growth, differentiation, maturation, and synaptic plasticity have led several research groups also to search for an association with suicidal behaviors by measuring its peripheral levels [12], BDNF gene expression, and epigenetic changes [13], but also the influence of BDNF polymorphisms [14]. A functional polymorphism of the BDNF gene, the Val66Met (rs6265) has been relatively widely investigated with regard to suicidal behavior; however, the findings are inconsistent. This single-nucleotide polymorphism (SNP) might increase the risk of suicidal behavior in certain ethnicities such as Caucasians and Asians, but not in the overall population [14]. Among other BDNF gene polymorphisms, rs962369 was found to have a strong association with suicidal ideation during antidepressant treatment [15] as well as with suicide completion [16]. We have previously demonstrated a significant association between BDNF (rs6265) and highly polygenic region 5-HTTLPR of the SLC6A4 (a gene-encoding serotonin transporter) genetic variations in patients suffering from affective disorders [17]. The latter genetic variant has also been studied with suicide. However, a recent meta-analysis did not confirm its association with suicidal behavior [18]. On the other hand, the authors found a significant association with the risk of violent suicide attempts. Another gene that is potentially involved in psychiatric disorders and suicide attempts could be the fat mass and obesity-associated gene (FTO), also known as alpha-ketoglutarate-dependent dioxygenase. Although the FTO gene was primarily found to be related to obesity and higher BMI (body mass index) risk [19], abundant expression of FTO in the brain [20] led to subsequent association studies with affective disorders [21,22] or completed suicide [23]. The important role of FTO in modulating brain functions was suggested by several studies in animal models [24,25,26]. Additionally, it was demonstrated that FTO is necessary for the correct function of the hippocampus by regulating BDNF processing [27]. The aim of this study was to evaluate the associations between completed suicide and polymorphisms in genes BDNF (rs6265, rs962369), SLC6A4 (5-HTTLPR), and FTO (rs9939609) in the Slovak population. We put extra attention on the evaluation of these polymorphisms in relation to the sex and BMI of the study subjects. ## 2.1. Study Sample The current case-control study was performed in two groups of adult participants of Slovak origin (Caucasians). The case (suicide) group ($$n = 119$$) comprised individuals who committed suicide. In Slovakia, all deaths due to suicide or suspected suicide are referred to a coroner’s investigation and reported to the police. Suicide belongs to violent death, and it is necessary to order an autopsy according to legislation in Slovakia. Autopsies, including of suicides, were performed at the Medico-Legal and Pathological-Anatomical Department of the Health Care Surveillance Authority in Kosice (one of the seven departments in Slovakia). The control group ($$n = 137$$) was matched by age, sex, and ethnicity to the suicide group. The control group included adult volunteers with no relation to the cases, no known psychiatric disorders, and with no history of suicidal behavior. Blood samples from both groups were collected from November 2018 to November 2022, and the participation of control subjects in testing was voluntary and could be canceled by any individual at any time during the study. Classification of the subjects into the categories of underweight, normal weight, overweight, or obese was based on the cut-off values of BMI recommended for white populations [28]. This study was approved by the Ethics Committee of the University Hospital of Louis Pasteur in Kosice ($\frac{149}{2018}$/OPVaV), and control subjects provided written informed consent. The study was conducted in accordance with the Helsinki Declaration. ## 2.2. Genotyping DNA from peripheral blood was extracted using the QIAamp DNA Blood Mini QIAcube Kit according to the manufacturer’s instructions on the QIAcube—robotic workstation for automated purification of DNA, RNA, or proteins (QIAGEN, Hilden, Germay). Genotyping of the FTO rs9939609, BDNF rs6265, and BDNF rs962369 was performed using asymmetric (primers ratio 1:10) real-time polymerase chain reaction and subsequent high-resolution melting analysis in the presence of an unlabeled probe on the Eco Real-Time PCR System (Illumina, Inc., San Diego, CA, USA). The reaction mixture contained 1× MeltDoctor™ HRM Master Mix (Applied Biosystems™, Waltham, MA, USA), appropriate oligonucleotides, and 20 ng of template DNA in a final volume of 15 μL. Genotypes were identified using EcoTM Software 4.1. The oligonucleotides were designed in our laboratory (Table 1). The 5-HTTLPR genetic variation was genotyped using forward 5′-GGCGTTGCCGCTCTGAATGC-3′ and reverse 5′-GAGGGACTGAGCTGGACAACCAC-3′ primers according to Murakami et al. [ 29]. The PCR products were separated by electrophoresis on a $2\%$ agarose gel. ## 2.3. Statistical Analysis The Hardy–Weinberg equilibrium (HWE) assumption was assessed for the tested groups by comparing the observed numbers of each genotype with those expected under the HWE for the estimated allele frequency. Online software SNPstats was used to assess the strength of the relative associations via odds ratios (ORs) with their corresponding $95\%$ confidence intervals (CIs) and p-value [30]. Codominant, dominant, overdominant, and recessive genetic models were used to analyze the association between genetic variations and phenotypes. Akaike’s Information Criteria (AIK) and Bayesian Information Criteria (BIC) were considered in model selection. Analysis of association was based on linear or logistic regression according to the response variable (quantitative or binary status (case-control), respectively). The Bonferroni correction was applied to correct for multiple testing. Because four polymorphisms were evaluated, a p-value of <0.0125 (a nominal value <$\frac{0.05}{4}$) was considered statistically significant. ## 3.1. Characterization of the Subjects A total of 256 subjects were included in the current study. Out of 119 suicides, 92 ($77.3\%$) were males, and 27 ($22.7\%$) were females (female: male ratio was 1:3.4). The mean age ± standard deviation (SD) of the suicide cases was 47.71 ± 17.57 years: 46.85 ± 16.65 in males and 50.63 ± 20.11 in females (Table 2). Obtained data showed seven categories of suicide methods. The most common suicide method was hanging or suffocation (71 persons, $59.66\%$), jumping (16 persons; $13.45\%$), and collision with a train (12 persons; $10.08\%$). The remaining suicide methods were categorized as shooting ($9\%$), cutting ($4\%$), and less than $2\%$ frequency for electrocution or drug poisoning. Overall, $98\%$ of cases completed suicide with the violent method. Suicide methods’ frequencies and BMI of suicide cases are shown in Table 3. Age-, gender-, and ethnicity-matched adults served as the control group. The mean age ± SD of the control group was 48.23 ± 18.85 years. Of 137 individuals, 107 ($78.1\%$) were males, and 30 ($21.9\%$) were females. The mean age was 47.36 ± 18.75 years in males and 51.33 ± 18.86 years in females (Table 2). All collected blood samples were successfully genotyped for all genetic variations. The genotype distribution among controls and cases did not deviate from HWE (p ≥ 0.05) for all tested genetic variations. ## 3.2. Distribution of Genotypes of Selected Genetic Variants As not all analyzed genetic models were statistically significant for 5-HTTLPR (ins/del), FTO (rs9939609), and BDNF (rs6265) variants, the distribution of genotypes and their possible association with completed suicide are presented under a codominant genetic model. In the case of the BDNF (rs962369), the results are also presented using a recessive model, since it achieved lower AIK and BIC scores than the co-dominant model (Table 4). The distribution of 5-HTTLPR genotypes was similar between controls and cases (Table 4). There were no significant differences in male or female groups when evaluated separately. There was a trend for a higher frequency of the SS genotype in female suicide completers in comparison with the control female group ($37\%$ vs. $16.7\%$); however, the difference did not reach statistical significance. No statistically significant association was found between FTO or BDNF (rs6265) and completed suicide under four studied genetic models nor after sex stratification (data are shown only for the codominant model in Table 4 and Table 5, respectively). We have, however, revealed a nominally significant increased risk of suicide in the subjects with the CC genotype in BDNF (rs962369) using a recessive model (CC vs. TT + TC OR = 3.39, $95\%$ CI = 1.05–10.94, $$p \leq 0.03$$), irrespective of sex (Table 5). Having considered sex differences, a significant association between BDNF rs962369 and suicide was found only in males under the recessive model (CC vs. TT+TC; OR = 4.71, $95\%$ CI = 1.27–17.43, $$p \leq 0.01$$). The results show that $12\%$ of male suicide completers had the CC genotype, whereas in the female suicides, we did not find any individual with the CC genotype out of 27 suicide completers. ## 3.3. Association between HTTLPR, FTO, and BDNF Gene Variants and BMI in Suicide Completers BMI values of $59.6\%$ of suicide completers were within the normal range (18.5–25), $27.73\%$ were overweight, $5\%$ of subjects were underweight, and $7.6\%$ were obese. The average BMI in all genotype categories ranged from 22.68–25.27 (Table 6). We assessed the association between four gene variants and the BMI of the suicide completers. A nominally significant association between the gene variant and BMI was observed for BDNF rs962369 under the overdominant model ($$p \leq 0.02$$). Heterozygotes with the TC genotype showed a lower average BMI of about 1.69 ($95\%$ C.I. −3.09–−0.29) than homozygotes with TT or CC genotypes. No statistically significant association between HTTLPR (ins/del), FTO rs9939609, or BDNF (rs6265) genotypes and BMI was found either in the codominant or in the dominant, recessive, or overdominant genetic models. ## 4. Discussion Here, we present an association study of selected gene polymorphisms with suicide completion within the Slovak population. We investigated a serotonin system candidate, a functional polymorphism 5-HTTLPR (ins/del) in the SLC6A4 gene, two common polymorphisms in the widely studied neurotrophic factor gene BDNF (rs6265, rs962369), and finally, an FTO polymorphism (rs9939609). This is the first study exploring the risk of committing completed suicide in the Slovak population related to these selected variants. In our study, the female-to-male ratio in the case subjects was 1:3.4, which is in line with the sex discrepancy in suicide rates in Slovakia [4,5]. It is estimated that around $20\%$ of global suicides are due to pesticide self-poisoning (nonviolent method), most of which occur in rural agricultural areas in low- and middle-income countries. Other common methods of suicide are hanging and firearms [1]. In our study, $98\%$ of cases overall completed suicide with the violent method; the most frequent method (around $60\%$) was hanging or suffocation, a method with an increasing trend in many countries over the last 30 years [31,32]. ## 4.1. Evaluation of an Association of BDNF Genetic Variants with Suicide A promising candidate for an association with suicidal behavior is the BDNF gene. Two SNPs previously associated with suicidal behavior were analyzed in our study. The most studied BDNF polymorphism is rs6265 (Val66Met). In our study, we did not find significant differences in rs6265 under all analyzed genetic models, and no differences were found after gender stratification. Our results are in accordance with a meta-analysis study in which a significant association between BDNF rs6265 polymorphism and the risk of suicidal behavior in the overall population was not confirmed [14]. This meta-analysis included studies from three populations of Slavic origin (Croatia, Slovenia, and Poland), out of which three did not confirm any association [33,34,35], and one found a higher risk for carriers of at least one minor allele (Met-Met or Val-Met) but only in female suicide victims [36]. Neither the more recent study from Central Europe [37] nor an earlier European multicenter study [38] has found an association between rs6365 and suicidal behavior. Although this broadly studied missense variant of BDNF did not show significant results, another polymorphism, rs962369, could be another candidate for predicting increased suicide risk. In our study, out of four observed polymorphisms, the only association with completed suicide was found for BDNF rs962369. The association with BDNF rs962369 and susceptibility to suicide has been significant after gender stratification in males under recessive models. On the contrary, in the recent Slovenian study, none of the seven BDNF polymorphisms showed an association with completed suicide when used as a single marker. However, haplotype analysis of five selected polymorphisms showed that in such a combination, major allele T of rs962369 contributed to a higher suicide risk association [16]. Out of 123 polymorphisms in the GENDEP project, the strongest association with suicidal ideation was observed in rs962369 genetic variants [15]. Although the study assessed only suicidal ideation during antidepressant treatment, the outcomes correspond with our observation. Studies on animal models show the tendency of developing BDNF-deficient-related diseases such as depression or anxiety is higher in female animals [39], and that sex hormones or steroids can modulate the activities of BDNF, which may account for its functional discrepancy in different sexes [40,41]. Although the number of female cases and controls in our study was too low to make any conclusions, we speculate that the BDNF rs962369 variant could act in a sex-dependent manner. ## 4.2. Evaluation of an Association of 5-HTTLPR (ins/del) Genetic Variants with Suicide One of the most studied genetic variations associated with suicide behavior is a highly polymorphic region 5-HTTLPR (ins/del) of the SLC6A4 gene-encoding serotonin transporter. So far, controversial results have been obtained. Out of systematic reviews and meta-analyses, some studies confirmed the higher susceptibility of short allele (S) carriers for suicidal behavior [42,43,44], but others have not [18,45], or contradictory results have been found in which a positive association was found with long allele (L) carriers [46]. Interestingly, discrepancies have also been found when distinct phenotypes such as suicidal behavior generally, suicidal ideation, history of suicide attempts, or a complete suicide only are considered. The 5-HTTLPR polymorphism has been found to be significantly associated with suicide attempts, but not with completed suicides [42,45]. Other meta-analyses did not confirm the association of the S allele and suicidal behavior generally; however, they found an association of the S allele with suicide attempts within the same psychiatric diagnosis, also with violent suicide [47] or violent suicide attempt [18]. In the present study, we did not reveal any significant difference between genetic variations of the 5-HTT gene promoter comparing suicide cases and controls. The result follows the studies performed in other Central Europe populations [48,49] and within individuals of Slavic origin [50,51,52]. Interestingly, a huge study that involved more than 100,000 subjects from 38 countries investigating an association between allelic frequencies in various countries and their suicide rates found that the S allele acts as a protective factor in Caucasians, whereas it acts as a risk factor in non-Caucasian populations [53]. We also evaluated HTTLPR genetic variants after gender stratification, as some studies found an association only in males [54] and others only in females [55]. Our results did not show either of these associations, although there was a trend for a higher frequency of the SS genotype in female suicide completers. ## 4.3. The FTO Genetic Variant (rs9939609) in the Context of Suicide and BMI Within the genes related to obesity, the FTO gene has one of the strongest links with this condition in the human population. In the most studied polymorphism of this gene, rs9939609, minor allele A was found to positively affect obesity and BMI [19,56]. The FTO gene has recently been studied in association with affective disorders given its high abundance in the brain and often comorbid obesity and depression. A bidirectional relationship between obesity and higher BMI with depression has been proposed lately [57], although the underlying mechanisms have not yet been elucidated [58]. Up to $12\%$ of a shared genetic component was found between depression and obesity [59]. However, there are various phenotypes within depressive disorder, and though one type is characterized by hyperphagia, another can lead to weight loss [60]. The meta-analysis by Rivera et al. [ 61] supported a significant interaction between FTO, depression, and BMI, indicating that depression increases the effect of FTO on BMI; depressed subjects had an additional effect of FTO on BMI that corresponded to a BMI increase of $2.2\%$ for each A allele. The mentioned meta-analysis did not confirm an association between the rs9939609 A risk allele and depression, similar to that in the earlier studies [21,22]. Considering the association studies between this polymorphism and suicide, an inverse association between the A allele and complete suicide was reported in the Polish population, suggesting a codominant effect of the risk allele [23]. One study has suggested that the A allele could be a protective variant for depression development [62]. We aimed to test the correlation between the BMI and FTO rs9939609 genotypes in a group of suicide completers. In our study, we did not reveal any significant association between FTO gene polymorphism and BMI in suicide completers, even after gender stratification. This study has limitations such as its relatively small number of cases and controls, particularly in the female groups. However, this corresponds with the discrepancy in suicide rate frequency in women and men in the Slovak population. The study’s strength is that the control subjects were cautiously matched by age, gender, and ethnicity to their suicide counterparts. Another strength is that this is the first Slovak association study of variants 5-HTTLPR (ins/del) in the SLC6A4 gene, BDNF (rs6265, rs962369), and FTO (rs9939609) with completed suicide. Our main result is that the BDNF rs962369 variant could play a role in completed suicide, as individuals with the CC genotype were more often found among suicides than in control subjects. The finding was confirmed mainly for male individuals. In the female group, the result cannot be interpreted unequivocally due to the small number of cases and controls. Another important finding of this study is that FTO polymorphism (rs9939609) did not affect BMI in the group of Slovak suicide completers, but our findings follow an inverse association between BMI and completed suicide. ## 5. Conclusions The results of this study suggest that rs962369 polymorphism of the BDNF gene could potentially be involved in the higher risk of committing complete suicide in the Slovak male population. We did not find any association between other evaluated genetic variants, but studies with more included subjects are needed to verify this finding. Interestingly, FTO genetic variants did not affect the BMI of Slovak suicide completers. ## References 1. Suicide 2. Pathirathna M.L., Nandasena H.M.R.K.G., Atapattu A.M.M.P., Weerasekara I.. Impact of the COVID-19 Pandemic on Suicidal Attempts and Death Rates: A Systematic Review. BMC Psychiatry (2022.0) 22 1-15. DOI: 10.1186/S12888-022-04158-W/FIGURES/2 3. Farooq S., Tunmore J., Ali W., Ayub M.. Suicide, Self-Harm and Suicidal Ideation during COVID-19: A Systematic Review. Psychiatry Res. (2021.0) 306 114228. DOI: 10.1016/j.psychres.2021.114228 4. Suicide Rates 5. Brazinova A., Moravansky N., Gulis G., Skodacek I.. Suicide Rate Trends in the Slovak Republic in 1993–2015. Int. J. Soc. Psychiatry (2017.0) 63 161-168. DOI: 10.1177/0020764016688715 6. Preventing Suicide: A Global Imperative. (2014.0) 1-89 7. Hawton K., van Heeringen K.. Suicide. Lancet (2009.0) 373 1372-1381. DOI: 10.1016/S0140-6736(09)60372-X 8. Diblasi E., Kang J., Docherty A.R.. Genetic Contributions to Suicidal Thoughts and Behaviors. Psychol. Med. (2021.0) 51 2148-2155. DOI: 10.1017/S0033291721001720 9. Brent D.A., Mann J.J.. Family Genetic Studies, Suicide, and Suicidal Behavior. Am. J. Med. Genet. C Semin. Med. Genet. (2005.0) 133C 13-24. DOI: 10.1002/ajmg.c.30042 10. Mirkovic B., Laurent C., Podlipski M.A., Frebourg T., Cohen D., Gerardin P.. Genetic Association Studies of Suicidal Behavior: A Review of the Past 10 Years, Progress, Limitations, and Future Directions. Front. Psychiatry (2016.0) 7 1. DOI: 10.3389/fpsyt.2016.00158 11. Schenkel L.C., Segal J., Becker J.A., Manfro G.G., Bianchin M.M., Leistner-Segal S.. The BDNF Val66Met Polymorphism Is an Independent Risk Factor for High Lethality in Suicide Attempts of Depressed Patients. Prog. Neuropsychopharmacol. Biol. Psychiatry (2010.0) 34 940-944. DOI: 10.1016/j.pnpbp.2010.04.023 12. Fusar-Poli L., Aguglia A., Amerio A., Orsolini L., Salvi V., Serafini G., Volpe U., Amore M., Aguglia E.. Peripheral BDNF Levels in Psychiatric Patients with and without a History of Suicide Attempt: A Systematic Review and Meta-Analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry (2021.0) 111 110342. DOI: 10.1016/j.pnpbp.2021.110342 13. Ropret S., Kouter K., Zupanc T., Paska A.V.. BDNF Methylation and MRNA Expression in Brain and Blood of Completed Suicides in Slovenia. World J. Psychiatry (2021.0) 11 1301. DOI: 10.5498/wjp.v11.i12.1301 14. González-Castro T.B., Salas-Magaña M., Juárez-Rojop I.E., López-Narváez M.L., Tovilla-Zárate C.A., Hernández-Díaz Y.. Exploring the Association between BDNF Val66Met Polymorphism and Suicidal Behavior: Meta-Analysis and Systematic Review. J. Psychiatr Res. (2017.0) 94 208-217. DOI: 10.1016/j.jpsychires.2017.07.020 15. Perroud N., Aitchison K.J., Uher R., Smith R., Huezo-Diaz P., Marusic A., Maier W., Mors O., Placentino A., Henigsberg N.. Genetic Predictors of Increase in Suicidal Ideation During Antidepressant Treatment in the GENDEP Project. Neuropsychopharmacology (2009.0) 34 2517-2528. DOI: 10.1038/npp.2009.81 16. Ropret S., Zupanc T., Komel R., Videtič Paska A.. Single Nucleotide Polymorphisms in the BDNF Gene and Suicide in the Slovenian Sample. Neurosci. Lett. (2015.0) 602 12-16. DOI: 10.1016/j.neulet.2015.06.027 17. Bednarova A., Cizmarikova M., Habalova V., Jarcuskova D.. Evaluation of 5-HTTLPR (Insertion/Deletion) and BDNF (Rs6265) Genetic Variations in the Slovakian Individuals Suffering from Affective Disorders. Gen. Physiol. Biophys. (2021.0) 40 365-376. DOI: 10.4149/gpb_2021025 18. Fanelli G., Serretti A.. The Influence of the Serotonin Transporter Gene 5-HTTLPR Polymorphism on Suicidal Behaviors: A Meta-Analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry (2019.0) 88 375-387. DOI: 10.1016/j.pnpbp.2018.08.007 19. Frayling T.M., Timpson N.J., Weedon M.N., Zeggini E., Freathy R.M., Lindgren C.M., Perry J.R.B., Elliott K.S., Lango H., Rayner N.W.. A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity. Science (2007.0) 316 889. DOI: 10.1126/science.1141634 20. Dina C., Meyre D., Gallina S., Durand E., Körner A., Jacobson P., Carlsson L.M.S., Kiess W., Vatin V., Lecoeur C.. Variation in FTO Contributes to Childhood Obesity and Severe Adult Obesity. Nat. Genet. (2007.0) 39 724-726. DOI: 10.1038/ng2048 21. Yao Y., Wen Y., Du T., Sun N., Deng H., Ryan J., Rao S.. Meta-Analysis Indicates That SNP Rs9939609 within FTO Is Not Associated with Major Depressive Disorder (MDD) in Asian Population. J. Affect. Disord. (2016.0) 193 27-30. DOI: 10.1016/j.jad.2015.12.048 22. Du T., Rao S., Wu L., Ye N., Liu Z., Hu H., Xiu J., Shen Y., Xu Q.. An Association Study of the M6A Genes with Major Depressive Disorder in Chinese Han Population. Artic. J. Affect. Disord. (2015.0) 183 279-286. DOI: 10.1016/j.jad.2015.05.025 23. Chojnicka I., Fudalej S., Walczak A., Wasilewska K., Fudalej M., Stawinski P., Strawa K., Pawlak A., Wojnar M., Krajewski P.. Inverse Association between Obesity Predisposing FTO Genotype and Completed Suicide. PLoS ONE (2014.0) 9. DOI: 10.1371/journal.pone.0108900 24. Li L., Zang L., Zhang F., Chen J., Shen H., Shu L., Liang F., Feng C., Chen D., Tao H.. Fat Mass and Obesity-Associated (FTO) Protein Regulates Adult Neurogenesis. Hum. Mol. Genet. (2017.0) 26 2398-2411. DOI: 10.1093/hmg/ddx128 25. Hess M.E., Hess S., Meyer K.D., Verhagen L.A.W., Koch L., Brönneke H.S., Dietrich M.O., Jordan S.D., Saletore Y., Elemento O.. The Fat Mass and Obesity Associated Gene (Fto) Regulates Activity of the Dopaminergic Midbrain Circuitry. Nat. Neurosci. (2013.0) 16 1042-1048. DOI: 10.1038/nn.3449 26. Widagdo J., Zhao Q.Y., Kempen M.J., Tan M.C., Ratnu V.S., Wei W., Leighton L., Spadaro P.A., Edson J., Anggono V.. Experience-Dependent Accumulation of N6-Methyladenosine in the Prefrontal Cortex Is Associated with Memory Processes in Mice. J. Neurosci. (2016.0) 36 6771. DOI: 10.1523/JNEUROSCI.4053-15.2016 27. Spychala A., Rüther U.. FTO Affects Hippocampal Function by Regulation of BDNF Processing. PLoS ONE (2019.0) 14. DOI: 10.1371/journal.pone.0211937 28. Weir C.B., Jan A.. BMI Classification Percentile and Cut Off Points (2022.0) 29. Murakami F., Shimomura T., Kotani K., Ikawa S., Nanba E., Adachi K.. Anxiety Traits Associated with a Polymorphism in the Serotonin Transporter Gene Regulatory Region in the Japanese. J. Hum. Genet. (1999.0) 44 15-17. DOI: 10.1007/s100380050098 30. Solé X., Guinó E., Valls J., Iniesta R., Moreno V.. SNPStats: A Web Tool for the Analysis of Association Studies. Bioinformatics (2006.0) 22 1928-1929. DOI: 10.1093/bioinformatics/btl268 31. Sabrinskas R., Hamilton B., Daniel C., Oliffe J.. Suicide by Hanging: A Scoping Review. Int. J. Ment. Health Nurs. (2022.0) 31 278-294. DOI: 10.1111/inm.12956 32. Gunnell D., Bennewith O., Hawton K., Simkin S., Kapur N.. The Epidemiology and Prevention of Suicide by Hanging: A Systematic Review. Int. J. Epidemiol. (2005.0) 34 433-442. DOI: 10.1093/ije/dyh398 33. Chojnicka I., Strawa K., Fudalej S., Fudalej M., Pawlak A., Kostrzewa G., Wojnar M., Krajewski P., Poski R.. Analysis of Four Genes Involved in the Neurodevelopment Shows Association of Rs4307059 Polymorphism in the Cadherin 9/10 Region with Completed Suicide. Neuropsychobiology (2012.0) 66 134-140. DOI: 10.1159/000339559 34. Nedic G., Nikolac Perkovic M., Nenadic Sviglin K., Muck-Seler D., Borovecki F., Pivac N.. Brain-Derived Neurotrophic Factor Val66Met Polymorphism and Alcohol-Related Phenotypes. Prog. Neuropsychopharmacol. Biol. Psychiatry (2013.0) 40 193-198. DOI: 10.1016/j.pnpbp.2012.09.005 35. Zarrilli F., Angiolillo A., Castaldo G., Chiariotti L., Keller S., Sacchetti S., Marusic A., Zagar T., Carli V., Roy A.. Brain Derived Neurotrophic Factor (BDNF) Genetic Polymorphism (Val66Met) in Suicide: A Study of 512 Cases. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. (2009.0) 150B 599-600. DOI: 10.1002/ajmg.b.30849 36. Pregelj P., Nedic G., Paska A.V., Zupanc T., Nikolac M., Balažic J., Tomori M., Komel R., Seler D.M., Pivac N.. The Association between Brain-Derived Neurotrophic Factor Polymorphism (BDNF Val66Met) and Suicide. J. Affect. Disord. (2011.0) 128 287-290. DOI: 10.1016/j.jad.2010.07.001 37. Berent D., Szymańska B., Kulczycka-Wojdala D., Macander M., Pawłowska Z., Wojnar M.. The Role of Childhood Adversities, FKBP5, BDNF, NRN1, and Generalized Self-Efficacy in Suicide Attempts in Alcohol-Dependent Patients. Pharmacol. Rep. (2020.0) 72 730. DOI: 10.1007/s43440-020-00080-8 38. Schosser A., Carlberg L., Calati R., Serretti A., Massat I., Spindelegger C., Linotte S., Mendlewicz J., Souery D., Zohar J.. The Impact of BDNF Polymorphisms on Suicidality in Treatment-Resistant Major Depressive Disorder: A European Multicenter Study. Int. J. Neuropsychopharmacol. (2017.0) 20 782. DOI: 10.1093/ijnp/pyx028 39. Monteggia L.M., Luikart B., Barrot M., Theobold D., Malkovska I., Nef S., Parada L.F., Nestler E.J.. Brain-Derived Neurotrophic Factor Conditional Knockouts Show Gender Differences in Depression-Related Behaviors. Biol. Psychiatry (2007.0) 61 187-197. DOI: 10.1016/j.biopsych.2006.03.021 40. Fanaei H., Karimian S.M., Sadeghipour H.R., Hassanzade G., Kasaeian A., Attari F., Khayat S., Ramezani V., Javadimehr M.. Testosterone Enhances Functional Recovery after Stroke through Promotion of Antioxidant Defenses, BDNF Levels and Neurogenesis in Male Rats. Brain Res. (2014.0) 1558 74-83. DOI: 10.1016/j.brainres.2014.02.028 41. Li M., Masugi-Tokita M., Takanami K., Yamada S., Kawata M.. Testosterone Has Sublayer-Specific Effects on Dendritic Spine Maturation Mediated by BDNF and PSD-95 in Pyramidal Neurons in the Hippocampus CA1 Area. Brain Res. (2012.0) 1484 76-84. DOI: 10.1016/j.brainres.2012.09.028 42. Anguelova M., Benkelfat C., Turecki G.. A Systematic Review of Association Studies Investigating Genes Coding for Serotonin Receptors and the Serotonin Transporter: II. Suicidal Behavior. Mol. Psychiatry (2003.0) 8 646-653. DOI: 10.1038/sj.mp.4001336 43. Li D., He L.. Meta-Analysis Supports Association between Serotonin Transporter (5-HTT) and Suicidal Behavior. Mol. Psychiatry (2007.0) 12 47-54. DOI: 10.1038/sj.mp.4001890 44. Schild A.H.E., Pietschnig J., Tran U.S., Voracek M.. Genetic Association Studies between SNPs and Suicidal Behavior: A Meta-Analytical Field Synopsis. Prog. Neuropsychopharmacol. Biol. Psychiatry (2013.0) 46 36-42. DOI: 10.1016/j.pnpbp.2013.06.014 45. Clayden R.C., Zaruk A., Meyre D., Thabane L., Samaan Z.. The Association of Attempted Suicide with Genetic Variants in the SLC6A4 and TPH Genes Depends on the Definition of Suicidal Behavior: A Systematic Review and Meta-Analysis. Transl. Psychiatry (2012.0) 2 e166. DOI: 10.1038/tp.2012.96 46. De Medeiros Alves V., Bezerra D., de Andrade T., de Melo Neto V., Nardi A.. Genetic Polymorphisms Might Predict Suicide Attempts in Mental Disorder Patients: A Systematic Review and Meta-Analysis. CNS Neurol. Disord. Drug Targets (2015.0) 14 820-827. DOI: 10.2174/1871527314666150713104619 47. Lin P.Y., Tsai G.. Association between Serotonin Transporter Gene Promoter Polymorphism and Suicide: Results of a Meta-Analysis. Biol. Psychiatry (2004.0) 55 1023-1030. DOI: 10.1016/j.biopsych.2004.02.006 48. Bokor J., Gonda X., Dome P., Faludi G., Dinya E., Laszik A.. Association between the 5-HTTLPR Polymorphism of the Serotonin Transporter Gene and Suicide: A Case-Control Pilot Study. Neuropsychopharmacol. Hung. (2017.0) 19 5-10. PMID: 28467954 49. Pawlak J., Dmitrzak-Weglarz M., Wilkosc M., Szczepankiewicz A., Leszczynska-Rodziewicz A., Zaremba D., Kapelski P., Rajewska-Rager A., Hauser J.. Suicide Behavior as a Quantitative Trait and Its Genetic Background. J. Affect. Disord. (2016.0) 206 241-250. DOI: 10.1016/j.jad.2016.07.029 50. Božina N., Jovanović N., Podlesek A., Rojnić Kuzman M., Kudumija Slijepčević M., Roguljić A., Dimitrović A., Božina T., Lovrić J., Ljubić H.. Suicide Ideators and Attempters with Schizophrenia—The Role of 5-HTTLPR, Rs25531, and 5-HTT VNTR Intron 2 Variants. J. Psychiatr. Res. (2012.0) 46 767-773. DOI: 10.1016/j.jpsychires.2012.03.008 51. Hranilovic D., Stefulj J., Furac I., Kubat M., Balija M., Jernej B.. Serotonin Transporter Gene Promoter (5-HTTLPR) and Intron 2 (VNTR) Polymorphisms in Croatian Suicide Victims. Biol. Psychiatry (2003.0) 54 884-889. DOI: 10.1016/S0006-3223(03)00179-3 52. Pungercic G., Videtic A., Pestotnik A., Pajnic I.Z., Zupanc T., Balazic J., Tomori M., Komel R.. Serotonin Transporter Gene Promoter (5-HTTLPR) and Intron 2 (VNTR) Polymorphisms: A Study on Slovenian Population of Suicide Victims. Psychiatr. Genet. (2006.0) 16 187-191. DOI: 10.1097/01.ypg.0000218617.65633.9e 53. Schild A.H.E., Nader I.W., Pietschnig J., Voracek M.. Ethnicity Moderates the Association Between 5-HTTLPR and National Suicide Rates. Arch. Suicide Res. (2014.0) 18 1-13. DOI: 10.1080/13811118.2013.803447 54. Limosin F., Loze J.Y., Boni C., Hamon M., Adès J., Rouillon F., Gorwood P.. Male-Specific Association between the 5-HTTLPR S Allele and Suicide Attempts in Alcohol-Dependent Subjects. J. Psychiatr. Res. (2005.0) 39 179-182. DOI: 10.1016/j.jpsychires.2004.06.007 55. Gaysina D., Zainullina A., Gabdulhakov R., Khusnutdinova E.. The Serotonin Transporter Gene: Polymorphism and Haplotype Analysis in Russian Suicide Attempters. Neuropsychobiology (2006.0) 54 70-74. DOI: 10.1159/000096041 56. Scuteri A., Sanna S., Chen W.M., Uda M., Albai G., Strait J., Najjar S., Nagaraja R., Orrú M., Usala G.. Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits. PLoS Genet. (2007.0) 3 1200-1210. DOI: 10.1371/journal.pgen.0030115 57. Luppino F.S., de Wit L.M., Bouvy P.F., Stijnen T., Cuijpers P., Penninx B.W.J.H., Zitman F.G.. Overweight, Obesity, and Depression: A Systematic Review and Meta-Analysis of Longitudinal Studies. Arch. Gen. Psychiatry (2010.0) 67 220-229. DOI: 10.1001/archgenpsychiatry.2010.2 58. Zarza-Rebollo J.A., Molina E., Rivera M.. The Role of the FTO Gene in the Relationship between Depression and Obesity. A Systematic Review. Neurosci. Biobehav. Rev. (2021.0) 127 630-637. DOI: 10.1016/j.neubiorev.2021.05.013 59. Wray N.R., Ripke S., Mattheisen M., Trzaskowski M., Byrne E.M., Abdellaoui A., Adams M.J., Agerbo E., Air T.M., Andlauer T.M.F.. Genome-Wide Association Analyses Identify 44 Risk Variants and Refine the Genetic Architecture of Major Depression. Nat. Genet. (2018.0) 50 668-681. DOI: 10.1038/s41588-018-0090-3 60. Milaneschi Y., Lamers F., Berk M., Penninx B.W.J.H.. Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression. Biol. Psychiatry (2020.0) 88 369-380. DOI: 10.1016/j.biopsych.2020.01.014 61. Rivera M., Locke A.E., Corre T., Czamara D., Wolf C., Ching-Lopez A., Milaneschi Y., Kloiber S., Cohen-Woods S., Rucker J.. Interaction between the FTO Gene, Body Mass Index and Depression: Meta-Analysis of 13701 Individuals. Br. J. Psychiatry (2017.0) 211 70-76. DOI: 10.1192/bjp.bp.116.183475 62. Samaan Z., Anand S., Zhang X., Desai D., Rivera M., Pare G., Thabane L., Xie C., Gerstein H., Engert J.C.. The Protective Effect of the Obesity-Associated Rs9939609 A Variant in Fat Mass- and Obesity-Associated Gene on Depression. Mol. Psychiatry (2013.0) 18 1281-1286. DOI: 10.1038/mp.2012.160
--- title: 'Expression of GADD45G and CAPRIN1 in Human Nucleus Pulposus: Implications for Intervertebral Disc Degeneration' authors: - Koki Kawaguchi - Koji Akeda - Junichi Yamada - Takahiro Hasegawa - Norihiko Takegami - Tatsuhiko Fujiwara - Akihiro Sudo journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10059755 doi: 10.3390/ijms24065768 license: CC BY 4.0 --- # Expression of GADD45G and CAPRIN1 in Human Nucleus Pulposus: Implications for Intervertebral Disc Degeneration ## Abstract Marked cellular changes occur in human intervertebral disc (IVD) degeneration during disc degeneration with biochemical changes. Genome-wide analysis of the DNA methylation profile has identified 220 differentially methylated loci associated with human IVD degeneration. Among these, two cell-cycle–associated genes, growth arrest and DNA damage 45 gamma (GADD45G) and cytoplasmic activation/proliferation-associated protein-1 (CAPRIN1), were focused on. The expression of GADD45G and CAPRIN1 in human IVDs remains unknown. We aimed to examine the expression of GADD45G and CAPRIN1 in human nucleus pulposus (NP) cells and evaluate those in human NP tissues in the early and advanced stages of degeneration according to Pfirrmann magnetic resonance imaging (MRI) and histological classifications. Human NP cells were cultured as monolayers after isolation from NP tissues by sequential enzyme digestion. Total RNA was isolated, and the mRNA expression of GADD45G and CAPRIN1 was quantified using real-time polymerase chain reaction. To examine the effects of pro-inflammatory cytokines on mRNA expression, human NP cells were cultured in the presence of IL-1β. Protein expression was evaluated using Western blotting and immunohistochemistry. GADD45G and CAPRIN1 expression was identified in human NP cells at both mRNA and protein levels. The percentage of cells immunopositive for GADD45G and CAPRIN1 significantly increased according to the Pfirrmann grade. A significant correlation between the histological degeneration score and the percentage of GADD45G-immunopositive cells was identified, but not with that of CAPRIN1-immunopositive cells. The expression of cell-cycle-associated proteins (GADD45G and CAPRIN1) was enhanced in human NP cells at an advanced stage of degeneration, suggesting that it may be regulated during the progression of IVD degeneration to maintain the integrity of human NP tissues by controlling cell proliferation and apoptosis under epigenetic alteration. ## 1. Introduction Low back pain (LBP) is one of the most common health concerns in patients, ranging from children to the elderly, and it affects daily activities and quality of life [1]. Recent epidemiological and clinical studies have reported that intervertebral disc (IVD) degeneration is associated with the occurrence of LBP [2,3,4,5]. Clinically, IVD degeneration is well assessed using magnetic resonance imaging (MRI), and its severity and characteristic findings are reported to be associated with discogenic LBP [6,7]. IVDs are complex structures that consist of a thick outer ring of fibrous cartilage termed the annulus fibrosus (AF), which surrounds a more gelatinous core known as the nucleus pulposus (NP); the NP is sandwiched inferiorly and superiorly by cartilage end-plates [8]. IVD degeneration is defined as “the structural and functional failure of the disc as a result of aberrant, pathological cellular and extracellular matrix (ECM) changes” [9]. Although the precise causes remain to be elucidated, IVD degeneration is considered to be related to genetic factors, environmental factors, sex, age, and poor nutrition [10]. The progression of IVD degeneration is suggested to be a consequence of an imbalance between the anabolism and catabolism of the ECM [11]. The biochemical characteristics of IVD degeneration, especially NP degeneration, include changes in ECM molecules (mainly, the loss of proteoglycan and water content in the NP). Along with biochemical changes, marked cellular changes occur in the human NP during the progression of IVD degeneration. Cells in the human nucleus are initially notochordal but are gradually replaced during childhood by rounded cells resembling the chondrocytes of articular cartilage, which is considered to be the first sign of disc degeneration [12]. With increasing degeneration, the proportion of single cells in the human NP continuously decreases and thereafter becomes extremely low in adulthood. Simultaneously, cell clusters increase, leading to cell-mediated tissue remodeling with the progression of disc degeneration. A previous study evaluated the association between DNA methylation and disc degeneration by genome-wide association analysis of human NP tissues and identified 220 differentially methylated loci associated with human IVD degeneration [13]. Among the differentially methylated genes in the advanced stages of disc degeneration, those related to the cell cycle were extracted. Thereafter, two pivotal genes functionally associated with the cell cycle, one for cell death (apoptosis) and the other for cell proliferation, were identified for investigation in this study. Growth arrest and DNA damage 45 gamma (GADD45G) has been implicated in regulating cell survival, apoptosis, senescence, cell cycle arrest, and genomic stability (see review in [14,15,16]). Cytoplasmic activation/proliferation-associated protein-1 (CAPRIN1) is an RNA-binding protein that has been identified as a promoter of cell proliferation [17] and participates in the regulation of post-transcriptional responses to stress [18,19,20]. Therefore, we hypothesized that these two cell-cycle–associated genes are expressed in the human NP and play a role in cellular changes in the process of disc degeneration. This study aims to [1] examine the expression of GADD45G and CAPRIN1 in human NP cells and [2] compare the expression of GADD45G and CAPRIN1 in human NP tissues at different stages of degeneration, as evaluated by Pfirrmann MRI classification and the newly modified histological human NP classification score. ## 2.1. Histological Classification of Human NP Tissues at Different Stages of Degeneration In the hematoxylin and eosin (H-E) staining samples, a number of chondrocyte-like single cells was observed in the NP of Pfirrmann grade 2 (Figure 1a,d); however, as degeneration progressed, the number of single cells decreased and cell cluster formations were observed in the Pfirrmann grade 3 (Figure 1b,e) and 4 samples (Figure 1c,f). In the Safranin-O staining samples, a red-stained matrix was evenly distributed in the NP of Pfirrmann grade 2 (Figure 2a,d). On the other hand, the intense staining of Safranin-O around the cell clusters (pericellular matrix) was identified in Pfirrmann grade 3 (Figure 2b,e) and 4 samples (Figure 2c,f). The staining property of Safranin-O was remarkably decreased, and micro-fissures were observed in the Pfirrmann grade 4 sample (Figure 2c,f). In all NP samples ($$n = 30$$), the mean total histological score was 2.50 ± 1.3. In the subclass analysis, the mean histological score of “cellularity” was 0.76 ± 0.62, “matrix” was 1.00 ± 0.58, and “matrix staining” was 0.73 ± 0.58. The total histological score in the Pfirrmann grade 4 group was significantly higher than that in the grade 2 group ($p \leq 0.01$; grade 2 ($$n = 4$$), 1.50 ± 0.57; grade 3 ($$n = 11$$), 1.90 ± 1.44; grade 4 ($$n = 15$$), 3.20 ± 1.08) (Figure 3a). The histological score of “cellularity” in the Pfirrmann grade 4 group was significantly higher than in the grade 2 group ($p \leq 0.01$; grade 2: 0, grade 3: 0.72 ± 0.64, grade 4: 1.00 ± 0.53) (Figure 3b). The score of “matrix” in the Pfirrmann grade 4 group was significantly higher than that in the grade 3 group ($p \leq 0.05$; grade 2: 0.75 ± 0.50, grade 3: 0.63 ± 0.50, grade 4: 1.33 ± 0.48) (Figure 3c). There was no significant difference in the histological score of “matrix staining” among the groups ($$p \leq 0.39$$; grade 2: 0.75 ± 0.50, grade 3: 0.54 ± 0.68, grade 4: 0.86 ± 0.51) (Figure 3d). ## 2.2. Correlation between Pfirrmann MRI Classification and Histological Classification Scores The Pfirrmann MRI classification score was positively correlated with the total histological classification score ($r = 0.54$, $p \leq 0.01$). The subclass analysis showed that the Pfirrmann MRI classification score was significantly correlated with the histological classification score of “cellularity” ($r = 0.49$, $p \leq 0.01$) and “matrix” ($r = 0.53$, $p \leq 0.01$); however, no significant correlation with “matrix staining” was identified. ## 2.3. Total Cell Number by MRI Grade Classification The total number of cells in each view captured by microscopy was counted and compared by Pfirrmann MRI classification. No significant difference in the total number of cells was found among the three grades (grade 2: 14.6 ± 10.5, grade 3: 15.8 ± 16.7, grade 4: 15.5 ± 1.4, Figure 4). ## 2.4. Gene Expression of GADD45G and CAPRIN1 and Effect of Interleukin-1β Stimulation The mRNA expressions of GADD45G and CAPRIN1 were clearly identified in human NP cells (Figure 5). IL-1β increased the mRNA expression of GADD45G in a dose-dependent manner (IL-1β 0.1 [ng/mL]: 1.62 ± 1.30, IL-1β 1.0: 4.59 ± 5.56, IL-1β 10: 10.08 ± 10.44-fold vs. IL-1β 0); however, it did not reach statistical significance (IL-1β 10 ng/mL group vs. IL-1β 0 control group, $$p \leq 0.053$$, Figure 5a). Similarly, an increase of relative mRNA expression of CAPRIN1 stimulated by IL-1β was found (IL-1β 0.1 [ng/mL]: 0.96 ± 0.93, IL-1β 1.0: 1.38 ± 1.17, IL-1β 10: 1.59 ± 1.07-fold vs. IL-1β 0). However, no significant differences were identified among the groups (IL-1β 0 control vs. IL-1β 10 ng, $$p \leq 0.50$$, Figure 5b). ## 2.5. Western Blot Analysis of Human NP Cells for GADD45G and CAPRIN1 Western blot analysis identified a single band directed against GADD45G (60 kDa) and CAPRIN1 (116 kDa) in the protein extracts from NP cells. β-actin expression was clearly identified in the NP cells. The relative intensity of GADD45G normalized to β-actin was 0.81, and that of CAPRIN1 was 0.63 (Figure 6). ## 2.6. Immunohistochemical Expression of GADD45G in Human NP Tissues at Different Stages of Degeneration Immunoreactivity directed against GADD45G was clearly found in human NP cells in NP tissues for all Pfirrmann MRI classifications (Figure 7). Immunoreactivity against GADD45G was observed around the nuclei of chondrocyte-like cells in the NP region of the MRI grade 2 sample (Figure 7a). Immunopositive cells with intense staining were identified in the cells of cluster-forming MRI grade 3 and 4 samples (Figure 7b,c). No immunoreactivity was found in the isotype controls (Figure 7d). The percentage of GADD45G-immunopositive cells in the NP lesions in the Pfirrmann grade 4 group ($74.2\%$ ± $11.3\%$) was significantly higher than that in the grade 2 ($45.7\%$ ± $15.8\%$, $p \leq 0.01$) and grade 3 ($61.5\%$ ± $17.1\%$, $p \leq 0.05$) groups (Figure 7e). The percentage of 2+ positive cells in the Pfirrmann grade 4 group ($49.2\%$ ± $15.2\%$) was significantly higher than that in the Pfirrmann grade 2 ($23.5\%$ ± $16.0\%$, $p \leq 0.01$) and 3 ($32.1\%$ ± $17.7\%$, $p \leq 0.05$) groups, respectively (Figure 7f). The Pfirrmann MRI grade was positively correlated with the percentage of GADD45G-immunopositive cells ($r = 0.55$, $p \leq 0.01$). ## 2.7. Immunohistochemical Expression of CAPRIN1 in Human NP Tissues at Different Stages of Degeneration CAPRIN1 immunopositive cells were also found in the cytoplasm of chondrocyte-like cells of the NP regions at all Pfirrmann MRI classifications (Figure 8a–c). No immunoreactivity was found in the isotype controls (Figure 8d). The percentage of CAPRIN1-immunopositive cells in the Pfirrmann grade 3 ($84.0\%$ ± $4.0\%$) and grade 4 ($85.9\%$ ± $9.2\%$) groups was significantly higher than that in the grade 2 ($71.3\%$ ± $7.4\%$) group ($p \leq 0.05$, $p \leq 0.01$, respectively) (Figure 8e). The percentage of 1+ positive cells in the Pfirrmann grade 4 group ($72.1\%$ ± $12.1\%$) was significantly higher than those in the Pfirrmann grade 2 ($55.3\%$ ± $6.6\%$) and 3 ($54.4\%$ ± $13.2\%$) groups, respectively ($p \leq 0.05$, $p \leq 0.01$). The percentage of 2+ positive cells in the Pfirrmann grade 3 group ($29.5\%$ ± $13.3\%$) was significantly higher than those in the Pfirrmann grade 4 group ($13.7\%$ ± $12.9\%$, $p \leq 0.01$) (Figure 8f). The Pfirrmann MRI grade was positively correlated with the percentage of CAPRIN1-immunopositive cells ($r = 0.50$, $p \leq 0.01$). ## 2.8. Correlation between Percentage of Immunopositive Cells and Histological Classification Score The percentage of GADD45G-immunopositive cells positively correlated with the total histological score ($r = 0.47$, $p \leq 0.01$). The subclass analysis showed that the percentage of GADD45G-immunopositive cells was significantly correlated with the histological scores of “matrix” ($r = 0.39$, $p \leq 0.05$) and “matrix staining” ($r = 0.40$, $p \leq 0.05$); however, no significant correlation with that of “cellularity” was found (Figure 9). In contrast, there were no significant correlations between the percentage of CAPRIN1-immunopositive cells and the histological score (Figure 10a) or its subtype score (Figure 10b–d). ## 3. Discussion This study shows, for the first time, that GADD45G and CAPRIN1 are expressed in human NP cells at both mRNA and protein levels. GADD45G and CAPRIN1 expression in NP cells tend to be stimulated by pro-inflammatory cytokines (IL-1β), and their expression is significantly upregulated in degenerated IVD tissues. The expression of both GADD45G and CAPRIN1 is intermediately correlated with Pfirrmann MRI classification, and the expression of GADD45G is also significantly correlated with the histological classification score of the NP tissue. MRI is considered the best imaging instrument for evaluating IVD degeneration. The most widely known classification of IVD degeneration was reported by Pfirrmann et al. [ 21]; it evaluates the disc signal intensity, disc structure, distinction between the nucleus and annulus, and disc height for classifying the degree of disc degeneration into five grades with adequate inter- and intra-observer agreement [21,22]. Thus, the Pfirrmann classification is graded to reflect whole-disc degeneration, including NP and AF. Previous studies have shown that the Pfirrmann MRI classification in animal IVDs has a significant correlation with the histological score of disc degeneration [23,24,25], in particular using the degeneration score by Boos classification [26]; however, few studies have evaluated these correlations in human IVDs [27]. Human IVD tissues obtained during spinal surgeries, particularly lumbar interbody fusion surgeries, largely contain NP tissues. Therefore, macroscopically identified human NP tissues were used in this study. The histology of the human NP tissues was evaluated using the new histological scoring system by Rutges et al. [ 28] with modifications, in particular, by adopting three items associated with the histology of NP. It has been reported that the Rutges scoring system has been biochemically validated with high intra- and inter-observer reliability [28]; however, the association with MRI-graded human disc degeneration remains unknown. The results of this study revealed that the Pfirrmann classification was significantly correlated with the total histological grading scores and subclass grading scores, except for “NP matrix staining”. Our results suggest that our modified Rutges classification for human NP shows a statistically significant correlation with MRI-graded disc degeneration evaluated using the Pfirrmann classification [21]. GADD45G, a member of the GADD45 family, encodes a small (18 kDa) protein that negatively regulates cell growth. Importantly, GADD45 proteins can form homo- and/or hetero-oligomers with other family members [29] and play a role by interacting with cell-cycle-related proteins. In our study, Western blot analysis showed a single band of GADD45G at 60 kDa in human NP samples, which reflects the oligomerization of GADD45G with other GADD proteins and/or cell-cycle-related proteins. Previous reports have shown that GADD45G interacts with and inhibits the kinase activity of the Cdk1/CyclinB1 complex [30], which plays a key role in the progression from the G2 to M phase of the cell cycle [31]. The expression of GADD45 family members, including GADD45G, is known to be induced by various physiological stresses, including irradiation, ultraviolet radiation, and inflammatory cytokines [14,32,33]. Our results showed that the mRNA expression of GADD45G was upregulated by stimulation with the pro-inflammatory cytokine IL-1β. Furthermore, the percentage of GADD45G-immunopositive cells in the human NP of the Pfirrmann grade 4 samples was significantly higher than that of the grade 2 and 3 samples. These results suggest that GADD45G expression is upregulated in human NP at an advanced stage of degeneration, where the aberrant expression of pro-inflammatory cytokines, such as IL-1β or TNF-α, is found. CAPRIN1 was purified from activated T-lymphocytes, as reported by Grill et al. [ 17]. CAPRIN1 is a ubiquitously expressed RNA-binding protein that participates in the regulation of cell-cycle-control–associated genes in the G1 to S phase transition [19,34,35]. The expression of CAPRIN1 was reported to be high in the thymus and spleen and low in tissues with a low proportion of dividing cells, such as the kidney or muscle [17]. Importantly, CAPRIN1 was reported to be a core nucleating component of stress granules, which are dense aggregates in the cytosol composed of proteins and RNAs that appear when the cell is under stress [36]. Gene expression analysis by real-time PCR in this study did not show a significant association between CAPRIN1 expression and the pro-inflammatory cytokine stimulus IL-1β, which plays a major role in matrix degradation and pain generation by promoting the expression of degradative enzymes, such as MMP-3 or -13 and ADAMTS-4 or -5 [37], and pain-related molecules, such as nerve growth factor [38] and glial-cell-line-derived neurotrophic factor [39]. The regulatory mechanism of CAPRIN1 expression remains largely unknown; however, it is speculated that CAPRIN1 and stress granules can regulate the cellular post-transcriptional response to various types of stress, such as osmotic pressure or nitric oxide [40,41]. Histological evaluation in this study showed that the percentage of CAPRIN1 immunopositive cells in Pfirrmann grade 3 or 4 samples was higher than that in grade 2 samples. Furthermore, a higher percentage of strongly immunopositive cells was observed in the grade 3 samples than in the grade 4 samples. In the process of disc degeneration, IVD cells attempt to restore the damaged matrix by forming cell clusters, and a subsequent reduction in viable cell numbers appears as degeneration advances [42]. The change in the number and intensity of CAPRIN1-immunopositive cells among the different grades of Pfirrmann MRI classifications may reflect a change in cell proliferative activity during the progression of disc degeneration. The results of this study may explain the mechanism of disc degeneration from the viewpoint of cell proliferation; however, further investigation is needed to elucidate the CAPRIN1 response to various stressors that have been reported to be associated with disc degeneration, such as oxidative stress [43]. GADD45G and CPRIN1 are representative hypermethylated genes in the advanced degenerated IVD group and are associated with cell cycling. Theoretically, when methylation is located in gene promoter and enhancer regions, DNA methylation typically acts to silence genes, whereas methylation in gene body regions usually induces enhanced gene expression [44]. However, recent genome-wide studies of DNA methylation have reported that gene expression differs significantly depending on the methylation site of the promoter lesion (5′UTR or 3′UTR side) [45]. Previous genome-wide DNA methylation analysis has shown that GADD45G and CAPRIN1 are representative genes in which core promoter lesions were hypermethylated in the NP in the advanced stage of disc degeneration [13]; however, the percentage of GADD45G- and CAPRIN1-immunopositive cells was higher in the human NP with advanced stages of disc degeneration. Therefore, authors have speculated that the epigenetic regulation of gene transcription is not only confined to DNA methylation but is also intricately cooperative, including with the influence of chromatin variation and noncoding RNA [46]. There were several limitations in this study. First, mRNA was not extracted from the human NP tissues; therefore, the mRNA expression of GADD45G and CPRIN1 was not quantitatively evaluated. Second, our study representatively focused on GADD45G and CAPRIN1, which were differentially methylated in advanced-stage degenerated IVD tissues [13]; however, other cell-cycle-related molecules may also be responsive to the progression of IVD degeneration. Thirdly, the characterization of the cellular phenotype in the human NP is important for the immunohistological evaluation of the cell-cycle-associated proteins; however, it was not evaluated in this study. NP chondrocyte markers such as Krt19, Pax1, and FoxF1 [47] should be evaluated in future studies. ## 4. Materials and Methods This study was approved by the Institutional Clinical Research Ethics Review Committee of Mie University Hospital (approval number: H2022-178) and performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients. ## 4.1. Human NP Tissues and Cell Isolation Human IVDs were obtained from surgical specimens (five women, 50–75 years of age (mean 67 years) and with Pfirrmann MRI grades 4 [21]). NP tissues were macroscopically separated from other structures of human IVD tissues, including the AF and cartilaginous end-plates. Human NP cells were cultured in a monolayer after isolation from NP tissues through sequential enzyme digestion, as previously reported [48]. Briefly, following $0.4\%$ pronase and $0.025\%$ collagenase P digestion, the cells were washed with Dulbecco’s modified Eagle’s medium and Ham’s F-12 medium (DMEM/F12; Gibco, Palo Alto, CA, USA), and cultured in a monolayer at 4.0 × 104 cells/mL with $5\%$ CO2 and $95\%$ air in complete medium (DMEM/F12 containing $10\%$ fetal bovine serum, 25 μg/mL ascorbic acid, 10,000 units/mL penicillin, and 10,000 μg/mL streptomycin) (Figure 11). The medium was changed every three days. Primary cultured cells were used in all experiments conducted in this study. The cells in six-well plates were pre-cultured to $80\%$ confluence (approximately 14 d) in all sets of experiments. ## 4.2. Western Blotting Cell lysates (containing 20 μg of protein) of monolayer-cultured NP cells were analyzed using Western blotting under reducing conditions, as previously reported [49]. For GADD45G-immunoblotting, the cell lysates were treated with chondroitinase-ABC, degrading the proteoglycans’ chondroitin sulfate chains. Immunostaining was performed using a mouse monoclonal antibody raised against GADD45G (OTI2F12, 1:4000; Novus Biologicals, Littleton, CO, USA) and CAPRIN1 (15112-1-AP, 1:500; Proteintech, Manchester, UK), diluted with $5\%$ skim milk in Tris buffered saline (TBS). β-actin served as a loading control for the Western blot assay. The densitometry analysis was performed using Image J software (version 1.53 m; National Institutes of Health, Bethesda, MD, USA). ## 4.3. RNA Isolation Total RNA was isolated from human NP cells in monolayer culture using the ISOGEN PB kit (NipponGene, Toyama, Japan) according to the manufacturer’s instructions. Total RNA was reverse-transcribed using the first strand cDNA synthesis kit (Roche Applied Science, Mannheim, Germany) with a DNA thermal cycler (Veriti, Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s protocol. ## 4.4. Quantitative Real-Time Polymerase Chain Reaction (PCR) The expression levels of GADD45G (Hs02566147 s1, TaqMan Gene Expression Assay; Applied Biosystems) and CAPRIN1 (Hs00195416 m1; Applied Biosystems) were quantified via real-time PCR with TaqMan gene expression assays (Applied Biosystems) using the primer pairs for TaqMan genomic assays. The assay was calibrated using 18S RNA (Hs99999901 s1) as an internal control. To determine the expression levels of GADD45G and CAPRIN1, the resultant cDNA (three replicates) was amplified for the target genes. The cycle used a 15 s denaturation at 95 °C and 1 min annealing and extension at 60 ℃, utilizing the ABI PRISM 7000 sequence detection system (Applied Biosystems). The relative expression levels of GADD45G and CAPRIN1 were calculated using the comparative threshold method [50]. ## 4.5. Effect of Interleukin-1β on the Gene Expression of GADD45G and CAPRIN1 To examine the effect of pro-inflammatory cytokines on the mRNA expression of GADD45G and CAPRIN1, human NP cells were cultured in the presence of IL-1β (0.1, 1.0, and 10 ng/mL) for 48 h after serum starvation. The mRNA expression levels of GADD45G and CAPRIN1 were quantified as described above. ## 4.6. Histological Grading of Human NP Tissues Human NP tissues obtained from spine surgeries were divided into three groups using Pfirrmann MRI classification (14 men, 16 women, 34–85 years old, mean age: 64.5 years old, grade 2: $$n = 4$$, grade 3: $$n = 11$$, grade 4: $$n = 15$$) (Table 1), according to the extent of disc degeneration evaluated via MRI. The samples were fixed in $4\%$ paraformaldehyde for 48 h at 4 ℃ and embedded in paraffin. The sections (5 μm) were stained with H-E, Safranin-O, and fast green. The histological grading of human NP tissues was evaluated based on Rutges’s classification [28] with modifications. NP tissues were evaluated in three subcategories. Each item was graded as 0, 1, or 2 on the H-E sections for cellularity NP and matrix NP and Safranin-O sections for NP matrix staining. Cellularity NP: 0: Normal cellularity, 1: Mixed cellularity, 2: Mainly clustered cellularity; Matrix NP: 0: Well-organized structure of nucleus matrix, 1: Partly disorganized structure of nucleus matrix, 2: Complete disorganization and loss of nucleus matrix; NP matrix staining: 0: Intense staining (red stain dominates), 1: Reduced staining (mixture of red and slight green staining), 2: Faint staining (increased green staining). The total score of the modified classification is the sum of the three different scoring items of NP tissues, resulting in a minimum score of 0 points for a completely healthy NP and a maximum of 6 points for an entirely degenerated NP. ## 4.7. Immunohistochemistry of Human NP Tissues For the immunohistochemistry of GADD45G and CAPRIN1, the human NP tissues used in the histological analysis were analyzed. For the immunostaining of GADD45G, after epitope retrieval with citrate buffer (pH 6.0), sections were incubated with a primary anti-GADD45G mouse monoclonal antibody (Novus Biologicals, Littleton, CO, USA). For the staining of CAPRIN1, after epitope retrieval using proteinase K treatment, the sections were incubated with primary anti-CAPRIN1 rabbit polyclonal antibody (Proteintech, Manchester, UK) diluted with $1\%$ bovine serum albumin (BSA) in phosphate buffered saline (PBS). The primary antibody was visualized using the Histofine Simple Stain MAX-PO (MULTI) kit (Nichirei Bioscience, Tokyo, Japan), according to the manufacturer’s instructions, with some modifications. Peroxidase activity was detected using 3,3′-diaminobenzidine tetrahydrochloride (DAB; Dojindo, Toyama, Japan). The sections were counterstained with Mayer’s hematoxylin. The isotype control was processed using mouse IgG. Five views of each section of the NP area at 200× magnification were randomly captured, and immunopositive or -negative cells were manually counted using conventional microscopy (OLYMPUS BX53, Tokyo, Japan). Immunopositive cells were classified as slightly positive (1+) or strongly positive (2+) according to the staining area and intensity (Table 2). ## 4.8. Statistical Analysis Data are expressed as the mean ± standard deviation. One-way analysis of variance (ANOVA) was used to assess the effects of the culture conditions in vitro. Post-hoc analyses were performed using Fisher’s least significant difference (LSD). Statistical differences in histological grading scores by Pfirrmann MRI classification were determined using the Kruskal–Wallis test. Statistical correlations between Pfirrmann MRI classification and histological classification scores and between the percentage of immunopositive cells and Pfirrmann MRI classification score or histological classification score were evaluated using Spearman’s correlation test. All statistical analyses were performed using IBM SPSS Statistics software (version 28.0; IBM Japan, Tokyo, Japan). Statistical significance was set at $p \leq 0.05.$ ## 5. Conclusions Our study showed, for the first time, that GADD45G and CAPRIN1 were expressed in human NP cells at both the mRNA and protein levels. Immunohistochemical analysis showed the enhanced expression of GADD45G and CAPRIN1 in advanced stages of degenerated NP tissues. We speculated that the expression of these two cell-cycle-related molecules may be regulated during IVD degeneration progression to maintain the integrity of human NP tissues by controlling cell proliferation and apoptosis (cell death) under epigenetic alteration. ## References 1. Knezevic N.N., Candido K.D., Vlaeyen J.W.S., Van Zundert J., Cohen S.P.. Low Back Pain. Lancet (2021) 398 78-92. DOI: 10.1016/S0140-6736(21)00733-9 2. Samartzis D., Karppinen J., Mok F., Fong D., Luk K., Cheung K.. A Population-Based Study of Juvenile Disc Degeneration and Its Association with Overweight and Obesity, Low Back Pain, and Diminished Functional Status. J. Bone Jt. Surg. Am. (2011) 93 662-670. DOI: 10.2106/JBJS.I.01568 3. Teraguchi M., Yoshimura N., Hashizume H., Muraki S., Yamada H., Minamide A., Oka H., Ishimoto Y., Nagata K., Kagotani R.. Prevalence and Distribution of Intervertebral Disc Degeneration over the Entire Spine in a Population-Based Cohort: The Wakayama Spine Study. Osteoarthr. Cartil. (2013) 22 104-110. DOI: 10.1016/j.joca.2013.10.019 4. Teraguchi M., Yoshimura N., Hashizume H., Muraki S., Yamada H., Oka H., Minamide A., Nakagawa H., Ishimoto Y., Nagata K.. The Association of Combination of Disc Degeneration, End Plate Signal Change, and Schmorl Node with Low Back Pain in a Large Population Study: The Wakayama Spine Study. Spine J. (2015) 15 622-628. DOI: 10.1016/j.spinee.2014.11.012 5. Suzuki H., Kanchiku T., Imajo Y., Yoshida Y., Nishida N., Taguchi T.. Diagnosis and Characters of Non-Specific Low Back Pain in Japan: The Yamaguchi Low Back Pain Study. PLoS ONE (2016) 11. DOI: 10.1371/journal.pone.0160454 6. Mallow G.M., Zepeda D., Kuzel T.G., Barajas J.N., Aboushaala K., Nolte M.T., Espinoza-Orias A., Oh C., Colman M., Kogan M.. Issls Prize in Clinical Science 2022: Epidemiology, Risk Factors and Clinical Impact of Juvenile Modic Changes in Paediatric Patients with Low Back Pain. Eur. Spine J. (2022) 31 1069-1079. DOI: 10.1007/s00586-022-07125-x 7. Wang Z.X., Hu Y.G.. Imaging Analysis of the High-Intensity Zone on Lumbar Spine Magnetic Resonance Images: Classification, Features and Correlation with Low Back Pain. J. Pain Res. (2021) 14 2981-2989. DOI: 10.2147/JPR.S332509 8. Urban J.P., Roberts S.. Degeneration of the Intervertebral Disc. Arthritis Res. Ther. (2003) 5 120-130. DOI: 10.1186/ar629 9. Adams M.A., Roughley P.J.. What Is Intervertebral Disc Degeneration, and What Causes It?. Spine (2006) 31 2151-2161. DOI: 10.1097/01.brs.0000231761.73859.2c 10. Battie M.C., Videman T., Levalahti E., Gill K., Kaprio J.. Genetic and Environmental Effects on Disc Degeneration by Phenotype and Spinal Level: A Multivariate Twin Study. Spine (2008) 33 2801-2808. DOI: 10.1097/BRS.0b013e31818043b7 11. Vo N.V., Hartman R.A., Patil P.R., Risbud M.V., Kletsas D., Iatridis J.C., Hoyland J.A., Le Maitre C.L., Sowa G.A., Kang J.D.. Molecular Mechanisms of Biological Aging in Intervertebral Discs. J. Orthop. Res. (2016) 34 1289-1306. DOI: 10.1002/jor.23195 12. Pattappa G., Li Z., Peroglio M., Wismer N., Alini M., Grad S.. Diversity of Intervertebral Disc Cells: Phenotype and Function. J. Anat. (2012) 221 480-496. DOI: 10.1111/j.1469-7580.2012.01521.x 13. Ikuno A., Akeda K., Takebayashi S.-I., Shimaoka M., Okumura K., Sudo A.. Genome-Wide Analysis of DNA Methylation Profile Identifies Differentially Methylated Loci Associated with Human Intervertebral Disc Degeneration. PLoS ONE (2019) 14. DOI: 10.1371/journal.pone.0222188 14. Liebermann D.A., Hoffman B.. Gadd45 in Stress Signaling. J. Mol. Signal. (2008) 3 15. DOI: 10.1186/1750-2187-3-15 15. Liebermann D., Tront J.S., Sha X., Mukherjee K., Mohamed-Hadley A., Hoffman B.. Mohamed-Hadley, and B. Hoffman. Gadd45 Stress Sensors in Malignancy and Leukemia. Crit. Rev. Oncog. (2011) 16 129-140. DOI: 10.1615/CritRevOncog.v16.i1-2.120 16. Humayun A., Fornace A.J.. Gadd45 in Stress Signaling, Cell Cycle Control, and Apoptosis. Adv. Exp. Med. Biol. (2022) 1360 1-22. PMID: 35505159 17. Grill B., Wilson G.M., Zhang K.-X., Wang B., Doyonnas R., Quadroni M., Schrader J.W.. Activation/Division of Lymphocytes Results in Increased Levels of Cytoplasmic Activation/Proliferation-Associated Protein-1: Prototype of a New Family of Proteins. J. Immunol. (2004) 172 2389-2400. DOI: 10.4049/jimmunol.172.4.2389 18. Solomon S., Xu Y., Wang B., David M.D., Schubert P., Kennedy D., Schrader J.W.. Distinct Structural Features of Caprin-1 Mediate Its Interaction with G3bp-1 and Its Induction of Phosphorylation of Eukaryotic Translation Initiation Factor 2alpha, Entry to Cytoplasmic Stress Granules, and Selective Interaction with a Subset of Mrnas. Mol. Cell Biol. (2007) 27 2324-2342. DOI: 10.1128/MCB.02300-06 19. Yang Z.S., Qing H., Gui H., Luo J., Dai L.J., Wang B.. Role of Caprin-1 in Carcinogenesis. Oncol. Lett. (2019) 18 15-21. PMID: 31289466 20. Nolan L.S., Chen J., Gonçalves A.-C., Bullen A., Towers E.R., Steel K.P., Dawson S.J., Gale J.E.. Targeted Deletion of the Rna-Binding Protein Caprin1 Leads to Progressive Hearing Loss and Impairs Recovery from Noise Exposure in Mice. Sci. Rep. (2022) 12 2444. DOI: 10.1038/s41598-022-05657-2 21. Pfirrmann C.W.A., Metzdorf A., Zanetti M., Hodler J., Boos N.. Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration. Spine (2001) 26 1873-1878. DOI: 10.1097/00007632-200109010-00011 22. Urrutia J., Besa P., Campos M., Cikutovic P., Cabezon M., Molina M., Cruz J.P.. The Pfirrmann Classification of Lumbar Intervertebral Disc Degeneration: An Independent Inter- and Intra-Observer Agreement Assessment. Eur. Spine J. (2016) 25 2728-2733. DOI: 10.1007/s00586-016-4438-z 23. Clouet J., Pot-Vaucel M., Grimandi G., Masson M., Lesoeur J., Fellah B.H., Gauthier O., Fusellier M., Cherel Y., Maugars Y.. Characterization of the Age-Dependent Intervertebral Disc Changes in Rabbit by Correlation between Mri, Histology and Gene Expression. BMC Musculoskelet. Disord. (2011) 12. DOI: 10.1186/1471-2474-12-147 24. Ohnishi T., Sudo H., Tsujimoto T., Iwasaki N.. Age-Related Spontaneous Lumbar Intervertebral Disc Degeneration in a Mouse Model. J. Orthop. Res. (2018) 36 224-232. DOI: 10.1002/jor.23634 25. Bouhsina N., Decante C., Hardel J., Madec S., Abadie J., Hamel A., Le Visage C., Lesoeur J., Guicheux J., Clouet J.. Correlation between Magnetic Resonance, X-Ray Imaging Alterations and Histological Changes in an Ovine Model of Age-Related Disc Degeneration. Eur. Cells Mater. (2021) 42 166-178. DOI: 10.22203/eCM.v042a13 26. Boos N., Weissbach S., Rohrbach H., Weiler C., Spratt K.F., Nerlich A.G.. Classification of Age-Related Changes in Lumbar Intervertebral Discs: 2002 Volvo Award in Basic Science. Spine (2002) 27 2631-2644. DOI: 10.1097/00007632-200212010-00002 27. Canbay S., Turhan N., Bozkurt M., Arda K., Caglar S.. Correlation of Matrix Metalloproteinase-3 Expression with Patient Age, Magnetic Resonance Imaging and Histopathological Grade in Lumbar Disc Degeneration. Turk. Neurosurg. (2013) 23 427-433. PMID: 24101259 28. Rutges J., Duit R., Kummer J., Bekkers J., Oner F., Castelein R., Dhert W., Creemers L.. A Validated New Histological Classification for Intervertebral Disc Degeneration. Osteoarthr. Cartil. (2013) 21 2039-2047. DOI: 10.1016/j.joca.2013.10.001 29. Kovalsky O., Lung F.-D.T., Roller P.P., Fornace A.J.. Oligomerization of Human Gadd45a Protein. J. Biol. Chem. (2001) 276 39330-39339. DOI: 10.1074/jbc.M105115200 30. Vairapandi M., Balliet A.G., Hoffman B., Liebermann D.A.. Gadd45b and Gadd45g Are Cdc2/Cyclinb1 Kinase Inhibitors with a Role in S and G2/M Cell Cycle Checkpoints Induced by Genotoxic Stress. J. Cell Physiol. (2002) 192 327-338. DOI: 10.1002/jcp.10140 31. Elledge S.J.. Cell Cycle Checkpoints: Preventing an Identity Crisis. Science (1996) 274 1664-1672. DOI: 10.1126/science.274.5293.1664 32. Gupta M., Gupta S.K., Balliet A.G., Hollander M.C., Fornace A.J., Hoffman B., Liebermann D.A.. Hematopoietic Cells from Gadd45a- and Gadd45b-Deficient Mice Are Sensitized to Genotoxic-Stress-Induced Apoptosis. Oncogene (2005) 24 7170-7179. DOI: 10.1038/sj.onc.1208847 33. Gupta S.K., Gupta M., Hoffman B., Liebermann D.A.. Hematopoietic Cells from Gadd45a-Deficient and Gadd45b-Deficient Mice Exhibit Impaired Stress Responses to Acute Stimulation with Cytokines, Myeloablation and Inflammation. Oncogene (2006) 25 5537-5546. DOI: 10.1038/sj.onc.1209555 34. Gan Y., Li S., Li P., Xu Y., Wang L., Zhao C., Ouyang B., Tu B., Zhang C., Luo L.. A Controlled Release Codelivery System of Mscs Encapsulated in Dextran/Gelatin Hydrogel with Tgf-Β3-Loaded Nanoparticles for Nucleus Pulposus Regeneration. Stem Cells Int. (2016) 2016 9042019. DOI: 10.1155/2016/9042019 35. Wang B., David M.D., Schrader J.W.. Absence of Caprin-1 Results in Defects in Cellular Proliferation. J. Immunol. (2005) 175 4274-4282. DOI: 10.4049/jimmunol.175.7.4274 36. Protter D.S., Parker R.. Principles and Properties of Stress Granules. Trends Cell Biol. (2016) 26 668-679. DOI: 10.1016/j.tcb.2016.05.004 37. Risbud M.V., Shapiro I.M.. Role of Cytokines in Intervertebral Disc Degeneration: Pain and Disc Content. Nat. Rev. Rheumatol. (2014) 10 44-56. DOI: 10.1038/nrrheum.2013.160 38. Abe Y., Akeda K., An H.S., Aoki Y., Pichika R., Muehleman C., Kimura T., Masuda K.. Proinflammatory Cytokines Stimulate the Expression of Nerve Growth Factor by Human Intervertebral Disc Cells. Spine (2007) 32 635-642. DOI: 10.1097/01.brs.0000257556.90850.53 39. Yamada J., Akeda K., Sano T., Iwasaki T., Takegami N., Sudo A.. Expression of Glial Cell Line-Derived Neurotrophic Factor in the Human Intervertebral Disc. Spine (2020) 45 E768-E775. DOI: 10.1097/BRS.0000000000003418 40. Aulas A., Lyons S.M., Fay M.M., Anderson P., Ivanov P.. Nitric Oxide Triggers the Assembly of “Type Ii” Stress Granules Linked to Decreased Cell Viability. Cell Death Dis. (2018) 9 1129. DOI: 10.1038/s41419-018-1173-x 41. Song D., Kuang L., Yang L., Wang L., Li H., Li X., Zhu Z., Shi C., Zhu H., Gong W.. Yin and Yang Regulation of Stress Granules by Caprin-1. Proc. Natl. Acad. Sci. USA (2022) 119 e2207975119. DOI: 10.1073/pnas.2207975119 42. Vergroesen P.-P., Kingma I., Emanuel K., Hoogendoorn R., Welting T., van Royen B., van Dieën J., Smit T.. Mechanics and Biology in Intervertebral Disc Degeneration: A Vicious Circle. Osteoarthr. Cartil. (2015) 23 1057-1070. DOI: 10.1016/j.joca.2015.03.028 43. Cao G., Yang S., Cao J., Tan Z., Wu L., Dong F., Ding W., Zhang F.. The Role of Oxidative Stress in Intervertebral Disc Degeneration. Oxidative Med. Cell. Longev. (2022) 2022 2166817. DOI: 10.1155/2022/2166817 44. Jones P.A.. Functions of DNA Methylation: Islands, Start Sites, Gene Bodies and Beyond. Nat. Rev. Genet. (2012) 13 484-492. DOI: 10.1038/nrg3230 45. Mishra N.K., Guda C.. Genome-Wide DNA Methylation Analysis Reveals Molecular Subtypes of Pancreatic Cancer. Oncotarget (2017) 8 28990-29012. DOI: 10.18632/oncotarget.15993 46. Goldberg A.D., Allis C.D., Bernstein E.. Epigenetics: A Landscape Takes Shape. Cell (2007) 128 635-638. DOI: 10.1016/j.cell.2007.02.006 47. Richardson S.M., Ludwinski F.E., Gnanalingham K.K., Atkinson R.A., Freemont A.J., Hoyland J.A.. Notochordal and Nucleus Pulposus Marker Expression Is Maintained by Sub-Populations of Adult Human Nucleus Pulposus Cells through Aging and Degeneration. Sci. Rep. (2017) 7 1501. DOI: 10.1038/s41598-017-01567-w 48. Sano T., Akeda K., Yamada J., Takegami N., Sudo T., Sudo A.. Expression of the Rank/Rankl/Opg System in the Human Intervertebral Disc: Implication for the Pathogenesis of Intervertebral Disc Degeneration. BMC Musculoskelet. Disord. (2019) 20. DOI: 10.1186/s12891-019-2609-x 49. Iida R., Akeda K., Kasai Y., Masuda K., Morimoto R., Sakakibara T., Sato M., Uchida A.. Expression of Proteinase-Activated Receptor-2 in the Intervertebral Disc. Spine (2009) 34 470-478. DOI: 10.1097/BRS.0b013e318195a67d 50. Daum L.T., Ye K., Chambers J.P., Santiago J., Hickman J.R., Barnes W.J., Kruzelock R.P., Atchley D.H.. Comparison of Taqman and Epoch Dark Quenchers During Real-Time Reverse Transcription Pcr. Mol. Cell. Probes (2004) 18 207-209. DOI: 10.1016/j.mcp.2004.01.005
--- title: 'Impact of COVID-19 Pandemic on Women’s Health and Obstetric Outcomes after Assisted Reproduction: A Survey from an Italian Fertility Center' authors: - Michela Cirillo - Valentina Basile - Letizia Mazzoli - Maria Elisabetta Coccia - Cinzia Fatini journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10059757 doi: 10.3390/jpm13030563 license: CC BY 4.0 --- # Impact of COVID-19 Pandemic on Women’s Health and Obstetric Outcomes after Assisted Reproduction: A Survey from an Italian Fertility Center ## Abstract Background: the restrictive measures that were adopted during three waves of the COVID-19 pandemic had an impact on both the emotional state and lifestyle of the general population. We evaluated the impact of COVID-19 pandemic on lifestyles and emotional states of women planning assisted reproductive technology (ART), and whether these changes affected ART outcomes. Methods: quantitative research, using a web-based survey, was performed on 289 Caucasian women. Results: In preconception, we observed higher percentage of women with positive obstetric outcomes who reduced body weight ($52.4\%$ vs. $27.2\%$, $$p \leq 0.09$$). Over $60\%$ of women with positive outcomes practiced physical activity vs. $47\%$ of women with negative outcomes ($$p \leq 0.03$$), as well as having better quality of sleep ($45\%$ vs. $35\%$), and a more solid relationships with their partners ($65.1\%$ vs. $51.7\%$, $$p \leq 0.03$$). Women who increased their intake of whole grains, fruits, vegetables, and legumes ($p \leq 0.05$), according to the Mediterranean diet, showed positive outcomes. We observed that participants who experienced “very much” or “extreme” anxiety, sadness, and fear ($p \leq 0.05$) during pandemic were clearly more numerous in the group with negative pregnancy outcomes. Conclusions: healthy lifestyle together with a positive emotional state in preconception can positively influence the obstetric outcomes after ART. ## 1. Introduction The COVID-19 pandemic had a significant impact on the field of reproductive medicine: in Italy on 18 March 2020, the Group of Special Interest on Sterility (GISS) of the Italian Society of Gynecology and Obstetrics (ISGO) and its federates declared the suspension of all the Assisted Reproductive Technology (ART) treatments to prevent the moving of people and their access to clinical facilities, in order to contain the spread of the virus [1]. Exceptions were made for ongoing stimulation cycles and fertility cryopreservation procedures in oncological patients [2]. The Italian infertile population is the oldest among Western countries: in about a third of cases, the female partner is over the age of 40, and time is fundamental to achieve a good outcome of ART treatments [3]. Moreover, in Italy, every month of ART inactivity causes a failure to the performance of about 8000 treatments with a potential falling of the birth rate of about 1500 children. Therefore, from May 2020 onwards, when the general lockdown was already left behind, all reproductive medicine activities resumed [4]. Infertility is a distressing condition that induce emotional, financial, and social strain [5]. The COVID-19 pandemic intensifies this stressful status in infertile women attempting ART, due to its impact on women’s life (isolation, economic shutdowns, unemployment, and fear of contamination) [6]. However, the restrictive measures that were adopted during the first, the second and the third wave of the pandemic had an impact not only on the emotional state (fear, anxiety, and distress), but also on the dietary habits and lifestyle of the general population. These two aspects, associated with systemic inflammation and endothelial dysfunction, play a major role in the development and in the progression of cardiovascular diseases [7,8]. As a matter of fact, people having a positive psychological attitude tend also to have healthy habits, such as a healthy diet, an active lifestyle, and a low incidence of sleep-related disorders. In addition, the Mediterranean diet has positive effects on health, as it reduces the risk of developing cardiovascular diseases, the incidence of developing malignant tumors, neurodegenerative diseases, and diabetes [9]. On the other hand, during the first wave of the pandemic, women waiting to undergo ARTs have shown changes in weight, an increase of BMI, a high consumption of sweets, cheese, meat, and snacks [10]. Besides several gynecological and systemic diseases affecting women’s fertility, lifestyle factors and environmental conditions, such as stress induced by COVID-19 pandemic, contribute to interfere with reproductive health in both women and men. The literature shows that an unbalanced and unhealthy diet can lead to adverse ART outcomes, while physical activity and healthy eating can lead to improvements on menstrual cycles and female fertility [11]. Specifically, nutritional factors may affect not only oocyte maturation, but also quality of embryos and implantation [12]. This study aimed to evaluate the impact of COVID-19 pandemic restrictive measures on lifestyle and emotional state of women who have undergone ART from May 2020 to February 2021, and whether changes related to pandemic period had affected or not ARTs’ outcomes. ## 2. Materials and Methods We performed a study in a sample of Caucasian women, referred to the Internal Medicine Clinic at the Assisted Reproductive Technology Centre, using a web-based survey. The survey was conducted in Italian, according to the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) Statement [13]. The Italian version of the questionnaire was created online by using Google Forms. We sent the questionnaire to 480 women by email using a validated account of the University of Florence. The survey was addressed from 3 May to 31 July 2021. We included women aged between 18–49 years, planning homologous or heterologous infertility treatments at ART Centre from May 2020 to February 2021. Non-Caucasian women were excluded. An information sheet was set as the first page of the web survey, and participants had the opportunity to give informed consent, according to Ethical principles of the Declaration of Helsinki, before accessing the survey. All the potential participants were fully informed about the study, the extent of privacy, anonymity and confidentiality, the voluntary nature of participating, and the lack of negative consequences in case of decline. The Local Ethics Commitee (Azienda Ospedaliero-Universitaria Careggi) approved the study (Prot. 19901_OSS). The 46 self-administered questions were designed to assess the impact of COVID-19 pandemic on lifestyle habits as well as psychological behavior and obstetric outcomes of the ART procedure. Basic demographic data were recorded: age, level of education, and region of domicile. Socio-economic status comprised types of job before and during pandemic. Clinical data included: types of procedures (homologous or heterologous), height, weight gain during the pandemic (Body Mass Index, BMI, calculated by dividing weight in kilograms by the square of height in meters. According to the World Health Organization criteria, overweight was defined as BMI values ≥ 25 kg/m2), and presence of chronic diseases. Nutritional habits, physical activity, smoking habit, quality, and quantity of sleep during the pandemic were evaluated (multiple choice). The emotional state, including anxiety, sadness, and fear during the pandemic, using a verbal rating scale (not at all, slightly, moderately, very much, extremely) was assessed. ## Statistical Analysis Statistical analysis was performed by using the SPSS (Statistical Package for Social Sciences, Chicago, IL, USA) software for Windows (Version 28.0). Continuous variables were expressed as mean (±SD). The categorical variables were expressed as frequencies and percentages. Chi-square test was used to test for proportions. The continuous variables were analyzed by using a parametric test (t-Student test). A p-value < 0.05 was considered significant. ## 3. Results The questionnaire was sent to 480 women and was completed by 289 responders. Demographic, socio-economic, and clinical characteristics of the study population are reported in Table 1. The mean age was 39.4 (±4.7 years) and 118 responders ($40.8\%$) were more than 40 years old. More than $70\%$ of women came from Central Italian regions and more than $50\%$ of women had graduated (graduation and post-graduation). In comparison to our previous study performed during the first wave of pandemic [10], the percentage of women unemployed was lower ($43.6\%$ vs. $15.2\%$), as expected. In Table 2, we report lifestyle changes during the COVID-19 pandemic. Specifically, we observed that about $18\%$ of women reduced body weight and about $24\%$ quit smoking. Indeed, about $54\%$ of women practiced regularly physical activity (1–2 times a week in $29.8\%$, 3–4 times a week in $18.3\%$ and more than 4 times a week in $5.5\%$), a higher percentage in comparison to that observed in our previous study during the first wave of the pandemic [10]. We investigated quality and quantity of sleep habits during the pandemic and about $26\%$ of women reported feeling tired when waking up, about $28\%$ of them had difficulties falling asleep or reported fragmentated sleep, while about $6\%$ of the patients needed to take medications or supplements to treat sleep disorders. In our previous study, performed during the first wave of the pandemic, the percentage of women who reported fragmentated sleep or took medications or supplements to treat sleep disorders, was about twice as high ($60\%$ vs. $34\%$) [10]. ## 3.1. COVID-19 Pandemic and Diet We evaluated the habitual food intake of women. Their usual water intake was not optimal: only $9.7\%$ of women drank more than two liters of water a day. About $65\%$ of the participants did not usually drink other beverages such as sugar drinks (beverage with added sugar or other sweeteners such as high fructose corn syrup, sucrose, fruit juice concentrates), and this habit did not change during the pandemic. Most of the women did not change their intake of alcohol and coffee during the pandemic: as concerns alcohol consumption, we observed that the $51\%$ were non-drinkers, the $23\%$ drink during the weekend and $21\%$ of participants reduced alcohol consumption before ART. We also investigated the consumption of unplanned snacks during the pandemic and before ART. We focused on women’s motivation for snack consumption and we observed that most of the participants ($37.7\%$) ate snacks because of hunger, and more than $20\%$ ate snacks because of boredom or of anxiety. The answers most frequently selected as a response to the question of which type of food was eaten as unplanned snacks was fresh fruit in $37.7\%$ of women, yogurt in $30.1\%$, pastries, and savory snacks (pizza, chips, salted peanuts) in $28.2\%$ of women. Moreover, we evaluated the adherence to the Mediterranean diet pattern, and we observed that $34\%$ of the women ate whole grain; in addition, the $13.8\%$ of them declared that they started eating whole grain before ART procedure. Concerning the consumption of vegetables and fruits, we observed that a large percentage of women did not eat vegetables ($2.4\%$) and fruits ($8.3\%$) or did not eat them in adequate quantities ($41.1\%$ vegetables one portion a day; $50.2\%$ fruit 1one portion a day). In relation to legume consumption, about $60\%$ ate them once a week and $5.5\%$ started or increased legume intake before ART. Moreover, about $50\%$ of women ate fish once a week, and $9\%$ of women increased fish intake (two-to-three times a week) before ART procedure. On the other hand, regarding red and/or processed meat consumption, we observed that about $61\%$ of participants ate them with a frequency of one-to-two times a week, $18\%$ reduced consumption to less than two times a week but $8.6\%$ of women consumed them more than two times a week during COVID-19 pandemic and before ART. ## 3.2. COVID-19 Pandemic and Emotional State We investigated emotional state during COVID-19 pandemic and before ART procedure. We analyzed levels of anxiety, sadness, and fear in the population, using a verbal scale, and the reasons that determined emotion. We found that about $34\%$ of participants experienced moderate anxiety during the pandemic: $38.4\%$ of the population felt anxious because of the restrictions following the COVID-19 pandemic, $20.4\%$ felt anxious due to the future ART procedure, and $12.8\%$ for the ART outcome. As concerns sadness, we found similar percentages among the options “not at all”, “slightly”, “moderately” (respectively, $26\%$, $28\%$, $23.5\%$), whereas $19.7\%$ selected the option “very much”. The most frequent cause of sadness was economic condition, and in $17.6\%$ lack of parenthood. Regarding fear, we found that a high percentage ($45\%$) of women selected the option “slightly” and $27\%$ “moderately”. Of the sample, $45.3\%$ said they felt fear for “failing to carry out the ART procedure because of pandemic restrictions”. We analyzed the emotional reactions to the feelings felt, allowing the participants to select more than one possible option. Interestingly, we noted that the most-selected response was “I sought comfort in the partner” ($70.6\%$), followed by “I sought comfort in family members” ($28.7\%$). When asked if the partner had been supportive during the pandemic and the ART procedure, $94.8\%$ of participants answered “yes”. In addition, we investigated the reasons that motivated them to continue the ART plan despite the difficulties resulting from COVID-19 pandemic: the majority of participants ($96.9\%$) said they were driven by a strong desire for motherhood. ## 3.3. COVID-19 Pandemic and Obstetric Outcomes Obstetric and pregnancy outcomes of all women are reported in Figure 1. We observed that 143 ($49.5\%$) of women had implantation failure, 24 ($8.3\%$) had pregnancy and delivery, 59 ($20.4\%$) had ongoing pregnancy, 26 ($9\%$) had miscarriage, 34 ($11.8\%$) stopped treatment for several gynecological reasons, and 3 ($1\%$) stopped treatment for COVID-19 infection. We analyzed the arising emotions from the failure of the ART procedure and allowed participants to select as many options as possible; finding that sadness ($26.3\%$) and disappointment ($18\%$) were the emotions that they felt most. We compared the lifestyle and the emotional state in relation to the outcomes of the ART procedure, to understand the influence that these variables may have had on the ART outcomes (Figure 2, Figure 3 and Figure 4). In Figure 2, we observed a higher percentage of women with overweight/obesity who reduced their body weight before ART procedure by enhancing BMI in the group of women with positive outcomes ($52.4\%$ vs. $27.2\%$, $$p \leq 0.09$$). In addition, in the preconception period, over $60\%$ of women with positive outcomes practiced physical activity compared to the $47\%$ observed in the group of women with negative outcomes ($$p \leq 0.03$$). Concerning smoking habits, women who obtained positive outcomes were more virtuous, and had a better quality of sleep ($45\%$ vs. $35\%$), as well as having more solid relationships with their partners ($65.1\%$ vs. $51.7\%$, $$p \leq 0.03$$). Concerning eating habits (Figure 3), women who increased their adherence to the Mediterranean pattern in their preconception period had more positive outcomes. In particular, in women with positive outcomes we observed an increase in the intake of whole grains ($$p \leq 0.02$$), fruits ($$p \leq 0.04$$), vegetables ($$p \leq 0.01$$), and legumes ($$p \leq 0.05$$), and a reduction in the consumption of sweets and sugar drinks, even if not significant. Before ART the alcohol consumption was low, according to the Mediterranean diet, in women with positive obstetric outcomes in comparison to women with negative outcomes ($p \leq 0.0001$). Finally, regarding emotional state (Figure 4), we observed that participants who experienced “very much” or “extreme” anxiety ($$p \leq 0.001$$), sadness ($$p \leq 0.002$$), and fear were clearly more numerous in group with negative pregnancy outcomes. ## 4. Discussion The COVID-19 pandemic and the restrictions imposed on the population had important effects on people’s lifestyle and emotional state, especially on women planning ART [10]. To date, evidence confirms that implementing good practices in lifestyle significantly reduces fertility problems, improves pregnancy outcomes, and generally provides for a good state of health throughout life [14,15]. On the other hand, the COVID-19 pandemic affected personal freedom, led to uncertainty about ART procedures, and also had a negative impact on the psychological sphere and lifestyle [10,16]. To the best of our knowledge, no other studies have considered the impact of the COVID-19 pandemic—which we are still going through—on lifestyle and emotional state in relation to ART outcomes. Results coming from this survey showed that, in preconception, improving lifestyle in terms of better adherence to the Mediterranean diet pattern, adequate BMI, increased physical activity, and quitting smoking were associated with a higher probability of positive obstetric outcomes. It is well-known that female overweight/obesity has a significant harmful effect on live birth rate following ART, possibly due to impaired ovarian folliculogenesis, oocyte quality, embryonic development, and uterine environment [17]. Moreover, obese pregnant women are at established increased risk for maternal, perinatal, and fetal complications [18]. In our study, we observed a higher percentage of women with overweight/obesity who reduced body weight before ART procedure by enhancing BMI in the group of women with positive outcomes, thus permitting to hypothesize that preconceptional maternal weight loss might be benefit before ART and during pregnancy. The literature focusing on weight loss suggested that a weight reduction of 5–$10\%$ correlate with dietary and lifestyle improvements; moreover, weight loss is often sufficient to improve the chance of pregnancy and metabolic parameters [19]. Indeed, it is well known that adherence to the Mediterranean diet significantly reduces the risk of developing cardiovascular diseases, neurodegenerative diseases, diabetes, cancer development, and overall mortality [20]. Moreover, scientific evidence points out that the Mediterranean diet, rich in vegetables, fruits, whole grains, legumes, extra virgin olive oil, fish, and a reduced intake of red and/or processed meat determines a greater probability of success for ART, as well as preserving and improving fertility [21,22]. Most couples who refer to the ART Centre searching for pregnancy are extremely motivated to take any necessary measures to maximize the procedure chances of success. Our study showed that a good percentage of participants improved their nutrition habits during the pandemic in accordance with the Mediterranean diet pattern. In our study, which covered a period of about a year when restrictions were weaker if compared to the first phases of the pandemic, we observed that virtuous behaviors during preconception were more evident in women who had a positive outcome after ART. On the other hand, it is worth noting that $60\%$ of women with negative outcomes consumed unplanned snacks (especially pastries and savory snacks), of whom about $27\%$ did so because of boredom and anxiety. This emotional eating may contribute to excess energy intake and weight gain, as also observed in our previous study [10]. In keeping with this, recent scientific findings suggest that food consumption is an important factor that affects the occurrence of mental illness [23]. It is noteworthy that a diet rich in ultra-processed foods (UPFs), saturated fat, sugar, salt, strongly flavored ingredients, and chemical additives is related to an increase in neuroinflammation. UPFs’ formulation, presentation, and marketing often promote overconsumption, and the evidence so far shows that UPFs’ consumption is associated with unhealthy dietary nutrient profiles. In contrast, the consumption of complex carbohydrates, such as vegetables, fruits, and fiber, is recommended because they are sources of vitamins and polyphenols, and are metabolized into short-chain fatty acids, which are important anti-inflammatory agents [24]. Meanwhile, we must emphasize that following or switching to a Mediterranean diet pattern does not only imply making better food choices, but it means adopting a real and healthier lifestyle, characterized by regular physical activity, adequate rest, and conviviality, too; accordingly, we observed that in the group of women with positive outcomes, about $60\%$ continued to exercise regularly compared to $47.6\%$ of patients who had later a negative outcome. Several studies evaluated the effect of exercise on ART outcomes with conflicting results [25]. Some data indicate that physical activity may have beneficial effects on some reproductive health outcomes in young adult women, even if the type, intensity, frequency, and the role of physical activity independent of weight loss remain unclear [26]. On the other hand, we well know that sedentary behavior and sitting time (while watching TV) are associated with increased risk of cardiovascular disease and with several cardiometabolic and mental effects [8]. It is known that sleeping adequately helps to regulate appetite and to reduce cardiovascular risk [27]; in our study we found out that about $60\%$ of the participants who reported having difficulty falling asleep or felt tired when waking up needed to take medication/supplements to treat sleep disorders. Despite this, we have not observed significant differences in sleep quality between the two groups in relation to obstetric outcome. Not surprisingly, our data regarding the emotional status of women in relation to clinical outcomes showed that anxiety and sadness during the pandemic were associated with a higher frequency of negative outcome after ART. Purewal et al., evidenced that depression and anxiety during ART treatment are associated with poor ART outcome, nevertheless no evidence about changes in the levels of anxiety and depression from baseline to ART procedures are associated with ART outcome [28]. All of that could have a negative impact, as the literature data have confirmed that negative emotional factors, such as anxiety, sadness and stress, are considered predisposing factors potentially increasing the risk of cardiovascular diseases, also through potential influences on the lifestyle [29]. A recent survey capturing emotional reactions of people during lockdown, and before fertility clinics announced re-opening, reported more negative than positive emotions, in particular stress, worry, frustration, and anger [30]. Negative emotional state, in particular anxiety and sadness, was possibly associated with poorer ART outcome. These findings could help to identify women needing tailored psychological support during the different stages of infertility treatments [31,32]. Therefore, our findings suggest the necessity to implement adequate measures such as meetings and tailored information for the couples, with the aim to raise awareness about the importance of health in the preconception period, especially regarding lifestyle and nutrition. Moreover, all couples who are at risk to experience negative emotional states should be readily identified and referred to appropriate counselling to improve the chances of ART success. The limitations of this research include the lack of validated questionnaire; nevertheless, the survey was able to focus on specific group of women planning ART. Moreover, another limitation is represented by a self-reported questionnaire, which may be associated with the possibility of bias (social desirability), as well as the lack of inclusion of men’s behaviors and perspectives. ## 5. Conclusions In conclusion, a healthy lifestyle, characterized by regular physical activity, a normal BMI, a stable socioeconomic condition, a good quality of sleep, and a good diet in line with the Mediterranean diet pattern, together with a positive emotional state and an established relationship, can positively influence the obstetric ART outcomes. The creation of a path dedicated to women’s preconception health in ART requires a multidisciplinary approach in which professional figures such as gynecologists, midwives, dietitians, phycologists, and general practitioners cooperate for health promotion, during ART procedure and pregnancy, but also for women’s health later in life. ## References 1. 1.Available online: www.aogoi.it/media/6918/comunicato-stampa_giss_sigo_coronavirus-e-pma_17-03-2020.pdf(accessed on 24 March 2022) 2. Alviggi C., Esteves S.C., Orvieto R., Conforti A., La Marca A., Fischer R., Andersen C.Y., Bühler K., Sunkara S.K., Polyzos N.P.. COVID-19 and assisted reproductive technology services: Repercussions for patients and proposal for individualized clinical management. Reprod. Biol. Endocrinol. (2020.0) 18 45. DOI: 10.1186/s12958-020-00605-z 3. 3.Available online: worldpopulationreview.com/country-rankings/total-fertility-rate(accessed on 8 March 2022) 4. 4.Available online: www.sicp.it/wp-content/uploads/2020/03/PMA_raccomandazioni-6-apr-20.pdf(accessed on 15 March 2022) 5. Cavicchioli M., Ferrucci R., Guidetti M., Canevini M.P., Pravettoni G., Galli F.. What Will Be the Impact of the COVID-19 Quarantine on Psychological Distress? Considerations Based on a Systematic Review of Pandemic Outbreaks. Healthcare (2021.0) 9. DOI: 10.3390/healthcare9010101 6. Domar A.D., Shah J.S., Gompers A., Meyers A.J., Khodakhah D.R., Hacker M.R., Penzias A.S., Sakkas D., Toth T.L., Vaughan D.A.. The psychological impact of the coronavirus disease 2019 pandemic on women who become pregnant after receiving treatment for infertility: A longitudinal study. F&S Rep. (2022.0) 3 71-78. DOI: 10.1016/j.xfre.2022.01.004 7. Sin N.L.. The Protective Role of Positive Well-Being in Cardiovascular Disease: Review of Current Evidence, Mechanisms, and Clinical Implications. Curr. Cardiol. Rep. (2016.0) 18 106. DOI: 10.1007/s11886-016-0792-z 8. Mattioli A.V., Sciomer S., Cocchi C., Maffei S., Gallina S.. Quarantine during COVID-19 outbreak: Changes in diet and physical activity increase the risk of cardiovascular disease. Nutr. Metab. Cardiovasc. Dis. (2020.0) 30 1409-1417. DOI: 10.1016/j.numecd.2020.05.020 9. Guasch-Ferré M., Willett W.C.. The Mediterranean diet and health: A comprehensive overview. J. Intern. Med. (2021.0) 290 549-566. DOI: 10.1111/joim.13333 10. Cirillo M., Rizzello F., Badolato L., De Angelis D., Evangelisti P., Coccia M.E., Fatini C.. The effects of COVID-19 lockdown on lifestyle and emotional state in women undergoing assisted reproductive technology: Results of an Italian survey. J. Gynecol. Obstet. Hum. Reprod. (2021.0) 50 102079. DOI: 10.1016/j.jogoh.2021.102079 11. Garruti G., De Palo R., De Angelisc M.. Weighing the Impact of Diet and Lifestyle on Female Reproductive Function. Curr. Med. Chem. (2019.0) 26 3584-3592. DOI: 10.2174/0929867324666170518101008 12. Silvestris E., Lovero D., Palmirotta R.. Nutrition and Female Fertility: An Interdependent Correlation. Front. Endocrinol. (2019.0) 10 346. DOI: 10.3389/fendo.2019.00346 13. Eysenbach G.. Improving the Quality of Web Surveys: The Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J. Med. Internet Res. (2004.0) 6 e34. DOI: 10.2196/jmir.2042 14. Sharma R., Biedenharn K.R., Fedor J.M., Agarwal A.. Lifestyle factors and reproductive health: Taking control of your fertility. Reprod. Biol. Endocrinol. (2013.0) 11 66. DOI: 10.1186/1477-7827-11-66 15. Homan G., Davies M., Norman R.. The impact of lifestyle factors on reproductive performance in the general population and those undergoing infertility treatment: A review. Hum. Reprod. Update (2007.0) 13 209-223. DOI: 10.1093/humupd/dml056 16. Gürtin Z.B., Jasmin E., Da Silva P., Dennehy C., Harper J., Kanjani S.. Fertility treatment delays during COVID-19: Profiles, feelings and concerns of impacted patients. Reprod. Biomed. Soc. Online (2022.0) 14 251-264. DOI: 10.1016/j.rbms.2021.12.004 17. Sermondade N., Huberlant S., Bourhis-Lefebvre V., Arbo E., Gallot V., Colombani M., Fréour T.. Female obesity is negatively associated with live birth rate following IVF: A systematic review and meta-analysis. Hum. Reprod. Update (2019.0) 25 439-451. DOI: 10.1093/humupd/dmz011 18. Dornelles V.C., Hentschke M.R., Badalotti M., Badalotti-Teloken I., Trindade V.D., Cunegatto B., de Vasconcelos N.F., Petracco A., da Costa B.E.P., Padoin A.V.. Influence of overweight and obesity on perinatal outcomes in assisted reproduction: A retrospective cohort study. BMC Pregnancy Childbirth (2022.0) 22. DOI: 10.1186/s12884-022-04920-8 19. Twigt J.M., Bolhuis M.E.C., Steegers E.A.P., Hammiche F., van Inzen W.G., Laven J.S.E., Steegers-Theunissen R.P.M.. The preconception diet is associated with the chance of ongoing pregnancy in women undergoing IVF/ICSI treatment. Hum. Reprod. (2012.0) 27 2526-2531. DOI: 10.1093/humrep/des157 20. Dominguez L., Di Bella G., Veronese N., Barbagallo M.. Impact of Mediterranean Diet on Chronic Non-Communicable Diseases and Longevity. Nutrients (2021.0) 13. DOI: 10.3390/nu13062028 21. Vujkovic M., de Vries J.H., Lindemans J., Macklon N.S., van der Spek P.J., Steegers E.A., Steegers-Theunissen R.P.. The preconception Mediterranean dietary pattern in couples undergoing in vitro fertilization/intracytoplasmic sperm injection treatment increases the chance of pregnancy. Fertil. Steril. (2010.0) 94 2096-2101. DOI: 10.1016/j.fertnstert.2009.12.079 22. Karayiannis D., Kontogianni M.D., Mendorou C., Mastrominas M., Yiannakouris N.. Adherence to the Mediterranean diet and IVF success rate among non-obese women attempting fertility. Hum. Reprod. (2018.0) 33 494-502. DOI: 10.1093/humrep/dey003 23. Gómez-Donoso C., Sánchez-Villegas A., Martínez-González M.A., Gea A., de Deus Mendonça R., Lahortiga-Ramos F., Bes-Rastrollo M.. Ultra-Processed Food Consumption and the Incidence of Depression in a Mediterranean Cohort: The SUN Project. Eur. J. Nutr. (2020.0) 59 1093-1103. DOI: 10.1007/s00394-019-01970-1 24. Coletro H.N., Mendonça R.D.D., Meireles A.L., Machado-Coelho G.L.L., de Menezes M.C.. Ultra-processed and fresh food consumption and symptoms of anxiety and depression during the COVID-19 pandemic: COVID Inconfidentes. Clin. Nutr. ESPEN (2022.0) 47 206-214. DOI: 10.1016/j.clnesp.2021.12.013 25. Hornstein M.D.. Lifestyle and IVF Outcomes. Reprod. Sci. (2016.0) 23 1626-1629. DOI: 10.1177/1933719116667226 26. Mena G.P., Mielke G., Brown W.J.. The effect of physical activity on reproductive health outcomes in young women: A systematic review and meta-analysis. Hum. Reprod. Update (2019.0) 25 541-563. DOI: 10.1093/humupd/dmz013 27. Korostovtseva L., Bochkarev M., Sviryaev Y.. Sleep and Cardiovascular Risk. Sleep Med. Clin. (2021.0) 16 485-497. DOI: 10.1016/j.jsmc.2021.05.001 28. Purewal S., Chapman S.C.E., van den Akker O.B.A.. Depression and state anxiety scores during assisted reproductive treatment are associated with outcome: A meta-analysis. Reprod. Biomed. Online (2018.0) 36 646-657. DOI: 10.1016/j.rbmo.2018.03.010 29. Natt Och Dag Y., Mehlig K., Rosengren A., Lissner L., Rosvall M.. Negative emotional states and negative life events: Consequences for cardiovascular health in a general population. J. Psychosom. Res. (2020.0) 129 109888. DOI: 10.1016/j.jpsychores.2019.109888 30. Boivin J., Harrison C., Mathur R., Burns G., Pericleous-Smith A., Gameiro S.. Patient experiences of fertility clinic closure during the COVID-19 pandemic: Appraisals, coping and emotions. Hum. Reprod. (2020.0) 35 2556-2566. DOI: 10.1093/humrep/deaa218 31. Pozza A., Dèttore D., Coccia M.E.. Depression and Anxiety in Pathways of Medically Assisted Reproduction: The Role of Infertility Stress Dimensions. Clin. Pract. Epidemiol. Ment. Health (2019.0) 15 101-109. DOI: 10.2174/1745017901915010101 32. Vitale S.G., La Rosa V.L., Rapisarda A.M., Laganà A.S.. Psychology of infertility and assisted reproductive treatment: The Italian situation. J. Psychosom. Obstet. Gynaecol. (2017.0) 38 1-3. DOI: 10.1080/0167482X.2016.1244184
--- title: 'Associations between Health Literacy, Trust, and COVID-19 Vaccine Hesitancy: The Case of Hong Kong' authors: - Cindy Yue Tian - Phoenix Kit-Han Mo - Dong Dong - Hong Qiu - Annie Wai-Ling Cheung - Eliza Lai-Yi Wong journal: Vaccines year: 2023 pmcid: PMC10059763 doi: 10.3390/vaccines11030562 license: CC BY 4.0 --- # Associations between Health Literacy, Trust, and COVID-19 Vaccine Hesitancy: The Case of Hong Kong ## Abstract This study investigates how health literacy (HL) and trust in health information affected COVID-19 vaccine hesitancy among Chinese Hong Kong adults. A cross-sectional study was conducted in August 2022. A total of 401 participants completed the study. Participants completed a newly developed Hong Kong HL scale and self-reported their trust levels in health information from different resources. The proportions of early uptake of the first dose and booster dose of COVID-19 vaccine were $69.1\%$ and $71.8\%$, respectively. The risk of delaying the first dose was higher among participants with inadequate functional HL (OR = 0.58, $$p \leq 0.015$$), adequate levels of two subdomains of critical HL (OR = 1.82, $$p \leq 0.013$$; OR = 1.91, $p \leq 0.01$), and low-level trust in health information from the government (OR = 0.57, $$p \leq 0.019$$). Respondents with adequate interactive HL (OR = 0.52, $$p \leq 0.014$$) and inadequate level of one subdomain of critical HL (OR =1.71, $$p \leq 0.039$$) were more likely to delay the booster dose. This negative association between critical HL and vaccination was suppressed by trust in health information from the government. This study shows that HL and trust in health information from the government are associated with COVID-19 vaccine hesitancy. Efforts should be directed at providing tailored communication strategies with regard to people’s HL and increasing public confidence in health authorities to decrease vaccine hesitancy. ## 1. Introduction Vaccine hesitancy (i.e., delay in acceptance or refusal of vaccines) has been a common phenomenon during the pandemic. It also may be a key factor in leading Hong Kong to the highest daily death per capita in the fifth wave that started in early Jan 2022 [1,2]. During this wave, almost half of Hong Kong residents were estimated to have been infected with COVID-19 [3], and around $73\%$ of COVID-related deaths were unvaccinated [1]. Notably, when this wave hit this city, less than $67\%$ of the population was vaccinated with the first, and only $6\%$ was vaccinated with the booster dose [4]. Induced by the surge of confirmed cases and high death rates among unvaccinated people, the vaccination rates climbed; in early March, the proportion of those with first and booster doses increased to $88\%$ and $32\%$, respectively [4]. Although this devastating wave has subsided, identifying the determinants of vaccine hesitancy is critical to boosting vaccination rates in future vaccine campaigns globally. One potential factor affecting vaccine hesitancy could be health literacy (HL), which refers to an individual’s ability to process and use health information to promote health [5]. Theoretically, people with sophisticated HL are more likely to understand health information and respond in a manner that benefits their health, especially for vaccination programs that involve complex and evolving information. However, two recent systematic reviews highlighted that there is limited evidence to support the association between HL and vaccine hesitancy, and this association remains unclear across vaccine types [6,7]. Similarly, although the relationship between HL and COVID-19 vaccination has been investigated, the results are inconsistent [8,9,10,11,12,13,14,15,16]. One study conducted among midwifery students indicated that the students’ decisions to receive the COVID-19 vaccination were not affected by their HL levels [8], while one Australian study argued that inadequate HL was significantly associated with reluctance towards COVID-19 vaccination [12]. Therefore, further studies are needed to enable a more precise picture of the impact of HL on COVID-19 vaccine hesitancy. Moreover, a comprehensive measurement of HL is needed to investigate the association between HL and vaccination. According to Nutbeam’s theory, HL is not just about functional health literacy (FHL), accessing and reading the information; it also involves interactive health literacy (IHL), in which cognitive and social skills are needed to comprehend information from different forms of communication. Then, critical health literacy (CHL), which refers to a higher level of cognitive and social skills, is required to critically analyze information and employ this information to gain better control over life events [17]. However, previous studies investigating this association between HL and vaccination mainly focused on FHL, which is a basic level of HL [6,7]. Additionally, a major challenge in the pandemic is how individuals can integrate and transfer the abundance of information into proper behaviors that not only affect them, but also their families and community. CHL might be the key in light of such a challenge. According to the latest understanding of CHL [18,19,20,21], CHL includes the ability to judge the quality of information (i.e., CHL-1), be aware of the social structural factors that influence health outcomes (i.e., CHL-2), and actively transform knowledge into action to address the modifiable determinants of health for personal and community health (i.e., CHL-3). Linked to the scenario of the COVID-19 vaccination, a critical health-literate citizen is expected to be able to question information from the internet and understand herd immunity as well as make informed decisions to get vaccinated for self-protection and public good. However, most of the studies mainly focused on individuals’ ability to judge the information (i.e., CHL-1) and did not capture all the components of CHL mentioned above [9,22,23,24]. This is the gap this study aimed to address. Trust in health information (trust) has been identified as an essential factor associated with COVID-19 vaccination across countries [25,26,27]. Nevertheless, there is limited evidence [28,29,30] about how people intend to take COVID-19 vaccination considering their trust and HL levels. The Health Literacy Skills Framework highlights that greater levels of HL may lead to greater compliance with vaccine recommendations, and trust, as one potential mediator, may contribute to such engagement in vaccination programs [31]. Aligned with this, the present study aimed to provide empirical evidence of the Health Literacy Skills Framework by investigating how trust impacts vaccination uptake among people with inadequate HL. From all these perspectives, this study aimed to use a comprehensive HL scale to examine the associations between HL, trust, and COVID-19 vaccine hesitancy in Hong Kong. We hypothesized that low HL is directly associated with delayed COVID-19 vaccine uptake, and trust may mediate this association. ## 2.1. Study Design and Patient Participation A cross-sectional study was performed between August to September 2022. Participants who were aged 18 years or older and permanent Hong Kong residents who could read Chinese were recruited from the registrants of an internet research service company called Qualtrics. The participants were invited to complete the survey by email and message. If they accepted, they could click the survey link to fill out the online questionnaire. All participants provided informed electronic consent before participation. According to a systematic review of the prevalence of inadequate HL in Southeast Asian countries (range: 1.6–$99.5\%$; mean: $55.3\%$) [32], we took $55.3\%$ as the expected prevalence of inadequate HL in Hong Kong. With consideration of a $95\%$ confidence level and $5\%$ allowable error, the estimated adequate sample size should be 380 or above [30]. Given that online surveys may be biased towards young people who may possess better digital literacy, we used quota sampling with consideration of age. We also took the distributions of gender and living district into account to reach a regionally representative sample. Hence, a quota sampling was conducted to match the distribution of participants by gender (i.e., female and male), age group (i.e., 18–24, 25–34, 35–44, 45–54, and ≥55 years), and living district (i.e., Hong Kong Island, Kowloon, and New Territories) to the results of the 2020 Hong Kong census [33]. We piloted the survey among 30 participants and found that the median time for them to complete the survey was 9 min. Based on the results, we added half of the median completion time (i.e., 270 s) as the speeding check to terminate those who did not respond thoughtfully. We also added two attention-check questions to ensure that participants were reading each question carefully. If participants failed the two questions, their survey would be terminated as well. As far as the survey went, we monitored who took the survey and the number of participants for each quota sample. This survey stopped when the quota for each stratum was met. ## 2.2.1. Study Variables Participants’ HL was measured using the Hong Kong Health Literacy Scale (HLS-HK) developed and validated by our research team. The scale comprehensively operationalized the five domains mentioned above: FHL, IHL, CHL-1, CHL-2, and CHL-3 (see Figure 1). Its content validity was examined by local healthcare providers and consumers in one Delphi study [34]. Furthermore, its internal consistency, factorial structural validity, convergent validity, and predictive validity were assessed in one cross-sectional survey among 433 Hong Kong Chinese [35]. The results demonstrate overall good psychometric qualities of the scale in the context of Hong Kong [34,35]. The process of the scale development and validation has been documented elsewhere [34,35], and the present study was the first to explore the association between HL and other variables in Hong Kong using the scale after its development. In the present study, Cronbach’s alpha of the total scale was 0.90. The confirmatory factor analysis shows an acceptable fit of the five-domain framework, with a comparative fit index (CFI) = 0.90, standardized root mean square residual (SRMA) = 0.07, and root mean square error of approximation (RMSEA) = 0.06. All items were rated on a 5-point Likert scale, and the scores were summed. Participants who scored a higher score on this scale had a higher level of HL. The mean values of the individual domains of HL were used to divide the sample into inadequate vs. adequate groups. Additionally, given that participant HL was measured after the fifth wave and their HL might evolve within health education programs during this wave, we conducted propensity score matching (PSM) to examine whether subjects’ HL were relatively stable over time. Based on age, gender, income, education, and self-reported health status, participants in our previous survey (conducted before and in the fifth wave) [35] and the current survey (conducted after the fifth wave) were propensity score matched at a 1:1 ratio. The results show no difference in the HL levels between the two groups (see Tables S1 and S2 in the supplement). Therefore, it is reasonable to assume that the participants’ HL did not change substantively across the fifth wave. Participants’ trust levels were measured by items adopted from previous studies [36,37]. They were asked about their perceived trust in health information from the government, healthcare professionals, family members and friends, social media (e.g., Facebook, Instagram), and mass media (e.g., newspapers, magazines) by rating a 5-point Likert scale (1 = distrust completely to 5 = trust completely). The responses were then grouped into “distrust” (scored 1–3) and “trust” (scored 4–5). ## 2.2.2. Study Outcomes Participants were asked about their COVID-19 vaccination records. Given the low vaccine coverage rate before the fifth wave, we used the start date (1 January 2022) of this wave to dichotomize individuals into two groups: early and late first dose vaccinees. Considering the vaccination schedule (i.e., the earlier to receive the first dose vaccine, the earlier to be fully vaccinated), we also used booster dose uptake to indicate vaccine hesitancy. Namely, those who received the booster dose and those who did not receive the booster dose during the date of the survey were categorized as early and late booster dose vaccinees, respectively. ## 2.2.3. Sociodemographic and Health-Related Characteristics Age, gender, educational attainment, monthly household income, employment, marital status, health status, chronic condition, and health behaviors related to smoking, physical activity, and alcohol were self-reported and collected and then grouped as dichotomous variables. ## 2.3. Statistical Analysis Descriptive statistics with proportions were calculated. Sociodemographic and health-related variables, HL and trust were stratified by individuals’ COVID-19 vaccination uptake, and Chi-square tests were performed to assess variation across the categories. Multivariable binary logistic regression using the forward procedure was performed to examine the effects of HL and trust on individuals’ COVID-19 vaccine hesitancy. The first multivariable model (Model 1) tested the association between the individual domains of HL and vaccine hesitancy. The second multivariable model (Model 2) examined the impact of the individual domains of HL and trust on vaccine hesitancy. Age, gender, educational attainment, income, health status, and chronic disease status were the covariates in the two models. Sensitivity analyses using the original categorizations for Likert questions were performed to ensure the robustness of the results. Additionally, according to Baron and Keeny’s method for mediation [38], we only included variables with statistical significance in Model 1 and Model 2 in the mediation models to test any potential indirect effects of trust on the association between HL and delayed vaccination. All data analyses were conducted using SPSS version 26 [39] and R software (MatchIt package [40] and mediation package [41]). p values of 0.05 were used to determine statistical significance. ## 3. Results A total of 401 participants completed the survey. Table 1 presents the sociodemographic and health-related characteristics of the participants. The sample distribution in terms of age, gender, and living district was almost in accordance with the distribution of these metrics in the Hong Kong population (see Table S3 in the Supplementary Materials) [33]. The proportions of early first and booster dose vaccinees were $69.1\%$ and $71.8\%$, respectively. ## 3.1. Health Literacy Overall, most of the participants had insufficient HL (see Table 2). Specifically, over half of the participants had inadequate CHL-1 ($55.9\%$) and CHL-3 ($59.9\%$), and nearly half of them had inadequate FHL ($44.4\%$), IHL ($42.9\%$), and CHL-2 ($46.6\%$). FHL ($$p \leq 0.030$$), IHL ($$p \leq 0.014$$), and CHL-3 ($$p \leq 0.004$$) were significantly associated with first dose vaccine hesitancy. The differences between IHL ($$p \leq 0.002$$), CHL-1 ($$p \leq 0.041$$), CHL-3 ($$p \leq 0.029$$) and booster dose vaccine hesitancy were significant. ## 3.2. Trust Table 2 indicates that the proportion of trust in health information from healthcare professionals ($69.6\%$) was the highest, followed by government ($45.9\%$), family members and friends ($39.9\%$), mass media ($27.2\%$) and social media ($15.7\%$). Although there was no significant difference between trust and vaccine hesitancy, trust in information from the government (early vs. late first dose: $48.0\%$ vs. $41.1\%$) and healthcare professionals (early vs. late booster dose: $71.5\%$ vs. $65.3\%$) tended to reduce vaccine hesitancy. ## 3.3. Health Literacy, Trust, and COVID-19 Vaccine Hesitancy In multivariable analysis (see Table 3), Model 1 indicated that respondents with a higher level of FHL were more likely to receive an early first dose vaccine (OR = 0.56, $95\%$ CI = 0.36–0.88, $p \leq 0.05$). Contrarily, people with an adequate level of CHL-1 (OR = 1.63, $95\%$ CI = 1.03–2.58, $p \leq 0.05$) or CHL-3 (OR = 1.67, $95\%$ CI = 1.06–2.63, $p \leq 0.05$) were more likely to receive their first dose vaccine late. For the booster dose hesitancy, the risk of delaying this dose was significantly higher among participants with inadequate IHL (OR = 0.51, $95\%$ CI = 0.33–0.81, $p \leq 0.01$) and adequate CHL-3 (OR = 1.82, $95\%$ CI = 1.15–2.87, $p \leq 0.05$). With the inclusion of trust in Model 2, the effect of FHL, CHL-1, and CHL-3 on the first dose vaccine and the effect of IHL and CHL-3 on the booster dose remained strong. Trust in information from the government was significantly positively associated with first dose vaccine hesitancy (OR = 0.57, $95\%$ CI = 0.35–0.91, $p \leq 0.05$). However, there were no significant associations between trust in health information from other resources and vaccine hesitancy. The sensitive analysis confirmed these findings (see Table S4 in the Supplementary Materials). ## 3.4. Mediation Effect of Trust on Health Literacy and First Dose COVID-19 Vaccine Uptake Based on the results of the above multivariable analysis, we only tested the potential mediation effects of trust in health information from the government on the association between certain domains of HL (i.e., FHL, CHL-1, CHL-3) and first dose vaccine hesitancy after controlling all other variables. Table 4 indicates that the effect of CHL on first dose vaccine hesitancy was significantly suppressed by trust in information from the government (CHL-1-> trust in health information from the government -> first dose vaccine hesitancy: standardized indirect effects = −0.012 < 0, $95\%$CI = −0.028–0.00, $$p \leq 0.040$$; CHL_3 -> trust in health information from the government -> first dose vaccine hesitancy: standardized indirect effects = −0.016 < 0, $95\%$ CI = −0.033–0.00, $$p \leq 0.014$$). There was no indirect effect of trust in information from the government on the association between FHL and first dose vaccination. ## 4.1. Main Results Decisions to vaccinate are complex, requiring an understanding of the scientific evidence and the adverse events that may occur during immunization schedules. HL is an important factor affecting this decision making. In this study, we used HLS-HK, which has shown good reliability and validity in measuring HL in a standardized process of scale development and validation. Although this is the first time this scale was adopted to explore the impact of HL on vaccine hesitancy, it provided important insights into the barriers and facilitators affecting vaccine uptake via comprehensively measuring FHL, IHL, and CHL. Specially, the multivariable analysis indicates that the basic acquisition of information at the FHL level is positively associated with the early first uptake of vaccination. This result is consistent with other studies [42,43,44]. It is reasonable to assume that people who were better at searching and understanding health-related information at the early stage of the pandemic were more likely to actively get vaccinated. Regarding IHL, participants with sufficient IHL were less likely to delay the booster dose. It seems that good communication with healthcare professionals could help increase immunization levels and reduce withdrawal from vaccination campaigns. However, higher levels of CHL are negatively associated with first and booster doses. This negative association is contrary to the expectation that people with high HL adopt more positive health behaviors. This behavior, however, has been documented in several other studies [45,46,47]. There are some potential explanations for this result. First, people with lower CHL might be less concerned about the effectiveness and side effects of the vaccines [46]. Thus, such people might be less hesitant to take the vaccine. Second, according to the theory of confirmation bias, when people who distrust vaccination also have higher HL, they are even more likely to choose the information that matches their biases and supports their beliefs [47]. These skeptics are unlikely to be reassured by healthcare authorities to get vaccinated. In these complex situations, health practitioners may need to listen to public concerns and facilitate targeted communication strategies to improve vaccine coverage. Additionally, this study revealed that over half of the participants were found to have a low score of HL. Those people were grouped as inadequate HL groups in our study. Other similar studies may group those people as insufficient or limited or low HL groups [32,48,49,50,51]. As these and our surveys revealed, the poor state of HL is a public health problem that is common across the globe. For example, one survey conducted among 8698 Chinese found that around $80\%$ of subjects have inadequate HL [50]; another survey in Turkey found that $81.5\%$ of individuals with diabetes have an inadequate level of HL [51]. Therefore, health education is needed to improve public HL and ensure equal access to health information not only physically but also literally. Although the information from the government was not the most trustworthy for the respondents in our study, it significantly affected their vaccination uptake. This study highlights that individuals with a higher level of trust in information from the government were more likely to receive the first dose vaccine early. This result is similar to several local studies [52,53,54] and reveals that people learn about the pandemic and take action to prevent infection not only considering the content and quality of the information but also their levels of trust in information from their government [25,55]. We also found that the standardized indirect/mediated effects of trust in information from the government on the associations between two subdomains of CHL and first dose vaccination were negative. This means the direct pathway (i.e., higher CHL -> later vaccination) was counteracted by the indirect pathway (i.e., higher CHL -> more trust -> earlier vaccination); as a result, the total effect of CHL on vaccination became smaller because the direct and indirect effects cancelled each other out. Therefore, trust in health information from the government served as a possible mechanism for suppressing this negative association between CHL and first dose vaccination hesitancy. Namely, as individuals with higher levels of CHL were less likely to engage with the vaccination protocol, building trust may be helpful to mitigate and eliminate vaccine hesitancy among these people. This may be because a higher level of trust in health authorities may repress the spread of vaccine conspiracy theories and consequently increase vaccine coverage rates [56,57]. ## 4.2. Implications This study indicates that different aspects of HL have different influences on vaccination hesitancy. Given the positive association between FHL and first dose vaccination and IHL and booster dose vaccination, healthcare practitioners may need to ensure that the public is given equal access to health information in the early stage of vaccine campaigns and use effective communication channels to remind people to adhere to clinical recommendations after they complete a primary series of vaccines. Regarding the negative association between CHL and vaccination, disclosing transparent information about the development and features of vaccination is the key to improve vaccine coverage rates [58,59]. In this way, people can critically analyze clear and unbiased information and thereby potentially avoid vaccine hesitancy. Moreover, considering that public trust in health information from the government might suppress the negative association between CHL and vaccine uptake, the government and health authorities may need to build, rebuild, or maintain trustful relationships with the public in future vaccine protocols. ## 4.3. Strengths and Limitations A major strength of this study is the use of a comprehensive HL scale. Different standardized measurements [23,44,60,61,62,63] were adopted in previous studies investigating the relationship between HL and vaccination uptake. Unfortunately, they do not capture all related skills of HL, especially the skills related to the aspect of CHL. In this study, we used HLS-HK to address more dimensions of HL and added empirical evidence on the association between HL and vaccine hesitancy across immunization schedules. We also provided insights into how CHL may cause people’s under-reacting to vaccination during the pandemic. However, this study has some limitations. First, all data collected were self-reported and recall biases might exist. Second, it might be possible that some aspects of HL were not measured in the HLS-HK due to the complexity of this concept. Third, the psychometric properties of HLS-HK are well proven but it is the first time this scale has been applied in a study, so the generalization of results may need more evidence to be supported. In addition, the validity of the HLS-HK across populations and contexts needs to be examined in future studies. Fourth, due to limited resources, we did not include a performance-based measure as a comparison scale to examine participants’ HL levels. Participants may overestimate their HL when they complete this self-reported scale. This may cause self-efficacy (i.e., an individual’s perception of their ability to get vaccinated) to confound the relationship between HL and vaccine uptake. Usually, those who overestimated their HL levels may tend to have a higher level of self-efficacy; people with sufficient self-efficacy are more likely to engage in vaccine campaigns. Fifth, selection biases might exist. Although we used quota sampling to reach a regionally representative sample, the study subjects, who were recruited from an online questionnaire platform panel, may be better at seeking and understanding information. Sixth, no causal relationship could be inferred by this cross-sectional study. ## 5. Conclusions In this study, HL and trust in health information from the government are important factors affecting vaccine hesitancy. By using the newly developed scale HLS-HK to measure HL, we found that FHL, IHL and trust in information from the government are positively associated with COVID-19 vaccination. However, a negative association between CHL and this vaccine uptake was reported. These findings indicate a challenge for public health practitioners to effectively deliver health messages about vaccination to the general public, especially for those with inadequate FHL and IHL and adequate CHL. Efforts should be directed at providing tailored communication strategies about people’s HL and increasing public confidence in health authorities to raise vaccine coverage rates. In addition, given that the validity of HLS-HK across populations and countries is required, more studies are in demand to extend the implication of the results of this study to a broader context. ## References 1. Kwan R.. How Hong Kong’s vaccination missteps led to the world’s highest COVID-19 death rate. BMJ (2022.0) 377 1127. DOI: 10.1136/bmj.o1127 2. Cheung P.-H.H., Chan C.-P., Jin D.-Y.. Lessons learned from the fifth wave of COVID-19 in Hong Kong in early 2022. Emerg. Microbes Infect. (2022.0) 11 1072-1078. DOI: 10.1080/22221751.2022.2060137 3. Chow W., Chow C., Cheng C.. Estimation of the Total Number of Infected Cases in the 5th Wave of COVID-19 in Hong Kong. medRxiv (2022.0). DOI: 10.1101/2022.08.12.22278708 4. Hong Kong Vaccination Dashboard 5. Nutbeam D.. Health promotion glossary. Health Promot. Int. (1998.0) 13 349-364. DOI: 10.1093/heapro/13.4.349 6. Lorini C., Santomauro F., Donzellini M., Capecchi L., Bechini A., Boccalini S., Bonanni P., Bonaccorsi G.. Health literacy and vaccination: A systematic review. Hum. Vaccines Immunother. (2018.0) 14 478-488. DOI: 10.1080/21645515.2017.1392423 7. Siena L.M., Isonne C., Sciurti A., De Blasiis M.R., Migliara G., Marzuillo C., De Vito C., Villari P., Baccolini V.. The Association of Health Literacy with Intention to Vaccinate and Vaccination Status: A Systematic Review. Vaccines (2022.0) 10. DOI: 10.3390/vaccines10111832 8. Aslantekin-Özçoban F., Uluşen M., Yalnız-Dilcen H., Çilesiz E.. Are midwifery students ready for the COVID-19 vaccine? The decision to vaccinate and affecting factors. Human Vaccines Immunother. (2021.0) 17 4896-4903. DOI: 10.1080/21645515.2021.2003648 9. Arvanitis M., Opsasnick L., O’Conor R., Curtis L.M., Vuyyuru C., Benavente J.Y., Bailey S.C., Jean-Jacques M., Wolf M.S.. Factors associated with COVID-19 vaccine trust and hesitancy among adults with chronic conditions. Prev. Med. Rep. (2021.0) 24 101484. DOI: 10.1016/j.pmedr.2021.101484 10. Patil U., Kostareva U., Hadley M., Manganello J.A., Okan O., Dadaczynski K., Massey P.M., Agner J., Sentell T.. Health literacy, digital health literacy, and COVID-19 pandemic attitudes and behaviors in US college students: Implications for interventions. Int. J. Environ. Res. Public Health (2021.0) 18. DOI: 10.3390/ijerph18063301 11. Van Duong T., Lin C.-Y., Chen S.-C., Huang Y.-K., Okan O., Dadaczynski K., Lai C.-F.. Oxford COVID-19 Vaccine Hesitancy in School Principals: Impacts of Gender, Well-Being, and Coronavirus-Related Health Literacy. Vaccines (2021.0) 9. DOI: 10.3390/vaccines9090985 12. Dodd R.H., Cvejic E., Bonner C., Pickles K., McCaffery K.J., Ayre J., Batcup C., Copp T., Cornell S., Dakin T.. Willingness to vaccinate against COVID-19 in Australia. Lancet Infect. Dis. (2021.0) 21 318-319. DOI: 10.1016/S1473-3099(20)30559-4 13. Zhang H., Li Y., Peng S., Jiang Y., Jin H., Zhang F.. The Effect of Health Literacy on COVID-19 Vaccine Hesitancy among community population in China: The Moderating Role of Stress. Vaccine (2022.0) 40 4473-4478. DOI: 10.1016/j.vaccine.2022.06.015 14. Umakanthan S., Lawrence S.. Predictors of COVID-19 vaccine hesitancy in Germany: A cross-sectional, population-based study. Postgrad. Med. J. (2022.0) 98 756-764. DOI: 10.1136/postgradmedj-2021-141365 15. Naveed M.A., Shaukat R.. Health literacy predicts COVID-19 awareness and protective behaviours of university students. Health Inf. Libr. J. (2022.0) 39 46-58. DOI: 10.1111/hir.12404 16. Chaniaud N., Jeanpierre P., Laguette V., Loup-Escande E.. Impact of Psycho-Social Factors, E-health Literacy and Information Access on COVID-19 Vaccination Perceptions and Intentions: Online Survey. Rev. Eur. Stud. (2022.0) 14 55. DOI: 10.5539/res.v14n2p55 17. Nutbeam D.. Health literacy as a public health goal: A challenge for contemporary health education and communication strategies into the 21st century. Health Promot. Int. (2000.0) 15 259-267. DOI: 10.1093/heapro/15.3.259 18. Renwick K.. Critical health literacy in 3D. Frontiers in Education (2017.0) Volume 2 40 19. Abel T., McQueen D.. Critical health literacy and the COVID-19 crisis. Health Promot. Int. (2020.0) 35 1612-1613. DOI: 10.1093/heapro/daaa040 20. De Wit L., Fenenga C., Giammarchi C., Di Furia L., Hutter I., de Winter A., Meijering L.. Community-based initiatives improving critical health literacy: A systematic review and meta-synthesis of qualitative evidence. BMC Public Health (2018.0) 18. DOI: 10.1186/s12889-017-4570-7 21. Pavelić A., Špiranec S.. Critical Health Literacy and Critical Information Literacy: Bridging Research Discourses from Different Domains (2022.0) 52-61 22. Longchamps C., Ducarroz S., Crouzet L., Vignier N., Pourtau L., Allaire C., Colleville A.C., El Aarbaoui T., Melchior M.. COVID-19 vaccine hesitancy among persons living in homeless shelters in France. Vaccine (2021.0) 39 3315-3318. DOI: 10.1016/j.vaccine.2021.05.012 23. Okan O., Bollweg T.M., Berens E.-M., Hurrelmann K., Bauer U., Schaeffer D.. Coronavirus-related health literacy: A cross-sectional study in adults during the COVID-19 infodemic in Germany. Int. J. Environ. Res. Public Health (2020.0) 17. DOI: 10.3390/ijerph17155503 24. Fukuda Y., Ando S., Fukuda K.. Knowledge and preventive actions toward COVID-19, vaccination intent, and health literacy among educators in Japan: An online survey. PLoS ONE (2021.0) 16. DOI: 10.1371/journal.pone.0257552 25. Park H.K., Ham J.H., Jang D.H., Lee J.Y., Jang W.M.. Political ideologies, government trust, and COVID-19 vaccine hesitancy in South Korea: A cross-sectional survey. Int. J. Environ. Res. Public Health (2021.0) 18. DOI: 10.3390/ijerph182010655 26. Lazarus J.V., Ratzan S.C., Palayew A., Gostin L.O., Larson H.J., Rabin K., Kimball S., El-Mohandes A.. A global survey of potential acceptance of a COVID-19 vaccine. Nat. Med. (2021.0) 27 225-228. DOI: 10.1038/s41591-020-1124-9 27. Schernhammer E., Weitzer J., Laubichler M.D., Birmann B.M., Bertau M., Zenk L., Caniglia G., Jäger C.C., Steiner G.. Correlates of COVID-19 vaccine hesitancy in Austria: Trust and the government. J. Public Health (2022.0) 44 e106-e116. DOI: 10.1093/pubmed/fdab122 28. Turhan Z., Dilcen H.Y., Dolu İ.. The mediating role of health literacy on the relationship between health care system distrust and vaccine hesitancy during COVID-19 pandemic. Curr. Psychol. (2022.0) 41 8147-8156. DOI: 10.1007/s12144-021-02105-8 29. Weerakoon S.M., Henson-Garcia M., Valerio-Shewmaker M.A., Messiah S.E., Knell G.. Contributions of Trustworthiness, Health Literacy, and Self-Efficacy in Communicating With COVID-19 Vaccine–Hesitant Audiences: Web-Based Survey Study. JMIR Form. Res. (2022.0) 6 e38076. DOI: 10.2196/38076 30. Heiniger S., Schliek M., Moser A., von Wyl V., Höglinger M.. Differences in COVID-19 vaccination uptake in the first 12 months of vaccine availability in Switzerland-a prospective cohort study. Swiss Med. Wkly. (2022.0) 152 w30162. DOI: 10.4414/SMW.2022.w30162 31. Squiers L., Peinado S., Berkman N., Boudewyns V., McCormack L.. The health literacy skills framework. J. Health Commun. (2012.0) 17 30-54. DOI: 10.1080/10810730.2012.713442 32. Rajah R., Hassali M., Murugiah M.. A systematic review of the prevalence of limited health literacy in Southeast Asian countries. Public Health (2019.0) 167 8-15. DOI: 10.1016/j.puhe.2018.09.028 33. Hong Kong in Figures (Latest Figures) 34. Tian C.Y., Wong E.L.-Y., Xu R.H., Cheung A.W.-L., Dong D., Mo P.K.-H.. Developing a comprehensive health literacy scale for adults in Hong Kong: A modified e-Delphi study in healthcare consumers and providers. Health Expect. (2022.0) 1-11. DOI: 10.1111/hex.13651 35. Tian C.Y., Mo P.K.-H., Dong D., Cheung A.W.-L., Wong E.L.-Y.. Development and validation of a comprehensive health literacy tool for adults in Hong Kong. Front. Public Health (2022.0) 10 1043197. DOI: 10.3389/fpubh.2022.1043197 36. Gehrau V., Fujarski S., Lorenz H., Schieb C., Blöbaum B.. The impact of health information exposure and source credibility on COVID-19 vaccination intention in germany. Int. J. Environ. Res. Public Health (2021.0) 18. DOI: 10.3390/ijerph18094678 37. Qiao S., Friedman D.B., Tam C.C., Zeng C., Li X.. Vaccine acceptance among college students in South Carolina: Do information sources and trust in information make a difference?. MedRxiv (2020.0). DOI: 10.1101/2020.12.02.20242982 38. Baron R.M., Kenny D.A.. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. (1986.0) 51 1173. DOI: 10.1037/0022-3514.51.6.1173 39. 39.IBM SPSS Statistics for MacintoshVersion 26.0IBM CorpArmonk, NY, USA2019. IBM SPSS Statistics for Macintosh (2019.0) 40. Ho D., Imai K., King G., Stuart E.A.. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J. Stat. Softw. (2011.0) 42 1-28. DOI: 10.18637/jss.v042.i08 41. Tingley D.Y.T., Hirose K., Keele L., Imai K.. Mediation: R Package for Causal Mediation Analysis. J. Stat. Softw. (2014.0) 59 1-38. DOI: 10.18637/jss.v059.i05 42. Sherman S.M., Smith L.E., Sim J., Amlôt R., Cutts M., Dasch H., Rubin G.J., Sevdalis N.. COVID-19 vaccination intention in the UK: Results from the COVID-19 vaccination acceptability study (CoVAccS), a nationally representative cross-sectional survey. Hum. Vaccines Immunother. (2021.0) 17 1612-1621. DOI: 10.1080/21645515.2020.1846397 43. Engelbrecht M.C., Kigozi N.G., Heunis J.C.. Factors Associated with Limited Vaccine Literacy: Lessons Learnt from COVID-19. Vaccines (2022.0) 10. DOI: 10.3390/vaccines10060865 44. Biasio L.R., Bonaccorsi G., Lorini C., Pecorelli S.. Assessing COVID-19 vaccine literacy: A preliminary online survey. Hum. Vaccines Immunother. (2021.0) 17 1304-1312. DOI: 10.1080/21645515.2020.1829315 45. Aharon A.A., Nehama H., Rishpon S., Baron-Epel O.. Parents with high levels of communicative and critical health literacy are less likely to vaccinate their children. Patient Educ. Couns. (2017.0) 100 768-775. DOI: 10.1016/j.pec.2016.11.016 46. Pati S., Feemster K.A., Mohamad Z., Fiks A., Grundmeier R., Cnaan A.. Maternal health literacy and late initiation of immunizations among an inner-city birth cohort. Matern. Child Health J. (2011.0) 15 386-394. DOI: 10.1007/s10995-010-0580-0 47. Meppelink C.S., Smit E.G., Fransen M.L., Diviani N.. “I was right about vaccination”: Confirmation bias and health literacy in online health information seeking. J. Health Commun. (2019.0) 24 129-140. DOI: 10.1080/10810730.2019.1583701 48. Baccolini V., Rosso A., Di Paolo C., Isonne C., Salerno C., Migliara G., Prencipe G.P., Massimi A., Marzuillo C., De Vito C.. What is the prevalence of low health literacy in european union member states? A systematic review and meta-analysis. J. Gen. Intern. Med. (2021.0) 36 753-761. DOI: 10.1007/s11606-020-06407-8 49. Sørensen K., Pelikan J.M., Röthlin F., Ganahl K., Slonska Z., Doyle G., Fullam J., Kondilis B., Agrafiotis D., Uiters E.. Health literacy in Europe: Comparative results of the European health literacy survey (HLS-EU). Eur. J. Public Health (2015.0) 25 1053-1058. DOI: 10.1093/eurpub/ckv043 50. Chen W., Ren H., Wang N., Xiong Y., Xu F.. The relationship between socioeconomic position and health literacy among urban and rural adults in regional China. BMC Public Health (2021.0) 21. DOI: 10.1186/s12889-021-10600-7 51. İlhan N., Telli S., Temel B., Aştı T.. Health literacy and diabetes self-care in individuals with type 2 diabetes in Turkey. Prim. Care Diabetes (2021.0) 15 74-79. DOI: 10.1016/j.pcd.2020.06.009 52. Wong M.C., Wong E.L., Huang J., Cheung A.W., Law K., Chong M.K., Ng R.W., Lai C.K., Boon S.S., Lau J.T.. Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong. Vaccine (2021.0) 39 1148-1156. DOI: 10.1016/j.vaccine.2020.12.083 53. Yan E., Lai D.W., Ng H.K., Lee V.W.. Predictors of COVID-19 actual vaccine uptake in Hong Kong: A longitudinal population-based survey. SSM-Popul. Health (2022.0) 18 101130. DOI: 10.1016/j.ssmph.2022.101130 54. Xiao J., Cheung J.K., Wu P., Ni M.Y., Cowling B.J., Liao Q.. Temporal changes in factors associated with COVID-19 vaccine hesitancy and uptake among adults in Hong Kong: Serial cross-sectional surveys. Lancet Reg. Health-West. Pac. (2022.0) 23 100441. DOI: 10.1016/j.lanwpc.2022.100441 55. Chung G.K.K., Chan L., Chan S.-M., Chen J.-K., Wong H., Chung Y.-N.. The impact of trust in government on pandemic management on the compliance with voluntary COVID-19 vaccination policy among adolescents after social unrest in Hong Kong. Front. Public Health (2022.0) 10 3230. DOI: 10.3389/fpubh.2022.992895 56. De Freitas L., Basdeo D., Wang H.-I.. Public trust, information sources and vaccine willingness related to the COVID-19 pandemic in Trinidad and Tobago: An online cross-sectional survey. Lancet Reg. Health-Am. (2021.0) 3 100051. DOI: 10.1016/j.lana.2021.100051 57. Jennings W., Stoker G., Bunting H., Valgarðsson V.O., Gaskell J., Devine D., McKay L., Mills M.C.. Lack of trust, conspiracy beliefs, and social media use predict COVID-19 vaccine hesitancy. Vaccines (2021.0) 9. DOI: 10.3390/vaccines9060593 58. Machingaidze S., Wiysonge C.S.. Understanding COVID-19 vaccine hesitancy. Nat. Med. (2021.0) 27 1338-1339. DOI: 10.1038/s41591-021-01459-7 59. Petersen M.B., Bor A., Jørgensen F., Lindholt M.F.. Transparent communication about negative features of COVID-19 vaccines decreases acceptance but increases trust. Proc. Natl. Acad. Sci. USA (2021.0) 118 e2024597118. DOI: 10.1073/pnas.2024597118 60. Weiss B.D., Mays M.Z., Martz W., Castro K.M., DeWalt D.A., Pignone M.P., Mockbee J., Hale F.A.. Quick assessment of literacy in primary care: The newest vital sign. Research Support, Non-U.S. Gov’t. Ann. Fam. Med. (2005.0) 3 514-522. DOI: 10.1370/afm.405 61. Osborne R.H., Batterham R.W., Elsworth G.R., Hawkins M., Buchbinder R.. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). Validation Studies. BMC Public Health (2013.0) 13. DOI: 10.1186/1471-2458-13-658 62. Ishikawa H., Takeuchi T., Yano E.. Measuring functional, communicative, and critical health literacy among diabetic patients. Research Support, Non-U.S. Gov’t. Diabetes Care (2008.0) 31 874-879. DOI: 10.2337/dc07-1932 63. Sørensen K., Van den Broucke S., Pelikan J.M., Fullam J., Doyle G., Slonska Z., Kondilis B., Stoffels V., Osborne R.H., Brand H.. Measuring health literacy in populations: Illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q.). Research Support, Non-U.S. Gov’t Validation Studies. BMC Public Health (2013.0) 13. DOI: 10.1186/1471-2458-13-948
--- title: Bioactive Compounds, Antioxidant Activities, and HPLC Analysis of Nine Edible Sprouts in Cambodia authors: - Visessakseth So - Philip Poul - Sokunvary Oeung - Pich Srey - Kimchhay Mao - Huykhim Ung - Poliny Eng - Mengkhim Heim - Marnick Srun - Chantha Chheng - Sin Chea - Tarapong Srisongkram - Natthida Weerapreeyakul journal: Molecules year: 2023 pmcid: PMC10059773 doi: 10.3390/molecules28062874 license: CC BY 4.0 --- # Bioactive Compounds, Antioxidant Activities, and HPLC Analysis of Nine Edible Sprouts in Cambodia ## Abstract The non-nutritional health benefits of sprouts are unconfirmed. Thus, nine sprout methanolic extracts were tested for phytoconstituents and antioxidant activity. The TPC, TCC, TFC, TAC, and TALC were measured. ABTS and DPPH radical scavenging and ferric-reducing antioxidant power assays were used to assess the antioxidant activity. HPLC detected gallic acid, vanillin, syringic acid, chlorogenic acid, caffeic acid, and rutin in the extracts. The sprout extracts contained six compounds, with caffeic acid being the most abundant. Gallic acid, syringic acid, chlorogenic acid, caffeic acid, vanillin, and rutin were highest in soybean, black sesame, mustard, sunflower, white radish, and black sesame sprouts, respectively. Sunflower sprouts had the highest level of TCC while soybean sprouts had the highest level of TFC, Taiwanese morning glory had the highest level of TPC, mustard sprouts had the highest level of TALC, and black sesame sprouts had the highest level of TAC. Taiwanese morning glories scavenged the most DPPH and ABTS radicals. Colored and white radish sprouts had similar ferric-reducing antioxidant power. Antioxidation mechanisms varied by compound. Our findings demonstrated that sprouts have biological effects, and their short time for mass production offers an alternative food source for health benefits, and that they are useful for future research development of natural products and dietary supplements. ## 1. Introduction Various sprouted foods come from different types of seed, such as alfalfa, buckwheat, red cabbage, and broccoli sprouts. Consumption of these sprouts has been increasing and they are considered healthy foods. Sprouts grow from seeds during the germination process that involves various physiological processes. Seeds in the early stage of germination absorb water and comprise hydrolytic enzymes. After activation, these enzymes degrade macromolecules (i.e., proteins, polysaccharides, and lipids) into small molecules that can be easily absorbed into the human body [1]. Secondary metabolic pathways in sprouts are also activated during germination. In addition to their crispness, flavor, and aroma, sprouts contain phytochemicals such as sulforaphane, sulforaphene, isothiocyanates, glucosinolates, enzymes, antioxidants, and vitamins [2]. These secondary metabolites exhibit a wide range of functional activities such as anti-oxidation, anti-inflammatory, anti-cancer, and anti-diabetic activities [3,4]. People have become more interested in eating healthy foods as changes in eating habits have been associated with a reduced risk of diseases, particularly those that are oxidative stress-related. Researchers have, thus, focused on free radicals since these are generated by endogenous (normal cell metabolism, auto-oxidation, electron transport system, enzymatic production by peroxidases) and exogenous sources (pollutants, radiation, cigarette smoking, foods and nutrients, drugs, and xenobiotics). Oxidative stress is caused by an excess of free radicals in the body. Oxidative stress plays a role in chronic and degenerative diseases such as cancer, autoimmune disorders, aging, cataracts, rheumatoid arthritis, cardiovascular disease, and neurodegenerative diseases. The human body has several mechanisms to counteract oxidative stress by producing antioxidants [5]. A balance between free radicals and antioxidants is necessary for proper physiological functions. Since humans are continually exposed to oxidant agents endogenously and exogenously, naturally produced antioxidants in situ cannot neutralize free radicals and overcome oxidative stress. Therefore, antioxidants from external sources through foods and/or supplements are required to maintain this balance. Antioxidants from our diet can aid endogenous antioxidants to relieve some oxidative stress [6,7], which is the subject of our research. The presence of bioactive substances from plant secondary metabolites in plant-based diets are known to have positive health benefits [8,9,10,11]. Numerous epidemiological studies have demonstrated a link between daily plant-based food consumption, and a decrease in the risk factors for chronic diseases such as cardiovascular disease, diabetes, and obesity [12]. Among the promising vegetables, ready-to-eat sprouts reportedly contain high nutrients and phytochemicals. A previous study demonstrated that alfalfa sprouts contain a high content of saponins and other bioactive compounds with anti-oxidant, anti-diabetic, and anti-viral activity [13]. Buckwheat sprouts, usually eaten with noodles by Asian people, possess anti-oxidant, anti-hypocholesterolemic, and neuroprotective functions [14,15]. Brassica vegetables such as red cabbage and broccoli sprouts also contain anti-microbial, anti-cancer, and anti-obesity properties [16]. The germination of sprouts is a low-cost and efficient technique to activate bioactive chemicals in cereals, vegetables, fruits, flowers, and medicinal plant seeds [17]. Sprouts have gained popularity around the world due to their nutritional value, low cost, convenience, and short production time. Soybean and mung bean sprouts have long been popular and widely consumed in Asia. They are mostly served as fresh salad or porridge, but they are also processed into pickles and cooked. With the demand for functional foods to improve immunity, especially during the COVID-19 pandemic, there is an increase in knowledge about the health benefits of sprouts. A diverse range of sprout germination from a wide range of seeds has been grown and is becoming more popular among health-conscious Cambodians. However, there have been few studies on the bioactive compounds and antioxidant activities of different types of sprouts grown in Cambodia. In this study, nine species of sprouts—sunflower, mustard, black sesame, Taiwanese morning glory, mung bean, soybean, white radish, colored radish, and green pea—were investigated for their total phenolic, anthocyanin, flavonoid, chlorophyll, and alkaloid contents, as well as antioxidant activities (detected by DPPH, ABTS, and FRAP assays). High-performance liquid chromatography (HPLC) analysis was used to identify and quantify some phenolics and flavonoid contents by comparing them to six standard compounds. The discussion of phytochemicals found in sprouts focuses on the health benefits. The information obtained can be used to identify promising sprouts for future research and development of healthy food products and pharmaceuticals. ## 2.1. Extraction Yield Different young sprouts were collected in this study, which were Helianthus annuus L. (sunflower, SF); Brassica juncea (L.) Czern. ( mustard, MT); Sesamum indicum L. (black sesame, BS); Ipomoea aquatica Forssk. ( Taiwanese morning glory, TG); Vigna radiata (L.) R. Wilczek (mung bean, MB); Glycine max (L.) Merr. ( soybean, SB); Raphanus sativus L. (white radish, WR); Raphanus sativus L. (colored radish, CR); and Pisum sativum L. (green pea, GP). They were cultivated at the ages of 4–7 days and prepared as methanolic extracts (Figure 1). The rank order from the highest to the lowest percentage of yields is SB ($7.64\%$) > MB ($6.01\%$) > CR ($6.0\%$) > WR ($5.4\%$) > SF ($4.94\%$) > TG ($4.92\%$) > GP ($3.6\%$) > MT ($3.32\%$) > BS ($2.68\%$), respectively (Figure 1). ## 2.2. Identification of Phenolics and Flavonoids Using HPLC The phenolic and flavonoid contents in the extracts were identified using HPLC analysis at three different wavelengths (280 nm, 320 nm, and 370 nm) by comparing the retention times with the standards (i.e., gallic acid, syringic acid, chlorogenic acid, caffeic acid, vanillin, and rutin) (Table 1 and Figure 2). The phenolic and flavonoid contents of the sprout extracts were calculated from the peak area compared to those of the standard compounds and the results are shown in Table 2. Six compounds were identified in the sprout extracts. The highest content of each compound (expressed in milligrams per gram of crude extract) was detected in the plants; gallic acid in SB (4.06 ± 0.01 mg), vanillin in WR (2.03 ± 0.01 mg), chlorogenic acid in MT (2.39 ± 0.01 mg), syringic acid in BS (0.46 ± 0.003 mg), caffeic acid in SF (14.91 ± 0.08 mg), and rutin in BS (5.29 ± 0.02 mg). The phenolic compound with hydroxycinnamic acid structure (i.e., caffeic acid) was the highest amount among all compounds tested and existed in the highest amount in SF. The other hydroxycinnamic acid, chlorogenic acid, was detected as the highest compound compared to other compounds in three sprouts (MT, GP, and CR), while gallic acid was detected as the highest compound in two sprouts (SB and GP). Syringic acid was detected in a small amount in most sprouts except SF and MT, where it was not detected. ## 2.3.1. Total Chlorophyll Content (TCC) Chlorophyll a and chlorophyll b were detected at 645 and 663 nm, respectively (Figure 3A). The results of the TCC were expressed as milligrams per grams of crude extract. The TCCs in MT, BS, and WR were not significantly different, while the TCCs in the other sprouts were significantly different ($p \leq 0.05$). SF contained the highest amount of TCC (0.031 ± 0.00045), followed by TG (0.024 ± 0.0026), SB (0.020 ± 0.0016), MB (0.008 ± 0.001), WR (0.006 ± 0.0001), BS (0.006 ± 0.0004), MT (0.006 ± 0.001), GP (0.004 ± 0.000), and CR (0.004 ± 0.000), respectively. ## 2.3.2. Total Flavonoid Content (TFC) The total flavonoid content was reported as milligrams of quercetin equivalent (QE) per grams of crude extract (Figure 3B). The sprouts that contained non-significantly different TFCs included a group of WR and SF, BS and CR, and TG and GP. The high to low ranking of flavonoid content was first SB (98.9 ± 4.8), followed by MB (71.8 ± 7.97), WR (29.9 ± 2.43), SF (29.3 ± 1.34), BS (23.6 ± 2.49), CR (21.8 ± 2.61), TG (14.1 ± 1.59), GP (13.6 ± 0.528), and MT (12.5 ± 1.14). ## 2.3.3. Total Phenolic Content (TPC) The total phenolic content of various methanolic sprout extracts was determined using Folin-Ciocalteu’s reagents. The results are expressed as milligrams of gallic acid equivalent (GAE) per gram of crude extract (Figure 3C). The sprouts that contained non-significantly different TPCs included a group of BS and WR; and MT and MB. The ranking of the phenolic contents from high to low were TG (65.0 ± 2.60), CR (50.2 ± 2.74), WR (40.6 ± 1.13), BS (40.5 ± 3.67), MT (15.5 ± 1.94), MB (13.6 ± 0.98), SB (11.3 ± 1.06), GP (7.4 ± 0.69), and SF (2.6 ± 0.21), respectively. ## 2.3.4. Total Alkaloid Content (TALC) The results of the total alkaloid content were expressed as milligrams of atropine equivalent (AE) per gram of crude extract (Figure 3D). The high to low ranking of alkaloid content was first MT (0.33 ± 0.00), then BS (0.24 ± 0.00), WR (0.23 ± 0.00), TG (0.08 ± 0.00), CR (0.07 ± 0.00), SF (0.07 ± 0.00), GP (0.02 ± 0.00), SB (0.01 ± 0.00), and MB (0.01 ± 0.00). The respective total alkaloid contents of GP, SB and MB were not significantly different. ## 2.3.5. Total Anthocyanin Content (TAC) The results of the TAC were expressed as milligrams of cyanidin 3-glycocide equivalent per gram of crude extract (Figure 3E). The high to low ranking of the anthocyanin content was first expressed in BS (3.5 ± 0.38), then CR (2.5 ± 0.37), MT (2.3 ± 0.53), SF (1.6 ± 0.12), WR (1.4 ± 0.13), TG (1.1 ± 0.10), MB (0.6 ± 0.1), SB (0.6 ± 0.1), and GP (0.5 ± 0.03). BS showed a significantly higher TAC than the other sprouts ($p \leq 0.05$). Furthermore, MB, SB, and GP contained significantly lower TACs than the other sprouts ($p \leq 0.05$). ## 2.4.1. DPPH Radical Scavenging Activities Assay The results are expressed as the inhibitory concentration at $50\%$ (IC50) (Table 3). The low IC50 value indicates a high scavenging activity of the extract. Trolox exhibited a DPPH radical scavenging effect with an IC50 value of 32.6 ± 4.14 µg/mL. TG exhibited the highest DPPH radical scavenging effect with an IC50 value of 283.6 ± 25.87 µg/mL, following by SB (403.5 ± 36.78 µg/mL) (Table 3). The sprouts that possessed non-significantly different DPPH radical scavenging activity included a group of BS (486.3 ± 58.03 µg/mL) and CR (422.70 ± 43.05 µg/mL); and of MB (556.0 ± 18.30 µg/mL) and WR (527.91 ± 14.87 µg/mL). SF and MT exerted significantly weak DPPH radical scavenging activities (IC50 values of 1201.01 ± 38.47 and 1480.7 ± 154.93 µg/mL, respectively) ($p \leq 0.05$). ## 2.4.2. ABTS Radical Scavenging Activities Assay The results are expressed as the percent of inhibition. The concentration of the extract used in this study was within the range of 50–1000 µg/mL. The activities were based on concentration (Figure 4). The highest to lowest ABTS radical scavenging activities at 1000 µg/mL were BS (74.4 ± $3.1\%$), CR (73.4 ± $3.0\%$), TG (60.6 ± $3.4\%$), WR (53.4 ± $4.8\%$), SF (40.6 ± $4.9\%$), MT (35.8 ± $1.4\%$), SB (22.4 ± $1.0\%$), MB (22.0 ± $1.8\%$), and GP (16.3 ± $1.2\%$). The inhibitions of the ABTS radical are expressed as IC50 values (Table 3). Trolox exhibited an ABTS radical scavenging effect with an IC50 value of 44.0 ± 0.680 µg/mL. Among the sprout extracts, TG exhibited the highest ABTS radical scavenging effect with the lowest IC50 value. ## 2.4.3. Ferric-Reducing Antioxidant Power Assay The single electron transfer mechanism (SET) was indicated by the ferric-reducing antioxidant power assay (FRAP), and this test was performed at a sprout concentration of 500 µg/mL. The result was expressed as the millimoles of FeSO4 per gram of dry crude extract. Quercetin, a positive control, showed the highest ferric-reducing antioxidant power (0.56 ± 0.019 mM FeSO4 g−1 extract). Among the sprout extracts, CR and WR exerted the highest reducing antioxidant power with non-significantly differences showing 0.059 ± 0.002 and 0.064 ± 0.004 mM FeSO4 g−1 extract, respectively, while SB possessed the lowest reducing antioxidant power (0.0044 ± 0.00014 mM FeSO4 g−1 extract) (Table 3). ## 2.5. The Principal Component Analysis (PCA) of Bioactive Compounds and Antioxidant Activities The correlations of bioactive compounds and the antioxidant capacity of all sprout extracts are presented in Figure 5. Figure 5A,C,E presents the principal analysis between the sprout extracts and their antioxidant activity based on the IC50 values inhibiting DPPH radicals, % scavenging of ABTS at 500 µg/mL extract, and the FRAP value at 500 µg/mL extract, respectively. The compounds that contributed to the antioxidant activity are shown using loading plots (Figure 5B,D); namely, TPC, TFC, TCC, TALC, TAC, gallic acid (GA), syringic acid (SA), chlorogenic acid (CHA), caffeic acid (CFA), vanillin (VN), and rutin (RT). The results in the upper quadrant of Figure 5A showed that TG and CR, which had high DPPH scavenging activities, were separated from the other sprouts above PC3, which explained $15.5\%$ of the variance (Figure 5A). Chlorogenic acid and TPC contributed to the high DPPH scavenging activity of TG and CR based on moderate loading, as shown in Figure 5B. A moderate loading of vanillin indicated that vanillin was the predominant compound found in WR, which had a low activity and separated WR from the other extracts. BS was in the lower left quadrant, while SB and SF were in the lower right quadrant (explaining $21.7\%$ of the variance) (Figure 5A). Along with PC2, rutin had a moderate loading with BS, which showed a high DPPH scavenging activity (Figure 5B). A higher rutin content was present in BS, with a higher DPPH scavenging activity. The TCC and caffeic acid moderately contributed to the low DPPH scavenging activity of SF. Figure 5C,D showed a group of sprout extracts (i.e., SB, MB, GP, and MT) with low ABTS inhibitory activity separated from the other groups along PC1 (explaining $38.9\%$ of the variance). Gallic acid and chlorogenic acid contents had a moderate loading with SB, MB, GP, and MT, a group of sprouts with a low ABTS inhibitory activity. These data conformed to the results obtained from the HPLC analysis that gallic acid was high in SB and MB, while chlorogenic acid was high in MT. The same pattern was observed for the ferric-reducing antioxidant activity of the SB, MB, GP, and MT extracts and the attributed compounds (i.e., gallic acid and chlorogenic acid) by PC1 (explaining $38.9\%$ of the variance) (Figure 5E,D). High gallic acid and chlorogenic acid contents were detected in SB, MB, GP, and MT, a group of sprouts with low ferric-reducing antioxidant activity. Vanillin was a compound presented in an extract with a high ABTS inhibitory activity and ferric-reducing antioxidant activity of WR along PC1 (Figure 5C–E). Vanillin was the marker found in WR, which exhibited a high ABTS inhibitory activity and ferric-reducing antioxidant activities. Data suggested that TPC and chlorogenic acid moderately contributed to the stronger DPPH scavenging activity. Vanillin moderately contributed to a moderate to high ABTS inhibitory activity and ferric-reducing antioxidant. Notably, other phytochemicals were not able to discriminate the antioxidants of sprout extracts due to their weak loading or contributions. ## 2.6. Correlation Coefficient (r) of Sprouts at Different Species with Antioxidant Capacity The correlational characteristics of the nine sprouts are illustrated in Figure 6. We focused on the antioxidant capacities and determined their correlation with bioactive compounds from the chemical screening (i.e., TCC, TPC, TFC, TALC, and TAC) and the HPLC analysis (i.e., GA, SA, CHA, CFA, VN, and RT). A high DPPH scavenging activity was based on a low IC50 value. The results illustrate a significant moderate negative correlation of IC50 value from DPPH inhibitory activity with TPC (r = −0.617, $p \leq 0.01$) and vanillin (r = −0.507, $p \leq 0.01$). This indicates that TPC and vanillin were present in the extracts with a high DPPH scavenging activity, while vanillin had a high DPPH scavenging activity (low IC50 value). The ABTS scavenging activity, determined from % ABTS inhibition, showed a significant strong positive correlation with the TPC ($r = 0.730$, $p \leq 0.01$), TAC ($r = 0.777$, $p \leq 0.01$), and vanillin ($r = 0.711$, $p \leq 0.01$), respectively. However, this activity had a significantly strong negative correlation with gallic acid (r = −0.857, $p \leq 0.01$). This indicates that the TPC, TAC, and vanillin existed in the extract with a high % inhibition of ABTS, while gallic acid was present in the extract with a low % ABTS inhibition. The ferric-reducing antioxidant power was determined from the FRAP value. The higher the value, the better the activity. TPC and vanillin showed a significantly strong positive correlation with the FRAP value ($r = 0.636$, and 0.702, respectively; $p \leq 0.01$). Gallic acid, however, had a significantly strong negative correlation with the FRAP value (r = −0.665, $p \leq 0.01$). TPC and vanillin could be biomarkers in extracts with high reducing powers, while gallic acid could be for low reducing power. This information is in agreement with the data from PCA. Interestingly, rutin does not correlate with any antioxidant capacity of sprout extracts (Figure 6). Rutin, which is a flavonoid compound, has a weak negative correlation with the TFC. SB comprised the highest TFC but detected gallic acid as the highest compound compared to rutin. This indicates that other flavonoids are also present in SB. All other phenolic compounds identified by HPLC analysis also showed moderate to weak, positive, and negative correlations with the TCC. Data suggest that some unidentified compounds might be present in the sprout extracts. ## 3. Discussion Reactive oxygen species are formed from endogenous sources (normal cell metabolism, electron transport system, and enzymatic production by peroxidases) or exogenous sources (pollutants, radiation, smoking, nutrients, drugs, and xenobiotics). The overproduction of free radicals in the body likely results in oxidative stress. Oxidative stress plays a big role in chronic and degenerative diseases such as cancer, inflammation, rheumatoid arthritis, cardiovascular, neurodegenerative disease, and aging. Many studies have reported the antioxidant activities of plant extracts. The antioxidant mechanism proceeds through two processes, including: [1] giving the free radical one electron, which is a chain-breaking mechanism, and [2] quenching chain-initiating catalysts in order to remove reactive oxygen or nitrogen species initiators [18]. This study determined the scavenging of stable radicals (i.e., DPPH and cation radical ABTS+), and the reducing potential of an antioxidant via the FRAP assay. The stable radical scavenging capacity of the antioxidants can be obtained by receiving an electron or hydrogen from the antioxidant or sprout samples. These radicals are stable and suitable models that are strongly absorbed in the visible region, and, thus, can be measured by spectrophotometry based on the absorption change. DPPH and ABTS+ scavenging assays are commonly used models because they are simple, rapid, sensitive, and consistently reproducible. The mechanism of antioxidant action in DPPH assay is through donating hydrogen to reduce the stable radical DPPH to the non-radical diphenyl-picrylhydrazine (DPPH-H) and ABTS+ radicals’ scavenging activity by single-electron transfer. The extract or compound contains molecular structures that bear active hydroxyl groups, such as polyphenols and flavonoids, which are potent radical scavengers [19]. The antioxidant activities of plant extracts were enhanced by the presence of phenolic compounds, flavonoids, alkaloids, anthocyanin compounds, and other secondary metabolites [20,21]. Secondary plant metabolites, produced in response to different stresses for the defensive mechanisms of plants, have been reported to possess many biological activities [22]. In our study, methanol was selected for the extraction of nine different sprouts. The polarity of the extraction solvent and the solubility of chemical constituents in the extraction solvent lead to different types of extraction compounds from plant materials. Based on many reports, methanol is one of the common solvents for extraction, producing a higher percentage yield of phenolic compounds than other solvents [23,24,25]. Examples of notable secondary metabolites extracted by polar solvent are as follows. Gallic acid, a phenolic compound containing three hydroxyl groups at positions three, four, and five, acts as an antioxidant, an antineoplastic agent, an apoptosis inducer, a gene protector, and an arachidonate 15-lipoxygenase inhibitor to protect human spermatozoa against oxidative stress [26,27]. Syringic acid is a dimethoxybenzene derivative of gallic acid. This substance was suggested to have a variety of biological effects, including anti-oxidant and anti-nitrosant capabilities, as well as anti-cancer, anti-bacterial, anti-inflammatory, and anti-diabetic activity [28,29]. Chlorogenic acid is a polyphenol found in coffee and black tea, which is an ester of caffeic acid and quinic acid. It exerts antioxidant and chemopreventive properties, preventing the development of cancer by scavenging free radicals, preventing DNA damage, and promoting the expression of genes related to immune system activation and increasing the activation and proliferation of cytotoxic T-lymphocytes, macrophages, and natural killer cells [30,31]. Caffeic acid, also known as a hydroxycinnamic acid with hydroxyl groups substituting for the phenyl ring at positions three and four, functions as an antioxidant, inhibiting histone deacetylase, arachidonate 15-lipoxygenase, glutathione transferase, and arachidonate 5-lipoxygenase [32,33]. Vanillin belongs to benzaldehydes that contain methoxy and hydroxy substituents at positions three and four, respectively. It acts as an anti-oxidant, anti-convulsant, anti-inflammatory, and flavoring [34,35]. Rutin is quercetin that has had the hydroxyl group at position C-3 replaced with sugar groups such as glucose and rhamnose and is known as an anti-oxidant [36,37]. Based on in vitro chemical screening of phytochemicals using a spectroscopic method, the total contents of phenolic, flavonoids, anthocyanin, chlorophyll, and alkaloids have been identified and quantified in our selected sprouts. They have been reported to exert various biological activities. Phenolics act as anti-oxidants by interacting with free radicals through different reactions. The hydroxyl group of phenolic compounds is responsible for anti-oxidant activity [38,39,40,41]. Both phenolic and flavonoid compounds are significant anti-oxidants due to their capacity to donate hydrogen atoms to free radicals and deactivate free radicals. They also possess the perfect structural qualities for free radical scavenging [42]. Anthocyanin and chlorophyll have also been reported to contribute to anti-oxidant activity in many plants [43,44]. Chlorophyll provides health advantages and helps prevent chronic diseases such as coronary heart disease, various cancers, and obesity [45,46,47]. Chlorophyll a and its breakdown products are widely used as anti-inflammatory agents and to prevent the uptake of carcinogens and carcinogenesis [48,49]. Alkaloid has been reported to exhibit anti-oxidant activity [50], anti-bacterial activity [51,52], anti-malarial activity [53], an anti-inflammatory effect [54,55], and anti-diabetic [56] and anti-cancer activities [57]. Some reports of biological activity and compounds have been published on the nine sprouts used in this study. Sunflower seeds and sprouts have significant anti-oxidant, anti-bacterial, anti-inflammatory, anti-hypertensive, wound-healing, and cardiovascular effects due to their phenolic compounds, flavonoids, polyunsaturated fatty acids, and vitamins [58]. Additionally, they comprised proline, chlorophyll, carbohydrates, proteins, and lipids. In ethnomedicine, they have been used to cure a variety of diseases, including whooping cough and bronchial, laryngeal, and pulmonary infections, as well as heart disease [59]. In the middle of flowering, the aerial parts of sunflowers are a rich source of phenolic compounds with anti-oxidant properties [60]. Mustard sprouts contain anti-oxidants that can neutralize reactive oxygen species without being transformed into destructive radicals [61]. The total phenolic compounds, total flavonoids, vitamins, and minerals are present in this plant [62]. The black sesame sprouts contained high levels of total phenolic and flavonoid content, proteins, unsaturated and saturated fatty acids, vitamins, minerals, and lignans such as sesamin, sesamol, sesamolin, and tocopherols [23]. Taiwanese morning glory sprouts exhibited anti-cancer, anti-bacterial, anti-oxidant, anti-calcification, and anti-mutagenic effects [63]. This plant has been used in Cambodia and Myanmar to cure febrile delirium and as a source of protein, vitamin, and mineral in chicken feed to promote growth [64]. The mung bean sprout has been reported to possess vitamin C, phenolics, carotenes, chlorophyll, and other nutrients [65]. The bioactivities of this sprout include detoxification, reducing the incidence of coronary heart disease, hypercholesterolemia, preventing hair and nail loss, and exhibiting antioxidant, anti-diabetic, and hypocholesterolemic properties [66]. Soybeans are an important crop for food security in Asia and also in Cambodia [67,68]. The phenolic, flavonoid, and anthocyanin profiles, as well as anti-oxidant activities, of soybean seeds were different across soybean varieties. Colored radish sprouts showed scavenging activities in the ABTS assay [69,70]; anti-microbial activity against E. coli, S. pneumonia, and S. aureus; and antiproliferation in the HT29 (colon cancer) and MCF7 (breast cancer) cell lines. The bioactive compounds in the colored radish sprouts determined by HPLC were anthocyanins such as pelargonidin, cyanidin, and delphinidin [71]. A previous study reported that flavonoids, phenolic acids, vitamins, and trace elements were abundant in radishes and were attributed to anti-oxidants. White radish sprouts were also reported for anti-oxidant activities via DPPH and FRAP assays. Asian white radishes possess anti-cancerous and anti-inflammatory properties in their edible solid taproot. Radish sprouts and mustard contain derivatives of hydroxycinnamic acids, including sinapic acid, chlorogenic acid, and flavonols [72]. Green pea pods were found to have a flavonoid and phenolic content, as well as other antioxidant activities according to the DPPH, FRAP, and ABTS assay [73,74]. HPLC analysis shows that the sprouts expressed various phytochemical compounds [75,76]. However, HPLC conditions and extraction methods can affect the presence and content of the extracted compounds [77,78]. Moreover, other unidentified peaks also appeared in our chromatogram. This study has some limitations; not all of the compounds in the extraction samples were identified by comparing them with the standard compounds or from the chemical screening of phytochemical analysis. Moreover, the lack of identical compounds between plant collections and the present study could be due to natural diversity and/or a difference in the growing conditions in the location of the collection. The presence of significant amounts and types of bioactive components in the sprouts under the present study and the quantity determined based on the selected solvent for the extraction process ensures its unequivocal recommendation for use in the pharmaceutical and nutraceutical sectors. However, more structured elucidation should be performed to disclose other compounds and a more detailed study of antioxidant activity to create a nutritional and medicinal reference for these sprouts and to evaluate their health benefits. ## 4.1. Material and Reagents Analytical grade methanol was purchased from Merck KGaA (Darmstadt, Germany). HPLC grade methanol and acetonitrile were purchased from RCI Labscan V.S. CHEM HOUSE (Bangkok, Thailand). Syringic acid was purchased from Thermo Scientific (Fair Lawn, NJ, USA). Chlorogenic acid was purchased from AK Scientific (Union, CA, USA). Rutin was obtained from HiMedia Laboratories Pvt. Ltd. (Mumbai, India). Vanillin was purchased from Acros Organic, Janssen Pharmaceuticalaan 3a (Geel, Belgium). Caffeic acid was obtained from Thermo Scientific (Fair Lawn, NJ, USA). Gallic acid was purchased from Acros Organic, Janssen Pharmaceuticalaan 3a (Geel, Belgium). Trolox was obtained from Thermo Scientific (Fair Lawn, NJ, USA). Quercetin was obtained from HiMedia Laboratories Pvt. Ltd. (Mumbai, India). Furthermore, 2,4,6-Tri(2-pyridyl)-s-triazine (TPTZ) was purchased from Thermo Scientific (Fair Lawn, NJ, USA); 1,1-Diphenyl-2-picrylhydrazyl Free Radical (DPPH) was purchased from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan); 2,2′-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) was Chem Cruz (Dallas, TX, USA). The HPLC standard atropine was purchased from HPC Standards GmbH (Cunnersdorf, Germany). Folin-Ciocalteu’s phenol reagent, aluminum chloride, Iron(III) chloride hexahydrate, potassium sulfate, ferric (II) sulfate hexahydrate, and hydrochloric acid were purchased from Merck KGaA (Darmstadt, Germany). The other reageants were purchased from standard commercial suppliers. ## 4.2. Plant Extraction The extraction was performed following the previous report [79]. First, 1 kg of seeds was soaked in 2 L of water for 4 h, then evenly spread out on a wet towel for 8 h. Next, the seeds were prepared by soil germination in mesh greenhouses and sprayed with water twice daily at room temperature (25–30 °C). In total, 9 sprouts were harvested at the ages of 4 to 7 days. Finally, the aerial part of the sprouts was cut and collected for the experiment when they were mature, 5 cm long, and had 2 leaves (at 4–7 days). They were Helianthus annuus L. (sunflower, SF); Brassica juncea (L.) Czern. ( mustard, MT); Sesamum indicum L. (black sesame, BS); Ipomoea aquatica Forssk. ( Taiwanese morning glory, TG); Vigna radiata (L.) R. Wilczek (mung bean, MB); Glycine max (L.) Merr. ( soybean, SB); Raphanus sativus L. (white radish, WR); Raphanus sativus L. (colored radish, CR); and Pisum sativum L. (green pea, GP). The sprouts were gently washed to remove dirt. The excess water was drained and the sprouts were dried in an oven (Biobase, Shandong, China) at 30 °C for 2 h then chopped into small fragments. The dried plant (20 g) was transferred to $1\%$ concentrated hydrochloric acid in methanol and ultrasonicated at 30 °C for 30 min before filtering. The solvent was removed by a rotary evaporator (IKA RV10, Selangor, Malaysia) at room temperature to obtain the dried crude residue. The crude extract was stored in the refrigerator at 4 °C. ## 4.3. Identification of Phenolics and Flavonoids Using HPLC The phenolic and flavonoid contents in the sprout extracts were determined by high-performance liquid chromatography (HPLC) using the modified method from a previous study [77]. HPLC chromatograms can be found in the Supplementary Materials (Figures S1–S10). Analyses were performed using an LC−2030C3D quaternary pump (Shimadzu, Kyoto, Japan) equipped with a diode array detector (DAD), according to the method of [80]. The extracts were dissolved in methanol (HPLC grade), filtered through a 0.45 µm membrane filter, and injected into the GIST C18 shim-pack column (4.6 × 250 mm, 5 µm) (KTA Technologies Corporation, Tokyo, Japan). The column temperature was 38 °C. The injection volume was 20 µL using an autosampler. The flow rate of the mobile phase was 0.8 mL/min. The mobile phase consisted of $1\%$ acetic acid in purified water (A) and acetonitrile (B). The gradient elution was performed as follows: from 0 to 5 min, linear gradient from 5 to $9\%$ of acetonitrile; from 5 to 15 min, 9 to $11\%$ of acetonitrile; from 15 to 22 min, linear gradient from 11 to $15\%$ of acetonitrile; from 22 to 30 min, linear gradient from 15 to $18\%$ of acetonitrile; and from 30 to 38 min and a re-equilibration period of 10 min with $5\%$ of acetonitrile used between individual runs. The detector wavelength was set at 280, 320, and 370 nm. The compounds in the sample were identified by comparing their retention time and UV spectral matching to standards. The phenolic and flavonoid standards were gallic acid, chlorogenic acid, vanillin, caffeic acid, syringic acid, and rutin. ## 4.4. Total Chlorophyll Content (TCC) The TCC was performed following the previous report [81]. The stock solution of the crude extract in methanol was prepared with the final concentration of 2.5 mg/mL. The TCC was detected at wavelengths of 645 and 663 nm by microplate spectrophotometer (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA). The TCC was calculated using Equation [1]:Total chlorophyll content (mg/g) = (20.2 × A645) + (8.02 × A663)/1000[1] ## 4.5. Total Flavonoid Contents (TFC) The TFC was determined per previous studies [82,83]. The crude extracts were prepared by dissolving 100 mg of the extract in 1000 µL of methanol. Briefly, 100 µL of the extract was mixed with 50 µL of $2\%$ aluminum chloride as a buffer. Quercetin was dissolved in methanol (10 mg/mL), then diluted at various concentrations (10, 20, 30, 40, 50, and 60 µg/mL). The experiments were carried out with five replications. The absorbance of the test solution was measured at 400–415 nm with a microplate spectrophotometer (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA). The TFC was calculated from a standard curve of quercetin ($y = 0.014$x − 0.0014, R² = 0.9907). The results expressed the total flavonoid content as milligrams of quercetin equivalent (QE)/g of the crude extracts. ## 4.6. Total Phenolic Content (TPC) by Folin Ciocalteu’s Reagent The TPC was determined using the Folin-Ciocalteu method [84,85]. The crude extracts were dissolved in methanol (final concentration of 10 mg/mL). Briefly, 15 µL of extract was mixed with 120 µL of prepared Folin-Ciocalteu’s reagents and placed at room temperature away from light for 5 min. Subsequently, 120 µL of sodium carbonate buffer (pH 7.5) was added to the mixture and kept for another 90 min under the same condition. The absorbance of the blue solution of molybdenum (V) in Folin-Ciocalteu’s reagents was measured at 725 nm with the microplate spectrophotometer (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA). Gallic acid was used as a positive control. The gallic acid solution was dissolved in methanol (10 mg/mL) and prepared at different final concentrations (10, 20, 30, 40, and 50 µg/mL). The experiments were carried out in five replicates. The standard curve of gallic acid was created from the plot between the absorbance of the blue solutions of molybdenum (V) against the gallic acid concentrations. TPC was calculated from a standard gallic acid curve ($y = 0.0166$x + 0.1135, R2 = 0.9908). The results were expressed as milligrams of gallic acid equivalent (GAE) per gram of crude extract. ## 4.7. Total Alkaloids Content (TALC) The TALC was performed according to previous studies [83,86]. The solution of crude extracts was prepared by dissolving 10 mg in 1000 µL of 2N HCL. Briefly, 1000 µL of the extract was mixed with 5 mL of bromocresol 0.2 mM and 5 mL of the citrate phosphate buffer (pH 4.7). The solution mixture was added by 5 mL of chloroform and shaken vigorously. The solution mixture was incubated at room temperature for 30 min. The chloroform layer appeared below the layer of the sample/standard solution, which was collected for analysis at 420 nm using the UV-spectrometry (GENESYSTM 10S UV-Visible Spectrophotometer, Thermo Fisher Scientific, Madison, WI, USA). Atropine, a positive control, was dissolved in 2N HCL at different final concentrations (0.01–0.1 mg/mL). The experiments were carried out in five replications. The standard curve of atropine was created from the plot between the absorbance and the blank solutions, which do not contain any sample mixtures. The TALC was calculated from a standard curve of atropine ($y = 5.9942$x + 0.0479, R2 = 0.9997). The results are expressed as milligrams of atropine equivalent (AE) per gram of crude extracts. ## 4.8. Total Anthocyanin Content (TAC) The TAC was evaluated using the pH differential method, as described in previous reports [77,87]. Briefly, the two different pH solutions were prepared as follows: [1] potassium chloride buffer (pH 1.0 KCl buffer) and [2] pH 4.5 sodium acetate buffer (pH 4.5 CH3CO2Na buffer). The extract in methanol was diluted 10 times with the 2 buffers, shaken under dark conditions for 15 min, and then centrifuged at 419× g at room temperature before measuring the absorbance at 510 nm and 700 nm using a microplate reader (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA). The blank determination (DI water) was also performed. The total anthocyanin of the extract was calculated following Equation [2] in terms of cyanidin-3-glucoside. The results were expressed as milligrams of equivalent cyanidin-3-glucoside per gram of crude extract and the average of the four replicates. Total anthocyanin content (mg/g) = [Adiff × Mw × DF × 1000]/[ε][2] ## 4.9.1. DPPH Radical Scavenging Activities The DPPH assay was based on hydrogen atom transfer (HAT) reactions. In addition, 2,2-Diphenyl-1-picrylhydrazyl or DPPH will generate a stable free radical with an unpaired electron, delocalized throughout the molecule, producing stabilized molecules [84,88]. Briefly, various concentrations of the extracts dissolved in methanol (between 10 and 1000 g/mL) and the DPPH reagents were added and mixed in a 1:1 ratio in the 96-well plates for 30 min in the dark at room temperature. The loss of absorbance of the DPPH radical at 515 nm was measured using a microplate spectrophotometer (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA). Trolox, a standard antioxidant, was used as a positive control. The linear curve ($y = 0.0051$x + 0.0225, R² = 0.9939) was obtained from the plot between the Trolox concentration and the DPPH radical scavenging power. The experiments were carried out in four replicates. The DPPH radical scavenging capacity was represented as the percentage of DPPH radical inhibition at $50\%$ (IC50). The percentage of inhibition or % of the scavenging effect of DPPH was calculated following Equation [3]: %DPPH scavenging effect = [Abs of control − Abs of sample]/[Abs of control] × 100[3] ## 4.9.2. ABTS Radical Scavenging Activity The 2,2′-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) assay measures the antioxidant scavenging activity of ABTS free radicals generated by potassium persulfate in the aqueous phase [85,89]. ABTS and potassium persulfate were dissolved in distilled water with final concentrations of 7 mM and 2.45 mM, respectively. The ABTS radical cation produced by the addition of both solutions in a 1:0.5 ratio was then incubated at room temperature for 12 h in a dark room. The ABTS solution was diluted in ethanol to give an absorbance of 0.7 ± 0.02 at 743 nm. Various concentrations of sprout extract (50–1000 µg/mL) were added to 1 mL of diluted ABTS solution in a ratio of 1:100 and incubated for 30 min. The absorbance was measured at 734 nm using a microplate spectrophotometer (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA). Trolox was used as a standard solution (0.1–0.8 mM). The result of the ABTS radical scavenging was expressed as an IC50 value (inhibition at $50\%$) or as a percentage of inhibition using Equation [4]. % ABTS scavenging effect = [Abs of control − Abs of sample]/[Abs of control] × 100[4] ## 4.9.3. Reducing Antioxidant Power based on FRAP Assay The FRAP assay describes the ferric-reducing antioxidant power. This assay method measures the reduction in the ferric ion Fe3+-TPTZ complex to Fe2+-TPTZ [89,90]. Various concentrations of each extract, ferrous sulfate, and positive control such as quercetin were dissolved with methanol. The FRAP reagent comprised 300 mM of acetate buffer (pH 3.6), 10 mM of 2,4,6-tripyridyls-triazine (TPTZ) solution, and 20 mM of FeCl3.6H2O in a 10:1:1 ratio. The reaction mixture was pipetted into each well of a 96-well plate and incubated for 30 min in the dark at room temperature. The absorbance of the colored product (ferrous tripyridyltriazine complex) was measured at 593 nm with the microplate spectrophotometer (Thermo ScientificTM MultiskanTM FC, Boston, MA, USA) against a blank with methanol. The experiments were tested in five replicates. The standard curve of ferrous sulfate with various concentrations (30, 35, 40, 45, 100, 150, and 200 mM) was linear ($y = 0.0013$x + 0.0049, R2 = 0.9949). Quercetin was stated as a positive control with various final concentrations (10, 20, 30, and 40 µg/mL) and was linear ($y = 0.0101$x + 0.0601, R² = 0.9917). The FRAP value was calculated from the standard curve and expressed in mmol Fe2+ per gram of extract (mmol Fe2+/g extract). ## 4.10. Stastistical Analysis The results are reported as the mean ± standard deviation (SD). Statistical analyses between samples were performed using nonparametric tests followed Kruskal-Wallis tests (SPSS version 26, SPSS Inc. Armonk, NY, USA). A significant difference was set at $p \leq 0.05.$ Correlation analysis was performed using SPSS Version 26, SPSS Inc., Armonk, NY, USA. Principal component analysis (PCA) of bioactive components between sprouts and their antioxidant activities was performed using Python software version 3.10.5 (Python Software Foundation, Fredericksburg, VA, USA). The bioactive compounds were normalized before computing the PCA analysis. The PCA scores and loading variables were obtained from the PCA model. The PCA score plot was used to illustrate the correlation between bioactive compounds and its antioxidant. The loading plot indicated the correlation between the bioactive compounds and their antioxidant activity at 0.5 (moderate correlation) and 1.0 (strong correlation) as a black dashed circle and a red solid circle, respectively. ## 5. Conclusions Based on the constitutive bioactive compounds and antioxidant activities of the nine species of sprouts studied, our findings suggest that the nine sprouts chosen for the study have health benefits beyond their nutritional value. Sunflower sprouts contained the highest total chlorophyll content. Soybean sprouts contained the highest total flavonoid content. The mustard sprouts were rich in total alkaloid content but contained the least total flavonoid content. DPPH and ABTS scavenging activity had a strong positive correlation to the total phenolic (TPC) and total anthocyanin (TAC) contents. The sprouts that exerted high TPCs and TACs also had high DPPH or ABTS scavenging activities (i.e., Taiwanese morning glory, colored radish, black sesame, white radish). Sunflowers, which were composed of the lowest TPC, had low DPPH and ABTS scavenging activities. The FRAP values had a strong positive correlation with vanillin and a moderate to weak correlation with the other compounds. The white radish that contained the highest vanillin, therefore, exhibited the highest FRAP value. The findings of our study could be viewed as exploratory and preliminary scientific evidence. Further research is needed to confirm the efficacy of safe sprout consumption in vitro, in vivo, and clinically, as well as to elucidate a more detailed structure and mechanism of action. ## References 1. Wang H., Zhang Y., Jiang H., Cao J., Jiang W.. A Comprehensive Review of Effects of Electrolyzed Water and Plasma-Activated Water on Growth, Chemical Compositions, Microbiological Safety and Postharvest Quality of Sprouts. Trends Food Sci. Technol. (2022.0) 129 449-462. DOI: 10.1016/j.tifs.2022.10.017 2. Ikram A., Saeed F., Afzaal M., Imran A., Niaz B., Tufail T., Hussain M., Anjum F.M.. Nutritional and End-Use Perspectives of Sprouted Grains: A Comprehensive Review. Food Sci. Nutr. (2021.0) 9 4617-4628. DOI: 10.1002/fsn3.2408 3. Liu H.K., Kang Y.F., Zhao X.Y., Liu Y.P., Zhang X.W., Zhang S.J.. Effects of Elicitation on Bioactive Compounds and Biological Activities of Sprouts. J. Funct. Foods (2019.0) 53 136-145. DOI: 10.1016/j.jff.2018.12.019 4. Hao J., Li J., Zhao D.. Effect of Slightly Acidic Electrolysed Water on Functional Components, Antioxidant and α-Glucosidase Inhibitory Ability of Buckwheat Sprouts. Int. J. Food Sci. Technol. (2021.0) 56 3463-3473. DOI: 10.1111/ijfs.14972 5. Pham-Huy L.A., He H., Pham-Huy C.. Free Radicals, Antioxidants in Disease and Health. Free Radic. Antioxid. (2008.0) 4 89 6. Willcox J.K., Ash S.L., Catignani G.L.. Antioxidants and Prevention of Chronic Disease. Crit. Rev. Food Sci. Nutr. (2004.0) 44 275-295. DOI: 10.1080/10408690490468489 7. Donaldson M.S.. Nutrition and Cancer: A Review of the Evidence for an Anti-Cancer Diet. Nutr. J. (2004.0) 3 19. DOI: 10.1186/1475-2891-3-19 8. Barba F.J., Mariutti L.R.B., Bragagnolo N., Mercadante A.Z., Barbosa-Cánovas G.V., Orlien V.. Bioaccessibility of Bioactive Compounds From Fruits and Vegetables After Thermal and Nonthermal Processing. Trends Food Sci. Technol. (2017.0) 67 195-206. DOI: 10.1016/j.tifs.2017.07.006 9. Ahmed A.F., Attia F.A.k., Liu Z., Li C., Wei J., Kang W.. Antioxidant Activity and Total Phenolic Content of Essential Oils and Extracts of Sweet Basil (. Food Sci. Hum. Wellness (2019.0) 8 299-305. DOI: 10.1016/j.fshw.2019.07.004 10. Derakhshan Z., Ferrante M., Tadi M., Ansari F., Heydari A., Hosseini M.S., Conti G.O., Sadrabad E.K.. Antioxidant Activity and Total Phenolic Content of Ethanolic Extract of Pomegranate Peels, Juice And Seeds. Food Chem. Toxicol. (2018.0) 114 108-111. DOI: 10.1016/j.fct.2018.02.023 11. Ayoub I., El-Shazly M., Lu M.-C., Singab A.. Antimicrobial and Cytotoxic Activities of the Crude Extracts of. S. Afr. J. Bot. (2014.0) 95 97-101. DOI: 10.1016/j.sajb.2014.08.012 12. Raiola A., Errico A., Petruk G., Monti D.M., Barone A., Rigano M.M.. Bioactive Compounds in Brassicaceae Vegetables with a Role in the Prevention of Chronic Diseases. Molecules (2017.0) 23. DOI: 10.3390/molecules23010015 13. Shi Y.H., Wang J., Guo R., Wang C.Z., Yan X.B., Xu B., Zhang D.Q.. Effects of Alfalfa Saponin Extract on Growth Per-Formance and Some Antioxidant Indices of Weaned Piglets. Livest. Sci. (2014.0) 167 257-262. DOI: 10.1016/j.livsci.2014.05.032 14. Yılmaz H.Ö., Ayhan N.Y., Meric C.S.. Buckwheat: A Useful Food and Its Effects on Human Health. Curr. Nutr. Food Sci. (2020.0) 16 29-34. DOI: 10.2174/1573401314666180910140021 15. Bastida J.A.G., Zielinski H.. Buckwheat as a Functional Food and Its Effects on Health. J. Agric. Food Chem. (2015.0) 63 7896-7913. DOI: 10.1021/acs.jafc.5b02498 16. Shah M.A., Sarker M.M.R., Gousuddin M.. Antidiabetic Potential of. Int. J. Pharmacogn. Phytochem. Res. (2016.0) 8 462-469. DOI: 10.13140/RG.2.1.4509.3529 17. Gan R.-Y., Lui W.-Y., Wu K., Chan C.-L., Dai S.-H., Sui Z.-Q., Corke H.. Bioactive Compounds and Bioactivities of Germi-Nated Edible Seeds and Sprouts: An Updated Review. Trends Food Sci. Technol. (2017.0) 59 1-14. DOI: 10.1016/j.tifs.2016.11.010 18. Yan Z., Zhong Y., Duan Y., Chen Q., Li F.. Antioxidant Mechanism of Tea Polyphenols and Its Impact on Health Benefits. Anim. Nutr. (2020.0) 6 115-123. DOI: 10.1016/j.aninu.2020.01.001 19. Lü J.-M., Lin P.H., Yao Q., Chen C.. Chemical and Molecular Mechanisms of Antioxidants: Experimental Approaches and Model Systems. J. Cell. Mol. Med. (2010.0) 14 840-860. DOI: 10.1111/j.1582-4934.2009.00897.x 20. Tungmunnithum D., Thongboonyou A., Pholboon A., Yangsabai A.. Flavonoids and other Phenolic Compounds from Medicinal Plants For Pharmaceutical and Medical Aspects: An Overview. Medicines (2018.0) 5. DOI: 10.3390/medicines5030093 21. Shi J., Wu Q., Deng J., Balfour K., Chen Z., Liu Y., Kumar S., Chen Y., Zhu Z., Zhu G.. Metabolic Profiling and Antioxidant Analysis for the Juvenile Red Fading Leaves of Sweetpotato. Plants (2022.0) 11. DOI: 10.3390/plants11223014 22. Yeshi K., Crayn D., Ritmejerytė E., Wangchuk P.. Plant Secondary Metabolites Produced in Response to Abiotic Stresses Has Potential Application in Pharmaceutical Product Development. Molecules (2022.0) 27. DOI: 10.3390/molecules27010313 23. Saeed N., Khan M.R., Shabbir M.. Antioxidant Activity, Total Phenolic and Total Flavonoid Contents of Whole Plant Extracts. BMC Complement. Altern. Med. (2012.0) 12. DOI: 10.1186/1472-6882-12-221 24. Babbar N., Oberoi H.S., Sandhu S.K., Bhargav V.K.. Influence of Different Solvents in Extraction of Phenolic Compounds From Vegetable Residues and Their Evaluation As Natural Sources of Antioxidants. J. Food Sci. Technol. (2014.0) 51 2568-2575. DOI: 10.1007/s13197-012-0754-4 25. Agidew M.G.. Phytochemical Analysis of Some Selected Traditional Medicinal Plants in Ethiopia. Bull. Natl. Res. Cent. (2022.0) 46 87. DOI: 10.1186/s42269-022-00770-8 26. PubChem Compound Summary for CID 370, Gallic Acid. PubChem 27. Alavi R.S., Farhoosh R., Sharif A.. Antioxidant Activity of Gallic Acid as Affected by an Extra Carboxyl Group than Pyrogallol in Various Oxidative Environments. Eur. J. Lipid Sci. Technol. (2018.0) 120 1800319. DOI: 10.1002/ejlt.201800319 28. Vo Q.V., Van Bay M., Nam P.C., Quang D.T., Flavel M., Hoa N.T., Mechler A.. Theoretical and Experimental Studies of the Antioxidant and Antinitrosant Activity of Syringic Acid. J. Org. Chem. (2020.0) 85 15514-15520. DOI: 10.1021/acs.joc.0c02258 29. Li Y., Zhang L., Wang X., Wu W., Qin R.. Effect of Syringic Acid on Antioxidant Biomarkers and Associated Inflammatory Markers in Mice Model of Asthma. Drug Dev. Res. (2019.0) 80 253-261. DOI: 10.1002/ddr.21487 30. PubChem Compound Summary for CID 1794427, Chlorogenic Acid. PubChem 31. Xu J.-G., Hu Q.-P., Liu Y.. Antioxidant and DNA-Protective Activities of Chlorogenic Acid Isomers. J. Agric. Food Chem. (2012.0) 60 11625-11630. DOI: 10.1021/jf303771s 32. PubChem Compound Summary for CID 689043, Caffeic Acid. PubChem 33. Genaro-Mattos T.C., Maurício Â.Q., Rettori D., Alonso A., Hermes-Lima M.. Antioxidant Activity of Caffeic Acid Against Iron-Induced Free Radical Generation—A chemical approach. PLoS ONE (2015.0) 10. DOI: 10.1371/journal.pone.0129963 34. PubChem Compound Summary for CID 1183, Vanillin. PubChem 35. Tai A., Sawano T., Yazama F., Ito H.. Evaluation of Antioxidant Activity of Vanillin by Using Multiple Antioxidant Assays. Biochim. Biophys. Acta (BBA)—Gen. Subj. (2011.0) 1810 170-177. DOI: 10.1016/j.bbagen.2010.11.004 36. PubChem Compound Summary for CID 5280805, Rutin. PubChem 37. Yang J., Guo J., Yuan J.. In Vitro Antioxidant Properties of Rutin. LWT—Food Sci. Technol. (2008.0) 41 1060-1066. DOI: 10.1016/j.lwt.2007.06.010 38. Agidew M.G., Dubale A.A., Atlabachew M., Abebe W.. Fatty Acid Composition, Total Phenolic Contents and Antioxidant Activity of White and Black Sesame Seed Varieties from Different Localities of Ethiopia. Chem. Biol. Technol. Agric. (2021.0) 8 14. DOI: 10.1186/s40538-021-00215-w 39. Rice-Evans C.A., Diplock A.T.. Current Status of Antioxidant Therapy. Free. Radic. Biol. Med. (1993.0) 15 77-96. DOI: 10.1016/0891-5849(93)90127-G 40. Krinsky N.I.. Mechanism of Action of Biological Antioxidants. Proc. Soc. Exp. Biol. Med. (1992.0) 200 248-254. DOI: 10.3181/00379727-200-43429 41. Zeb A.. Concept, Mechanism, and Applications of Phenolic Antioxidants in Foods. J. Food Biochem. (2020.0) 44 e13394. DOI: 10.1111/jfbc.13394 42. Tang Y.-Y., He X.-M., Sun J., Li C.-B., Li L., Sheng J.-F., Xin M., Li Z.-C., Zheng F.-J., Liu G.-M.. Polyphenols and Alkaloids in Byproducts of Longan Fruits (. Molecules (2019.0) 24. DOI: 10.3390/molecules24061186 43. Jin H.-M., Dang B., Zhang W.-G., Zheng W.-C., Yang X.-J.. Polyphenol and Anthocyanin Composition and Activity of Highland Barley with Different Colors. Molecules (2022.0) 27. DOI: 10.3390/molecules27113411 44. Pérez-Gálvez A., Viera I., Roca M.. Carotenoids and Chlorophylls as Antioxidants. Antioxidants (2020.0) 9. DOI: 10.3390/antiox9060505 45. Li Y., Cui Y., Hu X., Liao X., Zhang Y.. Chlorophyll Supplementation in Early Life Prevents Diet-Induced Obesity and Modulates Gut Microbiota in Mice. Mol. Nutr. Food Res. (2019.0) 63 1801219. DOI: 10.1002/mnfr.201801219 46. Vaňková K., Marková I., Jašprová J., Dvořák A., Subhanová I., Zelenka J., Novosádová I., Rasl J., Vomastek T., Sobotka R.. Chlorophyll-Mediated Changes in the Redox Status of Pancreatic Cancer Cells are Associated with Its Anticancer Effects. Oxidative Med. Cell. Longev. (2018.0) 2018 4069167. DOI: 10.1155/2018/4069167 47. Li Y., Cui Y., Lu F., Wang X., Liao X., Hu X., Zhang Y.. Beneficial Effects of A Chlorophyll-Rich Spinach Extract Supplementation on Prevention of Obesity and Modulation of Gut Microbiota In High-Fat Diet-Fed Mice. J. Funct. Foods (2019.0) 60 103436. DOI: 10.1016/j.jff.2019.103436 48. Subramoniam A., Asha V.V., Nair S.A., Sasidharan S.P., Sureshkumar P.K., Rajendran K.N., Karunagaran D., Ramalingam K.. Chlorophyll Revisited: Anti-inflammatory Activities of Chlorophyll a and Inhibition of Expression of TNF-α Gene by the Same. Inflammation (2012.0) 35 959-966. DOI: 10.1007/s10753-011-9399-0 49. McQuistan T.J., Simonich M.T., Pratt M.M., Pereira C.B., Hendricks J.D., Dashwood R.H., Williams D.E., Bailey G.S.. Cancer Chemoprevention by Dietary Chlorophylls: A 12,000-Animal Dose–Dose Matrix Biomarker and Tumor Study. Food Chem. Toxicol. (2012.0) 50 341-352. DOI: 10.1016/j.fct.2011.10.065 50. Gan J., Feng Y., He Z., Li X., Zhang H.. Correlations between Antioxidant Activity and Alkaloids and Phenols of Maca (. J. Food Qual. (2017.0) 2017 3185945. DOI: 10.1155/2017/3185945 51. Villinski J., Dumas E., Chai H.B., Pezzuto J., Angerhofer C., Gafner S.. Antibacterial Activity and Alkaloid Content of. Pharm. Biol. (2003.0) 41 551-557. DOI: 10.1080/13880200390500768 52. Otshudi A.L., Apers S., Pieters L., Claeys M., Pannecouque C., De Clercq E., Van Zeebroeck A., Lauwers S., Frédérich M., Foriers A.. Biologically Active Bisbenzylisoquinoline Alkaloids from the Root Bark of. J. Ethnopharmacol. (2005.0) 102 89-94. DOI: 10.1016/j.jep.2005.05.021 53. Wirasathien L., Boonarkart C., Pengsuparp T., Suttisri R.. Biological Activities of Alkaloids from. Pharm. Biol. (2006.0) 44 274-278. DOI: 10.1080/13880200600714111 54. Li L.S., Chiroma S.M., Hashim T., Adam S.K., Moklas M.A.M., Yusuf Z., Rahman S.A.. Antioxidant and Anti-Inflammatory Properties of. Heliyon (2020.0) 6 e04141. DOI: 10.1016/j.heliyon.2020.e04141 55. Bribi N., Algieri F., Rodriguez-Nogales A., Garrido-Mesa J., Vezza T., Maiza F., Utrilla M.P., Rodriguez-Cabezas M.E., Galvez J.. Antinociceptive and Anti-Inflammatory Effects of Total Alkaloid Extract from. Evid. Based Complement. Altern. Med. (2015.0) 2015 127414. DOI: 10.1155/2015/736895 56. Adhikari B.. Roles of Alkaloids from Medicinal Plants in the Management of Diabetes Mellitus. J. Chem. (2021.0) 2021 2691525. DOI: 10.1155/2021/2691525 57. Al-Hakeim H.K., Al-Zabibah R.S., Alzihari H.F., Almensoori A.K., Al-Zubaidi H.A., Hassan L.E.A.H.. Anticancer and Antiangiogenic Activities of Alkaloids Isolated from. Karbala Int. J. Mod. Sci. (2021.0) 7 11. DOI: 10.33640/2405-609X.2577 58. De’Nobili M.D., Bernhardt D.C., Basanta M.F., Rojas A.M.. Sunflower (. Front. Nutr. (2021.0) 8 777214. DOI: 10.3389/fnut.2021.777214 59. Bashir T., Zia-Ur-Rehman Mashwani K.Z., Haider S., Shaista T.M.. Chemistry, Pharmacology and Ethnomedicinal Uses of. Pure Appl. Biol. (2021.0) 4 226-235. DOI: 10.19045/bspab.2015.42011 60. Gai F., Karamać M., Janiak M.A., Amarowicz R., Peiretti P.G.. Sunflower (. Antioxidants (2020.0) 9. DOI: 10.3390/antiox9060535 61. Iqbal N., Masood A., Nazar R., Syeed S., Khan N.A.. Photosynthesis, Growth and Antioxidant Metabolism in Mustard (. Agric. Sci. Chin. (2010.0) 9 519-527. DOI: 10.1016/S1671-2927(09)60125-5 62. Khyade V.B., Jagtap S.G.. Sprouting Exert Significant Influence on the Antioxidant Activity in Selected Pulses (Black Gram, Cowpea, Desi Chickpea and Yellow Mustard). World Sci. News (2016.0) 35 3-86 63. Manvar M.N.. Pharmacognostical Investigations on. Int. J. Pharm. Sci. (2011.0) 2 2812 64. Kusumah V., Pertiwi H.. Potential of. Ecol. Environ. Conserv. (2021.0) 28 33-41. DOI: 10.53550/EEC.2022.v28i01s.004 65. Mastropasqua L., Dipierro N., Paciolla C.. Effects of Darkness and Light Spectra on Nutrients and Pigments in Radish, Soybean, Mung Bean and Pumpkin Sprouts. Antioxidants (2020.0) 9. DOI: 10.3390/antiox9060558 66. Kalim A., Zaheer M., Siddiqui M.U.A., Ahmed S., Hassan M.M.. Nutritional value, Ethnomedicine, Phytochemistry and Pharmacology of. J. Pharmacogn. Phytochem. (2021.0) 10 54-58. DOI: 10.22271/phyto.2021.v10.i2a.13821 67. Nget R., Aguilar E.A., Cruz P.C.S., Reaño C.E., Sanchez P.B., Reyes M.R., Prasad P.V.V.. Overview of Farmers’ Perceptions of Current Status and Constraints to Soybean Production in Ratanakiri Province of Cambodia. Sustainability (2021.0) 13. DOI: 10.3390/su13084433 68. Peiretti P.G., Karamać M., Janiak M., Longato E., Meineri G., Amarowicz R., Gai F.. Phenolic Composition and Antioxidant Activities of Soybean (. Agronomy (2019.0) 9. DOI: 10.3390/agronomy9030153 69. Noman O.M., Nasr F.A., Alqahtani A.S., Al-zharani M., Cordero M.A., Alotaibi A.A., Bepari A., Alarifi S., Daoud A.. Comparative Study of Antioxidant and Anticancer Activities and HPTLC Quantification of Rutin in White Radish (. Open Chem. (2021.0) 19 408-416. DOI: 10.1515/chem-2021-0042 70. Yücetepe A., Altin G., Özçelik B.. A Novel Antioxidant Source: Evaluation of in vitro Bioaccessibility, Antioxidant Activity and Polyphenol Profile of Phenolic Extract from Black Radish Peel Wastes (. Int. J. Food Sci. (2021.0) 56 1376-1384. DOI: 10.1111/ijfs.14494 71. Chorol S.. Antioxidant Content in Different Parts of Radish (. Pharmacogn. J. (2019.0) 11 1064-1070. DOI: 10.5530/pj.2019.11.166 72. Banihani S.A.. Radish (. Nutrients (2017.0) 9. DOI: 10.3390/nu9091014 73. Guo F., Xiong H., Wang X., Jiang L., Yu N., Hu Z., Sun Y., Tsao R.. Phenolics of Green Pea (. J. Agric. Food Chem. (2019.0) 67 11955-11968. DOI: 10.1021/acs.jafc.9b04501 74. Hossain M.A., Arafat M.Y., Alam M., Hossain M.M.. Effect of Solvent Types on the Antioxidant Activity and Total Flavonoids of Some Bangladeshi Legumes. Food Res. (2021.0) 5 329-335. DOI: 10.26656/fr.2017.5(4).035 75. Paśko P., Tyszka-Czochara M., Galanty A., Gdula-Argasińska J., Żmudzki P., Bartoń H., Zagrodzki P., Gorinstein S.. Comparative Study of Predominant Phytochemical Compounds and Proapoptotic Potential of Broccoli Sprouts and Florets. Plant Foods Hum. Nutr. (2018.0) 73 95-100. DOI: 10.1007/s11130-018-0665-2 76. Sousa C., Lopes G., Pereira D., Taveira M., Valentao P., Seabra R., Pereira J.A., Baptista P., Ferreres F., Andrade P.. Screening of Antioxidant Compounds During Sprouting of. Comb. Chem. High Throughput Screen. (2007.0) 10 377-386. DOI: 10.2174/138620707781662817 77. Tamprasit K., Weerapreeyakul N., Sutthanut K., Thukhammee W., Wattanathorn J.. Harvest Age Effect on Phytochemical Content of White and Black Glutinous Rice Cultivars. Molecules (2019.0) 24. DOI: 10.3390/molecules24244432 78. Seal T., Pillai B., Chaudhuri K.. Identification and Quantification of Phenolic Acids by HPLC, In Two Wild Edible Plants Viz.. J. Chem. Biol. Phys. Sci. (2016.0) 6 1108-1121 79. So V., Pocasap P., Sutthanut K., Sethabouppha B., Thukhammee W., Wattanathorn J., Weerapreeyakul N.. Effect of Harvest Age on Total Phenolic, Total Anthocyanin Content, Bioactive Antioxidant Capacity and Antiproliferation of Black and White Glutinous Rice Sprouts. Appl. Sci. (2020.0) 10. DOI: 10.3390/app10207051 80. Zohra T., Ovais M., Khalil A.T., Qasim M., Ayaz M., Shinwari Z.K.. Extraction Optimization, Total Phenolic, Flavonoid Contents, HPLC-DAD Analysis and Diverse Pharmacological Evaluations of. Nat. Prod. Res. (2019.0) 33 136-142. DOI: 10.1080/14786419.2018.1437428 81. Zkan G., Ersus B.S.. Enzyme-Assisted Extraction of Stabilized Chlorophyll from Spinach. Food Chem. (2015.0) 176 152-157. DOI: 10.1016/j.foodchem.2014.12.059 82. Elufioye T.O., Olusola D.M., Oyedeji A.O.. Correlation of Total Phenolic, Flavonoid and Tannin Content of. Pharmacogn. J. (2019.0) 11 1003-1009. DOI: 10.5530/pj.2019.11.158 83. Ghane S., Attar U., Yadav P., Lekhak M.. Antioxidant, Anti-Diabetic, Acetylcholinesterase Inhibitory Potential and Estimation of Alkaloids (Lycorine and Galanthamine) from Crinum Species: An Important Source of Anticancer and Anti-Alzheimer Drug. Ind. Crop. Prod. (2018.0) 125 168-177. DOI: 10.1016/j.indcrop.2018.08.087 84. Pauliuc D., Dranca F., Oroian M.. Antioxidant Activity, Total Phenolic Content, Individual Phenolics and Physicochemical Parameters Suitability for Romanian Honey Authentication. Foods (2020.0) 9. DOI: 10.3390/foods9030306 85. Senhaji S., Lamchouri F., Boulfia M., Lachkar N., Bouabid K., Toufik H.. Mineral Composition, Content of Phenolic Compounds and in vitro Antioxidant and Antibacterial Activities of Aqueous and Organic Extracts of the Seeds of. S. Afr. J. Bot. (2022.0) 147 697-712. DOI: 10.1016/j.sajb.2022.03.005 86. Lee C.H., Lee T.H., Ong P.Y., Wong S.L., Hamdan N., Elgharbawy A.A., Azmi N.A.. Integrated Ultrasound-Mechanical Stirrer Technique for Extraction of Total Alkaloid Content from. Process. Biochem. (2021.0) 109 104-116. DOI: 10.1016/j.procbio.2021.07.006 87. Paśko P., Bartoń H.J., Zagrodzki P., Gorinstein S., Fołta M., Zachwieja Z.. Anthocyanins, Total Polyphenols and Antioxidant Activity in Amaranth and Quinoa Seeds and Sprouts During Their Growth. Food Chem. (2009.0) 115 994-998. DOI: 10.1016/j.foodchem.2009.01.037 88. Ali A.M.A., El-Nour M.E.M., Yagi S.M.. Total Phenolic and Flavonoid Contents and Antioxidant Activity of Ginger (. J. Genet. Eng. Biotechnol. (2018.0) 16 677-682. DOI: 10.1016/j.jgeb.2018.03.003 89. Chen G.-L., Chen S.-G., Xiao Y., Fu N.-L.. Antioxidant Capacities and Total Phenolic Contents of 30 Flowers. Ind. Crop. Prod. (2018.0) 111 430-445. DOI: 10.1016/j.indcrop.2017.10.051 90. Zhong Y., Shahidi F.. Methods for the Assessment of Antioxidant Activity in Foods. Handbook of Antioxidants for Food Preservation (2015.0) 287-333
--- title: Selenized Polymer-Lipid Hybrid Nanoparticles for Oral Delivery of Tripterine with Ameliorative Oral Anti-Enteritis Activity and Bioavailability authors: - Yuehong Ren - Chunli Qi - Shuxian Ruan - Guangshang Cao - Zhiguo Ma - Xingwang Zhang journal: Pharmaceutics year: 2023 pmcid: PMC10059782 doi: 10.3390/pharmaceutics15030821 license: CC BY 4.0 --- # Selenized Polymer-Lipid Hybrid Nanoparticles for Oral Delivery of Tripterine with Ameliorative Oral Anti-Enteritis Activity and Bioavailability ## Abstract The oral delivery of insoluble and enterotoxic drugs has been largely plagued by gastrointestinal irritation, side effects, and limited bioavailability. Tripterine (Tri) ranks as the hotspot of anti-inflammatory research other than inferior water-solubility and biocompatibility. This study was intended to develop selenized polymer-lipid hybrid nanoparticles loading Tri (Se@Tri-PLNs) for enteritis intervention by improving its cellular uptake and bioavailability. Se@Tri-PLNs were fabricated by a solvent diffusion-in situ reduction technique and characterized by particle size, ζ potential, morphology, and entrapment efficiency (EE). The cytotoxicity, cellular uptake, oral pharmacokinetics, and in vivo anti-inflammatory effect were evaluated. The resultant Se@Tri-PLNs were 123 nm around in particle size, with a PDI of 0.183, ζ potential of −29.70 mV, and EE of $98.95\%$. Se@Tri-PLNs exhibited retardant drug release and better stability in the digestive fluids compared with the unmodified counterpart (Tri-PLNs). Moreover, Se@Tri-PLNs manifested higher cellular uptake in Caco-2 cells as evidenced by flow cytometry and confocal microscopy. The oral bioavailability of Tri-PLNs and Se@Tri-PLNs was up to $280\%$ and $397\%$ relative to Tri suspensions, respectively. Furthermore, Se@Tri-PLNs demonstrated more potent in vivo anti-enteritis activity, which resulted in a marked resolution of ulcerative colitis. Polymer-lipid hybrid nanoparticles (PLNs) enabled drug supersaturation in the gut and the sustained release of Tri to facilitate absorption, while selenium surface engineering reinforced the formulation performance and in vivo anti-inflammatory efficacy. The present work provides a proof-of-concept for the combined therapy of inflammatory bowel disease (IBD) using phytomedicine and Se in an integrated nanosystem. Selenized PLNs loading anti-inflammatory phytomedicine may be valuable for the treatment of intractable inflammatory diseases. ## 1. Introduction Inflammatory bowel disease (IBD) is a group of disorders that cause chronic inflammation (pain and swelling) in the intestine, mainly including Crohn’s disease (CD) and ulcerative colitis (UC) [1]. The incidence and prevalence of IBD are increasing worldwide year by year. It can affect people of all ages, from children to the aged, and affects all aspects of life. A clinical survey disclosed that the risk of colon cancer in IBD patients is higher than that in non-IBD patients [2]. Currently, the pathogenesis of IBD is still unclear. The basic options for treatment include surgery [3], medication [4], dietary intervention [5], and biologic therapy [6]. However, these medical strategies come with some downsides, such as a long course of treatment, side effects, and high recurrence. Therefore, it is important to develop novel medications for IBD therapy. For the past few years, phytochemicals or phytocomponents have aroused considerable interest as therapeutic candidates in confronting various chronic diseases by virtue of their pleiotropic bioactivity and lower toxicity. Triperine (Tri) is a typical phytomedicine that has demonstrated good results in preclinical trials to treat diverse immune diseases, including IBD and rheumatoid arthritis (RA) [7]. It has been reported that Tri can upregulate protective autophagy through the PI3K/Akt/mTOR signaling pathway to improve experimental colitis in IL-10 deficient mice [8]. It can also ameliorate DSS-induced ulcerative colitis by modulating oxidative stress, the expression of inflammatory cytokines, and intestinal homeostasis [9]. These experimental studies indicate that Tri has a broad therapeutic prospect for IBD. However, its clinical translation is highly challenged by its lower aqueous solubility, poor oral bioavailability, and potential cytotoxicity. The water-solubility of Tri is merely 13.25 μg/mL around at 37 °C [10], but the oil-water partition coefficient (LogP) is as high as 5.63 [11], showing BCS IV-type drug properties. To address the formulation challenge of Tri, a variety of pharmaceutical nanotechnologies have emerged, including polymerized prodrug micelles [12], enzyme-responsive nanoparticles [13], and bio-mimicking nanoparticles [14]. Polymer-lipid hybrid nanoparticles (PLNs) are next-generation core-shell nanostructures derived from liposome and polymeric nanoparticles, where a polymer core remains encysted by a lipid corona [15]. It relies on the lipid components to improve the intestinal permeability and biocompatibility of nanocarriers. The polymers help enhance drug encapsulation and gastrointestinal stability and take charge of the sustained release. PLNs have exhibited enormous potential in improving the physicochemical properties of drugs, overcoming the biological barriers, modulating drug release, and improving oral bioavailability [16]. Selenium (Se), as an essential trace element and physiological regulator, plays an important role in inflammation and immunity [17]. Previous studies have shown that Se functionalization can not only stabilize nanocarriers to facilitate GI transport, but also potentiate the curative effect of payloads through a synergistic mechanism [18,19,20,21,22,23]. Surface engineering with Se may be extra contributory to PLNs to maximize the antiphlogistic action of Tri. In this study, selenized PLNs (Se@PLNs) were tactfully developed for the oral delivery of Tri with the intention of ameliorating its bioavailability and anti-enteritis activity. Tri-loaded Se@PLNs (Se@Tri-PLNs) were characterized by particle size, ζ potential, morphology, drug entrapment and release, and in vitro/vivo stability. The cellular uptake and transport feature of Se@Tri-PLNs were evaluated in Caco-2 cells. In addition, the in vivo pharmacokinetics and pharmacodynamics of Se@Tri-PLNs were demonstrated in normal rats and a DSS-induced murine ulcerative colitis (UC) model via oral dosing, respectively. ## 2.1. Materials Tripterine was purchased from Anhui Zesheng Technology Co., Ltd. (Hefei, China). Soybean lecithin with phosphatidylcholine (PC) over $90\%$ was obtained from Alfa Aesar Chemicals Co. Ltd. (Shanghai, China). Poly (lactic-co-glycolic acid) (PLGA 75:25) was provided by Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China). Sodium selenite (Na2SeO3) and reduced glutathione (r-GSH) were bought from Aladdin Reagent (Shanghai, China). Dulbecco’s Modified Eagle’s Minimal Essential Medium (DMEM), fetal bovine serum (FBS), and penicillin-streptomycin solution were purchased from Gibco BRL (Carlsbad, CA, USA). 3,3′-diocta-decyloxacarbocyanine perchlorate (DiO) and Hoechst 33258 were products of Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China). Chlorpromazine, simvastatin, and genistein were obtained from Sigma–Aldrich (Shanghai, China). Dextran sodium sulfate (DSS) came from Coolaber Science and Technology Co., Ltd. (Beijing, China). Acetonitrile and methanol were provided by Merck (Darmstadt, Germany). All other chemicals were of analytical grade and used as received. ## 2.2. Cell Line and Animals The Caco-2 cell line was obtained from ATCC (American Type Culture Collection). The cells were maintained in DMEM supplemented with $10\%$ FBS and 100 U/mL penicillin and 100 μg/mL streptomycin at 37 °C in a humidified incubator of $5\%$ CO2 and sub-cultured every 2–3 days. Male Sprague Dawley (SD) rats (220 ± 20 g) and male BALB/c mice (18–22 g) were purchased from the Guangdong Medical Laboratory Animal Center (Guangzhou, China). All animals were housed under controlled conditions (23 ± 2 °C, 55 ± $10\%$ humidity, and 12 h dark/light cycle) and free access to a standard laboratory diet and water. All mice were acclimatized for one week prior to the study. All of the protocols of animal experiments were reviewed and approved by the Experimental Animal Ethical Committee of Jinan University (No. 20220812-01). Animals were handled as per the Guidelines on the Care and Use of Animals for Scientific Purposes [2004]. ## 2.3. Preparation of Se@Tri-PLNs Se@Tri-PLNs were fabricated by solvent diffusion, followed by the in situ reduction technique, as shown in Figure 1 [19]. Briefly, Tri, lecithin and PLGA at a fixed ratio were weighed into a small flask, where a certain amount of binary organic solvent (acetone-ethanol, 4:1) was used to facilitate their dissolution to form an organic phase under ultrasonication. The organic solution was then dripped into deionized water dropwise in a fixed proportion under stirring at 1500 rpm for 0.5 h, resulting in the self-assembly of drug molecules and carrier materials into hybrid nanoparticles. Afterwards, the resultant nanoparticles were subjected to sonication for 6 min. After removal of organic solvents via evaporation, Tri-loaded polymer-lipid hybrid nanoparticles (Tri-PLNs) were obtained. Subsequently, Na2SeO3 was introduced into the nanosuspensions of Tri-PLNs and stirred for 0.5 h at room temperature. After that, r-GSH was added into the system to trigger the reduction reaction at a molar ratio of 4:1 to Na2SeO3. The reaction was maintained for 2.5 h at 37 °C under an agitation of 1000 rpm. Se@Tri-PLNs were formed upon Se4+ being reduced into elemental Se and precipitating onto the surface of Tri-PLNs. The residual reactants were dialyzed out against deionized water twice. To obtain a preferred formulation, we screened the factors affecting the formulation performance of Se@Tri-PLNs, including the mass ratio of lecithin to PLGA, the mass ratio of Tri to carrier materials, the volume ratio of organic phase to the water phase upon self-assembly, and the concentration of Na2SeO3 upon reaction. ## 2.4. Characterization of Nanocarriers Tri-PLNs and Se@Tri-PLNs were characterized by particle size, ζ potential, morphology, entrapment efficiency (EE), and drug loading (DL). The particle size and ζ potential of nanoparticles were measured by size and potential analyzer (Zetasizer Nano ZS, Malvern, UK) at 25 °C after dilution with deionized water approximately 50 times in a quartz cell, based on dynamic light scattering and Doppler velocimetry, respectively. The data were obtained using the built-in software for analysis of particle size and ζ potential. To inspect the morphology of nanocarriers, the samples were diluted properly with deionized water and fixed on a carbon-coated copper grid by evaporating the residual water naturally. Microimaging was performed on a JEM-1230 transmission electron microscope (TEM) (JEOL, Tokyo, Japan). The micrographs of Tri-PLNs and Se@Tri-PLNs were taken at an acceleration voltage of 100 kV. The EE and DL of Tri-PLNs and Se@Tri-PLNs were determined after separating unentrapped and free Tri from nanosuspensions by the centrifugal ultrafiltration technique. Freshly prepared Tri-PLNs and Se@Tri-PLNs were first centrifuged at 5000 rpm for 10 min to remove the unentrapped coarse Tri, and then subjected to centrifugal ultrafiltration against a MWCO 30 K filter device (Amicon®, Merck, Darmstadt, Germany). The total drug and free drug in the filtrate were quantified by UPLC, as described below. EE and DL were calculated as follows:EE(%)=(1−MfreMtot)×100; DL(%)=Mtot−Mfre(Mtot+Mexc)×100 where Mfre, Mtot and Mexc denote the amounts of free Tri, total Tri, and excipients used in the formulation, respectively. A Waters ACQUITY H-Class ULPC system equipped with a quaternary pump, an autosampler, and a PDA detector was employed for the Tri assay. The samples were eluted against a Poroshell HPH-C18 column (2.6 μm, 2.1 × 50 mm, Waters) at 45 °C with 5 μL of injection. A mobile phase composed of methanol and $0.25\%$ phosphoric acid solution ($\frac{80}{20}$) pumped at a flow rate of 0.2 mL/min was utilized to elute the samples. The elution signals were collected at 425 nm. ## 2.5. Biorelevant Stability Study The stability of nanocarriers in the simulated gastric fluid (SGF) and simulated intestinal fluid (SIF) was evaluated. With deionized water medium as the control, the changes in particle size, PDI and ζ potential of Tri-PLNs and Se@Tri-PLNs in SGF and SIF were investigated, respectively. In detail, an appropriate amount of nanosuspensions was added into SGF (containing $0.32\%$ pepsin, pH 1.2) or SIF (containing $1\%$ trypsin, pH 6.8) at a volume of 1:5 and incubated at 37°C under stirring at 100 rpm. At predetermined intervals, 1 mL of sample was withdrawn and immediately analyzed for particle size, PDI and ζ potential. To get insight into the mechanism of particle size change, we reassessed the indices of particle size by changing the pH of the test medium that nanocarriers were susceptible to after incubation. The same sample was divided into two parts: one was directly determined for particle size, PDI and ζ potential after incubation, and the other was determined after neutralization with a pH conditioner. ## 2.6. In Vitro Release Study The in vitro release of Tri-PLNs and Se@Tri-PLNs were conducted in deionized water, pH 1.2 HCl solution and pH 6.8 phosphate buffered saline (PBS) containing $0.5\%$ Tween 80 as a solubilizing agent, respectively. Briefly, 2.5 mL of Tri-PLNs or Se@Tri-PLNs were placed into dialysis bags loading 50 mL of release medium and subjected to agitation at 100 rpm and 37 °C for 12 h in the dissolution cups using a ZRS-8GD dissolution tester (TIANDA TIANFA, Tianjing, China). At 0.5, 1, 2, 4, 6, 8 and 12 h, 1 mL of release solution was withdrawn and filtered against a 0.22 μM filter membrane. Tri concentration in the filtrates was analyzed by UPLC, as described above. The release profiles were plotted according to the accumulative release percentage with the time. ## 2.7. Cytotoxicity Assay The cytotoxicity of Tri, Tri-PLNs and Se@Tri-PLNs was evaluated by the MTT method. Briefly, Caco-2 cells were cultured in DMEM medium supplemented with $10\%$ inactivated FBS and $1\%$ dual antibiotic solution (100 U/mL of penicillin-100 μg/mL of streptomycin) at 37 °C under a $5\%$ CO2/$95\%$ air atmosphere. Caco-2 cells were seeded into a 96-well plate and cultured over 24 h. The cell viability was evaluated by colorimetry using a Synergy H1 Microplate Reader (BioTek, Winooski, VT, USA) after incubation for 8 h, 12 h and 24 h with a series of concentrations of Tri. After finalizing the test concentration and time, the cell viability was assessed again in the presence of Tri, Tri-PLNs, Se@Tri-PLNs, or blank carrier (Se@PLNs). ## 2.8. Cellular Uptake and Internalization Cellular uptake and internalization of Tri-PLNs and Se@Tri-PLNs were analyzed by flow cytometry and CLSM imaging, respectively [20]. DiO-labeled Tri-PLNs and Se@Tri-PLNs were freshly prepared and utilized for observation of cellular uptake and internalization. Except for light avoidance, the preparation of DiO-labeled Tri-PLNs and Se@Tri-PLNs followed the same procedures of Tri-PLNs and Se@Tri-PLNs by synchronously dissolving DiO into the organic phase upon fabrication. Fluorescent Tri-PLNs and Se@Tri-PLNs were incubated with Caco-2 cells at a Tri concentration of 5 μg/mL for 0.5, 1, and 2 h at 37 °C, respectively. The cells were then rinsed twice with a pH 7.4 HBSS and the uptake rate was examined using a flow cytometer (FACSCanto, BD, New York, NY, USA). To identify the cellular internalization, Caco-2 cells were seeded in a confocal Petri dish that was balanced with a cell culture medium in advance. Cells were cultured for 24 h at a density of 5 × 104 cell/mL. DiO-labeled Tri-PLNs and Se@Tri-PLNs were then introduced into the confocal dish to incubate for 1 h. After incubation, the cells were washed two times with cold HBSS, fixed in $4\%$ paraformaldehyde for 0.5 h, and stained with Hoechst 33258. The internalized nanoparticles by Caco-2 cells were visualized on a LSM800 confocal laser scanning microscope (CLSM, Zeiss, Wetzlar, Germany). ## 2.9. Cellular Trafficking Pathway The fluorescent nanoparticles prepared above were used to illustrate the cellular trafficking pathway of Tri-PLNs and Se@Tri-PLNs using various transmembrane transport inhibitors for intervention [24]. Caco-2 cells were pretreated with inhibitors at 37 °C or treated at 4 °C for 0.5 h. DiO-labeled Tri-PLNs and Se@Tri-PLNs were then introduced into the cell wells and incubated for another 2 h. Thereafter, the cells were washed, trypsinized with trypsin-EDTA ($0.25\%$) solution, and centrifuged at 1000 rpm to collect cell pellets. The fluorescence intensity in the sample of cell pellets was quantified by flow cytometry. The internalization or endocytosis pathway of nanoparticles was deciphered according to the altered cellular uptake in the presence of various inhibitors. The concentration and function of physiological inhibitors used are listed in Table 1. ## 2.10. Oral Pharmacokinetics Male SD rats were fasted overnight prior to administration, but were allowed free access to water. The rats were randomized into three groups ($$n = 5$$) and orally administered with Tri suspensions, Tri-PLNs or Se@Tri-PLNs at a dose of 40 mg/kg, respectively. At the time points of 0.25, 0.5, 1, 2, 4, 6, 8, 12 and 24 h, aliquots of blood (~0.25 mL) were sampled from the caudal vein. Fresh plasma was immediately prepared by centrifugation at 3000 rpm for 10 min, during which the concentration of Tri was measured by an UPLC-MS method, as described below. The blood drug concentration versus time curve was plotted, and pharmacokinetic parameters were analyzed by PKSolver 2.0. To quantify the plasma Tri, a liquid-liquid extraction procedure was used to extract Tri from the plasma [25]. Briefly, 100 mL of plasma were combined with 500 mL of ethyl acetate supplemented with 0.1 μg of emodin as an internal standard and then centrifuged to separate the supernatant. The plasma was extracted twice, and the extracting solution was merged followed by evaporation at 30 °C under a vacuum. The residues were reconstituted in 100 μL of methanol for UPLC-MS analysis (SCIEX Triple Quad LC-MS/MS system, AB SCIEX, Framingham, MA, USA). The samples were eluted against a Poroshell HPH-C18 column (2.6 μm, 2.1 × 50 mm, Waters) at 35 °C with 5 μL of injection. A gradient elution was applied using $1\%$ formic acid in water (mobile phase A) versus $0.1\%$ formic acid in acetonitrile (mobile phase B) at a flow rate of 0.4 mL/min. The gradient elution program was $60\%$ B at 0–1 min, $68\%$ B at 1–5 min, $100\%$ B at 5–9.5 min, and $60\%$ B at 9.4–12 min. ## 2.11. In Vivo Anti-Enteritis Activity Evaluation A mouse UC model was adopted to evaluate the in vivo anti-inflammatory activity of Se@Tri-PLNs. UC was induced by DSS in the drinking water ($3\%$, w/v) for one week to establish the UC model. In an experiment, mice were randomly divided into five groups, i.e., control, DSS (model), DSS + Tri suspensions, DSS + Tri-PLNs, and DSS + Se@Tri-PLNs ($$n = 5$$). The mice in the control group were provided with normal drinking water, while all mice in other groups were given water containing $3\%$ DSS for seven days. From the fourth day, the mice in the treatment group were orally administered with Tri suspensions, either Tri-PLNs or Se@Tri-PLNs by gavage at a dose of 1 mg/kg every day for four consecutive days. The mice were monitored for body weight, disease activity index (DAI), and colon morphology. Weight change, fecal viscosity and occult blood were collectively scored from 0 to 4 to calculate DAI according to the previous report [26]. On the 11th day, the mice were euthanized, and the serum, thymus, spleen, and colon tissues were collected immediately. All serum and colon samples were stored at −80 °C until analysis. A histological examination was performed on the colon tissue by blinded analysis. The severity of colon damage was checked by routine H.E. staining. The levels of TNF-α, IL-1β and IL-6 in the serum were measured using commercial mouse TNF-α, IL-1β, and IL-6 ELISA kits (Andygene, Beijing, China), respectively. The thymus and spleen were weighed under sterile conditions and used to calculate the thymus index [(thymus weight (mg)/mouse weight (g)) × 10] and spleen index [(spleen weight (mg)/mouse weight (g)) × 10]. ## 3.1. Preparation and Characterization of Se@Tri-PLNs Solvent diffusion or the nanoprecipitation technique is the most common method for preparing matrix nanoparticles [27,28,29]. When water-miscible organic solvents diffuse towards the aqueous phase, self-assembly that forms nanoparticles takes place due to the sharp decline in the solubility of materials. In the preliminary study, we found that the ratios of lecithin/PLGA, drug/carrier materials, organic/aqueous phase upon self-assembly, and the concentration of Na2SeO3 upon selenization affected the formulation performance. Figure 2 shows the effects of these formulation variables on the particle size and PDI or EE of PLNs. It was observed that the mass ratio of lecithin to PLGA exhibited a peculiar effect on the particle size and PDI of Tri-PLNs (Figure 2A). The high ratio of lecithin resulted in smaller nanoparticles, but they were broadly dispersed. This may be related to the formation of heterogeneous nanoparticles, such as vesicles and micelles, in addition to PLNs. The formulation with a mass ratio of lecithin/PLGA at 1:2 produced the smallest nanoparticles with the narrowest polydispersity. Therefore, it could be argued that lecithin only played a surfactant role in the formulation to facilitate the formation of PLNs as a lipid corona, rather than as the principal carrier material. As the lecithin/PLGA ratio fixed, the ratios of drug/carrier materials and the organic/aqueous phase upon self-assembly had negative effects on the particle size of Tri-PLNs (Figure 2B,C). They increased with the increase of the ratios of Tri in the formulation and aqueous phase upon preparation. However, there was no significant difference in EE, which may be attributed to the high lipophilicity of Tri [30]. Interestingly, the particle size of Se@Tri-PLNs was significantly increased with the increased concentration of Na2SeO3 upon in situ reduction (Figure 2D). This phenomenon illustrated that the nascent elemental selenium had successfully precipitated on the surface of Tri-PLNs. The concentration of Na2SeO3 at 0.25 mg/mL was found to be optimal for decorating Tri-PLNs to fabricate Se@Tri-PLNs with a suitable particle size. Taking the above together, the formulation and process of Se@Tri-PLNs were finalized as lecithin to PLGA at a ratio of 1:2, Tri to lecithin and PLGA at a ratio of 1:4, the organic phase to the water phase at a volume ratio of 1:5 upon self-assembly, and 0.25 mg/mL of Na2SeO3 upon in situ reduction. Based on the preferred formulation and process, Se@Tri-PLNs were prepared using 3 mg of Tri, 4 mg of SPC, and 8 mg of PLGA. Drug and carrier materials were dissolved in 3 mL of an acetone-ethanol binary organic system that was then dripped into 15 mL of water followed by in situ reduction with 3.75 mg of Na2SeO3 and a quadruple mole of r-GSH. The resultant Se@Tri-PLNs possessed a particle size of 123.1 nm around with a PDI of 0.183 (Figure 3A). The particle size was larger than that of unmodified Tri-PLNs (103.5 nm). The increase in particle size can be attributable to the Se precipitation onto the surface of Tri-PLNs. The nanosuspensions of Se@Tri-PLNs were reddish-brown in appearance, which differed from that of Tri-PLNs (yellow) due to the red Se attachment (Figure 3B). The ζ potential of nanoparticles changed from −39.40 mV to −29.70 mV before and after selenization. The absolute ζ potentials were all greater than 25 mV, suggesting good colloidal stability for them. Both Tri-PLNs and Se@Tri-PLNs were spherical in morphology, as revealed by TEM. Otherwise, Se@Tri-PLNs exhibited an apparent corona around the nanoparticles, manifesting the occurrence of Se precipitation. The EE of Se@Tri-PLNs was as high as $98.95\%$, with a DL of $17.07\%$. The merits of small particle size and high encapsulation with Se@Tri-PLNs create conditions for synergy and attenuation of Tri after oral administration. ## 3.2. Gastrointestinal Stability The in vivo fate of nanocarriers is closely associated with their absorption and drug delivery efficacy. The physicochemical stability of Tri-PLNs and Se@Tri-PLNs can be manifested by the particle size, PDI and ζ potential. Biorelevant media including deionized water, SGF and SIF were applied for evaluating the GI stability of Tri-PLNs and Se@Tri-PLNs. Figure 4 shows the changes in particle size, PDI and ζ potential of two nanocarriers with time upon incubation with deionized water, SGF and SIF. It turned out that Tri-PLNs and Se@Tri-PLNs were rather stable both in deionized water and SIF, except in SGF, with no significant changes in particle size, PDI and ζ potential (Figure 4A–C). However, in the case of SGF, these indicators changed dramatically, whereby the particle size increased significantly along with the rise of PDI and the inversion of interfacial charge. The alterations were more pronounced in terms of Tri-PLNs. In order to elucidate the underlying cause, we conducted pH neutralization to the samples incubated with SGF and determined the parameters again. After being neutralized to pH 7.4 with sodium hydroxide, the particle size, PDI and ζ potential of Tri-PLNs and Se@Tri-PLNs was restored to almost their initial levels. The results indicated that Tri-PLNs and Se@Tri-PLNs were not digested or degraded in the harsh gastric condition, but rather protonated. Both Tri-PLNs and Se@Tri-PLNs were negatively charged on the surface, and thus they tended to appear protonated in the strong acidic environment. Protonation tends to cause the aggregation of nanoparticles, the enlargement of PDI, and the reversal of interfacial charge [31]. Of note, Se@Tri-PLNs exhibited a stronger resistance to acid protonation compared with Tri-PLNs, owing to surface Se attachment. ## 3.3. In Vitro Drug Release The release profiles of Tri from Tri-PLNs and Se@Tri-PLNs in different media are delimitated in Figure 5. In pH 1.2 HCl solution, both Tri-PLNs and Se@Tri-PLNs exhibited an extremely low release, where merely $5.02\%$ and $4.89\%$ of Tri were released from Tri-PLNs and Se@Tri-PLNs within 12 h, respectively. As suggested in the stability study, the marginal release of Tri from nanoparticles may result from protonation of the surface of the nanoparticles. Protonation causes nanoparticle aggregation and contraction that retards drug release. Drug release was accelerated in deionized water. Nevertheless, the accumulative release percentages were still limited for both Tri-PLNs and Se@Tri-PLNs. The maximal release percentage was not more than $38\%$ within the predictable time of GI transportation, showing an obvious sustained release effect. Quicker drug release was presented in the medium of pH 6.8 PBS. This may be related to the ionization of Tri in the buffering system. Tri contains a structural carboxyl that can react with alkali ions such as Na+ and K+ to form salts, thus promoting the dissolution of Tri from PLNs. In comparison, Se@Tri-PLNs displayed slower drug release both in deionized water and pH 6.8 PBS. The accumulative release rate of Tri exceeded $81.50\%$ within 12 h in the case of Tri-PLNs in the medium of pH 6.8 PBS, whereas it was $71.03\%$ with regard to Se@Tri-PLNs. The reduced release indicated that a thin Se layer was provided with Se@Tri-PLNs as a result of in situ reduction. The characteristics of pH-dependent drug release reduces the premature release of Tri in the stomach, which will be conducive to its intestinal absorption through integral nanoparticles, hence improving the oral bioavailability. ## 3.4. Cytotoxicity Figure 6 depicts the cytotoxicity of free Tri, formulated Tri and a blank carrier in Caco-2 cells. As shown in Figure 6A, the cytotoxicity of Tri was time- and concentration-dependent. When the concentration of Tri was 1–5 μg/mL, the cell survival rate was higher than $80\%$ over 24 h. Above this concentration, the cytotoxicity of Tri became apparent (Figure 6A). To this end, it is of interest to formulate Tri into nanomedicine so as to overcome the issues of low solubility and limited oral absorption due to drug efflux [21,32]. To further examine the cytotoxicity of nanoscaled Tri (Tri-PLNs and Se@Tri-PLNs), the cell viability was measured at a given concentration equivalent to 5 μg/mL of Tri after incubation for 24 h. A blank carrier (Se@PLNs) exhibited lower cytotoxicity parallel to free Tri. However, Tri-PLNs and Se@Tri-PLNs produced a certain cytotoxicity to Caco-2 cells, where the cell viability was reduced to $58.36\%$ and $52.01\%$ after treatment for 24 h at the concentration of 5 μg/mL, respectively, which were significantly lower than that of free Tri at the same level. This can be explained by the increased cellular uptake of Tri-PLNs and Se@Tri-PLNs that enhances the intracellular concentration of Tri, which results in more cell necrosis. Tri has been proven to be a substrate of P-glycoprotein with significant intestinal efflux [33]. Thus, the increased cytotoxicity of formulated Tri results from incremental cellular uptake rather than the nanotoxicity from the carriers. In addition, there is no need to worry about damage to enterocytes caused by nanoparticles, since the concentration exposed to the intestinal epithelial cells will be much lower than the tested concentration after oral administration [34]. ## 3.5. Cellular Uptake, Internalization, and Transport Mechanisms The in vitro cellular uptake of nanoparticles tested on enterocytes is normally used to predict the in vivo absorption thereof. Figure 7 shows the flow cytometric events of cellular uptake in Caco-2 cells with regard to Tri-PLNs and Se@Tri-PLNs. A time-dependent cellular uptake was presented by both nanocarriers. The relative cell numbers stained by DiO-labeled Tri-PLNs and Se@Tri-PLNs were as high as $64.3\%$ and $92.5\%$, respectively, after incubation for 2 h. By contrast, the cellular uptake rate of Se@Tri-PLNs was higher. It was reported that this was a result of the heavy density of selenized nanocarriers, which resulted in enhancive nonspecific phagocytosis [22]. Oral vehicles that we developed produced a similar cellular uptake to cationic liposomes layered by trimethylated chitosan [35], showing stronger intestinal epithelial permeability. The excellent cell affinity of Tri-PLNs and Se@Tri-PLNs can also be reflected by CLSM imaging of cellular internalization. The apparent fluorescence staining associated with Tri-PLNs and Se@Tri-PLNs was observed to diffusely distribute in the cell colony (Figure 8). The fluorescence intensity in the group of Se@Tri-PLNs was slightly stronger than that of Tri-PLNs, consistent with the results of cellular uptake. The findings suggest that selenized nanoparticles take on a certain absorption-promoting effect which may contribute to the enhancement of oral bioavailability. Furthermore, it could be seen from the micrograph that nanoparticle-associated fluorescence mainly distributed within the cytoplasm rather than the nucleus (stained by Hoechst 33258). Therefore, it is fairly beneficial to the translocation of nanoparticles from the apical membrane of intestinal epithelium to the basolateral side. The cellular uptake of Tri-PLNs and Se@Tri-PLNs in the presence of various inhibitors treated at 4 °C is shown in Figure 9. The cellular uptake rate of Tri-PLNs was significantly affected by hypertonic sucrose, chlorpromazine, and simvastatin, whereas genistein exerted less effect on the cellular uptake. Hypertonic sucrose markedly inhibited the uptake of Se@Tri-PLNs, with a reduction of $10.83\%$ in the uptake rate compared with the control group. The effects of chlorpromazine, simvastatin and genistein on the cellular uptake of Se@Tri-PLNs were relatively insignificant. In addition, the cellular uptakes of Tri-PLNs and Se@Tri-PLNs were markedly exhibited at 4 °C, resulting in an uptake reduction of $89.3\%$ and $70.6\%$, respectively. These results suggest that Tri-PLNs and Se@Tri-PLNs share different cellular uptake mechanisms. Clathrin-mediated endocytosis and nonspecific caveolin-mediated endocytosis are involved in the uptake process of Tri-PLNs, but ATP-dependent transport through a special transporter or pinocytosis may be mainly responsible for the cellular uptake of Se@Tri-PLNs. It is known that some transporters are sensitive to temperature, and thus active transport is greatly inhibited under a lower temperature [36]. However, the specific transporter responsible for the transport of Se@Tri-PLNs is not clear. Nevertheless, the active transport manner enables Se@Tri-PLNs to be more readily assimilated by the absorptive intestinal epithelia. ## 3.6. Enhanced Bioavailability The pharmacokinetic profiles of Tri suspensions, Tri-PLNs and Se@Tri-PLNs in SD rats after oral administration are shown in Figure 10. It could be seen that the blood drug concentration in the group of Tri suspensions went up quickly but declined quickly as well, presenting a short in vivo residence of free Tri. As formulated into Tri-PLNs and Se@Tri-PLNs, the absorption extent of Tri was dramatically enhanced, though the rate of absorption decreased. The maximum plasma concentration (Cmax) did not drop much for Tri-PLNs, but declined a little for Se@Tri-PLNs. The time to Cmax (Tmax) lagged significantly behind the suspension formulation, from approximately 4 h to 8 h (Table 2). Meanwhile, the mean residence time (MRT) of Tri-PLNs and Se@Tri-PLNs increased accordingly, increasing to approximately 13.8 h. Between Tri-PLNs and Se@Tri-PLNs, there was no significant difference in the absorption rate as signified by the parameters of Cmax and Tmax. However, the degree of absorption was enhanced substantially in terms of Se@Tri-PLNs compared to Tri-PLNs. The high blood drug concentration was maintained, even at the end of sampling. These results suggest that Se@Tri-PLNs possesses a prolonged absorption profile, which shows good in vitro and in vivo correlation (IVIVC), combined with the in vitro release. The relative oral bioavailability of Tri-PLNs and Se@Tri-PLNs calculated by the trapezoidal method was up to $280\%$ and $397\%$ compared with the Tri suspensions, respectively. Furthermore, the oral bioavailability that resulted from Se@Tri-PLNs was greater than that of Tri-PLNs, just as Se@Tri-PLNs could better sustain the release of Tri. Sustained and everlasting absorption can maintain a long-term curative effect in vivo, which is beneficial for the treatment of chronic diseases [37]. To improve the bioavailability of Tri, a variety of oral nano-vehicles have been explored, including phytosomes [20,38], silk fibroin nanoparticles [39], and lactosylated albumin nanoparticles [40]. Compared with silk fibroin nanoparticles and lactosylated albumin nanoparticles, the selenized PLN system developed by us resulted in higher blood drug concentrations, in addition to longer in vivo residence times. The Cmax that resulted from silk fibroin nanoparticles was only 90.5 ± 49.2 ng/mL, while this parameter of lactosylated albumin nanoparticles was lower than 3.5 ng/mL. Depending on the superior gastrointestinal stability, our constructed nano-vehicle exhibits an advantage in ameliorating the oral absorption of Tri. ## 3.7. Ameliorative In Vivo Anti-Enteritis A murine UC model was adopted to evaluate the anti-enteritis effect of Se@Tri-PLNs. As shown in Figure 11B,D, the control mice exhibited unceasing weight gain and normal colon histology. However, the model mice emerged with significant weight loss along with rectal bleeding and diarrhea after DSS induction for three days. Meanwhile, the length of the colon of the model mice was shortened, and the colon tissue presented inflammatory symptoms, such as ulcers, crypt abscesses, loss of goblet cells and mucus layers, and considerable neutrophil infiltration into the lamina propria (Figure 11D,I). These results indicated that the UC model based on BALB/c mice was successfully established by the oral administration of $3\%$ DSS in drinking water. After treatment with Tri suspensions, either with Tri-PLNs or Se@Tri-PLNs for one week at the dose of 1 mg/kg, the mice in the trial groups had increased body weight, a decreased DAI score, elongated colon length, and alleviated colon injury to different extents in comparison with the model group (Figure 11B–E,I). It is clear that both free Tri and formulated Tri are provided with good anti-UC activities, among which the curative effect of Se@Tri-PLNs was most prominent. The results also demonstrated that Se could potentiate the anti-inflammatory action of Tri on the base of PLNs. The synergistic effect between Se and therapeutic molecules has been widely confirmed [34,41,42]. This was well supported by the declined levels of inflammatory factors in the peripheral blood (Figure 11F–H). The normal mice (control) maintained lower levels of serumal TNF-α, IL-1β, and IL-6, whereas three inflammatory factors were clearly upregulated in the DSS-induced group. After treatment with Tri suspensions, Tri-PLNs and Se@Tri-PLNs for one week, these inflammatory markers significantly decreased. In comparison, Se@Tri-PLNs gave rise to a remarkable downregulation toward the inflammatory factors. This may be attributable to the sensitization of Se to Tri that cooperatively inhibits the release of pro-inflammatory cytokines. Additional evidence comes from the thymus index and spleen index that mirror the status of the immune function [43]. It was found that the thymus index and spleen index of the DSS-induced group were significantly lower than that of the control group (Table 3), signifying the immune function of mice being inhibited. For the treatment group, both indexes escalated to varying degrees, wherein Se@Tri-PLNs demonstrated an optimal immune boosting effect. A histopathological check further validated that Tri-PLNs and Se@Tri-PLNs possessed excellent anti-UC activities, even at a considerably low dose (1 mg/kg) (Figure 11I). Compared with Tri-PLNs, the anti-UC efficacy of Se@Tri-PLNs was more prominent, which markedly reduced the lymphocytic infiltration of the mucosal epithelium and the severity of UC. Se@Tri-PLNs not only have advantages in cellular uptake and transport, but also show better in vivo anti-UC action. The synergy between Se and Tri in inflammation resolution has been clearly documented. In fact, enteritis correlates with the progression of inflammation and oxidative stress. As a promising phytomedicine for IBD, Tri can act on the disease target by modulating oxidative stress, inflammatory cytokines, and intestinal homeostasis [44], suppressing the RIP3/MLIC necroptosis pathway [45] and maintaining immune balance by regulating gut microbiota [9]. Moreover, Se has been proven to be qualified with direct immunomodulatory and anti-inflammatory properties as an oxidative stress alleviator [46]. Therefore, selenization contributed substantially to the enhancement of nanoscaled Tri against UC. ## 4. Conclusions In this work, we developed selenized polymer-lipid hybrid nanoparticles for oral delivery of Tri in an attempt to potentiate its anti-enteritis efficacy. We successfully prepared Se@Tri-PLNs through the solvent diffusion combined with in situ reduction technology. The resultant nanomedicine demonstrated numerous advantages in stability, drug release, cellular uptake, bioavailability, and anti-UC activity. Se@Tri-PLNs effectively prevented the progression of UC, lowered the levels of inflammatory factors in vivo, and alleviated DSS-associated injury to the colon compared with free Tri and Tri-PLNs. Ameliorative cellular uptake and pharmacokinetics were responsible for enhanced anti-enteritis activity. Se, as a sensitizer, further strengthened the curative effect of Tri. These findings provide a formulation solution to integrating water-insoluble API and Se into clinically applicable medications for the management of IBD. Selenized PLNs may serve as a promising nanotechnology platform for the oral delivery of anti-inflammatory agents to treat IBD. ## References 1. De Mattos B.R., Garcia M.P., Nogueira J.B., Paiatto L.N., Albuquerque C.G., Souza C.L., Fernandes L.G., Tamashiro W.M., Simioni P.U.. Inflammatory bowel disease: An overview of immune mechanisms and biological treatments. Mediators Inflamm. (2015) 2015 493012. DOI: 10.1155/2015/493012 2. Shah S.C., Itzkowitz S.H.. Colorectal cancer in inflammatory bowel disease: Mechanisms and management. Gastroenterology (2022) 162 715-730. DOI: 10.1053/j.gastro.2021.10.035 3. Crippa J., Carvello M., Kotze P.G., Spinelli A.. Robotic surgery in inflammatory bowel disease. Curr. Drug Targets (2021) 22 112-116. DOI: 10.2174/1389450121999200820125918 4. Santos J.D.M., Peña-Sánchez J.N., Fowler S.A.. Patients’ perspectives on medication for inflammatory bowel disease: A mixed-method systematic review. Eur. J. Gastroenterol. Hepatol. (2021) 33 1139-1147. DOI: 10.1097/MEG.0000000000001861 5. Levine A., Sigall Boneh R., Wine E.. Evolving role of diet in the pathogenesis and treatment of inflammatory bowel diseases. Gut (2018) 67 1726-1738. DOI: 10.1136/gutjnl-2017-315866 6. Lowe S.C., Sauk J.S., Limketkai B.N., Kwaan M.R.. Declining rates of surgery for inflammatory bowel disease in the era of biologic therapy. J. Gastrointest. Surg. (2021) 25 211-219. DOI: 10.1007/s11605-020-04832-y 7. Cascão R., Fonseca J.E., Moita L.F.. Celastrol: A spectrum of treatment opportunities in chronic diseases. Front. Med. (2017) 4 69. DOI: 10.3389/fmed.2017.00069 8. Zhao J., Sun Y., Shi P., Dong J.N., Zuo L.G., Wang H.G., Gong J.F., Li Y., Gu L.L., Li N.. Celastrol ameliorates experimental colitis in IL-10 deficient mice via the up-regulation of autophagy. Int. Immunopharmacol. (2015) 26 221-228. DOI: 10.1016/j.intimp.2015.03.033 9. Li M., Guo W., Dong Y., Wang W., Tian C., Zhang Z., Yu T., Zhou H., Gui Y., Xue K.. Beneficial effects of celastrol on immune balance by modulating gut microbiota in experimental ulcerative colitis mice. Genom. Proteom. Bioinform. (2022) 20 288-303. DOI: 10.1016/j.gpb.2022.05.002 10. Qi X., Qin J., Ma N., Chou X., Wu Z.. Solid self-microemulsifying dispersible tablets of celastrol: Formulation development, charaterization and bioavailability evaluation. Int. J. Pharm. (2014) 472 40-47. DOI: 10.1016/j.ijpharm.2014.06.019 11. Song J., Shi F., Zhang Z., Zhu F., Xue J., Tan X., Zhang L., Jia X.. Formulation and evaluation of celastrol-loaded liposomes. Molecules (2011) 16 7880-7892. DOI: 10.3390/molecules16097880 12. Li J., Jia Y., Zhang P., Yang H., Cong X., An L., Xiao C.. Celastrol self-stabilized nanoparticles for effective treatment of melanoma. Int. J. Nanomed. (2020) 15 1205-1214. DOI: 10.2147/IJN.S232603 13. Deng C., Zhang Q., He P., Zhou B., He K., Sun X., Lei G., Gong T., Zhang Z.. Targeted apoptosis of macrophages and osteoclasts in arthritic joints is effective against advanced inflammatory arthritis. Nat. Commun. (2021) 12 2174. DOI: 10.1038/s41467-021-22454-z 14. Cao X., Hu Y., Luo S., Wang Y., Gong T., Sun X., Fu Y., Zhang Z.. Neutrophil-mimicking therapeutic nanoparticles for targeted chemotherapy of pancreatic carcinoma. Acta Pharm. Sin. B (2019) 9 575-589. DOI: 10.1016/j.apsb.2018.12.009 15. Mukherjee A., Waters A.K., Kalyan P., Achrol A.S., Kesari S., Yenugonda V.M.. Lipid-polymer hybrid nanoparticles as a next-generation drug delivery platform: State of the art, emerging technologies, and perspectives. Int. J. Nanomed. (2019) 14 1937-1952. DOI: 10.2147/IJN.S198353 16. Rao S., Prestidge C.A.. Polymer-lipid hybrid systems: Merging the benefits of polymeric and lipid-based nanocarriers to improve oral drug delivery. Expert Opin. Drug Deliv. (2016) 13 691-707. DOI: 10.1517/17425247.2016.1151872 17. Huang Z., Rose A.H., Hoffmann P.R.. The role of selenium in inflammation and immunity: From molecular mechanisms to therapeutic opportunities. Antioxid. Redox Signal. (2012) 16 705-743. DOI: 10.1089/ars.2011.4145 18. Deng W., Xie Q., Wang H., Ma Z., Wu B., Zhang X.. Selenium nanoparticles as versatile carriers for oral delivery of insulin: Insight into the synergic antidiabetic effect and mechanism. Nanomedicine (2017) 13 1965-1974. DOI: 10.1016/j.nano.2017.05.002 19. Deng W., Wang H., Wu B., Zhang X.. Selenium-layered nanoparticles serving for oral delivery of phytomedicines with hypoglycemic activity to synergistically potentiate the antidiabetic effect. Acta Pharm. Sin. B (2019) 9 74-86. DOI: 10.1016/j.apsb.2018.09.009 20. Zhu S., Luo C., Feng W., Li Y., Zhu M., Sun S., Zhang X.. Selenium-deposited tripterine phytosomes ameliorate the antiarthritic efficacy of the phytomedicine via a synergistic sensitization. Int. J. Pharm. (2020) 578 119104. DOI: 10.1016/j.ijpharm.2020.119104 21. Liu S., Chen Q., Yan L., Ren Y., Fan J., Zhang X., Zhu S.. Phytosomal tripterine with selenium modification attenuates the cytotoxicity and restrains the inflammatory evolution via inhibiting nlrp3 inflammasome activation and pyroptosis. Int. Immunopharmacol. (2022) 108 108871. DOI: 10.1016/j.intimp.2022.108871 22. Yin J., Hou Y., Yin Y., Song X.. Selenium-coated nanostructured lipid carriers used for oral delivery of berberine to accomplish a synergic hypoglycemic effect. Int. J. Nanomed. (2017) 12 8671-8680. DOI: 10.2147/IJN.S144615 23. Chen S., Xing C., Huang D., Zhou C., Ding B., Guo Z., Peng Z., Wang D., Zhu X., Liu S.. Eradication of tumor growth by delivering novel photothermal selenium-coated tellurium nanoheterojunctions. Sci. Adv. (2020) 6 eaay6825. DOI: 10.1126/sciadv.aay6825 24. Zhang X., Qi J., Lu Y., He W., Li X., Wu W.. Biotinylated liposomes as potential carriers for the oral delivery of insulin. Nanomedicine (2014) 10 167-176. DOI: 10.1016/j.nano.2013.07.011 25. Zhou X., Zhang X., Ye Y., Zhang T., Wang H., Ma Z., Wu B.. Nanostructured lipid carriers used for oral delivery of oridonin: An effect of ligand modification on absorption. Int. J. Pharm. (2015) 479 391-398. DOI: 10.1016/j.ijpharm.2014.12.068 26. Zhu L., Gu P., Shen H.. Protective effects of berberine hydrochloride on dss-induced ulcerative colitis in rats. Int. Immunopharmacol. (2019) 68 242-251. DOI: 10.1016/j.intimp.2018.12.036 27. Martínez Rivas C.J., Tarhini M., Badri W., Miladi K., Greige-Gerges H., Nazari Q.A., Galindo Rodríguez S.A., Román R., Fessi H., Elaissari A.. Nanoprecipitation process: From encapsulation to drug delivery. Int. J. Pharm. (2017) 532 66-81. DOI: 10.1016/j.ijpharm.2017.08.064 28. Du M., Ouyang Y., Meng F., Zhang X., Ma Q., Zhuang Y., Liu H., Pang M., Cai T., Cai Y.. Polymer–lipid hybrid nanoparticles: A novel drug delivery system for enhancing the activity of psoralen against breast cancer. Int. J. Pharm. (2019) 561 274-282. DOI: 10.1016/j.ijpharm.2019.03.006 29. Gan Z., Huang C., Shen Y., Zhou Q., Han D., Ma J., Liu S., Zhang Y.. Preparation of carbon nitride nanoparticles by nanoprecipitation method with high yield and enhanced photocatalytic activity. Chin. Chem. Lett. (2020) 31 513-516. DOI: 10.1016/j.cclet.2019.04.065 30. Zhang X., Zhang T., Zhou X., Liu H., Sun H., Ma Z., Wu B.. Enhancement of oral bioavailability of tripterine through lipid nanospheres: Preparation, characterization, and absorption evaluation. J. Pharm. Sci. (2014) 103 1711-1719. DOI: 10.1002/jps.23967 31. Wu S., Sun K., Wang X., Wang D., Wan X., Zhang J.. Protonation of epigallocatechin-3-gallate (EGCG) results in massive aggregation and reduced oral bioavailability of EGCG-dispersed selenium nanoparticles. J. Agric. Food Chem. (2013) 61 7268-7275. DOI: 10.1021/jf4000083 32. Shan W.G., Wang H.G., Wu R., Zhan Z.J., Ma L.F.. Synthesis and anti-tumor activity study of water-soluble PEG-celastrol coupling derivatives as self-assembled nanoparticles. Bioorg. Med. Chem. Lett. (2019) 29 685-687. DOI: 10.1016/j.bmcl.2019.01.042 33. Li H., Li J., Liu L., Zhang Y., Luo Y., Zhang X., Yang P., Zhang M., Yu W., Qu S.. Elucidation of the intestinal absorption mechanism of celastrol using the Caco-2 cell transwell model. Planta Med. (2016) 82 1202-1207. DOI: 10.1055/s-0035-1568597 34. Guan B., Yan R., Li R., Zhang X.. Selenium as a pleiotropic agent for medical discovery and drug delivery. Int. J. Nanomed. (2018) 13 7473-7490. DOI: 10.2147/IJN.S181343 35. Xian J., Zhong X., Gu H., Wang X., Li J., Li J., Wu Y., Zhang C., Zhang J.. Colonic delivery of celastrol-loaded layer-by-layer liposomes with pectin/trimethylated chitosan coating to enhance its anti-ulcerative colitis effects. Pharmaceutics (2021) 13. DOI: 10.3390/pharmaceutics13122005 36. Dos Santos T., Varela J., Lynch I., Salvati A., Dawson K.A.. Effects of transport inhibitors on the cellular uptake of carboxylated polystyrene nanoparticles in different cell lines. PLoS ONE (2011) 6. DOI: 10.1371/journal.pone.0024438 37. Chen Y., Liang X., Ma P., Tao Y., Wu X., Wu X., Chu X., Gui S.. Phytantriol-based in situ liquid crystals with long-term release for intra-articular administration. AAPS PharmSciTech (2015) 16 846-854. DOI: 10.1208/s12249-014-0277-6 38. Freag M.S., Saleh W.M., Abdallah O.Y.. Self-assembled phospholipid-based phytosomal nanocarriers as promising platforms for improving oral bioavailability of the anticancer celastrol. Int. J. Pharm. (2018) 535 18-26. DOI: 10.1016/j.ijpharm.2017.10.053 39. Zhan S., Paik A., Onyeabor F., Ding B., Prabhu S., Wang J.. Oral bioavailability evaluation of celastrol-encapsulated silk fibroin nanoparticles using an optimized LC-MS/MS method. Molecules (2020) 25. DOI: 10.3390/molecules25153422 40. Fan N., Zhao J., Zhao W., Zhang X., Song Q., Shen Y., Shum H.C., Wang Y., Rong J.. Celastrol-loaded lactosylated albumin nanoparticles attenuate hepatic steatosis in non-alcoholic fatty liver disease. J. Control. Release (2022) 347 44-54. DOI: 10.1016/j.jconrel.2022.04.034 41. Krug P., Mielczarek L., Wiktorska K., Kaczyńska K., Wojciechowski P., Andrzejewski K., Ofiara K., Szterk A., Mazur M.. Sulforaphane-conjugated selenium nanoparticles: Towards a synergistic anticancer effect. Nanotechnology (2019) 30 065101. DOI: 10.1088/1361-6528/aaf150 42. Abdulmalek S.A., Balbaa M.. Synergistic effect of nano-selenium and metformin on type 2 diabetic rat model: Diabetic complications alleviation through insulin sensitivity, oxidative mediators and inflammatory markers. PLoS ONE (2019) 14. DOI: 10.1371/journal.pone.0220779 43. Bi Y., Zhang W., Sun J., Gao R.. Therapeutic and immune function improvement of vitamin D combined with IFN-α on mouse with hepatitis B infection. Int. J. Immunopathol. Pharmacol. (2018) 32 2058738418775250. DOI: 10.1177/2058738418775250 44. Shaker M.E., Ashamallah S.A., Houssen M.E.. Celastrol ameliorates murine colitis via modulating oxidative stress, inflammatory cytokines and intestinal homeostasis. Chem. Biol. Interact. (2014) 210 26-33. DOI: 10.1016/j.cbi.2013.12.007 45. Jia Z., Xu C., Shen J., Xia T., Yang J., He Y.. The natural compound celastrol inhibits necroptosis and alleviates ulcerative colitis in mice. Int. Immunopharmacol. (2015) 29 552-559. DOI: 10.1016/j.intimp.2015.09.029 46. Mal’tseva V.N., Goltyaev M.V., Turovsky E.A., Varlamova E.G.. Immunomodulatory and anti-inflammatory properties of selenium-containing agents: Their role in the regulation of defense mechanisms against COVID-19. Int. J. Mol. Sci. (2022) 23. DOI: 10.3390/ijms23042360
--- title: Risperidone Administration Attenuates Renal Ischemia and Reperfusion Injury following Cardiac Arrest by Antiinflammatory Effects in Rats authors: - Yang Hee Kim - Tae-Kyeong Lee - Jae-Chul Lee - Dae Won Kim - Hyun-Jin Tae - Joon Ha Park - Ji Hyeon Ahn - Choong-Hyun Lee - Moo-Ho Won - Seongkweon Hong journal: Veterinary Sciences year: 2023 pmcid: PMC10059783 doi: 10.3390/vetsci10030184 license: CC BY 4.0 --- # Risperidone Administration Attenuates Renal Ischemia and Reperfusion Injury following Cardiac Arrest by Antiinflammatory Effects in Rats ## Abstract ### Simple Summary Risperidone has been reported to show other beneficial effects instead of its original effectiveness. This experiment was conducted for the effects of risperidone on renal ischemia and reperfusion injury (IRI) following cardiac arrest. The increased levels of serum blood urea nitrogen, creatinine, and lactate dehydrogenase after cardiac arrest were significantly decreased by risperidone treatment. IRI-induced histopathological injury was attenuated by risperidone administration, showing that pro-inflammatory and anti-inflammatory cytokine immunoreactivities were apparently controlled by risperidone administration. Based on these findings, risperidone administration after cardiac arrest can protect kidneys from IRI via anti-inflammatory effects. ### Abstract Multi-organ dysfunction following cardiac arrest is associated with poor outcome as well as high mortality. The kidney, one of major organs in the body, is susceptible to ischemia and reperfusion; however, there are few studies on renal ischemia and reperfusion injury (IRI) following the return of spontaneous circulation (ROSC) after cardiac arrest. Risperidone, an atypical antipsychotic drug, has been discovered to have some beneficial effects beyond its original effectiveness. Therefore, the aim of the present study was to investigate possible therapeutic effects of risperidone on renal IRI following cardiac arrest. Rats were subjected to cardiac arrest induced by asphyxia for five minutes followed by ROSC. When serum biochemical analyses were examined, the levels of serum blood urea nitrogen, creatinine, and lactate dehydrogenase were dramatically increased after cardiac arrest, but they were significantly reduced by risperidone administration. Histopathology was examined using hematoxylin and eosin staining. Histopathological injury induced by cardiac arrest was apparently attenuated by risperidone administration. Furthermore, alterations in pro-inflammatory cytokines (interleukin-6 and tumor necrosis factor-α) and anti-inflammatory cytokines (interleukin-4 and interleukin-13) were examined by immunohistochemistry. Pro-inflammatory and anti-inflammatory cytokine immunoreactivities were gradually and markedly increased and decreased, respectively, in the kidneys following cardiac arrest; however, risperidone administration after cardiac arrest significantly attenuated the increased pro-inflammatory cytokine immunoreactivities and the decreased anti-inflammatory cytokine immunoreactivities. Collectively, our current results revealed that, in rats, risperidone administration after cardiac arrest protected kidneys from IRI induced by cardiac arrest and ROSC through anti-inflammatory effects. ## 1. Introduction It is known that renal diseases in domestic animals are commonly caused by aging, congenital factors, pathogenic infections and toxicosis. In the present study, renal injury was induced by whole-body ischemia and reperfusion in rats, which is pathogenetically different from the renal diseases commonly diagnosed in domestic animals. In this regard, this experiment was conducted from the aspects of comparative medicine. Cardiac arrest (CA) refers to the sudden loss of heart function that results in an abrupt halt of effective blood flow to the body. The morbidity and mortality due to CA have increased worldwide [1]. Many studies have focused on myocardial dysfunction and brain injury following CA and return of spontaneous circulation (ROSC) [2,3,4,5]. Dysfunctions in various organs are common after ROSC following cardiopulmonary resuscitation (CPR) from CA [6]. Nevertheless, few studies on renal ischemia and reperfusion injury (IRI) following CA and ROSC (CA/ROSC) have been performed. The recovery of blood flow after CA/ROSC can cause renal injury, known as renal IRI [7,8]. Renal IRI following CA/ROSC can cause acute kidney dysfunction, further contributing to high mortality [9]. It has been reported that kidney injury in patients with CA/ROSC occurs frequently, occurring in ~$50\%$ of cases of CA/ROSC [10]. The pathophysiology following renal IRI is complex and not fully understood yet; however, renal inflammation is accepted as one of the important pathogenic components [11,12]. Inflammatory cascade in an ischemic kidney is induced a few hours after IRI and lasts for a long time (days or weeks) as a delayed reaction to the damage [13,14]. In the early phase after renal IRI, macrophages promote inflammation and amplify IRI through the release of pro-inflammatory cytokines (tumor necrosis factor-α, TNFα and interleukin-4, IL4) [15,16]. However, in the late phase after renal IRI, macrophages express anti-inflammatory cytokines (IL4 and IL13) and play an important role for the repair process [17,18]. Risperidone (Risp, a benzoxazole derivative), as a second-generation antipsychotic drug, has been primarily used to treat schizophrenia and bipolar disorder [19,20]. It has been reported that Risp induces hypothermia in patients with mental disorders, such as schizophrenia [21,22]. In recent experimental studies with Risp, the administration of Risp after brain-IRI-induced hypothermia and effectively protected neurons from IRI in gerbil hippocampus and rat spinal cord [23,24]. It is generally admitted that body temperature influences, to a certain extent, the outcomes of IRI in patients after CA/ROSC [25,26,27]. It has been reported that therapeutic hypothermia can improve the survival rate and the neurological outcomes of post-CA patients for several years [28,29]. Hypothermia can attenuate ischemia-induced tissue damage in important organs, including the heart, kidney, and liver [30,31]. It has been reported that Risp induces hypothermia in patients with mental disorders, such as schizophrenia [21,22]. However, the effects of Risp on the survival rate and kidney IRI in a rat model of CA/ROSC has not been studied. Therefore, this study investigated the effects of Risp on mortality and renal histopathology using rats with CA/ROSC. In addition, with regard to the mechanisms of the effects, changes in inflammatory cytokines were evaluated in rat ischemic kidney treated with Risp. ## 2.1. Experimental Protocol and Animals The protocol of this experiment (approval number, KW-200113-1) was authorized by the Institutional Animal Care and Use Committee affiliated with Kangwon National University (Chuncheon, Republic of Korea) on 18 February 2020. All animal procedures were conducted in compliance with the guideline of the “Current International Laws and Policies” which is a part of the “Guide for the Care and Use of Laboratory Animals” [32]. Male rats (Sprague-Dawley; 10-week-old; body weight, 310 ± 7 g) were supplied from the Experimental Animal Center, an affiliated institution of Kangwon National University (Chuncheon, Republic of Korea). Until the rats acclimatize to the laboratory environment, they were kept in a conventional room for two weeks controlled at about 24 °C of room temperature and $55\%$ of relative humidity was used and provided steady dark and light cycle every 12 h and freely accessible pellet feed and water. Four groups were used for this study: [1] Sham-vehicle group ($$n = 7$$) received sham operation and was administered a vehicle (saline), [2] Sham-Risp group ($$n = 7$$) received sham operation and was administered Risp, [3] CA-vehicle group ($$n = 21$$) received CA/ROSC operation and was administered vehicle, and [4] CA-Risp group ($$n = 21$$) received CA/ROSC operation and was administered Risp. ## 2.2. Operation of CA/ROSC and Risp Administration As shown in Figure 1, the CA/ROSC operation was conducted according to previous studies [24,33] with modifications. Briefly, the rats were fasted for eight hours excepting free access to water and anesthetized with 2.5–$3\%$ isoflurane (Hana Pharmaceutical Co., Ltd.; Seoul, Republic of Korea). Under the anesthesia, each rat was placed on a surgical board in the supine position; the trachea of the rat was intubated with a 14-gauge cannula through a tracheotomy under mechanical ventilation using a ventilator (Harvard Apparatus, Holliston, MA, USA). A PE-50 catheter (ADInstruments Ltd., Sydney, Australia) was canulated in the left femoral artery to measure mean arterial pressure (MAP) and to take a blood sample. The MAP was monitored with MLT 1050/D (ADInstruments Ltd.). Another PE-50 catheter was canulated in the right femoral vein for the delivery of fluids and drugs. The catheters were intermittently cleaned with sterilized normal saline. A pulse oximetry saturation probe (Nonin Medical Inc., Plymouth, MN, USA) was connected to the left foot in order to monitor peripheral oxygen saturation, as saturation of percutaneous oxygen (SpO2). For the monitoring of electrocardiogram (ECG), three electrode leads (GE healthcare, Chicago, IL, USA) were, respectively, connected to both forelimbs and left hind limb. To control body temperature, the rats were monitored with a TR-100 rectal temperature probe obtained from Fine Science Tools (Foster City, CA, USA) and controlled at 37 ± 0.5 °C (normothermia) using a thermometric blanket (Harvard Apparatus). After collection of baseline data and stabilization for five minutes, a single dose of 2 mg/kg vecuronium bromide (Reyon Pharmaceutical Co., Ltd., Seoul, Republic of Korea) was intravenously injected to induce respiratory paralysis and immobilize the rats. Three to four minutes later, the ventilator was removed and the endotracheal tube was clamped to induce asphyxia. Asphyxial CA was defined by confirming pulseless electric activity of ECG and less than 25 mmHg of MAP. ROSC was performed immediately after confirming CA as follows. CPR by chest compression and ventilation was begun five minutes of the CA for 60 s according to manual chest compression. Simultaneously, 0.005 mg/kg of epinephrine (Dai Han Pharmaceutical Co., Ltd., Seoul, Republic of Korea) and 1 mEq/kg of sodium bicarbonate (Daewon Pharmaceutical Co., Ltd., Seoul, Republic of Korea) were intravenously injected. In this experiment, the rats with sham operation received the same operation except CA/ROSC. For the experimental groups, 10 mg of Risp (Sigma-Aldrich, St. Louis, MO, USA) was dissolved in $0.85\%$ saline, and vehicle (saline) or Risp was intravenously injected after CA/CPR operation. The rats with CA/ROSC were respired spontaneously one hour after ROSC and the hemodynamics became stable. After confirming the stability, the cannulated catheters, endotracheal intubation, electrode leads and oximetry probe were removed. Finally, the rats were subcutaneously administered isotonic saline (20 mL/kg/d) containing $5\%$ dextrose until the rats were able to drink and eat by themselves. ## 2.3. Biochemical Analysis of Serum An intraperitoneal injection of pentobarbital sodium (180 mg/kg; JW Pharmaceutical Co., Ltd., Seoul, Republic of Korea) was used to anesthetize all animals. Blood sampling was conducted from the abdominal veins of all rats. Serum was collected by blood centrifugation (2774× g, 15 min, 4 °C) and preserved at −80 °C until the analysis. According to a method outlined by the International Federation of Clinical Chemistry [34], the levels of blood urea nitrogen (BUN), creatinine, and lactate dehydrogenase (LDH) were determined using an automated Olympus AU2700 Analyzer (Olympus, Optical Co., Tokyo, Japan). This analysis was conducted in triplicate using fresh serum. ## 2.4. Hematoxylin and Eosin (HE) Staining HE staining and analysis were performed according a previous study [35] with minor modification. In short, at the designated points in time after CA, all rats were deeply anesthetized via intraperitoneal injection of 180 mg/kg of pentobarbital sodium (JW Pharmaceutical Co.) and flushed with heparinized saline by transcardial perfusion until all blood was fully cleared. Continuously, the rats were flushed with $4\%$ paraformaldehyde (in 0.1 M phosphate buffer (PB), pH 7.4). The kidneys were removed and postfixed in the $4\%$ paraformaldehyde for three days. The kidneys were trimmed, dehydrated, cleared and embedded into paraffin wax. Paraffin sections (six-μm thickness) were made using Leica microtome (Wezlar, Germany). Thereafter, the sections were deparaffinized, reacted with hematoxylin (ThermoScientific, Waltham, MA, USA), washed, and reacted with Eosin Y (ThermoScientific). Finally, the sections were dehydrated, cleared and coverslipped with Canada balsam (Kanto Chemical Co., Inc., Tokyo, Japan). For examination of histopathological changes in the kidneys, the stained tissue slides were observed and captured the images using BX53 light microscope (Olympus, Tokyo, Japan). The scoring was performed in accordance with injured level as zero to five scale: zero, none; one, 0–$10\%$; two, 11–$25\%$; three, 26–$45\%$; four, 46–$75\%$; and five, 76–$100\%$. ## 2.5. Immunohistochemistry To compare alterations in pro-inflammatory (TNFα and IL6) and anti-inflammatory (IL4 and IL13) cytokines between the four groups, TNFα, IL4, IL6 and IL13 expressions were evaluated by a standard immunostaining method. In brief, as described in the “Section 2.4”, the paraffin sections were deparaffinated and hydrated. Then the sections were incubated in a blocking reagent containing $1\%$ hydrogen peroxide in methanol and $5\%$ horse and/or rabbit serum (in 100 mM PBS, pH 7.4) for 25 min at room temperature, respectively. The sections were then immunoreacted with each primary antibody (Table 1) for 12 hours at 4 °C. Afterward, the sections were reacted with secondary antibody for one hour at room temperature and incubated in avidin-biotin complex (diluted, 1:250; Vector Laboratories, Burlingame, CA, USA). Thereafter, the sections were washed with 0.1 M PBS (pH 7.4) and visualized using $0.05\%$ 3,3′-diaminobenzidine tetrahydrochloride (Sigma-Aldrich Co., St. Louis, MO, USA) in 100 mM PBS containing $0.1\%$ hydrogen peroxide. Then the sections were dehydrated, cleared and covered with cover glasses and Canada balsam (Kanto Chemical Co.). The TNFα, IL4, IL6 and IL13 immunostained structures were compared between the four groups as follows. Five sections per rat were selected, and each image was taken like the method described in the “Section 2.4”. The image was converted into eight-bit grey scale (from zero to 255) image and assessed for grey scale intensity. The immunoreactive intensity was calculated using Image J 1.46 obtained from National Institutes of Health (Bethesda, Maryland, MD, USA) and presented as ROD as percentage (ROD of Sham + vehicle group, $100\%$). ## 2.6. Data and Statistical Analyses In this study, all experiments were carried out in a randomized manner. Using GPower 3 software [36], sample size was calibrated with an alpha error of 0.05 and a power of >$80\%$, resulting in two animals per group for the minimum. The results obtained from this experiment were expressed as means ± standard error of the mean (SEM). To calculate identical SEM, Bartlett test was performed, and to evaluate normal distribution, a Kolmogorov and Smirnov test was carried out. All data were analyzed using SPSS 18.0 (SPSS, Chicago, IL, USA). Kaplan–Meier method and the log-rank test were used to analyze cumulative survival rate. One- and two-way ANOVA was used to analyze MAP and SpO2. A post hoc Tukey’s test was used to analyze the significance of statistical differences among all groups. p values less than 0.05 were statistically considered significant. ## 3.1. Body Temperature and Survival Rate In this experiment, body temperature was recorded before and after CA. Body temperature before CA was similar to the baseline obtained in the Sham + vehicle group. In the Sham-Risp and CA-Risp groups, body temperature was significantly different from that shown in the Sham-vehicle group. In the two groups, a significantly low body temperature (33 ± 0.5 °C) was observed from one to two hours after CA (Figure 2A). Thereafter, body temperature was spontaneously and gradually increased to 37 ± 0.5 °C with intermittently shivering (Figure 2A). In each group, the survival rate of the rats was determined two days after ROSC by Kaplan–Meier analysis* which demonstrated a significant difference in the survival rate. In the Sham-vehicle group, the survival rate was $100\%$. The survival rate in the CA-vehicle group was $6.25\%$; however, the rate in the CA-Risp group was $63.64\%$ (Figure 2B). ## 3.2. Levels of BUN, Creatinine and LDH The levels of serum BUN, creatinine, and LDH were analyzed to evaluate the renal function of the experimental rats (Figure 3). The rats of the CA-vehicle group showed significant increases ($p \leq 0.05$) in BUN (SEM: 1.17, 1.16, 1.12, 1.03 at sham, 12 h, 1 d and 2 d after CA/ROSC), creatinine (SEM: 0.020, 0.027, 0.022, 0.028 at sham, 12 h, 1 d and 2 d after CA/ROSC), and LDH (SEM: 35.9, 55.2, 53.1, 52.3 at sham, 12 h, 1 d and 2 d after CA/ROSC) levels when compared with those obtained from the Sham-vehicle group. In the CA-Risp group, however, the serum BUN (SEM: 1.03, 0.96, 1.30, 0.88 at sham, 12 h, 1 d and 2 d after CA/ROSC), creatinine (SEM: 0.019, 0.020, 0.020, 0.025 at sham, 12 h, 1 d and 2 d after CA/ROSC), and LDH (SEM: 57.4, 49.6, 34.7, 33.8 at sham, 12 h, 1 d and 2 d after CA/ROSC) levels were significantly ($p \leq 0.05$) decreased from 12 h to two days after CA/ROSC when compared with the CA-vehicle group. ## 3.3. Histopathology by HE Staining Based on the HE staining, in the Sham-vehicle group, renal tissue had typical structures, with the infiltration of only a few inflammatory cells (Figure 4A). In the CA-vehicle group, one days after CA, renal lesion (patchy denudation of renal tubular cells with loss of brush border, vacuoles in tubular cells, dilated lumen of tubules, glomerular capillary dilatation, inflammatory cell infiltration, etc.) was apparent when compared with those in the Sham-vehicle group (Figure 4B). Two days after CA/ROSC. renal injury was significantly increased (Figure 4C,G,H). The morphology of renal tissue in the Sham-Risp group was similar to that of the Sham-vehicle group (Figure 4D). In the CA-Risp group, kidney injury was apparently attenuated one and two days after CA/ROSC (Figure 4E,F). In particular, Risp treatment after CA/ROSC significantly attenuated the damage of brush border and the expansion of the tubules, decreased vacuolization in the tubular cells, and improved glomerular injury as compared with those obtained from the CA-vehicle group (Figure 4E–H). ## 3.4.1. IL6 Immunoreactivity Weak IL6 immunoreactivity was observed in the renal tissue of the Sham-vehicle group (Figure 5Aa). IL6 immunoreactivity in the CA-vehicle group was slightly increased ($122.0\%$ of Sham-vehicle group) 12 h after CA/ROSC, showing that the increased immunoreactivity was shown mainly in tubular cells (Figure 5Ab,B). Thereafter, IL6 immunoreactivity was more increased ($166.8\%$ of Sham-vehicle group) on day 1 after CA/ROSC and highest ($207.9\%$ of Sham-vehicle group) on day 2 after CA/ROSC, showing that strong IL6 immunoreactivity was shown in tubular cells on day 2 after CA/ROSC (Figure 5A(c,d),B). In the Sham-Risp group, IL6 immunoreactivity in the renal tissue was similar to that shown in the Sham-vehicle group (Figure 5Ae). In the CA-Risp group, IL6 immunoreactivity was also gradually increased with time after CA/ROSC, but the immunoreactivity was significantly lower ($77.4\%$ and $69.7\%$ of CA-vehicle group on day 1 and 2, respectively, after CA/ROSC) than that in the CA-vehicle group (Figure 5B). ## 3.4.2. TNFα Immunoreactivity Weak TNFα immunoreactivity was detected in the kidney of the Sham-vehicle group: the immunoreactivity was generally expressed in tubular cells (Figure 5Ca). In the rats of the CA-vehicle group, TNFα immunoreactivity was slightly increased in the tubules at 12 h (Figure 5Cb). One and two days after CA/ROSC, TNFα immunoreactivity was markedly increased (Figure 5C(c,d)), showing that the ROD of the TNFα immunoreactivity was $205.6\%$ and $248.8\%$ of the Sham-vehicle group, respectively, one day and two days after CA/ROSC (Figure 5D). TNFα immunoreactivity shown in the tubules of the Sham-Risp group was not different from that shown in the Sham-vehicle group (Figure 5Ce). In the CA-Risp group, TNFα immunoreactivity was also increased in the tubules after CA/CPR (Figure 5C(f–h)), but the immunoreactivities were significantly lower ($70.9\%$ and $73.1\%$ of CA-vehicle group one and two days, respectively, after CA/ROSC) than that in the CA-vehicle group (Figure 5D). ## 3.5.1. IL4 Immunoreactivity Weak IL4 immunoreactivity was mainly observed in the renal tubules of the Sham-vehicle group (Figure 6Aa). In the CA-vehicle group, IL4 immunoreactivity was gradually decreased after CA/CPR, and, on day 2 after CA/ROSC, the immunoreactivity was very low ($42.6\%$ of the Sham-vehicle group) (Figure 6A(b–d),B). In the Sham-Risp group, IL4 immunoreactivity in the tubules was similar to that in the Sham-vehicle group (Figure 6Ae). In the CA-Risp group, IL4 immunoreactivity was slightly decreased after CA/ROSC (Figure 6A(f–h)), showing that the ROD of the IL4 immunoreactivity was $94.7\%$ and $90.0\%$ of the Sham-vehicle group, respectively, on day 1 and 2 after CA/ROSC (Figure 6B). ## 3.5.2. IL13 Immunoreactivity Strong IL13 immunoreactivity was easily found in the kidney of the Sham-vehicle group: the IL13 immunoreactivity was generally shown in the tubules (Figure 6Ca). However, IL13 immunoreactivity in the CA-vehicle group was gradually and dramatically reduced after CA/ROSC, showing that showing that the ROD of the IL13 immunoreactivity was $47.5\%$ and $21.8\%$ of the Sham-vehicle group, respectively, one day and two days after CA/ROSC (Figure 6C(b–d),D). IL13 immunoreactivity in the Sham-Risp group was similar to that obtained from the Sham-vehicle group (Figure 6Ce,D). In the CA-Risp group, IL13 immunoreactivity was not significantly altered after CA/ROSC (Figure 6A(f–h),D). ## 4. Discussion In the present study, histopathological alteration and changes in the immunoreactivities of pro- and anti-inflammatory cytokines were found in rat kidney due to IRI induced by CA/ROSC. In addition, therapeutic administration of Risp after CA/ROSC significantly attenuated histopathological damage and controlled the increases of pro-inflammatory cytokine immunoreactivities and the decreases of anti-inflammatory cytokine immunoreactivities. The recovery rate from acute renal injury after out-of-hospital CA is $39\%$, and recovery from acute renal injury is a strong predictor of survival and good neurological outcomes at discharge from the hospital [37]. Acute renal injury occurs in 30–$50\%$ of the patients who survive from CA/ROSC [10] and complicates 12–$40\%$ of hospitalized CA patients [38]. Renal injury resulting from CA/ROSC is a complicated process and related with a high mortality rate [39]. Therefore, it is important to investigate acute kidney injury following CA/ROSC. However, many studies have focused on brain and heart injury following CA/ROSC, while renal failure following CA/ROSC has not been largely studied [40]. It is well accepted that, in animal studies, the heart and brain are the most affected organs following IRI induced by CA/ROSC [41,42]. In this study using a rat model of CA/ROSC, the survival rate decreased time-dependently and reached to $6.25\%$ two days after CA/ROSC. It has been reported that, at the early-phase in the post CA syndrome, the survival rate in patients is $4\%$ to $33\%$ [43]. In this study, histopathological damage in the rat kidney with CA/ROSC-induced IRI by histopathological score (tubular injury score and glomeruli lesion score), including the dilatation of the renal tubules, loss of the brush border of the tubules, tubular necrosis, vacuoles in tubular cells, glomerular capillary dilatation, and inflammatory cell infiltration, which were severe two days after CA/ROSC. These findings showed consistency with the results of the previous studies using rats with IRI [44,45]. Taken together, we suggest that the transient block of blood supply to the kidney can evoke severe structural alteration (damage) in the kidney. Physical hypothermia (i.e., surface cooling) can lead to protection and improve functional impairment or damage in animal models of brain and spinal cord IRI [46,47,48,49,50], although clinical data concerning the effects of hypothermia on brain and spinal cord IRI are disputed. Nevertheless, several studies have reported that hypothermia reduces the severity of renal injury and increases the survival rate in rat models of CA/ROSC [44,51]. In this regard, we need the experiments of the effects of hypothermia-inducing drugs in major organs (i.e., brain, spinal cord, heart, liver, and kidney) of animal models of IRI. It has been demonstrated that Risp exhibits hypothermia [21,23,52]. For several decades, Risp has been widely used for the treatment of schizophrenia [19,20]. Risp, as a second-generation antipsychotic drug, is a benzoxazole derivative and a selective monoaminergic antagonist having high affinity for serotonin type 2 and dopamine type 2 receptors in the limbic system [19,20]. We reported that hypothermia induced by Risp revealed an effective protection of hippocampal neurons from IRI following transient forebrain ischemia in gerbils [23] and positive effects in ischemic liver and spinal cord following CA/ROSC in rats [24,34]. Our present study showed for the first time the beneficial effects of Risp on acute renal IRI following CA/ROSC in rats. Risp induced hypothermia and significantly attenuated histopathological injury in the renal tissues of the rats with CA/ROSC. In addition, the serum levels of renal injury markers (BUN, creatinine, and LDH) were significantly suppressed in the rats with CA/ROSC by Risp administration. Based on our results, we strongly suggest that Risp treatment after renal IRI improves renal damage and dysfunction in patients suffering from CA/ROSC. Over the past few years, in kidney IRI, pro-inflammatory cytokines enhance kidney damage and contribute to cell death (apoptosis and necrosis) in the tubules [53,54]. In recent years, TNF-α has been well-established as an essential mediator of kidney IRI [55,56]. While renal TNFα expression mediates neutrophil infiltration and injury after renal ischemic insult [57], the mechanisms of TNFα-induced kidney injury are multiple [58,59,60,61]. For example, it has reported that TNFα plays a key role in inflammation after kidney ischemia and reperfusion by up-regulating inflammatory genes [62], and that the inhibition of TNFα activity has antioxidant and anti-inflammatory effects, and protects kidneys from IRI [63]. For the function of IL6 in renal IRI, IL6 emission promotes the expression of oxidative stress and enhances the degree of renal injury including inflammation [64,65]. In acute kidney injury in mice, IL6 can also be upregulated and released from the epithelial cells of the renal tubules in response to the injury and plays an essential role for renal pathophysiology [66]. Additionally, IL6 production is enhanced from the myocardium in states of cardiac ischemia, congestive heart failure, and congenital heart disease in humans and mice [66,67,68]. These findings suggest that pro-inflammatory cytokines can contribute to cell death in the kidney after CA/ROSC. In this regard, we found, in this study, that the immunoreactivities of TNFα and IL6 were significantly increased in the renal tubular cells of the CA-vehicle group as compared with the Sham + vehicle group. However, the immunoreactivities were controlled in the CA-Risp group. Thus, our current findings indicate that Risp can prevent the abnormal increases of TNFα and IL6 in ischemic kidney induced by CA/ROSC. On the other hand, renal tissue damage can be alleviated by an anti-inflammatory response. Yokota et al. [ 69] have shown that the deletion of IL4 leads to markedly worse renal function and tubular injury in murine renal IRI. In addition, Jayaraj et al. [ 70] have reported that endogenous IL13 controls brain inflammation induced by lipopolysaccharide treatment in rats by inhibiting pro-inflammatory cytokine expression, resulting in an enhancement of neuronal survival [70]. These results collectively support the beneficial effects of IL4 and IL13 as anti-inflammatory cytokines. In our current research, significant decreases in immunoreactivities of IL-4 and IL-13 were observed in the kidney of the CA-vehicle group, but, unlike the immunoreactivities of TNFα and IL6, the IL4 and IL13 immunoreactivities in the CA-Risp group were not significantly reduced when compared with the Sham + vehicle group. Our findings are supported by some papers showing that maintained expressions of endogenous anti-inflammatory cytokines, such as IL4 and IL13, contribute to neuronal survival from brain IRI in gerbils [71,72], and that IL4 and IL13 suppress the abnormal expression and production of pro-inflammatory cytokines, such as TNF-α and IL-6, in human monocytes [73,74,75]. Therefore, the maintained expression of anti-inflammatory cytokines (IL4 and IL13) in the kidney of the CA-Risp group may be related with the protective effect of Risp against renal IRI following CA/ROSC. ## 5. Conclusions This study revealed that CA/ROSC in rats showed high mortality with severe histopathological injury, the increases of renal injury markers (BUN, creatinine, and LDH) and the expressions of TNFα and IL6, and the decrease of IL4 and IL13 expressions in ischemic kidney induced by CA/ROSC. However, Risp administration after CA/ROSC significantly improved the survival rate, reduced the increased expressions of pro-inflammatory cytokines (TNFα and IL6), and maintained the expressions of anti-inflammatory cytokines (IL4 and IL13), ameliorating the renal injury induced by CA/ROSC. These results indicate that Risp treatment after CA/ROSC reduces kidney damage, which is closely associated with the attenuation of renal inflammation following CA/ROSC after CA. Nevertheless, the exact role of Risp is still unclear in the protection of the kidney from IRI following CA/ROSC. Further study is needed to more identify the protective mechanisms of Risp in the kidney after CA/ROSC. ## References 1. Girotra: S., Chan P.S., Bradley S.M.. Post-resuscitation care following out-of-hospital and in-hospital cardiac arrest. Heart (2015) 101 1943-1949. DOI: 10.1136/heartjnl-2015-307450 2. Laurent I., Monchi M., Chiche J.D., Joly L.M., Spaulding C., Bourgeois B., Cariou A., Rozenberg A., Carli P., Weber S.. Reversible myocardial dysfunction in survivors of out-of-hospital cardiac arrest. J. Am. Coll. Cardiol. (2002) 40 2110-2116. DOI: 10.1016/S0735-1097(02)02594-9 3. Madl C., Holzer M.. Brain function after resuscitation from cardiac arrest. Curr. Opin. Crit. Care (2004) 10 213-217. DOI: 10.1097/01.ccx.0000127542.32890.fa 4. Zhong S., Guo H., Wang H., Xing D., Lu T., Yang J., Wang C.. Apelin-13 alleviated cardiac fibrosis via inhibiting the pi3k/akt pathway to attenuate oxidative stress in rats with myocardial infarction-induced heart failure. Biosci. Rep. (2020) 40 BSR20200040. DOI: 10.1042/BSR20200040 5. Chang Y., Zhu J., Wang D., Li H., He Y., Liu K., Wang X., Peng Y., Pan S., Huang K.. Nlrp3 inflammasome-mediated microglial pyroptosis is critically involved in the development of post-cardiac arrest brain injury. J. Neuroinflamm. (2020) 17 219. DOI: 10.1186/s12974-020-01879-1 6. Roberts B.W., Kilgannon J.H., Chansky M.E., Mittal N., Wooden J., Parrillo J.E., Trzeciak S.. Multiple organ dysfunction after return of spontaneous circulation in postcardiac arrest syndrome. Crit. Care Med. (2013) 41 1492-1501. DOI: 10.1097/CCM.0b013e31828a39e9 7. Tujjar O., Mineo G., Dell’Anna A., Poyatos-Robles B., Donadello K., Scolletta S., Vincent J.L., Taccone F.S.. Acute kidney injury after cardiac arrest. Crit. Care (2015) 19 169. DOI: 10.1186/s13054-015-0900-2 8. Sandroni C., Dell’anna A.M., Tujjar O., Geri G., Cariou A., Taccone F.S.. Acute kidney injury after cardiac arrest: A systematic review and meta-analysis of clinical studies. Minerva Anestesiol. (2016) 82 989-999. PMID: 26957119 9. Uchino S., Kellum J.A., Bellomo R., Doig G.S., Morimatsu H., Morgera S., Schetz M., Tan I., Bouman C., Macedo E.. Acute renal failure in critically ill patients: A multinational, multicenter study. JAMA (2005) 294 813-818. DOI: 10.1001/jama.294.7.813 10. Hasper D., von Haehling S., Storm C., Jorres A., Schefold J.C.. Changes in serum creatinine in the first 24 hours after cardiac arrest indicate prognosis: An observational cohort study. Crit. Care (2009) 13 R168. DOI: 10.1186/cc8144 11. Bonventre J.V., Zuk A.. Ischemic acute renal failure: An inflammatory disease?. Kidney Int. (2004) 66 480-485. DOI: 10.1111/j.1523-1755.2004.761_2.x 12. Molitoris B.A., Sutton T.A.. Endothelial injury and dysfunction: Role in the extension phase of acute renal failure. Kidney Int. (2004) 66 496-499. DOI: 10.1111/j.1523-1755.2004.761_5.x 13. Ceulemans A.G., Zgavc T., Kooijman R., Hachimi-Idrissi S., Sarre S., Michotte Y.. The dual role of the neuroinflammatory response after ischemic stroke: Modulatory effects of hypothermia. J. Neuroinflamm. (2010) 7 74. DOI: 10.1186/1742-2094-7-74 14. Lakhan S.E., Kirchgessner A., Hofer M.. Inflammatory mechanisms in ischemic stroke: Therapeutic approaches. J. Transl. Med. (2009) 7 97. DOI: 10.1186/1479-5876-7-97 15. Bao N., Dai D.. Dexmedetomidine protects against ischemia and reperfusion-induced kidney injury in rats. Mediators Inflamm. (2020) 2020 2120971. DOI: 10.1155/2020/2120971 16. Prieto-Moure B., Lloris-Carsi J.M., Belda-Antoli M., Toledo-Pereyra L.H., Cejalvo-Lapena D.. Allopurinol protective effect of renal ischemia by downregulating tnf-alpha, il-1beta, and il-6 response. J. Invest. Surg. (2017) 30 143-151. DOI: 10.1080/08941939.2016.1230658 17. Zhang M.Z., Wang X., Wang Y., Niu A., Wang S., Zou C., Harris R.C.. Il-4/il-13-mediated polarization of renal macrophages/dendritic cells to an m2a phenotype is essential for recovery from acute kidney injury. Kidney Int. (2017) 91 375-386. DOI: 10.1016/j.kint.2016.08.020 18. Moledina D.G., Mansour S.G., Jia Y., Obeid W., Thiessen-Philbrook H., Koyner J.L., McArthur E., Garg A.X., Wilson F.P., Shlipak M.G.. Association of t cell-derived inflammatory cytokines with acute kidney injury and mortality after cardiac surgery. Kidney Int. Rep. (2019) 4 1689-1697. DOI: 10.1016/j.ekir.2019.09.003 19. Schotte A., Janssen P.F., Gommeren W., Luyten W.H., Van Gompel P., Lesage A.S., De Loore K., Leysen J.E.. Risperidone compared with new and reference antipsychotic drugs: In vitro and in vivo receptor binding. Psychopharmacology (1996) 124 57-73. DOI: 10.1007/BF02245606 20. Corena-McLeod M.. Comparative pharmacology of risperidone and paliperidone. Drugs RD (2015) 15 163-174. DOI: 10.1007/s40268-015-0092-x 21. Razaq M., Samma M.. A case of risperidone-induced hypothermia. Am. J. Ther. (2004) 11 229-230. DOI: 10.1097/00045391-200405000-00012 22. Brevik A., Farver D.. Atypical antipsychotic induced mild hypothermia. South Dak. J. Med. (2003) 56 67-70 23. Yang G.E., Tae H.J., Lee T.K., Park Y.E., Cho J.H., Kim D.W., Park J.H., Ahn J.H., Ryoo S., Kim Y.M.. Risperidone treatment after transient ischemia induces hypothermia and provides neuroprotection in the gerbil hippocampus by decreasing oxidative stress. Int. J. Mol. Sci. (2019) 20. DOI: 10.3390/ijms20184621 24. Lee T.K., Lee J.C., Tae H.J., Kim H.I., Shin M.C., Ahn J.H., Park J.H., Kim D.W., Hong S., Choi S.Y.. Therapeutic effects of risperidone against spinal cord injury in a rat model of asphyxial cardiac arrest: A focus on body temperature, paraplegia, motor neuron damage, and neuroinflammation. Vet. Sci. (2021) 8. DOI: 10.3390/vetsci8100230 25. Gebhardt K., Guyette F.X., Doshi A.A., Callaway C.W., Rittenberger J.C.. Prevalence and effect of fever on outcome following resuscitation from cardiac arrest. Resuscitation (2013) 84 1062-1067. DOI: 10.1016/j.resuscitation.2013.03.038 26. Cronberg T., Lilja G., Horn J., Kjaergaard J., Wise M.P., Pellis T., Hovdenes J., Gasche Y., Aneman A., Stammet P.. Neurologic function and health-related quality of life in patients following targeted temperature management at 33 degrees c vs 36 degrees c after out-of-hospital cardiac arrest: A randomized clinical trial. JAMA Neurol. (2015) 72 634-641. DOI: 10.1001/jamaneurol.2015.0169 27. Arrich J., Holzer M., Havel C., Mullner M., Herkner H.. Hypothermia for neuroprotection in adults after cardiopulmonary resuscitation. Cochrane Database Syst. Rev. (2016) 2 CD004128. DOI: 10.1002/14651858.CD004128.pub4 28. Kida K., Shirozu K., Yu B., Mandeville J.B., Bloch K.D., Ichinose F.. Beneficial effects of nitric oxide on outcomes after cardiac arrest and cardiopulmonary resuscitation in hypothermia-treated mice. Anesthesiology (2014) 120 880-889. DOI: 10.1097/ALN.0000000000000149 29. Miao Y.F., Wu H., Yang S.F., Dai J., Qiu Y.M., Tao Z.Y., Zhang X.H.. 5′-adenosine monophosphate-induced hypothermia attenuates brain ischemia/reperfusion injury in a rat model by inhibiting the inflammatory response. Mediators Inflamm. (2015) 2015 520745. DOI: 10.1155/2015/520745 30. Niemann C.U., Malinoski D.. Therapeutic hypothermia in deceased organ donors and kidney-graft function. N. Engl. J. Med. (2015) 373 2687. DOI: 10.1056/NEJMoa1501969 31. Niemann C.U., Xu F., Choi S., Behrends M., Park Y., Hirose R., Maher J.J.. Short passive cooling protects rats during hepatectomy by inducing heat shock proteins and limiting the induction of pro-inflammatory cytokines. J. Surg. Res. (2010) 158 43-52. DOI: 10.1016/j.jss.2008.08.030 32. 32. National Research Council Guide for the Care and Use of Laboratory AnimalsNational Research CouncilWashington, DC, USA2010. Guide for the Care and Use of Laboratory Animals (2010) 33. Kim S.E., Shin H.Y., Lee E.Y., Yoo Y.J., Kim R.H., Cho J.H., Lee T.K., Ahn D., Park B.Y., Yoon J.C.. Effect of therapeutic hypothermia against renal injury in a rat model of asphyxial cardiac arrest: Alpha focus on the survival rate, pathophysiology and antioxidant enzymes. Mol. Med. Rep. (2022) 25 19. DOI: 10.3892/mmr.2021.12535 34. Park Y., Ahn J.H., Cho J.H., Tae H.J., Lee T.K., Kim B., Lee J.C., Park J.H., Shin M.C., Ohk T.G.. Effects of hypothermia on inflammatory cytokine expression in rat liver following asphyxial cardiac arrest. Exp. Ther. Med. (2021) 21 626. DOI: 10.3892/etm.2021.10058 35. Jackson T.C., Dixon C.E., Janesko-Feldman K., Vagni V., Kotermanski S.E., Jackson E.K., Kochanek P.M.. Acute physiology and neurologic outcomes after brain injury in scop/phlpp1 ko mice. Sci. Rep. (2018) 8 7158. DOI: 10.1038/s41598-018-25371-2 36. Faul F., Erdfelder E., Lang A.G., Buchner A.. *Gpower 3: A flexible statistical power analysis program for the social, behav-ioral, and biomedical sciences*. Behav. Res. Methods* (2007) 39 175-191. DOI: 10.3758/BF03193146 37. Park Y.S., Choi Y.H., Oh J.H., Cho I.S., Cha K.C., Choi B.S., You J.S.. Recovery from acute kidney injury as a potent predictor of survival and good neurological outcome at discharge after out-of-hospital cardiac arrest. Crit. Care (2019) 23 256. DOI: 10.1186/s13054-019-2535-1 38. Geri G., Guillemet L., Dumas F., Charpentier J., Antona M., Lemiale V., Bougouin W., Lamhaut L., Mira J.P., Vinsonneau C.. Acute kidney injury after out-of-hospital cardiac arrest: Risk factors and prognosis in a large cohort. Intensive Care Med. (2015) 41 1273-1280. DOI: 10.1007/s00134-015-3848-4 39. Damman K., Valente M.A., Voors A.A., O’Connor C.M., van Veldhuisen D.J., Hillege H.L.. Renal impairment, worsening renal function, and outcome in patients with heart failure: An updated meta-analysis. Eur. Heart. J. (2014) 35 455-469. DOI: 10.1093/eurheartj/eht386 40. Greite R., Thorenz A., Chen R., Jang M.S., Rong S., Brownstein M.J., Tewes S., Wang L., Baniassad B., Kirsch T.. Renal ischemia-reperfusion injury causes hypertension and renal perfusion impairment in the cd1 mice which promotes progressive renal fibrosis. Am. J. Physiol. Ren. Physiol. (2018) 314 F881-F892. DOI: 10.1152/ajprenal.00519.2016 41. Nguyen Thi P.A., Chen M.H., Li N., Zhuo X.J., Xie L.. Pd98059 protects brain against cells death resulting from ros/erk activation in a cardiac arrest rat model. Oxid. Med. Cell Longev. (2016) 2016 3723762. DOI: 10.1155/2016/3723762 42. Yin X.L., Shen H., Zhang W., Yang Y.. Inhibition of endoplasm reticulum stress by anisodamine protects against myocardial injury after cardiac arrest and resuscitation in rats. Am. J. Chin. Med. (2011) 39 853-866. DOI: 10.1142/S0192415X11009251 43. Lopez-Herce J., del Castillo J., Matamoros M., Canadas S., Rodriguez-Calvo A., Cecchetti C., Rodriguez-Nunez A., Carrillo A.. Post return of spontaneous circulation factors associated with mortality in pediatric in-hospital cardiac arrest: A prospective multicenter multinational observational study. Crit. Care (2014) 18 607. DOI: 10.1186/s13054-014-0607-9 44. Jawad A., Yoo Y.J., Yoon J.C., Tian W., Islam M.S., Lee E.Y., Shin H.Y., Kim S.E., Ahn D., Park B.Y.. Changes of renal histopathology and the role of nrf2/ho-1 in asphyxial cardiac arrest model in rats. Acta Cir. Bras. (2021) 36 e360607. DOI: 10.1590/acb360607 45. Kocoglu H., Ozturk H., Ozturk H., Yilmaz F., Gulcu N.. Effect of dexmedetomidine on ischemia-reperfusion injury in rat kidney: A histopathologic study. Ren. Fail. (2009) 31 70-74. DOI: 10.1080/08860220802546487 46. Dietrich W.D., Atkins C.M., Bramlett H.M.. Protection in animal models of brain and spinal cord injury with mild to moderate hypothermia. J. Neurotrauma. (2009) 26 301-312. DOI: 10.1089/neu.2008.0806 47. Ahn J.H., Lee T.K., Kim B., Lee J.C., Tae H.J., Cho J.H., Park Y., Shin M.C., Ohk T.G., Park C.W.. Therapeutic hypothermia improves hind limb motor outcome and attenuates oxidative stress and neuronal damage in the lumbar spinal cord following cardiac arrest. Antioxidants (2020) 9. DOI: 10.3390/antiox9010038 48. Horiuchi T., Kawaguchi M., Kurita N., Inoue S., Nakamura M., Konishi N., Furuya H.. The long-term effects of mild to moderate hypothermia on gray and white matter injury after spinal cord ischemia in rats. Anesth. Analg. (2009) 109 559-566. DOI: 10.1213/ane.0b013e3181aa96a1 49. Saito T., Saito S., Yamamoto H., Tsuchida M.. Neuroprotection following mild hypothermia after spinal cord ischemia in rats. J. Vasc. Surg. (2013) 57 173-181. DOI: 10.1016/j.jvs.2012.05.101 50. Wang L.M., Yan Y., Zou L.J., Jing N.H., Xu Z.Y.. Moderate hypothermia prevents neural cell apoptosis following spinal cord ischemia in rabbits. Cell Res. (2005) 15 387-393. DOI: 10.1038/sj.cr.7290306 51. Islam A., Kim S.E., Yoon J.C., Jawad A., Tian W., Yoo Y.J., Kim I.S., Ahn D., Park B.Y., Hwang Y.. Protective effects of therapeutic hypothermia on renal injury in an asphyxial cardiac arrest rat model. J. Therm. Biol. (2020) 94 102761. DOI: 10.1016/j.jtherbio.2020.102761 52. van Marum R.J., Wegewijs M.A., Loonen A.J., Beers E.. Hypothermia following antipsychotic drug use. Eur. J. Clin. Pharmacol. (2007) 63 627-631. DOI: 10.1007/s00228-007-0294-4 53. Williams T.M., Wise A.F., Layton D.S., Ricardo S.D.. Phenotype and influx kinetics of leukocytes and inflammatory cytokine production in kidney ischemia/reperfusion injury. Nephrology (2018) 23 75-85. DOI: 10.1111/nep.12941 54. Bonventre J.V., Yang L.. Cellular pathophysiology of ischemic acute kidney injury. J. Clin. Investig. (2011) 121 4210-4221. DOI: 10.1172/JCI45161 55. Meldrum K.K., Meldrum D.R., Meng X., Ao L., Harken A.H.. Tnf-alpha-dependent bilateral renal injury is induced by unilateral renal ischemia-reperfusion. Am. J. Physiol. Heart Circ. Physiol. (2002) 282 H540-H546. DOI: 10.1152/ajpheart.00072.2001 56. Eraslan E., Tanyeli A., Polat E., Yetim Z.. Evodiamine alleviates kidney ischemia reperfusion injury in rats: A biochemical and histopathological study. J. Cell Biochem. (2019) 120 17159-17166. DOI: 10.1002/jcb.28976 57. Partrick D.A., Moore F.A., Moore E.E., Barnett C.C., Silliman C.C.. Neutrophil priming and activation in the pathogenesis of postinjury multiple organ failure. New Horiz. (1996) 4 194-210. PMID: 8774796 58. Zhang Y., Hu F., Wen J., Wei X., Zeng Y., Sun Y., Luo S., Sun L.. Effects of sevoflurane on nf-small ka, cyrillicb and tnf-alpha expression in renal ischemia-reperfusion diabetic rats. Inflamm. Res. (2017) 66 901-910. DOI: 10.1007/s00011-017-1071-1 59. Liang S., Xu Z., Ruan Y., Niu T., Guo W., Jiang W., Hou J.. Isoquercitrin attenuates renal ischemia/reperfusion injury through antioxidation, anti-inflammation, and antiapoptosis in mice. Transplant Proc. (2020) 52 1014-1019. DOI: 10.1016/j.transproceed.2019.12.038 60. Miklos Z., Kurthy M., Degrell P., Ranczinger E., Vida M., Lantos J., Arato E., Sinay L., Hardi P., Balatonyi B.. Ischaemic postconditioning reduces serum and tubular tnf-alpha expression in ischaemic-reperfused kidney in healthy rats. Clin. Hemorheol. Microcirc. (2012) 50 167-178. DOI: 10.3233/CH-2011-1414 61. Gao G., Zhang B., Ramesh G., Betterly D., Tadagavadi R.K., Wang W., Reeves W.B.. Tnf-alpha mediates increased susceptibility to ischemic aki in diabetes. Am. J. Physiol. Renal Physiol. (2013) 304 F515-F521. DOI: 10.1152/ajprenal.00533.2012 62. Lee I.T., Yang C.M.. Inflammatory signalings involved in airway and pulmonary diseases. Mediators Inflamm. (2013) 2013 791231. DOI: 10.1155/2013/791231 63. Mozaffari Godarzi S., Valizade Gorji A., Gholizadeh B., Mard S.A., Mansouri E.. Antioxidant effect of p-coumaric acid on interleukin 1-beta and tumor necrosis factor-alpha in rats with renal ischemic reperfusion. Nefrologia (2020) 40 311-319. DOI: 10.1016/j.nefro.2019.10.003 64. Patel N.S., Chatterjee P.K., Di Paola R., Mazzon E., Britti D., De Sarro A., Cuzzocrea S., Thiemermann C.. Endogenous interleukin-6 enhances the renal injury, dysfunction, and inflammation caused by ischemia/reperfusion. J. Pharmacol. Exp. Ther. (2005) 312 1170-1178. DOI: 10.1124/jpet.104.078659 65. Thurman J.M.. Triggers of inflammation after renal ischemia/reperfusion. Clin. Immunol. (2007) 123 7-13. DOI: 10.1016/j.clim.2006.09.008 66. Nechemia-Arbely Y., Barkan D., Pizov G., Shriki A., Rose-John S., Galun E., Axelrod J.H.. Il-6/il-6r axis plays a critical role in acute kidney injury. J. Am. Soc. Nephrol. (2008) 19 1106-1115. DOI: 10.1681/ASN.2007070744 67. Kavsak P.A., Ko D.T., Newman A.M., Palomaki G.E., Lustig V., MacRae A.R., Jaffe A.S.. Risk stratification for heart failure and death in an acute coronary syndrome population using inflammatory cytokines and n-terminal pro-brain natriuretic peptide. Clin. Chem. (2007) 53 2112-2118. DOI: 10.1373/clinchem.2007.090613 68. Afify M.F., Mohamed G.B., El-Maboud M.A., Abdel-Latif E.A.. Serum levels of ghrelin, tumor necrosis factor-alpha and interleukin-6 in infants and children with congenital heart disease. J. Trop. Pediatr. (2009) 55 388-392. DOI: 10.1093/tropej/fmp036 69. Yokota N., Burne-Taney M., Racusen L., Rabb H.. Contrasting roles for stat4 and stat6 signal transduction pathways in murine renal ischemia-reperfusion injury. Am. J. Physiol. Ren. Physiol. (2003) 285 F319-F325. DOI: 10.1152/ajprenal.00432.2002 70. Jayaraj R.L., Azimullah S., Beiram R., Jalal F.Y., Rosenberg G.A.. Neuroinflammation: Friend and foe for ischemic stroke. J. Neuroinflamm. (2019) 16 142. DOI: 10.1186/s12974-019-1516-2 71. Yu J.T., Lee C.H., Yoo K.Y., Choi J.H., Li H., Park O.K., Yan B., Hwang I.K., Kwon Y.G., Kim Y.M.. Maintenance of anti-inflammatory cytokines and reduction of glial activation in the ischemic hippocampal ca1 region preconditioned with lipopolysaccharide. J. Neurol. Sci. (2010) 296 69-78. DOI: 10.1016/j.jns.2010.06.004 72. Yan B.C., Kim S.K., Park J.H., Ahn J.H., Lee C.H., Yoo K.Y., Choi J.H., Lee D.S., Kim M.J., Kim Y.M.. Comparison of inflammatory cytokines changes in the hippocampal ca1 region between the young and adult gerbil after transient cerebral ischemia. Brain Res. (2012) 1461 64-75. DOI: 10.1016/j.brainres.2012.04.025 73. Wang P., Wu P., Siegel M.I., Egan R.W., Billah M.M.. Interleukin (il)-10 inhibits nuclear factor kappa b (nf kappa b) activation in human monocytes. Il-10 and il-4 suppress cytokine synthesis by different mechanisms. J. Biol. Chem. (1995) 270 9558-9563. DOI: 10.1074/jbc.270.16.9558 74. te Velde A.A., Huijbens R.J., Heije K., de Vries J.E., Figdor C.G.. Interleukin-4 (il-4) inhibits secretion of il-1 beta, tumor necrosis factor alpha, and il-6 by human monocytes. Blood (1990) 76 1392-1397. DOI: 10.1182/blood.V76.7.1392.1392 75. de Waal Malefyt R., Figdor C.G., Huijbens R., Mohan-Peterson S., Bennett B., Culpepper J., Dang W., Zurawski G., de Vries J.E.. Effects of il-13 on phenotype, cytokine production, and cytotoxic function of human monocytes. Comparison with il-4 and modulation by ifn-gamma or il-10. J. Immunol. (1993) 151 6370-6381. DOI: 10.4049/jimmunol.151.11.6370
--- title: Association between Systemic Factors and Vitreous Fluid Cytokines in Proliferative Diabetic Retinopathy authors: - Tomohito Sato - Rina Okazawa - Koichi Nagura - Hideaki Someya - Yoshiaki Nishio - Toshio Enoki - Masataka Ito - Masaru Takeuchi journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10059790 doi: 10.3390/jcm12062354 license: CC BY 4.0 --- # Association between Systemic Factors and Vitreous Fluid Cytokines in Proliferative Diabetic Retinopathy ## Abstract Proliferative diabetic retinopathy (PDR) is a vision-threatening complication of diabetes mellitus (DM). Systemic and intraocular factors are intricately related to PDR, and vitreous fluid (VF) cytokines are representative intraocular biomarkers. However, the associations between systemic factors and VF cytokines and their influence on PDR pathology are unclear. This study aimed to examine the correlation between systemic factors and VF cytokines and analyze their contributions to the pathology of PDR using multivariate analyses. We conducted a retrospective observational study on 26 PDR eyes of 25 patients with type 2 DM, and 30 eyes of 30 patients with idiopathic macular hole or epiretinal membrane as controls. Fifteen systemic and laboratory tests including blood pressure (BP) and body mass index (BMI), and 27 cytokines in VF were analyzed. BP and BMI correlated positively with VF levels of IL-6 and IP-10 in PDR patients, while no significant correlation was found between systemic factors and VF cytokines in controls. MCP-1 and VEGF-A in VF separately clustered with different systemic factors in controls, but these cytokines lost the property similarity with systemic factors and acquired property similarity with each other in PDR. Systemic factors contributed to only $10.4\%$, whereas VF cytokines contributed to $42.3\%$ out of $52.7\%$ variance of the whole PDR dataset. Our results suggest that intraocular factors play a major role in the pathology of PDR, whereas systemic factors may have limited effects, and that BP and BMI control in PDR could be useful interventions to improve intraocular immune condition. ## 1. Introduction Diabetes mellitus (DM) is a metabolic disease characterized by absolute or relative insulin deficiency [1]. Diabetic retinopathy (DR) is a major complication of DM, and is potentially vision-threatening in DM patients aged 20 to 75 years [2]. DR is characterized by retinal microvascular damage leading to vascular leakage and ischemia-induced retinal neovascularization [3,4]. Numerous etiologic factors potentially involved in the onset and progression of DR have been investigated, including hypertension, obesity, blood glucose level, glycated hemoglobin (Hb)A1c, hyperlipidemia, dietary style, exercise, and smoking [5,6]. Proliferative DR (PDR) is an advanced stage of DR, in which vitreous hemorrhage (VH) and tractional retinal detachment (TRD) occur due to proliferative membrane traction [7,8]. Cytokines are small and nonstructural proteins secreted by a variety of immune and non-immune cells, playing a key role in immune responses in various cells [9,10]. Intraocular fluids comprising aqueous humor (AH) and vitreous fluid (VF) are unique specimens used for the direct analysis of intraocular immune conditions in various chorioretinal diseases such as age-related macular degeneration (AMD) [11,12], uveitis [13,14] and PDR [15,16,17]. In PDR eyes, VF levels of inflammatory cytokines including interleukin (IL)-6, interferon gamma-induced protein 10 (IP-10), monocyte chemotactic protein 1 (MCP-1) and vascular endothelial growth factor (VEGF)-A are elevated [15,16,17]. DR develops through complex interactions among various systemic and intraocular factors [5,18]. Previous studies on DR pathogenesis mainly concentrated on ocular morphological changes observed on color fundus photographs (CFP) and optical coherence tomography (OCT) images. The purposes of this study were to examine the correlations between systemic factors and VF cytokines and to analyze their contributions to the pathology of PDR. ## 2.1. Subjects This retrospective observational study was performed on 26 PDR eyes of 25 patients with type 2 DM who underwent pars plana vitrectomy (PPV) for VH and/or TRD in National Defense Medical College between 1 January 2014 and 31 August 2021. PDR was diagnosed according to the international clinical disease severity classification of DR [8]. The control group was composed of 14 idiopathic macular hole (MH) eyes of 14 patients, and 16 idiopathic epiretinal membrane (ERM) eyes of 16 patients. The inclusion criteria were [17,19]: [1] no current or past history of intraocular inflammatory diseases including retinal artery occlusion, retinal vein occlusion, AMD, ocular tumor, uveitis, endophthalmitis, and dialysis therapy for renal failure; [2] no history of previous pars plana vitrectomy (PPV), ocular trauma, and prior intravitreal therapies including steroid and anti-VEGF agents such as bevacizumab; and [3] no history of cataract surgery performed within 6 months before the date of enrollment. In one patient with bilateral PDR, both eyes were analyzed separately. When PPV was performed on both eyes in the control group, the VF specimens collected from the first operation were used. The disposition of PDR patients is summarized in Figure S1. In the PDR group, panretinal photocoagulation was not performed within 7 days before PPV (per inclusion criterion), but was performed more than 7 days before PPV in 16 eyes ($61.5\%$). Twenty-three eyes ($88.5\%$) had VH-obscuring fundus findings. DME and TRD developed in 21 eyes ($80.8\%$) and 16 eyes ($61.5\%$), respectively. DME was defined as retinal edema in the central 3 mm circle on the Early Treatment Diabetic Retinopathy Study (ETDRS) grid [20] in the macula, measured by spectral-domain OCT (SD-OCT; Cirrus HD-OCT, Carl Zeiss Meditec, Dublin, CA, USA). DME and TRD were confirmed by CFP, fluorescein fundus angiography (FAG) and SD-OCT before PPV. When dense VH impeded SD-OCT examination before PPV, the examination was conducted during PPV using an intraoperative OCT system (EnFocus, Leica Microsystems/Bioptigen, Morrisville, NC). In all cases, TRD did not occur within the central 6 mm circle on the ETDRS grid in the macula. In the present study, we performed a priori power calculation using previous clinical data [17]. We calculated effect sizes (Hedges’ g) for VF IL-6 concentration, which is a representative cytokine with a significant difference between PDR and non-DR control (PDR patients; $$n = 27$$, IL-6 = 658.5 ± 402.1 pg/mL, ERM patients as control; $$n = 27$$, IL-6 = 45.7 ± 54.9) [17]. The effect size for VF IL-6 was 2.14. To demonstrate significant differences in VF IL-6 concentration with a statistical power of 0.80 [21], the sample sizes of our study should be at least 4.3 for two-tailed tests. On the other hand, the number of samples used in multivariate analyses should be one or more than that of the explanatory variables [22]. From our preliminary study, 23 variables comprising 11 systemic factors and 12 VF cytokines were expected to be used as explanatory variables in multivariate analyses. Therefore, we attempted to include 25 or more cases each for the PDR and control groups, allowing margin of error. ## 2.2. Diagnostics PDR, MH and ERM were diagnosed based on a full ophthalmological examination including best-corrected visual acuity (BCVA) test using a decimal chart, intraocular pressure measurement, slit-lamp biomicroscopy, dilated fundus examination, and SD-OCT. Furthermore, CFP and FAG were performed in PDR patients. BCVA was converted to a logarithm of the minimum angle of resolution units (logMAR VA) for statistical analysis. Counting fingers, hand motion, light perception and no light perception were converted to 1.85, 2.30, 2.80 and 2.90 logMAR, respectively [23,24]. Central retinal thickness (CRT) was defined as the mean retinal thicknesses in the central 1 mm circle on the ETDRS grid in the macula [11,12] measured by SD-OCT. Three retinal specialists (members of Japanese Retina and Vitreous Society) confirmed the diagnoses and reviewed the clinical findings. In case of a discrepancy among the three assessors, the decision was adjudicated by majority rule. ## 2.3. Systemic Factors Health condition factors of age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure difference between SBP and DBP (PPD) and heart rate (HR) were collected at admission for PPV. Systemic condition factors comprising the international normalized ratio of prothrombin time (PT-INR), activated partial thromboplastin time (APTT), random blood glucose (RBG), glycated hemoglobin (Hb) A1c, blood urea nitrogen (BUN), creatinine, estimated glomerular filtration rate (eGFR), C-reactive protein (CRP), urinary glucose and urinary protein were measured at the time of decision to perform PPV. Estimated GFR was calculated using the following formula [25]: 194 × serum creatinine−1.094 × age−0.287. CRP values lower than 0.3 mg/dL were treated as 0 in statistical analysis. Notation of ± in urine test was assigned a value of 0.5. ## 2.4. Vitreous Fluid Sample Collection and Cytokine Assay Approximately 0.5 mL of undiluted VF was collected using a 25G vitreous cutter inserted into the mid-vitreous cavity at the beginning of PPV before active infusion [17,19]. Uncentrifuged VF samples were transferred into sterile tubes and stored at −80 °C until processing. No complication associated with VF sampling was observed. Before analysis, undiluted VF samples were centrifuged at 10,000× g for 10 min, and 50 μL of the supernatant was used in cytokine assay [17,19]. All standards and samples were assayed in duplicate. A multiplex bead analysis platform (Bio-Plex Suspension Array System; Bio-Rad) and a multiplex cytokine panel (Bio-Plex Human Cytokine 27-plex panel; Bio-Rad, Hercules, CA, USA) that provides comprehensive coverage of inflammatory mediators were used to detect 27 VF cytokines comprising platelet-derived growth factor-BB, interleukin (IL)-1β, IL-1 receptor antagonist (ra), IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17A, eotaxin, basic fibroblast growth factor, granulocyte colony-stimulating factor (G-CSF), granulocyte macrophage colony-stimulating factor, interferon-gamma (IFN-γ), IP-10, MCP-1, MIP (macrophage inflammatory protein)-1α, MIP-1β, regulated on activation, normal T-cell expressed and secreted (RANTES), tumor necrosis factor alpha (TNFα) and VEGF-A. The levels of VF cytokines below detectable levels were treated as 0 in statistical analysis [11,12,17,19]. ## 2.5. Statistical Analysis Statistical analyses were performed using the statistic add-in software for Excel (BellCurve for Excel®, SSRI Co., Ltd., Tokyo, Japan, and XLSTAT®, Addinsoft company, Paris, France). Data are expressed as mean ± standard deviation [12,17,19]. Mann–Whitney U test and Spearman’s rank correlation test were used for nonparametric comparison and correlation, respectively, between two unpaired groups. Pearson’s chi-squared test (for n ≥ 4) and Fisher’s exact test (for n < 4) were used to compare categorical variables. Cytokines with detection rates over $60\%$ and high concentrations of 10 pg/mL or above in PDR group were selected as explanatory variables in hierarchical cluster analysis, principal component analysis (PCA), and Spearman’s rank correlation test [11,12]. Hierarchical cluster analysis was performed using Ward’s method with Euclidean distance as the distance metric [11,12]. A two-tailed p value less than 0.05 was considered to be statistically significant. In subsequent multivariate analyses, the number of samples must be one or more than the number of explanatory variables [22]. Therefore, the systemic factors used in the analyses were carefully selected by the following criteria: [1] the systemic factor is recognized as a DR-related factor [6,26,27]; [2] when multiple systemic factors shared the same purpose of evaluation, the factor that provided the most appropriate evaluation was selected; and [3] the systemic test value should be quantitative on a proportional scale. Based on these criteria, age, BMI, SBP, DBP, PPD, HR, PT-INR, APTT, RBG level, HbA1c% and eGFR were chosen as appropriate explanatory variables in subsequent analyses. ## 3.1. Subjects The demographic and clinical data of the PDR and control groups are shown in Table 1. The age was 58.6 ± 13.5 years (range 32–82 years) in the PDR group and 70.7 ± 7.94 years (range 48–82) in the control group. The PDR group was significantly younger than the control group, similar to previous studies [17,28]. The Male/female ratio was $\frac{18}{7}$ in the PDR group and $\frac{13}{17}$ in the control group. LogMAR VA was higher and CRT was lower in the PDR group than in the control group. RBG, HbA1c, BUN, serum creatinine, eGFR, urine glucose and urine protein were higher in the PDR group than in the control group. There were no significant differences in BMI, SBP, DBP, PPD, HR, PT-INR, APTT and CRP between the two groups. ## 3.2. Vitreous Fluid Cytokine Levels The profiles of VF cytokine levels in the PDR and control groups are summarized in Table 2. The VF levels of IL-1ra, IL-6, IL-7, IL-8, IL-13, IL-15, eotaxin, G-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, RANTES, TNFα and VEGF-A were higher in PDR group than in control group. Furthermore, IL-1ra, IL-6, IL-7, IL-8, IL-13, eotaxin, IFN-γ, IP-10, MCP-1, MIP-1β, TNFα and VEGF-A had detection rates with higher than $60\%$ and high concentrations of 10 pg/mL or above in the PDR group, and were also detectable in the control group. These 12 VF cytokines that were reliably detectable and had reasonably high concentrations were considered appropriate as explanatory variables in subsequent multivariate analyses [11]. In the subgroup analysis of VF cytokines between TRD and VH eyes in PDR group, there was no significant difference with the levels of all VF cytokines measured (Tables S1 and S2). ## 3.3. Correlation between Systemic Factors and Vitreous Fluid Cytokines Matrices of p values obtained from Spearman’s rank correlation test between systemic factors and VF cytokines are shown in Table 3. In control group (Table 3), positive correlations were found between DBP and HR, and between PT-INR and APTT. SBP correlated positively with DBP, PPD and HR. BMI had negative correlation with APTT, and DBP correlated negatively with RBG and HbA1c. Regarding correlations between VF cytokines, significant correlations were observed among IL-1ra, IL-6, IL-7, IL-8, eotaxin, IFN-γ, IP-10, MCP-1 and MIP-1β. No significant correlation was found between systemic factors and VF cytokines. In the PDR group (Table 3), positive correlations were observed between age and PPD, and between PT-INR and APTT. SBP correlated positively with BMI, DBP, PPD and RBG. Age correlated negatively with DBP, and PT-INR correlated negatively with RBG. Concerning correlations between systemic factors and VF cytokines, BMI correlated positively with IL-6 and IP-10. Positive correlations were found between SBP and IL-6, IL-7, IL-13, eotaxin or IP-10, and between DBP and IL-6, eotaxin or IP-10. IL-1ra correlated negatively with BMI. Regarding correlations between VF cytokines, all cytokines correlated with one another. In particular, VEGF-A correlated positively with IL-7, IL-13, eotaxin and TNFα. These results suggest that the correlation between systemic factors and VF cytokines could be a pathological feature of PDR. Spearman’s rank correlation coefficients between systemic factors and VF cytokines are presented in Table S3. ## 3.4. Expression Patterns of Systemic Factors and Vitreous Fluid Cytokines in Hierarchical Cluster Analysis Cluster analysis was performed to classify systemic factors and VF cytokines into groups with relatively similar properties called clusters [29] (pp. 603–16). In the control group (Figure 1), hierarchical cluster analysis classified the explanatory variables broadly into single cytokine and two principal clusters as follows: [1] the single cytokine was IP-10; [2] one cluster consisted of one cytokine MCP-1 and a subcluster formed by age, SBP, DBP, PPD, HR, RBG and eGFR; and [3] another cluster was composed of BMI, PT-INR, APTT, HbA1c and the remaining cytokines including VEGF-A. In the PDR group (Figure 1), the explanatory variables were roughly classified into single cytokine and two principal clusters as follows: [1] the single cytokine was IP-10; [2] one cluster consisted of two cytokines, MCP-1 and VEGF-A; and [3] another cluster was composed of all the systemic factors and the remaining cytokines. In summary, although MCP-1 and VEGF-A clustered independently with different systemic factors in control group, the two cytokines were altered in the PDR group, losing property similarity with the systemic factors and acquiring property similarity with each other, independent of the influences of systemic factors. ## 3.5. Principal Component Analysis for Expression Patterns of Systemic Factors and Vitreous Fluid Cytokines in Controls In the PCA of the control group, the variance of VEGF-A was less than 1.0 × 10−10, and PPD showed multicollinearity with other explanatory variables. Therefore, VEGF-A and PPD were excluded from PCA. The contribution rates (CRs) of the first (PC1), second (PC2) and third principal component (PC3) were, respectively, $23.5\%$, $14.7\%$ and $10.3\%$ (cumulative $48.5\%$) of the total variance of the entire dataset (Figure 2D). In the principal component loading (PCL) analysis of PC1 (Figure 2A), IP-10, MCP-1, IFN-γ, IL-7, eotaxin and MIP-1β had high loadings of over 0.6, and the loading of age was approximately 0.35, while eGFR, SBP and DBP had negative loadings. The results suggest that intraocular immune regulation by immunocompetent cells including T cells, macrophages and eosinophils would work in an exquisitely balanced manner, and the cytokines secreted tend to increase with aging, while systemic hemodynamics and renal function generally deteriorate with aging. Therefore, PC1 as a dominant unsupervised summary axis may be interpreted as the primary intraocular immune condition in older healthy subjects. In the PCL analysis of PC2 (Figure 2B), DBP, HR and SBP had high loadings of over 0.6. HbA1c, RBG and age had negative loadings of −0.61, −0.60 and −0.22, respectively. These results imply that BP and HR decrease, whereas RBG and HbA1c increase with aging. PC2 may be interpreted as the functions of systemic hemodynamics and glucose tolerance. In the PCL analysis of PC3 (Figure 2C), the loadings of eGFR, HbA1c and RBG were, respectively, 0.55, 0.47 and 0.37, whereas PT-INR and APTT had negative loadings of −0.623 and −0.618. The loading of age was approximately 0.30, suggesting some influence on PC3. PC3 may be interpreted as the relationship among glucose tolerance, renal function and blood coagulability. Based on the interpretation of each principal component, the cumulative CR of systemic factors dominating PC2 and PC3 was $25.0\%$, and that of VF cytokines being the major constituents of PC1 was $23.5\%$, out of $48.5\%$ variance of the whole dataset. The plot of eigenvalues of all principal components are presented in Figure 2E. ## 3.6. Principal Component Analysis for Expression Patterns of Systemic Factors and Vitreous Fluid Cytokines in PDR Patients In the PCA of the PDR group, PPD was excluded from analysis because of multicollinearity with other explanatory variables. The CRs of PC1, PC2 and PC3 were, respectively, $25.2\%$, $17.1\%$ and $10.4\%$ (cumulative $52.7\%$) of the total variance of the dataset (Figure 2I). In the PCL analysis of PC1 (Figure 2F), VEGF-A and IL-6 had the highest loadings of 0.86 and 0.83, while DBP and SBP also had high loadings (0.81 and 0.70). Age had a negative loading of −0.42. The results suggest that in PDR patients, younger age is associated with higher VF levels of VEGF-A and IL-6 accompanied by BP elevation. PC1 may be interpreted as the primary intraocular immune condition, implying angiogenesis and inflammation in response to retinal ischemia, which is also associated with systemic hemodynamics. In the PCL analysis of PC2 (Figure 2G), MCP-1, IFN-γ, IL-1ra, MIP-1β and IL-8 had high loadings of over 0.6, whereas PT-INR and APTT had negative loadings (−0.58 and −0.33). The loading of age was only 0.03, indicating no influence on PC2. PC2 may be interpreted as the second major intraocular immune condition, possibly influenced by blood coagulability. In the PCL analysis of PC3 (Figure 2H), APTT, TNFα and PT-INR had high loadings of 0.80, 0.76 and 0.53, respectively. RBG and HbA1c had negative loadings of −0.31 and −0.22. The loading of age was only 0.12, indicating almost no effect on PC3. The results suggest that the decreases of APTT and PT-INR are accompanied by the elevation of RBG and HbA1c. PC3 may be interpreted as the relationship between glucose tolerance and blood coagulability, similar to PC3 in controls. Significant variables in PC1 and PC2 mainly consisted of VF cytokines, while those in PC3 were primarily systemic factors. Based on the interpretation of each principal component, the cumulative CR of VF cytokines dominating PC1 and PC2 was $42.3\%$ and that of systemic factors mainly constituting PC3 was $10.4\%$, out of $52.7\%$ variance of the entire dataset. The eigenvalues of all principal components are plotted in Figure 2J. ## 4. Discussion In this study, we demonstrated the correlations between systemic factors and VF cytokines as representative intraocular factors in PDR, and comprehensively evaluated the influences of systemic factors and VF cytokines on the pathology of PDR using multivariate analyses. The primary findings of the current study were as follows: [1] BP and BMI correlated positively with VF levels of IL-6 and IP-10 in PDR patients, while there was no significant correlation between systemic factors and VF cytokines in controls; [2] MCP-1 and VEGF-A in the VF independently clustered with different systemic factors in controls, but these cytokines lost the property similarity with systemic factors and acquired property similarity with each other in PDR patients; [3] systemic factors contributed to only $10.4\%$, whereas VF cytokines contributed to $42.3\%$ out of $52.7\%$ variance of the whole PDR dataset. A large number of health and systemic conditions including hypertension, obesity, hyperlipidemia, blood glucose, HbA1c as well as dietary style, exercise, and smoking are known to be potentially related to DR [5,6]. In particular, hypertension is consistently associated positively with the development and progression of DR [6,30,31]. For DM, intensive blood glucose control from the early stage was found to suppress the development and progression of DM-related complications over the long-term, a phenomenon termed “legacy effect” [27,32] or “metabolic memory [33,34,35]. Concerning renal function, lower GFR and proteinuria increased the prevalence and progression risk of DR in several clinical studies [36,37,38,39]. A retrospective analysis of non-PDR patients conducted in the United States showed that nephropathy increased the risk of progression to PDR by $29\%$ [40]. Based on the reported DR-related factors, we selected those related to systemic hemodynamics, renal function, glucose tolerance, and anticoagulant activity as systemic factors potentially associated with PDR in this study. Various intraocular inflammatory and angiogenic factors are involved in the development and progression of DR. The VF levels of IL-6, IL-8, TNFα and VEGF-A were elevated in PDR eyes compared to non-diabetic eyes [15,17,19]. Retinal ischemia increases compensatory angiogenesis, tissue remodeling and inflammation, presumably mediated by elevated expression of IL-6, IL-1𝛽, TNF𝛼 and VEGF [41]. On the other hand, cytokines demonstrate functional multiplicity and diversity by interacting with one another [10,11,42]. Therefore, we conducted a comprehensive analysis of the influences of systemic factors and VF cytokines on the pathology of PDR using PCA, aiming to elucidate the intricately intertwined relationship among those factors. Correlation existed between systemic factors and VF cytokines in PDR patients, but not in older healthy subjects (Table 3). In addition, BP and BMI were associated with increases or decreases of VF IL-6 and IP-10 levels. IL-6 is involved in low-grade inflammation via immune responses in type 2 DM [43], and directly or indirectly induces numerous angiogenic and inflammatory cytokines including VEGF [44]. IP-10 is a specific chemokine of type 1 helper cells [45], and acts as antiangiogenic and antifibrotic factors [46,47]. We speculate that elevated IP-10 level in the VF may be a homeostatic response for suppressing angiogenesis via low-grade inflammation in PDR. Regarding BP in DR, the UKPDS reported that a 10 mmHg reduction in SBP reduced the risk of developing DR by approximately $10\%$, and strict BP control decreased DR progression by $35\%$ and vision loss by $47\%$ [27,32]. As for dietary style in DR, several studies suggested that greater adherence to the Mediterranean diet and higher intake of fruit, vegetable and fish may protect against the development of DR [48,49,50]. Therefore, our results support proper management of BP and dietary style as useful systemic interventions to improve intraocular immune conditions in PDR eyes, which is generally consistent with previous studies. On the other hand, VEGF-A in the VF did not correlate with systemic factors or IL-6, implying that the VF level of VEGF-A could not be reduced sufficiently by controlling systemic factors in PDR patients. In real-world practice, intravitreal injection of an anti-VEGF agent induces marked regression of retinal neovascularity secondary to DR, especially in the cases with neovascular glaucoma [51]. Therefore, our results and previous studies suggest that both systemic and topical eye treatments are required for managing PDR. Cluster analysis is a summary statistical method that classifies explanatory variables into relatively similar groups called clusters [29] (pp. 603–616). Cluster analysis results suggest that the property similarity of systemic factors with VF cytokines was weakened and limited in PDR patients compared to controls. In particular, the property similarities of MCP-1 and VEGF-A in the VF deviated from all systemic factors in PDR, suggesting that these VF cytokines may not be significantly affected by systemic factors. VEGF-A is a crucial mediator of angiogenesis and vascular permeability [52], and recruits macrophages by binding VEGF receptor-1 in the process of inflammatory neovascularization [53]. MCP-1 is a chemokine regulating migration and infiltration of macrophages [54], plays pathogenic roles in DR via low-grade inflammation [55,56], and is also related to vascular permeability [57]. Currently, intravitreal anti-VEGF injection [58], intravitreal triamcinolone acetonide (TA) [59] and sub-Tenon TA [60] are the standard treatments for DME [61]. The present study could provide a rationale that topical ophthalmic treatment is an effective therapy to inhibit the activity of PDR compared to systemic treatment. PCA is a statistical technique that summarizes quantitative multivariate data by converting many correlated variables into fewer uncorrelated variables called principal components [62] (pp. 1094–1096). In this study, 23 variables comprising 11 systemic factors and 12 VF cytokines were reconstructed into a new unsupervised summary axis according to the order of influence on the pathology of PDR, and the interpretations of the summary axis were based on the magnitude and plus or minus sign of the PCL of explanatory variables constituting that axis. In PDR group, significant variables in PC1 and PC2 were dominantly VF cytokines, with cumulative CR of $42.3\%$, whereas significant variables in PC3 were primarily systemic factors possibly interpreted as functions of glucose tolerance and blood coagulability, with CR of only $10.4\%$. It is clinically important to assess whether systemic treatment impacts the pathogenesis of PDR. In the Wisconsin Epidemiologic Study of Diabetic Retinopathy, metabolic controls of HbA1c, BP and total cholesterol, and disease duration accounted for only $11\%$ of the risk of DR in type 1 DM patients, while the remaining $89\%$ was due to other factors [63,64]. DR and DME disease severity scales have been proposed [8], but the influence of systemic factors on the pathology of DR in each stage with or without DME has not been examined in detail. Further research is needed to elucidate the impact of the complex relationship between systemic and intraocular factors on the pathology of DR. Aqueous humor (AH) is an intraocular specimen that can be collected repeatedly and less invasively compared to VF [65], and will be a useful source of biomarkers to explore intraocular immune environment in DR. In the present study, serum levels of cytokines were not examined. Since the blood-retinal barrier could be disrupted in PDR, serum cytokines may affect the VF cytokine levels. Takeuchi et al. [ 17] reported a markedly lower serum IL-6 level (2.82 ± 10.2 pg/mL) than VF IL-6 level (658.5 ± 402.1 pg/mL) in PDR patients. In addition, when comparing the levels of VF cytokines between PDR patients with or without VH, there was no significant difference with those of 15 VF cytokines. Nevertheless, the contamination of VF samples by serum cytokines and blood cells is a potential bias, leading to misinterpretation of VF cytokines. A further comprehensive study analyzing complex data composed of AH, VF, blood and systemic factors will help to understand the pathogenesis of PDR. The present study had several limitations. [ 1] This study was a retrospective observational study, and the data analyzed were limited to routine examinations and laboratory tests covered by health insurance. Potential DR-related risk factors [5] including disease duration, hyperlipidemia, dietary style, exercise, and smoking were not included due to incomplete medical records. BCVA, CRT and fundus findings were not adopted as explanatory variables in multivariate analyses due to the high prevalence of VH ($88.5\%$) that significantly undermined the reliability of ophthalmic findings. [ 2] The sample size was small, which limits the generalizability to all types of PDR patients. [ 3] The PDR group consisted of younger patients compared to the control group, similar to previous studies [17,28]. A relative disease control group was used, not a legitimate control group composed of DM patients with no DR or PDR, because PPV is not performed in such patients and VF specimens are not available. In future, further case–control studies based on a baseline of systemic disease need to be performed. [ 4] The instability of systemic disease markers as poor controls of systemic treatment could be a potential bias in the interpretation of association between systemic factors and VF cytokines in the pathology of PDR. [ 5] Hierarchical cluster analysis and PCA are unsupervised analyses, and some degrees of interpretation freedom are allowed. The interpretation criteria are based on the distance (property similarity) and expression intensity (influence) of the sorted explanatory variables in hierarchical cluster analysis, and the order of PC (contribution) and the magnitude of PCL of the PC (property similarity and influence) in PCA. However, the sorting of explanatory variables in those analyses were automatically calculated, and there was no bias in the presented data. A key strength of our study is that systemic and intraocular PDR-related factors were examined simultaneously, and their complex relations and influences on the pathology of PDR were evaluated comprehensively by multivariate analyses. Furthermore, the data used in this study were obtained from real-world management of PDR, and there was no arbitrary intervention bias in the treatment process. ## 5. Conclusions The influence of systemic factors on the pathology of PDR may be weakened compared to that of intraocular factors. BP and BMI were important systemic factors that correlate positively with VF levels of inflammatory cytokines. Therefore, in addition to intraocular treatments, BP and BMI managements could be useful interventions to improve intraocular immune conditions. Ophthalmologists and physicians need to be aware of the effectiveness of both systemic and topical eye treatments, and work closely together to prevent the progression of DR, a vision-threatening complication of DM. ## References 1. Dejkhamron P., Menon R.K., Sperling M.A.. Childhood diabetes mellitus: Recent advances & future prospects. Indian J. Med. Res. (2007) 125 231-250. PMID: 17496353 2. Kempen J.H., O’colmain B., Leske M., Haffner S., Klein R., Moss S., Taylor R.H., Hamman R.F.. The prevalence of diabetic retinopathy among adults in the United States. Arch Ophthalmol. (2004) 122 552-563. PMID: 15078674 3. Tang J., Kern T.S.. Inflammation in diabetic retinopathy. Prog. Retin. Eye Res. (2011) 30 343-358. DOI: 10.1016/j.preteyeres.2011.05.002 4. Durham J.T., Herman I.M.. Microvascular modifications in diabetic retinopathy. Curr. Diabetes Rep. (2011) 11 253-264. DOI: 10.1007/s11892-011-0204-0 5. Jenkins A.J., Joglekar M.V., Hardikar A.A., Keech A.C., O’Neal D.N., Januszewski A.S.. Biomarkers in Diabetic Retinopathy. Rev. Diabet. Stud. (2015) 12 159-195. DOI: 10.1900/RDS.2015.12.159 6. Wat N., Wong R.L., Wong I.Y.. Associations between diabetic retinopathy and systemic risk factors. Hong Kong Med. J. (2016) 22 589-599. DOI: 10.12809/hkmj164869 7. Gologorsky D., Thanos A., Vavvas D.. Therapeutic interventions against inflammatory and angiogenic mediators in proliferative diabetic retinopathy. Mediat. Inflamm. (2012) 2012 629452. DOI: 10.1155/2012/629452 8. Wilkinson C.P., Ferris 3rd F.L., Klein R.E., Lee P.P., Agardh C.D., Davis M., Dills D., Kampik A., Pararajasegaram R., Verdaguer J.T.. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology (2003) 110 1677-1682. DOI: 10.1016/S0161-6420(03)00475-5 9. Rubio-Perez J.M., Morillas-Ruiz J.M.. A review: Inflammatory process in Alzheimer’s disease, role of cytokines. Sci. World J. (2012) 2012 756357. DOI: 10.1100/2012/756357 10. Zhang J.M., An J.. Cytokines, inflammation, and pain. Int. Anesthesiol. Clin. (2007) 45 27-37. DOI: 10.1097/AIA.0b013e318034194e 11. Sato T., Takeuchi M., Karasawa Y., Takayama K., Enoki T.. Comprehensive expression patterns of inflammatory cytokines in aqueous humor of patients with neovascular age-related macular degeneration. Sci. Rep. (2019) 9 19447. DOI: 10.1038/s41598-019-55191-x 12. Sato T., Enoki T., Karasawa Y., Someya H., Taguchi M., Harimoto K., Takayama K., Kanda T., Ito M., Takeuchi M.. Inflammatory Factors of Macular Atrophy in Eyes with Neovascular Age-Related Macular Degeneration Treated with Aflibercept. Front. Immunol. (2021) 2 738521. DOI: 10.3389/fimmu.2021.738521 13. Takeuchi M., Taguchi M., Sato T., Karasawa Y., Sakurai Y., Harimoto K., Ito M.. Association of High-Mobility Group Box-1 With Th Cell-Related Cytokines in the Vitreous of Ocular Sarcoidosis Patients. Investig. Ophthalmol. Vis. Sci. (2017) 58 528-537. DOI: 10.1167/iovs.16-20324 14. Takeuchi M., Karasawa Y., Harimoto K., Tanaka A., Shibata M., Sato T., Caspi R.R., Ito M.. Analysis of Th Cell-related Cytokine Production in Behcet Disease Patients with Uveitis Before and After Infliximab Treatment. Ocul. Immunol. Inflamm. (2017) 25 52-61. DOI: 10.3109/09273948.2016.1158276 15. Wu F., Phone A., Lamy R., Ma D., Laotaweerungsawat S., Chen Y., Zhao T., Ma W., Zhang F., Psaras C.. Correlation of aqueous, vitreous, and plasma cytokine levels in patients with proliferative diabetic retinopathy. Investig. Ophthalmol. Vis. Sci. (2020) 61 26. DOI: 10.1167/iovs.61.2.26 16. Suzuki Y., Nakazawa M., Suzuki K., Yamazaki H., Miyagawa Y.. Expression profiles of cytokines and chemokines in vitreous fluid in diabetic retinopathy and central retinal vein occlusion. Jpn. J. Ophthalmol. (2011) 55 256-263. DOI: 10.1007/s10384-011-0004-8 17. Takeuchi M., Sato T., Tanaka A., Muraoka T., Taguchi M., Sakurai Y., Karasawa Y., Ito M.. Elevated levels of cytokines associated with Th2 and Th17 cells in vitreous fluid of proliferative diabetic retinopathy patients. PLoS ONE (2015) 10. DOI: 10.1371/journal.pone.0137358 18. Adki K.M., Kulkarni Y.A.. Potential Biomarkers in Diabetic Retinopathy. Curr. Diabetes Rev. (2020) 16 971-983. PMID: 32065092 19. Takeuchi M., Sato T., Sakurai Y., Taguchi M., Harimoto K., Karasawa Y., Ito M.. Association between aqueous humor and vitreous fluid levels of Th17 cell-related cytokines in patients with proliferative diabetic retinopathy. PLoS ONE (2017) 12. DOI: 10.1371/journal.pone.0178230 20. Photocoagulation for diabetic macular edema: Early Treatment Diabetic Retinopathy Study report no. 4. Int. Ophthalmol. Clin. (1987) 27 265-272. DOI: 10.1097/00004397-198702740-00006 21. Cohen J.. A power primer. Psychol. Bull. (1992) 112 155-159. DOI: 10.1037/0033-2909.112.1.155 22. Sato T., Takeuchi M., Karasawa Y., Ito M.. Profiles of Cytokines Secreted by ARPE-19 Cells Exposed to Light and Incubated with Anti-VEGF Antibody. Biomedicines (2021) 9. DOI: 10.3390/biomedicines9101333 23. Grover S., Fishman G.A., Anderson R.J., Tozatti M.S., Heckenlively J.R., Weleber R.G., Edwards A.O., Brown J.. Visual acuity impairment in patients with retinitis pigmentosa at age 45 years or older. Ophthalmology (1999) 106 1780-1785. DOI: 10.1016/S0161-6420(99)90342-1 24. Schulze-Bonsel K., Feltgen N., Burau H., Hansen L., Bach M.. Visual acuities “hand motion” and “counting fingers” can be quantified with the Freiburg visual acuity test. Investig. Ophthalmol. Vis. Sci. (2006) 47 1236-1240. DOI: 10.1167/iovs.05-0981 25. Matsuo S., Imai E., Horio M., Yasuda Y., Tomita K., Nitta K., Yamagata K., Tomino Y., Yokoyama H., Hishida A.. Revised equations for estimated GFR from serum creatinine in Japan. Am. J. Kidney Dis. (2009) 53 982-992. DOI: 10.1053/j.ajkd.2008.12.034 26. Klein R., Klein B.E., Moss S.E., Davis M.D., DeMets D.L.. The Wisconsin epidemiologic study of diabetic retinopathy. II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch. Ophthalmol. (1984) 102 520-526. DOI: 10.1001/archopht.1984.01040030398010 27. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet (1998) 352 837-853. DOI: 10.1016/S0140-6736(98)07019-6 28. Tsai T., Kuehn S., Tsiampalis N., Vu M.-K., Kakkassery V., Stute G., Dick H.B., Joachim S.C.. Anti-inflammatory cytokine and angiogenic factors levels in vitreous samples of diabetic retinopathy patients. PLoS ONE (2018) 13. DOI: 10.1371/journal.pone.0194603 29. Wilks D.S.. Cluster analysis. International Geophysics. 100 (2011) 603-616 30. Zhang X., Saaddine J.B., Chou C.-F., Cotch M.F., Cheng Y.J., Geiss L.S., Gregg E.W., Albright A.L., Klein B.E.K., Klein R.. Prevalence of diabetic retinopathy in the United States, 2005–2008. JAMA (2010) 304 649-656. DOI: 10.1001/jama.2010.1111 31. Stratton I., Kohner E., Aldington S., Turner R.C., Holman R.R., Manley S.E., Matthews D.R.. UKPDS 50: Risk factors for incidence and progression of retinopathy in Type II diabetes over 6 years from diagnosis. Diabetologia (2001) 44 156-163. DOI: 10.1007/s001250051594 32. Holman R.R., Paul S.K., Bethel M.A., Matthews D.R., Neil H.A.. 10-year follow-up of intensive glucose control in type 2 diabetes. N. Engl. J. Med. (2008) 359 1577-1589. DOI: 10.1056/NEJMoa0806470 33. Nathan D.M., Genuth S., Lachin J., Cleary P., Crofford O., Davis M., Rand L., Siebert C.. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. (1993) 329 977-986. PMID: 8366922 34. Lachin J.M., Genuth S., Cleary P., Davis M.D., Nathan D.M.. Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. N. Engl. J. Med. (2000) 342 381-389. PMID: 10666428 35. Nathan D.M., Cleary P.A., Backlund J.Y., Genuth S.M., Lachin J.M., Orchard T.J., Raskin P., Zinman B.. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N. Engl. J. Med. (2005) 353 2643-2653. PMID: 16371630 36. Man R.E., Sasongko M.B., Wang J.J., MacIsaac R., Wong T.Y., Sabanayagam C., Lamoureux E.L.. The Association of Estimated Glomerular Filtration Rate with Diabetic Retinopathy and Macular Edema. Investig. Ophthalmol. Vis. Sci. (2015) 56 4810-4816. DOI: 10.1167/iovs.15-16987 37. Chen H., Zheng Z., Huang Y., Guo K., Lu J., Zhang L., Yu H., Bao Y., Jia W.. A microalbuminuria threshold to predict the risk for the development of diabetic retinopathy in type 2 diabetes mellitus patients. PLoS ONE (2012) 7. DOI: 10.1371/journal.pone.0036718 38. Park Y.H., Shin J.A., Han J.H., Park Y.M., Yim H.W.. The association between chronic kidney disease and diabetic retinopathy: The Korea National Health and Nutrition Examination Survey 2008–2010. PLoS ONE (2015) 10. DOI: 10.1371/journal.pone.0125338 39. Yamamoto M., Fujihara K., Ishizawa M., Osawa T., Kaneko M., Ishiguro H., Matsubayashi Y., Seida H., Yamanaka N., Tanaka S.. Overt Proteinuria, Moderately Reduced eGFR and Their Combination Are Predictive of Severe Diabetic Retinopathy or Diabetic Macular Edema in Diabetes. Investig. Ophthalmol. Vis. Sci. (2019) 60 2685-2689. DOI: 10.1167/iovs.19-26749 40. Harris N.K., Talwar N., Gardner T.W., Wrobel J.S., Herman W.H., Stein J.D.. Predicting development of proliferative diabetic retinopathy. Diabetes Care (2013) 36 1562-1568. DOI: 10.2337/dc12-0790 41. Semeraro F., Cancarini A., dell’Omo R., Rezzola S., Romano M.R., Costagliola C.. Diabetic Retinopathy: Vascular and Inflammatory Disease. J. Diabetes Res. (2015) 2015 582060. DOI: 10.1155/2015/582060 42. Cohen M.C., Cohen S.. Cytokine function: A study in biologic diversity. Am. J. Clin. Pathol. (1996) 105 589-598. DOI: 10.1093/ajcp/105.5.589 43. Papanicolaou D.A., Wilder R.L., Manolagas S.C., Chrousos G.P.. The pathophysiologic roles of interleukin-6 in human disease. Ann. Intern. Med. (1998) 128 127-137. DOI: 10.7326/0003-4819-128-2-199801150-00009 44. Holzinger C., Weissinger E., Zuckermann A., Imhof M., Kink F., Schöllhammer A., Kopp C., Wolner E.. Effects of interleukin-1,-2,-4,-6, interferon-gamma and granulocyte/macrophage colony stimulating factor on human vascular endothelial cells. Immunol. Lett. (1993) 35 109-117. DOI: 10.1016/0165-2478(93)90078-G 45. Neumann B., Emmanuilidis K., Stadler M., Holzmann B.. Distinct functions of interferon-gamma for chemokine expression in models of acute lung inflammation. Immunology (1998) 95 512-521. DOI: 10.1046/j.1365-2567.1998.00643.x 46. Angiolillo A.L., Sgadari C., Taub D.D., Liao F., Farber J.M., Maheshwari S., Kleinman H.K., Reaman G.H., Tosato G.. Human interferon-inducible protein 10 is a potent inhibitor of angiogenesis in vivo. J. Exp. Med. (1995) 182 155-162. DOI: 10.1084/jem.182.1.155 47. Tager A.M., Kradin R.L., LaCamera P., Bercury S.D., Campanella G.S., Leary C.P., Polosukhin V., Zhao L.-H., Sakamoto H., Blackwell T.S.. Inhibition of pulmonary fibrosis by the chemokine IP-10/CXCL10. Am. J. Respir. Cell Mol. Biol. (2004) 31 395-404. DOI: 10.1165/rcmb.2004-0175OC 48. Dow C., Mancini F., Rajaobelina K., Boutron-Ruault M.C., Balkau B., Bonnet F., Fagherazzi G.. Diet and risk of diabetic retinopathy: A systematic review. Eur. J. Epidemiol. (2018) 33 141-156. DOI: 10.1007/s10654-017-0338-8 49. Wong M.Y.Z., Man R.E.K., Fenwick E.K., Gupta P., Li L.J., van Dam R.M., Chong M.F., Lamoureux E.L.. Dietary intake and diabetic retinopathy: A systematic review. PLoS ONE (2018) 13. DOI: 10.1371/journal.pone.0186582 50. Tanaka S., Yoshimura Y., Kawasaki R., Kamada C., Tanaka S., Horikawa C., Ohashi Y., Araki A., Ito H., Akanuma Y.. Fruit intake and incident diabetic retinopathy with type 2 diabetes. Epidemiology (2013) 24 204-211. DOI: 10.1097/EDE.0b013e318281725e 51. Osaadon P., Fagan X., Lifshitz T., Levy J.. A review of anti-VEGF agents for proliferative diabetic retinopathy. Eye (2014) 28 510-520. DOI: 10.1038/eye.2014.13 52. Bhisitkul R.B.. Vascular endothelial growth factor biology: Clinical implications for ocular treatments. Br. J. Ophthalmol. (2006) 90 1542-1547. DOI: 10.1136/bjo.2006.098426 53. Cursiefen C., Chen L., Borges L.P., Jackson D., Cao J., Radziejewski C., D’Amore P.A., Dana M.R., Wiegand S.J., Streilein J.W.. VEGF-A stimulates lymphangiogenesis and hemangiogenesis in inflammatory neovascularization via macrophage recruitment. J. Clin. Investig. (2004) 113 1040-1050. DOI: 10.1172/JCI20465 54. Deshmane S.L., Kremlev S., Amini S., Sawaya B.E.. Monocyte chemoattractant protein-1 (MCP-1): An overview. J. Interferon Cytokine Res. (2009) 29 313-326. DOI: 10.1089/jir.2008.0027 55. Canataroglu H., Varinli I., Ozcan A.A., Canataroglu A., Doran F., Varinli S.. Interleukin (IL)-6, interleukin (IL)-8 levels and cellular composition of the vitreous humor in proliferative diabetic retinopathy, proliferative vitreoretinopathy, and traumatic proliferative vitreoretinopathy. Ocul. Immunol. Inflamm. (2005) 13 375-381. DOI: 10.1080/09273940490518900 56. Kakehashi A., Inoda S., Mameuda C., Kuroki M., Jono T., Nagai R., Horiuchi S., Kawakami M., Kanazawa Y.. Relationship among VEGF, VEGF receptor, AGEs, and macrophages in proliferative diabetic retinopathy. Diabetes Res. Clin. Pract. (2008) 79 438-445. DOI: 10.1016/j.diabres.2007.10.018 57. Hong K.H., Ryu J., Han K.H.. Monocyte chemoattractant protein-1–induced angiogenesis is mediated by vascular endothelial growth factor-A. Blood (2005) 105 1405-1407. DOI: 10.1182/blood-2004-08-3178 58. Nguyen Q.D., Brown D.M., Marcus D.M., Boyer D.S., Patel S., Feiner L., Gibson A., Sy J., Rundle A.C., Hopkins J.J.. Ranibizumab for diabetic macular edema: Results from 2 phase III randomized trials: RISE and RIDE. Ophthalmology (2012) 119 789-801. DOI: 10.1016/j.ophtha.2011.12.039 59. Martidis A., Duker J.S., Greenberg P.B., Rogers A.H., Puliafito C.A., Reichel E., Baumal C.. Intravitreal triamcinolone for refractory diabetic macular edema. Ophthalmology (2002) 109 920-927. DOI: 10.1016/S0161-6420(02)00975-2 60. Bakri S.J., Kaiser P.K.. Posterior subtenon triamcinolone acetonide for refractory diabetic macular edema. Am. J. Ophthalmol. (2005) 139 290-294. DOI: 10.1016/j.ajo.2004.09.038 61. Tomita Y., Lee D., Tsubota K., Negishi K., Kurihara T.. Updates on the Current Treatments for Diabetic Retinopathy and Possibility of Future Oral Therapy. J. Clin. Med. (2021) 10. DOI: 10.3390/jcm10204666 62. Jolliffe I.. Principal component analysis. International Encyclopedia of Statistical Science (2011) 1094-1096 63. Klein R., Knudtson M.D., Lee K.E., Gangnon R., Klein B.E.. The Wisconsin Epidemiologic Study of Diabetic Retinopathy XXII: The twenty-five-year progression of retinopathy in persons with type 1 diabetes. Ophthalmology (2008) 115 1859-1868. DOI: 10.1016/j.ophtha.2008.08.023 64. Hirsch I.B., Brownlee M.. Beyond hemoglobin A1c--need for additional markers of risk for diabetic microvascular complications. JAMA (2010) 303 2291-2292. DOI: 10.1001/jama.2010.785 65. Saxena S., Lai T.Y., Koizumi H., Farah M.E., Ferrara D., Pelayes D., Sato T., Meyer C.H., Murray T.. Anterior chamber paracentesis during intravitreal injections in observational trials: Effectiveness and safety and effects. Int. J. Retina Vitr. (2019) 5 8. DOI: 10.1186/s40942-019-0157-z
--- title: Denervation Drives YAP/TAZ Activation in Muscular Fibro/Adipogenic Progenitors authors: - Felipe S. Gallardo - Adriana Córdova-Casanova - Alexia Bock-Pereda - Daniela L. Rebolledo - Andrea Ravasio - Juan Carlos Casar - Enrique Brandan journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10059792 doi: 10.3390/ijms24065585 license: CC BY 4.0 --- # Denervation Drives YAP/TAZ Activation in Muscular Fibro/Adipogenic Progenitors ## Abstract Loss of motoneuron innervation (denervation) is a hallmark of neurodegeneration and aging of the skeletal muscle. Denervation induces fibrosis, a response attributed to the activation and expansion of resident fibro/adipogenic progenitors (FAPs), i.e., multipotent stromal cells with myofibroblast potential. Using in vivo and in silico approaches, we revealed FAPs as a novel cell population that activates the transcriptional coregulators YAP/TAZ in response to skeletal muscle denervation. Here, we found that denervation induces the expression and transcriptional activity of YAP/TAZ in whole muscle lysates. Using the PdgfraH2B:EGFP/+ transgenic reporter mice to trace FAPs, we demonstrated that denervation leads to increased YAP expression that accumulates within FAPs nuclei. Consistently, re-analysis of published single-nucleus RNA sequencing (snRNA-seq) data indicates that FAPs from denervated muscles have a higher YAP/TAZ signature level than control FAPs. Thus, our work provides the foundations to address the functional role of YAP/TAZ in FAPs in a neurogenic pathological context, which could be applied to develop novel therapeutic approaches for the treatment of muscle disorders triggered by motoneuron degeneration. ## 1. Introduction Excessive and pathological deposition of skeletal muscle extracellular matrix (ECM)—fibrosis—defines one of the most common outcomes of many chronic skeletal muscle pathologies [1,2]. Degeneration of the neuromuscular junction (NMJ), which can be experimentally induced by the transection of motor nerves, precedes muscular fibrosis. This process occurs during aging and neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS), leading to muscle atrophy and decreased muscle performance [3]. Although crucial signaling pathways, such as the signaling mediated by the transforming growth factor type β1 (TGF-β1) [4,5] and the bioactive lipid lysophosphatidic acid (LPA) [6], have been described as promoters of skeletal muscle fibrosis, their molecular mediators are still poorly understood. Thus, discovering downstream molecules that transduce these signals may be relevant for the development of novel therapies that target skeletal muscle fibrosis and neurogenic degeneration. Myofibroblasts are the primary cell type responsible for tissue fibrosis across organs [7]. In the skeletal muscle, these cells are mainly derived from the differentiation of a resident multipotent stromal cell population, also known as fibro/adipogenic progenitors (FAPs), which express the mesodermal markers PDGFRα and TCF4 (Tcf7l2) [8,9,10,11,12,13,14]. Activated FAPs accumulate in denervated skeletal muscles and skeletal muscles affected by ALS [15,16,17]. However, the molecular mechanisms that regulate FAPs behavior are far from being understood. In this context, Yes-associated protein 1 (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) have been shown to participate in the fibrogenic process in various organs, including kidney, lung, and liver [18,19,20,21]. YAP and TAZ (referred together as YAP/TAZ) are paralogs transcriptional coregulators that orchestrate several biological processes, such as stem cell specification, differentiation and proliferation, and organ size control [22]. These proteins act as co-activators of the transcriptional enhancer factor (TEA)-domain (TEAD1-4) family of transcription factors to control the expression of their target genes, such as Ankrd1, Cyr61/Ccn1, and the pro-fibrotic factor Ctgf/Ccn2 [23,24]. Although they were initially discovered as final effectors of the Hippo signaling pathway, which modulates their phosphorylation status through the action of a serine kinase relay module, their participation in other pathways has been documented, revealing the complex nature of YAP/TAZ activity and proteostasis [22,25]. For instance, YAP/TAZ act in concert with the canonical TGF-β1–SMAD signaling [21,26,27], are involved in the destruction complex of WNT/β-catenin signaling [28,29], and participate as downstream effectors of the LPA–LPA receptors–G-proteins signaling [30]. In addition to biochemical regulation, YAP/TAZ behave as mechanosensors, shuttling between the nucleus and cytoplasm in response to extra- and intracellular forces [31]. For instance, high ECM stiffness and consequent increase in actomyosin tension promote the nuclear accumulation and activity of YAP/TAZ, whereas cells in soft extracellular environment show cytoplasmic localization and low TEAD-dependent transcription [32,33,34]. In the skeletal muscle, YAP/TAZ have been described as regulators of muscle stem cell (satellite cell) function and muscle mass [35,36]. Only one report has demonstrated that skeletal muscle denervation (by peroneal nerve transection) upregulates the expression of YAP in whole muscle and enhances its localization in myonuclei, a response related to the control of skeletal muscle atrophy [37]. The participation of other cell types residing in the skeletal muscle in the denervation process, such as FAPs, has not been addressed. Using an experimental model of neurogenic degeneration of limb skeletal muscle (sciatic nerve transection) combined with transcriptomics analyses, we report that YAP and TAZ accumulate in atrophic-fibrotic denervated skeletal muscles and that FAPs represent one of the cell types where YAP/TAZ are activated. Our study establishes a fundamental groundwork to determine the function of YAP/TAZ during skeletal muscle fibrosis. ## 2.1. YAP and TAZ Are Increased in Denervated Skeletal Muscles To understand the dynamics of YAP/TAZ activity in the skeletal muscle, we performed skeletal muscle denervation by sciatic nerve transection, a well-established muscle wasting and degeneration model. Limb muscles lacking motoneuron innervation undergo atrophy, FAPs expansion, and fibrosis, which becomes evident 14 days post-surgery [16,17]. Using this approach, we found that in whole tibial anterior (TA) muscle lysates from denervated muscles, both YAP and TAZ protein levels are increased compared to control muscles. Such increase was associated with fibrosis illustrated by the elevated ECM component fibronectin (Figure 1). To explore whether YAP/TAZ protein increase could be explained by its induction at the mRNA level, we processed and analyzed publicly available bulk RNA-sequencing data from mice gastrocnemius (GST) skeletal muscle, either denervated for 14 days or its control (SRA: SRP196460) [38]. Differential expression analysis using a p value of 0.05 and log2FC of 0.6 as thresholds revealed that of 29,327 mapped genes, 3121 were upregulated, 2683 downregulated, and 23,523 were not affected 14 days after denervation. The volcano plot shows that Yap1 (log2FC = 0.72, $$p \leq 9.86$$ × 10−6) can be found among the upregulated genes. Although not included in the upregulated group, Wwtr1 (TAZ) expression was significantly induced with a lower fold-change (log2FC = 0.55, $$p \leq 1.37$$ × 10−3) (Figure 2a). Replotting of the counts per million values from the differential expression analysis (TMM method) demonstrates increased expression of both genes (Figure 2b). The results from the RNA-seq expression analysis were further supported by RT-qPCR (Figure 2c). Despite the high variability in the data, RT-qPCR analysis demonstrated a similar upward trend in both Yap1 and Wwtr1 mRNA levels, which at least partially supports the RNA-seq findings. This additional validation using RT-qPCR strengthens the evidence of YAP and TAZ induction at the protein level in response to denervation and strongly suggests that an increase in their mRNA levels may partly explain this. ## 2.2. YAP/TAZ Activity Is Augmented in Denervated Skeletal Muscle To assess whether increased expression of YAP/TAZ translates into increased transcriptional activity, we evaluated the gene expression of classical YAP/TAZ target genes, i.e., Ankrd1, Cyr61/Ccn1, and Ctgf/Ccn2. Fourteen days post-denervation, denervated muscles exhibited increased expression levels of Ankrd1 and Cyr61/Ccn1 compared to control muscles analyzed by RT-qPCR (Figure 3a). We did not find any changes in Ctgf/Ccn2 mRNA levels. Again, we complemented our RT-qPCR results with the RNA-seq data and found the same results (Figure 3b). Nevertheless, previous results from our lab showed increased CTGF/CCN2 protein levels, suggesting an earlier transcriptional upregulation [17]. These initial observations suggest that denervation triggers an active state of YAP/TAZ. Thus, we next decided to model transcriptional activity by gene set enrichment analysis (GSEA) using a defined set of YAP/TAZ/TEAD target genes previously reported [39]. GSEA works by ranking whole transcriptomic data in an ordered gene list based on differential expression analysis, from upregulated in denervation (DEN; red color) to upregulated in control (CTL; blue color) (bottom colored bar). Individual genes from the YAP/TAZ signature are scanned throughout the ranked list, and their positions are annotated (black lines above the colored bar). This analysis revealed that most of the target genes of YAP/TAZ fall within the upregulated genes of a denervated muscle obtaining a positive enrichment score (NES = 1.64, nominal $$p \leq 0$$), thus indicating increased transcriptional activity of YAP/TAZ under denervation (Figure 3c). ## 2.3. Expanded FAPs Accumulate YAP upon Denervation The development of fibrosis triggered by denervation correlates with FAPs expansion [8,16,40]. To reveal whether muscular FAPs show regulation of YAP/TAZ upon denervation, we decided to use the transgenic knock-in mouse PdgfraH2B:EGFP/+, which expresses the H2B:EGFP fusion protein in cells with an active Pdgfra promoter, allowing us to specifically target FAPs in the skeletal muscle [6,40,41]. This mouse strain exhibits atrophy, fibrosis, FAPs accumulation, and induction of Ankrd1 and Cyr61/Ccn1 triggered by denervation similar to wild-type mice (Supplementary Figure S1). Western blot analysis of denervated muscle from PdgfraH2B:EGFP/+ mice shows that YAP/TAZ were also induced in this genetic model following denervation, indicating that modification of the Pdgfra locus does not impact YAP/TAZ induction (Figure 4a). Thus, we carried out immunostaining of YAP in the PdgfraH2B:EGFP/+ mice and found that although YAP is basally expressed in the nucleus of a fraction of FAPs in control muscles, denervation significantly increases the number of FAPs expressing YAP, as well as its abundance within the nuclei measured by signal intensity (Figure 4b,c). Thus, part of the effect of YAP/TAZ induction seen at the whole muscle level could be explained by its accumulation in expanded FAPs. ## 2.4. Single-Cell Transcriptomics of Denervated Muscles Reveal Increased YAP/TAZ Activity in FAPs Our previous in vivo findings showing YAP/TAZ induction in FAPs motivated us to investigate whether this accumulation correlates with transcriptional activation of the YAP/TAZ system in FAPs during denervation. To address this inquiry, we analyzed the gene expression profile of FAPs using publicly available single-nucleus RNA sequencing (snRNA-seq) data of whole GST muscle from control and denervated limbs for 14 days [42]. Exploration of gene expression levels at single-nucleus resolution reveals that YAP and TAZ are expressed across nearly every cluster and in the one identified as FAPs (Pdgfra+/Tcf7l2+) (Figure 5a). Surprisingly, when comparing gene expression of YAP and TAZ in FAPs from control and denervated muscles, we found a reduction in the number of FAPs expressing high levels of YAP and TAZ (Figure 5b; Expression Level ≥ 1). This result contrasts with the accumulation at the protein level and suggests differential regulation of mRNA export from the nucleus or increased YAP/TAZ proteostasis. Nevertheless, gene expression of Ankrd1, Cyr61/Ccn1, and Ctgf/Ccn2 demonstrates a considerable number of FAPs expressing high levels of the three target genes during denervation (Figure 5b). We analyzed the enrichment score of the same gene set (YAP/TAZ/TEAD_DIRECT_TARGET_GENES) evaluated in the previous whole muscle analysis (Figure 2c). Figure 5c shows that FAPs from denervated muscle have an increased transcriptional signature of YAP/TAZ compared to FAPs from control muscles, consistent with our results of protein accumulation in these cells. In summary, all our findings, from in vivo to in silico, indicate that FAPs are a novel cell type activating YAP/TAZ in denervated muscle and suggest a potential role of these transcriptional regulators during FAPs activation. ## 3. Discussion In this work, we studied the role of YAP/TAZ under a neurodegenerative experimental model that leads to the establishment of skeletal muscle fibrosis. Our results prove the involvement of this signaling pathway and assess for the first time its activation in skeletal muscle FAPs. Here, in the muscle as a whole, we showed that the transcriptional coregulators YAP/TAZ are induced after sciatic nerve transection, suggesting their participation in the development of the fibrogenic process. To the best of our knowledge, we are the first to show that TAZ, and not only YAP [37], potentially plays a role in skeletal muscle fibrosis triggered by denervation. Importantly, the ability of YAP/TAZ to drive fibrosis in other organs indicates that YAP/TAZ activation is a hallmark feature of the fibrotic process possibly conserved in all tissues. For instance, in mice, administration of a known YAP/TAZ-TEAD complex inhibitor, verteporfin (VP) [43], decreased CTGF/CCN2 and collagen I expression in fibrotic kidneys [18,21]. Moreover, TAZ-hemizygous mice resist fibrotic onset triggered by bleomycin in the lungs [44]. In addition, the induction of liver fibrosis by carbon tetrachloride confirms the protective effect of YAP/TAZ inhibition with VP [20]. Whether the skeletal muscle responds similarly to YAP/TAZ inhibition in a fibrotic context requires further research. Judson and colleagues [2013] have already demonstrated that myofiber-specific overexpression of a constitutively active form of YAP (resistant to Hippo-dependent inhibition) in wild-type mice induces features of muscular dystrophy, such as myofiber necrosis, regeneration, and tissue degeneration [45]. However, contrary to this idea, decreased expression of YAP is evident in muscle samples from patients affected by Duchenne muscular dystrophy [46]. At the molecular level, the capacity of YAP/TAZ to regulate fibrogenic processes could be attributed to their participation as molecular mediators of the differentiation of fibroblasts/stromal cells into myofibroblasts. Our results show that FAPs, characterized as multipotent stromal cells, activate YAP/TAZ during denervation, suggesting a role in their activation, proliferation, and/or differentiation. YAP/TAZ activation could be explained by the fact that these transcriptional coregulators act as nodes of several signaling pathways associated with fibrosis [25]. Among the factors that stimulate the differentiation of fibroblasts derived from diverse tissues, the most studied are those related to TGF-β1 signaling, whose effects can interestingly be prevented through YAP/TAZ inhibition [21,47,48,49]. We have previously shown that TGF-β1 signaling is upregulated during denervation and in a neurodegenerative genetic model of ALS [15,17]. TGF-β1-SMAD and YAP/TAZ pathways regulate multiple common highly inducible target genes, such as Ctgf/Ccn2 or Cyr61/Ccn1; thus, cooperative action between these pathways is suggested to be at the transcriptional level through the assembly of a large transcriptional complex. In the skeletal muscle, Ctgf/Ccn2 acts as a pro-fibrotic factor [50,51]. Overexpression of Ctgf/Ccn2 in healthy muscle by adenovirus infection induces tissue degeneration and muscle fibrosis, leading to reduced muscle functionality [50]. Moreover, blockade or decreased expression of Ctgf/Ccn2 in skeletal muscle fibrotic models results in reduced tissue fibrosis [15,17,51]. Our snRNA-seq analysis reveals that after denervation, FAPs upregulate the expression of YAP/TAZ target genes, including Ctgf/Ccn2 (Figure 5b). These observations suggest a possible mechanism where TGF-β1 signaling in FAPs activates YAP/TAZ, which, coupled with SMAD mediators, induces Ctgf/Ccn2 to control ECM remodeling and fibrotic development. Another possible mechanism of YAP/TAZ activation in FAPs during the fibrotic stage of denervation may be due to increased ECM stiffness. Indeed, cellular responses to matrix stiffness have been widely reported as critical inductors of tissue fibrosis [52]. Other groups have demonstrated increased YAP/TAZ signals in cells that reside and accumulate in high-stiffness zones in fibrotic tissues [18,19,47]. Fibrotic skeletal muscle ECM exhibits high stiffness [53,54,55]. Recently, the pro-fibrotic response of FAPs to ECM stiffness was reported for the first time [56]. In response to biomimetic substrates of high stiffness, FAPs display nuclear accumulation of YAP and induction of myofibroblast differentiation, which can be prevented by VP addition. This demonstrates the conserved ability of FAPs to adopt a pro-fibrotic phenotype in response to ECM stiffness. Adding more complexity, YAP/TAZ have also been shown to participate in the Wnt/β-catenin signaling, intimately related to the destruction complex of β-catenin [28,29]. Activation of the pathway induces the release of β-catenin and YAP/TAZ from the destruction complex allowing β-catenin/TCF/LEF and YAP/TAZ/TEAD transcriptional activities. Interestingly, the Wnt3a ligand is upregulated in denervated muscles [57]. Whether FAPs induce YAP/TAZ signaling by a mechanism dependent on Wnt/β-catenin remains unexplored; however, FAPs are known to express high levels of the canonical Wnt/β-catenin transcription factor Tcf7l2 (Figure 5a) [8,9,11,12], suggesting a possible target cell responding to high levels of Wnt ligands during denervation. In conclusion, this work is the first to reveal the activation of YAP/TAZ in FAPs associated with skeletal muscle fibrosis in vivo and with single-nucleus resolution. We believe that our results could establish a starting point to assess the functional role of YAP/TAZ in the fibrogenic process of the skeletal muscle and the cell fate decisions of FAPs, resulting in a better understanding of FAPs biology and fibrosis. ## 4.1. Animal Experiments Animal experiments were performed with approval and in accordance with the Animal Ethics Committee of Pontificia Universidad Católica de Chile (Protocol 220427014). Mice were maintained before and during experiments in a 12 h light-dark cycle with access to food and water. Limb muscle denervation was conducted in 5–6 months old C57BL/10 wild-type or Pdgfratm11(EGFP)Sor (JAX stock #007669; [41]) mice as previously described [17]. Briefly, 2–5 mm of the sciatic nerve was transected at the gluteal and biceps femoris muscles level of the left hindlimb, whereas right-side muscles without surgery were used as controls. Fourteen days post-surgery, animals were sacrificed. TA, GST, or soleus muscle from denervated and control hindlimbs were collected, frozen in liquid nitrogen (plus isopentane for cryosectioning) and stored at −80 °C until processing. ## 4.2. Protein Extraction and Western Blot Protein extraction, SDS-PAGE, and Western blot were performed as previously described [6]. Briefly, 40–60 μg of protein was separated by SDS-PAGE, electrophoretically transferred to PVDF membranes, blocked with $5\%$ milk in TBS (50 mM Tris-HCl, pH 7.6; 150 mM NaCl), and probed overnight at 4 °C with the following antibodies: anti-YAP/TAZ (8418, Cell Signaling, Danvers, MA, USA), anti-Fibronectin (F3648, Sigma-Aldrich, St. Louis, MO, USA), and anti-GAPDH (sc-365062, Santa Cruz Biotechnology, Santa Cruz, CA, USA). To detect the primary antibody, horseradish-peroxidase-conjugated secondary antibodies and SuperSignal™ Luminol/Enhancer substrates (Thermo, Waltham, MA, USA) were used to generate chemiluminescence. Protein expression, measured as absolute pixel density, was determined using ImageJ software (v.1.53k, NIH, Bethesda, MD, USA). ## 4.3. RNA Isolation and RT-qPCR RNA extraction from the soleus muscle was performed using TRIzol (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. One μg of isolated total RNA was reverse-transcribed into complementary DNA using random primers and M-MLV reverse transcriptase (Invitrogen, Carlsbad, CA, USA). RT-qPCR was performed in triplicate on an Eco Real-Time PCR System (Illumina, San Diego, CA, USA) using PowerUp™ SYBR™ Green Master Mix (Applied Biosystems, Foster City, CA, USA) and primer sets for mouse Yap1 (F: 5′-GGA AGG AGA AGC AAT GAA CAT AGA-3′ and R: 5′-CGT CCA AGA TTT CGG AAC TCA-3′), Wwtr1 (F: 5′-CTT GCT GGT GTT GTT GAT TC-3′ and R: 5′-ATC AGC CTC TGA ATC ATG TGA A-3′), Ankrd1 (F: 5′-GGA TGT GCC GAG GTT TCT GAA-3′ and R: 5′-GTC CGT TTA TAC TCA CAG AC-3′), Cyr61/Ccn1 (F: 5′-TAA GGT CTG CGC TAA ACA ACT C-3′ and R: 5′-CAG ATC CCT TTC AGA GCG GT-3′), Ctgf/Ccn2 (F: 5′-CAG GCT GGA GAA GCA GAG TCG T-3′ and R: 5′-CTG GTG CAG CCA GAA AGC TCA A-3′), and for reference mouse Gapdh (F: 5′-TGA TGA CAT CAA GAA GGT GGT GAA G-3′ and R: 5′-TCC TTG GAG GCC ATG TAG GCC AT-3′) or 18s (F: 5′-TGA CGG AAG GGC ACC ACC AG-3′ and R: 5′-GTT TGC GAT GGT ACA GCT TAT TC-3′) at a final concentration of 300 nM (Integrated DNA Technologies, Coralville, IA, USA). mRNA expression was determined using the comparative 2−ΔΔCt method and expressed as fold-changes relative to control muscles. ## 4.4. Immunofluorescence Tissue sectioning and immunofluorescence were performed as previously described [6]. Samples were incubated with anti-YAP (1:100, sc-376830, Santa Cruz Biotechnology, Santa Cruz, CA, USA) overnight at 4 °C. To prevent endogenous mouse IgG detection, Ready ProbesTM Mouse on Mouse IgG Blocking Solution (Invitrogen, Carlsbad, CA, USA) was used according to the manual’s instructions and before primary antibody incubation. The primary antibody was detected by incubation with a secondary antibody Alexa Fluor® 568 goat anti-mouse IgG (H + L) (1:500, A11004, Invitrogen, Carlsbad, CA, USA). Samples were stained for total nuclei with Hoechst 33342 (2 mg/mL) for 10 min at RT and mounted with a fluorescent mounting medium (Dako, Glostrup, Denmark). Fluorescent images were acquired using LSM 880 ZEISS microscope with Airyscan detector mounted with an LD LCI Plan-Apochromat 40× objective (NA = 1.20). Images were presented as inverted grayscale. Analysis of images in Figure 4d,e was performed using ImageJ software (v.1.53k, NIH, Bethesda, MD, USA). Briefly, to determine the number of YAP+ and EGFP+ cells, the image of the YAP channel was converted to 8-bit format and segmented (threshold = MaxEntropy dark) to obtain only YAP-positive areas above the defined threshold. The resulting binary image was analyzed for colocalization with EGFP+ cells, and the number of double positive cells (YAP+ and EGFP+) was counted. For YAP signal intensity, the EGFP channel was converted to 8-bit format, blurred with a Gaussian filter (sigma = 2.0), segmented (threshold = MaxEntropy dark), analyzed for particles (size = 2.0), and added to ROI Manager. YAP channel intensity was measured within every segmented EGFP+ nuclei and normalized to the whole image. ## 4.5. Transcriptomics Analyses Bulk RNA-seq data from control and denervated muscles were downloaded from NBCI Sequenced Read Archive (SRA) under accession code SRP196460 [38] (samples: CTL_1: SRR9026466; CTL_2: SRR9026467; CTL_3: SRR9026508; CTL_4: SRR9026456; DEN_1: SRR9026506; DEN_2: SRR9026501; DEN_3: SRR9026504; DEN_4: SRR9026499). The high quality of sequenced libraries was determined using the FastQC package (v.0.11.9). Reads were aligned into the mouse reference genome assembly GRCm39 using Hisat2 with default parameters (v.2.2.1). Mapped read files were converted to reads per annotated gene counts using version 108 of GRCm39 for transcriptome annotation and processed in R for Mac OS X GUI (v.4.2.1), RStudio (v.2022.07.1), and the R package Rsubread (v.2.12.0). Raw counts were processed for normalization using the Trimmed mean of M-values (TMM) methods in the R package edgeR (v.3.40.0). GSEA of YAP/TAZ signature was analyzed using a custom-built gene set of previously documented YAP/TAZ target genes [39] and the desktop version of GSEA (v.4.3.2 Mac App). Single-nucleus transcriptomics of denervated and control muscles was obtained from Gene Expression Omnibus (GEO) GSE183802 [42] (supplementary file: GSE183802_snRNA-seq_data_CTL_and_denervation.rds.gz). Processed data were analyzed in R and with the Seurat package (v.4.2.1). AddModuleScore function was used to assign YAP/TAZ signature score to the Seurat object. ## 4.6. Statistical Analysis The number of replicates used for each experiment is indicated in the figure legends. Data are presented as the mean ± SEM. Statistical significance between two groups was determined by unpaired Student’s t-test using Prism 9 for macOS (v. 9.5.0, Graphpad Software Inc., Boston, MA, USA). Differences were considered statistically significant with a $p \leq 0.05.$ ## References 1. Rebolledo D.L., Lipson K.E., Brandan E.. Driving fibrosis in neuromuscular diseases: Role and regulation of Connective tissue growth factor (CCN2/CTGF). Matrix Biol. Plus (2021) 11 100059. DOI: 10.1016/j.mbplus.2021.100059 2. Sheets K., Overbey J., Ksajikian A., Bovid K., Kenter K., Li Y.. The pathophysiology and treatment of musculoskeletal fibrosis. J. Cell. Biochem. (2022) 123 843-851. DOI: 10.1002/jcb.30217 3. Iyer S., Shah S., Lovering R.. The Neuromuscular Junction: Roles in Aging and Neuromuscular Disease. Int. J. Mol. Sci. (2021) 22. DOI: 10.3390/ijms22158058 4. Accornero F., Kanisicak O., Tjondrokoesoemo A., Attia A.C., McNally E.M., Molkentin J.D.. Myofiber-specific inhibition of TGFβ signaling protects skeletal muscle from injury and dystrophic disease in mice. Hum. Mol. Genet. (2014) 23 6903-6915. DOI: 10.1093/hmg/ddu413 5. Cohn R.D., van Erp C., Habashi J.P., Soleimani A., Klein E.C., Lisi M.T., Gamradt M., ap Rhys C.M., Holm T.M., Loeys B.L.. Angiotensin II type 1 receptor blockade attenuates TGF-β–induced failure of muscle regeneration in multiple myopathic states. Nat. Med. (2007) 13 204-210. DOI: 10.1038/nm1536 6. Córdova-Casanova A., Cruz-Soca M., Chun J., Casar J.C., Brandan E.. Activation of the ATX/LPA/LPARs axis induces a fibrotic response in skeletal muscle. Matrix Biol. (2022) 109 121-139. DOI: 10.1016/j.matbio.2022.03.008 7. Henderson N.C., Rieder F., Wynn T.A.. Fibrosis: From mechanisms to medicines. Nature (2020) 587 555-566. DOI: 10.1038/s41586-020-2938-9 8. Contreras O., Rebolledo D.L., Oyarzún J.E., Olguín H.C., Brandan E.. Connective tissue cells expressing fibro/adipogenic progenitor markers increase under chronic damage: Relevance in fibroblast-myofibroblast differentiation and skeletal muscle fibrosis. Cell Tissue Res. (2016) 364 647-660. DOI: 10.1007/s00441-015-2343-0 9. Contreras O., Soliman H., Theret M., Rossi F.M.V., Brandan E.. TGF-beta-driven downregulation of the tran-scription factor TCF7L2 affects Wnt/beta-catenin signaling in PDGFRalpha(+) fibroblasts. J. Cell Sci. (2020) 133 jcs242297. DOI: 10.1242/jcs.242297 10. Joe A.W., Yi L., Natarajan A., Le Grand F., So L., Wang J., Rudnicki M.A., Rossi F.. Muscle injury activates resident fibro/adipogenic progenitors that facilitate myogenesis. Nat. Cell Biol. (2010) 12 153-163. DOI: 10.1038/ncb2015 11. Mathew S., Hansen J.M., Merrell A.J., Murphy M.M., Lawson J.A., Hutcheson D.A., Hansen M.S., Angus-Hill M., Kardon G.. Connective tissue fibroblasts and Tcf4 regulate myogenesis. Development (2011) 138 371-384. DOI: 10.1242/dev.057463 12. Murphy M.M., Lawson J.A., Mathew S.J., Hutcheson D.A., Kardon G.. Satellite cells, connective tissue fibroblasts and their interactions are crucial for muscle regeneration. Development (2011) 138 3625-3637. DOI: 10.1242/dev.064162 13. Uezumi A., Fukada S.-I., Yamamoto N., Takeda S., Tsuchida K.. Mesenchymal progenitors distinct from satellite cells contribute to ectopic fat cell formation in skeletal muscle. Nat. Cell Biol. (2010) 12 143-152. DOI: 10.1038/ncb2014 14. Uezumi A., Ito T., Morikawa D., Shimizu N., Yoneda T., Segawa M., Yamaguchi M., Ogawa R., Matev M.M., Miyagoe-Suzuki Y.. Fibrosis and adipogenesis originate from a common mesenchymal progenitor in skeletal muscle. J. Cell Sci. (2011) 124 3654-3664. DOI: 10.1242/jcs.086629 15. González D., Contreras O., Rebolledo D.L., Espinoza J.P., van Zundert B., Brandan E.. ALS skeletal muscle shows enhanced TGF-β signaling, fibrosis and induction of fibro/adipogenic progenitor markers. PLoS ONE (2017) 12. DOI: 10.1371/journal.pone.0177649 16. Madaro L., Passafaro M., Sala D., Etxaniz U., Lugarini F., Proietti D., Alfonsi M.V., Nicoletti C., Gatto S., De Bardi M.. Denervation-activated STAT3–IL-6 signalling in fibro-adipogenic progenitors promotes myofibres atrophy and fibrosis. Nat. Cell Biol. (2018) 20 917-927. DOI: 10.1038/s41556-018-0151-y 17. Rebolledo D.L., González D., Faundez-Contreras J., Contreras O., Vio C.P., Murphy-Ullrich J.E., Lipson K.E., Brandan E.. Denervation-induced skeletal muscle fibrosis is mediated by CTGF/CCN2 independently of TGF-β. Matrix Biol. (2019) 82 20-37. DOI: 10.1016/j.matbio.2019.01.002 18. Chen J., Wang X., He Q., Bulus N., Fogo A.B., Zhang M.-Z., Harris R.C.. YAP Activation in Renal Proximal Tubule Cells Drives Diabetic Renal Interstitial Fibrogenesis. Diabetes (2020) 69 2446-2457. DOI: 10.2337/db20-0579 19. Liu F., Lagares D., Choi K.M., Stopfer L., Marinković A., Vrbanac V., Probst C.K., Hiemer S.E., Sisson T.H., Horowitz J.C.. Mechanosignaling through YAP and TAZ drives fibroblast activation and fibrosis. Am. J. Physiol. Lung Cell. Mol. Physiol. (2015) 308 L344-L357. DOI: 10.1152/ajplung.00300.2014 20. Mannaerts I., Leite S.B., Verhulst S., Claerhout S., Eysackers N., Thoen L.F., Hoorens A., Reynaert H., Halder G., van Grunsven L.A.. The Hippo pathway effector YAP controls mouse hepatic stellate cell activation. J. Hepatol. (2015) 63 679-688. DOI: 10.1016/j.jhep.2015.04.011 21. Szeto S.G., Narimatsu M., Lu M., He X., Sidiqi A.M., Tolosa M.F., Chan L., De Freitas K., Bialik J.F., Majumder S.. YAP/TAZ Are Mechanoregulators of TGF-β-Smad Signaling and Renal Fibrogenesis. J. Am. Soc. Nephrol. (2016) 27 3117-3128. DOI: 10.1681/ASN.2015050499 22. Totaro A., Panciera T., Piccolo S.. YAP/TAZ upstream signals and downstream responses. Nat. Cell Biol. (2018) 20 888-899. DOI: 10.1038/s41556-018-0142-z 23. Zhang H., Liu C.-Y., Zha Z.-Y., Zhao B., Yao J., Zhao S., Xiong Y., Lei Q.-Y., Guan K.-L.. TEAD Transcription Factors Mediate the Function of TAZ in Cell Growth and Epithelial-Mesenchymal Transition. J. Biol. Chem. (2009) 284 13355-13362. DOI: 10.1074/jbc.M900843200 24. Zhao B., Ye X., Yu J., Li L., Li W., Li S., Yu J., Lin J.D., Wang C.-Y., Chinnaiyan A.M.. TEAD mediates YAP-dependent gene induction and growth control. Genes Dev. (2008) 22 1962-1971. DOI: 10.1101/gad.1664408 25. Piersma B., Bank R.A., Boersema M.. Signaling in Fibrosis: TGF-β, WNT, and YAP/TAZ Converge. Front. Med. (2015) 2 59. DOI: 10.3389/fmed.2015.00059 26. Labibi B., Bashkurov M., Wrana J.L., Attisano L.. Modeling the Control of TGF-β/Smad Nuclear Accumulation by the Hippo Pathway Effectors, Taz/Yap. Iscience (2020) 23 101416. DOI: 10.1016/j.isci.2020.101416 27. Wang Y., Tu K., Liu D., Guo L., Chen Y., Li Q., Maiers J.L., Liu Z., Shah V.H., Dou C.. p300 Acetyltransferase Is a Cytoplasm-to-Nucleus Shuttle for SMAD2/3 and TAZ Nuclear Transport in Transforming Growth Factor β–Stimulated Hepatic Stellate Cells. Hepatology (2019) 70 1409-1423. DOI: 10.1002/hep.30668 28. Azzolin L., Panciera T., Soligo S., Enzo E., Bicciato S., Dupont S., Bresolin S., Frasson C., Basso G., Guzzardo V.. YAP/TAZ Incorporation in the β-Catenin Destruction Complex Orchestrates the Wnt Response. Cell (2014) 158 157-170. DOI: 10.1016/j.cell.2014.06.013 29. Azzolin L., Zanconato F., Bresolin S., Forcato M., Basso G., Bicciato S., Cordenonsi M., Piccolo S.. Role of TAZ as Mediator of Wnt Signaling. Cell (2012) 151 1443-1456. DOI: 10.1016/j.cell.2012.11.027 30. Yu F.-X., Zhao B., Panupinthu N., Jewell J.L., Lian I., Wang L.H., Zhao J., Yuan H., Tumaneng K., Li H.. Regulation of the Hippo-YAP Pathway by G-Protein-Coupled Receptor Signaling. Cell (2012) 150 780-791. DOI: 10.1016/j.cell.2012.06.037 31. Panciera T., Azzolin L., Cordenonsi M., Piccolo S.. Mechanobiology of YAP and TAZ in physiology and disease. Nat. Rev. Mol. Cell Biol. (2017) 18 758-770. DOI: 10.1038/nrm.2017.87 32. Aragona M., Panciera T., Manfrin A., Giulitti S., Michielin F., Elvassore N., Dupont S., Piccolo S.. A Mechanical Checkpoint Controls Multicellular Growth through YAP/TAZ Regulation by Actin-Processing Factors. Cell (2013) 154 1047-1059. DOI: 10.1016/j.cell.2013.07.042 33. Dupont S., Morsut L., Aragona M., Enzo E., Giulitti S., Cordenonsi M., Zanconato F., Le Digabel J., Forcato M., Bicciato S.. Role of YAP/TAZ in mechanotransduction. Nature (2011) 474 179-183. DOI: 10.1038/nature10137 34. Scott K.E., Fraley S.I., Rangamani P.. A spatial model of YAP/TAZ signaling reveals how stiffness, dimensionality, and shape contribute to emergent outcomes. Proc. Natl. Acad. Sci. USA (2021) 118 e2021571118. DOI: 10.1073/pnas.2021571118 35. Fischer M., Rikeit P., Knaus P., Coirault C.. YAP-Mediated Mechanotransduction in Skeletal Muscle. Front. Physiol. (2016) 7 41. DOI: 10.3389/fphys.2016.00041 36. Silver J.S., Günay K.A., Cutler A.A., Vogler T.O., Brown T.E., Pawlikowski B.T., Bednarski O.J., Bannister K.L., Rogowski C.J., Mckay A.G.. Injury-mediated stiffening persistently activates muscle stem cells through YAP and TAZ mechanotransduction. Sci. Adv. (2021) 7 eabe4501. DOI: 10.1126/sciadv.abe4501 37. Watt K.I., Turner B.J., Hagg A., Zhang X., Davey J.R., Qian H., Beyer C., Winbanks C.E., Harvey K.F., Gregorevic P.. The Hippo pathway effector YAP is a critical regulator of skeletal muscle fibre size. Nat. Commun. (2015) 6 6048. DOI: 10.1038/ncomms7048 38. Ehmsen J.T., Kawaguchi R., Mi R., Coppola G., Höke A.. Longitudinal RNA-Seq analysis of acute and chronic neurogenic skeletal muscle atrophy. Sci. Data (2019) 6 179. DOI: 10.1038/s41597-019-0185-4 39. Zanconato F., Forcato M., Battilana G., Azzolin L., Quaranta E., Bodega B., Rosato A., Bicciato S., Cordenonsi M., Piccolo S.. Genome-wide association between YAP/TAZ/TEAD and AP-1 at enhancers drives oncogenic growth. Nat. Cell Biol. (2015) 17 1218-1227. DOI: 10.1038/ncb3216 40. Contreras O., Cruz-Soca M., Theret M., Soliman H., Tung L.W., Groppa E., Rossi F.M., Brandan E.. Cross-talk between TGF-beta and PDGFRalpha signaling pathways regulates the fate of stromal fibro-adipogenic progenitors. J. Cell Sci. (2019) 132 jcs232157. DOI: 10.1242/jcs.232157 41. Hamilton T.G., Klinghoffer R.A., Corrin P.D., Soriano P.. Evolutionary Divergence of Platelet-Derived Growth Factor Alpha Receptor Signaling Mechanisms. Mol. Cell. Biol. (2003) 23 4013-4025. DOI: 10.1128/MCB.23.11.4013-4025.2003 42. Lin H., Ma X., Sun Y., Peng H., Wang Y., Thomas S.S., Hu Z.. Decoding the transcriptome of denervated muscle at single-nucleus resolution. J. Cachex-Sarcopenia Muscle (2022) 13 2102-2117. DOI: 10.1002/jcsm.13023 43. Liu-Chittenden Y., Huang B., Shim J.S., Chen Q., Lee S.-J., Anders R.A., Liu J.O., Pan D.. Genetic and pharmacological disruption of the TEAD-YAP complex suppresses the oncogenic activity of YAP. Genes Dev. (2012) 26 1300-1305. DOI: 10.1101/gad.192856.112 44. Mitani A., Nagase T., Fukuchi K., Aburatani H., Makita R., Kurihara H.. Transcriptional Coactivator with PDZ-binding Motif Is Essential for Normal Alveolarization in Mice. Am. J. Respir. Crit. Care Med. (2009) 180 326-338. DOI: 10.1164/rccm.200812-1827OC 45. Judson R.N., Gray S.R., Walker C., Carroll A.M., Itzstein C., Lionikas A., Zammit P.S., De Bari C., Wackerhage H.. Constitutive Expression of Yes-Associated Protein (Yap) in Adult Skeletal Muscle Fibres Induces Muscle Atrophy and Myopathy. PLoS ONE (2013) 8. DOI: 10.1371/journal.pone.0059622 46. Vita G.L., Polito F., Oteri R., Arrigo R., Ciranni A.M., Musumeci O., Messina S., Rodolico C., Di Giorgio R.M., Vita G.. Hippo signaling pathway is altered in Duchenne muscular dystrophy. PLoS ONE (2018) 13. DOI: 10.1371/journal.pone.0205514 47. Liang M., Yu M., Xia R., Song K., Wang J., Luo J., Chen G., Cheng J.. Yap/Taz Deletion in Gli+ Cell-Derived Myofibroblasts Attenuates Fibrosis. J. Am. Soc. Nephrol. (2017) 28 3278-3290. DOI: 10.1681/ASN.2015121354 48. Piersma B., de Rond S., Werker P.M., Boo S., Hinz B., van Beuge M.M., Bank R.A.. YAP1 Is a Driver of Myofibroblast Differentiation in Normal and Diseased Fibroblasts. Am. J. Pathol. (2015) 185 3326-3337. DOI: 10.1016/j.ajpath.2015.08.011 49. Shi-Wen X., Racanelli M., Ali A., Simon A., Quesnel K., Stratton R.J., Leask A.. Verteporfin inhibits the persistent fibrotic phenotype of lesional scleroderma dermal fibroblasts. J. Cell Commun. Signal. (2021) 15 71-80. DOI: 10.1007/s12079-020-00596-x 50. Morales M.G., Cabello-Verrugio C., Santander C., Cabrera D., Goldschmeding R., Brandan E.. CTGF/CCN-2 over-expression can directly induce features of skeletal muscle dystrophy. J. Pathol. (2011) 225 490-501. DOI: 10.1002/path.2952 51. Morales M.G., Gutierrez J., Cabello-Verrugio C., Cabrera D., Lipson K., Goldschmeding R., Brandan E.. Reducing CTGF/CCN2 slows down mdx muscle dystrophy and improves cell therapy. Hum. Mol. Genet. (2013) 22 4938-4951. DOI: 10.1093/hmg/ddt352 52. Dey A., Varelas X., Guan K.-L.. Targeting the Hippo pathway in cancer, fibrosis, wound healing and regenerative medicine. Nat. Rev. Drug Discov. (2020) 19 480-494. DOI: 10.1038/s41573-020-0070-z 53. Engler A., Griffin M.A., Sen S., Bonnemann C.G., Sweeney H.L., Discher D.E.. Myotubes differentiate optimally on substrates with tissue-like stiffness: Pathological implications for soft or stiff microenvironments. J. Cell Biol. (2004) 166 877-887. DOI: 10.1083/jcb.200405004 54. Pavan P., Monti E., Bondí M., Fan C., Stecco C., Narici M., Reggiani C., Marcucci L.. Alterations of Extracellular Matrix Mechanical Properties Contribute to Age-Related Functional Impairment of Human Skeletal Muscles. Int. J. Mol. Sci. (2020) 21. DOI: 10.3390/ijms21113992 55. Wood L.K., Kayupov E., Gumucio J.P., Mendias C., Claflin D., Brooks S.V.. Intrinsic stiffness of extracellular matrix increases with age in skeletal muscles of mice. J. Appl. Physiol. (2014) 117 363-369. DOI: 10.1152/japplphysiol.00256.2014 56. Loomis T., Hu L.-Y., Wohlgemuth R.P., Chellakudam R.R., Muralidharan P.D., Smith L.R.. Matrix stiffness and architecture drive fibro-adipogenic progenitors’ activation into myofibroblasts. Sci. Rep. (2022) 12 13582. DOI: 10.1038/s41598-022-17852-2 57. Kurimoto S., Jung J., Tapadia M., Lengfeld J., Agalliu D., Waterman M., Mozaffar T., Gupta R.. Activation of the Wnt/β-catenin signaling cascade after traumatic nerve injury. Neuroscience (2015) 294 101-108. DOI: 10.1016/j.neuroscience.2015.02.049
--- title: Impact of Obesity on the IL-6 Immune Marker and Th17 Immune Cells in C57BL/6 Mice Models with Imiquimod-Induced Psoriasis authors: - So Hee Park - Kyung Ah Lee - Jae-Hyeog Choi - SaeGwang Park - Dae-Wook Kim - So Young Jung journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10059802 doi: 10.3390/ijms24065592 license: CC BY 4.0 --- # Impact of Obesity on the IL-6 Immune Marker and Th17 Immune Cells in C57BL/6 Mice Models with Imiquimod-Induced Psoriasis ## Abstract Obese psoriatic patients experience higher disease severity and exhibit poorer treatment responses and clinical outcomes. It has been proposed that proinflammatory cytokines produced by adipose tissue exacerbate psoriasis; however, the role of obesity in psoriasis remains unclear. This study aimed to elucidate the role of obesity in the pathogenesis of psoriasis, focusing on immunological changes. To induce obesity, mice were fed a high-fat diet for 20 weeks. We then applied imiquimod to the skin on a mouse’s back for seven consecutive days to induce psoriasis and scored lesion severity every day for seven days. Cytokine levels in serum and the Th17 cell population in the spleen and draining lymph nodes were studied to identify immunological differences. The clinical severity was more remarkable, and histologically the epidermis was also significantly thicker in the obese group. Increased levels of IL-6 and TNF-α were observed in serum after psoriasis. They were elevated to a greater degree, with greater expansion of the functional Th17 cell population in the obese group. It is concluded that obesity could exacerbate psoriasis through mechanisms that involve elevated proinflammatory cytokine secretion and an expanded Th17 cell population. ## 1. Introduction Psoriasis is a chronic inflammatory skin disorder that mostly manifests in the form of psoriasis vulgaris, characterized by raised, well-demarcated, erythematous plaques with silvery scales [1]. The mechanisms of pathogenesis for psoriasis remain poorly understood. In the past, hyper-proliferation and altered differentiation of keratinocytes was thought to be the main cause. It is now thought that complex alterations in keratinocytes, accompanied by immunological and vascular abnormalities, contribute to pathogenesis in psoriasis [2]. Among described immunological abnormalities, T-cell mediated processes, and the IL-23/Th17 axis in particular, have been implicated in psoriasis [3,4]. Th1-related cytokines such as IFN-γ and IL-12 were thought to be the driving force for pathophysiology in psoriasis. However, this understanding has evolved with the identification of IL-17-producing Th17 cells and their crucial role in psoriasis, which is analogous to their role in other autoimmune diseases, such as ankylosing spondylitis, multiple sclerosis, and Crohn’s disease [5]. IL-6, TGF-β, and IL-23 are required for Th17 cell differentiation. Initially, IL-6 and TGF-β drive naïve T-cell differentiation to Th17 cells, through activating STAT3 and subsequently promoting the transcription of Th17-specific genes such as Rorc, Il17, and IL23r. However, Th17 cells produced from TGF-β- and IL-6-driven-differentiation have limited pathogenicity and require subsequent exposure to IL-23 for their maturation and development into pathogenic Th17 cells. Finally, these pathogenic Th17 cells produce cytokines, including IL-17 and IL-22, which play crucial roles in psoriasis pathogenesis [6,7,8]. Psoriasis has recently been recognized as a form of systemic inflammation, and several clinical studies have indicated its linkage to psoriatic arthritis, cardiovascular disease, metabolic syndrome, and obesity [9]. Particularly, obese psoriatic patients show more severe clinical symptoms and poorer responses to regular treatment [10,11,12,13]. Additionally, weight loss alleviates symptoms of psoriasis [14]. Nevertheless, the role played by obesity in psoriasis is still unclear. Recent studies have indicated that elevated serum levels of IL-6 and IL-17 in mice with diet-induced obesity (DIO) promoted Th17 cell differentiation [15]. DIO was also associated with increased disease severity in Th17-dependent mouse models of disease, such as colitis and experimental autoimmune encephalomyelitis [15]. Accordingly, this study aimed to determine the potential role of obesity in exacerbating psoriasis in a C57BL/6 murine model with imiquimod-induced psoriasis by comparing the immunological differences between mice reared on obesity-inducing diet (OID) with mice reared on a regular diet (RD). In addition, this study is seeking a profound understanding of the role of obesity in the pathogenesis of psoriasis, based on immunological changes, helping determine appropriate immunomodulatory treatment in obese psoriatic patients. ## 2.1. Severity of Skin Inflammation To assess the effect of obesity on the severity of skin inflammation, a topical imiquimod cream was applied to shaved skin on the backs of OID and RD mice for 7 consecutive days. Erythema and increases in skin thickness were observed from the second day, and scaling from the third day in the RD and OID groups. Scores on each symptom parameter increased up to the seventh day of the experiment and were significantly higher in the OID mouse group (Figure 1A). H&E staining revealed that the epidermis became thicker after 7 days of imiquimod treatment in the RD and OID groups (Figure 1B). However, the epidermal thickness was significantly greater in the OID group (69.2 ± 4.26 μm) than in the RD group (58.11 ± 1.52 μm) (Figure 1C). Histological changes were observed over the time course (Figure 1D). From the second day to the seventh day, there were visible changes in epidermal thickness and cell infiltration into the dermis. The epidermis gradually thickened with time and became prominent in the OID mice. Additionally, intra-corneal microabscess formation, a characteristic feature of psoriasis, was observed earlier in OID mice (at day 2) than in RD mice (at day 4). ## 2.2. Serum Levels of Psoriasis-Associated Proinflammatory Cytokines We next examined if obesity affected the production of psoriasis-related proinflammatory cytokines IL-6 and TNF-α in the serum after imiquimod treatment. Serum samples were collected at 5 different time-points: before imiquimod treatment and days 1, 2, 4, and 7 after treatment. There was no difference in serum IL-6 and TNF-α between the OID and RD groups before imiquimod treatment (Figure 2A). IL-6 and TNFα increased one day after treatment, and elevated levels of both cytokines remained unchanged until the seventh day in both the OID and RD groups. Although the levels of the cytokines were higher in the OID group, there were no statistical differences from those in the RD group (Figure 2B). ## 2.3. Th17 Cell Population in Spleen and Lymph Node As Th17 cell populations have been identified to play a key role in the pathogenesis of psoriasis, we investigated cells obtained from spleens and lymph nodes at day 7 after imiquimod treatment. The strategy of IL-17+CD4 T cells gating was identified in Figure 3A. In the spleens, the highest percentage of Th17 cells was in the imiquimod-treated OID group (8.82 ± $0.51\%$), followed by the imiquimod-treated RD group (6.74 ± $0.45\%$), the non-treated OID group (6.15 ± $0.63\%$), and the non-treated RD group (4.10 ± $0.38\%$) (Figure 3B). As such, imiquimod treatment increased the percentage of Th17 cells in both the OID and RD groups. A more considerable increase in Th17 cells was observed in the imiquimod-treated OID group than in the imiquimod-treated RD group. The Th17 cell population was also larger in the non-treated OID group than in the non-treated RD group. The differences between the imiquimod-treated OID group and the imiquimod-treated RD group, and between the non-treated OID group and the non-treated RD group were statistically significant. In the draining lymph nodes, we observed similar trends in Th17 cells. The percentage of Th17 cells was the highest in the imiquimod-treated OID group (5.64 ± $0.51\%$), followed by the imiquimod-treated RD group (4.15 ± $0.09\%$) and the non-treated OID group (3.94 ± $0.11\%$), with the smallest percentage observed in the non-treated RD group (3.19 ± $0.19\%$) (Figure 3C). ## 2.4. IL-17 Secretion of Th17 Cell Population in Spleen and Lymph Node Th17 cells characteristically secrete IL-17. We previously found that the Th17 cell population was the largest in OID mice treated with imiquimod. IL-17 levels were measured to determine if increased Th17 cell counts led to increased cytokine secretion. Cells were isolated from the spleens and draining lymph nodes of mice in the imiquimod-treated groups and were stimulated in vitro with anti-CD3 and anti-CD28 for 72 h, following which cytokines in the culture supernatant were measured. The level of IL-17 was significantly higher in the OID group than in the RD group (Figure 4). Meanwhile, the levels of IL-6 and TNF-α were higher in the OID group than in the RD group, but the recorded differences between the two groups were insignificant. ## 3. Discussion In this study, we investigated the role of obesity in psoriasis and how obesity aggravates psoriasis using an imiquimod-induced murine model of psoriasis in C57BL/6 mice. Imiquimod was observed to induce more severe psoriasis in obese mice, and the levels of IL-6, TNF- α, and Th17 cells, were simultaneously elevated. Several different mouse models of psoriasis have been developed, including a genetically engineered model and models induced by xenotransplantation or transfer of immune cells [16]. In this study, we used the imiquimod-induced psoriasis mouse model, which was developed in 2009 by van der Fits et al. [ 17]. Imiquimod is a ligand for Toll-like receptor (TLR) 7 and TLR8 and is used in dermatology for the treatment of several cutaneous diseases, including actinic keratosis and basal cell carcinoma, as well as viral diseases such as condyloma accuminatum. It is known that the treatment effect of imiquimod in these diseases is mediated through activation of monocytes, macrophages, and plasmacytoid dendritic cells expressing TLR7 or TLR8 [18]. A recent study showed that TLR7 is also expressed in differentiated keratinocytes and that imiquimod can stimulate keratinocyte production of proinflammatory cytokines via NF-κB [19]. TLR7 and TLR8 are also expressed in the subcutaneous adipose tissue of the C57BL/6 mice [20]. Based on this information, we deduced that topical application of imiquimod could stimulate cytokine production via keratinocytes and immune cells as well as adipose tissue. For this reason, we chose an imiquimod-induced model of psoriasis for our experiments. Several clinical studies have associated psoriasis with obesity. Firstly, children who are overweight are at increased risk of early onset psoriasis [21]. The risk of developing psoriasis is higher in overweight or obese people, who also present with more severe clinical symptoms [22,23]. Treatment responses to both systemic and biological therapies are poorer in obese individuals [12,13,24]. Additionally, weight loss reduces the severity of psoriasis symptoms and increases response to systemic treatment [14]. We observed a similar relationship between obesity and severity of symptoms in an imiquimod-induced mouse model of psoriasis. Although OID and RD groups first showed similar clinical symptoms two days after imiquimod treatment, progression was more rapid in the first group, coupled with higher disease severity. Histological results showed that epidermal thickness was significantly greater in the OID mice. Additionally, collections of neutrophils in the stratum corneum (also known as Munro’s microabscess) which is a characteristic histologic finding in psoriasis tended to be seen earlier in the OID group than in the RD group. For histological examination, we made more than three slides stained with hematoxylin and eosin (H&E) with the skin obtained from one mouse. Munro’s microabscess, if present, was not always observed in all slides made from the same mouse. It is thought that whether Munro’s microabscess can be observed or not is dependent on where the tissue cut is sampled from. Additionally, there were biopsy results from the OID group, in which Munro’s microabscess was not found on the second day. However, it was found in the skin tissues of all the sacrificed mice on the fourth day. On the other hand, in the RD group, Munro’s microabscess was not found at all on the second day and began to be observed on the fourth day. Same as similar to the second day of the OID group, in the RD group, Munro’s microabscess was not observed in the skin tissue of all the sacrificed mice on all samples of the fourth day except one. Based on the results, we can predict that formation of Munro’s microabscess occurs earlier in the OID group. Taken together, the differences in clinical symptoms and histology suggest that in this disease model, OID exacerbates the progression of psoriasis, but does not appear to play a considerable role in initial psoriasis development. Recently, obesity has come to be thought of as a condition of systemic inflammation. Chronic low-grade inflammation in obese individuals is associated with metabolic diseases, cancers, and psoriasis [25,26]. While the association between psoriasis and obesity is well known, the mechanism of this association and how it results in more severe pathophysiology remains unclear. It has been proposed that adipokines and proinflammatory cytokines produced by adipose tissue play a role in exacerbating psoriasis [9,27]. Accumulated adipose tissue in obese individuals do not simply act as an energy store, but also has endocrine and immune functions. Adipocytes and macrophages in adipose tissue can produce various proinflammatory cytokines, and elevated concentrations of IL-6 and TNF-α are increased in adipose tissue and serum during obesity [28,29,30]. It is thought that obesity-linked elevated cytokine levels are associated with accelerated macrophage accumulation in adipose tissue [31,32]. In this study, we found no differences in the serum levels of IL-6 and TNF-α in the OID and RD groups before imiquimod treatment. This is different from what previous studies have found [28]; however, we observed significantly higher IL-6 and TNF-α levels in the OID group than in the RD group after imiquimod treatment. Since TLR7 and TLR8 are expressed in the adipocytes of C57BL/6 mice [20], it can be explained that the enlarged adipose cells in the OID group were more stimulated by imiquimod, affecting more increased IL-6 or TNF-α secretion. It is unclear if the elevated IL-6 and TNF-α levels directly caused the increased disease severity in this model. However, both IL-6 and TNF-α are important immune mediators during initiation and maintenance periods for psoriatic lesions, and elevated cytokine levels would have either direct or indirect effects on disease severity in OID mice [33]. IL-6 has long been associated with the pathogenesis of psoriasis and is elevated in the serum and skin of patients with psoriasis [34,35]. IL-6 is known to be a cytokine that is essential for initial differentiation of Th17 cells, a key population of cells associated with pathogenesis in psoriasis [6,7,36]. A recent study showed that IL-6 was required for obesity-associated Th17 cell expansion during induction of EAE with myelin oligodendrocyte glycoprotein 35–55 [15]. We hypothesized in this study that elevated IL-6 would affect Th17 cell differentiation, and that increased numbers of Th17 cells would consequently result in more severe clinical symptoms in OID mice. To test this hypothesis, we analyzed Th17 cell populations present in the spleens and draining lymph nodes at day 7. Greater Th17 cell populations were observed in the RD and OID groups treated with imiquimod compared to their control groups. In addition, the imiquimod-treated OID group recorded the highest number of Th17 cells. As was previously reported [15], we found a larger Th17 cell population in OID mice than in RD mice without imiquimod treatment. We also tested the larger population of Th17 cells in the OID group treated with imiquimod for cytokine production. Cells were isolated from the spleens and draining lymph nodes, activated with anti-CD3 and anti-CD28 for 3 days, and elevated levels of IL-17 were observed. As mentioned before, the role of IL -23 is important for maturation of Th17 cells. Although we have not evaluated IL-23, we can predict that IL-23 would have worked well enough in our experiment. This is because IL-17 secretion was found to increase due to larger Th17 cell papulation in the spleens and lymph nodes of OID mice treated with imiquimod. However, our study is limited in its ability to explain fully the process of changing from non-pathogenic Th17 cells to pathogenic Th17 cells. In our further studies, to examine whether the Th17 cells are functional or not, we will investigate upstream cytokine IL-23 as well as downstream cytokine IL-17. In summary, imiquimod more highly stimulated the production of IL-6 from enlarged adipose tissue, and this may have increased differentiation of IL-17-producing Th17 cells in OID mice than in normal ones. Higher levels of IL-17 produced by larger numbers of Th17 cells may be the reason for more severe psoriasis symptoms in OID mice than in RD mice. ## 4.1. Mice and Treatment Female C57BL/6 mice aged 8–10 weeks were purchased from Orient Bio, Inc. (Sungnam, Republic of Korea). All mice were housed in the Animal Care Facility at the College of Medicine, Inje University. Mice were randomly divided into two groups and were fed either a control regular diet (RD group) or an obesity-inducing high-fat diet (OID group) for 20 weeks. The commercially available high-fat diet (Dyets, Inc. Bethlehem, PA, USA) consisted of $40\%$ fat. The high-fat diet group were defined as having OID when the mean body weight reached > 3SD above the mean bodyweight of the regular diet group [28,37,38]. ## 4.2. Induction of Psoriasis We used a method previously described by van der Fits et al. to induce psoriasis [17]. The experimental groups were as follows: RD control group, 24.63 ± 1.27 g ($$n = 36$$), RD disease group, 24.56 ± 1.09 g ($$n = 40$$), OID control group, 39.15 ± 4.27 g ($$n = 36$$), and OID disease group, 42.07 ± 6.38 g ($$n = 40$$). Briefly, mice were anesthetized with an intra-peritoneal injection of keratmin (100 mg/kg) and xylazine (10 mg/kg). Anesthesia was administered only on the first day for shaving. Mice had their backs shaved, and any remaining hair was completely removed with depilatory cream. A 2.5 × 3 cm rectangle was drawn on the back of all mice to demarcate the treated area. Mice in the induced psoriasis group were treated with commercially available $5\%$ imiquimod cream (Aldara®; 3M Pharmaceuticals, Saint Paul, MN, USA). A daily dose of 80 mg of cream containing 4 mg of imiquimod was applied for 7 consecutive days. A vehicle cream, Vaseline, was similarly applied to the control treatment group. ## 4.3. Physical Scoring of Skin Inflammation Severity of psoriasis was measured every day for seven days using a method that was also developed by van der Fits et al. [ 17]. Two dermatologists performed the scoring evaluation to the groups in a blinded manner. The symptoms of erythema, scaling, and thickness were scored independently on a scale from 0–4: 0 representing no symptoms, 1 representing slight symptoms, 2 indicating moderate symptoms, 3 indicating marked symptoms, and 4 representing very marked symptoms. A cumulative score (scale: 0–12) was then calculated by adding the scores for all three symptoms. ## 4.4. Histopathology of Skin Tissue Skin tissue from the backs of the mice was collected on days 1, 2, 4, and 7, and was fixed in $10\%$ buffered formalin solution and embedded in paraffin. Sections (5 μm) were prepared and stained with hematoxylin and eosin (H&E). The tissue sections were histologically examined, and epidermal thickness was scored. ## 4.5. Serum Cytokine Levels 100 μL of blood was collected using a heparinized capillary tube from the retro-orbital vascular plexus at days 1, 2, 4 and 7. Additionally, the serum was separated and stored at −20 °C. Levels of IL-6, TNF-α, and IL-17 were measured in serum using a mouse Th1/Th2/Th17 cytokine Cytometrric Bead Array (CBA) kit (BD Biosciences, Franklin Lakes, NJ, USA). ## 4.6. Flow Cytometry Single cells were prepared from the draining lymph nodes and spleens on day 7. Cells were incubated with an Fc receptor blocking antibody 2.4G2 for 5 min, following which they were stained with the following fluorophore-conjugated antibodies: CD3-APC and CD4-PE/Cy5. For intracellular IL-17 staining, 1.5 × 106 cells/mL were stimulated with PMA (50 ng/mL) and ionomycin (750 ng/mL) for 6 h, and Golgi stop was added for the last 3 h, then labeled with anti-mouse IL-17 FITC antibody. Intracellular labeling was performed using the BD Cytofix/CytopermTM kit (BD Biosciences) according to the manufacturer’s instructions. All antibodies were purchased from eBiosciences, Inc. (San Diego, CA, USA). FACS data were acquired using a FACS Canto II flow cytometer (BD Biosciences) and analyzed using FlowJo Software V10 (FlowJo LLC, Ashland, OR, USA). ## 4.7. T Cell Activation and Supernatants Cytokine Assay Single cell suspensions were prepared from spleens and draining lymph nodes from imiquimod-treated RD and OID mice on day 7. For T cell activation, single cells were plated at a density of 7.5 × 105 cells/well (48-well culture plate) that had been precoated with anti-CD3 antibodies (0.5 μg/mL). Anti-mouse CD28 antibodies (1 μg/mL) were added to the cell cultures. After 3 days, cell-free supernatant was collected for cytokine detection by CBA kit (BD Biosciences, Franklin Lakes, NJ, USA). ## 4.8. Statistical Analysis Statistical analysis was performed using GraphPad Prism (Version 6 for Windows; GraphPad Software, Inc., La Jolla, CA, USA). Data are represented as means ± SEM of at least two or three independently performed experiments. Differences between groups were compared using an unpaired t-test, and the level of significance was set at $p \leq 0.05.$ $p \leq 0.05$, 0.01, and 0.001 are marked with , , and , respectively. ## 5. Conclusions In conclusion, this study demonstrated that obesity exacerbates psoriasiform dermatitis in an imiquimod-induced murine model of psoriasis. Clinical symptoms such as erythema, scales, and increased epidermal thickness were more severe, and a histological feature characteristic of psoriasis was observed earlier in obese mice. The aggravation of symptoms was related to increased production of the psoriasis-related proinflammatory cytokines IL-6 and TNF-α. Obesity resulted in increased differentiation to Th17 cells, and the greatest number of Th17 cells was observed with IL-17 production in obese mice with imiquimod-induced psoriasis. We propose that this model of imiquimod-induced psoriasis in obese mice might be valuable for investigation of the role that obesity plays in psoriasis and to test different modes of treatment for patients with psoriasis and obesity. ## References 1. Nestle F.O., Kaplan D.H., Barker J.. Psoriasis*. N. Eng. J. Med.* (2009) 361 496-509. DOI: 10.1056/NEJMra0804595 2. Nickoloff B.J., Xin H., Nestle F.O., Qin J.Z.. The cytokine and chemokine network in psoriasis. Clin. Dermatol. (2007) 25 568-573. DOI: 10.1016/j.clindermatol.2007.08.011 3. Kickloff B.J., Qin J.Z., Nestle F.O.. Immunopathogenesis of psoriasis. Clin. Rev. Allergy Immunol. (2007) 33 45-56. PMID: 18094946 4. Di Cesare A., Di Meglio P., Nestle F.O.. The IL-23/Th17 axis in the immunopathogenesis of psoriasis. J. Investig. Dermatol. (2009) 129 1339-1350. DOI: 10.1038/jid.2009.59 5. Iwakura Y., Ishigame H.. The IL-23/IL-17 axis in inflammation. J. Clin. Investig. (2006) 116 1218-1222. DOI: 10.1172/JCI28508 6. Gaffen S.L., Jain R., Garg A.V., Cua D.J.. The IL-23-IL-17 immune axis: From mechanisms to therapeutic testing. Nat. Rev. Immunol. (2014) 14 585-600. DOI: 10.1038/nri3707 7. Zuniga L.A., Jain R., Haines C., Cua D.J.. Th17 cell development: From the cradle to the grave. Immuno. Rev. (2013) 252 78-88. DOI: 10.1111/imr.12036 8. Basu R., Hatton R.D., Weaver C.T.. The Th17 family: Flexibility follows function. Immuno. Rev. (2013) 252 89-103. DOI: 10.1111/imr.12035 9. Reich K.. The concept of psoriasis as a systemic inflammation: Implications for disease management. J. Eur. Acad. Dermatol. Venereol. (2012) 26 S3-S11. DOI: 10.1111/j.1468-3083.2011.04410.x 10. Amstrong A.W., Harskamp C.T., Armstrong E.J.. The association between psoriasis and obesity: A systematic review and meta-analysis of observational studies. Nutr. Diabetes (2012) 2 e54. DOI: 10.1038/nutd.2012.26 11. Huang Y.H., Yang L.C., Hui R.Y., Chang Y.C., Yang Y.W., Yang C.H., Chen Y.H., Chung W.H., Kuan Y.Z., Chiu C.S.. Relationships between obesity and the clinical severity of psoriasis in Taiwan. J. Eur. Acad. Dermatol. Venereol. (2010) 24 1035-1039. DOI: 10.1111/j.1468-3083.2010.03573.x 12. Carrascosa J.M., Rocamora V., Fernandez-Torres R.M., Jimenez-Puya R., Moreno J.C., Coll-Puigserver N., Fonseca E.. Obesity and psoriasis: Inflammatory nature of obesity, relationship between psoriasis and obesity, and therapeutic implications. Actas. Dermosifiliogr. (2014) 105 31-44. DOI: 10.1016/j.ad.2012.08.003 13. Bremmer S., Van Voorhees A.S., Hsu S., Korman N.J., Lebwohl M.G., Young M., Bebo B.F., Blauvelt A.. Obesity and psoriasis: From the medical board of the national psoriasis foundation. J. Am. Acad. Dermatol. (2010) 63 1058-1069. DOI: 10.1016/j.jaad.2009.09.053 14. Gisondi P., Del Giglio M., Di Francesco V., Zamboni M., Girolomoni G.. Weight loss improves the response of obese patients with moderate-to-severe chronic plaque psoriasis to low-dose cyclosporine therapy: A randomized, controlled, investigator-blinded clinical trial. Am. J. Clin. Nutr. (2008) 88 1242-1247. PMID: 18996858 15. Winer S., Paltser G., Chan Y., Tsui H., Engleman E., Winer D., Dosch H.M.. Obesity predisposes to Th17 bias. Eur. J. Immunol. (2009) 39 2629-2635. DOI: 10.1002/eji.200838893 16. Gudjonsson J.E., Johnston A., Dyson M., Valdimarsson H., Elder J.T.. Mouse models of psoriasis. J. Investig. Dermatol. (2007) 127 1292-1308. DOI: 10.1038/sj.jid.5700807 17. van der Fits L., Mourits S., Voerman J.S., Kant M., Boon L., Laman J.D., Cornelissen F., Mus A.M., Florencia E., Prens E.P.. Imiquimod-induced psoriasis-like skin inflammation in mice is mediated via the IL-23/IL-17 axis. J. Immunol. (2009) 182 5836-5845. DOI: 10.4049/jimmunol.0802999 18. Patel U., Mark N.M., Machler B.C., Levine V.J.. Imiquimod 5% cream induced psoriasis: A case report, summary of the literature and mechanism. Br. J. Dermatol. (2011) 164 670-672. DOI: 10.1111/j.1365-2133.2010.10124.x 19. Li Z.J., Sohn K.C., Choi D.K., Shi G., Hong D., Lee H.E., Whang K.U., Lee Y.H., Im M., Lee Y.. Roles of TLR7 in activation of NF-κB signaling of keratinocytes by imiquimod. PLoS ONE (2013) 8. DOI: 10.1371/journal.pone.0077159 20. Kim S.J., Choi Y., Choi Y.H., Park T.. Obesity activates toll-like receptor-mediated proinflammatory signaling cascades in the adipose tissue of mice. J. Nutr. Biochem. (2012) 23 113-122. DOI: 10.1016/j.jnutbio.2010.10.012 21. Boccardi D., Menni S., La Vecchia C., Nobile M., Decarli A., Volpi G., Ferraroni M.. Overweight and childhood psoriasis. Br. J. Dermatol. (2009) 161 484-486. DOI: 10.1111/j.1365-2133.2009.09276.x 22. Naldi L., Chatenoud L., Linder D., Belloni Fortina A., Peserico A., Virgili A.R., Bruni P.L., Ingordo V., Scocco G.L., Solaroli C.. Cigarette smoking, body mass index, and stressful life events as risk factors for psoriasis: Results from an Italian case-control study. J. Investig. Dermatol. (2005) 125 61-67. DOI: 10.1111/j.0022-202X.2005.23681.x 23. Murray M.L., Bergstresser P.R., Adams-Huet B., Cohen J.B.. Relationship of psoriasis severity to obesity using same-gender siblings as controls for obesity. Clin. Exp. Dermatol. (2009) 34 140-144. DOI: 10.1111/j.1365-2230.2008.02791.x 24. Bardazzi F., Balestri R., Baldi E., Antonucci A., De Tommaso S., Patrizi A.. Correlation between BMI and PASI in patients affected by moderate to severe psoriasis undergoing biological therapy. Dermatol. Ther. (2010) 23 S12-S19. DOI: 10.1111/j.1529-8019.2009.01281.x 25. Schaffler A., Scholmerich J.. Innate immunology and adipose tissue biology. Trens. Immunol. (2010) 31 228-235. DOI: 10.1016/j.it.2010.03.001 26. Greenberg A.S., Obin M.S.. Obesity and the role of adipose tissue in inflammation and metabolism. Am. J. Clin. Nutr. (2006) 83 S461-S465. DOI: 10.1093/ajcn/83.2.461S 27. Shipman A.R., Millington G.W.. Obesity and the skin. Br. J. Dermatol. (2011) 165 743-750. DOI: 10.1111/j.1365-2133.2011.10393.x 28. Fenton J.I., Nunez N.P., Yakar S., Perkins S.N., Hord N.G., Hursting S.L.. Diet-induced adiposity alters the serum profile of inflammation in C57BL/6N mice as measured by antibody array. Diabetes Obes. Metab. (2009) 11 343-354. DOI: 10.1111/j.1463-1326.2008.00974.x 29. Bastard J.P., Jardel C., Bruckert E., Blondy P., Capeau J., Laville M.. Elevated levels of interleukin 6 are reduced in serum and subcutnaeous adipose tissue of obese women after weight loss. J. Clin. Endocrinol. Metab. (2000) 85 3338-3342. PMID: 10999830 30. Makki K., Froguel P., Wolowczuk I.. Adipose tissue in obesity-related inflammation and insulin resistance: Cells, cytokines, and chemokines. ISRN Inflamm. (2013) 22 139-239. DOI: 10.1155/2013/139239 31. Weisberg S.P., McCann D., Desai M., Rosenbaum M., Leibel R.L., Ferrante A.W.. Obesity is associated with macrophage accumulation in adipose tissue. J. Clin. Investig. (2003) 112 1796-1808. DOI: 10.1172/JCI200319246 32. Ramkhelawon B., Hennessy E.J., Menager M., Ray T.D., Sheedy F.J., Hutchison S., Wanschel A., Oldebeken S., Geoffrion M., Spiro W.. Netrin-1 promotes adipose tissue macrophage retention and insulin resistance in obesity. Nat. Med. (2014) 20 377-384. DOI: 10.1038/nm.3467 33. Gaspari A.A.. Innate and adaptive immunity and the pathyphysiology of psoriasis. J. Am. Acad. Dermatol. (2006) 54 S67-S80. DOI: 10.1016/j.jaad.2005.10.057 34. Saggini A., Chimenti S., Chiricozzi A.. IL-6 as a druggable target in psoriasis: Focus on pustular variants. J. Immunol. Res. (2014) 2014 964069. DOI: 10.1155/2014/964069 35. Neuner P., Urbanski A., Trautinger F., Moller A., Kirnbauer R., Kapp A., Schöpf E., Schwarz T., Luger T.A.. Increased IL-6 production by monocytes and keratinocytes in patients with psoriasis. J. Investig. Dermatol. (1991) 97 27-33. DOI: 10.1111/1523-1747.ep12477880 36. Hunter C.A., Jones S.A.. IL-6 as a keystone cytokine in health and disease. Nat. Immunol. (2015) 16 448-457. DOI: 10.1038/ni.3153 37. Karlsson E.A., Sheridan P.A., Beck M.A.. Diet-induced obesity impairs the T cell memory response to influenze virus infection. J. Immunol. (2010) 184 3127-3133. DOI: 10.4049/jimmunol.0903220 38. James B.R., Tomanek-Chalkley A., Askeland E.J., Kucaba T., Griffith T.S., Norian L.A.. Diet-induced obesity alters dendritic cell function in the presence and absence of tumor growth. J. Immunol. (2012) 189 1311-1321. DOI: 10.4049/jimmunol.1100587
--- title: Associations between Body Mass Index and Probable Sarcopenia in Community-Dwelling Older Adults authors: - Molly Curtis - Lauren Swan - Rebecca Fox - Austin Warters - Maria O’Sullivan journal: Nutrients year: 2023 pmcid: PMC10059806 doi: 10.3390/nu15061505 license: CC BY 4.0 --- # Associations between Body Mass Index and Probable Sarcopenia in Community-Dwelling Older Adults ## Abstract [1] Background/Objectives: The relationship between body mass index (BMI) and probable sarcopenia, a precursor to sarcopenia diagnosis, is unclear. While low BMI has been associated with sarcopenia risk, some evidence suggests that obesity may confer protection. We aimed to investigate the association between probable sarcopenia and BMI and, furthermore, to explore associations with waist circumference (WC). [ 2] Methods: This cross-sectional study included 5783 community-dwelling adults (mean age 70.4 ± 7.5 years) from Wave 6 of the English Longitudinal Study of Ageing (ELSA). Probable sarcopenia was defined using the European Working Group on Sarcopenia in Older People (EWGSOP2) criteria for low hand grip strength and/or slow chair rise. Associations between BMI and probable sarcopenia were examined using multivariable regression analysis and were similarly performed for WC. [ 3] Results: Our overall findings show that an underweight BMI was significantly associated with an increased likelihood of probable sarcopenia [OR (CI) 2.25 (1.17, 4.33), $$p \leq 0.015$$]. For higher BMI categories, the findings were conflicting. Overweight and obesity were associated with an increased likelihood of probable sarcopenia when defined by lower limb strength alone, [OR (CI), 2.32 (1.15, 4.70), $$p \leq 0.019$$; 1.23 (1.02, 1.49), $$p \leq 0.35$$, and 1.49 (1.21, 1.83), $p \leq 0.001$, respectively]. In contrast, overweight and obesity appeared protective when probable sarcopenia was assessed by low hand grip strength alone [OR (CI) 0.72 (0.60, 0.88), $$p \leq 0.001$$, and 0.64 (0.52, 0.79), $p \leq 0.001$, respectively]. WC was not significantly associated with probable sarcopenia on multivariable regression analysis. [ 4] Conclusion: This study supports the evidence that low BMI is associated with an increased likelihood of probable sarcopenia, highlighting an important at-risk group. The findings for overweight and obesity were inconsistent and may be measurement dependent. It seems prudent that all older adults at risk of probable sarcopenia, including those with overweight/obesity, are assessed to prevent underdetection of probable sarcopenia alone or with the double burden of obesity. ## 1. Introduction By 2050, it is estimated that approximately $20\%$ of the global population will be over the age of 60 [1]. Consequently, core elements of National and International health policies include enabling older people to remain healthy and independent for as long as possible [1]. An important aspect of enabling independence is maintaining skeletal muscle function [2]. Sarcopenia is a muscle disease characterized by an accelerated loss of muscle strength, mass, and function [2], and its incidence increases with age. The condition results in an increased risk of falls, functional decline, disability, and mortality along with a reduced quality of life [2,3,4,5,6]. The concept of ‘probable sarcopenia’, introduced by the European Working Group on Sarcopenia in Older People (EWGSOP2) in 2018, is defined by the presence of low muscle strength alone [2]. This can be assessed using simple measures of muscle strength, such as hand grip strength or chair rise tests [2], making the detection of sarcopenia in community-dwelling older populations more straightforward. Importantly, the identification of probable sarcopenia is sufficient evidence to initiate treatment, through physical activity and dietary approaches [2,3]. Thus, the concept of probable sarcopenia is pragmatic and applicable to large populations due to its ease of use as both a screening tool and a basis for intervention. The prevalence of probable sarcopenia among community-dwelling older adults is relatively common, with estimates ranging from $19\%$ to $34\%$ [7,8,9,10,11]. Recognized risk factors for probable and confirmed sarcopenia include older age, physical inactivity, and comorbidity [7,8,9,12,13,14]. Older adults with malnutrition or low BMI are at increased risk of sarcopenia [15,16]. However, for probable sarcopenia the evidence is less clear. In a cross-sectional analysis of the Irish Longitudinal Study on Ageing, underweight BMI was not identified as a risk factor for probable sarcopenia, although few participants were underweight [9]. Thus, while underweight BMI is consistently identified as a determinant of confirmed or diagnosed sarcopenia [12,17,18], the relationship between BMI and probable sarcopenia has not been fully elucidated. This is particularly the case for higher BMI categories, including overweight and obesity, where the reported research into probable sarcopenia is inconsistent [7,8,10]. Previously, we reported that overweight and obesity were associated with lower odds of probable sarcopenia, which was assessed by hand grip strength only [9]. Similarly, an analysis from the Brazilian Longitudinal Study of Ageing (ELSI-Brazil) showed that BMI was inversely associated with muscle weakness measured by hand grip strength [19]. In contrast, others report an increased risk of sarcopenia/probable sarcopenia in older adults with obesity [20]. A further consideration is that the loss of muscle mass in aging is frequently offset by increases in fat mass, meaning that BMI may therefore remain unchanged [21]. Assessment of waist circumference (WC) as a clinical indicator of central obesity may provide further insight into associations between BMI and probable sarcopenia. Recent research has highlighted that an increased WC was associated with stronger hand grip strength cross-sectionally, but over an eight-year follow-up was associated with an accelerated decline in hand grip strength [22]. Both probable sarcopenia and obesity are prevalent in older populations, and each is independently associated with adverse health outcomes [23]. This issue is complex and compounded by evidence that obesity may be protective against sarcopenia and probable sarcopenia. A better understanding of these areas is important to ensure appropriate prevention, detection, and treatment for probable sarcopenia in older populations and with co-existing obesity. In the present study, we aimed to investigate the association between probable sarcopenia and BMI in a large sample of community-dwelling older adults from the English Longitudinal Study of Ageing and to examine whether the mode of assessment of muscle strength influenced the findings. In addition, we explored if WC would provide further insight into BMI and probable sarcopenia findings. We hypothesized that probable sarcopenia would be significantly associated with BMI categories and with WC. ## 2. Materials and Methods This study is a cross-sectional analysis of Wave 6 of the English Longitudinal Study of Ageing (ELSA), an ongoing study of community-dwelling adults aged ≥50 years in England. Full details are reported elsewhere [24]. In brief, Wave 6 took place in 2012–2013 and included 10,601 participants [24]. Data were collected through interviews and self-completion questionnaires, with 8054 completing a health assessment of physical function [25]. ELSA was conducted in line with the Declaration of Helsinki and was approved by the London Multicenter Research Ethics Committee. Written ethical consent was obtained for all waves and components of ELSA, according to the ethical approval system in operation at the time [24]. The inclusion criteria for the present study were as follows: adults > 60 years, who participated in the health assessment, with recorded data for hand grip strength, chair rise test, and BMI (Figure 1). ## 2.1. Determining Probable Sarcopenia In accordance with the EWGSOP2 criteria, probable sarcopenia was defined as weak hand grip strength (males: <27 kg; females: <16 kg), and/or time to complete five chair rises of >15 s [2]. Hand grip strength was measured using a Smedley dynamometer [25]. Three measures were taken per hand, and the maximum score for the dominant hand was used in the analysis [7]. For the chair rise test, participants were asked to stand up and down from a firm chair, as quickly as possible, without using their arms [25]. Time taken to complete five rises was recorded. Participants deemed unable to complete the test without using their arms, or who did not attempt the test because they felt unsafe, were assumed to have probable sarcopenia [7]. ## 2.2. BMI and Waist Circumference Weight was measured without shoes and in light clothing, using Tanita™ electronic scales [24]. Height was measured using a stadiometer with the head in the Frankfurt plane [25]. BMI was categorized according to the World Health Organisation (WHO) definition [26], as underweight (<18.5 kg/m2), healthy (18.5–25 kg/m2), overweight (25–30 kg/m2), and obese (≥30 kg/m2). BMI was further analysed using a cut-off of <20 kg/m2 for underweight [27,28]. WC was measured using a flexible metric tape at the midpoint between the iliac crest and the last rib [24]. The mean of two valid measurements was included in the analysis. WC was grouped into metabolic risk categories as low-risk (males: <94 cm; females: <80 cm), medium-risk (males: 94–102 cm; females: 80–88 cm), and high-risk (males: ≥102 cm; females: 88 cm) [25,29,30]. ## 2.3. Covariates Demographic characteristics included sex (male; female), ethnicity (white; non-white), and age. Participants ≥ 90 years old were coded as 90 to avoid disclosure. Educational attainment was used as a marker of socioeconomic position [31]. Potential risk factors for probable sarcopenia and obesity were selected based on current evidence. In line with previous studies, comorbidities were defined using the Functional Comorbidity Index (FCI), which was modified by adding the presence of self-reported physician-diagnosed conditions to generate a score (0–8) [9]. For the purpose of the analysis, the number of conditions was then categorized as 0, 1, or 2 or more. Osteoarthritis was analysed separately due to its associations with probable sarcopenia [7]. Diabetes and cardiovascular disease (CVD) were also analysed separately, given their associations with obesity [32]. CVD was classified as any self-reported physician-diagnosed heart condition including angina, heart attack, congestive heart failure, heart murmur, abnormal heart rhythm, stroke, or other heart disease [33,34]. The number of falls in the last two years and difficulty with one or more activities of daily living (ADLs) or instrumental activities of daily living (IADLs) were self-reported [6,8]. Physical activity level was based on self-reported participation in mild, moderate, or vigorous activities at least once a week [35]. Smoking status and weekly frequency of alcohol intake were self-reported [36]. Information on alcohol history was unavailable. ## 3. Statistical Analysis Participant characteristics were described using means and standard deviations or counts and percentages. Categorical variable characteristics of the probable sarcopenic and reference groups were compared using chi-square tests, and continuous variables were compared using independent t-tests. In multivariable models, we adjusted for covariates using backwards stepwise logistic regression. Adjusted odds ratios (OR) and $95\%$ confidence intervals (CI) were derived. Analyses were performed using IBM SPSS Statistics, version 28.0. ## 4.1. Characteristics of the Study Population Overall and by Probable Sarcopenia The characteristics of the study population overall and according to probable sarcopenia are outlined in Table 1. Participants ($$n = 5783$$) were a mean age of 70.4 ± 7.5 years and $54.6\%$ were female. Overweight and obesity ($73.4\%$) along with increased WC ($79.5\%$) were prevalent. Overall, $31.8\%$ of the study population met the criteria for probable sarcopenia. Participants with probable sarcopenia had a significantly higher frequency of underweight BMI based on both the <18.5 and <20 kg/m2 cut-off criteria at $1.6\%$ and $3.9\%$, respectively, compared with the reference group. However, it is important to note that <$4\%$ of participants were underweight in this population. In the probable sarcopenia group, there was a significantly higher proportion of obesity ($34.1\%$ vs. $29.2\%$) but fewer overweight ($40.0\%$ vs. $43.8\%$). In addition, a greater proportion met the criteria for high-risk WC in the probable sarcopenia group compared with the reference group. With respect to other health and lifestyle characteristics, participants with probable sarcopenia were significantly older, more physically inactive, and experienced more chronic conditions, previous falls, and difficulty with ADLs/IADLs than the reference group (Table 1). ## 4.2. Associations between Probable Sarcopenia and BMI Based on Regression Analysis Probable sarcopenia was defined by the EWGSOP2 criteria in all regression models. In Model 1, muscle strength was assessed by hand grip strength and/or chair rise test, in Model 2 by hand grip strength alone, and in Model 3 by the chair rise test alone (Table 2). In a multivariable analysis, older adults with underweight BMI had a 2.25-fold increased likelihood of probable sarcopenia [OR 2.25, CI 1.17, 4.33, $$p \leq 0.015$$] compared with those within a healthy BMI range (model 1), with similar findings reported for Model 3. When captured by hand grip strength alone (model 2), associations between probable sarcopenia and underweight BMI were not noted. Both overweight and obese BMI was associated with significantly greater odds of probable sarcopenia detected by slow chair rise (model 3) [OR, CI, 1.23 (1.02, 1.49), $$p \leq 0.35$$, and 1.49 (1.21–1.83), $p \leq 0.001$, respectively]. Conversely, overweight and obesity were associated with reduced odds of probable sarcopenia (Model 2), as detected by hand grip strength alone [OR, CI 0.72 (0.60, 0.88), $$p \leq 0.001$$, and 0.64 (0.52, 0.79), $p \leq 0.001$, respectively]. Elevated BMI was not significantly associated with probable sarcopenia in Model 1. Underweight, overweight, and obese BMI were consistently associated with an increased likelihood of probable sarcopenia in Model 3 (chair rise test), with divergent findings for Model 2 (hand grip strength). In addition, all three regression models provided further evidence of a higher likelihood of probable sarcopenia associated with older age, low physical activity, lower educational attainment, chronic conditions, osteoarthritis, recurrent falls, and difficulty with ADLs or IADLs. Finally, regression analysis for WC (Table 3) was not statistically significant overall in a model controlled for other covariates (model 1). Increased odds of probable sarcopenia was suggested for high-risk WC measurements in the lower limb assessment model, in contrast to reduced odds in the hand grip strength sarcopenia model and, importantly, the latter was not statistically significant. ## 5. Discussion Probable sarcopenia, defined by low muscle strength [2], is a practical measure to apply in population settings. Determinants of probable sarcopenia include older age, physical inactivity, and co-morbidity [7,8,9], but findings for BMI remain inconsistent particularly for overweight and obesity [9]. In the present study, we investigated associations between BMI and probable sarcopenia in a large population of community-dwelling older adults ($$n = 5783$$) with a mean age of 70.4 ± 7.5 years. An underweight BMI was significantly associated with increased odds of probable sarcopenia. BMI in the overweight or obese category was not significantly associated with probable sarcopenia in the overall model, however, the results differed according to the mode of assessment employed for low muscle strength in further regression models. The association between low BMI and increased likelihood of probable sarcopenia is consistent with evidence that an underweight BMI may be a marker of malnutrition in older adults [27,28,37]. The observations for underweight BMI were noted in the bivariate analysis, in the overall and lower limb strength regression models, but not in the hand grip strength model. Collectively, the finding suggests that older adults with underweight BMI would benefit from screening for probable sarcopenia in addition to malnutrition screening, with appropriate interventions if indicated. Indeed, low BMI, low skeletal muscle mass, or muscle strength (when mass cannot be readily assessed) are among the recommended phenotypic criteria for malnutrition by the Global Leadership Initiative on the Malnutrition (GLIM) working group [37]. Overweight and obese BMI was not significantly associated with probable sarcopenia when controlled for known risk factors in the overall model, with divergent findings based on the mode of sarcopenia assessment employed (Models 2 and 3). In this regard, overweight and obesity were significantly associated with an increased likelihood of probable sarcopenia as defined by lower limb strength alone. Conversely, higher BMI suggested protective effects when probable sarcopenia was defined by low hand grip strength alone. Previous studies have reported obesity as a risk factor for sarcopenia; recently, Crovetto Mattassi et al. [ 22] highlighted that participants with obesity had a 3.2 times greater risk of presenting with sarcopenia (probable and severe sarcopenia combined) compared with healthy nutritional status in a relatively small study sample. Much of the published evidence appears to favour inverse associations between probable sarcopenia risk and overweight/obesity, which is in agreement with the finding from our hand grip strength model. An analysis of the Irish Longitudinal Study on Ageing, which employed hand grip strength only, found that overweight and obesity were associated with lower odds of probable sarcopenia [9]. Consistent with this, in the Brazilian Longitudinal Study of Ageing, obesity was inversely associated with hand grip strength [19]. Others similarly observed that a larger overall body mass, indicated by higher BMI, was associated with stronger hand grip strength [38]. Recently, obesity accompanying probable sarcopenia defined by hand grip strength showed favourable trends for frailty, compared with probable sarcopenia alone [39]. Though other findings are more complex, for example, in the Korean Frailty and Ageing Cohort Study, high BMI was not associated with muscle strength (using hand grip strength cut-offs equating to probable sarcopenia) but appeared protective against low muscle mass [40]. Of note, the latter study had a modest sample size and included community and residential care participants. The trajectory of probable sarcopenia with obesity and their combined impact on health outcomes remains to be clarified. Indeed, the proposition that overweight or obesity might confer health benefits in older populations fits with the obesity paradox [41]; for example, a higher BMI may mitigate against unintentional weight loss or reflect fewer chronic conditions. The association between higher hand grip strength values and obesity may be explained by a greater muscle mass [40], which was not assessed in the present study. It is likely that overweight and obesity may not necessarily protect against probable sarcopenia, but rather, the finding is in part related to the mode of detection when applying hand grip strength only in assessment. The identification of probable sarcopenia by hand grip strength alone may underdetect probable sarcopenia in older adults with overweight and obesity [42]. Positive cross-sectional associations between greater muscle strength and obesity may reverse over periods of time, as noted for longitudinal WC findings [22]. BMI may also remain stable on follow-up, while muscle-related parameters decline [43]. To expand the findings beyond BMI, we included waist circumference as a measure of central obesity. On analysis, this parameter was not significantly associated with probable sarcopenia in the overall model and did not substantially add to the BMI results. Keevil et al. reported that a high WC was associated with lower grip strength [38]. Other authors observed that abdominal obesity, defined by higher WC, was associated with stronger hand grip strength at baseline in older adults, but this effect was not maintained over time and was associated with accelerated muscle strength decline in men over 8 years of follow-up [22]. Further research into probable sarcopenia and WC is warranted. The present study supports previously identified risk factors for probable sarcopenia, namely older age, low physical activity, socioeconomic disadvantage, chronic conditions, recurrent falls, and difficulty with ADLs or IADLs [6,7,8,9,11]. Although there is a lack of consistency around BMI as a determinant of probable sarcopenia, there is a growing body of evidence around established risk factors for probable sarcopenia. Both overweight and probable sarcopenia are public health issues prevalent across older populations. Based on the current evidence, it cannot necessarily be assumed that older people with overweight or obesity are protected from sarcopenia. Probable sarcopenia may coexist with low BMI and increase the risk of poor outcomes. Equally, probable sarcopenia and obesity may coexist, and each increases the risk of poor health outcomes [44]. Obesity is characterized by low-grade inflammation and insulin resistance which negatively impact skeletal muscle mass. A systematic review and meta-analysis [45] demonstrated that older adults with sarcopenic obesity were at increased risk of adverse musculoskeletal outcomes compared with individuals with obesity, sarcopenia, or neither condition. Treating diagnosed sarcopenia with obesity is more complex and may be associated with poorer outcomes [45], which needs further exploration in probable sarcopenia. There is ongoing work to examine the complex condition of sarcopenic obesity, its definitions, clinical relevance, and the most effective prevention and treatment strategies [23]. The present study has a number of strengths. It is based on an analysis of a large robust nationally representative sample of community-dwelling older adults in England [24], with objective health measures, including markers of obesity and muscle strength. Limitations are acknowledged, such as the cross-sectional nature of the study meaning that we cannot infer causality nor predict the impact of BMI on probable or diagnosed sarcopenia trajectories over time. BMI as a measure has inherent limitations and does not reflect weight history, unintentional weight loss, body composition, or muscle parameters. Moreover, relatively few participants were classified with underweight BMI in this ELSA population. Non-participation in health assessments, such as BMI and muscle strength measures, may under-represent those with poorer health or reduced mobility and impact the generalisability Sof the findings. Notably, the population was not ethnically diverse as previously reported [11]. Further research is needed into the detection of probable sarcopenia, its longer-term trajectory, and optimal management in community-dwelling older adults with overweight and obesity. ## 6. Conclusions In conclusion, underweight BMI was significantly associated with an increased likelihood of probable sarcopenia in a large cohort of community-dwelling older adults, representing an important at-risk group for screening and intervention. The findings for overweight and obese BMI were conflicting and appeared, at least in part, to be measurement dependent. Overweight and obese BMI was consistently associated with an increased likelihood of probable sarcopenia, determined by lower limb strength, but protective when determined by hand grip strength. The lack of consistency suggests that high BMI values alone may be a poor determinant of probable sarcopenia. It seems sensible that all older adults at risk of probable sarcopenia should be assessed, regardless of BMI, including those with overweight or obesity, to enable early detection and treatment. Moreover, there is a need to prevent the under-detection of probable sarcopenia alone or with the double burden of obesity. Further longitudinal research is needed to understand probable sarcopenic obesity, its trajectory, and impact on health outcomes [23]. ## References 1. 1. World Health Organization Decade of Healthy Ageing: Baseline ReportWHOGeneva, Switzerland2020. Decade of Healthy Ageing: Baseline Report (2020) 2. Cruz-Jentoft A.J., Bahat G., Bauer J., Boirie Y., Bruyère O., Cederholm T., Cooper C., Landi F., Rolland Y., Sayer A.A.. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing (2019) 48 16-31. DOI: 10.1093/ageing/afy169 3. Cruz-Jentoft A.J., Sayer A.A.. Sarcopenia. Lancet (2019) 393 2636-2646. DOI: 10.1016/S0140-6736(19)31138-9 4. Van Leeuwen K.M., Van Loon M.S., Van Nes F.A., Bosmans J.E., de Vet H.C., Ket J.C., Widdershoven G.A., Ostelo R.W.. What does quality of life mean to older adults? A thematic synthesis. PLoS ONE (2019) 14. DOI: 10.1371/journal.pone.0213263 5. Kojima G., Iliffe S., Jivraj S., Walters K.. Association between frailty and quality of life among community-dwelling older people: A systematic review and meta-analysis. J. Epidemiol. Community Health (2016) 70 716-721. DOI: 10.1136/jech-2015-206717 6. Marincolo J.C.S., Aprahamian I., Corona L.P., Neri A.L., Yassuda M.S., Borim F.S.A.. Three definitions of probable sarcopenia and associations with falls and functional disability among community-dwelling older adults. Osteoporos. Sarcopenia (2021) 7 69-74. DOI: 10.1016/j.afos.2021.05.001 7. Dodds R., Murray J., Robinson S., Sayer A.. The identification of probable sarcopenia in early old age based on the SARC-F tool and clinical suspicion: Findings from the 1946 British birth cohort. Eur. Geriatr. Med. (2020) 11 433. DOI: 10.1007/s41999-020-00310-5 8. Wearing J., Konings P., de Bie R.A., Stokes M., de Bruin E.D.. Prevalence of probable sarcopenia in community-dwelling older Swiss people—A cross-sectional study. BMC Geriatr. (2020) 20 307. DOI: 10.1186/s12877-020-01718-1 9. Swan L., Warters A., O’Sullivan M.. Socioeconomic Inequality and Risk of Sarcopenia in Community-Dwelling Older Adults. Clin. Interv. Aging (2021) 16 1119-1129. DOI: 10.2147/CIA.S310774 10. Kim M., Won C.W.. Prevalence of sarcopenia in community-dwelling older adults using the definition of the European Working Group on Sarcopenia in Older People 2: Findings from the Korean Frailty and Aging Cohort Study. Age Ageing (2019) 48 910-916. DOI: 10.1093/ageing/afz091 11. Swan L., Warters A., O’Sullivan M.. Socioeconomic Disadvantage is Associated with Probable Sarcopenia in Community-Dwelling Older Adults: Findings from the English Longitudinal Study of Ageing. J. Frailty Aging (2022) 11 398-406. DOI: 10.14283/jfa.2022.32 12. Nasimi N., Dabbaghmanesh M.H., Sohrabi Z.. Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults. Exp. Gerontol. (2019) 122 67-73. DOI: 10.1016/j.exger.2019.04.009 13. Beaudart C., Sanchez-Rodriguez D., Locquet M., Reginster J.Y., Lengelé L., Bruyère O.. Malnutrition as a Strong Predictor of the Onset of Sarcopenia. Nutrients (2019) 11. DOI: 10.3390/nu11122883 14. Volkert D., Beck A.M., Cederholm T., Cruz-Jentoft A., Goisser S., Hooper L., Kiesswetter E., Maggio M., Raynaud-Simon A., Sieber C.C.. ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin. Nutr. (2019) 38 10-47. DOI: 10.1016/j.clnu.2018.05.024 15. Schneider S.M., Correia M.. Epidemiology of weight loss, malnutrition and sarcopenia: A transatlantic view. Nutrition (2020) 69 110581. DOI: 10.1016/j.nut.2019.110581 16. Boirie Y., Morio B., Caumon E., Cano N.J.. Nutrition and protein energy homeostasis in elderly. Mech. Ageing Dev. (2014) 136 76-84. DOI: 10.1016/j.mad.2014.01.008 17. Tramontano A., Veronese N., Sergi G., Manzato E., Rodriguez-Hurtado D., Maggi S., Trevisan C., de Zaiacomo F., Giantin V.. Prevalence of sarcopenia and associated factors in the healthy older adults of the Peruvian Andes. Arch. Gerontol. Geriatr. (2017) 68 49-54. DOI: 10.1016/j.archger.2016.09.002 18. Dodds R.M., Granic A., Davies K., Kirkwood T.B., Jagger C., Sayer A.A.. Prevalence and incidence of sarcopenia in the very old: Findings from the Newcastle 85+ Study. J. Cachexia Sarcopenia Muscle (2017) 8 229-237. DOI: 10.1002/jcsm.12157 19. de Souza Moreira B., de Souza Andrade A.C., Lustosa Torres J., de Souza Braga L., de Carvalho Bastone A., de Melo Mambrini J.V., Lima-Costa M.F.. Nationwide handgrip strength values and factors associated with muscle weakness in older adults: Findings from the Brazilian Longitudinal Study of Aging (ELSI-Brazil). BMC Geriatr. (2022) 22. DOI: 10.1186/s12877-022-03721-0 20. Crovetto Mattassi M., Henríquez Mella C., Pérez Bocaz L.. Association between Sarcopenia and Nutritional Status in Chilean Older People Aged 65 Years and Older. Nutrients (2022) 14. DOI: 10.3390/nu14245228 21. Ponti F., Santoro A., Mercatelli D., Gasperini C., Conte M., Martucci M., Sangiorgi L., Franceschi C., Bazzocchi A.. Aging and Imaging Assessment of Body Composition: From Fat to Facts. Front. Endocrinol. (2019) 10 861. DOI: 10.3389/fendo.2019.00861 22. de Carvalho D.H.T., Scholes S., Santos J.L.F., de Oliveira C., Alexandre T.D.S.. Does Abdominal Obesity Accelerate Muscle Strength Decline in Older Adults? Evidence from the English Longitudinal Study of Ageing. J. Gerontol. Ser. A Biol. Sci. Med. Sci. (2019) 74 1105-1111. DOI: 10.1093/gerona/gly178 23. Donini L.M., Busetto L., Bauer J.M., Bischoff S., Boirie Y., Cederholm T., Cruz-Jentoft A.J., Dicker D., Frühbeck G., Giustina A.. Critical appraisal of definitions and diagnostic criteria for sarcopenic obesity based on a systematic review. Clin. Nutr. (2020) 39 2368-2388. DOI: 10.1016/j.clnu.2019.11.024 24. Steptoe A., Breeze E., Banks J., Nazroo J.. Cohort profile: The English longitudinal study of ageing. Int. J. Epidemiol. (2013) 42 1640-1648. DOI: 10.1093/ije/dys168 25. Batty D., Blake M., Bridges S., Crawford R., Demakakos P., de Oliveira C., Hussey D., Jackowska M., Jackson S., Marmot M.. The Dynamics of Ageing: Evidence from the English Longitudinal Study of Ageing 2002–2012 (Wave 6) (2014) 26. 26. World Health Organisation Obesity: Preventing and Managing the Global EpidemicWHOGeneva, Switzerland1998. Obesity: Preventing and Managing the Global Epidemic (1998) 27. Cederholm T., Bosaeus I., Barazzoni R., Bauer J., Van Gossum A., Klek S., Muscaritoli M., Nyulasi I., Ockenga J., Schneider S.M.. Diagnostic criteria for malnutrition–an ESPEN consensus statement. Clin. Nutr. (2015) 34 335-340. DOI: 10.1016/j.clnu.2015.03.001 28. Bardon L.A., Streicher M., Corish C.A., Clarke M., Power L.C., Kenny R.A., O’Connor D.M., Laird E., O’Connor E.M., Visser M.. Predictors of Incident Malnutrition in Older Irish Adults from the Irish Longitudinal Study on Ageing Cohort-A MaNuEL study. J. Gerontol. Ser. A Biol. Sci. Med. Sci. (2020) 75 249-256. DOI: 10.1093/gerona/gly225 29. Ashwell M., Gibson S.. Waist-to-height ratio as an indicator of ‘early health risk’: Simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference. BMJ Open (2016) 6 e010159. DOI: 10.1136/bmjopen-2015-010159 30. 30. World Health Organization Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert ConsultationWHOGeneva, Switzerland2008. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation (2008) 31. Grundy E., Holt G.. The socioeconomic status of older adults: How should we measure it in studies of health inequalities?. J. Epidemiol. Community Health (2001) 55 895-904. DOI: 10.1136/jech.55.12.895 32. Agha M., Agha R.. The rising prevalence of obesity: Part A: Impact on public health. Int. J. Surg. Oncol. (2017) 2 e17. DOI: 10.1097/IJ9.0000000000000017 33. Valtorta N.K., Kanaan M., Gilbody S., Hanratty B.. Loneliness, social isolation and risk of cardiovascular disease in the English Longitudinal Study of Ageing. Eur. J. Prev. Cardiol. (2018) 25 1387-1396. DOI: 10.1177/2047487318792696 34. Zhu D., Chung H.F., Pandeya N., Dobson A.J., Hardy R., Kuh D., Brunner E.J., Bruinsma F., Giles G.G., Demakakos P.. Premenopausal cardiovascular disease and age at natural menopause: A pooled analysis of over 170,000 women. Eur. J. Epidemiol. (2019) 34 235-246. DOI: 10.1007/s10654-019-00490-w 35. Smith L., Gardner B., Fisher A., Hamer M.. Patterns and correlates of physical activity behaviour over 10 years in older adults: Prospective analyses from the English Longitudinal Study of Ageing. BMJ Open (2015) 5 e007423. DOI: 10.1136/bmjopen-2014-007423 36. Aspell N., Laird E., Healy M., Lawlor B., O’Sullivan M.. Vitamin D Deficiency Is Associated with Impaired Muscle Strength and Physical Performance in Community-Dwelling Older Adults: Findings from The English Longitudinal Study of Ageing. Clin. Interv. Aging (2019) 14 1751-1761. DOI: 10.2147/CIA.S222143 37. Cederholm T., Jensen G.L., Correia M.I.T.D., Gonzalez M.C., Fukushima R., Higashiguchi T., Baptista G., Barazzoni R., Blaauw R., Coats A.J.S.. GLIM criteria for the diagnosis of malnutrition–A consensus report from the global clinical nutrition community. J. Cachexia Sarcopenia Muscle (2019) 10 207-217. DOI: 10.1002/jcsm.12383 38. Keevil V.L., Luben R., Dalzell N., Hayat S., Sayer A.A., Wareham N.J., Khaw K.T.. Cross-sectional associations between different measures of obesity and muscle strength in men and women in a British cohort study. J. Nutr. Health Aging (2015) 19 3-11. DOI: 10.1007/s12603-014-0492-6 39. Ozkok S., Aydin C.O., Sacar D.E., Catikkas N.M., Erdogan T., Bozkurt M.E., Kilic C., Karan M.A., Bahat G.. Sarcopenic obesity versus sarcopenia alone with the use of probable sarcopenia definition for sarcopenia: Associations with frailty and physical performance. Clin. Nutr. (2022) 41 2509-2516. DOI: 10.1016/j.clnu.2022.09.005 40. Yoo M.C., Won C.W., Soh Y.. Association of high body mass index, waist circumference, and body fat percentage with sarcopenia in older women. BMC Geriatr. (2022) 22. DOI: 10.1186/s12877-022-03643-x 41. Wang S., Ren J.. Obesity Paradox in Aging: From Prevalence to Pathophysiology. Prog. Cardiovasc. Dis. (2018) 61 182-189. DOI: 10.1016/j.pcad.2018.07.011 42. Johansson J., Strand B.H., Morseth B., Hopstock L.A., Grimsgaard S.. Differences in sarcopenia prevalence between upper-body and lower-body based EWGSOP2 muscle strength criteria: The Tromsø study 2015–2016. BMC Geriatr. (2020) 20. DOI: 10.1186/s12877-020-01860-w 43. Guo J., Shang Y., Fratiglioni L., Johnell K., Welmer A.K., Marseglia A., Xu W.. Individual changes in anthropometric measures after age 60 years: A 15-year longitudinal population-based study. Age Ageing (2021) 50 1666-1674. DOI: 10.1093/ageing/afab045 44. Schoufour J.D., Tieland M., Barazzoni R., Ben Allouch S., Bie J.V.D., Boirie Y., Cruz-Jentoft A.J., Eglseer D., Topinková E., Visser B.. The Relevance of Diet, Physical Activity, Exercise, and Persuasive Technology in the Prevention and Treatment of Sarcopenic Obesity in Older Adults. Front. Nutr. (2021) 8 661449. DOI: 10.3389/fnut.2021.661449 45. Gandham A., Mesinovic J., Jansons P., Zengin A., Bonham M.P., Ebeling P.R., Scott D.. Falls, fractures, and areal bone mineral density in older adults with sarcopenic obesity: A systematic review and meta-analysis. Obes. Rev. (2021) 22 e13187. DOI: 10.1111/obr.13187
--- title: Mendelian Randomization Analysis Provides Insights into the Pathogenesis of Serum Levels of Branched-Chain Amino Acids in Cardiovascular Disease authors: - Wenxi Jiang - Ke Lu - Zhenhuang Zhuang - Xue Wang - Xun Tang - Tao Huang - Pei Gao - Yuan Wang - Jie Du journal: Metabolites year: 2023 pmcid: PMC10059809 doi: 10.3390/metabo13030403 license: CC BY 4.0 --- # Mendelian Randomization Analysis Provides Insights into the Pathogenesis of Serum Levels of Branched-Chain Amino Acids in Cardiovascular Disease ## Abstract Several observational studies have indicated an association between high serum levels of branched-chain amino acids (BCAAs) and an increased risk of cardiovascular disease (CVD). To assess whether theses associations reflect causality, we carried out two-sample Mendelian randomization (MR). Single-nucleotide polymorphisms (SNPs) associated with BCAA were evaluated in 10 studies, including 24,925 participants. The association between SNPs and coronary artery disease (CAD) were assessed using summary estimates from the CARDIoGRAMplusC4D consortium. Further MR analysis of BCAAs and seven CVD outcomes was performed. The BCAA-raising gene functions were also analyzed. MR analyses revealed a risk-increasing causal relationship between serum BCAA concentrations and CAD (odds ratio 1.08; $95\%$ confidence interval (CI) 1.02–1.14), which was partly mediated by blood pressure and type 2 diabetes. BCAA also demonstrated a causal relationship with ischemic CVD events induced by plaque rupture and thrombosis (false discovery rate <0.05). Two BCAA-raising genes (MRL33 and CBLN1) were preferentially associated with myocardial infarction risk in the presence of atherosclerosis ($p \leq 0.003$). Functional analysis of the BCAA-raising genes suggested the causal involvement of two pathophysiological pathways, including glucose metabolism (PPM1K and TRMT61A) related to plaque progression, and the newly discovered neuroendocrine disorders regulating blood pressure (MRPL33, CBLN1, and C2orf16) related to plaque rupture and thrombosis. This comprehensive MR analysis provided insights into the potential causal mechanisms linking BCAA with CVD risk and suggested targeting neuroendocrine disorders as a potential strategy for the prevention of CVD. These results warrant further studies to elucidate the mechanisms underlying these reported causal associations. ## 1. Introduction Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide [1]. Data from cohort and case–control studies have suggested that raised circulating concentrations of branched-chain amino acids (BCAAs) are associated with an increased risk of coronary artery disease (CAD) [2,3,4,5]. As important energy metabolism substrates, BCAAs, including leucine, isoleucine, and valine, are important regulators of systemic metabolism, energy expenditure, and muscle protein synthesis. Abnormal accumulation of BCAAs increases the risk of CAD and subsequent serious cardiovascular events, such as myocardial infraction (MI), sudden death, or stroke, due to the rupture of atherosclerotic plaques and thrombus formation [5,6]. BCAA catabolism has been shown to damage cardiovascular endothelial cells in mice and promotes the development of CVD [7]. However, data from experimental models cannot explain the causal relationship between circulating BCAAs and CVD in a heterogeneous population in a complex environment. Well-powered, genome-wide association studies (GWAS) have identified hundreds of single-nucleotide polymorphisms (SNPs) associated with CVD-related traits or circulating BCAAs [8,9]. This creates the opportunity to test potentially causal genetic relationships between these SNPs and other clinically relevant cardiovascular traits using a Mendelian randomization (MR) approach. Multiple serum BCAA-associated genetic variants can thus be used as instrumental variables to assess the relationship between serum BCAA concentrations and CVD risk. Recent studies have focused on how BCAAs promote insulin resistance and diabetes, which in turn cause CVD [10]. However, the molecular mechanism underpinning this causal association remains unclear, and there is thus a need to explore and understand this mechanism to identify potential new interventions for patients with CVD. Based on the results of projects such as the Gene-Tissue Expression Project (GTEx) [11], the Human Protein Atlas (HPA) [12], and Phenome Wide Association Studies (PWAS), we hypothesized that this would provide an opportunity to explore the pathophysiological mechanisms by which BCAA affects CVD. In the present study, we carried out an MR analysis using data generated from various GWAS datasets to detect potentially causal links between BCAAs and CAD. We further aimed to identify intermediate phenotypes that may mediate any causal effects of BCAAs on CAD. We also investigated the potential causal associations between BCAAs and a broad range of CAD-related clinical events, including ischemic events (MI, heart attack, stroke, deep venous thrombosis, or pulmonary embolism) and bleeding events (intracerebral and subarachnoid hemorrhages). We then evaluated the relationship between BCAA-raising SNPs and the risk of MI (GWAS data with MI vs. CAD only) due to plaque rupture and thrombosis. In the present study, we sought to address the hypothesis that genetically determined BCAAs may lead to CAD and subsequent ischemic events through multiple pathophysiological pathways to vulnerable plaques and thrombus formation. Therefore, we seek to BCAA-raising locus to putative effector genes through integrated analysis of expression data from disease-relevant tissues in order to broaden our understanding of the pathophysiological basis of the impact of BCAAs on the biological mechanisms responsible for plaque initiation, progression, rupture, and thrombosis in patients with CVD. ## 2.1. Study Overview We derived causal estimates for the association of serum BCAAs with the risk of CAD using six different MR approaches based on multiple serum BCAA-associated genetic variants, identified intermediate phenotypes that may mediate causal effects, and compared the association with the results of a meta-analysis of observational studies. We further evaluated the causal link between BCAAs and CAD-related clinical events and the association between BCAA-raising SNPs and the risk of MI caused by plaque rupture/thrombosis (Figure 1). Finally, we carried out a functional analysis of effector genes using tissue-specific transcriptome, proteome, and phenotype-related GWAS datasets. We used summary estimates for the effects of genotype on exposures and outcomes from the largest available meta-analyses of previous GWAS studies, focusing on datasets including participants predominantly of European descent, to ensure consistent allele frequencies across the datasets and avoid possible modifications of genetic effects by ancestral origin. ## 2.2. GWAS Summary Level Data on BCAA and CAD The current analysis involved the use of publicly available, de-identified data (Table 1). All the original studies included appropriate ethical review and informed consent. The serum BCAA-associated SNP set was derived from a previous GWAS [8]. The primary outcome data (CAD) for the analysis were retrieved from the CARDIoGRAMplusC4D Consortium database. The analytic sample included up to 60,801 cases of CAD (approximately $70\%$ with MI) and 123,504 non-cases from 48 cohort and case–control studies. Most participants were of European ($77\%$), South Asian ($13\%$), or East Asian ($6\%$) ancestry [9]. Table 1 provides further details on the sources of the GWAS summary statistics. ## 2.3. GWAS Summary Level Data on CAD Clinical Event and Mediation CAD clinical outcome factors consisted of seven diseases that have been published in relevant GWAS summary data. Stroke was obtained from the MEGASTROKE consortium [13]. Intracranial haemorrhage and cardiac arrest were obtained from the FinnGen consortium (https://finngen.gitbook.io/documentation/, accessed on 26 May 2022). Atrial fibrillation was obtained from the GWAS Catalog (GCST006414) [14]. Heart attack/myocardial infarction, deep venous thrombosis, and pulmonary embolism were obtained from the UK Biobank consortium (http://www.nealelab.is/uk-biobank, accessed on 26 May 2022) (Table 1). Potential mediator GWAS summary statistics data were collected from the following resources: the Giant Investigation of Anthropometric Traits (GIANT) consortium [15] (Body mass index, waist-to-hip ratio), the Meta-Analyses of Glucose and Insulin-related traits Consortium [16] (MAGIC Fasting glucose Fasting insulin HbA1C 2h-glucose HOMA-IR), the UK Biobank consortium (Blood pressure), the Tobacco and Genetics Consortium (TAGC, smoking) [17], and the Global Lipids Genetics Consortium (GLGC LDL, HDL, triglycerides, Total cholesterol) [18]. Table 1 \| Exposure/Outcome \| Consortium \| Participants \| Web Source If Publicly Available \| \| --- \| --- \| --- \| --- \| \| Mendelian randomization analysis (BCAA to CAD) \| Mendelian randomization analysis (BCAA to CAD) \| Mendelian randomization analysis (BCAA to CAD) \| Mendelian randomization analysis (BCAA to CAD) \| \| Serum BCAA \| MAGNETIC NMR-GWAS [8] \| 24,925 individuals of European ancestry \| http://www.computationalmedicine.fi/, accessed on 27 August 2021 \| \| Coronary artery disease \| CARDIoGRAMplusC4Dconsortium’s 1000 Genomes-based GWAS [9] \| 184,305 individuals (60,801 CAD cases and 123,504 non-cases) of mainly European (77%) and Asian (19%) ancestry \| www.cardiogramplusc4d.org/, accessed on 26 May 2022 \| \| Mediation analysis \| Mediation analysis \| Mediation analysis \| Mediation analysis \| \| Lipids \| GLGC [18] \| 188,577 individuals of European ancestry \| csg.sph.umich.edu/abecasis/public/lipids2013/, accessed on 26 May 2022 \| \| Body mass index \| GIANT [15] \| 339,224 individuals of mainly European (95%) ancestry \| portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium, accessed on 26 May 2022 \| \| Waist-to-hip ratio \| GIANT [15] \| 224,459 individuals of mainly European (94%) ancestry \| portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium \| \| Smoking \| TAGC [17] \| 74,053 individuals of European ancestry \| www.med.unc.edu/pgc/results-and-downloads, accessed on 26 May 2022 \| \| Blood pressure \| UK Biobank \| 317,756 individuals of European ancestry \| http://www.nealelab.is/uk-biobank, accessed on 26 May 2022 \| \| Glycaemic traits \| MAGIC [16] \| 46,186 non-diabetic individuals of European ancestry \| www.magicinvestigators.org/, accessed on 26 May 2022 \| \| Mendelian randomization analysis (BCAA to CAD clinical event) \| Mendelian randomization analysis (BCAA to CAD clinical event) \| Mendelian randomization analysis (BCAA to CAD clinical event) \| Mendelian randomization analysis (BCAA to CAD clinical event) \| \| Stroke \| MEGASTROKE [13] \| 446,696 individuals of European ancestry \| http://www.megastroke.org/acknowledgments.html, accessed on 26 May 2022 \| \| Intracranial haemorrhage \| FinnGen \| 202,568 individuals of European ancestry \| https://finngen.gitbook.io/documentation/, accessed on 26 May 2022 \| \| Atrial fibrillation \| GCST006414 [14] \| 65,446 individuals of mainly European (91%) ancestry \| http://www.broadcvdi.org/, accessed on 26 May 2022 \| \| Cardiac arrest \| FinnGen \| 73,969 individuals of European ancestry \| https://finngen.gitbook.io/documentation/, accessed on 26 May 2022 \| \| heart attack/ myocardial infarction \| UK Biobank \| 317,756 individuals of European ancestry \| http://www.nealelab.is/uk-biobank, accessed on 26 May 2022 \| \| deep venous thrombosis \| UK Biobank \| 317,756 individuals of European ancestry \| http://www.nealelab.is/uk-biobank, accessed on 26 May 2022 \| \| Pulmonary embolism \| UK Biobank \| 317,756 individuals of European ancestry \| http://www.nealelab.is/uk-biobank, accessed on 26 May 2022 \| ## 2.4. Development of a Genetic Instrument for Serum BCAA Concentrations A total of 196 SNPs associated with serum BCAA (isoleucine, leucine, or valine) levels were identified in a meta-analysis of 24,925 individuals of European origin (Supplementary Data S1) [8]. These SNPs were associated with serum BCAA levels at a genome-wide significance level ($p \leq 5.0$ × 10−8) in the meta-analysis of the discovery and replication cohorts. If an SNP was associated with three BCAAs, we chose the strongest correlation as the association between the SNP and BCAA. We used linkage disequilibrium score regression to estimate the genetic correlation among the investigated traits [19]. The variants were defined as being independent of each other based on imperfect linkage disequilibrium (r2 < 0.8) in the 1000 Genome project data. In addition, two different protocols were established for IV screening approaches (r2 < 0.001 or lead SNP), respectively. MR analyses were performed based on these two sets of IV protocols. ## 2.5. Mediation Analysis We analyzed available data for the association between the BCAA genetic instrument and risk factors to evaluate the potential mediating effects of risk factors in the association between BCAA levels and CAD risk. We used MR to obtain effect estimates of the exposure-outcome (i.e., BCAA levels and CAD risk), exposure-mediator (i.e., BCAA levels and risk factors), and mediator-outcome (i.e., risk factors and CAD risk) associations. The exposure–mediator and mediator–outcome associations could then be used to estimate the expected effect of BCAA levels on CAD risk, assuming that the association was mediated by the risk factors. This effect estimate was then contrasted with the observed exposure–outcome association to gain insights into the mediating effect of the putative mediator [20]. The calculation is described in detail in Text S1. ## 2.6. Meta-Analysis of Observational Studies of BCAA Levels and CAD Events We conducted a systematic review and meta-analysis of published observational studies examining the association between BCAA levels and incident CVD. The details of the search strategies and the eligibility and exclusion criteria are presented in the Supplementary Material (Texts S2 and S3, and Figure S1). The results of seven studies were included in the meta-analysis using random effect models. Heterogeneity was quantified using the I2 statistic. ## 2.7. Gene Set Enrichment Analysis We carried out a gene-based and gene set enrichment analysis of variant associations using MAGMA, implemented by FUMA. This analysis was performed using summary-level meta-analysis results. Gene- and pathway-based association tests were performed using study summary statistics in MAGMA v1.0680 in accordance with recommended procedures using reference files available at https://ctg.cncr.nl/software/magma (accessed on 24 February 2023) via the FUMA online server (http://fuma.ctglab.nl/, accessed on 24 February 2023). On top of the single gene level analyses, FUMA also provides information on association overrepresentation in sets of differentially expressed genes (DEG) to identify tissue specificity of prioritized genes. We conducted a gene-based association analysis to identify candidate genes associated with BCAA, followed by tissue enrichment analysis of BCAA-associated genes using gene expression data for 30 tissues from GTEx. The expression levels of genes and proteins in each tissue were obtained from https://tsomics.shinyapps.io/RNA_vs_protein/ (accessed on 24 February 2023) or http://genome-asia.ucsc.edu/ (accessed on 24 February 2023) [21]. ## 2.8. Association of BCAA-Raising Loci with Other Phenotypes We examined the association between BCAA-raising SNPs and MI induced by plaque rupture and thrombosis using a recently published GWAS meta-analysis (MI vs. restricted CAD-only) [22]. This meta-analysis included publicly available summary statistics for MI with 9,289,491 SNPs from the CARDIoGRAMplusC4D Consortium combined with GWAS results for MI in the UK Biobank. The odds ratio (OR) and associated $95\%$ confidence intervals (CIs) were calculated for each potential BCAA-raising SNP. To understand the BCAA-raising gene functions, we reported associations ($p \leq 1$ × 10−5) for sentinel variants with traits in the UK Biobank cohort using the MRBase PheWAS database (http://phewas.mrbase.org/, accessed 1 June 2022), which contains genome-wide association summary data for 4203 phenotypes measured in 361,194 unrelated individuals of European ancestry from the UK Biobank data. We queried GWAS data for eight traits related to pathological risk factors, endophenotypes, and related disease traits using summary-level data from the largest publicly available GWAS study. The possible metabolic regulatory factors leading to cardiovascular events due to the occurrence and progression of plaque have been focused on, including glucose and lipid metabolism (glycated hemoglobin, low-density lipoprotein-cholesterol (LDL-C), and body mass index (BMI) (from UK Biobank)), neuroendocrine regulation (renin [23], angiotensin-converting enzyme 2 [23], testosterone, and total choline (from UK Biobank)), and platelet count [24]. Specifically, it is known that the BCAA gene PPM1K affects diabetes [25], and defective BCAA catabolism disrupts glucose metabolism and sensitizes the heart to ischemia-reperfusion injury [26]. Neurohormonal activation is important in the development of cardiovascular diseases such as CAD, especially the renin-angiotensin system [27]. Several observational studies show an inverse association between serum testosterone concentration and adverse cardiovascular outcomes, metabolic syndrome, and mortality [28]. Ritchel et al. reported that central nervous system control of BCAAs may be mediated in part by vagal outflow [29]. In humans, elevated plasma levels of choline and products of its metabolism have been linked to the risk of CAD-related outcomes [30]. In addition, BCAA could regulate energy metabolism to promote the progression of cardiovascular disease, so BMI and lipids were included as candidate traits [26]. Finally, platelet dysfunction is an important cause of subsequent ischemic events in CAD, which may be regulated by BCAA [31]. We thus propose these eight traits as candidate pathologic features. ## 2.9. Hierarchical Agglomerative Clustering, Gene Interactions and Epigenetic Effects Phenotypic clustering was estimated through agglomerative hierarchical clustering (AHC), calculating the Euclidean distance and using Ward’s agglomeration method [32]. We performed agglomerative hierarchical clustering to identify sets of genes sharing similar profiles. Hierarchical clustering based on effect size and direction of the association. We performed agglomerative hierarchical clustering across the top 17 independent loci using the directional Z -score (beta of continuous traits or log odds of disease risk divided by the standard error of the cross-trait associations) obtained from logistic regression analyses in each of the eight disease-specific GWASs. Where a sentinel variant was not available in any of the other trait summary results, a common proxy was used in place of the sentinel variant. We accounted for multiple testing at a family-wise error rate of 0.05 by Bonferroni correction for the eight traits tested per BCAA-raising locus (136 tests) and considered $p \leq 3.7$ × 10−4 ($\frac{0.05}{136}$) as the significance threshold for an association. Gene interactions and networks were analyzed using the GeneMANIA prediction server (v3.5.1) (http://genemania.org, accessed on 24 February 2023) [33] and plotted using Cytoscape (v3.6.1). Pathway-based association testing was achieved by defining a biological pathway incorporating the gene targets of interest. In this study, we used the mQTL database (http://www.mqtldb.org, accessed on 24 February 2023) for this purpose, which reports SNP-methylation effect estimates obtained from the ARIES project (http://www.ariesepigenomics.org.uk/, accessed on 24 February 2023). ## 2.10. MR Analyses The primary analysis was conducted using a random-effects inverse-variance-weighted method [34]. The secondary MR methods included MR-Egger [35], simple median, weighted median [36], MR–Robust Adjusted Profile Score (MR-RAPS) [37], and MR–Pleiotropy Residual Sum and Outlier (MR-PRESSO) [38] methods. The weighted median method can reduce the bias of valid inverse variances and is suitable for applications with multiple genetic analyses. The MR-Egger regression can detect and adjust for pleiotropy, but the precision of the estimates is low. The MR-RAPS approach was designed to identify and estimate confounded associations using weak genetic instrument variables. The MR-PRESSO method was used to identify potential outliers in multi-instrument summary-level MR testing and provide robust estimates with outlier correction. ## 2.11. Sensitive Analyses To increase the reliability of MR results, we conducted multiple sensitivity analyses with respect to the MR tests to exclude possible biases (horizontal pleiotropy, i.e., variants included in the genetic instrument having an effect on the disease outside their effects on the exposure in MR) under different scenarios in the MR estimates [39]. Finally, a modified Cochran Q statistic and leave-one-out analysis were conducted to detect heterogeneity in the results [39]. As a result of these different methods used to compare the results, better agreement and higher reliability could be obtained. ## 2.12. Statistical Analyses MR analyses were conducted using the TwoSampleMR R package [34]. All statistical tests were two-sided. All statistical analyses were conducted using Stata version 15.1 (Stata Corp, College Station, TX, USA) and R version 4.1 (R Foundation). ## 3.1. MR Findings Thirty-four SNPs were associated with BCAA concentrations after the screening. Seven genes related to confounder factors for CAD were not included because of the limitations of the MR method, and seventeen SNPs were finally included (Table 2; See Text S4 for details). The variation caused by these 17 SNPs accounted for $6.26\%$ of the serum BCAA concentration. The proportion of variance in serum BCAAs explained by each SNP was calculated as r2 = 2 × minor allele frequency × (1 − minor allele frequency) × (β/SD)2, where the standard deviation (SD) = 1, because the data have been standardized. In the overall inverse-variance-weighted meta-analysis of the 17 SNPs, the OR of CAD per SD increase in genetically predicted serum BCAA was 1.08 ($95\%$CI, 1.02–1.14; $$p \leq 0.007$$). Concordant results were observed with the other MR methods. The significance was replicated with the weighted median (OR = 1.08; $95\%$CI, 1.01–1.16; $$p \leq 0.037$$), simple median (OR = 1.10; $95\%$CI, 1.02–1.19; $$p \leq 0.011$$), MR-RAPS (OR = 1.08; $95\%$CI, 1.02–1.14; $$p \leq 0.009$$), and MR-PRESSO (OR = 1.08; $95\%$CI, 1.04–1.12; $$p \leq 0.0003$$) approaches. The OR of the MR-Egger method was 0.99 ($95\%$CI, 0.78–1.24) (intercept = 0.009 [−0.014, 0.033], $$p \leq 0.445$$) (Table 3 and Figure S2). We also conducted heterogeneity and pleiotropy tests to ensure the reliability of the MR results. Cochran Q tests for IVW ($$p \leq 0.99$$) indicated no heterogeneity in SNPs included in the study. No outliers were identified by the leave-one-out analysis (Figure S2). These results support the reliability of the MR results. The results of MR analysis showed the following relationships between the three BCAAs and CAD: leucine, OR = 1.08 ($95\%$CI, 1.01–1.16, $$p \leq 0.036$$); isoleucine, OR = 1.13 ($95\%$CI, 1.03–1.24, $$p \leq 0.006$$); and valine, OR = 1.07 ($95\%$CI, 1.00–1.14, $$p \leq 0.058$$) (Table S1 and Figure S3). The results showed that the partial association of BCAA with MR of cardiovascular disease showed a correlation when strict LD cut-offs were used (r2 < 0.001 or lead SNP), but the direction remained consistent (Tables S2 and S3). ## 3.2. Mediating Effects In conventional MR analyses, a genetic predisposition towards higher serum BCAA levels was associated with lower high-density lipoprotein (HDL) cholesterol ($$p \leq 0.001$$), higher systolic blood pressure (SBP) ($p \leq 0.001$), higher diastolic blood pressure (DBP) ($p \leq 0.001$), and type 2 diabetes mellitus ($p \leq 0.001$), but not with LDL-C, total cholesterol, triglycerides, fasting glucose, fasting insulin, insulin resistance, body mass index, waist-to-hip ratio, or smoking ($p \leq 0.05$) (Table S4). Given that previous MR studies found no causal relationship between HDL and CAD [40], we only analyzed the mediating effects of SBP, DBP, and type 2 diabetes, which were identified as important mediators involved in the regulation of CAD by BCAAs ($34.2\%$, $15.2\%$, and $33.5\%$, respectively) (Table 4). Surprisingly, blood pressure appeared to exhibit a stronger mediating effect than diabetes in terms of both proportion and effect value (SBP: beta [se] = 0.566 [0.091]; DBP: beta [se] = 0.581 [0.076]; type 2 diabetes mellitus: beta [se] = 0.110 [0.028]). ## 3.3. Meta-Analysis Findings A meta-analysis of observational studies revealed that high levels of BCAAs were associated with a high risk of developing CAD (risk ratio [RR] = 1.18 [$95\%$CI, 1.11–1.24 from 7 studies with 38,975 individuals; 4685 CAD cases/events; I2 $44.3\%$; random-effects meta-analysis]), after adjustment for age, sex, smoking status, BMI, blood pressure, and lipids. The RR was 1.31 ($95\%$CI, 1.10–1.51) in three case–control studies and 1.14 ($95\%$CI, 1.10–1.17) in three prospective cohort studies. The specific results are shown in the Supplementary Materials (Text S3, Table S5 and Figure S4). ## 3.4. BCAA Linked to Ischemic CVD Events We applied MR analyses to investigate the effects of BCAAs on seven CVD clinical events including ischemic events (MI, heart attack, stroke, and its four subtypes (including ischemic stroke, cardioembolic ischemic stroke, small-vessel and large artery atherosclerosis ischemic stroke), deep venous thrombosis, and pulmonary embolism) and bleeding events (subarachnoid hemorrhage, intracerebral hemorrhage) (Figure 2a). Using conventional MR, 5 of the 11 analyses provided evidence of an impact of BCAAs on the risk of ischemic events based on a false discovery rate (FDR) <$5\%$. Stronger evidence was found for the risk of ischemic events due to plaque rupture and thrombus: stroke risk (OR = 1.13, $95\%$CI, 1.07–1.19, $$p \leq 1.61$$ × 10−5), MI (OR = 1.08, $95\%$CI, 1.01–1.16, $$p \leq 1.93$$ × 10−2), and heart attack (OR = 1.004, $95\%$CI, 1.002–1.007, $$p \leq 8.70$$ × 10−5). The effect remained robust after correcting the multivariable analyses for FDR (FDR < 0.05). In addition, it did not show that BCAAs were causally linked to the risk of simple thrombosis (including deep venous thrombosis and pulmonary embolism) or bleeding events ($p \leq 0.05$). All MR estimates derived in these analyses are shown in Figure 2a. ## 3.5. Preferential Associations of MRPL33 and C2orf16 Loci with MI in the Presence of Atherosclerosis Evidence from the large-scale human genetic analysis was consistent with a causal role of BCAA metabolism in ischemic CVD events due to plaque rupture and thrombus formation. Therefore, we assumed that BCAAs might be involved not only in plaque initiation and progression (chronic impairment factor in CAD) but also in plaque rupture and thrombosis (trigger for ischemic events). We evaluated the relationship between BCAA-raising genes (PPM1K, TRMT61A, CBLN1, MRPL33, and C2orf16) and the risk of MI (GWAS data with MI vs. CAD only). Two loci were related to plaque rupture: rs13030345 (OR = 1.05, $95\%$CI, 1.02–1.08; $$p \leq 3$$ × 10−4) in MRPL33 and rs1919128 (OR = 1.04, $95\%$CI, 1.02–1.07, $$p \leq 1.3$$× 10−3) in C2orf16, while the other loci were not related to the risk of MI ($p \leq 0.05$) (Figure 2b). ## 3.6. Functional Analysis of BCAA-Raising Genes Tissue-specificity analysis across tissue types from the GTEx project identified the organs with the greatest gene expression enrichment (Figure S5). Only PPM1K was specifically expressed in the heart, while the other three genes were mainly expressed in the central nervous system and the endocrine system, including the hypothalamus (CBLN1), adrenal gland (MRPL33), and testis (C2orf16). In addition, TRMT61A was mainly expressed in the esophagus (Figure S5). In addition to the single gene level analyses, we also identified tissue specificity of prioritized genes by looking at overrepresentation in sets of differentially expressed genes (DEG) (Figure S6). DEG for each tissue was calculated in FUMA. We found the colon, salivary gland, blood, bladder, and blood vessels to be the top five tissues with the most DEG. We then investigated the associations between the BCAA-raising genes and plaque rupture/thrombosis traits to provide potential insights into the etiology. We first queried the large database of genetic associations in the UK Biobank (http://www.nealelab.is/uk-biobank, accessed on 26 May 2022) and identified several biomarkers and disease associations at each locus. PPM1K has been found to be an important regulator of glucose metabolism and diabetes, which also revealed the casual association for type 2 diabetes [25]. The functions of MRPL33 and C2orf16 as solute carriers are not entirely known, but they have been reported to involve neuroendocrine diseases, such as autism spectrum disorder and type 2 diabetes [41,42]. We tested for associations of the BCAA-raising genes with eight putative risk factors of plaque rupture and thrombus, including neuroendocrine, platelet, and glucose metabolism factors, using GWAS summary data (Figure 3a). Sites on PPM1K and TRMT61A were mainly related to hemoglobin levels. CBLN1 was weakly correlated with renin levels, while MRPL33 and C2orf16 were correlated with angiotensin, testosterone, total choline, platelet count, glucose metabolism, BMI, and LDL, which differed significantly from the PPM1K gene-phenotype. Elevated plasma levels of choline and products of its metabolism have been linked to the risk of MI-related outcomes in humans [43]. Gene interaction analysis demonstrates high network connectivity between most of the identified BCAA genes (Figure 3b). By way of physical interactions, co-expression, prediction, colocalization, genetic interactions, pathways, and common protein domains, nodes in the network demonstrated that certain genes were linked to TRMT61A and PPM1K. GO results analysis showed that it mainly enriched to tRNA catalytic activity, RNA methylation, and branched-chain amino acid metabolic process. Therefore, we focused on exploring the relationship between these BCAA SNPs and DNA methylation. Seven out of the seventeen loci had at least one genetic variant (mQTL), in cis and/or trans, associated with methylation levels (Additional file: Supplementary Data S2). Collectively, these results suggested that high circulating BCAA levels can influence CAD and subsequent plaque rupture events via two distinct pathways: glucose metabolism disorder (as shown by PPM1K and TRMT61A) through processes leading to chronic vulnerability to CAD and neuroendocrine dysregulation (as shown by CBLN1, MRL33, and C2orf16) affecting hemodynamic factors and platelets through neuroendocrine factors, which in turn affect subsequent plaque rupture and thrombosis in CAD, triggering ischemic events. Taken together, these data provide supportive, functional evidence that genetically elevated BCAA levels may contribute to an increased risk of plaque rupture and thrombosis not only through glucose metabolism but, more likely, through neuroendocrine mechanisms (Figure 4). ## 4. Discussion The results of this MR study supported a potential causal link between elevated BCAA levels and an increased risk of CVD. The principal findings confirmed the results of several observational studies, suggesting that elevated serum BCAA levels may translate into an increased risk of CAD. The findings were robust in sensitivity analyses with different instruments and statistical models. Complementary analyses provided further evidence of the adverse effects of genetically predicted serum BCAA levels on the risks of ischemic CVD events caused by plaque rupture and thrombosis. In addition to chronic vulnerability to CAD, the results also suggested that BCAA may affect the neuroendocrine promotion of plaque rupture and thrombosis through platelet and hemodynamic factors (such as blood pressure), leading to ischemic CVD events. The circulating pool of free BCAAs is determined by a balance between their input (e.g., diet and proteolysis) and output (e.g., protein synthesis and catabolism for energy) [44]. BCAAs are essential amino acids that can only be obtained from external food sources, and their homeostasis must therefore be maintained by catabolism. The first two steps in the pathway are common to all three BCAAs: the first reaction, catalyzed by branched-chain aminotransferase (BCAT), is a reversible transamination to form branched-chain α-keto acids (BCKAs), and the second reaction, catalyzed by the branched-chain α-keto acid dehydrogenase (BCKD) complex, is an irreversible oxidative decarboxylation of BCKAs and is the rate-limiting step in the overall BCAA catabolic pathway (Figure S7) [45]. GWAS analysis indicated that most BCAA-related SNPs were located in the PPM1K gene, in which loss-of-function mutations result in impaired BCKD activity [46]. Impaired BCKD signaling, in turn, contributes to disturbed catabolism of BCAAs and their accumulation in circulation. The genetic variation found in this study may thus reflect impaired catabolism of BCAAs, leading to higher circulating levels. GWAS data also showed that BCAT-related genes were not significantly associated with circulating BCAAs. This might be because of the reversible steps of BCAT mediation or because its genes are more uniformly distributed across the population. The instrumental variables used in the current study provide the key to adjustments in BCAA concentrations. BCAA supplementation may improve energy expenditure [47]; however, increased circulating levels of BCAAs are also associated with diabetes, coronary heart disease, and heart failure [48]. Yoneshiro et al. proposed a unique, non-mutually exclusive theory to explain this apparent contradiction. Following cold exposure in mice and humans, brown adipose tissue (BAT) actively utilized BCAAs in the mitochondria for thermogenesis and promoted systemic BCAA clearance. In turn, a BAT-specific defect in BCAA catabolism attenuated systemic BCAA clearance, BAT fuel oxidation, and thermogenesis, leading to glucose intolerance [49]. Similarly, the increase in circulating BCAAs caused by genetic mutations may be due to catabolism disorders. Supplemented BCAAs may thus be unable to participate effectively in improving the body’s energy metabolism. The current MR analysis of BCAA and CVD risk factors indicated that BCAAs were related to SBP, DBP, and type 2 diabetes. The relationship between circulating BCAA levels and coronary heart disease was unlikely to be explained by the main lipids because genetically higher BCAA levels are unrelated to cholesterol and triglyceride levels, and existing MR studies have found an unclear relationship between increased HDL and CVD [50,51]. Mediation analysis suggested that the effect of BCAAs on CVD was partly mediated by blood pressure and type 2 diabetes, and GWAS analysis of BCAA concentrations confirmed that BCAA-related genes affecting CVD were related to disorders of glucose metabolism. The strongest signal was located in PPM1K, which encodes BCKD and mitochondrial phosphatase. Moreover, mutation or loss of PPM1K leads to maple diabetes. Luca et al. found a causal relationship between BCAAs and type 2 diabetes, with the strongest signal also located on the PPM1K gene [25]. Some studies suggested that insulin resistance in endothelial cells was the main cause of coronary atherosclerosis [52], and BCAAs have been shown to upregulate glucose transporters and activate insulin secretion [53,54]. BCAA catabolism disorders lead to impaired mitochondrial activity and redox capacity, increased glucose, glycolysis intermediates, and glucose-derived sugars [44]. Long-term accumulation of BCAAs will lead to down-regulation of the biological exposure pathway of glucosamine, including the inactivation of pyruvate dehydrogenase, making the cardiovascular system vulnerable to damage [26]. Interestingly, the current results showed that BCAAs might also affect ischemic events caused by plaque rupture or thrombosis through the neuroendocrine system, especially via the regulation of blood pressure and platelets. Tissue expression and putative gene function analysis showed that three BCAA-related genes were involved in the modulation of neuroendocrine circuits. The CBLN1 gene encodes a cerebellar precursor-specific protein that acts primarily on post-synaptic Purkinje fibers, which is a component of the signaling pathway [55]. CBLN1 is a novel appetite-stimulating peptide related to renin expression that may be mediated by hypothalamic neuropeptide [56], which is a sympathetic neurotransmitter related to hypertension and a variety of CVDs [57]. MRPL33 has been causally associated with a higher risk of autism spectrum disorder [41] and with angiotensin-converting enzyme 2 and testosterone levels. C2orf16 is mainly expressed in the testis and has also been associated with angiotensin-converting enzyme 2 and testosterone. In addition, MRPL33 and C2orf16 were also correlated with choline and platelet count, while choline, which is involved in central and peripheral sympathetic innervation, participates in neuroendocrine regulation and is closely related to platelet activation [58,59]. Recent studies have examined the relationship between BCAAs and the central nervous system. Ritchel et al. reported that central nervous system control of BCAAs may be mediated in part by vagal outflow [29]. Recent articles showed that plaques could be regulated by the brain via the central nervous system, whereas BCAAs may also participate in CVD as a neuroregulatory signal [29,60]. In general, these three genes may affect the sympathetic renin–angiotensin axis, which may provide an explanation for the mediating roles of hypertension and platelets. Functional analysis of these genes suggested that BCAAs may promote CVD via neuroendocrine pathways, and further studies are needed to clarify the molecular mechanisms linking BCAA metabolism to an increased risk of CVD. Many conventional observational studies have confirmed a longitudinal association between high BCAA concentrations and increased risks of many CVDs [61,62]. The current MR analysis suggested that the effect of BCAAs on CVD could be generalized to cardiovascular ischemic outcomes caused by plaque rupture or thrombosis. This finding included evidence for a total effect of BCAA on the risk of ischemia CVD events later in life, corroborating recent basic studies suggesting that the increased event risk may be explained by defective BCAA catabolism determined by genetic switching [26]. Most studies have consistently reported that BCAA mediators influence alterations in glucose, oxidative stress, and inflammation, leading to endothelial dysfunction and atherosclerosis [7,63]. Atherosclerosis is the main cause of CVD and interacts synergistically with hypertension and diabetes, with these three factors aggravating each other [64]. Regarding ischemic events, gene function analysis suggested that BCAAs might affect hemodynamics and platelets through neuroendocrine regulation and might trigger plaque rupture or thrombosis. Furthermore, recent studies found that BCAA catabolism promoted the risk of thrombosis by enhancing tropomodulin-3 propionylation in platelets, which may act as a trigger for ischemic events [31]. This was also confirmed by the current finding that MRPL33 and C2orf16 affected platelet function. Overall, the causal effect may involve two pathophysiological pathways, including glucose metabolism (PPM1K and TRMT61A) related to plaque progression and the newly discovered neuroendocrine disorders (CBLN1, MRPL33, and C2orf16) related to plaque rupture and thrombosis (Figure 4). BCAAs may increase the risk of CVD via several other mechanisms. Leucine may inhibit endothelial cell synthesis, thus promoting endothelial cell dysfunction and increasing the risk of atherosclerosis [65]. TRMT61A encodes a methyltransferase that plays an important role in tRNA synthesis [66]. High concentrations of BCAAs have been shown to upregulate mammalian targets of rapamycin activity, affecting cardiac protein, lipid, glucose, and nucleotide metabolism and autophagy [67]. BCAAs/BCKA is known to inhibit the transport and utilization of pyruvate and fatty acids, thus increasing the sensitivity of cardiovascular cells to chemical damage. Pyruvate and fatty acids are the main sources of energy in the heart and are in persistently high demand by heart cells, and elevated BCAAs/BCKA may thus affect normal cardiovascular energy homeostasis leading to increased cell load, vasoconstriction, and accelerated heart failure [68]. Higher BCAA/BCKA levels may also have a potentially negative cardiovascular effect by regulating the mitochondrial electron transport chain and mitochondrial permeability transition pores [69]. BCAA metabolism is closely linked to other metabolic pathways, and changes in this metabolic pathway may, therefore, only influence CVD risk in certain metabolic contexts. Lipid metabolism-related genes also affect blood levels of metabolites other than BCAAs, including total cholesterol, LDL, and triglycerides, which have been associated with a higher incidence of CVD in observational studies [70,71]. The effects of BCAA-related lipid metabolism genes on lipids and coronary heart disease might mask the relationship between BCAAs and coronary heart disease. To avoid violating the MR hypothesis, we excluded horizontal genes from the current study. However, the results indicated that activation of BCAAs by specific pathways may increase the risk of CVD and subsequent ischemic events. This study had several limitations. Samples contributing to the study-level MR were also included in the much larger CAD GWAS used for the summary-level MR, implying some dependence between the results, even though the methodologies were different. The predominantly European ancestry of the subjects also limited the generalizability of the results to other ethnic groups. In addition to the vertical pleiotropy considered above, there was evidence for horizontal pleiotropy that was addressed through current best practices for MR sensitivity analysis, all of which supported the primary conclusions. However, as with all MR studies, we could not address unobserved pleiotropy. In addition, a potential non-linear association between serum BCAA levels and CAD could not be evaluated. Limited by the summary data of BCAA GWAS, we found that the recent MR analysis of BCAA also did not carry out non-linear analysis, including the MR analysis of BCAA and type 2 diabetes, insulin resistance and stroke [25,72,73]. Furthermore, a further limitation of MR methods is that the effects of the exposure or mediator are non-linear. Although some methods are emerging for carrying out MR analysis with non-linear exposures [74,75], these methods have not yet been extended to MR mediation analyses. Current MR methods for mediation analysis will assume a linear association between exposure and outcome. In addition, a conservative approach was adopted, and at the cost of reducing power, the use of loci specific to each phenotypic trait (r2 < 0.001 or lead SNP) showed a weak correlation. Furthermore, we only considered changes in BCAA concentrations as a result of genetic factors, which only accounted for a small proportion of the changes in BCAA levels. Although we evaluated blood pressure and type 2 diabetes as mediators of the causal link between BCAAs and CAD, BCAA metabolism is a complex process that is closely related to other pathways, and other unknown pathways and environmental factors will thus also affect CAD. BCAA-SNPs are involved in methylation maintenance. The role of epigenetic effects in modifying the effects of the BCAA on CAD deserves future research. Further investigations are warranted to illuminate the underlying pathological mechanisms by which BCAAs affect coronary heart disease, including their possible involvement in neuroendocrine regulation. ## 5. Conclusions Evidence from this large-scale human genetic and metabolomic study was consistent with a causal role of BCAA metabolism in CVD and subsequent ischemic events. The causal effect was partly mediated by blood pressure and type 2 diabetes. Functional analysis of the BCAA-raising gene signatures revealed that the causal effect might involve two pathophysiological pathways, including glucose metabolism (PPM1K and TRMT61A) related to plaque progression, and the newly discovered neuroendocrine disorders (CBLN1, MRPL33, and C2orf16) related to plaque rupture and thrombosis. We speculate that future analyses of BCAA in relation to neuroendocrine system function may yield additional insights into the genetic architecture of CVD to inform new approaches to its prevention and treatment. ## References 1. Gaziano T.A.. Cardiovascular disease in the developing world and its cost-effective management. Circulation (2005.0) 112 3547-3553. DOI: 10.1161/CIRCULATIONAHA.105.591792 2. Bhattacharya S., Granger C.B., Craig D., Haynes C., Bain J., Stevens R.D., Hauser E.R., Newgard C.B., Kraus W.E., Newby L.K.. Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization. Atherosclerosis (2014.0) 232 191-196. DOI: 10.1016/j.atherosclerosis.2013.10.036 3. Hu W., Sun L., Gong Y., Zhou Y., Yang P., Ye Z., Fu J., Huang A., Fu Z., Yu W.. Relationship between Branched-Chain Amino Acids, Metabolic Syndrome, and Cardiovascular Risk Profile in a Chinese Population: A Cross-Sectional Study. Int. J. Endocrinol. (2016.0) 2016 8173905. DOI: 10.1155/2016/8173905 4. Wurtz P., Havulinna A.S., Soininen P., Tynkkynen T., Prieto-Merino D., Tillin T., Ghorbani A., Artati A., Wang Q., Tiainen M.. Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts. Circulation (2015.0) 131 774-785. DOI: 10.1161/CIRCULATIONAHA.114.013116 5. Tobias D.K., Lawler P.R., Harada P.H., Demler O.V., Ridker P.M., Manson J.E., Cheng S., Mora S.. Circulating Branched-Chain Amino Acids and Incident Cardiovascular Disease in a Prospective Cohort of US Women. Circ. Genom. Precis. Med. (2018.0) 11 e002157. DOI: 10.1161/CIRCGEN.118.002157 6. Ruiz-Canela M., Toledo E., Clish C.B., Hruby A., Liang L., Salas-Salvado J., Razquin C., Corella D., Estruch R., Ros E.. Plasma Branched-Chain Amino Acids and Incident Cardiovascular Disease in the PREDIMED Trial. Clin. Chem. (2016.0) 62 582-592. DOI: 10.1373/clinchem.2015.251710 7. Zhenyukh O., Gonzalez-Amor M., Rodrigues-Diez R.R., Esteban V., Ruiz-Ortega M., Salaices M., Mas S., Briones A.M., Egido J.. Branched-chain amino acids promote endothelial dysfunction through increased reactive oxygen species generation and inflammation. J. Cell. Mol. Med. (2018.0) 22 4948-4962. DOI: 10.1111/jcmm.13759 8. Kettunen J., Demirkan A., Würtz P., Draisma H.H.M., Haller T., Rawal R., Vaarhorst A., Kangas A.J., Lyytikäinen L.-P., Pirinen M.. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat. Commun. (2016.0) 7 11122. DOI: 10.1038/ncomms11122 9. Nikpay M., Goel A., Won H.H., Hall L.M., Willenborg C., Kanoni S., Saleheen D., Kyriakou T., Nelson C.P., Hopewell J.C.. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. (2015.0) 47 1121-1130. DOI: 10.1038/ng.3396 10. Uddin G.M., Zhang L., Shah S., Fukushima A., Wagg C.S., Gopal K., Al Batran R., Pherwani S., Ho K.L., Boisvenue J.. Impaired branched chain amino acid oxidation contributes to cardiac insulin resistance in heart failure. Cardiovasc. Diabetol. (2019.0) 18 86. DOI: 10.1186/s12933-019-0892-3 11. Consortium G.T.. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science (2015.0) 348 648-660. DOI: 10.1126/science.1262110 12. Uhlen M., Fagerberg L., Hallstrom B.M., Lindskog C., Oksvold P., Mardinoglu A., Sivertsson A., Kampf C., Sjostedt E., Asplund A.. Proteomics. Tissue-based map of the human proteome. Science (2015.0) 347 1260419. DOI: 10.1126/science.1260419 13. Malik R., Chauhan G., Traylor M., Sargurupremraj M., Okada Y., Mishra A., Rutten-Jacobs L., Giese A.K., van der Laan S.W., Gretarsdottir S.. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. (2018.0) 50 524-537. DOI: 10.1038/s41588-018-0058-3 14. Nielsen J.B., Thorolfsdottir R.B., Fritsche L.G., Zhou W., Skov M.W., Graham S.E., Herron T.J., McCarthy S., Schmidt E.M., Sveinbjornsson G.. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat. Genet. (2018.0) 50 1234-1239. DOI: 10.1038/s41588-018-0171-3 15. Yang J., Loos R.J., Powell J.E., Medland S.E., Speliotes E.K., Chasman D.I., Rose L.M., Thorleifsson G., Steinthorsdottir V., Magi R.. FTO genotype is associated with phenotypic variability of body mass index. Nature (2012.0) 490 267-272. DOI: 10.1038/nature11401 16. Chen J., Spracklen C.N., Marenne G., Varshney A., Corbin L.J., Luan J., Willems S.M., Wu Y., Zhang X., Horikoshi M.. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. (2021.0) 53 840-860. DOI: 10.1038/s41588-021-00852-9 17. Tobacco C.. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. (2010.0) 42 441-447. DOI: 10.1038/ng.571 18. Willer C.J., Schmidt E.M., Sengupta S., Peloso G.M., Gustafsson S., Kanoni S., Ganna A., Chen J., Buchkovich M.L., Mora S.. Discovery and refinement of loci associated with lipid levels. Nat. Genet. (2013.0) 45 1274-1283. DOI: 10.1038/ng.2797 19. Bulik-Sullivan B., Finucane H.K., Anttila V., Gusev A., Day F.R., Loh P.R., Duncan L.. An atlas of genetic correlations across human diseases and traits. Nat. Genet. (2015.0) 47 1236-1241. DOI: 10.1038/ng.3406 20. Richmond R.C., Hemani G., Tilling K., Davey Smith G., Relton C.L.. Challenges and novel approaches for investigating molecular mediation. Hum. Mol. Genet. (2016.0) 25 R149-R156. DOI: 10.1093/hmg/ddw197 21. Jiang L., Wang M., Lin S., Jian R., Li X., Chan J., Dong G., Fang H., Robinson A.E., Consortium G.T.. A Quantitative Proteome Map of the Human Body. Cell (2020.0) 183 269-283.e19. DOI: 10.1016/j.cell.2020.08.036 22. Hartiala J.A., Han Y., Jia Q., Hilser J.R., Huang P., Gukasyan J., Schwartzman W.S., Cai Z., Biswas S., Tregouet D.A.. Genome-wide analysis identifies novel susceptibility loci for myocardial infarction. Eur. Heart J. (2021.0) 42 919-933. DOI: 10.1093/eurheartj/ehaa1040 23. Gilly A., Park Y.C., Png G., Barysenka A., Fischer I., Bjornland T., Southam L., Suveges D., Neumeyer S., Rayner N.W.. Whole-genome sequencing analysis of the cardiometabolic proteome. Nat. Commun. (2020.0) 11 6336. DOI: 10.1038/s41467-020-20079-2 24. Kanai M., Akiyama M., Takahashi A., Matoba N., Momozawa Y., Ikeda M., Iwata N., Ikegawa S., Hirata M., Matsuda K.. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. (2018.0) 50 390-400. DOI: 10.1038/s41588-018-0047-6 25. Lotta L.A., Scott R.A., Sharp S.J., Burgess S., Luan J., Tillin T., Schmidt A.F., Imamura F., Stewart I.D., Perry J.R.. Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis. PLoS Med. (2016.0) 13. DOI: 10.1371/journal.pmed.1002179 26. Li T., Zhang Z., Kolwicz S.C., Abell L., Roe N.D., Kim M., Zhou B., Cao Y., Ritterhoff J., Gu H.. Defective Branched-Chain Amino Acid Catabolism Disrupts Glucose Metabolism and Sensitizes the Heart to Ischemia-Reperfusion Injury. Cell Metab. (2017.0) 25 374-385. DOI: 10.1016/j.cmet.2016.11.005 27. Wu H., Cheng X.W., Hu L., Hao C.N., Hayashi M., Takeshita K., Hamrah M.S., Shi G.P., Kuzuya M., Murohara T.. Renin inhibition reduces atherosclerotic plaque neovessel formation and regresses advanced atherosclerotic plaques. Atherosclerosis (2014.0) 237 739-747. DOI: 10.1016/j.atherosclerosis.2014.10.098 28. Hudson J., Cruickshank M., Quinton R., Aucott L., Aceves-Martins M., Gillies K., Bhasin S., Snyder P.J., Ellenberg S.S., Grossmann M.. Adverse cardiovascular events and mortality in men during testosterone treatment: An individual patient and aggregate data meta-analysis. Lancet Healthy Longev. (2022.0) 3 e381-e393. DOI: 10.1016/S2666-7568(22)00096-4 29. Gannaban R.B., NamKoong C., Ruiz H.H., Choi H.J., Shin A.C.. Central Regulation of Branched-Chain Amino Acids Is Mediated by AgRP Neurons. Diabetes (2021.0) 70 62-75. DOI: 10.2337/db20-0510 30. Tang W.H., Wang Z., Levison B.S., Koeth R.A., Britt E.B., Fu X., Wu Y., Hazen S.L.. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. (2013.0) 368 1575-1584. DOI: 10.1056/NEJMoa1109400 31. Xu Y., Jiang H., Li L., Chen F., Liu Y., Zhou M., Wang J., Jiang J., Li X., Fan X.. Branched-Chain Amino Acid Catabolism Promotes Thrombosis Risk by Enhancing Tropomodulin-3 Propionylation in Platelets. Circulation (2020.0) 142 49-64. DOI: 10.1161/CIRCULATIONAHA.119.043581 32. Ward J.H.. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. (1963.0) 58 236-244. DOI: 10.1080/01621459.1963.10500845 33. Mostafavi S., Ray D., Warde-Farley D., Grouios C., Morris Q.. GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function. Genome Biol. (2008.0) 9 S4. DOI: 10.1186/gb-2008-9-s1-s4 34. Hemani G., Zheng J., Elsworth B., Wade K.H., Haberland V., Baird D., Laurin C., Burgess S., Bowden J., Langdon R.. The MR-Base platform supports systematic causal inference across the human phenome. Elife (2018.0) 7 e34408. DOI: 10.7554/eLife.34408 35. Burgess S., Thompson S.G.. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. (2017.0) 32 377-389. DOI: 10.1007/s10654-017-0255-x 36. Burgess S., Bowden J., Fall T., Ingelsson E., Thompson S.G.. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology (2017.0) 28 30-42. DOI: 10.1097/EDE.0000000000000559 37. Zhao Q., Wang J., Hemani G., Bowden J., Small D.S.. Statisticalinferenceintwo-Samplesummary-Datamendelian Randomization Using Robust Adjusted Profile Score 38. Verbanck M., Chen C.Y., Neale B., Do R.. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. (2018.0) 50 693-698. DOI: 10.1038/s41588-018-0099-7 39. Hemani G., Bowden J., Davey Smith G.. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. (2018.0) 27 R195-R208. DOI: 10.1093/hmg/ddy163 40. Vitali C., Khetarpal S.A., Rader D.J.. HDL Cholesterol Metabolism and the Risk of CHD: New Insights from Human Genetics. Curr. Cardiol. Rep. (2017.0) 19 132. DOI: 10.1007/s11886-017-0940-0 41. Yang J., He X., Qian L., Zhao B., Fan Y., Gao F., Yan B., Zhu F., Ma X.. Association between plasma proteome and childhood neurodevelopmental disorders: A two-sample Mendelian randomization analysis. EBioMedicine (2022.0) 78 103948. DOI: 10.1016/j.ebiom.2022.103948 42. Sakaue S., Kanai M., Tanigawa Y., Karjalainen J., Kurki M., Koshiba S., Narita A., Konuma T., Yamamoto K., Akiyama M.. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. (2021.0) 53 1415-1424. DOI: 10.1038/s41588-021-00931-x 43. Wang Z., Tang W.H., Buffa J.A., Fu X., Britt E.B., Koeth R.A., Levison B.S., Fan Y., Wu Y., Hazen S.L.. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur. Heart J. (2014.0) 35 904-910. DOI: 10.1093/eurheartj/ehu002 44. Neinast M.D., Jang C., Hui S., Murashige D.S., Chu Q., Morscher R.J., Li X., Zhan L., White E., Anthony T.G.. Quantitative Analysis of the Whole-Body Metabolic Fate of Branched-Chain Amino Acids. Cell Metab. (2019.0) 29 417-429.e4. DOI: 10.1016/j.cmet.2018.10.013 45. Shimomura Y., Yamamoto Y., Bajotto G., Sato J., Murakami T., Shimomura N., Kobayashi H., Mawatari K.. Nutraceutical effects of branched-chain amino acids on skeletal muscle. J. Nutr. (2006.0) 136 529S-532S. DOI: 10.1093/jn/136.2.529S 46. Oyarzabal A., Martinez-Pardo M., Merinero B., Navarrete R., Desviat L.R., Ugarte M., Rodriguez-Pombo P.. A novel regulatory defect in the branched-chain alpha-keto acid dehydrogenase complex due to a mutation in the PPM1K gene causes a mild variant phenotype of maple syrup urine disease. Hum. Mutat. (2013.0) 34 355-362. DOI: 10.1002/humu.22242 47. Giuseppe D.A., Maurizio R., Annalisa C., Laura T., Marta D., Flavia B., Francesca C., Giovanni C., Roberto B., Carruba M.O.. Branched-chain amino acid supplementation promotes survival and supports cardiac and skeletal muscle mitochondrial biogenesis in middle-aged mice. Cell Metab. (2010.0) 12 362-372. PMID: 20889128 48. Sun H., Olson K.C., Gao C., Prosdocimo D.A., Zhou M., Wang Z., Jeyaraj D., Youn J.Y., Ren S., Liu Y.. Catabolic Defect of Branched-Chain Amino Acids Promotes Heart Failure. Circulation (2016.0) 133 2038-2049. DOI: 10.1161/CIRCULATIONAHA.115.020226 49. Yoneshiro T., Wang Q., Tajima K., Matsushita M., Maki H., Igarashi K., Dai Z., White P.J., McGarrah R.W., Ilkayeva O.R.. BCAA catabolism in brown fat controls energy homeostasis through SLC25A44. Nature (2019.0) 572 614-619. DOI: 10.1038/s41586-019-1503-x 50. Voight B.F., Peloso G.M., Orho-Melander M., Frikke-Schmidt R., Barbalic M., Jensen M.K., Hindy G., Holm H., Ding E.L., Johnson T.. Plasma HDL cholesterol and risk of myocardial infarction: A mendelian randomisation study. Lancet (2012.0) 380 572-580. DOI: 10.1016/S0140-6736(12)60312-2 51. Holmes M.V., Asselbergs F.W., Palmer T.M., Drenos F., Lanktree M.B., Nelson C.P., Dale C.E., Padmanabhan S., Finan C., Swerdlow D.I.. Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J. (2015.0) 36 539-550. DOI: 10.1093/eurheartj/eht571 52. Kanter J.E., Bornfeldt K.E.. Evidence stacks up that endothelial insulin resistance is a culprit in atherosclerosis. Circ. Res. (2013.0) 113 352-354. DOI: 10.1161/CIRCRESAHA.113.301998 53. Shinobu N., Kenji T., Shoji F., Ichiro S.. Branched-chain amino acids improve glucose metabolism in rats with liver cirrhosis. Am. J. Physiol. Gastrointest. Liver Physiol. (2005.0) 288 1292-1300 54. Zhang S., Zeng X., Man R., Mao X., Qiao S.. Novel metabolic and physiological functions of branched chain amino acids: A review. J. Anim. Sci. Biotechnol. (2017.0) 8 10. DOI: 10.1186/s40104-016-0139-z 55. Hirai H., Pang Z.D., Miyazaki T., Li L., Miura E., Parris J., Rong Y., Watanabe M., Yuzaki M., Morgan J.I.. Cbln1 is essential for synaptic integrity and plasticity in the cerebellum. Nat. Neurosci. (2005.0) 8 1534-1541. DOI: 10.1038/nn1576 56. Gardiner J.V., Beale K.E., Roy D., Boughton C.K., Bataveljic A., Campbell D.C., Bewick G.A., Patel N.A., Patterson M., Leavy E.M.. Cerebellin1 is a novel orexigenic peptide. Diabetes Obes. Metab. (2010.0) 12 883-890. DOI: 10.1111/j.1463-1326.2010.01247.x 57. Wang L., Rao F., Zhang K., Mahata M., Rodriguez-Flores J.L., Fung M.M., Waalen J., Cockburn M.G., Hamilton B.A., Mahata S.K.. Neuropeptide Y(1) Receptor NPY1R discovery of naturally occurring human genetic variants governing gene expression in cella as well as pleiotropic effects on autonomic activity and blood pressure in vivo. J. Am. Coll. Cardiol. (2009.0) 54 944-954. DOI: 10.1016/j.jacc.2009.05.035 58. Collier B., Katz H.S.. Acetylcholine synthesis from recaptured choline by a sympathetic ganglion. J. Physiol. (1974.0) 238 639-655. DOI: 10.1113/jphysiol.1974.sp010548 59. Petty A.C., Scrutton M.C.. Release of Choline Metabolites from Human Platelets: Evidence for Activation of Phospholipase D and of Phosphatidylcholine-specific Phospholipase C. Platelets (1993.0) 4 23-29. DOI: 10.3109/09537109309013192 60. Mohanta S.K., Peng L., Li Y., Lu S., Sun T., Carnevale L., Perrotta M., Ma Z., Forstera B., Stanic K.. Neuroimmune cardiovascular interfaces control atherosclerosis. Nature (2022.0) 605 152-159. DOI: 10.1038/s41586-022-04673-6 61. Karwi Q.G., Lopaschuk G.D.. Branched-Chain Amino Acid Metabolism in the Failing Heart. Cardiovasc. Drugs Ther. (2022.0). DOI: 10.1007/s10557-022-07320-4 62. Portero V., Nicol T., Podliesna S., Marchal G.A., Baartscheer A., Casini S., Tadros R., Treur J.L., Tanck M.W.T., Cox I.J.. Chronically elevated branched chain amino acid levels are pro-arrhythmic. Cardiovasc. Res. (2022.0) 118 1742-1757. DOI: 10.1093/cvr/cvab207 63. Zhang X., Lin Q., Chen J., Wei T., Li C., Zhao L., Gao H., Zheng H.. High Glucose-Induced Cardiomyocyte Death May Be Linked to Unbalanced Branched-Chain Amino Acids and Energy Metabolism. Molecules (2018.0) 23. DOI: 10.3390/molecules23040807 64. Wang Z., Yang T., Fu H.. Prevalence of diabetes and hypertension and their interaction effects on cardio-cerebrovascular diseases: A cross-sectional study. BMC Public Health (2021.0) 21. DOI: 10.1186/s12889-021-11122-y 65. Yang Y., Wu Z., Meininger C.J., Wu G.. L-Leucine and NO-mediated cardiovascular function. Amino Acids (2015.0) 47 435-447. DOI: 10.1007/s00726-014-1904-y 66. Macari F., El-Houfi Y., Boldina G., Xu H., Khoury-Hanna S., Ollier J., Yazdani L., Zheng G., Bieche I., Legrand N.. TRM6/61 connects PKCalpha with translational control through tRNAi(Met) stabilization: Impact on tumorigenesis. Oncogene (2016.0) 35 1785-1796. DOI: 10.1038/onc.2015.244 67. Zhenyukh O., Civantos E., Ruiz-Ortega M., Sanchez M.S., Vazquez C., Peiro C., Egido J., Mas S.. High concentration of branched-chain amino acids promotes oxidative stress, inflammation and migration of human peripheral blood mononuclear cells via mTORC1 activation. Free Radic. Biol. Med. (2017.0) 104 165-177. DOI: 10.1016/j.freeradbiomed.2017.01.009 68. Ussher J.R., Elmariah S., Gerszten R.E., Dyck J.R.. The Emerging Role of Metabolomics in the Diagnosis and Prognosis of Cardiovascular Disease. J. Am. Coll. Cardiol. (2016.0) 68 2850-2870. DOI: 10.1016/j.jacc.2016.09.972 69. Sun H., Wang Y.. Branched Chain Amino Acid Metabolic Reprogramming in Heart Failure. BBA Mol. Basis Dis. (2016.0) 1862 2270-2275. DOI: 10.1016/j.bbadis.2016.09.009 70. Angeloni E., Paneni F., Landmesser U., Benedetto U., Melina G., Lüscher T.F., Volpe M., Sinatra R., Cosentino F.. Lack of protective role of HDL-C in patients with coronary artery disease undergoing elective coronary artery bypass grafting. Eur. Heart J. (2013.0) 34 3557-3562. DOI: 10.1093/eurheartj/eht163 71. Schnabel R.B., Schulz A., Messow C.M., Lubos E., Wild P.S., Zeller T., Sinning C.R., Rupprecht H.J., Bickel C., Peetz D.. Multiple marker approach to risk stratification in patients with stable coronary artery disease. Eur. Heart J. (2010.0) 31 3024-3031. DOI: 10.1093/eurheartj/ehq322 72. Mahendran Y., Jonsson A., Have C.T., Allin K.H., Witte D.R., Jørgensen M.E., Grarup N., Pedersen O., Kilpeläinen T.O., Hansen T.. Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels. Diabetologia (2017.0) 60 873-878. DOI: 10.1007/s00125-017-4222-6 73. Larsson S.C., Markus H.S.. Branched-chain amino acids and Alzheimer’s disease: A Mendelian randomization analysis. Sci. Rep. (2017.0) 7 13604. DOI: 10.1038/s41598-017-12931-1 74. Staley J.R., Burgess S.. Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization. Genet. Epidemiol. (2017.0) 41 341-352. DOI: 10.1002/gepi.22041 75. Burgess S., Davies N.M., Thompson S.G., Consortium E.P.-I.. Instrumental variable analysis with a nonlinear exposure-outcome relationship. Epidemiology (2014.0) 25 877-885. DOI: 10.1097/EDE.0000000000000161
--- title: Electrospun Polycaprolactone/Chitosan Nanofibers Containing Cordia myxa Fruit Extract as Potential Biocompatible Antibacterial Wound Dressings authors: - Amal A. Alyamani - Mastafa H. Al-Musawi - Salim Albukhaty - Ghassan M. Sulaiman - Kadhim M. Ibrahim - Elsadig M. Ahmed - Majid S. Jabir - Hassan Al-Karagoly - Abed Alsalam Aljahmany - Mustafa K. A. Mohammed journal: Molecules year: 2023 pmcid: PMC10059813 doi: 10.3390/molecules28062501 license: CC BY 4.0 --- # Electrospun Polycaprolactone/Chitosan Nanofibers Containing Cordia myxa Fruit Extract as Potential Biocompatible Antibacterial Wound Dressings ## Abstract The goal of the current work was to create an antibacterial agent by using polycaprolactone/chitosan (PCL/CH) nanofibers loaded with Cordia myxa fruit extract (CMFE) as an antimicrobial agent for wound dressing. Several characteristics, including morphological, physicomechanical, and mechanical characteristics, surface wettability, antibacterial activity, cell viability, and in vitro drug release, were investigated. The inclusion of CMFE in PCL/CH led to increased swelling capability and maximum weight loss. The SEM images of the PCL/CH/CMFE mat showed a uniform topology free of beads and an average fiber diameter of 195.378 nm. Excellent antimicrobial activity was shown towards Escherichia coli (31.34 ± 0.42 mm), Salmonella enterica (30.27 ± 0.57 mm), Staphylococcus aureus (21.31 ± 0.17 mm), Bacillus subtilis (27.53 ± 1.53 mm), and Pseudomonas aeruginosa (22.17 ± 0.12 mm) based on the inhibition zone assay. The sample containing 5 wt% CMFE had a lower water contact angle (47 ± 3.7°), high porosity, and high swelling compared to the neat mat. The release of the $5\%$ CMFE-loaded mat was proven to be based on anomalous non-Fickian diffusion using the Korsmeyer–Peppas model. Compared to the pure PCL membrane, the PCL-CH/CMFE membrane exhibited suitable cytocompatibility on L929 cells. In conclusion, the fabricated antimicrobial nanofibrous films demonstrated high bioavailability, with suitable properties that can be used in wound dressings. ## 1. Introduction The skin acts as a physical barrier to keep germs out of possible infection sites in living beings. However, traumas such as fractures, heat burns, lacerations, or surgical incisions can damage the skin’s structure and function and occasionally cause the skin to lose its protective function due to a disruption in the tissue’s healthy relationship caused by wounds [1,2]. Local antibacterial and dressing therapies are widely utilized to reduce drug resistance in the case of treatment with systemic antibiotics [3]. Additionally, wound healing is a complex process that includes epithelialization, matrix deposition, cell proliferation, tissue regeneration, and inflammation [4]. As a result, the optimal dressing should be moisturizing and breathable, absorb exudate, have anti-inflammatory, antibacterial, and antioxidative properties, and encourage cell proliferation and wound healing [5,6,7]. Due to their adjustable nanostructures and functionalities, nanomaterials, including the bactericidal performance of nanoparticles (NPs) and nanofibers, have recently received a significant promotion for the treatment of wounds [8,9,10,11,12]. Nanofibers are among the nanomaterials mentioned above that have high specific surface areas, microscopic pore diameters, moisturizing qualities, the capacity to allow air exchange, and morphologies that resemble the extracellular matrix; these features make nanofibers attractive options for wound dressings [13,14]. A remarkable method for producing nanofibers on a large scale from a polymer suspension is electrospinning. Under the influence of an electric field, electrospun fibers may be spun to nanoscale sizes [15,16]. Numerous natural and synthetic polymers have been electrospun into fibers, which have tremendous potential for medication delivery for the treatment of burn injuries [17]. Due to its appropriate mechanical behavior and biocompatibility, polycaprolactone (PCL), a biodegradable, stable, and non-hazardous synthetic polymer has been widely used in electrospun nanofiber membranes [18,19]. However, due to their hydrophobic properties, monomer PCL nanofibers may not be the best substrate for promoting cell growth and migration [20]. To enhance the surface-wetting capability of nanofibers, varieties of hydrophilic materials have been combined with PCL. Chitosan (CH) is a natural polysaccharide with biodegradable, biocompatible, and non-toxic properties and is thus proposed to be a safer substrate for use in biomedical applications, such as tissue engineering, the delivery of pharmaceutical drugs, and as wound dressings [21,22,23]. Chitosan has been studied extensively as a wound dressing because it can serve as a promoter of proliferation, an antibacterial agent, and an activator of macrophages [24]. Moreover, the inclusion of CH in PCL nanofibers may enhance the surface hydrophilicity of scaffolds [25]. The Cordia myxa tree, also known as “Bamber” in Iraq, was discovered to contain highly bioactive compounds. Cordia myxa can be used to treat respiratory tract infections as an expectorant and demulcent and can also act as a diuretic and anti-diarrheal medication. The compounds isolated from the genus Cordia have been identified as having antiviral, tumor cell cytotoxic, anti-inflammatory, and free radical scavenging properties [26,27,28,29]. Here, we propose that Cordia myxa fruit, by its antioxidant and antimicrobial properties, could eliminate chemicals such as reactive oxygen species generated via inflammatory responses and minimize the slow healing of wounds caused by infection. Herein, Cordia myxa fruit extract was loaded into poly(-caprolactone) chitosan-based nanofibrous mats to improve antimicrobial efficacy and biocompatibility for accelerated wound healing. SEM, XRD, and FT-IR studies of the morphological and structural properties of the fabricated nanofibers and their surface wettability, water absorption, and weight loss were also characterized. MTT assays on the L929 cell line were performed to assess the cytotoxicity. In addition, antibacterial properties against Escherichia coli, Salmonella enterica, Staphylococcus aureus, Bacillus subtilis, and Pseudomonas aeruginosa were studied via the inhibition zone test, which implied that the obtained PCL-CH/CMFE is a promising wound dressing for skin injuries. ## 2. Results and Discussion The current study focused on CMF fruit extracts stabilized on polycaprolactone/chitosan nanofibers for medical uses, such as antimicrobial properties for wound dressing applications. The rationale behind immobilizing plant materials with pharmacological significance, particularly for healing wounds, on a polymer matrix is fascinating [30]. The polymer matrix can encourage properties such as chemical and antimicrobial effectiveness to the plant extracts owing to its small size and surface area-to-volume ratio. Electrospinning was utilized to prepare PCL/CH nanofibrous scaffolds in the current study, where the CMF extract was immobilized into the created nanofibers. In spite of the fact that medicinal CMF extract has notable wound healing and antibacterial actions, it is preferable to improve them via immobilizing the extract on the nanofibrous surface of a scaffolding [31]. Electrospinning polymer nanofibers of PCL/CH containing a varied quantity of CMF extract was performed according to the criteria outlined in the methods section. Table 1 summarizes the composites and electrospinning parameters that were employed to create the samples. Figure 1 reveals SEM micrographs of PCL/CH electrospun nanofibers and CMF fruit extract-loaded PCL/CH nanofibers that showed a nano-scaled fibrous architecture without the existence of beads. This was obtained by utilizing the optimal spinning conditions used in this research. Round-shaped PCL nanofiber scaffolds with a smooth surface are optimum characteristics. Since plant extracts induce clusters to develop at branching points, a uniform fiber surface appears to be essential, even though the fiber diameter of the CMF-PCL/CH nanofiber scaffolds did not change significantly. However, due to the immobilization of the extract, the SEM micrographs of the CMF extract-loaded fibers seem to have a slightly swollen morphology. The diameter size of fibers was calculated using ImageJ, and it was shown to be in the range of 97 ± 7 nm for PCL nanofibers. CH tends to make the electrospinning suspension more polar due to its charged functional groups that cause fibers to stretch in an electrical field [32]. The diameter of PCL/CH fibers was 197 ± 23 nm, while the diameter of CH/PCL/CMF fibers was 295 ± 78 nm, with a slight increase due to swelling. To demonstrate the crystalline structure of CMFE-loaded PCL/CH nanofibers, X-ray diffraction (XRD) was investigated. XRD showed that the electrospun nanofibers contained crystals (Figure 2). The solvent medium, parameters, applied voltage, and polymer characteristics (molecular weight) all affect the crystal structure [33]. XRD was taken to analyze the modifications in the crystalline structure of the pristine electrospun PCL/CH/CMF nanofiber membrane. This is demonstrated in Figure 2. Two diffraction peaks at transmittance angles of 21.4° and 23.8° were observed in both cases, which were related to the semi-crystalline behavior of PCL [34,35,36]. The FTIR test was employed to investigate the functional groups found in PCL, CH, CMFE, and PCL/CH/CMFE (Figure 3). The pure PCL membrane had characteristic peaks at 2938, 2865, 1730,1408, 1106, and 730 cm−1, which are related to the (CH2), (CH2), (C=O), (CH2), (O-C), and (CH2) vibrations [37]. The FTIR spectra of chitosan showed peaks at 3440 cm−1, 1636–1650 cm−1, and 1107 cm−1 that contribute to chitosan groups, as stated in the FTIR spectra of chitosan [38]. The broad peak in the spectra of pure CMFE shown in Figure 3 at 3404 cm−1 revealed the existence of both free hydroxyl groups and hydroxyl groups involved in hydrogen bonding. The sharp peak at 1712 cm−1 may be caused by the carbonyl group (C=O), which could also point to the existence of compounds containing carboxylic acid. Similar results suggest that the C. dichotoma fruit extract contains some uronic acid in previous studies [39]. The band at 1434 cm−1 is caused by the C=C stretching vibration in aromatic rings, such as those present in phenolic and flavonoid compounds. The spectrum of the PCL/CH/CMFE mat (Figure 3) showed no major extra peaks or deviations from the peaks of the pure materials. This confirmed that CFE remained mostly intact and unreacted within the nanofibers. The PCL/CH/CMFE spectrum, as shown in Figure 3, suggests that there were no significant extra peaks or changes from the peaks of the pure materials. This confirmed that CMFE was mostly unreacted and retained inside the fibers. Stress–strain profiles exhibit two nanofiber samples made from pure PCL 12 w/v and PCL/CH/CMF $5\%$ volume ratios of $\frac{70}{30}$ (Figure 4A and Table 2). Adding the natural polymer to the scaffold reduces its mechanical strength. Lower fiber strength is detected in the sample containing $12\%$ PC, in addition to enhanced flexibility and elongation. PCL/CH/CMFE $3\%$ membranes presented a stress (MPa) of 20.1107 in a dry state, whereas PCL demonstrated 24.8931 MPa under the same conditions (Figure 4B). The mechanical behavior of wet membrane scaffolds was examined after being immersed in phosphate-buffered saline (PBS) for 24 h, and standard stress–strain curves were produced utilizing standard tensile stress–strain testing (Figure 4). Table 2 demonstrates that the PCL’s extension, thickness, tensile modulus, and stress were reduced by the addition of CH and CMFE. The PCL has $69.2\%$ porosity, while the PCL/CH/CMFE has $76.9\%$. Wettability is an essential factor to consider when choosing a wound dressing since it influences cell adherence, proliferation, and the ability to absorb exudates. The water contact angle can be used to determine the wettability of a surface. The water contact angle was measured to determine the behavior of the composite PCL/CH/CMFE mats and assess the hydrophilicity alterations in nanocomposite scaffolds. As presented in Figure 5, results obtained for PCL-$12\%$ film exhibit poor hydrophilicity with an average contact angle of 125.5°, which is in line with the hydrophobic nature of the polymer. The contact angle value of PCL decreased to 99.4°, 73°, and 47.4° after the addition of CH at $2\%$, CMFE at $3\%$, and CH at $2\%$ + CMFE at $3\%$, respectively. It has been demonstrated that greatly swelling matrices promote cell growth, adhesion, and internal migration of scaffolds [39]. Anionic and cationic polyelectrolytes improve the electrical conductivity of the electrospinning solution [40]. The swelling’s capacity to absorb wound exudates keeps the region surrounding the wound dry and reduces the risk of infection. The swelling ratio was increased by the use of CMFE in this study. The swelling behavior of the PCL/CH/CMFE nanofibrous performed better, with values of 101.2 ± 6.1 as compared to the PCL/CH mat, which was 86.7 ± 7.7, as shown in Table 2. The results of the wettability test supported our swelling findings. The incorporation of CH and CMFE within the PCL matrix may present some hydrophilic groups, such as NH and OH, on the surface of the nanocomposite membranes. Therefore, the wettability of the PCL scaffold can be seriously modified by the addition of CH/CMFE nanofiber. The higher hydrophilicity of the resultant scaffold is thought to be due to hydrophilic groups, such as hydroxyl and carboxyl groups, which are present in the CMF structure as identified by the GC-MS test. These findings are in agreement with the result of Allafchian et al., who reported that the contact angle for PCL is 120°, while after the addition of quince seed mucilage, the contact angle value of PCL decreased to about 40° [41]. The in vitro release of CMFE from the PCL/CH fibrous mat is depicted in Figure 6. The release curve showed that larger CMFE concentrations were released at equivalent time points. The PCL/CMFE $3\%$ mats released $94.5\%$ within the first 24 h. Thereafter, the slope of the curves decreased, indicating a more gradual release, which extended to 120 h. The maximum CMFE concentrations after 120 h were 62.1 ± 2.2 μg/mL for the PCL/CMFE $3\%$ mat. The antibacterial activity of the nanofibrous mat is important for the healing of skin lesions. Therefore, the antibacterial effectiveness of the produced CMFE-loaded PCL/CH nanofibrous mats was examined in this section against the most prevalent bacterial species that cause wound infections, including E. coli, S. Enterica, P. aeruginosa, S. aureus, and B. subtilis. Figure 7 illustrates a PCL/CH scaffold with and without CMFE antibacterial activity. PCL/CH fibers without CMFE extract exhibit antibacterial activity with various inhibition zones against B. subtilis (11.47 mm), S. aureus (12.5 mm), and E. coli, S. enterica, and P. aeruginosa with 22.5 mm, 22.1 mm, and 15.4 mm, respectively. PCL/CH/CMFE demonstrated significant antibacterial activity against S. aureus (21.31 ± 0.17 mm with IZ at 100 mg/mL concentration), E. coli (31.34 ± 0.42 mm), and S. enterica, B. subtilis, and P. aeruginosa (30.27 ± 0.57, 27.53 ± 1.53, and 22.17 ± 0.12 mm, respectively). As Gram-positive bacteria, S. aureus and B. subtilis are believed to have thicker cell walls and are, therefore, more resistant to action by chitosan than E. coli [42]. Additionally, it has been shown that chitosan has a much stronger affinity toward Gram-negative bacteria in comparison to Gram-positive bacteria [43]. It is well known and has been thoroughly researched that chitosan has antibacterial properties that are connected to its cationic structure, which results from its protonated amine groups when it comes into contact with liquids [44]. It is possible that this is connected to the positively charged NH2 groups of CH, which absorb and desorb negative and positively charged Gram-negative and Gram-positive bacterium, respectively, due to electrostatic forces [45]. It is important to emphasize that CMFE extracts added antibacterial action to chitosan-based nanofibrous mats against Gram-negative bacteria in addition to providing antibacterial activity against Gram-positive bacteria. This is attributed to CMFE’s natural ability to combat E. coli, S. enterica, P. aeruginosa, S. aureus, and B. subtilis [28,46] and its synergistic antimicrobial effect with chitosan against the above pathogens. Thus, the synergistic antibacterial action of PCL/CH-based nanofiber and coupled CMFE exhibited encouraging results to enhance antibacterial wound dressings. Further research is necessary to fully comprehend the mechanism of growth suppression in B. subtilis, S. aureus, E. coli, and S. enterica, as well as the detailed mode of action of the bioactive components of CMFE. This study showed positive results concerning the antimicrobial activity caused by PCL/CH/CMF to S. aureus, E. coli, S. enterica, B. subtilis, and P. aeruginosa. This corresponds to the previous study conducted by Stasiuk et al., [ 47] who reported antibacterial activities against S. aureus and E. coli. These results suggest that the ethanol extract of CMF can be utilized to inhibit foodborne diseases caused by microbial pathogens such as S. aureus, E. coli, S. enterica, B. subtilis, and P. aeruginosa. The current results are consistent with the previously published findings of Hamdia et al. [ 48] on the antibacterial activity of Cordia fruit extracts. These authors showed that C. myxa extracts (aqueous and alcoholic) exhibited concentration-dependent inhibition zones against P. fluorescens, S. enterica, S. dysenteriae, and E. coli. The results suggest that fruit extract could be used to reduce microbial infections caused by S. aureus, E. coli, S. enterica, B. subtilis, P. aeruginosa, A. brasiliensis, and S. cerevisiae. To evaluate the cytotoxicity of the nanofibers, we performed the MTT test. The viability of cells cultured in various nanofiber suspensions varied, as seen in Figure 8. Compared to the pure PCL membrane, the PCL-CH/CMF membrane exhibited almost negligible cytotoxicity. The enhanced biocompatibility of CMF may have increased cell viability. Results showed that dermal fibroblast cells were not significantly cytotoxic to the nanofiber scaffolds of PCL/CH/CMFE. The PCL/CH/CMFE electrospun nanofibers have improved surface area, density, and pore size characteristics. This occurred by changing critical manufacturing parameters, such as the distance between the needle and collector, electrical voltage, and the volume of the pumped polymer. Nanofibers have grown in importance in biotechnology due to their prospective uses and huge surface area, which might be employed to make a good matrix for biological activity [49]. It is crucial to employ biocompatible chemicals such as CH and CMFE; because of their greater bioactivity compared to synthetic polymers, natural polymers such as CH have demonstrated promising outcomes in vitro and in vivo [50]. Reactive oxygen species (ROS) depletion-based therapeutics like CMFE may help to reduce inflammation and facilitate a smooth transition from the inflammatory to the proliferative phase of the cell cycle. According to earlier research, natural antioxidants such as plant flavonoids may prevent free radical chain reactions or scavenge ROS to reduce the oxidation of cellular components and promote cell development [51]. The specific mechanisms by which CMFE stimulates the production of fibroblast growth factor and cell proliferation are still unknown. According to earlier studies, CMFE interacts with the growth factor receptors on fibroblasts to promote cell activity and proliferation. One of the key hypothesized mechanisms of topical CMFE activity on the wound area was related to the plant extract’s chemotactic action, which may have attracted inflammatory cells in addition to having antimicrobial properties [52]. ## 3.1. Chemicals Polycaprolactone (PCL, Mn 80,000 g/mol), chitosan (CH) (≥$85\%$ (degree of deacetylation) Mw 100 kDa, acetic acid, and formic acid were purchased from Sigma Aldrich at concentrations of 0.2 M and 0.6 M, respectively. All the compounds employed were of analytical reagent quality and required no further processing. ## 3.2. Plant Collection and Extraction Fresh C. myxa fruits (Figure 9) were harvested from trees in Baghdad, Iraq, between July and August 2020, and botanists from Al-Nahrain University confirmed their authenticity. The harvested plant material was packaged in a polyethylene bag to avoid moisture loss during transport to Al-Nahrain University’s Research Center laboratory in Baghdad. The procedure for C. myxa fruit extract was conducted according to the previously published procedure reported by El-Massry et al. [ 53]. Briefly, the fruits were cleaned and thoroughly inspected to remove those that were physically or microbiologically damaged. The gathered fruits were cleaned with tap water and passed through a deionized water solution. The harvest was collected, dried in the shade, and then processed into a powder. Then, 100 g of powder was mixed with 250 mL of ethanol in a round-bottom flask utilizing a magnetic stirrer for three hours at 25 °C, and the mixture was filtered under vacuum (using Whatman No. 1 filter paper, Cytiva, NY, USA). The materials of the filter paper were transferred to the conical flask once again, and the procedure was repeated. The extract was pooled and dried using a rotating evaporator. For yield computation, the resultant crude extract was weighed and kept at 4 °C until required. As a solvent for extraction, a $70\%$ ethanolic solution was utilized. In the following studies, the fruit sample was diluted in ethanol at an optimal concentration. The residual material was preserved at −20 °C until further use. All the tests were carried out within 72 h of extraction. ## 3.3. Electrospun The electrospinning variables, PCL and CH concentration, were assessed with minimal modifications based on a recently published study [32]. For the electrospun method, the optimum voltage, feeding rate, and distance between the needle and the collector were set at 12 kV, 0.6 mL/h, and 21 cm, respectively, and the fibers were collected using an aluminum sheet. A suspension of $12\%$ PCL, $2\%$ CH, and $5\%$ CMF was prepared separately using a $\frac{30}{70}$ solvent mixture of acetic acid and formic acid. Then, a mixture of PCL/CH/CMF in the ratio of $\frac{3}{1}$/2 was produced, and it was placed on a magnetic stirrer for 15 h to create a uniform mixture. As-prepared suspensions were placed into a 5 mL plastic syringe, whose nozzle was an 18-gauge stainless steel tip. For the gathering of the nanofibrous mats, several voltages and flow rates were applied to each of the blend suspensions, while the optimal values that produced uniform nanofibers were chosen. A total of 20 cm was maintained between the tip and the collection. Materials were gathered on a ground collector coated in aluminum plates and cotton gauze. During collection, a fixed collecting surface was utilized, while the spinneret moved transversely at 100 mm/s and was 100 mm in diameter. ## 3.4. Morphological Features SEM (TESCAN, MRA-3, Brno, Czech Republic) was used to analyze the morphological properties of the CMF/PCL/chitosan scaffold nanofibers, which were coated with an ultrathin 5-nm gold undercurrent and a voltage of 6 kV. XRD at the CuKα wavelength (0.15405 nm) was used to investigate the crystalline phase of CMFE-loaded PCL/CH. The current utilized was 25 mA, while the voltage used was 40 kV. ## 3.5. FTIR FTIR (IR Tracer 100, Shimadzu, Japan) measurements in the 500–4000 cm−1 range were used to investigate the functional groups in the nanofiber electrospun samples. ## 3.6. Porosity The tensile strength and elasticity of PCL/CH/CMFE were calculated during the examination of scaffold strength. The porosity of the PCL/CH/CMFE composite was determined using the gravimetric approach as in previously published work [25]:$$p \leq 1$$−Ad Bd ×$100\%$ where p indicates porosity, Ad represents the apparent density fibrous mat, and Bd indicates the bulk density fibrous mat. ## 3.7. Water Contact Angle The contact angle was dynamically calculated using the Wilhelmy plate approach [54]. The hydrophilicity or hydrophobicity of the sample was tested and evaluated using water with a surface tension of 72 dyn/cm. The liquid in the container increased until the metal plate’s intended surface was completely submerged in water, at which point it began to descend. The formula below can be used to determine the contact angle. Τcos θ=WsC where T is the water’s surface tension, θ indicates the water contact angle, Ws indicates the weight shift, and C is the fibrous mat’s diameter. ## 3.8. The Swelling Test The swelling level of the PCL/CH/CMFE samples was evaluated in PBS solution at a pH of 7.4 for 2 h at room temperature in accordance with the following relationship:St=Ws−WdWd×$100\%$ where St is the swelling test, Ws is the weight of each sample after it is immersed in the buffer solution for an hour, and Wd is the sample’s initial weight in its dry form. ## 3.9. CMFE Release Profile A 1 cm2 piece of CMFE-loaded PCL/CH mat was soaked in 14 mL of pH 7.4 PBS buffer at 37 °C for various times ranging from 1 to 120 h with moderate stirring. To measure the CMFE concentration, a UV spectrophotometer at 280 nm was used with a calibration curve of CMFE at ranges of 0 to 100 µg/mL, and aliquots of the solution (200 μL) were taken at each time point. To maintain the overall volume constant, a fresh 2 µL of PBS was added to the solution after the same volume was removed. ## 3.10. Antibacterial S. aureus, E. coli, S. enterica, B. subtilis, and P. aeruginosa were used for the evaluation of PCL/CH/CMFE in this part. The antibacterial activities of PCL/CH scaffolds with and without CMFE were evaluated by a zone inhibition test. Antibacterial activity was tested against E. coli, S. enterica, S. aureus, B. subtilis, and P. aeruginosa. Briefly, the synthetic corneal plates were cut to a diameter of 6 mm. UV light was used to sterilize the plates for one hour. Gentamycin was utilized as a control group, and the disks were cultured for 24 h at 37 °C. The growth inhibition zones were then calculated. ## 3.11. MTT Assay The MTT test was carried out on the normal fibroblast cells, L929 cells, in order to evaluate the biocompatibility effect of the PCL/CH/CMF fibrous mat. Briefly, DMEM enriched with $10\%$ fetal bovine serum, streptomycin (100 g/mL), and penicillin (100 U/mL) were used to cultivate the cells. They were then incubated at 37 °C with $5\%$ CO2. Therefore, cells were plated in a multi-well plate with DMEM-soaked fibers at a density of 2.4 × 104 cells per well and incubated for 24, 48, and 72 h. The MTT solution was then incorporated into the media in each well, and the cells were cultivated for an additional 4 h at 37 °C. The final step was dissolving the produced formazan crystals in DMSO and reading the absorbance at 570 nm on a microplate reader. ## 4. Conclusions In this study, a novel fibrous scaffold formulation was successfully prepared using electrospun PCL/CH fibers loaded with CMFE and was found to be safe for skin L929 fibroblast cells for use in wound dressing applications. After that, their structure, physicomechanical, antimicrobial, and in vitro characteristics were investigated. SEM analysis revealed thin fibers with mean diameters as low as 197 nm and a bead-free shape. PCL/CH fibers loaded with CMFE showed high porosity, promising tensile strength, enhanced hydrophilicity, and biocompatibility. Furthermore, the incorporation of CMFE in the PCL/CH composite was supported by the chemical analysis of the nanofibrous composite by FTIR spectroscopy and structural XRD data. CMFE-loaded PCL/CH fibers were effective against both Gram-positive (B. subtilis, S. aureus) and Gram-negative (S. enterica, E. coli, and P. aeruginosa) bacteria. The CMFE drug was rapidly released within 24 h. According to the findings, PCL/CH/CMFE electrospun nanofibers demonstrated outstanding promise and had the potential to be employed as wound dressings. ## References 1. Harris-Tryon T.A., Grice E.A.. Microbiota and maintenance of skin barrier function. Science (2022) 376 940-945. DOI: 10.1126/science.abo0693 2. Nguyen A.V., Soulika A.M.. The Dynamics of the Skin’s Immune System. Int. J. Mol. Sci. (2019) 20. DOI: 10.3390/ijms20081811 3. Baig M.S., Banu A., Zehravi M., Rana R., Burle S.S., Khan S.L., Islam F., Siddiqui F.A., Massoud E.E.S., Rahman M.H.. An Overview of Diabetic Foot Ulcers and Associated Problems with Special Emphasis on Treatments with Antimicrobials. Life (2022) 12. DOI: 10.3390/life12071054 4. Tottoli E.M., Dorati R., Genta I., Chiesa E., Pisani S., Conti B.. Skin Wound Healing Process and New Emerging Technologies for Skin Wound Care and Regeneration. Pharmaceutics (2020) 12. DOI: 10.3390/pharmaceutics12080735 5. Zhang K., Jiao X., Zhou L., Wang J., Wang C., Qin Y., Wen Y.. Nanofibrous composite aerogel with multi-bioactive and fluid gating characteristics for promoting diabetic wound healing. Biomaterials (2021) 276 121040. DOI: 10.1016/j.biomaterials.2021.121040 6. De Luca I., Pedram P., Moeini A., Cerruti P., Peluso G., Di Salle A., Germann N.. Nanotechnology Development for Formulating Essential Oils in Wound Dressing Materials to Promote the Wound-Healing Process: A Review. Appl. Sci. (2021) 11. DOI: 10.3390/app11041713 7. Nour S., Baheiraei N., Imani R., Khodaei M., Alizadeh A., Rabiee N., Moazzeni S.M.. A review of accelerated wound healing approaches: Biomaterial-assisted tissue remodeling. J. Mater. Sci. Mater. Med. (2019) 30 120. DOI: 10.1007/s10856-019-6319-6 8. Alberti T., Coelho D.S., Voytena A., Pitz H., de Pra M., Mazzarino L., Kuhnen S., Ribeiro-do-Valle R.M., Maraschin M., Veleirinho B.. Nanotechnology: A promising tool towards wound healing. Curr. Pharm. Des. (2017) 23 3515-3528. DOI: 10.2174/1381612823666170503152550 9. Albukhaty S., Al-Karagoly H., Dragh M.A.. Synthesis of zinc oxide nanoparticles and evaluated its activity against bacterial isolates. J. Biotech Res. (2020) 11 47-53 10. Safat S., Buazar F., Albukhaty S., Matroodi S.. Enhanced sunlight photocatalytic activity and biosafety of marine-driven synthesized cerium oxide nanoparticles. Sci. Rep. (2021) 11 14734. DOI: 10.1038/s41598-021-94327-w 11. Jihad M.A., Noori F.T.M., Jabir M.S., Albukhaty S., AlMalki F.A., Alyamani A.A.. Polyethylene Glycol Functionalized Graphene Oxide Nanoparticles Loaded with. Molecules (2021) 26. DOI: 10.3390/molecules26113067 12. Croitoru A.-M., Ficai D., Ficai A., Mihailescu N., Andronescu E., Turculet S.C.. Nanostructured Fibers Containing Natural or Synthetic Bioactive Compounds in Wound Dressing Applications. Materials (2020) 13. DOI: 10.3390/ma13102407 13. Al-Kaabi W.J., Albukhaty S., Al-Fartosy A.J.M., Al-Karagoly H.K., Al-Musawi S., Sulaiman G.M., Dewir Y.H., Alwahibi M.S., Soliman D.A.. Development of. Appl. Sci. (2021) 11. DOI: 10.3390/app11020828 14. Wang C., Wang J., Zeng L., Qiao Z., Liu X., Liu H., Zhang J., Ding J.. Fabrication of Electrospun Polymer Nanofibers with Diverse Morphologies. Molecules (2019) 24. DOI: 10.3390/molecules24050834 15. Albukhaty S., Al-Karagoly H., Allafchian A.R., Jalali S.A.H., Al-Kelabi T., Muhannad M.. Production and characterization of biocompatible nanofibrous scaffolds made of β-sitosterol loaded polyvinyl alcohol/tragacanth gum composites. Nanotechnology (2021) 33 085102. DOI: 10.1088/1361-6528/ac3789 16. Luo W.L., Zhang J., Qiu X., Chen L.J., Fu J., Hu P.Y., Li X., Hu R.J., Long Y.Z.. Electric-field-modified in situ precise deposition of electrospun medical glue fibers on the liver for rapid hemostasis. Nanoscale Res. Lett. (2018) 13 278. DOI: 10.1186/s11671-018-2698-8 17. Amini S., Salehi H., Setayeshmehr M., Ghorbani M.. Natural and synthetic polymeric scaffolds used in peripheral nerve tissue engineering: Advantages and disadvantages. Polym. Adv. Technol. (2021) 32 2267-2289. DOI: 10.1002/pat.5263 18. Malikmammadov E., Tanir T.E., Kiziltay A., Hasirci V., Hasirci N.. PCL and PCL-based materials in biomedical applications. J. Biomater. Sci. Polym. Ed. (2018) 29 863-893. DOI: 10.1080/09205063.2017.1394711 19. Kahdim Q.S., Abdelmoula N., Al-Karagoly H., Albukhaty S., Al-Saaidi J.. Fabrication of a Polycaprolactone/Chitosan Nanofibrous Scaffold Loaded with. BioTech (2023) 12. DOI: 10.3390/biotech12010019 20. Ghasemi-Mobarakeh L., Prabhakaran M.P., Morshed M., Nasr-Esfahani M.H., Ramakrishna S.. Bio-functionalized PCL nanofibrous scaffolds for nerve tissue engineering. Mater. Sci. Eng. C (2010) 30 1129-1136. DOI: 10.1016/j.msec.2010.06.004 21. Tao F., Cheng Y., Shi X., Zheng H., Du Y., Xiang W., Deng H.. Applications of Chitin and Chitosan Nanofibers in Bone Regenerative Engineering. Carbohydr. Polym. (2020) 230 115658. DOI: 10.1016/j.carbpol.2019.115658 22. Al-Musawi S., Albukhaty S., Al-Karagoly H., Almalki F.. Design and Synthesis of Multi-Functional Superparamagnetic Core-Gold Shell Coated with Chitosan and Folate Nanoparticles for Targeted Antitumor Therapy. Nanomaterials (2020) 11. DOI: 10.3390/nano11010032 23. Priya S., Batra U., Samshritha R.N., Sharma S., Chaurasiya A., Singhvi G.. Polysaccharide-based nanofibers for pharmaceutical and biomedical applications: A review. Int. J. Biol. Macromol. (2022) 218 209-224. DOI: 10.1016/j.ijbiomac.2022.07.118 24. Matica M.A., Aachmann F.L., Tøndervik A., Sletta H., Ostafe V.. Chitosan as a wound dressing starting material: Antimicrobial properties and mode of action. Int. J. Mol. Sci. (2019) 20. DOI: 10.3390/ijms20235889 25. Ghaee A., Bagheri-khoulenjani S., Afshar H.A., Bogheiri H.. Biomimetic nanocomposite scaffolds based on surface modified PCL-nanofibers containing curcumin embedded in chitosan/gelatin for skin regeneration. Compos. Part B (2019) 177 107339. DOI: 10.1016/j.compositesb.2019.107339 26. Sivalingam P.N., Singh D., Chauhan S.. Morphological and molecular diversity of an underutilized fruit crop—. Genet. Resour. Crop Evol. (2012) 59 305-316. DOI: 10.1007/s10722-011-9781-7 27. Yaermaimaiti S., Wu T., Aisa H.A.. Bioassay-guided isolation of antioxidant, antimicrobial, and antiviral constituents of. Ind. Crops Prod. (2021) 172 113977. DOI: 10.1016/j.indcrop.2021.113977 28. Al-Musawi M.H., Ibrahim K.M., Albukhaty S.. Phytochemical Analysis, and Anti-Microbial Activities of Ethanol Extract of. Res. J. Pharm. Technol. (2022) 15 2871-2876. DOI: 10.52711/0974-360X.2022.00479 29. Al-Musawi M.H., Ibrahim K.M., Albukhaty S.. In vitro study of antioxidant, antibacterial, and cytotoxicity properties of. Iran. J. Microbiol. (2022) 14 97-103. DOI: 10.18502/ijm.v14i1.8810 30. Khandaker M., Alkadhem N., Progri H., Nikfarjam S., Jeon J., Kotturi H., Vaughan M.B.. Glutathione Immobilized Polycaprolactone Nanofiber Mesh as a Dermal Drug Delivery Mechanism for Wound Healing in a Diabetic Patient. Processes (2022) 10. DOI: 10.3390/pr10030512 31. Liu Y., Li C., Feng Z., Han B., Yu D.-G., Wang K.. Advances in the Preparation of Nanofiber Dressings by Electrospinning for Promoting Diabetic Wound Healing. Biomolecules (2022) 12. DOI: 10.3390/biom12121727 32. Ma L., Shi X., Zhang X., Li L.. Electrospinning of polycaprolacton/chitosan core-shell nanofibers by a stable emulsion system. Colloids Surf. A Physicochem. Eng. Asp. (2019) 583 123956. DOI: 10.1016/j.colsurfa.2019.123956 33. Ahn H., Gwak H.J., Kim Y.M., Yu W.-R., Lee W.J., Yeo S.Y.. Microstructure Analysis of Drawing Effect and Mechanical Properties of Polyacrylonitrile Precursor Fiber According to Molecular Weight. Polymers (2022) 14. DOI: 10.3390/polym14132625 34. Pourhojat F., Sohrabi M., Shariati S., Mahdavi H., Asadpour L.. Evaluation of poly ε-caprolactone electrospun nanofibers loaded with Hypericum perforatum extract as a wound dressing. Res. Chem. Intermed. (2017) 43 297-320. DOI: 10.1007/s11164-016-2623-7 35. Al-Musawi S., Albukhaty S., Al-Karagoly H., Sulaiman G.M., Alwahibi M.S., Dewir Y.H., Soliman D.A., Rizwana H.. Antibacterial Activity of Honey/Chitosan Nanofibers Loaded with Capsaicin and Gold Nanoparticles for Wound Dressing. Molecules (2020) 25. DOI: 10.3390/molecules25204770 36. Liu M., Zhou Y., Zhang Y., Yu C., Cao S.. Preparation and structural analysis of chitosan films with and without sorbitol. Food Hydrocoll. (2013) 33 186-191. DOI: 10.1016/j.foodhyd.2013.03.003 37. Mosallanezhad P., Nazockdast H., Ahmadi Z., Rostami A.. Fabrication and characterization of polycaprolactone/chitosan nanofibers containing antibacterial agents of curcumin and ZnO nanoparticles for use as wound dressing. Front. Bioeng. Biotechnol. (2022) 10 1797. DOI: 10.3389/fbioe.2022.1027351 38. Tao F., Cheng Y., Tao H., Jin L., Wan Z., Dai F., Xiang W., Deng H.. Carboxymethyl chitosan/sodium alginate-based micron-fibers fabricated by emulsion electrospinning for periosteal tissue engineering. Mater. Des. (2020) 194 108849. DOI: 10.1016/j.matdes.2020.108849 39. Pawar H.A., Gavasane A.J., Choudhary P.D.. Extraction of polysaccharide from fruits of Cordia dichotoma G. Forst using acid precipitation method and its physicochemical characterization. Int. J. Biol. Macromol. (2018) 115 871-875. DOI: 10.1016/j.ijbiomac.2018.04.146 40. Suwantong O.. Biomedical applications of electrospun polycaprolactone fiber mats. Polym. Adv. Technol. (2016) 27 1264-1273. DOI: 10.1002/pat.3876 41. Allafchian A., Jalali S.A.H., Mousavi S.E., Hosseini S.S.. Preparation of cell culture scaffolds using polycaprolactone/quince seed mucilage. Int. J. Biol. Macromol. (2020) 155 1270-1276. DOI: 10.1016/j.ijbiomac.2019.11.096 42. Benhabiles M.S., Salah R., Lounici H., Drouiche N., Goosen M.F.A., Mameri N.. Antibacterial activity of chitin, chitosan and its oligomers prepared from shrimp shell waste. Food Hydrocoll. (2012) 29 48-56. DOI: 10.1016/j.foodhyd.2012.02.013 43. Mohamed A.E., Elgammal W.E., Eid A.M., Dawaba A.M., Ibrahim A.G., Fouda A., Hassan S.M.. Synthesis and characterization of new functionalized chitosan and its antimicrobial and in-vitro release behavior from topical gel. Int. J. Biol. Macromol. (2022) 207 242-253. DOI: 10.1016/j.ijbiomac.2022.02.173 44. Xie W., Xu P., Wang W., Liu Q.. Preparation and antibacterial activity of a water-soluble chitosan derivative. Carbohydr. Polym. (2002) 50 35-40. DOI: 10.1016/S0144-8617(01)00370-8 45. Abd El-Hack M.E., El-Saadony M.T., Shafi M.E., Zabermawi N.M., Arif M., Batiha G.E., Khafaga A.F., Abd El-Hakim Y.M., Al-Sagheer A.A.. Antimicrobial and antioxidant properties of chitosan and its derivatives and their applications: A review. Int. J. Biol. Macromol. (2020) 164 2726-2744. DOI: 10.1016/j.ijbiomac.2020.08.153 46. Seyedian R., Isavi F., Najafiasl M., Zaeri S.. Electrospun fibers loaded with. J. Drug Deliv. Sci. Technol. (2021) 63 102528. DOI: 10.1016/j.jddst.2021.102528 47. Stasiuk M., Kozubek A.. Biological activity of phenolic lipids. Cell. Mol. Life Sci. (2010) 67 841-860. DOI: 10.1007/s00018-009-0193-1 48. Hamdia M.S., Al-Faraji A.S.. Evaluation of inhibitory activity of. Evaluation (2017) 7 2224-3208 49. Angammana C.J., Jayaram S.H.. Fundamentals of electrospinning and processing technologies. Part. Sci. Technol. (2016) 34 72-82. DOI: 10.1080/02726351.2015.1043678 50. Yee Kuen C., Masarudin M.J.. Chitosan Nanoparticle-Based System: A New Insight into the Promising Controlled Release System for Lung Cancer Treatment. Molecules (2022) 27. DOI: 10.3390/molecules27020473 51. Alzubaidi A.K., Al-Kaabi W.J., Ali A.A., Albukhaty S., Al-Karagoly H., Sulaiman G.M., Asiri M., Khane Y.. Green Synthesis and Characterization of Silver Nanoparticles Using. Appl. Sci. (2023) 13. DOI: 10.3390/app13042182 52. Al-Khafaji S.A., Alsaadawi M.A., Al-Yasari A.M., Al-Saadawe M.A.. Article review:. Basrah J. Agric. Sci. (2021) 34 267-277. DOI: 10.37077/25200860.2021.34.2.20 53. El-Massry K.F., Farouk A., Mahmoud K.F., El-Ghorab A.H., Musa A., Mostafa E.M., Ghoneim M.M., Naguib I.A., Abdelgawad M.A.. Chemical characteristics and targeted encapsulated. Saudi J. Biol. Sci. (2021) 28 5349-5358. DOI: 10.1016/j.sjbs.2021.05.064 54. Karim A.M., Kavehpour H.P.. Effect of Viscous Force on Dynamic Contact Angle Measurement Using Wilhelmy Plate Method. Colloids Surf. A Physicochem. Eng. Asp. (2018) 548 54-60. DOI: 10.1016/j.colsurfa.2018.03.058
--- title: Inhibitors of the Ubiquitin-Mediated Signaling Pathway Exhibit Broad-Spectrum Antiviral Activities against New World Alphaviruses authors: - Niloufar A. Boghdeh - Brittany McGraw - Michael D. Barrera - Carol Anderson - Haseebullah Baha - Kenneth H. Risner - Ifedayo V. Ogungbe - Farhang Alem - Aarthi Narayanan journal: Viruses year: 2023 pmcid: PMC10059822 doi: 10.3390/v15030655 license: CC BY 4.0 --- # Inhibitors of the Ubiquitin-Mediated Signaling Pathway Exhibit Broad-Spectrum Antiviral Activities against New World Alphaviruses ## Abstract New World alphaviruses including Venezuelan Equine Encephalitis Virus (VEEV) and Eastern Equine Encephalitis Virus (EEEV) are mosquito-transmitted viruses that cause disease in humans and equines. There are currently no FDA-approved therapeutics or vaccines to treat or prevent exposure-associated encephalitic disease. The ubiquitin proteasome system (UPS)-associated signaling events are known to play an important role in the establishment of a productive infection for several acutely infectious viruses. The critical engagement of the UPS-associated signaling mechanisms by many viruses as host–pathogen interaction hubs led us to hypothesize that small molecule inhibitors that interfere with these signaling pathways will exert broad-spectrum inhibitory activity against alphaviruses. We queried eight inhibitors of the UPS signaling pathway for antiviral outcomes against VEEV. Three of the tested inhibitors, namely NSC697923 (NSC), bardoxolone methyl (BARM) and omaveloxolone (OMA) demonstrated broad-spectrum antiviral activity against VEEV and EEEV. Dose dependency and time of addition studies suggest that BARM and OMA exhibit intracellular and post-entry viral inhibition. Cumulatively, our studies indicate that inhibitors of the UPS-associated signaling pathways exert broad-spectrum antiviral outcomes in the context of VEEV and EEEV infection, supporting their translational application as therapeutic candidates to treat alphavirus infections. ## 1. Introduction New World encephalitic alphaviruses, including Venezuelan Equine Encephalitis Virus (VEEV) and the closely related Eastern Equine Encephalitis Virus (EEEV), are classified as re-emerging viruses and category B select agents by the National Institutes of Health (NIH) and the Centers for Disease Control and Prevention (CDC). VEEV and EEEV belong to the family of Togaviridae and are positive-strand RNA viruses [1,2,3]. Equine Encephalitis disease occurs naturally in humans in many parts of the world annually due to transmission by infected mosquitoes. Infections have been recorded for several decades in the Americas, primarily associated with natural transmission by infected mosquito vectors [4,5,6,7,8,9]. VEEV and EEEV are also highly stable and retain infectivity as aerosols, which greatly increases the possibility of encephalitic disease in infected individuals. VEEV or EEEV exposures due to deliberate aerosol dissemination pose encephalitis concerns because of their ability to establish a quick infection in the central nervous system (CNS) through the olfactory neuron, by-passing the blood–brain barrier (BBB) [10,11,12,13,14]. The establishment of a robust productive infection in the CNS triggers a strong inflammatory response that impacts the integrity of the BBB and contributes to encephalitic disease [15,16,17,18]. There are currently no FDA-approved small molecule therapeutic strategies to treat VEEV or EEEV exposures. Host-based signaling mechanisms play critical roles in the establishment of a productive VEEV and EEEV infection in in vitro and in vivo models, thus opening up the possibility of host-based proteins as broad-spectrum targets for therapeutic intervention [19,20,21,22,23,24]. We previously demonstrated that the ubiquitin proteasome system (UPS) is important for VEEV infection and inhibiting the UPS by small molecules elicits broad-spectrum antiviral activity against alphaviruses [25]. The UPS is also a critical requirement for several other acutely infectious viruses that possess epidemic/pandemic potential such as chikungunya virus, dengue virus and influenza virus, thus identifying this pathway as a broadly relevant target for epidemic/pandemic preparedness [25,26,27,28,29,30,31,32,33,34]. In addition to the UPS machinery itself, many signaling pathways that involve ubiquitination of specific signaling molecules have also been demonstrated to be viable targets for intervention against acutely infectious viruses. Notable among such signaling pathways with essential ubiquitination steps are the NFκB signaling pathway and the Nrf2 pathway, which are known to be targets for host–virus interaction for many acute viruses [34,35,36,37,38,39,40,41,42,43]. We previously demonstrated that the NFκB signaling pathways plays an important role in the establishment of a productive infection for VEEV and EEEV [36]. The wealth of data that exist regarding the important roles of the UPS, the NFκB and Nrf2 signaling pathways led us to hypothesize that inhibitors that interfere with UPS-mediated signaling events will exert an inhibitory outcome in the context of alphaviruses. We tested our hypothesis using eight small molecules that are well documented in the literature as inhibitors of NFκB and Nrf2 signaling events by interfering with ubiquitination modification. Our data demonstrate that two inhibitors, omaveloxolone (OMA), and bardoxolone methyl (BARM) exhibit potent, broad-spectrum inhibitory potential against VEEV and EEEV in a cell-type-independent manner. Treatment of VEEV-TC83-infected cells with OMA also resulted in the inhibition of several proinflammatory cytokines, thus adding support to the utility of these small molecules as broad-spectrum inhibitors of New World alphaviruses. ## 2.1. Cell Culture Vero African Green Monkey kidney epithelial cells (ATCC, CCL-81) were grown in Dulbecco’s Modified Eagle’s Medium (DMEM, Quality Biological, 112-013, 101CS, Gaithersburg, MD, USA) supplemented with $5\%$ heat-inactivated fetal bovine serum (FBS), $1\%$ penicillin and streptomycin (P/S) (Corning 30-003-CI, Corning, NY, USA), and $1\%$ L-glutamine (Corning, 25-005-CI, Corning NY, USA). Human microglial cells, HMC3 (CRL-3304, ATCC, Manassas, VA, USA) and astroglial cells SVG-p12, (ATCC, CRL-8621) were cultured in Eagle’s Minimum Essential Medium with $10\%$ FBS and $1\%$ P/S. Cells were plated per well at a density of 1.5 × 105 for 12-well plates, 5.0 × 104 for 24-well plates. For 96-well plates, HMC3 and SVG-p12 cells were plated at 100 µL at 1.0 × 104 or 200 µL at 2.0 × 104 per well, and Vero cells were plated at 100 µL 5.0 × 104 cells per well. All cell lines were maintained at 37 °C and $5\%$ CO2 culture conditions. ## 2.2. Viruses and Viral Infection Venezuelan Equine Encephalitis Virus (VEEV) TC83 strain was generated using a genomic clone that was kindly provided by Dr. Frolov (University of Alabama at Birmingham). VEEV Trinidad Donkey (TrD) strain and EEEV FL93 strains were kindly provided by Dr. Kehn-Hall (Virginia Polytechnic Institute and State University). All research activities involving select agents that are included in the manuscript were conducted at George Mason University’s Biomedical Research Laboratory with registration and compliance in accordance with Federal Select Agent regulations. ## 2.3. Inhibitors The inhibitors that were utilized in this study were purchased from MedChemExpress (Monmouth Junction, NJ, USA); BARM (bardoxolone methyl/RTA 402, Cat. HY-13324), OMA (omaveloxolone/RTA 408, Cat. HY-12212), NSC (NSC697923, Cat. HY-13811), BAR (bardoxolone, Cat. HY-14909), P00 (P005091, Cat. 87 HY-15667), YH1 (YH239-EE, Cat. HY-12287), JHS (JHS-23, Cat. HY-13982), ML (ML-323, Cat. 88 HY-17543). ## 2.4. Drug Treatment and Plaque Assay The treatment strategies for the small molecule inhibitors and the plaque assay methodology for quantification of virus infectious titer for VEEV and EEEV were carried out following procedures that are well described in the literature [19,20,21,22,23,24]. Briefly, cells were seeded at 1.0 × 104 in 96-well plate or 5.0 × 104 cells per well in 24-well plate. Following 24 h of culture at 37 °C and $5\%$ CO2, cells were pre-treated for 1 h with inhibitors or the DMSO control ($0.1\%$). For purpose of consistency, a DMSO control was included for all infections/treatment experiments at $0.1\%$ unless stated otherwise [44,45,46]. Concentrations for each inhibitor vary in each experiment unless stated otherwise. Cultured cells were infected at a multiplicity of infection (MOI) of 0.1, unless stated otherwise, for 1 h with VEEV-TC83, VEEV-TrD or EEEV FL93. Cells were washed with PBS after infection and the inhibitor-containing media with the same concentrations as in pre-treatment were added back to the cells after 1 h of infection. Culture supernatants were collected at different time points (6 or 18 h post-infection) and analyzed by plaque assays. For plaque assays, Vero cells were plated in 12-well plates at 1.5 × 105 cells per well. Supernatant samples were diluted in DMEM from 101 to 108 and infection was carried out for each dilution as described above. At 1 h post-infection, 1 mL of a 1:1 solution of $1\%$ agarose in distilled H2O with 2x Eagle’s Minimal Essential Medium was added to each well. Plates were allowed to solidify at room temperature and subsequently transferred to 37 °C, $5\%$ CO2 culture condition for 48 h. At 48 h post-infection, plates were fixed with $10\%$ formaldehyde overnight at room temperature. Approximately 24 h after fixation, the agar plugs were discarded and fixed cells were stained with $1\%$ crystal violet in $20\%$ methanol solution for 15 min. The plaques were counted for each plate and plaque forming units/mL (PFU/mL) for each sample was determined. The mean and standard deviation were determined using the average of 3 replicates for each sample. ## 2.5. Cell Viability Assay Cell viability assay was performed on inhibitor-treated cells to quantify cytotoxicity using CellTiterGlo Cell Luminescent Viability Assay according to manufacturer’s instructions (Promega, G7570, Madison, WI, USA). As readout, the ATP level in cells was detected via luminescence, and percent viability was quantified relative to the DMSO control. ## 2.6. RNA Extraction and qRT-PCR Assay The analysis of viral RNA by qRT-PCR was performed following methodologies that are published for VEEV and EEEV [19,20,21,22,23,24]. Briefly, cells were lysed with TriZol LS (ThermoFisher, Waltham, MA, USA), and total RNA was isolated from cells with the Direct-zol RNA miniprep kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol. The intracellular viral RNA quantification was performed using the RNA UltraSenseTM One-step Quantitative RT-PCR System (Applied Biosystems, Waltham, MA, USA). The experiments were performed according to a standardized protocol using 20 µL of master mix using Verso 1-step RT-qPCR Mix with ROX (Fischer Science, Hamptom, NH, USA) and 5 µL of sample RNA, using VEEV-TC83 positive-strand Probe (5′-TGTTGGAAGGAAGATAAACGGCTACGC-3′), forward primer (5′-TCTGACAAGACGTTCCCAATCA-3′) and reverse primer (5′-GAATAACTTCCCTCCGACCACA-3′). The samples were heated at 50 °C for 20 min, 95 °C for 15 min, followed by 40 cycles of 95 °C (15 s) and 60 °C (60 s). The standard curve was determined using serial dilutions of isolated VEEV-TC83 RNA. RNA genomic copies were determined relative to a standard curve containing known amount of viral RNA. Intracellular RNA data were determined per 10,000 cells, while the extracellular RNA data were determined based on supernatant volume. ## 2.7. Negative Strand RT-qPCR HMC3 cells were plated in a 12-well plate at a density of 1.5 × 105 cells per well and maintained at a 37 °C and $5\%$ CO2 culture conditions overnight. Cells were pre-treated with inhibitors OMA, BARM or DMSO control for 1 h, followed by infection with VEEV-TC83 at an Moi 0.1 for 1 h. Inhibitor-conditioning medium was added back after viral overlay was removed and cells were kept at 37 °C and $5\%$ CO2 culture conditions. Six hours post-infection, supernatants and intracellular RNA were collected and stored at −80 °C. Viral intracellular RNA was extracted as previously described. cDNA was generated using a specific primer to negative-strand RNA for VEEV TC-83, which contained a T7 promoter sequence attached at the 5′ end (T7-TC83-Neg 5′-GCGTAATACGACTCACTATATCCGTCAGCTCTCTCGCAGG-3′). A high-capacity cDNA reverse transcription kit (4368814, ThermoFisher, Waltham, MA, USA) was used to generated the negative-strand cDNA per the manufacturer’s instructions. For qPCR of negative-strand viral RNA, forward primer specific to the T7 promoter sequence (5′-GCGTAATACGACTCACTATA-3′) and reverse primer specific to VEEV TC-83 (5′-CAGGTACTAGGTTTATGCGC-3′) were utilized. qPCR for detection of viral negative strand used thermal cycling conditions adapted from PowerUp SYBR Green (A25742, ThermoFisher Scientific, Waltham, MA, USA) per the manufacturer’s instructions: 1 cycle at 50 °C for 2 min, 1 cycle at 95 °C for 2 min, 40 cycles at 95 °C for 15 s, 60 °C for 15 s and 72 °C for 1 min using StepOnePlus™ Real Time PCR system (ThermoFisher Scientific, Waltham, MA, USA). The ΔΔCt method was used to determine the fold change compared to the DSMO average. ## 2.8. Luciferase and Bradford Protein Assay HMC3 or SVGp12 cells were plated in 96-well plate at a density of 1.0 × 104 cells per well and pre-treated for 1 h with selected inhibitors and DMSO control, then infected for one hour with VEEV-TC83, and then post-treated with the same inhibitors, DMSO control or cell culture media. At 18 hpi, supernatants were collected and cellular lysates were obtained using 1X Passive Lysis Buffer (E1941, Promega, Madison, WI, USA), and Nano-Glo Luciferase Assay System (N1130, Promega, Madison, WI, USA) was used to measure the luciferase activity per the manufacturer’s instructions. Aliquots of the lysates were mixed with Bradford Reagent (5000006, Bio-Rad, Hercules, CA, USA) per manufacturer’s instructions. A standard curve for total protein was established using bovine serum albumin (BSA, BP1600, FisherSci, Hamptom, NH, USA) diluted in Passive Lysis Buffer at concentrations of 1, 2.5, 5, 10, 20 μg/μL. Mock-infected cells were used to establish the limit of detection for the luciferase assays. Luminescence and absorbance were measured using a GloMax Promega plate reader (Promega, Madison, WI, USA). Intracellular luciferase was normalized to total μg of protein. ## 2.9. Proinflammatory Cytokine Quantification Assay HMC3 cells were plated in 24-well plate at a density of 5.0 × 104 cells per well and pre-treated for 1 h with selected inhibitors, DMSO control or cell culture medium. After 1 h, inhibitors were removed and cells were infected for 1 h with VEEV-TC83. Medium by itself, or with inhibitors or DMSO was added back to the cells after infection. Supernatants were collected at 6 and 18 h post-infection and stored at −80. The collected supernatants from the two timepoints were assayed with MSD V-PLEX Proinflammatory Panel Human Kit (Cat. K15049D-2) as duplicates for 10 cytokines: IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13 and TNF-α. The assay was performed following the manufacturer’s protocol and read using MESO QuickPlex SQ 120 (MesoScale Discovery, Gaithersburg, MD, USA). ## 2.10. Statistics All quantifications were performed by incorporating data obtained from triplicate samples unless indicated otherwise. Error bars in all figures indicate standard deviations. Plaque assay, qRT-PCR and ELISA data calculations were performed using Microsoft Excel. Graphs and p-values were designed and calculated on GraphPad Prism version 9.2.0 for Windows 10 or 9.4.0 for MacOS. Significance values are indicated using One-way ANOVA with Dunnett’s post-test using asterisks as * $p \leq 0.05$, $p \leq 0.01$, * $p \leq 0.001$, **** $p \leq 0.0001$, or using unpaired two-tailed t-test * $p \leq 0.05$, $p \leq 0.01$, * $p \leq 0.001$, **** $p \leq 0.0001.$ ## 3.1. Inhibitors of UPS-Mediated Signaling Events Decrease VEEV-TC83 Load in Vero Cells Eight candidate inhibitors that target UPS-mediated signaling events, with an emphasis on NFkB signaling and Nrf2 signaling, were chosen to analyze their potential inhibitory effects on alphavirus multiplication using Venezuelan Equine Encephalitis Virus (VEEV) TC-83, the test pathogen (Table 1). As the first step, the cytotoxicity of these compounds was assessed in Vero cells and $50\%$ cytotoxic concentration (CC50) values were determined for each inhibitor, and DMSO ($0.1\%$) was included as the vehicle control (Figure 1A). The cells were incubated for 24 h with media containing increasing concentrations of each inhibitor and cell viability quantified by CellTiterGlo assay. The CC50 values for BARM (2.4 µM), OMA (3.5 µM), NSC (5.4 µM), BAR (11.6 µM), YH1 (24.7 µM), P00 (25.5 µM), JSH (27.9 µM) and ML (31.2 µM) were determined. To assess the inhibitory potential of each inhibitor, Vero cells were pre-treated with the inhibitor at 1 µM (BARM, OMA, NSC) or 2 µM (BAR, JSH, P00, YH1, ML) for 1 h, after which the cells were infected with VEEV-TC83 (multiplicity of infection (MOI): 0.1) for 1 h at 37 °C. After 1 h to permit infection, the virus overlay was replaced with the inhibitor-containing media and cells were maintained at 37 °C for 18 h. The culture supernatants were collected at 18 h post-infection and viral load quantified by plaque assay (Figure 1B). The data demonstrate that all the chosen inhibitors exerted an inhibitory effect, albeit to varying degrees, with BARM, BAR and OMA demonstrating a >2 log decrease as compared to the DMSO control. NSC, P00, ML, JHS and YH1 demonstrated a >1 log reduction in the TC-83 titer as compared to the DMSO control. Collectively, our initial assessment of the chosen inhibitors in Vero cells add support to the significance of UPS-mediated signaling events for the establishment of a productive VEEV infection. ## 3.2. UPS-Mediated Signaling Inhibitors Demonstrate Inhibition of VEEV-TC83 in Human Astroglial (SVG-p12) and Microglial (HMC3) Cells VEEV and EEEV are known to infect cells of the CNS that contribute to the encephalitic phenotype in infected individuals. Thus, we determined if the inhibitors that demonstrated successful antiviral outcomes in Vero cells were also able to elicit robust inhibition of VEEV in human-derived cells of the CNS, specifically, astroglial (SVG-p12) and microglial (HMC3) cells. OMA, BARM, NSC, YH1 and P00 were selected for further testing in HMC3 and SVG-p12 cells. The cells were independently treated with OMA (0.1 µM), BARM (0.1 µM), NSC (0.5 µM), YH1 (0.5 µM), P00 (1 µM) and DMSO ($0.1\%$) for 24 h and cytotoxicity assessed by CellTiterGlo assay (Figure 2A). The data demonstrate that >$90\%$ cell viability can be observed in all cases at selected concentrations. CC50 data for all five inhibitors in HMC3 and SVG-p12 cell types are included as supplemental data (Figure S1). The antiviral potential of these inhibitors in the two cell types was next assessed by preincubating the cells with inhibitor-containing media for 1 h, infection by VEEV-TC83 for 1 h and post-treatment of infected cells with inhibitors. DMSO was maintained as the vehicle-alone control. At 18 h post-infection, cell culture supernatants were collected and viral titers were quantified by plaque assay (Figure 2B,C). A fraction of the supernatant was used for RNA isolation to quantify extracellular viral RNA levels. The cells were then lysed and total RNA obtained to quantify the impact of the inhibitors on intracellular viral RNA levels (Figure 3A,B). In HMC3 cells, all five of the selected inhibitors decreased infectious virus titer when compared to the vehicle-alone control, while in SVG-p12 cells, only OMA and NSC demonstrated statistically significant inhibition (Figure 2C). The viral RNA levels in the cell culture supernatants and infected cells were quantified by qRT-PCR to ascertain the effect of the inhibitors on the extracellular and intracellular viral RNA levels, respectively. In the context of HMC3 cells, OMA treatment reduced the intracellular viral RNA level by >1 log, while NSC, BARM, YH1 and P00 treatment reduced the intracellular viral RNA by approximately 1 log (Figure 3A). In the context of SVG-p12 cells, OMA demonstrated a >4 log decrease and NSC showed a >3 log decrease in intracellular viral RNA, while BARM, YH1 and P00 did not demonstrate a statistically significant decrease. Quantification of extracellular viral RNA in HMC3 cells showed a uniform decrease of >1 log for all inhibitors (Figure 3B), agreeing with the trend seen with intracellular viral RNA. In the SVG-p12 cells, while OMA consistently reduced extracellular viral RNA by >3 logs (Figure 3B), trending similar to the intracellular viral RNA (Figure 3A), the extracellular viral RNA reduction by NSC was less significant as compared to the intracellular RNA. Cumulatively, the studies in CNS-relevant cell types, namely the astroglial and microglial cells, demonstrate that OMA demonstrated consistent inhibition of VEEV-TC83 in a cell-type-independent manner with reduction observed at the level of infectious titer and viral RNA. BARM and NSC continue to show promise as inhibitors of VEEV-TC83 in these cell lines, albeit at less robust levels than OMA. Additionally, CC50 and effective concentration at $50\%$ percent (EC50) further confirmed OMA, BARM and NSC as being effective inhibitors (Figure S2). ## 3.3. UPS Signaling Inhibitors Decrease VEEV-TC83 in a Dose-Dependent Manner OMA, BARM and NSC were selected to further assess dose dependency of their inhibitory outcomes using HMC3 cells as the cell type of choice. Viral inhibition was queried under three different treatment conditions as shown in the schematic (Figure 4A), namely pre-treatment only, pre- and post-treatment and post-treatment only. Under each condition, the inhibitors were tested at increasing concentrations (0.1 µM, 0.5 µM and 1.0 µM) while still staying within the cytotoxic concentration that resulted in >$90\%$ survival (Figure S1). Although all three inhibitors showed a dose-dependent reduction in viral infectious titer in pre- and post-treatment, and in post-treatment-only strategies (Figure 4B), OMA and BARM showed a significant reduction in infectious titer compared to NSC. At 0.1 µM concentration, the three inhibitors showed a ~1 log reduction in the pre- and post-treatment condition. OMA and BARM showed a >5 log reduction at the 1 µM concentration in the pre- and post-treatment condition. When the same groups are compared at the highest concentration tested (1.0 µM), OMA consistently exerted strong inhibition, while BARM showed less inhibition in the post-treatment condition. The pre-treatment-only condition did not result in a robust reduction in viral load (<1 log) although statistical significance could be observed. Interestingly, when the post-treatment-only condition at the lowest concentration (0.1 µM) for the three compounds was compared to the pre- and post-treatment condition, the former resulted in more inhibition but with lower statistical significance. Overall, OMA and BARM demonstrated clear dose dependency in HMC3 cells, with the pre- and post-treatment strategy eliciting strong, statistically significant inhibition of infectious titers at MOIs of 0.1 and 1 (Figure S3). The dose dependency of these inhibitors was also assessed at the level of intracellular and extracellular viral RNA by qRT-PCR in HMC3 cells. This analysis was restricted to only the pre- and post-treatment and the post-treatment-alone strategies (Figure 5A). The concentrations of OMA, BARM and NSC were maintained the same as described above for infectious viral titers. The analysis of intracellular RNA revealed that at the highest concentration tested (1 µM), the decrease in viral RNA in OMA, BARM and NSC treatments were highly comparable, producing a >3 log drop for OMA and BARM and a ~1 log drop for NSC (Figure 5B). At the lowest concentration tested (0.1 µM), there was no robust inhibitory effect noted for any of the inhibitors. At the mid-level concentration (0.5 µM), OMA and BARM elicited a >2 log inhibition, which was statistically significant. The overall trend for these three inhibitors, at these three concentrations, was fairly comparable when the extracellular viral RNA was quantified (Figure 5C). The lowest concentrations of the three compounds did not show robust inhibition, while very strong inhibition (>4 log) was seen for OMA and BARM in both conditions. Overall, the assessment of the dose dependency of the three inhibitors at the infectious titer (Figure 4) and viral RNA levels (Figure 5) indicate that pre- and post-treatment with OMA and BARM produce a clear, dose-dependent decrease in VEEV-TC83 infectious titer and viral RNA in HMC3 cells. Additionally, OMA and BARM at 1 µM concentration show a statistically significant reduction in viral negative-strand RNA levels in VEEV-TC83-infected HMC3 cells at 6 h post-infection (Figure 5D). ## 3.4. OMA, BARM and NSC Exhibit Differential Inhibitory Impact on Proinflammatory Cytokines in VEEV-TC83-Infected HMC3 Cells OMA, BARM and NSC are known to modulate the NFκB signaling cascade, which is an important modulator of proinflammatory cytokine expression. Therefore, the impact of OMA, BARM and NSC treatment on proinflammatory cytokine levels in the context of the pre- and post-infection treatment of infected HMC3 cells was analyzed by multiplexed ELISA. The supernatants from cells treated with the inhibitors or with the vehicle-alone control were obtained at 6 h and 18 h post-infection and quantified for the levels of 10 inflammatory cytokines (IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13 and TNF-α) using the V-PLEX proinflammatory human cytokine array (Mesoscale). At 6 h post-infection, OMA- and NSC-treated cells had an inhibitory effect on IL-1β, IL-6 and IL-8, while BARM inhibited IL-6 and IL-8 levels (Figure 6A). There was no significant change in IFNγ levels with any of the inhibitors. Similarly, at 18 h post-infection, OMA had an inhibitory effect on IL-1β, IL -6 and IL-8. BARM showed a more inhibitory effect compared to the control at 18 h post-infection with IL-6 and IL-8 but not for IL-1β (Figure 6B). At 18 h post-infection, NSC exerted inhibitory activity only on IL-6 (Figure 6B). The 6 and 18 h supernatant samples were also evaluated by plaque assay to measure the corresponding viral load at those time points (Figure 6C) and, as expected, the viral load was lower at the 18 h time point in the inhibitor-treated samples. The inhibitory activities of the three compounds on the other proinflammatory cytokines are included in the supplemental data (Figure S4). Overall, at the early and the later time points tested, OMA exerted an inhibitory effect on three major proinflammatory cytokines, IL-1β, IL-6, IL-8, and BARM demonstrated an inhibitory effect against IL-6 and IL-8 in VEEV-TC83-infected HMC3 cells. ## 3.5. OMA, BARM and NSC Exert Broad-Spectrum Viral Inhibitory Activity against Virulent Strains of VEEV and EEEV in HMC3 Cells The inhibitory potential of the three inhibitors against virulent strains of VEEV (VEEV-TrD) and Eastern Equine Encephalitis Virus (EEEV-FL93) were analyzed in HMC3 cells (Figure 7). The cells were pre- and post-infection treated with the inhibitors at 0.5 µM concentration and the effect on infectious viral titer was quantified at 18 h post-infection. With VEEV-TrD, a >2 log inhibition was observed with all three inhibitors, while EEEV, BARM and OMA exhibited a much stronger inhibition (>3 logs) than NSC, which exerted an inhibition of ~2 logs. Viral load analysis of VEEV-TrD with the three inhibitors at 1 µM concentration showed a >3 log reduction of both extracellular and intracellular RNA. Dose dependency of these inhibitors was also noted in the context of VEEV and EEEV infections (Figure 7A,C). Cumulatively, the data support the potential of these inhibitors to exert a broad-spectrum antiviral activity against virulent strains of New World alphaviruses. ## 4. Discussion VEEV and EEEV are New World alphaviruses that are transmitted by mosquito vectors and contribute to disease in humans in the Americas. These viruses are highly stable and infectious as aerosols, in which case the encephalitic outcomes are prominent in the infected individuals, leading to higher rates of mortality than the natural transmission by mosquitoes. FDA-approved therapeutic strategies are not available to treat VEEV or EEEV infection. Host-based inhibitors offer important advantages over virus-targeted inhibitors by decreasing the potential for the development of resistance and having a higher probability to exert broad-spectrum inhibitory outcomes in the context of closely related viruses. In this effort, we focused on a small selection of inhibitors that are known to influence the UPS-mediated signaling events in human host cells. Ubiquitination is an important post-translational modification that is mediated by the ubiquitin proteasome system, which includes enzymatic activities of several ubiquitin transferases and deubiquitinases [58]. Targeted protein degradation by the proteasome is also an important method for cell regulation that is dependent on the activity of the proteasome and the ubiquitination/deubiquitination enzymes. This is a multi-step process that is primarily carried out by E1 activating enzymes, E2 conjugating enzymes and E3 ubiquitin ligases, which ultimately leads to the proteasomal degradation of the ubiquitinated target by the 26S proteasome [58,59,60,61]. UPS is responsible for the degradation of targeted substrates (e.g., misfolded/unfolded proteins) and also functions to regulate many fundamental cellular processes such as stress response, signal transduction and transcriptional activation [58,59,60,61,62]. Many viral proteins are also regulated by differential ubiquitination and deubiquitination, thus suggesting that the associated enzymatic machinery can be targeted therapeutically to achieve virus inhibition. This has been documented for several viral proteins such as the NS1, NS3 and NS4B proteins of dengue virus [63]. The NS3 protease of dengue virus has been shown to interact with the E3 ligase, TRIM 69. Targeting the E3 ligase Cullin 2 exerts an inhibitory effect on dengue virus, adding support to the potential of these enzymes to be targeted to achieve viral inhibitory outcomes [64]. The Ebola virus protein VP35 is known to be ubiquitinated, which is required for the regulation of viral transcription and assembly [65]. The ubiquitination of VP40 has been demonstrated to be important for filovirus budding and egress [65,66,67,68]. In the context of New World alphaviruses, it has been demonstrated that inhibition of the proteasome function using the FDA-approved small molecule, bortezomib, exerted robust viral inhibitory activity against VEEV, by potentially impacting the ubiquitination status of the capsid protein [25]. Viral proteins also differentially regulate the availability and functionality of the UPS enzymatic machinery as evidenced in the context of chikungunya virus where the nsP2 protease downregulates an E2 conjugating enzyme [69]. Needless to say, there is a growing body of evidence in the literature that the UPS and the associated enzymatic machineries play critical roles in the establishment of productive infections in the context of several acutely infectious, enveloped RNA viruses, thus identifying them as valuable targets for therapeutic intervention. Several host signaling pathways also include modification of critical signaling proteins to achieve their intended cell response outcomes [70,71]. The NFκB signaling cascade is a central mediator of multiple host response events including cell growth, multiplication and apoptosis [70,71,72,73,74,75]. NFκB signaling requires the nuclear translocation of p65, which is restricted in the cytoplasm in non-stimulated conditions by IκBα [75]. Upon activation of the NFκB cascade, IκBα is phosphorylated by IKKβ kinase, and ubiquitinated on K48, which targets IκBα for degradation. This targeted degradation of IκBα is required for the nuclear translocation of p65, which acts to regulate the transcription of key response genes [75]. The NFκB cascade has been shown to play an important role in New World alphavirus infections because inhibition of the IKKβ kinase by small molecule inhibitors exerted a broad-spectrum inhibitory activity against VEEV and EEEV [36,73]. It has also been demonstrated that IKKβ kinase can phosphorylate the VEEV non-structural protein 3 (nsP3) and this phosphorylation event is also important for infection [76]. While the role of the phosphorylation by the NFκB signaling cascade has been shown, the impact of modulating the ubiquitination status of the NFκB signaling cascade on alphavirus infections has not been looked into. The data included in this manuscript demonstrate that modulation of the ubiquitination-dependent signaling aspect of the NFκB cascade can also exert a broad-spectrum inhibitory effect against alphaviruses. This step, however, is downstream of IKKβ activation and hence, from a mechanistic perspective, is likely to involve alternate host and/or viral targets than those demonstrated earlier such as the VEEV nsP3 protein. It is more likely that the ubiquitination/deubiquitination machinery that is likely targeted by the highly effective small molecules OMA, BARM and NSC are critical enablers for the establishment of productive alphavirus infection. However, the data included in this study also demonstrate inhibition of proinflammatory cytokines in the context of inhibitor treatment, which is likely to be directly related to the inhibition of the nuclear translocation of p65 and the lack of activation of gene expression of proinflammatory genes such as IL-1β, IL-6 and IL-8. The Nrf2 signaling pathway is well documented as playing important innate immune roles in cells including protective responses against oxidative stress and inflammation. VEEV infection has been shown to result in an increase in reactive oxygen species leading to mitochondrial damage and oxidative stress [77,78]. The Nrf2 signaling pathway is a target for multiple viruses including dengue virus [79,80], SARS-CoV-2 [81], hepatitis viruses [82], influenza virus [38] and Ebola virus [39,40,41,42], thus suggesting that this pathway can be targeted by therapeutic candidates to achieve a decrease in viral and/or inflammatory loads. Small molecule inhibitors that target the Nrf2 signaling pathway such as coumarin have been shown to exert an inhibitory effect in the context of alphavirus infections, specifically chikungunya virus [83]. The data included in this manuscript support the idea that the activation of Nrf2 signaling by small molecules can have broad-spectrum inhibitory outcomes against alphaviruses and also elicit protective outcomes by decreasing proinflammatory cytokine levels. Of high significance in the context of encephalitic alphaviruses is the damage to the blood–brain barrier (BBB), and inflammation has been heavily implicated in BBB disruption. For a therapeutic candidate to be effective in an encephalitic disease state, the ability to reduce viral and inflammatory load will be an important requirement. Modulating Nrf2 levels at the BBB in the context of proinflammatory states such as diabetes and intracerebral hemorrhage has been shown to have positive outcomes [84,85], thus presenting the attractive possibility of OMA and BARM functioning in the protection of BBB integrity and decreasing the inflammatory load across the BBB during alphavirus infections. ## 5. Conclusions Cumulatively, the data presented in this manuscript add support to the importance of UPS-mediated signaling events in the establishment of a productive infection by New World alphaviruses in cells of the CNS. The modulation of ubiquitination events in the NFκB and Nrf2 signaling pathways by small molecule inhibitors exert broad-spectrum inhibition of New World alphaviruses. It remains an attractive possibility that alphavirus proteins may be ubiquitinated/deubiquitinated by the same enzymatic machinery that results in ubiquitination modifications of host target proteins in the NFκB and Nrf2 signaling cascades, and hence, these small molecules may also have conserved viral targets. ## References 1. Azar S.R., Campos R.K., Bergren N.A., Camargos V.N., Rossi S.L.. Epidemic Alphaviruses: Ecology, Emergence and Outbreaks. Microorganisms (2020) 8. DOI: 10.3390/microorganisms8081167 2. Zacks M.A., Paessler S.. Encephalitic alphaviruses. Vet. Microbiol. (2010) 140 281-286. DOI: 10.1016/j.vetmic.2009.08.023 3. Weaver S.C., Ferro C., Barrera R., Boshell J., Navarro J.C.. Venezuelan equine encephalitis. Annu. Rev. Entomol. (2004) 49 141-174. DOI: 10.1146/annurev.ento.49.061802.123422 4. Forrester N.L., Wertheim J.O., Dugan V.G., Auguste A.J., Lin D., Adams A.P., Chen R., Gorchakov R., Leal G., Estrada-Franco J.G.. Evolution and spread of Venezuelan equine encephalitis complex alphavirus in the Americas. PLoS Negl. Trop. Dis. (2017) 11. DOI: 10.1371/journal.pntd.0005693 5. Guzmán-Terán C., Calderón-Rangel A., Rodriguez-Morales A., Mattar S.. Venezuelan equine encephalitis virus: The problem is not over for tropical America. Ann. Clin. Microbiol. Antimicrob. (2020) 19 19. DOI: 10.1186/s12941-020-00360-4 6. Guerrero-Arguero I., Tellez-Freitas C.M., Weber K.S., Berges B.K., Robison R.A., Pickett B.E.. Alphaviruses: Host pathogenesis, immune response, and vaccine & treatment updates. J. Gen. Virol. (2021) 102 001644. DOI: 10.1099/jgv.0.001644 7. Corrin T., Ackford R., Mascarenhas M., Greig J., Waddell L.A.. Eastern Equine Encephalitis Virus: A Scoping Review of the Global Evidence. Vector Borne Zoonotic Dis. (2021) 21 305-320. DOI: 10.1089/vbz.2020.2671 8. Lindsey N.P., Staples J.E., Fischer M.. Eastern Equine Encephalitis Virus in the United States, 2003–2016. Am. J. Trop. Med. Hyg. (2018) 98 1472-1477. DOI: 10.4269/ajtmh.17-0927 9. Armstrong P.M., Andreadis T.G.. Ecology and Epidemiology of Eastern Equine Encephalitis Virus in the Northeastern United States: An Historical Perspective. J. Med. Entomol. (2022) 59 1-13. DOI: 10.1093/jme/tjab077 10. Schäfer A., Brooke C.B., Whitmore A.C., Johnston R.E.. The role of the blood-brain barrier during Venezuelan equine encephalitis virus infection. J. Virol. (2011) 85 10682-10690. DOI: 10.1128/JVI.05032-11 11. Hollidge B.S., Cohen C.A., Akuoku Frimpong J., Badger C.V., Dye J.M., Schmaljohn C.S.. Toll-like receptor 4 mediates blood-brain barrier permeability and disease in C3H mice during Venezuelan equine encephalitis virus infection. Virulence (2021) 12 430-443. DOI: 10.1080/21505594.2020.1870834 12. Bocan T.M., Stafford R.G., Brown J.L., Akuoku Frimpong J., Basuli F., Hollidge B.S., Zhang X., Raju N., Swenson R.E., Smith D.R.. Characterization of Brain Inflammation, Apoptosis, Hypoxia, Blood-Brain Barrier Integrity and Metabolism in Venezuelan Equine Encephalitis Virus (VEEV TC-83) Exposed Mice by In Vivo Positron Emission Tomography Imaging. Viruses (2019) 11. DOI: 10.3390/v11111052 13. Cain M.D., Salimi H., Gong Y., Yang L., Hamilton S.L., Heffernan J.R., Hou J., Miller M.J., Klein R.S.. Virus entry and replication in the brain precedes blood-brain barrier disruption during intranasal alphavirus infection. J. Neuroimmunol. (2017) 308 118-130. DOI: 10.1016/j.jneuroim.2017.04.008 14. Phillips A.T., Rico A.B., Stauft C.B., Hammond S.L., Aboellail T.A., Tjalkens R.B., Olson K.E.. Entry Sites of Venezuelan and Western Equine Encephalitis Viruses in the Mouse Central Nervous System following Peripheral Infection. J. Virol. (2016) 90 5785-5796. DOI: 10.1128/JVI.03219-15 15. Sharma A., Bhomia M., Honnold S.P., Maheshwari R.K.. Role of adhesion molecules and inflammation in Venezuelan equine encephalitis virus infected mouse brain. Virol. J. (2011) 8 197. DOI: 10.1186/1743-422X-8-197 16. Sharma A., Bhattacharya B., Puri R.K., Maheshwari R.K.. Venezuelan equine encephalitis virus infection causes modulation of inflammatory and immune response genes in mouse brain. BMC Genom. (2008) 9. DOI: 10.1186/1471-2164-9-289 17. Gupta P., Sharma A., Han J., Yang A., Bhomia M., Knollmann-Ritschel B., Puri R.K., Maheshwari R.K.. Differential host gene responses from infection with neurovirulent and partially-neurovirulent strains of Venezuelan equine encephalitis virus. BMC Infect. Dis. (2017) 17. DOI: 10.1186/s12879-017-2355-3 18. Sharma A., Knollmann-Ritschel B.. Current Understanding of the Molecular Basis of Venezuelan Equine Encephalitis Virus Pathogenesis and Vaccine Development. Viruses (2019) 11. DOI: 10.3390/v11020164 19. Barrera M.D., Callahan V., Akhrymuk I., Bhalla N., Zhou W., Campbell C., Narayanan A., Kehn-Hall K.. Proteomic Discovery of VEEV E2-Host Partner Interactions Identifies GRP78 Inhibitor HA15 as a Potential Therapeutic for Alphavirus Infections. Pathogens (2021) 10. DOI: 10.3390/pathogens10030283 20. Carey B.D., Bakovic A., Callahan V., Narayanan A., Kehn-Hall K.. New World alphavirus protein interactomes from a therapeutic perspective. Antiviral. Res. (2019) 163 125-139. DOI: 10.1016/j.antiviral.2019.01.015 21. Yao Z., Zanini F., Kumar S., Karim M., Saul S., Bhalla N., Panpradist N., Muniz A., Narayanan A., Quake S.R.. The transcriptional landscape of Venezuelan equine encephalitis virus (TC-83) infection. PLoS Negl. Trop. Dis. (2021) 15. DOI: 10.1371/journal.pntd.0009306 22. Bakovic A., Bhalla N., Alem F., Campbell C., Zhou W., Narayanan A.. Inhibitors of Venezuelan Equine Encephalitis Virus Identified Based on Host Interaction Partners of Viral Non-Structural Protein 3. Viruses (2021) 13. DOI: 10.3390/v13081533 23. Risner K., Ahmed A., Bakovic A., Kortchak S., Bhalla N., Narayanan A.. Efficacy of FDA-Approved Anti-Inflammatory Drugs Against Venezuelan Equine Encephalitis Virus Infection. Viruses (2019) 11. DOI: 10.3390/v11121151 24. Lundberg L., Brahms A., Hooper I., Carey B., Lin S.C., Dahal B., Narayanan A., Kehn-Hall K.. Repurposed FDA-Approved drug sorafenib reduces replication of Venezuelan equine encephalitis virus and other alphaviruses. Antiviral. Res. (2018) 157 57-67. DOI: 10.1016/j.antiviral.2018.07.005 25. Amaya M., Keck F., Lindquist M., Voss K., Scavone L., Kehn-Hall K., Roberts B., Bailey C., Schmaljohn C., Narayanan A.. The ubiquitin proteasome system plays a role in venezuelan equine encephalitis virus infection. PLoS ONE (2015) 10. DOI: 10.1371/journal.pone.0124792 26. Fernandez-Garcia M.D., Meertens L., Bonazzi M., Cossart P., Arenzana-Seisdedos F., Amara A.. Appraising the roles of CBLL1 and the ubiquitin/proteasome system for flavivirus entry and replication. J. Virol. (2011) 85 2980-2989. DOI: 10.1128/JVI.02483-10 27. Byk L.A., Iglesias N.G., De Maio F.A., Gebhard L.G., Rossi M., Gamarnik A.V.. Dengue Virus Genome Uncoating Requires Ubiquitination. mBio (2016) 7 e00804-16. DOI: 10.1128/mBio.00804-16 28. Nag D.K., Finley D.. A small-molecule inhibitor of deubiquitinating enzyme USP14 inhibits Dengue virus replication. Virus Res. (2012) 165 103-106. DOI: 10.1016/j.virusres.2012.01.009 29. Zu S., Li C., Li L., Deng Y.-Q., Chen X., Luo D., Ye Q., Huang Y.-J., Li X.-F., Zhang R.-R.. TRIM22 suppresses Zika virus replication by targeting NS1 and NS3 for proteasomal degradation. Cell Biosci. (2022) 12 139. DOI: 10.1186/s13578-022-00872-w 30. Karpe Y.A., Pingale K.D., Kanade G.D.. Activities of proteasome and m-calpain are essential for Chikungunya virus replication. Virus Genes (2016) 52 716-721. DOI: 10.1007/s11262-016-1355-5 31. Lee H.-R., Lee M.K., Kim C.W., Kim M.. TRIM Proteins and Their Roles in the Influenza Virus Life Cycle. Microorganisms (2020) 8. DOI: 10.3390/microorganisms8091424 32. Biquand E., Poirson J., Karim M., Declercq M., Malausse N., Cassonnet P., Barbezange C., Straub M.L., Jones L., Munier S.. Comparative Profiling of Ubiquitin Proteasome System Interplay with Influenza A Virus PB2 Polymerase Protein Recapitulating Virus Evolution in Humans. mSphere (2017) 2 e00330-17. DOI: 10.1128/mSphere.00330-17 33. Widjaja I., de Vries E., Tscherne D.M., García-Sastre A., Rottier P.J., de Haan C.A.. Inhibition of the ubiquitin-proteasome system affects influenza A virus infection at a postfusion step. J. Virol. (2010) 84 9625-9631. DOI: 10.1128/JVI.01048-10 34. Park E.S., Dezhbord M., Lee A.R., Kim K.H.. The Roles of Ubiquitination in Pathogenesis of Influenza Virus Infection. Int. J. Mol. Sci. (2022) 23. DOI: 10.3390/ijms23094593 35. Joyce M.A., Berry-Wynne K.M., Dos Santos T., Addison W.R., McFarlane N., Hobman T., Tyrrell D.L.. HCV and flaviviruses hijack cellular mechanisms for nuclear STAT2 degradation: Up-regulation of PDLIM2 suppresses the innate immune response. PLoS Pathog. (2019) 15. DOI: 10.1371/journal.ppat.1007949 36. Amaya M., Voss K., Sampey G., Senina S., de la Fuente C., Mueller C., Calvert V., Kehn-Hall K., Carpenter C., Kashanchi F.. The Role of IKKβ in Venezuelan Equine Encephalitis Virus Infection. PLoS ONE (2014) 9. DOI: 10.1371/journal.pone.0086745 37. Nenasheva V.V., Kovaleva G.V., Uryvaev L.V., Ionova K.S., Dedova A.V., Vorkunova G.K., Chernyshenko S.V., Khaidarova N.V., Tarantul V.Z.. Enhanced expression of trim14 gene suppressed Sindbis virus reproduction and modulated the transcription of a large number of genes of innate immunity. Immunol. Res. (2015) 62 255-262. DOI: 10.1007/s12026-015-8653-1 38. Bergmann S., Elbahesh H.. Targeting the proviral host kinase, FAK, limits influenza a virus pathogenesis and NFkB-regulated pro-inflammatory responses. Virology (2019) 534 54-63. DOI: 10.1016/j.virol.2019.05.020 39. Ramanan P., Edwards M.R., Shabman R.S., Leung D.W., Endlich-Frazier A.C., Borek D.M., Otwinowski Z., Liu G., Huh J., Basler C.F.. Structural basis for Marburg virus VP35-mediated immune evasion mechanisms. Proc. Natl. Acad. Sci. USA (2012) 109 20661-20666. DOI: 10.1073/pnas.1213559109 40. Edwards M.R., Basler C.F.. Marburg Virus VP24 Protein Relieves Suppression of the NF-κB Pathway Through Interaction With Kelch-like ECH-Associated Protein 1. J. Infect. Dis. (2015) 212 S154-S159. DOI: 10.1093/infdis/jiv050 41. Edwards M.R., Johnson B., Mire C.E., Xu W., Shabman R.S., Speller L.N., Leung D.W., Geisbert T.W., Amarasinghe G.K., Basler C.F.. The Marburg virus VP24 protein interacts with Keap1 to activate the cytoprotective antioxidant response pathway. Cell Rep. (2014) 6 1017-1025. DOI: 10.1016/j.celrep.2014.01.043 42. Chien J.Y., Chou Y.Y., Ciou J.W., Liu F.Y., Huang S.P.. The Effects of Two Nrf2 Activators, Bardoxolone Methyl and Omaveloxolone, on Retinal Ganglion Cell Survival during Ischemic Optic Neuropathy. Antioxidants (2021) 10. DOI: 10.3390/antiox10091466 43. Abed D.A., Goldstein M., Albanyan H., Jin H., Hu L.. Discovery of direct inhibitors of Keap1-Nrf2 protein-protein interaction as potential therapeutic and preventive agents. Acta Pharm. Sin. B (2015) 5 285-299. DOI: 10.1016/j.apsb.2015.05.008 44. Bell T.M., Espina V., Lundberg L., Pinkham C., Brahms A., Carey B.D., Lin S.C., Dahal B., Woodson C., de la Fuente C.. Combination Kinase Inhibitor Treatment Suppresses Rift Valley Fever Virus Replication. Viruses (2018) 10. DOI: 10.3390/v10040191 45. Lundberg L., Pinkham C., de la Fuente C., Brahms A., Shafagati N., Wagstaff K.M., Jans D.A., Tamir S., Kehn-Hall K.. Selective Inhibitor of Nuclear Export (SINE) Compounds Alter New World Alphavirus Capsid Localization and Reduce Viral Replication in Mammalian Cells. PLOS Negl. Trop. Dis. (2016) 10. DOI: 10.1371/journal.pntd.0005122 46. Thomas D.R., Lundberg L., Pinkham C., Shechter S., DeBono A., Baell J., Wagstaff K.M., Hick C.A., Kehn-Hall K., Jans D.A.. Identification of novel antivirals inhibiting recognition of Venezuelan equine encephalitis virus capsid protein by the Importin α/β1 heterodimer through high-throughput screening. Antivir. Res. (2018) 151 8-19. DOI: 10.1016/j.antiviral.2018.01.007 47. Song M.K., Lee J.H., Ryoo I.G., Lee S.H., Ku S.K., Kwak M.K.. Bardoxolone ameliorates TGF-β1-associated renal fibrosis through Nrf2/Smad7 elevation. Free Radic. Biol. Med. (2019) 138 33-42. DOI: 10.1016/j.freeradbiomed.2019.04.033 48. Jiang Z., Qi G., Lu W., Wang H., Li D., Chen W., Ding L., Yang X., Yuan H., Zeng Q.. Omaveloxolone inhibits IL-1β-induced chondrocyte apoptosis through the Nrf2/ARE and NF-κB signalling pathways in vitro and attenuates osteoarthritis in vivo. Front. Pharmacol. (2022) 13 952950. DOI: 10.3389/fphar.2022.952950 49. Liang Q., Dexheimer T.S., Zhang P., Rosenthal A.S., Villamil M.A., You C., Zhang Q., Chen J., Ott C.A., Sun H.. A selective USP1-UAF1 inhibitor links deubiquitination to DNA damage responses. Nat. Chem. Biol. (2014) 10 298-304. DOI: 10.1038/nchembio.1455 50. Cheng J., Fan Y.H., Xu X., Zhang H., Dou J., Tang Y., Zhong X., Rojas Y., Yu Y., Zhao Y.. A small-molecule inhibitor of UBE2N induces neuroblastoma cell death via activation of p53 and JNK pathways. Cell Death Dis. (2014) 5 e1079. DOI: 10.1038/cddis.2014.54 51. Cao D.Y., Zhang Z.H., Li R.Z., Shi X.K., Xi R.Y., Zhang G.L., Li F., Wang F.. A small molecule inhibitor of caspase-1 inhibits NLRP3 inflammasome activation and pyroptosis to alleviate gouty inflammation. Immunol. Lett. (2022) 244 28-39. DOI: 10.1016/j.imlet.2022.03.003 52. Hodge C.D., Edwards R.A., Markin C.J., McDonald D., Pulvino M., Huen M.S., Zhao J., Spyracopoulos L., Hendzel M.J., Glover J.N.. Covalent Inhibition of Ubc13 Affects Ubiquitin Signaling and Reveals Active Site Elements Important for Targeting. ACS Chem. Biol. (2015) 10 1718-1728. DOI: 10.1021/acschembio.5b00222 53. Huang Y., Wolf S., Beck B., Köhler L.M., Khoury K., Popowicz G.M., Goda S.K., Subklewe M., Twarda A., Holak T.A.. Discovery of highly potent p53-MDM2 antagonists and structural basis for anti-acute myeloid leukemia activities. ACS Chem. Biol. (2014) 9 802-811. DOI: 10.1021/cb400728e 54. Nicholson B., Suresh Kumar K.G.. The multifaceted roles of USP7: New therapeutic opportunities. Cell Biochem. Biophys. (2011) 60 61-68. DOI: 10.1007/s12013-011-9185-5 55. Chen X., Liu G., Yuan Y., Wu G., Wang S., Yuan L.. NEK7 interacts with NLRP3 to modulate the pyroptosis in inflammatory bowel disease via NF-κB signaling. Cell Death Dis. (2019) 10 906. DOI: 10.1038/s41419-019-2157-1 56. Li X., Bao C., Ma Z., Xu B., Ying X., Liu X., Zhang X.. Perfluorooctanoic acid stimulates ovarian cancer cell migration, invasion via ERK/NF-κB/MMP-2/-9 pathway. Toxicol. Lett. (2018) 294 44-50. DOI: 10.1016/j.toxlet.2018.05.009 57. Sun Y., Sha B., Huang W., Li M., Zhao S., Zhang Y., Yan J., Li Z., Tang J., Duan P.. ML323, a USP1 inhibitor triggers cell cycle arrest, apoptosis and autophagy in esophageal squamous cell carcinoma cells. Apoptosis (2022) 27 545-560. DOI: 10.1007/s10495-022-01736-x 58. Park J., Cho J., Song E.J.. Ubiquitin-proteasome system (UPS) as a target for anticancer treatment. Arch Pharm. Res. (2020) 43 1144-1161. DOI: 10.1007/s12272-020-01281-8 59. Yuan T., Yan F., Ying M., Cao J., He Q., Zhu H., Yang B.. Inhibition of Ubiquitin-Specific Proteases as a Novel Anticancer Therapeutic Strategy. Front. Pharmacol. (2018) 9 1080. DOI: 10.3389/fphar.2018.01080 60. D’Arcy P., Wang X., Linder S.. Deubiquitinase inhibition as a cancer therapeutic strategy. Pharmacol. Ther. (2015) 147 32-54. DOI: 10.1016/j.pharmthera.2014.11.002 61. Eldridge A.G., O’Brien T.. Therapeutic strategies within the ubiquitin proteasome system. Cell Death Differ. (2010) 17 4-13. DOI: 10.1038/cdd.2009.82 62. Li Y., Li S., Wu H.. Ubiquitination-Proteasome System (UPS) and Autophagy Two Main Protein Degradation Machineries in Response to Cell Stress. Cells (2022) 11. DOI: 10.3390/cells11050851 63. Giraldo M.I., Vargas-Cuartas O., Gallego-Gomez J.C., Shi P.Y., Padilla-Sanabria L., Castaño-Osorio J.C., Rajsbaum R.. K48-linked polyubiquitination of dengue virus NS1 protein inhibits its interaction with the viral partner NS4B. Virus Res. (2018) 246 1-11. DOI: 10.1016/j.virusres.2017.12.013 64. Hao J., Li J., Zhang Z., Yang Y., Zhou Q., Wu T., Chen T., Wu Z., Zhang P., Cui J.. NLRC5 restricts dengue virus infection by promoting the autophagic degradation of viral NS3 through E3 ligase CUL2 (cullin 2). Autophagy (2022). DOI: 10.1080/15548627.2022.2126614 65. van Tol S., Kalveram B., Ilinykh P.A., Ronk A., Huang K., Aguilera-Aguirre L., Bharaj P., Hage A., Atkins C., Giraldo M.I.. Ubiquitination of Ebola virus VP35 at lysine 309 regulates viral transcription and assembly. PLOS Pathog. (2022) 18. DOI: 10.1371/journal.ppat.1010532 66. Shepley-McTaggart A., Schwoerer M.P., Sagum C.A., Bedford M.T., Jaladanki C.K., Fan H., Cassel J., Harty R.N.. Ubiquitin Ligase SMURF2 Interacts with Filovirus VP40 and Promotes Egress of VP40 VLPs. Viruses (2021) 13. DOI: 10.3390/v13020288 67. Han Z., Sagum C.A., Takizawa F., Ruthel G., Berry C.T., Kong J., Sunyer J.O., Freedman B.D., Bedford M.T., Sidhu S.S.. Ubiquitin Ligase WWP1 Interacts with Ebola Virus VP40 To Regulate Egress. J. Virol. (2017) 91 e00812-17. DOI: 10.1128/JVI.00812-17 68. Harty R.N., Brown M.E., Wang G., Huibregtse J., Hayes F.P.. A PPxY motif within the VP40 protein of Ebola virus interacts physically and functionally with a ubiquitin ligase: Implications for filovirus budding. Proc. Natl. Acad. Sci. USA (2000) 97 13871-13876. DOI: 10.1073/pnas.250277297 69. Ramphan S., Khongwichit S., Saisawang C., Kovanich D., Ketterman A.J., Ubol S., Auewarakul P., Roytrakul S., Smith D.R., Kuadkitkan A.. Ubiquitin-Conjugating Enzyme E2 L3 is Downregulated by the Chikungunya Virus nsP2 Protease. Proteomics. Clin. Appl. (2018) 12 e1700020. DOI: 10.1002/prca.201700020 70. Sun S.C.. Non-canonical NF-κB signaling pathway. Cell Res. (2011) 21 71-85. DOI: 10.1038/cr.2010.177 71. Vallabhapurapu S., Karin M.. Regulation and function of NF-kappaB transcription factors in the immune system. Annu. Rev. Immunol. (2009) 27 693-733. DOI: 10.1146/annurev.immunol.021908.132641 72. Santoro M.G., Rossi A., Amici C.. NF-κB and virus infection: Who controls whom. EMBO J. (2003) 22 2552-2560. DOI: 10.1093/emboj/cdg267 73. Yeh J.X., Park E., Schultz K.L.W., Griffin D.E.. NF-κB Activation Promotes Alphavirus Replication in Mature Neurons. J. Virol. (2019) 93 e01071-19. DOI: 10.1128/JVI.01071-19 74. Liu T., Zhang L., Joo D., Sun S.C.. NF-κB signaling in inflammation. Signal. Transduct. Target Ther. (2017) 2 17023. DOI: 10.1038/sigtrans.2017.23 75. Giridharan S., Srinivasan M.. Mechanisms of NF-κB p65 and strategies for therapeutic manipulation. J. Inflamm. Res. (2018) 11 407-419. DOI: 10.2147/JIR.S140188 76. Bakovic A., Bhalla N., Kortchak S., Sun C., Zhou W., Ahmed A., Risner K., Klimstra W.B., Narayanan A.. Venezuelan Equine Encephalitis Virus nsP3 Phosphorylation Can Be Mediated by IKKβ Kinase Activity and Abrogation of Phosphorylation Inhibits Negative-Strand Synthesis. Viruses (2020) 12. DOI: 10.3390/v12091021 77. Keck F., Khan D., Roberts B., Agrawal N., Bhalla N., Narayanan A.. Mitochondrial-Directed Antioxidant Reduces Microglial-Induced Inflammation in Murine In Vitro Model of TC-83 Infection. Viruses (2018) 10. DOI: 10.3390/v10110606 78. Keck F., Brooks-Faulconer T., Lark T., Ravishankar P., Bailey C., Salvador-Morales C., Narayanan A.. Altered mitochondrial dynamics as a consequence of Venezuelan Equine encephalitis virus infection. Virulence (2017) 8 1849-1866. DOI: 10.1080/21505594.2016.1276690 79. Zevini A., Ferrari M., Olagnier D., Hiscott J.. Dengue virus infection and Nrf2 regulation of oxidative stress. Curr. Opin. Virol. (2020) 43 35-40. DOI: 10.1016/j.coviro.2020.07.015 80. Ferrari M., Zevini A., Palermo E., Muscolini M., Alexandridi M., Etna M.P., Coccia E.M., Fernandez-Sesma A., Coyne C., Zhang D.D.. Dengue Virus Targets Nrf2 for NS2B3-Mediated Degradation Leading to Enhanced Oxidative Stress and Viral Replication. J. Virol. (2020) 94 e01551-20. DOI: 10.1128/JVI.01551-20 81. Olagnier D., Farahani E., Thyrsted J., Blay-Cadanet J., Herengt A., Idorn M., Hait A., Hernaez B., Knudsen A., Iversen M.B.. SARS-CoV2-mediated suppression of NRF2-signaling reveals potent antiviral and anti-inflammatory activity of 4-octyl-itaconate and dimethyl fumarate. Nat. Commun. (2020) 11 4938. DOI: 10.1038/s41467-020-18764-3 82. Bender D., Hildt E.. Effect of Hepatitis Viruses on the Nrf2/Keap1-Signaling Pathway and Its Impact on Viral Replication and Pathogenesis. Int. J. Mol. Sci. (2019) 20. DOI: 10.3390/ijms20184659 83. Mishra S., Pandey A., Manvati S.. Coumarin: An emerging antiviral agent. Heliyon (2020) 6 e03217. DOI: 10.1016/j.heliyon.2020.e03217 84. Sajja R.K., Prasad S., Tang S., Kaisar M.A., Cucullo L.. Blood-brain barrier disruption in diabetic mice is linked to Nrf2 signaling deficits: Role of ABCB10?. Neurosci. Lett. (2017) 653 152-158. DOI: 10.1016/j.neulet.2017.05.059 85. Xi Z., Chen X., Xu C., Wang B., Zhong Z., Sun Q., Sun Y., Bian L.. Protocatechuic acid attenuates brain edema and blood-brain barrier disruption after intracerebral hemorrhage in mice by promoting Nrf2/HO-1 pathway. Neuroreport (2020) 31 1274-1282. DOI: 10.1097/WNR.0000000000001542
--- title: Human Milk Oligosaccharides Are Associated with Lactation Stage and Lewis Phenotype in a Chinese Population authors: - Xiangnan Ren - Jingyu Yan - Ye Bi - Paul William Shuttleworth - Ye Wang - Shan Jiang - Jie Wang - Yifan Duan - Jianqiang Lai - Zhenyu Yang journal: Nutrients year: 2023 pmcid: PMC10059825 doi: 10.3390/nu15061408 license: CC BY 4.0 --- # Human Milk Oligosaccharides Are Associated with Lactation Stage and Lewis Phenotype in a Chinese Population ## Abstract Background: Human milk oligosaccharides (HMOs) are the third most abundant component of human milk. Various factors may affect the concentration of HMOs, such as the lactation period, Lewis blood type, and the maternal secretor gene status. Objectives: The purpose of this study is to investigate factors associated with HMO concentrations in Chinese populations. Methods: A sub-sample of 481 was randomly selected from a large cross-sectional study in China ($$n = 6481$$) conducted in eight provinces (Beijing, Heilongjiang, Shanghai, Yunnan, Gansu, Guangdong, Zhejiang, and Shandong) between 2011 and 2013. HMO concentrations were determined by a high-throughput UPLC-MRM method. Various factors were collected through face-to-face interviews. Anthropometric measurement was conducted by trained staff. Results: Median total HMO concentration was 13.6 g/L, 10.7 g/L, and 6.0 g/L for colostrum, transitional milk, and mature milk, respectively. HMO concentration decreased significantly as the lactation period increased ($p \leq 0.0001$). There were significant differences of average total HMO concentration between secretor mothers and non-secretor mothers (secretor 11.3 g/L vs. non-secretor 5.8 g/L, $p \leq 0.0001$). There were significant differences of average total HMO concentrations among three Lewis blood types ($$p \leq 0.003$$). Comparing with the concentration of total oligosaccharides of Le+ (a−b+), average of total oligosaccharides concentrations increased by 3.9 (Le+ (a+b−), $$p \leq 0.004$$) and 1.1 g/L (Le− (a−b−), $$p \leq 0.049$$). The volume of breast milk expressed and the province the mother came from affected the concentration of total oligosaccharides (all $p \leq 0.0001$). Maternal BMI ($$p \leq 0.151$$), age ($$p \leq 0.630$$), prematurity ($$p \leq 0.850$$), mode of delivery ($$p \leq 0.486$$), infants’ gender ($$p \leq 0.685$$), maternal education level ($$p \leq 0.989$$), maternal occupation ($$p \leq 0.568$$), maternal allergic history ($$p \leq 0.370$$), maternal anemia ($$p \leq 0.625$$), pregnancy-induced hypertension ($$p \leq 0.739$$), gestational diabetes ($$p \leq 0.514$$), and parity ($$p \leq 0.098$$) were not significantly correlated with the concentration of milk oligosaccharides. The concentrations of 2′-fucosyllactose (2′-FL), lacto-N-neotetraose (LNnT), sialyllacto-N-tetraose c (LSTc), lacto-N-fucopentaose I (LNFP-I), disialylated lacto-N-tetraose (DSLNT), difucosyl-para-lacto-N-neohexaose (DFpLNnH), difucosyl-lacto-N-hexaose (DFLNH[a]), and 3-sialyllactose (3′-SL) showed a gradual downward trend, while the concentration of 3-fucosyllactose (3-FL) showed a gradual upward trend among three lactation stages ($p \leq 0.05$). Conclusions: The concentration of HMOs changes throughout lactation, and it varies between different HMOs. HMO concentrations differed between lactation stage, maternal secretor gene status, Lewis blood type, volume of breast milk expressed, and the province the mother came from. Prematurity, mode of delivery, parity, infants’ gender, and maternal characteristics did not affect the HMO concentration. Geographical region may be not associated with HMOs concentration in human milk. There may be a mechanism for co-regulation of the secretion of some of the oligosaccharides such as 2′FL vs. 3FL, 2′FL vs. LNnT, and lacto-N-tetraose (LNT). ## 1. Introduction Human milk oligosaccharides (HMOs) are complex glycans that are indigestible by infants, and are the third most abundant component in human milk [1]. The mean total concentration of HMOs ranges from 5 g/L to 20 g/L, and there are different types and lower quantities of oligosaccharides in bovine and goat milk compared with human milk [1,2,3]. Recently, researchers have suggested that HMOs play an important role in biological functions, such as acting as prebiotics, antiadhesives preventing pathogen adhesion, and affecting immune modulation [2,3,4,5,6]. HMOs may play an important role in promoting a healthy, micro-ecological environment, nourishing health-promoting bacteria in an infant’s gastrointestinal tract [4,7,8]. HMOs could also play a role in modulating cognitive functions [9]. Several interventional trials have supported these potential roles, suggesting that HMOs could not only reduce the incidence of neonatal necrotizing enterocolitis, but could also be involved in the development of immune function and brain development [9,10]. HMO concentrations are affected by several factors, such as the lactation period, Lewis blood type and secretor gene type, regional characteristics, infants’ health and prematurity, maternal disease, and parity. Fucosylated HMO production is controlled by α-1-2, α-1-3 and α-1-4 fucosyltransferase [11]. The expression of α-1-2, fucosyltransferase is closely related to FUT2 (Se gene), and the expression of α-1-3, α-1-4 fucosyltransferase is closely related to FUT3 (Lewis gene) [12,13,14,15,16]. HMOs have been investigated in various countries, with a limited sample size ranging from 9 to 450 samples [1,17,18,19,20,21,22,23,24]. Numerous papers have shown that lactation time is the most significant factor affecting the concentration of HMOs, and the total concentration of HMO in colostrum is higher than those in mature milk [1,17,24]. Lewis blood type and maternal genetics may affect the HMO concentrations [17,19,23,24]. The relative concentrations of the various types of HMO have also been shown to vary among populations across different countries, suggesting that geographical location may be one of the potential influencing factors affecting their relative abundance [1]. In pregnancies carried to full term, the concentration of HMO secreted by mothers has been shown to be lower than in babies delivered prematurely [24]. However, sample sizes in the previously published evidence are small, and the type of available studies are limited. Subject populations generally came from the same region or the same living environment, which might limit the generalization of the results to a large population. Additionally, there were no standardized methods in analyzing, and the comparability of values in different studies still needs to be explored. Changes in the concentration of HMO throughout lactation in different living environments, and in different parts of China, have not been reported. The objectives of this study were to analyze dynamic changes of HMO concentration during different lactation periods (colostrum, transitional milk, mature milk) from a cross-sectional survey in China, and to explore the potential influencing factors that may affect concentrations of HMO. ## 2.1. Study Design The study design has been described previously in a paper reporting as part of building a regional breast milk composition database in China [25]. This study reports a sub sample of 481 samples from different women, stratified and randomly selected from 6481 human milk samples, from multiple sites including eight provinces (Beijing, Heilongjiang, Shanghai, Yunnan, Gansu, Guangdong, Zhejiang, and Shandong). It includes not only urban and rural areas, but also inland and coastal areas. The healthy lactating mothers were at different stages of lactation (0~340 days postpartum). Colostrum was 0~6 days postpartum, transitional milk was 7~14 days postpartum, and mature milk was 15~340 days postpartum. Ethical approval was given by the ethics committee of the National Institute for Nutrition and Health, China CDC. Written informed consent was obtained from all participants. ## 2.2. Human Milk Sample Collection Human milk samples were collected in a room without direct sunlight exposure. One full breast was emptied by a portable electric breast pump (HNR/X-2108Z, Shantou, Guangdong, China) into a feeding bottle in the morning (9:00~11:00 a.m.). After gently agitating the bottle for ~10 times, the samples were divided into 15 mL centrifuge tubes and were stored at −20 °C in a freezer. The frozen samples were shipped to a central laboratory at the National Institute for Nutrition and Health, CCDC, in Beijing and stored in a −80 °C freezer until analysis. The cold chain was preserved throughout. ## 2.3. Analysis of Human Milk Oligosaccharides Human milk oligosaccharide (HMO) content was determined using a recently published high-throughput UPLC-MRM method [26]. An ACQUITY Ultraperformance system (Waters, Milford, MA, USA) coupled to a Xevo TQ-XS triple quadrupole mass spectrometer (Waters) was used for analysis. The mobile phase solvents consisted of acetonitrile and water with ammonium acetate as the additive. ESI-MS detection was in the negative-ion mode, and collision-induced dissociation tandem MS (CID-MS/MS) was carried out using multi-reaction monitoring (MRM) for both sequence assignment and quantitation. The samples were centrifuged at 8000× g rpm for 6 min at 4 °C in order to remove the fat component. After removal of the top lipid layer, the sample was diluted by a factor of 15 using ultrapure water. For analysis, an aliquot (100 μL) of the sample solution was taken out and 2 volumes of ethanol added. The mixture was then centrifuged at 8000× g rpm for 6 min at 4 °C. The supernatant was 1:1 diluted with $50\%$ ACN in H2O (v/v). The overall dilution of the milk sample in various solvent was 1:90. The quantitation calibration curves of oligosaccharides were used, and each standard curve covered 8 concentration points. To make the standard concentrations closer to those of the real samples, we divided the oligosaccharide standards into high- and low-content groups and set different concentration ranges. In order to ensure a good parallelism in terms of MRM detection sensitivity and the chromatographic resolution (e.g., peak shape and retention time) for the large number of sample analyses, the mixed standard solution was used for quality control by injection between every 10 samples analyzed. ## 2.4. Maternal Secretor Status and Geographical Factors The subjects’ secretor status and Lewis blood type was determined by the presence of specific oligosaccharide and ion fragments. The presence of the product ion m/z 325 from 2′-fucosyllactose (2′-FL), Lactodifucotetraose (LDFT), lacto-N-fucopentaose I (LNFP-I), and lacto-N-neo-difucohexaose I (LNnDFH-I) can be used as an indicator of secretor’s status, while lacto-N-fucopentaose II (LNFP-II) with the fragment ion m/z 348 can be used as an indicator of Lewis blood-group phenotype [26,27]. Study sites were divided into rural and urban areas or were classified into coastal and inland areas based on geographic features. We used the sum of the concentrations of lacto-N-difucohexaose I (LNDFH-I) and LNnDFH-I for all the data analysis because the concentration of LNnDFH-I was so low that it was not even detected in some milk samples. These two oligosaccharides shared the common characteristics of only being found in secretors milk samples; therefore, we used the sum of two oligosaccharides instead of the individual concentrations. ## 2.5. Statistical Analysis Descriptive statistics were calculated for both continuous variables and categorical variables. All continuous variables were tested for normality. The data were expressed as mean ± SD and median. The median of concentrations of oligosaccharides was used in the data presented in chart form. The concentration of some oligosaccharides were heavily skewed, and therefore the natural log was used. The logarithmic values were used for statistical analysis. Nonparametric tests were used if the logarithmic data were still skewed. Analysis of variance (ANOVA) was used to analyze the effects of different lactating stages and blood type. Independent sample t-test was used to study the relationship between delivery time (term vs. preterm), between living environments (rural areas vs. urban areas), and between geographical location (coastal areas vs. inland areas). The Tukey–Kramer test was used for adjusting multiple comparisons if there were statistical significance for overall effects. The general linear model was selected to analyze the relationship between human milk oligosaccharides and influencing factors. Selecting the significant and possible influencing factors to enter the multiple regression model. Differences were considered significant at $p \leq 0.05.$ Principal component analysis (PCA) was used to obtain an overview of variations among oligosaccharides at different lactation stages. All analyses were conducted with SAS 9.4 (SAS Inc., Cary, NC, USA) or Origin software 2019 (Originlab, Northampton, MA, USA). ## 3.1. Characteristics of Study Subjects The characteristics of the subjects are shown in Table 1. The mean maternal age of lactating women was 26.8 years, and mean pre-pregnancy BMI of all mothers was 20.8 kg/m2; $54.3\%$ of the studied infants were boys. The proportion of preterm infants was $4.4\%$. Mean birth weights of term and preterm infants were 3491.3 g, and 2845.2 g, respectively. Colostrum, transitional milk, and mature milk samples each accounted for about one third of the total samples. The proportions of rural and urban areas were $36.8\%$ and $63.2\%$, respectively. The proportions of coastal and inland areas were $31.8\%$ and $68.2\%$, respectively. ## 3.2. Concentrations of Human Milk Oligosaccharides during Different Lactation Periods Principal component analysis (PCA) of HMOs showed a spectral separation among different lactation stages, indicating significant differences of concentrations of oligosaccharides (Figure 1). The first principal component (PC1) with PC2 and PC3 described $76.4\%$ of the variation contained in the concentrations of HMO from different lactation stages. The dispersion of within-particular groups can be observed on the PCA score plot and indicated a small variance in HMO concentrations among mature milk samples. There were significant differences in the concentrations of 23 oligosaccharides among three lactation stages based on ANOVA ($p \leq 0.05$). The box diagrams of oligosaccharides during three lactation stages are shown in Figure 2; lower case letters represent significant differences. Median total HMO concentration was 13.6 g/L, 10.7 g/L, and 6.0 g/L for colostrum, transitional milk, and mature milk, respectively, and decreased significantly the longer the interval since birth ($p \leq 0.0001$). The concentrations in colostrum were the highest and the concentrations in mature milk were the lowest, and showed a gradual downward trend in concentration for the following 14 oligosaccharides: 2′-FL, lacto-N-neotetraose (LNnT), sialyllacto-N-tetraose c (LSTc), LNFP-I, disialylated LNT (DSLNT), difucosyl-para-lacto-N-neohexaose (DFpLNnH), difucosyl-lacto-N-hexaose (DFLNH[a]), 3-sialyllactose (3′-SL). Through pairwise comparison, there were significant differences in HMO concentrations between any two groups (colostrum, transitional milk, mature milk) for these oligosaccharides ($p \leq 0.05$). The concentration of 3-fucosyllactose (3-FL) showed a gradual upward trend during lactation stages, and the concentrations in mature milk were higher than in colostrum and transitional milk ($p \leq 0.05$). The concentration of lacto-difucotetraose (LDFT) first decreased and then increased, and the concentration in colostrum was higher than in transitional milk, or mature milk ($p \leq 0.05$). The concentrations of other oligosaccharides also first increased then decreased, including sialyllacto-N-tetraose b (LSTb), 6-Sialyllactose (6′-SL), LNFP-II, monofucosyl-lacto-N-neohexaose (MFLNnH), and monofucosyl-lacto-N-hexaose III (MFLNH-III). The concentrations of oligosaccharides at different lactation stages are shown in Table S1. Concentrations of HMOs during different lactation periods are shown in Figure 3. The concentrations of total oligosaccharides showed a downward trend as lactation duration increased, with negative linear correlation between the total concentration of oligosaccharides and lactation time (r = −0.592, $p \leq 0.0001$). Comparing with the total concentration of oligosaccharides on lactation 0~3 day, the median of total oligosaccharides concentrations decreased by 2.5 g/L (day 8~10), 3.2 g/L (day 11~14), 4.2 g/L (day 15~30), 6.1 g/L (day 31~90), 8.3 g/L (day 91~180), and 9.0 g/L (day 181~340), respectively (all $p \leq 0.01$). There were no significant differences of total oligosaccharides between day 0~3 and day 4~7 ($$p \leq 0.713$$). Some specific oligosaccharides showed a downward trend in relation to lactation duration, such as 2′-FL, 3′-SL, LNnT, DSLNT, LSTc, DFLNH(a), and DFpLNnH. The concentrations of lacto-N-tetraose (LNT), LNFP-I, 6′-SL, and LSTb were highest during lactation day 4~7, day 4~7, day 11~14, and day 8~10, respectively. 3-FL showed an upward trend as the duration of lactation increased. ## 3.3. HMOs in Different Geographical Factors and Preterm Infants The province the subjects came from could affect the concentration of total oligosaccharides ($p \leq 0.0001$). Comparing the concentration of total oligosaccharides from Shanghai province, the average of total oligosaccharides concentrations increased by 2.3 g/L (Beijing), 3.0 g/L (Yunnan), 2.8 g/L (Gansu), and 2.9 g/L (Guangzhou), respectively (all $p \leq 0.05$) (Figure 4A). There were no significant differences between rural areas and urban areas ($$p \leq 0.922$$) or between coastal areas and inland areas ($$p \leq 0.244$$) (Figure 4C,D). There were no significant differences of total oligosaccharides secreted, between preterm milk and full-term milk ($$p \leq 0.618$$) (Figure 4B). ## 3.4. HMOs in Different Maternal Secretor Status and Lewis Blood Type The percentages of secretor and non-secretor mothers were $75.4\%$, and $24.6\%$, respectively. There were significant differences in the average of the total amount of HMOs secreted between secretor mothers and non-secretor mothers (secretor (11.3 g/L) vs. non-secretor (5.8 g/L), $p \leq 0.0001$) (Figure 4E). Concentrations of 2′-FL, LDFT, LNFP-I, and MFLNH-I particularly, were far lower in milk from non-secretor mothers than those from secretor mothers ($p \leq 0.0001$). The concentrations of 3-FL, LNT, LNFP-II, lacto-N-neo-difucohexaose II (LNnDFH-II), and monofucosyl-lacto-N-hexaose III (MFLNH-III) were approximately 2~5 times higher in milk from non-secretor mothers than those from secretor mothers ($p \leq 0.0001$). The percentages of Le+ (a+b−), Le+ (a−b+), and Le− (a−b−) were $22.9\%$, $68.6\%$, and $8.5\%$, respectively. The Lewis blood type also affected the concentration of total oligosaccharides ($$p \leq 0.003$$). Comparing the concentration of total oligosaccharides of Le+ (a−b+), the average total oligosaccharides concentrations increased by 3.9 (Le+ (a+b−), $$p \leq 0.004$$) and 1.1 g/L (Le− (a−b−), $$p \leq 0.049$$) (Figure 4F). Lewis phenotype takes into account both maternal secretor gene status and Lewis blood group. There is a significant difference between the percentage of different oligosaccharides in different Lewis phenotypes. ( Figure 5). The percentage of 2′-FL was highest (above $18.7\%$) compared with other oligosaccharides for Se+Le+ (a−b+) blood type. The percentage of 2′-FL was the highest specific oligosaccharide for Se+Le− (a−b−) blood type (colostrum: $33.8\%$, transitional milk: $23.9\%$, mature milk: $38.8\%$). The percentages of LNT, LNFP-II, and 3-FL were higher than other oligosaccharides for Se−Le+ (a+b−) blood type: $18.8\%$, $17.6\%$, $8.1\%$ in colostrum; $18.7\%$, $21.2\%$, $8.3\%$ in transitional milk; $11.0\%$, $19.5\%$, $24.0\%$ in mature milk. For colostrum, percentages of LNT ($39.2\%$) were higher than other oligosaccharides for Se−Le− (a−b−) blood type. For transitional milk, percentages of 6′-SL ($29.1\%$) and LNT ($11.8\%$) were higher than other oligosaccharides for Se−Le− (a−b−) blood type. For mature milk, the percentages of LNT ($55.2\%$) and LNFP-III ($17.4\%$) were higher than other oligosaccharides for Se−Le− (a−b−) blood type. The percentage of HMOs in different blood groups in the three lactation stages are shown in Table S2. ## 3.5. Influencing Factors on the Concentrations of HMOs According to a multiple regression model analysis, the total concentration of oligosaccharides was closely associated with lactation duration, maternal secretor gene type, Lewis blood type, the volume of breast milk expressed, and the province the mother came from. R2 was 0.6154. After correcting for other factors, Figure 4G shows a scatter plot charting the total concentration of HMOs, versus the number of days the mother has been lactating. The line of best fit shows a negative correlation, with total HMO concentration decreasing with time. Maternal BMI ($$p \leq 0.151$$), age ($$p \leq 0.630$$), prematurity ($$p \leq 0.850$$), mode of delivery ($$p \leq 0.486$$), infants’ gender ($$p \leq 0.685$$), maternal education level ($$p \leq 0.989$$), maternal occupation ($$p \leq 0.568$$), maternal allergic history ($$p \leq 0.370$$), maternal anemia ($$p \leq 0.625$$), pregnancy-induced hypertension ($$p \leq 0.739$$), gestational diabetes ($$p \leq 0.514$$), and parity ($$p \leq 0.098$$) were not significantly correlated with the concentration of milk oligosaccharides. ## 3.6. Correlation of Human Milk Oligosaccharides The correlations between the concentrations of the various human milk oligosaccharides are shown in Figure 6. The correlations were the absolute abundance of these specific HMOs over the period of lactation. 3-FL had a strong positive correlation with LNnDFH-II ($r = 0.804$, $p \leq 0.0001$). 3-FL had a strong negative correlation with MFLNH-I (r = −0.623, $p \leq 0.0001$), 2′-FL (r = −0.514, $p \leq 0.0001$), and LNFP-I (r = −0.676, $p \leq 0.0001$). LNnDFH-II had a strong negative correlation with MFLNH-I (r = −0.625, $p \leq 0.0001$), 2′-FL (r = −0.413, $p \leq 0.0001$), and LNFP-I (r = −0.503, $p \leq 0.0001$). MFLNH-III had a strong negative correlation with LDFT (r = −0.447, $p \leq 0.0001$). LNFP-II had a strong negative correlation with 2′-FL (r = −0.467, $p \leq 0.0001$). LNFP- III had a strong negative correlation with MFLNH-I (r = −0.484, $p \leq 0.0001$). 2′-FL had a strong positive correlation with LNFP-I ($r = 0.699$, $p \leq 0.0001$). LNT had a strong positive correlation with DSLNT ($r = 0.692$, $p \leq 0.0001$). Detailed information of the correlation of human milk oligosaccharides is shown in Table S3. ## 4. Discussion Our study analyzed the concentrations of 24 different HMOs in 481 human milk samples, in a Chinese population. We assessed for the influence of varying factors, including prematurity, geographical region, and others. The median of the total HMO concentration was 13.6 g/L, 10.7 g/L, and 6.0 g/L for colostrum, transitional milk, and mature milk, respectively, and decreased significantly with the extension of lactation days. There were significant differences of total human milk oligosaccharides between secretor mothers and non-secretor mothers. This study demonstrates that the concentrations of HMO changed dynamically the longer the mother had been lactating. The concentrations of total HMO gradually decreased as lactation extended, and the same trend was discovered in 2′-FL, 3′-SL, LNnT, DSLNT, and LSTc; this is consistent with previous studies [3,17,28]. 3-FL demonstrated a dynamic increase in concentration, which is also consistent with previous evidence [3,17,24,28,29]. The trends in the changes of the amount of LDFT, LSTb, 6′-SL, LNFP-I, and LNT secreted were also consistent with the published literature. Gabrielli et al. [ 24] have suggested that during the first month of lactation, decreases in total oligosaccharide concentrations occur for Se+Le+ groups and Se−Le− groups ($14.7\%$ and $21.5\%$, respectively), whereas minimal variations were present in Se−Le+ groups and Se+Le− groups. Total HMO concentrations decreased in four groups as the lactation period extended in our study. Their paper was a cohort study, while our study was a cross-sectional study. The sample size of the Se+Le− group ($$n = 7$$) and Se−Le− group ($$n = 7$$) was relatively small. Concentrations of DFLNH(a), LNFP-II, DFpLNnH, MFLNH-III, MFLNH-I, MFLNnH, and LNnDFH-II have not previously been reported in Chinese populations, and we present them for the first time. The concentrations of LNFP-II, MFLNnH, and MFLNH-III first increased, then decreased; the concentration of LNnDFH-II first decreased, then increased. The concentrations of DFLNH(a) and DFpLNnH showed a downward trend, which has not previously been reported with a clear trend in prior publications. The combined total concentrations of HMO was similar in colostrum, transitional milk, and mature milk from mothers of term and preterm infants, which is consistent with previous studies [19]. A slightly higher concentration of HMO was found in urban areas compared with rural areas, and a slightly higher concentration of HMO was found in coastal area compared with inland areas, but these were not statistically significant, $p \leq 0.05.$ The geographic region and ethnic group the mother comes from significantly affects the secretion of HMO. McGuire et al. [ 1] suggested normal HMO concentrations and profiles vary geographically. Li et al. [ 26] indicated that HMO concentration of the Tibetans was $56\%$ higher than the concentration of Zhuang, and the concentration of Han (6.3 g/L) was close to the average value (5.6 g/L) of all samples, which was within the scope of our research results. This result is the average of all data during different lactation periods (colostrum, transitional, and mature milk), with a mean lactation value of 143 days, with the earliest at day 6 and the longest at day 347. The total HMO concentrations in different lactation periods was reported instead of giving the average value of all data in our research because lactation stage was the important factor. Different ethnic groups may have different living environmental conditions and dietary habits that could explain these differences. The Han ethnic group was the largest, and the results of the Han ethnic group were close to the average level, therefore this article analyzes the oligosaccharide data of the Han population in detail. This can avoid the influence of too many confounding factors. However, living environments and geographical location are intimately related, and both may have some impact affecting the results. Therefore, these factors were analyzed using a multiple regression model. This study revealed that the total HMO concentrations, and the concentration of an individual HMO, showed large differences depending on the Lewis blood type (active or inactive FUT3 gene) and maternal secretor gene status (active or inactive FUT2 gene) [12,13,14,15,16]. The different expression of these two genes affects the Lewis blood group and maternal secretor status. The percentages of secretor (Se+) and non-secretor (Se−) status in this study were $75.4\%$, and $24.6\%$, respectively, which were similar to numerous studies [30,31]. The secretor (Se) gene encoded for the FUT2 is necessary for the synthesis of 2′FL and other α1-2-fucosylated HMOs [13]. There was an abundance of α1-2-fucosylated HMOs in milk from secretor mothers, such as 2′FL, LNFP I, and LDFT. By contrast, in non-secretors who lack the FUT2 enzyme, the concentrations of α1-2-fucosylated HMOs were only minimal, or not present. The absence of α-1-2-linked fucosylated oligosaccharides in milk from non-secretor mothers helps explain the lower total concentration of HMOs. 2′FL was highest in milk from secretor mothers. The percentages of 2′FL in colostrum, transitional milk, and mature milk from secretor mothers were $53.1\%$, $46.4\%$, and $58.8\%$, respectively, in this study. Because of high amounts secreted in breast milk, and the relative importance of 2′FL, it is available in some commercial infant formulas [32]. The USA’s FDA and the European Food Safety Authority (EFSA) considers that 2′FL is generally safe for infants under one year of age. 2′FL may improve infants gut microbiota composition, inhibit bacterial infection via antiadhesion mechanism, modulate the intestinal epithelial cell response, and regulate immunity [32,33]. It has also been shown to influence cognitive function and improve learning and memory in rodents [33,34,35,36,37]. The concentrations of LNT were the highest in milk from non-secretor mothers, and percentages in colostrum, transitional milk, and mature milk were $54.5\%$, $30.5\%$, and $65.8\%$, respectively. Sprenger et al. [ 22] have suggested that LNnT and LNT are ‘co-regulated’ with the FUT2-dependent 2′FL concentration. LNT concentration was negatively correlated with the amount of 2′FL, and LNnT concentration was positively correlated, which is consistent with the published literature [22]. It has been suggested that LNT and LNnT protect against important systemic infections of the newborn [13]. The secretion of 3-FL shows a gradual upward trend during lactation stages. 3-FL concentration was negatively correlated with the amount of 2′FL, which is consistent with previous studies [22]. Austin et al. [ 17] demonstrated the strong negative correlation between 2′-FL and 3-FL concentration, suggesting there is a possible mechanism of co-regulation. The EFSA Panel on Nutrition have evaluated the safety of 3-FL as a novel food and suggested 3-FL was safe under the proposed conditions of use, including the use as a food supplement [38]. A few studies have been conducted on the functionalities of 3-FL. 2′-FL and 3-FL have been reported to play an important role in the establishment of a healthy gut microbiome by selectively stimulating the growth of beneficial bacterium and suppressing the growth of harmful bacteria [39]. More research into the prebiotic effects of 3-FL is needed to confirm this. Lewis phenotype takes into account both maternal secretor gene status and Lewis blood group, and has been shown to play a key role in determining HMO secretion. They can be combined to generate an SeLe phenotype. The percentages of Lewis blood group (a−b+), (a+b−), and (a−b−) in this study were $68.6\%$, $22.9\%$, and $8.5\%$, respectively. The FUT3 gene expression produces α-1-$\frac{3}{4}$-L-fucosyltransferase, which controls the production of α-1-$\frac{3}{4}$-L-fucosylated oligosaccharides. The product of the FUT2 gene expression is α-1-2-L-fucosyltransferase, which controls the production of α-1-2-L-fucosylated oligosaccharides. 4 SeLe phenotype are controlled by FUT2 and FUT3 jointly [29,31]. Considering the maternal secretor status and Lewis blood group, the percentages of Se+Le+ (a−b+), Se+Le− (a−b−), Se−Le+ (a+b−), and Se−Le− (a−b−) were $68.1\%$, $7.3\%$, $22.7\%$, and $1.9\%$, respectively. The main types of maternal milk phenotype were Se+Le+ (a−b+) and Se−Le+ (a+b−), and the incidence of Se−Le− (a−b−) was less than $2\%$, which is consistent with the published evidence [24,31]. Overall, the proportion of these blood types were similar to the proportion found in other papers. The percentages of SeLe phenotypes varies among different regions and ethnic populations [15]. According to previous work, Se+Le+ varies by 54~$78\%$, Se−Le+ varies by 14~$31\%$, Se+Le− varies by 6~$25\%$, and Se−Le− varies by 3~$7\%$ [16,24,31]. The concentrations of LNFP II and LNDFH II in Se−Le+ (a+b−) maternal milk were higher than Se+Le+ (a−b+) maternal milk, while they were absent in the milk expressed by Lewis negative subjects. Lactation stage, secretor status, Lewis blood type, the volume of breast milk expressed, and the province the mother came from were found to be significant factors influencing the concentrations of expressed HMOs through multiple regression model analysis in this study. Kunz et al. [ 19] also suggested that differences in the total amount depended on the lactation time, the secretor status, and the Lewis blood group, but that prematurity did not have an influence. Austin et al. [ 17] indicated that lactation stage was the most significant influencing factor of HMO concentrations, and there were no significant correlations between HMO concentrations and geographical location or mode of delivery. Some studies found there were significant differences in oligosaccharide profiles between different geographical locations [1,40,41]. These studies generally included different countries or the ethnicity of mothers [17]. The province the subjects came from also affects the concentration of total oligosaccharides, and HMO concentration showed significant differences in our study between different provinces. There are many factors that may be closely associated with the province the mothers come from, which may be affecting this, such as their dietary habits. However, there were no significant differences between rural areas and urban areas, or between coastal areas and inland areas. The geographic regional difference was only reflected in the low total HMO concentration in one province. Although human milk samples were collected from mothers from eight provinces in China, they were mainly Han ethnicity mothers and presumably have similar cultural and dietary habits, as well as a similar genetic background, which may have led to regional similarities. In works by other authors, prematurity has been shown to be significant, which was not demonstrated in this study. Wang et al. [ 42] concluded that there were consistently higher levels (13~$23\%$) of oligosaccharide-bound sialic acid in preterm milk samples than in term milk samples. They had particularly low sample numbers for preterm infants, with only 21 ($4.4\%$), so this could just be related to the small sample size leading to a sampling error. Different oligosaccharides were analyzed, which may affect the total concentrations of HMO. The percentages of SeLe phenotypes varies among different regions and ethnic populations, and the phenotype of SeLe could affect the concentrations of HMO. This study is not without some limitations. It is known that more than 150 HMO exist in human milk; we have analyzed 24 representative HMO and more types of HMO need to be studied. Our study population is predominantly of Han Chinese, and findings may not be applicable to other ethnic groups within the wider Chinese population. As regards the HMO concentration in mothers with premature infants, our study population contained relatively low numbers, including only 21 subjects. This may not be sufficiently powered to demonstrate differences demonstrated in other papers. The relationship between infant growth and development and oligosaccharides has not been discussed, which will be the form of future research of this group. ## 5. Conclusions In conclusion, this is one of the largest cross-sectional studies conducted on factors affecting HMO concentrations and presents some findings in a Chinese population for the first time. HMO concentrations change throughout lactation and are influenced by the maternal secretor gene status, Lewis blood type, the volume of breast milk expressed, and the province the mother came from. There may be a mechanism for co-regulation of secretion for some oligosaccharides, such as 2′FL vs. 3FL, 2′FL vs. LNnT, and LNT. Our findings suggest that maternal BMI, maternal age, prematurity, mode of delivery, infants’ gender, maternal education level, maternal occupation, maternal allergic history, maternal anemia, pregnancy-induced hypertension, gestational diabetes, and parity were not significantly associated with total HMO concentration. ## References 1. McGuire M.K., Meehan C.L., McGuire M.A., Williams J.E., Foster J., Sellen D.W., Kamau-Mbuthia E.W., Kamundia E.W., Mbugua S., Moore S.E.. What’s normal? Oligosaccharide concentrations and profiles in milk produced by healthy women vary geographically. Am. J. Clin. Nutr. (2017) 105 1086-1100. DOI: 10.3945/ajcn.116.139980 2. Triantis V., Bode L., van Neerven R.J.J.. Immunological Effects of Human Milk Oligosaccharides. Front. Pediatr. (2018) 6 190. DOI: 10.3389/fped.2018.00190 3. Thurl S., Munzert M., Boehm G., Matthews C., Stahl B.. Systematic review of the concentrations of oligosaccharides in human milk. Nutr. Rev. (2017) 75 920-933. DOI: 10.1093/nutrit/nux044 4. Bode L.. Human milk oligosaccharides: Every baby needs a sugar mama. Glycobiology (2012) 22 1147-1162. DOI: 10.1093/glycob/cws074 5. Hill D.R., Chow J.M., Buck R.H.. Multifunctional Benefits of Prevalent HMOs: Implications for Infant Health. Nutrients (2021) 13. DOI: 10.3390/nu13103364 6. Okburan G., Kızıler S.. Human milk oligosaccharides as prebiotics. Pediatr. Neonatol. (2023). DOI: 10.1016/j.pedneo.2022.09.017 7. Wiciński M., Sawicka E., Gębalski J., Kubiak K., Malinowski B.. Human Milk Oligosaccharides: Health Benefits, Potential Applications in Infant Formulas, and Pharmacology. Nutrients (2020) 12. DOI: 10.3390/nu12010266 8. Donovan S.M., Comstock S.S.. Human Milk Oligosaccharides Influence Neonatal Mucosal and Systemic Immunity. Ann. Nutr. Metab. (2016) 69 42-51. DOI: 10.1159/000452818 9. Liu F., Simpson A.B., D’Costa E., Bunn F.S., van Leeuwen S.S.. Sialic acid, the secret gift for the brain. Crit. Rev. Food Sci. Nutr. (2022) 1-20. DOI: 10.1080/10408398.2022.2072270 10. Abbas S., Keir A.K., Makrides M., Klein L.D., Grzeskowiak L.E., McPhee A.J., Rumbold A.R.. Tailoring Human Milk Oligosaccharides to Prevent Necrotising Enterocolitis Among Preterm Infants. Front. Nutr. (2021) 8 702888. DOI: 10.3389/fnut.2021.702888 11. Kobata A.. Structures and application of oligosaccharides in human milk. Proc. Jpn. Academy Ser. B Phys. Biol. Sci. (2010) 86 731-747. DOI: 10.2183/pjab.86.731 12. Chaturvedi P., Warren C.D., Altaye M., Morrow A.L., Ruiz-Palacios G., Pickering L.K., Newburg D.S.. Fucosylated human milk oligosaccharides vary between individuals and over the course of lactation. Glycobiology (2001) 11 365-372. DOI: 10.1093/glycob/11.5.365 13. Vandenplas Y., Berger B., Carnielli V.P., Ksiazyk J., Lagström H., Sanchez Luna M., Migacheva N., Mosselmans J.M., Picaud J.C., Possner M.. Human Milk Oligosaccharides: 2’-Fucosyllactose (2’-FL) and Lacto-N-Neotetraose (LNnT) in Infant Formula. Nutrients (2018) 10. DOI: 10.3390/nu10091161 14. Figueroa-Lozano S., de Vos P.. Relationship Between Oligosaccharides and Glycoconjugates Content in Human Milk and the Development of the Gut Barrier. Compr. Rev. Food Sci. Food Saf. (2019) 18 121-139. DOI: 10.1111/1541-4337.12400 15. Tonon K.M., de Morais M.B., Abrão A.C.F.V., Miranda A., Morais T.B.. Maternal and Infant Factors Associated with Human Milk Oligosaccharides Concentrations According to Secretor and Lewis Phenotypes. Nutrients (2019) 11. DOI: 10.3390/nu11061358 16. Elwakiel M., Hageman J.A., Wang W., Szeto I.M., van Goudoever J.B., Hettinga K.A., Schols H.A.. Human Milk Oligosaccharides in Colostrum and Mature Milk of Chinese Mothers: Lewis Positive Secretor Subgroups. J. Agric. Food Chem. (2018) 66 7036-7043. DOI: 10.1021/acs.jafc.8b02021 17. Austin S., De Castro C.A., Bénet T., Hou Y., Sun H., Thakkar S.K., Vinyes-Pares G., Zhang Y., Wang P.. Temporal Change of the Content of 10 Oligosaccharides in the Milk of Chinese Urban Mothers. Nutrients (2016) 8. DOI: 10.3390/nu8060346 18. Zhang W., Wang T., Chen X., Pang X., Zhang S., Obaroakpo J.U., Shilong J., Lu J., Lv J.. Absolute quantification of twelve oligosaccharides in human milk using a targeted mass spectrometry-based approach. Carbohydr. Polym. (2019) 219 328-333. DOI: 10.1016/j.carbpol.2019.04.092 19. Kunz C., Meyer C., Collado M.C., Geiger L., García-Mantrana I., Bertua-Ríos B., Martínez-Costa C., Borsch C., Rudloff S.. Influence of Gestational Age, Secretor, and Lewis Blood Group Status on the Oligosaccharide Content of Human Milk. J. Pediatr. Gastroenterol. Nutr. (2017) 64 789-798. DOI: 10.1097/MPG.0000000000001402 20. Meredith-Dennis L., Xu G., Goonatilleke E., Lebrilla C.B., Underwood M.A., Smilowitz J.T.. Composition and Variation of Macronutrients, Immune Proteins, and Human Milk Oligosaccharides in Human Milk from Nonprofit and Commercial Milk Banks. J. Hum. Lact. (2018) 34 120-129. DOI: 10.1177/0890334417710635 21. Nijman R.M., Liu Y., Bunyatratchata A., Smilowitz J.T., Stahl B., Barile D.. Characterization and Quantification of Oligosaccharides in Human Milk and Infant Formula. J. Agric. Food Chem. (2018) 66 6851-6859. DOI: 10.1021/acs.jafc.8b01515 22. Sprenger N., Lee L.Y., De Castro C.A., Steenhout P., Thakkar S.K.. Longitudinal change of selected human milk oligosaccharides and association to infants’ growth, an observatory, single center, longitudinal cohort study. PLoS ONE (2017) 12. DOI: 10.1371/journal.pone.0171814 23. Thurl S., Henker J., Siegel M., Tovar K., Sawatzki G.. Detection of four human milk groups with respect to Lewis blood group dependent oligosaccharides. Glycoconj. J. (1997) 14 795-799. DOI: 10.1023/A:1018529703106 24. Gabrielli O., Zampini L., Galeazzi T., Padella L., Santoro L., Peila C., Giuliani F., Bertino E., Fabris C., Coppa G.V.. Preterm milk oligosaccharides during the first month of lactation. Pediatrics (2011) 128 e1520-e1531. DOI: 10.1542/peds.2011-1206 25. Yin S.A., Yang Z.Y.. An on-line database for human milk composition in China. Asia Pac. J. Clin. Nutr. (2016) 25 818-825. DOI: 10.6133/apjcn.092015.47 26. Li J., Bi Y., Zheng Y., Cao C., Yu L., Yang Z., Chai W., Yan J., Lai J., Liang X.. Development of high-throughput UPLC-MS/MS using multiple reaction monitoring for quantitation of complex human milk oligosaccharides and application to large population survey of secretor status and Lewis blood group. Food Chem. (2022) 397 133750. DOI: 10.1016/j.foodchem.2022.133750 27. Yan J., Ding J., Jin G., Duan Z., Yang F., Li D., Zhou H., Li M., Guo Z., Chai W.. Profiling of Human Milk Oligosaccharides for Lewis Epitopes and Secretor Status by Electrostatic Repulsion Hydrophilic Interaction Chromatography Coupled with Negative-Ion Electrospray Tandem Mass Spectrometry. Anal. Chem. (2019) 91 8199-8206. DOI: 10.1021/acs.analchem.9b00687 28. Thurl S., Munzert M., Henker J., Boehm G., Müller-Werner B., Jelinek J., Stahl B.. Variation of human milk oligosaccharides in relation to milk groups and lactational periods. Br. J. Nutr. (2010) 104 1261-1271. DOI: 10.1017/S0007114510002072 29. Sumiyoshi W., Urashima T., Nakamura T., Arai I., Saito T., Tsumura N., Wang B., Brand-Miller J., Watanabe Y., Kimura K.. Determination of each neutral oligosaccharide in the milk of Japanese women during the course of lactation. Br. J. Nutr. (2003) 89 61-69. DOI: 10.1079/BJN2002746 30. Soyyılmaz B., Mikš M.H., Röhrig C.H., Matwiejuk M., Meszaros-Matwiejuk A., Vigsnæs L.K.. The Mean of Milk: A Review of Human Milk Oligosaccharide Concentrations throughout Lactation. Nutrients (2021) 13. DOI: 10.3390/nu13082737 31. Guo M., Luo G., Lu R., Shi W., Cheng H., Lu Y., Jin K., Yang C., Wang Z., Long J.. Distribution of Lewis and Secretor polymorphisms and corresponding CA19-9 antigen expression in a Chinese population. FEBS Open Bio (2017) 7 1660-1671. DOI: 10.1002/2211-5463.12278 32. Reverri E.J., Devitt A.A., Kajzer J.A., Baggs G.E., Borschel M.W.. Review of the Clinical Experiences of Feeding Infants Formula Containing the Human Milk Oligosaccharide 2’-Fucosyllactose. Nutrients (2018) 10. DOI: 10.3390/nu10101346 33. Xiao L., Leusink-Muis T., Kettelarij N., van Ark I., Blijenberg B., Hesen N.A., Stahl B., Overbeek S.A., Garssen J., Folkerts G.. Human Milk Oligosaccharide 2’-Fucosyllactose Improves Innate and Adaptive Immunity in an Influenza-Specific Murine Vaccination Model. Front. Immunol. (2018) 9 452. DOI: 10.3389/fimmu.2018.00452 34. Hoeflinger J.L., Davis S.R., Chow J., Miller M.J.. In vitro impact of human milk oligosaccharides on Enterobacteriaceae growth. J. Agric. Food Chem. (2015) 63 3295-3302. DOI: 10.1021/jf505721p 35. Yu Z.T., Nanthakumar N.N., Newburg D.S.. The Human Milk Oligosaccharide 2’-Fucosyllactose Quenches Campylobacter jejuni-Induced Inflammation in Human Epithelial Cells HEp-2 and HT-29 and in Mouse Intestinal Mucosa. J. Nutr. (2016) 146 1980-1990. DOI: 10.3945/jn.116.230706 36. Zehra S., Khambati I., Vierhout M., Mian M.F., Buck R., Forsythe P.. Human Milk Oligosaccharides Attenuate Antigen-Antibody Complex Induced Chemokine Release from Human Intestinal Epithelial Cell Lines. J. Food Sci. (2018) 83 499-508. DOI: 10.1111/1750-3841.14039 37. Vázquez E., Barranco A., Ramírez M., Gruart A., Delgado-García J.M., Martínez-Lara E., Blanco S., Martín M.J., Castanys E., Buck R.. Effects of a human milk oligosaccharide, 2’-fucosyllactose, on hippocampal long-term potentiation and learning capabilities in rodents. J. Nutr. Biochem. (2015) 26 455-465. DOI: 10.1016/j.jnutbio.2014.11.016 38. Turck D., Castenmiller J., De Henauw S., Hirsch-Ernst K.I., Kearney J., Maciuk A., Mangelsdorf I., McArdle H.J., Naska A., Pelaez C.. Safety of 3-FL (3-Fucosyllactose) as a novel food pursuant to Regulation (EU) 2015/2283. EFSA J. Eur. Food Saf. Auth. (2021) 19 e06662. DOI: 10.2903/j.efsa.2021.6662 39. Shi R., Ma J., Yan Q., Yang S., Fan Z., Jiang Z.. Biochemical characterization of a novel α-L-fucosidase from. Appl. Microbiol. Biotechnol. (2020) 104 5813-5826. DOI: 10.1007/s00253-020-10630-y 40. Musumeci M., Simpore J., D’Agata A., Sotgiu S., Musumeci S.. Oligosaccharides in colostrum of Italian and Burkinabe women. J. Pediatr. Gastroenterol. Nutr. (2006) 43 372-378. DOI: 10.1097/01.mpg.0000228125.70971.af 41. Erney R.M., Malone W.T., Skelding M.B., Marcon A.A., Kleman-Leyer K.M., O’Ryan M.L., Ruiz-Palacios G., Hilty M.D., Pickering L.K., Prieto P.A.. Variability of human milk neutral oligosaccharides in a diverse population. J. Pediatr. Gastroenterol. Nutr. (2000) 30 181-192. DOI: 10.1097/00005176-200002000-00016 42. Wang B., Brand-Miller J., McVeagh P., Petocz P.. Concentration and distribution of sialic acid in human milk and infant formulas. Am. J. Clin. Nutr. (2001) 74 510-515. DOI: 10.1093/ajcn/74.4.510
--- title: Adipose Mesenchymal Stromal Cell-Derived Exosomes Carrying MiR-122-5p Antagonize the Inhibitory Effect of Dihydrotestosterone on Hair Follicles by Targeting the TGF-β1/SMAD3 Signaling Pathway authors: - Yunxiao Liang - Xin Tang - Xue Zhang - Cuixiang Cao - Miao Yu - Miaojian Wan journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10059832 doi: 10.3390/ijms24065703 license: CC BY 4.0 --- # Adipose Mesenchymal Stromal Cell-Derived Exosomes Carrying MiR-122-5p Antagonize the Inhibitory Effect of Dihydrotestosterone on Hair Follicles by Targeting the TGF-β1/SMAD3 Signaling Pathway ## Abstract Androgenic alopecia (AGA) is the most common type of hair loss, where local high concentrations of dihydrotestosterone (DHT) in the scalp cause progressive shrinkage of the hair follicles, eventually contributing to hair loss. Due to the limitations of existing methods to treat AGA, the use of multi-origin mesenchymal stromal cell-derived exosomes has been proposed. However, the functions and mechanisms of action of exosomes secreted by adipose mesenchymal stromal cells (ADSCs-Exos) in AGA are still unclear. Using Cell Counting Kit-8 (CCK8) analysis, immunofluorescence staining, scratch assays, and Western blotting, it was found that ADSC-Exos contributed to the proliferation, migration, and differentiation of dermal papilla cells (DPCs) and up-regulated the expression of cyclin, β-catenin, versican, and BMP2. ADSC-Exos also mitigated the inhibitory effects of DHT on DPCs and down-regulated transforming growth factor-beta1 (TGF-β1) and its downstream genes. Moreover, high-throughput miRNA sequencing and bioinformatics analysis identified 225 genes that were co-expressed in ADSC-Exos; of these, miR-122-5p was highly enriched and was found by luciferase assays to target SMAD3. ADSC-Exos carrying miR-122-5p antagonized DHT inhibition of hair follicles, up-regulated the expression of β-catenin and versican in vivo and in vitro, restored hair bulb size and dermal thickness, and promoted the normal growth of hair follicles. So, ADSC-Exos enhanced the regeneration of hair follicles in AGA through the action of miR-122-5p and the inhibition of the TGF-β/SMAD3 axis. These results suggest a novel treatment option for the treatment of AGA. ## 1. Introduction Androgenic alopecia (AGA) is the most common cause disease of chronic hair loss in men and women, with $50\%$ of people over the age of 40 affected by AGA, while affecting $73\%$ of men and $57\%$ of women over the age of 80 [1,2,3]. The patients’ characteristic, self-confidence and quality of life were affected. The pathogenesis of AGA has a genetic tendency and may be determined by multiple genes [4], lifestyle choices (smoking) and the exposed environment (chemical irritants, radiation, pollutants and microorganisms) [5]. The hair follicles (HFs) cycle was shown to be prolonged in the resting phase (telogen) and shortened in the active phase (anagen) in AGA patients. The traditional treatment methods are topical minoxidil, oral finasteride pills and hair transplant; topical and oral treatments do not always produce good results, and sometimes, they cause side effects [6]. Autologous hair transplant is the most effective treatment of AGA in the short term, but it is time-consuming, painful, and limited by the donor area follicles [7]. The development of various of different autologous cell biological techniques, such as autologous stem cells [8,9] and exosomes derived from cells [10,11,12], were reported in the treatment of AGA or hair regeneration. Although different kinds of methods are available to slow or reverse the progression of AGA, its therapy remains challenging [13]. Adipose-derived stromal cells (ADSCs) belong to the mesenchymal stem cells population and were found in the stromal-vascular fraction of fat tissue. Stromal-vascular fraction cells (SVFs) also have been used for many years for autologous applications in plastic surgery [14], wound healing [15], scar repair [16] and treatment of hair loss diseases [17]. The SVFs can be easily isolated from human fat and provide a rich source of ADSCs [18]. ADSCs have been used as a cellular therapeutic approach in AGA [19], knee arthritis [20], skin photoaging [16], wound repair [18], and harmful burn [21]. ADSC-derived proteins may be feasible clinical therapeutic agents for the treatment of hair loss. This is because ADSCs are easy to harvest from fat tissue, which can be taken from body, with the advantages of reduced trauma and fewer ethical issues, together with high yield. So, the cell products of ADSCs may be become alternative therapeutic treatment in hair loss disease. ADSC-derived cytokines, such as platelet-derived growth factor, vascular endothelial growth factor (VEGF) and hepatocyte growth factor (FGF) could activate hair development and promote hair growth in AGA [22]. Exosomes are extracellular vesicles with diameters between 30 and 120 nm that transport microRNAs, proteins, and lipids and function as communication bridges between donor and recipient cells [23,24]. It is well-known that exosomes are harvested from cell-conditioned medium and are derived from cells. The exosomes secreted from adipose-derived stromal cells (ADSC-Exos) have the advantages of being easily isolated and convenient to use, and they do not provoke a significant immune response [25]. Stromal cell exosomes can serve as novel treatment options to repair, regenerate, and rejuvenate skin tissue [26] and have been concerned in nerve repair [27], wound healing [28], and the vascularization of skin flaps [29]. Additionally, it had been reported that ADSC-Exos play an active role in hair loss [30,31]. ADSC-Exos can increase the proliferation and survival of dermal papilla cells (DPCs) and maintain their hair inductivity [31]. Additionally, they are considered the most effective methods in the in vivo and in vitro hair inductivity of DPCs [32]. However, the underlying mechanism of these exosomes on AGA are largely unknown. MicroRNAs can regulate genes expression by inducing the degradation or translation of targeted mRNA. As an epigenetic regulator, some research focuses on the exosome containing microRNAs from the DPCs and hair growth, which have been reported are related with hair growth [10,33,34]. However, a growing number of researchers have demonstrated positive results between hair growth and ADSC-Exos. However, little is known of the relationship between the exosomes containing microRNA from ADSCs and hair growth, let along with AGA and its mechanism. DPCs are located at the follicle base and activate follicle development and periodic regeneration during HF growth [35]. DPCs support hair growth and regulate the hair cycle. AGA involves the action of dihydrotestosterone (DHT) on DPCs. DHT decreased the levels of β-catenin and is a positive regulator of hair growth [36,37], and promotes the expression of TGF-β1, which is negative regulator of HFs growth [38]. However, the serum-free conditioned media of ADSCs can protect against the injury which DHT causes to DPCs [39]. In our study, miR-122-5p was highly expressed in ADSC-Exos and delivered to DPCs to regulate its biology. A previous study revealed that miR-122-5p was identified as a potential pro-angiogenic factor that promoted angiogenesis through shifting substrate preference to fatty acids in endothelial cells, activated vascular endothelial growth factor signaling and promoted angiogenesis [40]. In the research of spinal cord injury, the overexpression of miR-122-5p alleviated inflammatory response, reactive oxygen species and apoptosis [41]. Research showed that miR-122-5p has the function of down-regulating the TGF-β/Smad pathway in the regeneration of skeletal muscle repair [42]. To sum up, in our study, we speculated that miR-122-5p in ADSC-Exos may be involved in the process of hair growth through the levels of TGF-β/Smad pathway in DPCs, and may alleviate the inhibitory effect of DHT on HFs. This study aimed to investigate the effect of ADSC-Exos carrying miR-122-5p antagonizes DHT on HFs growth and its underlying mechanism. The results will show that the ADSC-Exos carried with miR-122-5p may represent a new treatment option for the clinical treatment of AGA. ## 2.1. Isolation and Characterization of ADSCs The ADSCs isolation steps are shown in Figure 1A. After isolation and three passages in vitro, most of the ADSCs were adherent with a fibroblast-like or spindle-shaped appearance under the microscope (10×) (Figure 1B). ADSCs were induced by adipogenic medium, and it could be seen that the lipid droplets gradually became larger after 21 days with bright red droplets visible under the microscope after Oil Red O staining. Meanwhile, ADSCs were also cultured separately in osteogenic differentiation induction medium or chondrogenic induction medium for approximately three weeks. Mineralization of the cell matrix was visible under the microscope after Alizarin Red S staining, indicating that osteogenesis had occurred, while pellet formation after Alcian Blue staining showed that chondrogenesis had occurred (Figure 1C). To confirm the characterization of the cells, flow cytometry analysis showed strong expression of ADSC biomarkers such as CD29, CD44, CD73, CD90, and CD105 in fourth-generation ADSC cells, compared with DLA-DR (Figure 1D). The isolation process of the exosomes is shown in Figure 1E. Furthermore, the exosomes derived from ADSCs were round- or cup-shaped under EM (Figure 1F) with diameters of 50–150 nm (Figure 1G). Western blot analysis demonstrated that the typical exosomal surface markers such as CD9, CD63 and TSG101 were present (Figure 1H). In addition, we also showed the characterization of ADSC-Exos harvested from conditioned media collected from cells that cultured on exosome-depleted fetal bovine serum (FBS) and full FBS (Figure S1A–C). ## 2.2. ADSC-Exos Promote Hair Growth and Induce HFs to Enter Anagen To investigate the effect of the ADSC-Exos on HFs, we observed their effects on HFs proliferation in vitro. The results showed that 10 µg/mL of ADSC-Exos could significantly promote HFs proliferation compared with the control group treated with phosphate buffer solution (PBS) (Figure S2A,B). β-catenin in the Wnt signaling pathway has previously been identified as a necessary signaling marker for the growth of HFs [43], and fluorescence immunoassay showed that FITC-labeled fluorescence signals of Ki-67 and β-catenin in HFs treated with 10 µg/mL ADSC-Exos were more obvious ($p \leq 0.05$) (Figure S2C). ## 2.3. ADSC-Exos Stimulate DPCs Proliferation and Migration To measure the uptake of ADSC-Exos by DPCs, the exosomes were labeled with the fluorescent dye PKH67 and co-cultured with DPCs in a Petri dish for 4 h, after which the fluorescence signals can be detected in the granule membranes as well as the plasma membranes of DPCs (Figure S3A). The CCK8 assays showed that after incubation with various concentrations of ADSC-Exos, DPCs proliferation reached a relatively optimal level after treatment with 10 µg/mL ADSC-Exos ($p \leq 0.01$) (Figure S3B,C). The results of the scratch wound assay also revealed that the migration ability of DPCs cultured with ADSC-Exos was enhanced compared with the PBS-treated group (Figure S3D,E). Furthermore, we also performed immunofluorescence staining for Ki-67, which was found to be strongly expressed in DPCs treated with ADSC-Exos, while only a few positive cells were observed in the PBS-treated group (Figure S3F,G). In addition, the expression of versican, β-catenin, and BMP2 were up-regulated in 10ug/mL ADSC-Exos group compared with control group, as were the levels of Cyclin D1 and Cyclin B1 (Figure S3H,I). These results showed that ADSC-Exos promoted the proliferation and migration of DPCs. ## 2.4. ADSC-Exos Counteracted the Inhibitory Effect of DHT on DPCs The effectiveness of ADSC-Exos on the proliferation of HFs/DPCs has been clari- fied. To explore whether ADSC-Exos can counteract the inhibitory effect of DHT (10−5 mol/L) on DPCs, we analyzed the effects of ADSC-Exos on the TGF-β/SMAD signaling induced by DHT, together with proliferation biomarker levels by Western blotting (Figure 2A,B). The results showed that the expression of versican and β-catenin were down-regulated in DHT treated group compared with control group, which indicatig that DHT could inhibit the proliferation of DPCs. However, ADSC-Exos showed the opposite trend, which exerts its pro-proliferation effect on DPCs. When ADSC-Exos and DHT act together on DPCs, compared with DHT group, the expression of SMAD3 and p-SMAD3 in the ADSC-Exos+DHT group showed a downward trend, while β-catenin and verican showed an increasing trend. The level of β-catenin, verican and SMAD3 and p-SMAD3 in ADSC-Exos+DHT group were no significant difference compared with the control group. Similarly, we also found that DHT down-regulated BCL2 and up-regulated Bax expression, while ADSC-Exos played the opposite regulatory role and reversed DHT-induced BCL2 and Bax expression levels. ( Figure S4). These results indicated that ADSC-Exos could mitigate the effects of DHT on TGF-β/SMAD protein levels. Our analysis suggested that ADSC-Exos potentially antagonize DHT-induced cell apoptosis or follicle development disorder. However, the specific mechanisms by which the exosomes down-regulate TGF-β signaling remain unclear. ## 2.5. Screening and Functional Analysis of Target Genes In this study, we sought to explore the miRNA expression profiles of ADSC-Exos and performed functional and pathway enrichment analyses. A total of 225 miRNAs were detected in the sample consensus expression (Figure 3A, Additional Table S1), and the relative expression levels are shown in the heatmap (Figure 3B). We then selected the eight most enriched miRNAs for further verification using semi-quantitative RT-PCR. The results showed that the expression of miR-122-5p was the highest of all the selected microRNAs (Figure 3C). Meanwhile, KEGG pathway analysis showed that miR-122-5p regulates the TGF-β signaling pathway (Figure 3D). This suggested that the exosomes could down-regulate TGF-β signaling induced by DHT, with miR-122-5p likely targeting the pathway to promote hair regeneration. Nevertheless, the details of the mechanism require further clarification. ## 2.6. miR-122-5p Targets and Negatively Regulates SMAD3 To investigate the function of miR-122-5p, we hypothesized that the binding of the miRNA to its putative target (SMAD3) can inhibit the TGF-β signaling pathway (Figure 4A). Given the proliferative effect of miR-122-5p, we investigated its potential target genes, especially those associated with the TGF-β/SMAD3 signaling pathway. Bioinformatics analysis using the online tools TargetScan and MiRwalk indicated that SMAD3 might be the target of miR-122-5p. In luciferase assays, plasmids containing the wild-type 3 ‘UTR of SMAD3 and the predicted binding site of miR-122-5p, or the mutant 3′ UTR without the binding site were transfected into DPCs. The result showed that the fluorescence decreased when miR-122-5p was bound to the 3′ UTR binding site of SMAD3. However, the fluorescence of the latter did not change significantly (Figure 4B). The quantitative analysis of exosomes collected after transfection demonstrated that miR-122-5p was highly expressed in Exo-miR-122-5p (Figure 4C). Moreover, the expression levels of SMAD3 and p-SMAD3 in Exo-miR-122-5p group were lower than Exo-NC group. This showed that the Exo-miR-122-5p mimic significantly reduced the expression of SMAD3 and p-SAMD3 after transfection (Figure 4D). ## 2.7. ADSC-Exo-miR-122-5p Counteracted the Inhibitory Effect of DHT on HFs in C57BL/6 Mice We further investigated whether TGF-β/SAMD3 plays a role in the protection against the effects of DHT. The results showed that the expression of SMAD3 and p-SMAD3 were down-regulated in the group treated with Exo-miR-122-5p, while the levels of β-catenin and versican were increased compared with the control group (Figure 5A,B). After DHT pretreatment, DPCs were incubated with Exo-miR-122-5p, and it was found that this intervention down-regulated the expression of SMAD3 and p-SMAD3 and enhanced the expression of β-catenin and versican (Figure 5A,B), thus indicating that Exo-miR-122-5p could inhibit the TGF-β pathway in DPCs and counteract the inhibitory effects of DHT on DPCs. In addition, we further explored the role of SMAD3 in the TGF-β signaling pathway. We transfected SMAD3 siRNA into cells to simulate the role of Exo-miR-122-5p. The results showed that both SMAD3 and p-SMAD3 levels decreased, while the expression of β-catenin and versican increased compared with the control group (Figure 5A,B). However, when co-cultured with Exo-miR-122-5p, the proliferation and induction of DPCs induced by DHT were restored, as the expression levels of β-catenin and versican were similar to those in the control group. While knockdown of SMAD3 resulted in reduced expression of SMAD3 and p-SMAD3, it is worth noting that both β-catenin and versican levels were significantly increased in this group. These results suggested that Exo-miR-122-5p targeted SMAD3 to down-regulate TGF-β signaling and to restore the proliferation and inducibility of DPCs. ## 2.8. Inhibition of TGF-β/SMAD Signaling Enhances the Protective Effects of ADSC-Exo-miR-122-5p in DHT-Induced HFs To investigate whether ADSC-Exo-miR-122-5p reverses the inhibitory effect of DHT on HFs, we depilated the dorsal hair of mice with depilatory cream to cause simultaneous entry of the HFs into catagen. The skin of the back in the catagen phase was pink (Figure 6A). After subcutaneous injection of 200 µL Exo-miR-122-5p and DHT, respectively, we found that the HFs of the mice in the two groups began to grow after seven days. By day 11, the dorsal skin of the Exo-miR-122-5p + DHT and minoxidil + DHT groups gradually become gray, indicating that the HFs had entered the anagen phase, while the dorsal skin of the mice in the DHT group was still pink. On day 15, compared with the DHT group, Exo-miR-122-5p + DHT and minoxidil + DHT groups showed larger fields range of hair coverage and were exuberant with the control group. Compared with the DHT groups, the Exo-miR-122-5p + DHT group showed a significant difference in HFs proliferation. The skin tissue from the backs of the mice were collected for histological analysis. This showed that the dermal thickness of the Exo-miR-122-5p + DHT group was greater than that of DHT group (Figure 6B,C), indicating that Exo-miR-122-5p could provide suitable culture conditions for HFs proliferation, and the size of the hair bulbs in this group was larger than those in the DHT group ($p \leq 0.01$) (Figure 6B,D); furthermore, the DHT + Exo-in-miR-122-5p group presented lower hair coverage, smaller dermal papilla and thinner thickness compared with the control group (Figure 6A–C). In addition, the fluorescence immunoassay results showed that compared with the DHT group, the expression of β-catenin in DHT+ Exo-miR-122-5p was upregulated, and was similar to control group, illustrating that Exo-miR-122-5p enhanced the growth of HFs even after the treatment with high DHT concentrations (Figure 6D), and it also down-regulated the expression of SMAD3 (Figure 6E). Otherwise, the expression of β-catenin and SMAD3 in the DHT + Exo-in-miR-122-5p group were similar to DHT group, illustrating that the Exo-in-miR-122-5p could not antagonize the inhibitory effect of DHT on HFs (Figure 6D,E). It has been reported that β-catenin is the main initiator of HF growth and plays a critical role in regulating the hair cycle and promoting hair growth. DHT significantly blocks hair development; thus, the expression of SMAD3 would be up-regulated, and the expression of β-catenin would be significantly down-regulated, indicating that antagonism existed between the TGF-β and Wnt pathways. The above evidence showed that Exo-miR-122-5p was delivered to HF cells, alleviated the inhibitory effect of DHT, and down-regulated TGF-β signaling by targeting SMAD3 to restore HFs proliferation. ## 3. Discussion Given the limited efficacy of traditional treatments of AGA, we investigated and successfully prepared a new biological product, ADSC-Exos. In the present study, we evaluated the effects of ADSC-Exos on the proliferation and cycle change in DPCs/HFs in vitro and explore the therapeutic effect of ADSC-Exos on HFs induced by DHT. The findings showed that ADSC-Exos could promote the proliferation of HFs/DPCs and alleviated the DHT-induced inhibition of DPCs and down-regulated the TGF-B/SMAD signaling pathway. MiR-122-5p is highly enriched in ADSC-Exos, which was found to specifically reduce SMAD3 expression in DPCs after the treatment of DHT. It was further confirmed that this effect was mainly exerted by exosomes transferring to the DPCs. These results suggest that miR-122-5p derived from ADSC-Exos may represent a promising strategy for the treatment of AGA. Due to the distribution of dermal papillae in the dermis and the subcutaneous fat layer, the periodic changes and regeneration of HFs are inseparable from the surrounding molecular environment [10]. A preliminary study observed that ADSC-Exos could promote HFs regeneration in mice [30], cell migration, proliferation, and the inhibited cell apoptosis of fibroblast and keratinocytes cells [44,45], although there is no clear conclusion regarding AGA. In our study, we found that the growth of HFs and proliferation of DPCs could be promoted after being incubated with ADSC-Exos, and the expression of ki-67 and cell migration increased compared with the control group. ADSC-Exos was also found to increase the expression of versican, β-catenin, BMP2 and cyclin. It would thus be expected, in principle, that increasing the ADSC-Exos concentration would result in the stronger promotion of DPCs proliferation. However, it was found that the response plateaued at 10 µg/mL, which differs from Enshell’s report [10] and suggests that additional factors may influence ADSC-Exos activity. The discrepancy may due to the differences in the viability of the stromal cells or the isolation methods [46]. It was also found that the ADSC-Exos could counteract the inhibitory effects of DHT on HFs. DHT is known to sensitize ARs in DPCs and to activate the TGF-β signaling pathway, and significant upregulation of phosphorylation SMAD2 and SMAD3 has also been observed [47], which is consistent with our results. Pretreatment of DPCs with DHT followed by treatment with ADSC-Exos resulted in significant down-regulation of TGF-B1, TGF-BR1, SMAD3 and p-SMAD3, in comparison with cells treated with DHT alone, while the expression of β-catenin and versican was restored to a relatively normal level compared with the control group. Meanwhile, the expression of anti-apoptotic protein BCL2 decreased and the apoptotic protein Bax increased in the DPCs induced by DHT. Under the action of ADSC-Exos, the apoptosis of DPCs could be restored. Studies have shown that TGF-B1 and TGF-B2 have long been identified as the major mediators in the development of AGA [48,49], and the down expression of BMP2 was observed in the DPCs of AGA patients, which would compromise HFs integrity and hair shaft differentiation [50]. Recent evidence has shown that the exosomes derived from human umbilical cord mesenchymal could promote wound healing by inhibiting TGF-β signaling [51]. The existing evidence indicated that MSC-Exos induce the proliferation of DPCs and secretion of VEGF, which is conducive to the development of HFs [52], it also accelerates the transition from the resting phase to the growth phase and stimulates the expression of Shh and β-catenin [53]. In addition, it is not difficult to get inspiration from the application of MSC-Exos in the treatment of wound, the TGF-β signaling pathway and the PI3K/Akt pathway could be regulated by MSC-Exos to accelerate the wound healing and improved skin regeneration [54,55,56]. These findings are in accordance with our experiment results and other treatment involving AGA [57]. MiRNAs within exosomes form an important component of paracrine signaling to mediate intercellular communication and functional interaction [53]. Further investigation of the mechanism by which ADSC-Exos mitigate the effects of DHT showed that miR-122-5p was a highly enriched component of the ADSC-Exos miRNA expression profile and that it modulated the TGF-β signaling pathway by targeting SMAD3 to reduce the effects of DHT on HFs growth, thus restoring their normal growth. One study has shown that miR-122-5p is an effective angiogenic factor that could activate VEGF and promote angiogenesis [40]. It is worth mentioning that the core mechanism of minoxidil in hair loss is to promote the expression of VEGF and promote hair regeneration [58]. In addition, it has been found that miR-122-5p suppresses the differentiation and collagen synthesis of TGF-β1-stimulated cardiac fibroblasts, reducing the expression of SMAD3 and p-SMAD3 [59], which is consistent with our results. Additionally, the overexpression of miR-122-5p in keratinocytes was reported to promote its proliferation [60]. The TGF-β signaling pathway is down-regulated in anagen, which would enhance the expression of Ki-67 and β-catenin [61]. SMAD3, as a downstream messenger and functional gene in TGF-β1 signaling, usually binds to SMAD2 and SMAD4 to enter the nucleus when the TGF-β signaling pathway is activated, which is consistent with our study. Our results also revealed that the expression of SMAD3 and p-SMAD3 was decreased in the Exo-miR-122-5p-treated cells compared with the cells treated with DHT only; correspondingly, the expression of versican and B-catenin in DPCs restored in the DHT+ Exo-miR-122-5p group. The directional knockdown of SMAD3 further illustrates the importance of TGF-β in AGA and the effectiveness of miR-122-5p. C57BL/6 mice are ideal for studying AGA. Our previous research showed that DHT with appropriate concentration can inhibit the growth of HFs [62]. We thus simulated the pathogenesis of AGA with DHT (10−5 mol/L). Compared with the control group, DHT significantly activated TGF-β signaling and enhanced the expression of SMAD3. The mice skin showed smaller dermal papilla size, thinner dermis and lower dorsal hair coverage. After treating with Exo-miR-122-5p, compared with the control group, the hair coverage rate on the back of mice in this group almost returned to the normal level, even seem to be more exuberant than control group, and the expression of SMAD3 in the dorsal skin of DHT-induced mice was significantly reduced compared with the DHT group. To our knowledge, extracellular vesicles contain various types of miRNAs, lipids, and proteins. In our experiments, we mainly analyzed miR-122-5p in ADSC-Exos, which exhibits a prominent role in resisting androgenetic alopecia. Meanwhile, the expression of β-catenin in the hair matrix cells and inner root sheath cells was also increased, indicating that DPCs may have a specific regulatory effect on the proliferation of these cells, especially in the endosomal environment of HFs. DPCs regulate the proliferation and migration of outer root sheath cells or hair matrix cells through the paracrine pathway [61,63]. ADSC-Exos could promote proliferation and migration in keratinocytes and endothelial cells [64]. Similarly, the β-catenin and Versican levels in the DHT + Exo-miR-122-5p group gradually returned to normal compared with the control group when TGF-β signaling was blocked. The injection of exosomes may be more advantageous than commercial minoxidil, because subcutaneous injection of ADSC-Exos can prevent the dormant state of DPCs. However, compared with the control group, the hair coverage was less, dermal papilla size was smaller and the thickness was thinner in DHT + Exo-in-miR-122-5p group, which could not reverse the inhibition effect of DHT on HFs. Thus, we concluded that ADSC-Exos carrying miR-122-5p could mitigate DHT inhibition of HFs by targeting SMAD3. However, a limitation to this study is that we focused only on miR-122-5p, which was highly expressed in ADSC-Exos, and the contribution of other miRNAs can’t be excluded. ## 4.1. Isolation and Culture of Human HFs The ethics committee of the Third Affiliated Hospital of University approved this research [[2022]-02-098-01]. Occipital scalp tissue was obtained from healthy adult men undergoing cosmetic surgery and was washed with normal saline to clear blood clots. The excess adipose tissue under the dermis was removed with sterile ophthalmic scissors. Single HF was isolated with the help of an anatomical microscope (Nikon, Tokyo, Japan). HFs in anagen were selected and cultured in 24-well plates. The medium was serum-free Williams’ medium (Gibco, Waltham, MA, USA), and 2 mM HEPES (Gibco), 2 mM/L-glutamine (Gibco), 10 mg/L insulin (Gibco), 10 µg/L hydrocortisone (Apexbio, Houston, TX, USA), and antibiotics (100 mg/L streptomycin and 100,000 U/mL penicillin), (Gibco) and were cultured, at 37 °C, in an atmosphere of $5\%$ CO2. After 24 h of culture, HFs that had grown to 0.3–0.5 mm were randomly divided into four groups. The lengths of the follicles were photographed and measured every two days. ## 4.2. DPCs Culture DPCs (Jian Daoshou, Nanjing, China) were cultured in Dulbecco Modified Eagle Medium (DMEM) (Gibco) supplemented with $1\%$ penicillin-streptomycin, $10\%$ fetal-bovine-serum (Gibco), and 1 ng/mL fibroblast growth factor (Jian Daoshou). DPCs were maintained in a 37 °C atmosphere with $5\%$ CO2. DPCs between passages 3 and 5 were used in this experiment. ## 4.3. Isolation and Identification of ADSCs Adipose tissue was obtained from the thighs of patients undergoing plastic revision surgery. After the removal of visible blood vessels and excessive anadesma, the adipose tissue was cut into small pieces of no more than 0.5 mm and was digested with $0.2\%$ type I collagenase (Sigma-Aldrich, St Louis, MO, USA) in a centrifuge tube, at 37 °C, for 20–30 min, after which it was thoroughly shaken to separate the stromal cells from the turbid liquid. Complete medium containing $10\%$ FBS was used to halt digestion. After centrifugation at 1000× g for 5 min, at room temperature, the upper adipose tissue was removed, and the tissue fragments and supernatant were removed with a 70 µm filter. The cell precipitate was obtained after further centrifugation at 1000× g for 3 min, and the cells were cultured in DMEM supplemented with $10\%$ FBS and $1\%$ penicillin/streptomycin, at 37 °C, in a $5\%$ CO2 atmosphere. For adipogenic and osteogenic differentiation, the cells were induced with adipogenic, osteogenic, and chondrogenesis medium, resulting in the formation of adipocytes after 21 d and the formation of osteocytes and chondrocytes after 23 d. The differentiated cells were then washed with PBS, fixed in $4\%$ paraformaldehyde (Macklin, Shanghai, China) for 30 min, stained with Oil Red O (Oricell, Guangzhou, China) for 20 min, stained with hematoxylin (Cyagen, Beijing, China) for 2 min, and observed under the microscope (Olympus Optical, Tokyo, Japan). Similarly, osteogenesis and chondrogenesis were induced in strict accordance with the operating instructions provided by Oricell. After culture in adipogenic and chondrogenic induction medium for approximately three weeks, the ADSCs were stained with Alizarin Red (Oricell) and Alcian Blue (Oricell) and observed under the microscope. For flow cytometry, ADSCs were harvested at the third passage by digestion with trypsin. The cells were then resuspended in 100 µL PBS and incubated with the relevant antibodies, namely, anti-HLA-DR, anti-CD29, anti-CD44, anti-CD73, anti-CD105, and anti-CD90 (Abcam, Cambridge, UK; 106 cells/1 µL) on ice for 5–10 min. The labeled cells were then analyzed on a flow cytometer (Cytek, Fremont, CA, USA), and the data were analyzed and processed with FlowJo 10 software (FlowJo vro 10.6.1, LLC, Ashland, OR, USA). ## 4.4. Extraction and Identification of ADSC-Deprived Exosomes ADSCs reaching $70\%$ confluence were cultured in DMEM supplemented with $10\%$ exosomes depleted FBS, and after 24 h culture, ADSC-Exos were extracted from culture supernatants by gradient ultracentrifugation; at the same time, we also collected ADSC-Exos of complete culture medium for comparison. The ADSCs culture supernatant was collected and centrifuged at 4 °C and 300× g 10 min, 2000× g 20 min, and 10,000× g 30 min to remove cell fragments and debris, and the large extracellular vesicles were filtered and removed through a 0.22 µm filter. The remaining supernatant was centrifuged at 100,000× g for 90 min to enrich the exosomes. Finally, after removing the supernatant, the exosomes were resuspended in PBS and stored, at −80 °C. It was worth noting that the centrifuge tubes need to be replaced with new tubes after each centrifugation. For electron microscopic analysis, the exosomes collected by gradient centrifugation were fixed in $1\%$ glutaraldehyde for 5 min, dehydrated with the same volume of $1\%$ nitrous oxide, stained with $1\%$ phosphotungstic acid for 5 min, and were observed under transmission electron microscopy (Hitachi, Tokyo, Japan). For NTA, 10 µL resuspended exosomes were diluted 1000 times with PBS, and the concentration and particle size distribution of exosomes were measured using nanoparticle size tracking analyzer (ViewSizer 3000, Irvine, CA, USA). ## 4.5. Cellular Uptake and Tracing of ADSC-Exos ADSC-Exos in PBS were labeled with the green-fluorescent dye PKH67 (Sigma) according to the provided instructions [65]. The labeled exosomes were collected by ultracentrifugation at 10 0000× g 90 min and were incubated with DPCs, at 37 °C, for 4 h. After fixing the cells with $4\%$ paraformaldehyde for about 10 min, the nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI). All procedures were conducted in the dark. The cells were observed under a fluorescence microscope (Nikon, Tokyo, Japan). ## 4.6. HF Treatment After 24 h of hair follicle culture, HFs in the growth stage were randomly divided into four groups and were co-cultured with different concentrations of ADSC-Exos for seven days. The hair lengths were measured and photographed every two days. On day 7, the growth of the HFs was evaluated by Ki-67 staining and hair stem length. ## 4.7. Cell Proliferation Assay/Cell Counting Kit-8 DPCs proliferation was measured using the CCK-8 (Dojindo, Shanghai, China). The total of 5 × 103 DPCs in a volume of 100 µL per well were inoculated into a 96-well plate, treated with different concentrations of ADSC-Exos for 24 h, 48 h, or 72 h and cultured, at 37 °C, with $5\%$ CO2. After that, 10 μL CCK-8 reagent was placed in each well and incubated, at 37 °C, for 2 h. The absorbance at 450 nm was measured by a microplate reader (BioTek elx-808, Winooski, VT, USA). The mean values of all wells were statistically analyzed, and the experiment was repeated three times. ## 4.8. Cell Migration Scratch Test In Vitro DPCs were inoculated at 2 × 105 cells per well and cultured in 6-well plates. The cells were cultured until nearly confluent in complete medium, after which scratches were made on the cells with a sterile 200 µL pipette tip, and suspended necrotic cells were washed off with PBS. The cells were incubated with different concentrations of ADSC-Exos for 24 h and 48 h and photographed under an inverted microscope. The scratch area was quantified using Image J software. Cell migration was calculated as the mobility rate (%) = (initial area—residual area)/initial area × $100\%$. ## 4.9. Cell Transfection and Exosome Editing Passage-3 ADSCs were cultured in DMEM with $10\%$ FBS. When the cells were approximately $70\%$ confluent, they were infected with lentivirus loaded with the lentivector constructs of the pre-miRNA-122-5p and anti-miRNA-122-5p clusters (Genechem, Shanghai, China; referred to as Exo-miR-122-5p and Exo-in-miR-122-5p, respectively) or the corresponding empty lentivector (Genechem, Shanghai, China; referred to Exo-NC and Exo-in-NC, respectively), as previously described [66]. Subsequently, 2 µg/mL puromycin was added for 3–4 days to produce a stable transduction cell line. The exosomes were harvested from the culture media of these ADSCs, respectively. Specifically, when the cells reached $80\%$ confluence, the normal culture medium was replaced with an exosome-free medium. The culture supernatant was retained, and the exosomes were collected by centrifugation as described above. The luciferase experiment can be performed according to the previous research protocol [66]. The expression of miR-122-5p in the exosomes was assessed by a semi-quantitative PCR assay. For siRNA knockdown, $60\%$-confluent DPCs were transfected with 50 nM si-SMAD3 using Lipofectamine 3000 (Thermo Fisher Scientific, Waltham, MA, USA). After 24 to 48 h of culture in double antibody-free medium, the cells were harvested for further analysis. ## 4.10. Hematoxylin and Eosin Staining and Immunofluorescence Staining For H&E staining, skin tissue sections from C57BL/6 mice were fixed with $4\%$ paraformaldehyde, cut into 4 µm sections, and stained with H&E (BBL-009, BASMEDTSCI, Beijing, China). For immunofluorescence staining, cell climbing tablets and dewaxed hydrated sections were permeabilized with $0.5\%$ Triton X-100 (diluted with PBS), at room temperature, for 10 min and blocked with $1\%$ BSA for 15 min, after which they were incubated with the appropriate primary antibodies and incubated, at 4 °C, overnight. Then, corresponding fluorescent labeled secondary antibodies (Affinity Biosciences, Cincinnati, OH, USA) was used to probe the primary antibody binding. The primary antibodies included anti-Ki-67 (ab92742,1:300, Abcam, Cambridge, UK), anti-β-catenin (ab223075, 1:300, Abcam), and anti-SMAD3 (AF6362, 1:300, Affinity Biosciences). After washing with PBS, the nuclei were counterstained with DAPI for about 5–8 min. The images of cells and sections were observed and photographed under the fluorescence microscope. ## 4.11. RNA Isolation and Quantitative Real Time-PCR Total RNA was isolated from cells and tissues using a total RNA Extraction Kit (Haigene, Harbin, China) according to the manufacturer’s instructions. Five hundred nanograms of RNA was used for reverse-transcription to cDNA with a TaqMan microRNA Reverse Transcription Kit (Takara, Dalian, China). To analyze miR-122-5p expression, qRT-PCR was performed using the FQTM miRNA poly (A) QRT PCR SYBR Kit (Enzy Valley, Guangzhou, China). The primers are shown in Table S2 (Supplementary Materials). The PCR reaction conditions were pre-denaturation, at 95 °C, for 10 min, followed by 40 reaction cycles (95 °C, 10 s), annealing (60 °C, 20 s), and extension (70 °C, 10 s). The relative expression of target genes was calculated using the 2−ΔΔCt method. All reactions were carried out in triplicate. The amount of miR-122-5p was determined by standardizing to U6. ## 4.12. miRNA Sequencing of ADSC-Exos and Bioinformatics Analysis For miRNA sequencing, total RNA was extracted from the ADSC-Exos using an Extraction Kit (Haigene, Harbin, China). A Nanodrop 2000 bioanalyzer (Thermo Fisher Scientific) was used for RNA quantification. cDNA libraries were generated by reverse-transcription using an Illumina TruSeq RNA Sample Preparation Kit (RS-122-2001; Illumina, San Diego, CA, USA) and the distribution of the template sizes was checked. The libraries were sequenced on an Illumina HiSeq 2500 system to produce paired-end readings. The raw read files were obtained with the CASAVA tool v1.8 (Illumina). The quality of the sequence reads was checked by the webservers FastQC. To obtain miRNA sequences, other ncRNA sequences, such as small nuclear RNA (snRNA), small nuclear RNA (snRNA), and small nuclear RNA (snRNA) were filtered out. The remaining sequences were analyzed with miRBase 22.0 (http://www.mirbase.org (accessed on 5 September 2020)) and miRWalk (http://mirwalk.uni-hd.de (accessed on 7 September 2020)) to detect the miRNAs. A heatmap of the top 50 most enriched miRNAs in the ADSC-Exos was generated, and their target genes were analyzed and predicted using the MiRanda database (http://www.microrna.org/microrna (accessed on 5 September 2020)). The Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp, (accessed on 9 September 2020)) pathway enrichment analysis was used to identify signaling pathways that could be regulated by the sequenced miRNAs. ## 4.13. Western Blotting Total protein was isolated from the cells using a whole protein extraction kit (KeyGEN, KGP2100, Nanjing, China), using lysis buffer containing 1 mM PMSF, 5 U/mL phosphatase inhibitor, and 1 µL/mL protease inhibitor. Twenty micrograms of protein was loaded onto $10\%$ SDS-PAGE gels and then transferred to polyvinylidene fluoride membranes (Pierce, Waltham, MA, USA). The membranes were blocked with $5\%$ skimmed milk, at room temperature, followed by incubation with the primary antibodies overnight at 4 °C. The antibodies included the Abcam antibodies anti-TSG101 (1:1000, ab83), anti-CD9 (1:1000, ab236630), anti-CD63 (1:1000, ab134045), anti-versican (1:1000,ab177480), anti-β-catenin (1:1000, ab223075), anti-cyclin D1 (1:1000, ab134175), anti-cyclin B1 (1:1000, ab32053), anti-BMP2 (1:1000, ab214821), and anti-GAPDH (1:1000, ab181602), as well as antibodies purchased from Affinity Biosciences, namely, anti-SAMD3 (AF6362, 1:1000, Affinity Biosciences), anti-p-SMAD3 (1:1000, ab52903), anti-BCL2 (1:1000, AF6139) and anti-Bax (1:1000, AF0083). The membranes were then incubated with horseradish peroxidase-conjugated goat anti-rabbit IgG (1:10 000, ab150077), at room temperature, for 1h. The protein bands were visualized using Kf003 Extremely Sensitive ECL chemiluminescence buffer (Affinity Biosciences) and the ChemiDoc Touch Imaging System (Bio-Rad, Hercules, CA, USA). The band densities were measured using BioRad Quality One and Image J software. ## 4.14. Hair Growth In Vivo Study Six-week-old male C57BL/6 mice were purchased from the Animal Core Facility of Guangzhou Animal Research Institute (Guangzhou, China), and maintained under controlled temperature (23 °C ± 1 °C) and humidity (55 ± $10\%$) conditions. After a one-week static observation period, all mice were randomly divided into control group ($$n = 6$$), DHT group ($$n = 6$$), DHT + Exo-miR-122-5p group ($$n = 6$$), DHT + Minoxidil group ($$n = 6$$) and DHT + Exo-in-miR-122-5p group ($$n = 6$$). A solution of PBS (200 µL), DHT (10−5 mol/L, 200 µL), and Exo-miR-122-5p (50 ug/mL, 200 µL) was injected subcutaneously at six points on the related dorsal skin once a day starting from the first day after depilation. All mice were observed and photographed every two days. After 15 days, skin samples were collected for further experimental analysis. ## 4.15. Statistical Analysis Data were expressed as means ±SD. The differences between groups were calculated by Student’s t-test, and the comparisons among the different interventions were analyzed by one-way analysis of variance, followed by Dunnett’s post hoc test. $p \leq 0.05$ was considered statistically significant. The above analysis was carried out using SPSS18.0 software (IBM R18.0.0, Armonk, New York, NY, USA). ## 5. Conclusions Our study provided further insights into the regulatory functions of ADSC-Exos cargos and their application in AGA. ADSC-Exos promoted HF growth, DPCs proliferation, and mitigated the inhibitory effect of DHT on DPCs by inhibiting the TGF-β signaling pathway. In addition, miR-122-5p, which was highly enriched in ADSC-Exos, up-regulated the expression of β-catenin and versican by targeting SMAD3 in vivo and in vitro, restored hair bulb size and dermal thickness, and accelerated the normal growth of HFs. The findings of this study provide a potential novel approach for further treatment strategies of AGA. ## References 1. Varothai S., Bergfeld W.. Androgenetic alopecia: An evidence-based treatment update. Am. J. Clin. Dermatol. (2014) 15 217-230. DOI: 10.1007/s40257-014-0077-5 2. Hordinsky M., Donati A.. Alopecia areata: An evidence-based treatment update. Am. J. Clin. Dermatol. (2014) 15 231-246. DOI: 10.1007/s40257-014-0086-4 3. Krupa Shankar D., Chakravarthi M., Shilpakar R.. Male androgenetic alopecia: Population-based study in 1005 subjects. Int. J. Trichology (2009) 1 131-133. DOI: 10.4103/0974-7753.58556 4. Cranwell W., Sinclair R., Feingold K.R., Anawalt B., Boyce A., Chrousos G., de Herder W.W., Dhatariya K., Dungan K., Hershman J.M., Hofland J., Kalra S.. Male Androgenetic Alopecia. Endotext (2000) 5. Trüeb R.M.. The impact of oxidative stress on hair. Int. J. Cosmet. Sci. (2015) 37 25-30. DOI: 10.1111/ics.12286 6. Talavera-Adame D., Newman D., Newman N.. Conventional and novel stem cell based therapies for androgenic alopecia. Stem Cells Cloning Adv. Appl. (2017) 10 11-19. DOI: 10.2147/SCCAA.S138150 7. Rose P.. Hair restoration surgery: Challenges and solutions. Clin. Cosmet. Investig. Dermatol. (2015) 8 361-370. DOI: 10.2147/CCID.S53980 8. Gentile P.. Autologous Cellular Method Using Micrografts of Human Adipose Tissue Derived Follicle Stem Cells in Androgenic Alopecia. Int. J. Mol. Sci. (2019) 20. DOI: 10.3390/ijms20143446 9. Gentile P., Scioli M., Cervelli V., Orlandi A., Garcovich S.. Autologous Micrografts from Scalp Tissue: Trichoscopic and Long-Term Clinical Evaluation in Male and Female Androgenetic Alopecia. BioMed Res. Int. (2020) 2020 7397162. DOI: 10.1155/2020/7397162 10. Hu S., Li Z., Lutz H., Huang K., Su T., Cores J., Dinh P., Cheng K.. Dermal exosomes containing miR-218-5p promote hair regeneration by regulating β-catenin signaling. Sci. Adv. (2020) 6 eaba1685. DOI: 10.1126/sciadv.aba1685 11. Ogawa M., Udono M., Teruya K., Uehara N., Katakura Y.. Exosomes Derived from Fisetin-Treated Keratinocytes Mediate Hair Growth Promotion. Nutrients (2021) 13. DOI: 10.3390/nu13062087 12. Yuan A., Bian Q., Gao J.. Current advances in stem cell-based therapies for hair regeneration. Eur. J. Pharmacol. (2020) 881 173197. DOI: 10.1016/j.ejphar.2020.173197 13. Nestor M., Ablon G., Gade A., Han H., Fischer D.. Treatment options for androgenetic alopecia: Efficacy, side effects, compliance, financial considerations, and ethics. J. Cosmet. Dermatol. (2021) 20 3759-3781. DOI: 10.1111/jocd.14537 14. Gentile P., Scioli M.G., Bielli A., Orlandi A., Cervelli V.. Concise Review: The Use of Adipose-Derived Stromal Vascular Fraction Cells and Platelet Rich Plasma in Regenerative Plastic Surgery. Stem. Cells (2017) 35 117-134. DOI: 10.1002/stem.2498 15. Bi H., Li H., Zhang C., Mao Y., Nie F., Xing Y., Sha W., Wang X., Irwin D.M., Tan H.. Stromal vascular fraction promotes migration of fibroblasts and angiogenesis through regulation of extracellular matrix in the skin wound healing process. Stem. Cell Res. (2019) 10 302. DOI: 10.1186/s13287-019-1415-6 16. Gentile P., Scioli M., Bielli A., Orlandi A., Cervelli V.. Comparing different nanofat procedures on scars: Role of the stromal vascular fraction and its clinical implications. Regen. Med. (2017) 12 939-952. DOI: 10.2217/rme-2017-0076 17. Butt G., Hussain I., Ahmad F., Choudhery M.. Stromal vascular fraction-enriched platelet-rich plasma therapy reverses the effects of androgenetic alopecia. J. Cosmet. Dermatol. (2020) 19 1078-1085. DOI: 10.1111/jocd.13149 18. Gentile P., Garcovich S.. Concise Review: Adipose-Derived Stem Cells (ASCs) and Adipocyte-Secreted Exosomal microRNA (A-SE-miR) Modulate Cancer Growth and proMote Wound Repair. J. Clin. Med. (2019) 8. DOI: 10.3390/jcm8060855 19. Andjelkov K., Eremin I., Korac A.. Different levels of EGF, VEGF, IL-6, MCP-1, MCP-3, IP-10, Eotaxin and MIP-1α in the adipose-derived stem cell secretome in androgenetic alopecia. Exp. Dermatol. (2022) 31 946-962. DOI: 10.1111/exd.14548 20. Zhao X., Ruan J., Tang H., Li J., Shi Y., Li M., Li S., Xu C., Lu Q., Dai C.. Multi-compositional MRI evaluation of repair cartilage in knee osteoarthritis with treatment of allogeneic human adipose-derived mesenchymal progenitor cells. Stem. Cell Res. Ther. (2019) 10 308. DOI: 10.1186/s13287-019-1406-7 21. Dong Y., Cui M., Qu J., Wang X., Kwon S., Barrera J., Elvassore N., Gurtner G.. Conformable hyaluronic acid hydrogel delivers adipose-derived stem cells and promotes regeneration of burn injury. Acta Biomater. (2020) 108 56-66. DOI: 10.1016/j.actbio.2020.03.040 22. Tak Y., Lee S., Cho A., Kim Y.. A randomized, double-blind, vehicle-controlled clinical study of hair regeneration using adipose-derived stem cell constituent extract in androgenetic alopecia. Stem Cells Transl. Med. (2020) 9 839-849. DOI: 10.1002/sctm.19-0410 23. Pegtel D., Gould S.. Exosomes. Annu. Rev. Biochem. (2019) 88 487-514. DOI: 10.1146/annurev-biochem-013118-111902 24. Li X., Yang N., Cheng Q., Zhang H., Liu F., Shang Y.. MiR-21-5p in Macrophage-Derived Exosomes Targets Smad7 to Promote Epithelial Mesenchymal Transition of Airway Epithelial Cells. J. Asthma Allergy (2021) 14 513-524. DOI: 10.2147/JAA.S307165 25. Mansoor H., Ong H., Riau A., Stanzel T., Mehta J., Yam G.. Current Trends and Future Perspective of Mesenchymal Stem Cells and Exosomes in Corneal Diseases. Int. J. Mol. Sci. (2019) 20. DOI: 10.3390/ijms20122853 26. Xiong M., Zhang Q., Hu W., Zhao C., Lv W., Yi Y., Wang Y., Tang H., Wu M., Wu Y.. The novel mechanisms and applications of exosomes in dermatology and cutaneous medical aesthetics. Pharmacol. Res. (2021) 166 105490. DOI: 10.1016/j.phrs.2021.105490 27. Feng N., Jia Y., Huang X.. Exosomes from adipose-derived stem cells alleviate neural injury caused by microglia activation via suppressing NF-kB and MAPK pathway. J. Neuroimmunol. (2019) 334 576996. DOI: 10.1016/j.jneuroim.2019.576996 28. Hu L., Wang J., Zhou X., Xiong Z., Zhao J., Yu R., Huang F., Zhang H., Chen L.. Exosomes derived from human adipose mensenchymal stem cells accelerates cutaneous wound healing via optimizing the characteristics of fibroblasts. Sci. Rep. (2016) 6 32993. DOI: 10.1038/srep32993 29. Xiong J., Liu Z., Wu M., Sun M., Xia Y., Wang Y.. Comparison of Proangiogenic Effects of Adipose-Derived Stem Cells and Foreskin Fibroblast Exosomes on Artificial Dermis Prefabricated Flaps. Stem. Cells Int. (2020) 2020 5293850. DOI: 10.1155/2020/5293850 30. Wu J., Yang Q., Wu S., Yuan R., Zhao X., Li Y., Wu W., Zhu N.. Adipose-Derived Stem Cell Exosomes Promoted Hair Regeneration. Tissue Eng. Regen. Med. (2021) 18 685-691. DOI: 10.1007/s13770-021-00347-y 31. Nilforoushzadeh M., Aghdami N., Taghiabadi E.. Effects of Adipose-Derived Stem Cells and Platelet-Rich Plasma Exosomes on The Inductivity of Hair Dermal Papilla Cells. Cell J. (2021) 23 576-583. PMID: 34837686 32. Hu M., Hong L., Liu C., Hong S., He S., Zhou M., Huang G., Chen Q.. Electrical stimulation enhances neuronal cell activity mediated by Schwann cell derived exosomes. Sci. Rep. (2019) 9 4206. DOI: 10.1038/s41598-019-41007-5 33. Kazi T., Nagata A., Nakagawa T., Matsuzaki T., Inui S.. Dermal Papilla Cell-Derived Extracellular Vesicles Increase Hair Inductive Gene Expression in Adipose Stem Cells via β-Catenin Activation. Cells (2022) 11. DOI: 10.3390/cells11020202 34. Yan H., Gao Y., Ding Q., Liu J., Li Y., Jin M., Xu H., Ma S., Wang X., Zeng W.. Exosomal Micro RNAs Derived from Dermal Papilla Cells Mediate Hair Follicle Stem Cell Proliferation and Differentiation. Int. J. Biol. Sci. (2019) 15 1368-1382. DOI: 10.7150/ijbs.33233 35. Driskell R.R., Clavel C., Rendl M., Watt F.M.. Hair follicle dermal papilla cells at a glance. J. Cell Sci. (2011) 124 1179-1182. DOI: 10.1242/jcs.082446 36. Kang J., Kim S., Kim M., Boo H., Kim E., Im G., Kim Y., Hyun J., Kang J., Koh Y.. Effects of dihydrotestosterone on rat dermal papilla cells in vitro. Eur. J. Pharmacol. (2015) 757 74-83. DOI: 10.1016/j.ejphar.2015.03.055 37. Won C., Park G., Wu X., Tran T., Park K., Park B., Kim D., Kwon O., Kim K.. The Basic Mechanism of Hair Growth Stimulation by Adipose-derived Stem Cells and Their Secretory Factors. Curr. Stem Cell Res. Ther. (2017) 12 535-543. DOI: 10.2174/1574888X12666170829161058 38. Fu D., Huang J., Li K., Chen Y., He Y., Sun Y., Guo Y., Du L., Qu Q., Miao Y.. Dihydrotestosterone-induced hair regrowth inhibition by activating androgen receptor in C57BL6 mice simulates androgenetic alopecia. Biomed. Pharmacother. Biomed. Pharmacother. (2021) 137 111247. DOI: 10.1016/j.biopha.2021.111247 39. Shin H., Won C.H., Chung W.K., Park B.S.. Up-to-date Clinical Trials of Hair Regeneration Using Conditioned Media of Adipose-Derived Stem Cells in Male and Female Pattern Hair Loss. Curr. Stem. Cell Res. (2017) 12 524-530. DOI: 10.2174/1574888X12666170504120244 40. Lou J., Wu J., Feng M., Dang X., Wu G., Yang H., Wang Y., Li J., Zhao Y., Shi C.. Exercise promotes angiogenesis by enhancing endothelial cell fatty acid utilization via liver-derived extracellular vesicle miR-122-5p. J. Sport Health Sci. (2021) 11 495-508. DOI: 10.1016/j.jshs.2021.09.009 41. Wei Z., Liu J., Xie H., Wang B., Wu J., Zhu Z.. MiR-122-5p Mitigates Inflammation, Reactive Oxygen Species and SH-SY5Y Apoptosis by Targeting CPEB1 After Spinal Cord Injury Via the PI3K/AKT Signaling Pathway. Neurochem. Res. (2021) 46 992-1005. DOI: 10.1007/s11064-021-03232-1 42. Ding Z., Lin J., Sun Y., Cong S., Liu S., Zhang Y., Chen Q., Chen J.. miR-122-5p negatively regulates the transforming growth factor-β/Smad signaling pathway in skeletal muscle myogenesis. Cell Biochem. Funct. (2020) 38 231-238. DOI: 10.1002/cbf.3460 43. Domenis R., Lazzaro L., Calabrese S., Mangoni D., Gallelli A., Bourkoula E., Manini I., Bergamin N., Toffoletto B., Beltrami C.. Adipose tissue derived stem cells: In vitro and in vivo analysis of a standard and three commercially available cell-assisted lipotransfer techniques. Stem Cell Res. Ther. (2015) 6 2. DOI: 10.1186/scrt536 44. He L., Zhu C., Jia J., Hao X.Y., Yu X.Y., Liu X.Y., Shu M.G.. ADSC-Exos containing MALAT1 promotes wound healing by targeting miR-124 through activating Wnt/β-catenin pathway. Biosci. Rep. (2020) 40 BSR20192549. DOI: 10.1042/BSR20192549 45. Ren S., Chen J., Duscher D., Liu Y., Guo G., Kang Y., Xiong H., Zhan P., Wang Y., Wang C.. Microvesicles from human adipose stem cells promote wound healing by optimizing cellular functions via AKT and ERK signaling pathways. Stem. Cell Res. (2019) 10 47. DOI: 10.1186/s13287-019-1152-x 46. Zaborowski M., Balaj L., Breakefield X., Lai C.. Extracellular Vesicles: Composition, Biological Relevance, and Methods of Study. Bioscience (2015) 65 783-797. DOI: 10.1093/biosci/biv084 47. Yoo H., Won C., Lee S., Kwon O., Kim K., Eun H., Cho K.. Expression of androgen and estrogen receptors in human scalp mesenchymal cells in vitro. Arch. Dermatol. Res. (2007) 298 505-509. DOI: 10.1007/s00403-006-0721-x 48. Inui S., Fukuzato Y., Nakajima T., Yoshikawa K., Itami S.. Androgen-inducible TGF-beta1 from balding dermal papilla cells inhibits epithelial cell growth: A clue to understand paradoxical effects of androgen on human hair growth. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. (2002) 16 1967-1969 49. Foitzik K., Lindner G., Mueller-Roever S., Maurer M., Botchkareva N., Botchkarev V., Handjiski B., Metz M., Hibino T., Soma T.. Control of murine hair follicle regression (catagen) by TGF-beta1 in vivo. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. (2000) 14 752-760 50. Michel L., Reygagne P., Benech P., Jean-Louis F., Scalvino S., Ly Ka So S., Hamidou Z., Bianovici S., Pouch J., Ducos B.. Study of gene expression alteration in male androgenetic alopecia: Evidence of predominant molecular signalling pathways. Br. J. Dermatol. (2017) 177 1322-1336. DOI: 10.1111/bjd.15577 51. Zhang Y., Pan Y., Liu Y., Li X., Tang L., Duan M., Li J., Zhang G.. Exosomes derived from human umbilical cord blood mesenchymal stem cells stimulate regenerative wound healing via transforming growth factor-β receptor inhibition. Stem Cell Res. Ther. (2021) 12 434. DOI: 10.1186/s13287-021-02517-0 52. Yin K., Wang S., Zhao R.. Exosomes from mesenchymal stem/stromal cells: A new therapeutic paradigm. Biomark. Res. (2019) 7 8. DOI: 10.1186/s40364-019-0159-x 53. Zhou L., Wang H., Jing J., Yu L., Wu X., Lu Z.. Regulation of hair follicle development by exosomes derived from dermal papilla cells. Biochem. Biophys. Res. Commun. (2018) 500 325-332. DOI: 10.1016/j.bbrc.2018.04.067 54. Zhang Y., Yan J., Liu Y., Chen Z., Li X., Tang L., Li J., Duan M., Zhang G.. Human Amniotic Fluid Stem Cell-Derived Exosomes as a Novel Cell-Free Therapy for Cutaneous Regeneration. Front. Cell Dev. Biol. (2021) 9 685873. DOI: 10.3389/fcell.2021.685873 55. Jiang T., Wang Z., Sun J.. Human bone marrow mesenchymal stem cell-derived exosomes stimulate cutaneous wound healing mediates through TGF-β/Smad signaling pathway. Stem Cell Res. Ther. (2020) 11 198. DOI: 10.1186/s13287-020-01723-6 56. Wang J., Wu H., Peng Y., Zhao Y., Qin Y., Zhang Y., Xiao Z.. Hypoxia adipose stem cell-derived exosomes promote high-quality healing of diabetic wound involves activation of PI3K/Akt pathways. J. Nanobiotechnol. (2021) 19 202. DOI: 10.1186/s12951-021-00942-0 57. Zhang X., Zhou D., Ma T., Liu Q.. Vascular Endothelial Growth Factor Protects CD200-Rich and CD34-Positive Hair Follicle Stem Cells Against Androgen-Induced Apoptosis Through the Phosphoinositide 3-Kinase/Akt Pathway in Patients with Androgenic Alopecia. Dermatol. Surg. Off. Publ. Am. Soc. Dermatol. Surg. (2020) 46 358-368. DOI: 10.1097/DSS.0000000000002091 58. Lachgar S., Charveron M., Gall Y., Bonafe J.. Minoxidil upregulates the expression of vascular endothelial growth factor in human hair dermal papilla cells. Br. J. Dermatol. (1998) 138 407-411. DOI: 10.1046/j.1365-2133.1998.02115.x 59. Feng W., Ying Z., Ke F., Mei-Lin X.. Apigenin suppresses TGF-β1-induced cardiac fibroblast differentiation and collagen synthesis through the downregulation of HIF-1α expression by miR-122-5p. Phytomed. Int. J. Phytother. Phytopharm. (2021) 83 153481. DOI: 10.1016/j.phymed.2021.153481 60. Jiang M., Ma W., Gao Y., Jia K., Zhang Y., Liu H., Sun Q.. IL-22-induced miR-122-5p promotes keratinocyte proliferation by targeting Sprouty2. Exp. Dermatol. (2017) 26 368-374. DOI: 10.1111/exd.13270 61. Li S., Chen J., Chen F., Wang C., Guo X., Wang C., Fan Y., Wang Y., Peng Y., Li W.. Liposomal honokiol promotes hair growth via activating Wnt3a/β-catenin signaling pathway and down regulating TGF-β1 in C57BL/6N mice. Biomed. Pharmacother. Biomed. Pharmacother. (2021) 141 111793. DOI: 10.1016/j.biopha.2021.111793 62. Chen X., Liu B., Li Y., Han L., Tang X., Deng W., Lai W., Wan M.. Dihydrotestosterone Regulates Hair Growth Through the Wnt/β-Catenin Pathway in C57BL/6 Mice and In Vitro Organ Culture. Front. Pharmacol. (2019) 10 1528. DOI: 10.3389/fphar.2019.01528 63. Chen Y., Huang J., Liu Z., Chen R., Fu D., Yang L., Wang J., Du L., Wen L., Miao Y.. miR-140-5p in Small Extracellular Vesicles From Human Papilla Cells Stimulates Hair Growth by Promoting Proliferation of Outer Root Sheath and Hair Matrix Cells. Front. Cell Dev. Biol. (2020) 8 593638. DOI: 10.3389/fcell.2020.593638 64. Ren S., Chen J., Guo J., Liu Y., Xiong H., Jing B., Yang X., Li G., Kang Y., Wang C.. Exosomes from Adipose Stem Cells Promote Diabetic Wound Healing through the eHSP90/LRP1/AKT Axis. Cells (2022) 11. DOI: 10.3390/cells11203229 65. Yang Z., Yang Y., Xu Y., Jiang W., Shao Y., Xing J., Chen Y., Han Y.. Biomimetic nerve guidance conduit containing engineered exosomes of adipose-derived stem cells promotes peripheral nerve regeneration. Stem Cell Res. Ther. (2021) 12 442. DOI: 10.1186/s13287-021-02528-x 66. Qu H., Wu S., Li J., Ma T., Li J., Xiang B., Jiang H., Zhang Q.. MiR-125b regulates the differentiation of hair follicles in Fine-wool Sheep and Cashmere goats by targeting MXD4 and FGFR2. Anim. Biotechnol. (2021) 1-8. DOI: 10.1080/10495398.2021.1968884
--- title: Metabolomics-Based Analysis of the Effects of Different Cultivation Strategies on Metabolites of Dendrobium officinale Kimura et Migo authors: - Da Yang - Yeyang Song - Anjin Lu - Lin Qin - Daopeng Tan - Qianru Zhang - Yuqi He - Yanliu Lu journal: Metabolites year: 2023 pmcid: PMC10059836 doi: 10.3390/metabo13030389 license: CC BY 4.0 --- # Metabolomics-Based Analysis of the Effects of Different Cultivation Strategies on Metabolites of Dendrobium officinale Kimura et Migo ## Abstract Dendrobium officinale Kimura et Migo is a famous plant with a high medicinal value which has been recorded in the Chinese Pharmacopoeia (2020 Edition). The medicinal properties of D. officinale are based on its chemical composition. However, there are no reports on how different cultivation methods affect its chemical composition. In order to reveal this issue, samples of the D. officinale were collected in this study through tree epiphytic cultivation, stone epiphytic cultivation, and greenhouse cultivation. Polysaccharides were determined by phenol sulfuric acid method and secondary metabolites were detected by the UPLC-MS technique. In addition, with regards to metabolomics, we used multivariate analyses including principal component analysis (PCA) and orthogonal partial least squares analysis (OPLS-DA) to screen for differential metabolites which met the conditions of variable importance projection values >1, fold change >4, and $p \leq 0.05.$ The differential metabolites were taken further for metabolic pathway enrichment analysis, which was based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and validated by antioxidant activity. Comparing the three groups of samples according to the standards of the ChP (2020 edition), the results showed that the polysaccharide content of the samples from stony epiphytic cultivation and greenhouse cultivation was significantly higher than that of the samples from live tree epiphytic cultivation. Metabolomic analysis revealed that there were 185 differential metabolites among the 3 cultivation methods, with 99 of the differential metabolites being highest in the stone epiphytic cultivation. The results of the metabolic pathway enrichment analysis showed that the different cultivation strategies mainly effected four carbohydrate metabolic pathways, five secondary metabolite synthesis pathways, six amino acid metabolic pathways, one nucleotide metabolism pathway, three cofactor and vitamin metabolism pathways, and one translation pathway in genetic information processing. Furthermore, D. officinale from stone epiphytic cultivation which had the best antioxidant activity was implicated in differential metabolite production. This study revealed the effects of different cultivation methods on the chemical composition of D. officinale and also provided a reference for establishing the quality control standards to aid its development and utilization. ## 1. Introduction Dendrobium officinale Kimura et Migo is a famous plant with high medicinal value which has been recorded in Chinese Pharmacopoeia due to its various remarkable effects, such as cardiovascular protection [1,2], hypoglycemia [3,4], gastrointestinal protection [5,6,7], immune modulation [8,9], antitumor [10,11,12], anti-aging [13,14], and anti-osteoporosis [15]. Its main chemical constituents include polysaccharides [16], flavonoids [17], phenolic acids [18], amino acids [19], bibenzyl [20], alkaloids [21], coumarins, lignans [22], and organic acids [23,24], which are closely related to its pharmacological effects. ( Specific information on some of the Dendrobium candidum compounds reported in the literature is shown in Table S2). Many studies have reported the pharmacological activities of D. officinale; however, few reports have considered the secondary metabolites or biological activities of D. officinale from the perspective of the whole plant system. D. officinale is mainly distributed south of the Yangtze River. Some studies suggest that it has a long history of application in the Yunnan-Guizhou Plateau, with its northward or eastward migration forming the first iteration of expansion [25]. The scarcity of wild resources [26] and the huge market demand led to the emergence of its artificial cultivation [27]. Since the 21st century, the cultivation of D. officinale has made significant progress, mainly through wild-like cultivation and greenhouse cultivation. The wild-like cultivation is further divided into live tree epiphytic cultivation and stone epiphytic cultivation [28,29]. This has the advantages of making full use of natural resources, having a low cost of cultivation, and having little difficulty in the management and care; greenhouse cultivation adds more artificial cultivation than wild cultivation, allowing it to grow in the greenhouse with the most suitable growth habits and preventing the occurrence of diseases as much as possible [27]. It is well known that the composition and content of chemical components of plants are influenced by their growth environment [30,31]. The content and variation of secondary metabolites in plants directly affects their quality differences and activity [32]. For this reason, many studies have also aimed to enhance the pharmacological activity of plants by altering their growth environment to increase the content of secondary metabolites [33,34]. Currently, commercially circulating D. officinale is mainly stone epiphytic cultivation, live tree epiphytic cultivation, and greenhouse cultivars [28,29] However, only the plant appearance of D. officinale has been reported to vary under different cultivation methods [35], and the effects of different cultivation methods on the chemical composition have not been compared. Therefore, in order to ensure the stable quality of D. officinale, it is necessary to carry out studies related to the differences in metabolites and the processes affecting D. officinale in different cultivation methods. In the present study, the samples of D. officinale from the three cultivation strategies, including live tree epiphytic cultivation, stone epiphytic cultivation, and greenhouse cultivation, were collected. The polysaccharide content determination was conducted by the phenol-sulphuric acid method and the metabolomic study conducted by UPLC-MS technology. The antioxidant activity of different cultivation methods was verified by DPPH and ABTS+ free radical scavenging assays. The results could also inform the establishment of quality control standards to aid in its development and utilization. ## 2.1. Effects of Different Cultivation Methods on the Polysaccharide Content of D. officinale The effects of different cultivation methods on the polysaccharide content of D. officinale were shown in Figure 1. The polysaccharide content of D. officinale cultivated epiphytically on trees was significantly lower than that of those cultivated in the other 2 ways ($p \leq 0.05$). There was no statistical difference in the polysaccharide content between samples from stone epiphytic cultivation and greenhouse cultivation. According to the Chinese Pharmacopoeia 2020 Edition, the polysaccharide content of D. officinale should not be less than $25\%$. The samples from stone epiphytic cultivation and greenhouse cultivation had the same qualification rate of polysaccharide content, both at $66.6\%$. ## 2.2. Metabolomic Determination of D. officinale by UPLC-MS We selected 18 representative samples of D. officinale. This study was conducted by a UPLC-MS system, which was used to determine the metabolites of the samples in the positive and negative ion mode, respectively. The quality control information is shown in Supplementary Figures S1 and S2. The results showed that a total of 949 metabolites were detected. As shown in Figure 2, the metabolites were analyzed and classified, including 177 phenolic acids, 153 lipids, 121 flavonoids, 86 amino acids and their derivatives, 83 organic acids, 66 alkaloids, 55 nucleotides and their derivatives, 28 lignans and coumarins, 27 terpenoids, 24 quinones, 16 vitamins, 7 stilbenes, 70 saccharides and alcohols, and 36 other compounds. ## 2.3. Effects of Different Cultivation Methods on the Metabolite Profile of D. officinale Unsupervised pattern recognition principal component analysis (PCA) was used to compare whether the secondary metabolites of D. officinale were affected under different cultivation conditions from the profiles. As shown in Figure 3, the points representing the samples from the three cultivation strategies were significantly distinguished in the PC1 and PC2 directions and did not intersect with each other. This study suggested that the three different cultivation methods had significant effects on the metabolite profile of D. officinale. ## 2.4. Effects of Different Cultivation Methods on the Metabolites of D. officinale Orthogonal partial least squares analysis (OPLS-DA) was used to compare and find out the different metabolites of D. officinale under the three cultivation conditions. As can be seen from Figure 4A–C, the OPLS-DA analysis models were stable and valid, and the variable importance projection values (VIP values) generated by the models were also reliable. The points representing the samples from the different cultivation strategies were clearly distinguished. In order to screen the most representative variable metabolites, the criteria were set to satisfy the conditions of VIP value >1, fold change >4, and $p \leq 0.05.$ Compared with the samples from stone epiphytic cultivation, the contents of 103 metabolites in the samples from greenhouse cultivation changed significantly, as shown in Figure 4D. There was an increase in the contents of 8 metabolites and a decrease in the contents of 95 metabolites. Compared with the samples from live tree epiphytic cultivation, the contents of 65 metabolites in the samples from greenhouse cultivation changed significantly, as shown in Figure 4E. There was an increase in the contents of 19 metabolites and a decrease in the contents of 46 metabolites. Compared with the samples from stone epiphytic cultivation, the contents of 117 metabolites in the samples from live tree epiphytic cultivation changed significantly, as shown in Figure 4F. There was an increase in the contents of 36 metabolites and a decrease in the contents of 81 metabolites. Through analysis, it was found that the changed metabolites included phenolic acids, flavonoids, lipids, amino acids and their derivatives, organic acids, alkaloids, nucleotides and their derivatives, lignans, coumarins, terpenoids, quinones, others, and so on. The details are shown in Table 1. There were 185 metabolites that were significantly altered in the samples from the 3 incubation methods after de-weighting. Among the metabolites, the contents of 99 metabolites were the highest under stone epiphytic cultivation, the contents of 71 metabolites were the highest under live tree epiphytic cultivation, and the contents of 15 metabolites were the highest under greenhouse cultivation, as shown in Figure 5. The content of secondary metabolites of the different species is significantly higher in stone epiphytic cultivation. Live tree epiphytic cultivation is the second most abundant, less so than in greenhouse cultivation. ( Detailed information is shown in Supplementary Table S1.) ## 2.5. Effects of Different Cultivation Methods on the Metabolic Pathways of D. officinale Metabolites were annotated and imported into Metabo Analyst 5.0 for KEGG metabolic pathway enrichment analysis. The top twenty enriched metabolic pathways were presented as enrichment bubble plots, as shown in Figure 6. Within these pathways, there are four carbohydrate metabolic pathways, five secondary metabolite synthesis pathways, six amino acid metabolic pathways, one nucleotide metabolism pathway, three cofactor and vitamin metabolism pathways, and one translation pathway in genetic information processing. The flavone and flavonol biosynthesis pathways were affected most by the three cultivation methods due to the highest degree of enrichment and the greatest number of changed metabolites. ## 2.6. Effects of Different Cultivation Methods on the Antioxidant Activity of D. officinale The metabolic pathways with the highest enrichment degree were the flavone and flavonol biosynthesis pathway and the ascorbic acid and aldose metabolism pathway, which were known to have powerful antioxidant capacities. Therefore, the antioxidant activity of D. officinale under different cultivation methods was verified. As shown in Figure 7, all samples showed a dose-dependent relationship in terms of DPPH and ABTS radical scavenging rates. However, the D. officinale from stone epiphytic cultivation showed higher DPPH and ABTS+ radical scavenging ability than the other two culture methods at the same concentration. ## 3. Discussion As mentioned before, D. officinale is a plant with a high value in clinical applications. The material basis of its multiple pharmacological activities is the various chemical compounds it contains. We demonstrate that the cultivation method influences the metabolite composition. Chemical compounds could be affected by the different cultivation methods. For example, the polysaccharide content of D. officinale from greenhouse cultivation and stone epiphytic cultivation was higher than that of D. officinale from live tree epiphytic cultivation. In addition, 185 metabolites were significantly altered by the different cultivation methods. Among the metabolites, the contents of 99 metabolites were the highest under stone epiphytic cultivation, the contents of 71 metabolites were the highest under live tree epiphytic cultivation, and the contents of 15 metabolites were the highest under greenhouse cultivation. The metabolites mainly included phenolic acids, flavonoids, lipids, amino acids, organic acids, alkaloids, and so on. The differences in metabolite content between cultivation conditions may be due to the plants’ self-regulation. In order to reveal the possible mechanism of the effects, metabolic pathway enrichment analysis was performed. The top twenty enriched metabolic pathways were found. The flavone and flavonol biosynthesis pathway was most affected by the three cultivation methods. Through this pathway, flavonoids and flavonols would be converted into lignin, which would accumulate in the secondary cell walls of plants, participate in mechanical support, and form conduits for transporting water and mineral elements, thus improving the drought tolerance of plants [36]. There were other pathways among the 20 pathways that can help plants with drought resistance. Lignin is also produced through the phenylpropanoid pathway, and thus the corresponding effects can be found [37]. Arginine had been reported to prevent water deficit-induced accumulation of proline, improve leaf gas exchange during water deficit, and enhance root antioxidant capacity in recovering plants, contributing to plant growth and development in water-stressed environments [38]. In the present study, the enrichment of arginine biosynthesis was most likely due to the self-regulation of D. officinale to improve the water deficit in stone and live tree epiphytic cultivation. In addition, β-alanine had multiple functions for plants. The β-alanine metabolic pathway was involved in protecting plants from extreme temperature, drought, hypoxia, heavy metal shock, and some biological stresses [39]. The self-regulation of D. officinale was manifested not only in drought resistance but also in other aspects. For example, the ascorbate and aldose metabolism pathway would help to regulate the plant’s own redox balance to adapt to nutrient-deficient survival conditions [40]. These results suggested that, under wild-like cultivation, D. officinale enhanced its ability to cope with the complex growth environment by instinctively self-regulating the levels of metabolites. In addition, different cultivation methods have the potential to affect the pharmacological activity of D. officinale by changing the metabolites levels. The polysaccharide of D. officinale has been reported to have a wide range of pharmacological activities such as antitumor [10], immunomodulatory [8], and hypoglycemic [2]. Compared to live-tree epiphytic cultivation, D. officinale from stone epiphytic cultivation and greenhouse cultivation contained more polysaccharide content with statistical-mathematical significance. The flavone and flavonol biosynthesis and the ascorbate and aldarate metabolism pathways, whose metabolites have important antioxidant effects [41,42],were the most variable among the metabolic pathways. To further compare the effect of these differential metabolites of D. officinale under three cultivation methods, we used DPPH and ABTS scavenging activities to analyze the antioxidant activity [43], while ascorbic acid (VitC) has been used as a positive agent in free radical scavenging assays [44]. D. officinale from stone epiphytic cultivation showed the best antioxidant ability than the other two culture methods at the same concentration, and the greenhouse cultivation showed the weakest. In the two pathways, six compounds underwent significant changes. Four of these compounds (Kaempferol 3-O-glucoside, D-Glucuronate, Dehydroascorbate, and D-Glucurono-6, 3-lactone) were most abundant in D. officinale from stone epiphytic cultivation. In addition, two of these compounds (Kaempferol and Quercetin-3-O-rutinoside) were higher in D. officinale from live-tree epiphytic cultivation. These results suggested that different cultivation methods do have a significant effect on the chemical composition of D. officinale and, therefore, on its biological activity. As mentioned earlier, there were many compounds that were the most abundant and had important pharmacological effects in D. officinale from stone epiphytic cultivation. For example, hircinol has been found to have antiproliferative and apoptosis-inducing effects on gastric cancer cell lines and was considered a promising candidate for drugs and nutrients [45]. Lumichrome has been reported to inhibit osteoclastogenesis and bone resorption by inhibiting RANKL-induced NFAT activation and calcium signaling, suggesting that lumichrome has potential as a therapeutic agent for osteolytic diseases [46]. The anti-inflammatory effects of rutinoside have been reported [47], and in recent studies its combination with ascorbic acid has been found to be effective at the treatment of pigmented purpuric skin disease [48]. Taxifolin has also been shown to have biological activity against a variety of cancers, such as osteosarcoma [49], colorectal cancer [50], breast cancer [51], and lung cancer [52]. In conclusion, the value of medicinal plants has been often influenced by their secondary metabolites, the production of which was inextricably linked to growth and development and environmental factors [53]. The results of this experiment showed that there are significant effects on the chemical composition of D. officinale due to differences in the growing environment. D. officinale attached to stones epiphytic cultivated with more flavonoid and phenolic acid metabolites, which enhanced its antioxidant capacity and ability to cope with the complex survival environment. We suggest that the choice of cultivation method for D. officinale is very important. Furthermore, we also suggest that the quality standard of D. officinale needs to consider not only the polysaccharide content, but also for the content of other compounds. ## 4.1. Collection Information of D. officinale Samples Fresh stems were collected from the Anlong and Xingyi areas of Guizhou Province, China. ( Photographs of the different cultivation methods of Dendrobium are shown in Figures S1 and S2).The collected samples were identified as D. officinale by Professor Jianwen Yang of Zunyi Medical University. Details of the sample collection are shown in Table 2. ## 4.2. Instruments and Reagents The instruments used were as follows: SHIMADZU Nexera X2 UPLC (Kyoto, Shimadzu, Japan), Applied Biosystems 4500 QTRAP mass spectrometer (Applied Biosystems, Waltham, MA, USA), MULTISKAN GO full-wavelength microplate reader (Thermo Fisher, Waltham, MA, USA), 3K15 centrifuge (Sigma-Aldrich, St. Louis, MO, USA), WP-UP-YJ-20 micro-organic heat removal ultra-pure water machine (Sichuan Water Treatment Equipment Co., Chengdu, China), and ME204E electronic balance (Shanghai Mettler-Toledo Instruments, Shanghai, China). The reagents used were as follows: 2,2-biphenyl-1-bitter hydrazinyl (Shanghai Macklin Biochemical Technology Co., Ltd., Shanghai, China), 2,2′-Hydrazinyl-bis(3-ethylbenzothiazoline-6-sulphonic acid) diamine salt (Shanghai Macklin Biochemical Technology Co., Ltd., Shanghai, China), glucose (Sigma-Aldrich, St. Louis, MO, USA), anhydrous ethanol (analytical grade, Chengdu Kelong Chemical Reagent Factory, Chengdu, China), phenol (analytical grade, Chengdu Kelong Chemical Reagent Factory, Chengdu, China), concentrated sulphuric acid (analytical grade, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China), acetonitrile (mass spectrometry grade, Merck & Co., Rahway, NJ, USA), and methanol (mass spectrometry grade, Merck & Co., Rahway, NJ, USA). ## 4.3.1. Extraction of Polysaccharide D. officinale was dried at 60 °C, crushed, and sieved through 50-mesh sieve to obtain the powder. The powder was precisely weighed to 60 mg and refluxed with 40 mL of water for 2 h. The solution was cooled and the volume fixed with water at 50 mL, mixed well, and centrifuged at 4000 rpm for 15 min. In total, 2.0 mL of the supernatant was mixed well with 10 mL of anhydrous ethanol and placed at 4 °C for 1 h, then centrifuged at 4000 rpm for 20 min. The precipitate was washed 2 times with 8 mL of $80\%$ (v/v) ethanol solution and centrifuged at 4000 rpm for 20 min. The sample was obtained by dissolving the precipitate using hot water and fixing its volume at 10 mL. ## 4.3.2. Polysaccharide Assay In total, 1.0 mL of $5\%$ phenol solution and 5.0 mL of concentrated sulfuric acid were added to 1.0 mL of the sample. The reaction solution was mixed and heated in water at 100 °C for 20 min, then put in an ice bath for 5 min. The absorbance of the reaction solution was measured at 488 nm using a microplate reader. ## 4.3.3. Preparation of Glucose Standard Curve In total, 9.00 mg of glucose standard was dissolved in water and the volume fixed to 50 mL to obtain the stock solution (containing 180 μg/mL of glucose). The stock solution was diluted to 0, 30, 60, 90, 120, and 150 μg/mL to obtain the standard solution. ## 4.4. Determination of the Metabolomics of D. officinale by UPLC -MS/MS The analysis was performed on the Agilent SB-C18 column (2.1 × 100 mm2, 1.6 μm). The mobile phase consisted of ultra-pure water (containing $0.1\%$ formic acid) for phase A and acetonitrile (containing $0.1\%$ formic acid) for phase B. The elution gradient was as follows: 0–9 min, 5–$95\%$ for phase B; and 9–10 min, $95\%$ for phase B. The flow rate was 0.35 mL/min, the column temperature was 40 °C, and the injection volume was 4 μL. The parameters of the ESI source were as follows: the source temperature was set to 550 °C; the ion spray voltage was set to 5500 V for the positive ion mode and −4500 V for the negative ion mode; the pressures of ion source gas I, gas II, and curtain gas were set to 50, 60, and 25.0 psi, respectively; and the collision-induced ionization parameter was set to high. Further DP and CE optimization was completed for individual MRM ion pairs. In total, 100 mg of D. officinale powder was added to 1.2 mL of $70\%$ (v/v) methanol and vortexed every 30 min for 30 s for 6 times. The samples were placed overnight at 4 °C and centrifuged for 10 min at 12,000 rpm. The supernatant was filtered through a microporous membrane (0.22 μm). The filtrate was used for UPLC-ESI-MS/MS analysis. The identification of the chemical composition was based on the MWDB (metware database, http://en.metware.cn/list/27.html (accessed on 26 December 2021)), which is a self-built database from Wuhan, China. The characterization of the substance was performed based on the secondary spectral information with the removal of isotopic signals and repeating signals. The quantification was achieved using triple quadrupole mass spectrometry in multiple reaction monitoring (MRM) mode. The mass spectra were integrated and calibrated using MultiQuant MD 3.0.3 software and the peak area represented the relative content of the corresponding substance [33]. ## 4.5. KEGG Metabolic Pathway Enrichment Based on the KEGG compound database, the metabolites were annotated. Furthermore, the annotated metabolites were then matched based on the KEGG pathway database. Significantly regulated metabolites were imported into the metabolite enrichment analysis MetaboAnalyst 5.0 for their metabolic pathway enrichment. The enrichment results were calculated for the significance (p-values) by hypergeometric tests and presented as bubble plots. ## 4.6.1. Sample Extraction In total, 0.9 g of D. officinale powder was added to 90 mL of $70\%$ (v/v) ethanol, extracted at reflux for 2 h, made up to the weight, filtered, and concentrated to 25 mg/mL. The concentrates were diluted to 0, 5, 10, 15, 20, and 25 mg/mL, and stored at 4 °C. ## 4.6.2. DPPH Radical Scavenging Assay In total, 0.5 mL of the sample solution was added to 1.75 mL of 1.9 × 10−4 mol/L DPPH solution, mixed immediately, and reacted for 30 min at room temperature and protected from light. Ascorbic acid (Vit C) with the same concentration as the sample was used as a positive control. The absorbance was measured at 517 nm. The clearance of DPPH (%) = [1 − (A − A1)/A0] × $100\%$ was calculated to compare the antioxidant activity between the different cultivation methods. A was the absorbance of the sample. A1 was the absorbance of the sample with the same volume of anhydrous ethanol as the DPPH solution. A0 was the absorbance of the same volume of $70\%$ (v/v) ethanol as the sample with the DPPH solution. ## 4.6.3. ABTS Radical Scavenging Assay ABTS free radical solution (ABTS+) was prepared by mixing 7 mol/L of ABTS solution and 2.45 mol/L of potassium persulfate solution at 1:1, which was then placed at room temperature and protected from light for 12 h before use. The solution was diluted with PBS (pH 7.4) until the absorbance at 734 nm was 0.70 (±0.02). In total, 0.2 mL of the sample solution was added to 5 mL of ABTS+ solution, mixed thoroughly, and reacted for 6 min at room temperature. Ascorbic acid (VitC) with the same concentration as the sample was used as the positive control. The absorbance at 734 nm was recorded immediately. The clearance of ABTS+ (%) was calculated = [1 − (A − A1)/A0] × $100\%$ to compare the antioxidant activity between the different cultivation methods. A was the absorbance of the sample. A1 was the absorbance of the sample with the same volume of distilled water as ABTS+ solution. A0 was the absorbance of the same volume of $70\%$ (v/v) ethanol as the sample with ABTS+ solution. ## 4.7. Data Analysis All visualization charts in the paper were performed in R programs. The FactoMineR package was used for PCA, the ropls package was used for OPLS-DA, the mixOmics package was used for volcano plots, and the other packages (ggsignif, gridExtra, reshape2 showtext, ggrepel, etc.) were also used. For multiple group comparisons, one-way analysis of variance (ANOVA) was performed using SPSS 18.0 (IBM, Chicago, IL, USA). $p \leq 0.05$ was considered statistically significant. ## 5. Conclusions In this study, the effects of three cultivation methods on the chemical composition of D. officinale were analyzed by comparing the polysaccharide content and the types of secondary metabolites in live tree epiphytic cultivation, stone epiphytic cultivation, and greenhouse cultivation for D. officinale and their intrinsic influencing factors. The results showed that the polysaccharide contents of D. officinale were comparable and significantly higher in stone epiphytic cultivation and greenhouse cultivation than in live tree epiphytic cultivation. The results of the secondary metabolite analysis showed that the secondary metabolites were significantly higher in the stone epiphytic cultivation D. officinale than in the live tree epiphytic cultivation and greenhouse cultivation. The content of flavonoids and phenolic acid was significantly higher in D. officinale stone epiphytic cultivation than in live tree epiphytic cultivation and greenhouse cultivation. The enrichment of the metabolic pathways in D. officinale indicates that there are differences in the synthesis of major flavonoid biosynthesis between cultivation methods. In addition, the results of antioxidant activity further demonstrated that the antioxidant activity of D. officinale stone epiphytic cultivation was significantly stronger than other cultivation methods. This study shows that the different cultivation methods of D. officinale have a great influence on its chemical composition. It also provides a reference for the selection of high-quality D. officinale cultivation methods and the selection of raw materials for product development. ## References 1. Zhang J.Y., Guo Y., Si J.P., Sun X.B., Sun G.B., Liu J.J.. A polysaccharide of Dendrobium officinale ameliorates H. Int. J. Biol. Macromol. (2017) 104 1-10. DOI: 10.1016/j.ijbiomac.2017.05.169 2. Su J., Wang T., Yan M.Q., Yu J.J., Xu J.B., Zhuge R.C., Yu H.H., Chen S.H., Lyu G.Y.. Effect of Dendrobii officinalis superfine powder on overeating greasy-induced metabolic hypertension in rats. Zhongguo Zhong Yao Za Zhi = China J. Chin. Mater. Med (2021) 46 1658-1666. DOI: 10.19540/j.cnki.cjcmm.20201027.403 3. Yang J., Chen H., Nie Q., Huang X., Nie S.. Dendrobium officinale polysaccharide ameliorates the liver metabolism disorders of type II diabetic rats. Int. J. Biol. Macromol. (2020) 164 1939-1948. DOI: 10.1016/j.ijbiomac.2020.08.007 4. Zeng J., Li D.N., Li Z.B., Zhang J., Zhao X.Y.. Dendrobium officinale attenuates myocardial fibrosis via inhibiting EMT signaling pathway in HFD/STZ-induced diabetic mice. Biol. Pharm. Bull. (2020) 43 864-872. DOI: 10.1248/bpb.b19-01073 5. Liu H., Liang J., Zhong Y., Xiao G., Efferth T., Georgiev M., Vargas-De-La-Cruz C., Bajpai V., Caprioli G., Liu J.. Dendrobium officinale Polysaccharide Alleviates Intestinal Inflammation by Promoting Small Extracellular Vesicle Packaging of miR-433-3p. J. Agric. Food Chem. (2021) 69 13510-13523. DOI: 10.1021/acs.jafc.1c05134 6. Liu C.Z., Chen W., Wang M.X., Wang Y., Chen L.Q., Zhao F., Shi Y., Liu H.J., Dou X.B., Liu C.. Dendrobium officinale Kimura et Migo and American ginseng mixture: A Chinese herbal formulation for gut microbiota modulation. Chin. J. Nat. Med. (2020) 18 446-459. DOI: 10.1016/S1875-5364(20)30052-2 7. Li L., Yao H., Li X., Zhang Q., Wu X., Wong T., Zheng H., Fung H., Yang B., Ma D.. Destiny of Dendrobium officinale Polysaccharide after Oral Administration: Indigestible and Nonabsorbing, Ends in Modulating Gut Microbiota. J. Agric. Food Chem. (2019) 67 5968-5977. DOI: 10.1021/acs.jafc.9b01489 8. He T.-B., Huang Y.-P., Yang L., Liu T.-T., Gong W.-Y., Wang X.-J., Sheng J., Hu J.-M.. Structural characterization and immunomodulating activity of polysaccharide from Dendrobium officinale. Int. J. Biol. Macromol. (2016) 83 34-41. DOI: 10.1016/j.ijbiomac.2015.11.038 9. Huang Y.P., He T.B., Cuan X.D., Wang X.J., Hu J.M., Sheng J.. 1, 4-β-d-Glucomannan from Dendrobium officinale activates NF-кB via TLR4 to regulate the immune response. Molecules (2018) 23. DOI: 10.3390/molecules23102658 10. Zhang K., Zhou X., Wang J., Zhou Y., Qi W., Chen H., Nie S., Xie M.. Dendrobium officinale polysaccharide triggers mitochondrial disorder to induce colon cancer cell death via ROS-AMPK-autophagy pathway. Carbohydr. Polym. (2021) 264 118018. DOI: 10.1016/j.carbpol.2021.118018 11. Guo Z., Zhou Y., Yang J., Shao X.. Dendrobium candidum extract inhibits proliferation and induces apoptosis of liver cancer cells by inactivating Wnt/β-catenin signaling pathway. Biomed. Pharmacother. (2019) 110 371-379. DOI: 10.1016/j.biopha.2018.11.149 12. Xing S.P., Yu W.X., Zhang X.F., Luo Y.Y., Lei Z.X., Huang D.D., Lin J., Huang Y.C., Huang S.W., Nong F.F.. Isoviolanthin Extracted from Dendrobium officinale Reverses TGF-β1-Mediated Epithelial–Mesenchymal Transition in Hepatocellular Carcinoma Cells via Deactivating the TGF-β/Smad and PI3K/Akt/mTOR Signaling Pathways. Int. J. Mol. Sci. (2018) 19. DOI: 10.3390/ijms19061556 13. Lin Y., Yang T., Shen L., Zhang J., Liu L.. Study on the properties of Dendrobiumofficinale fermentation broth as functional raw material of cosmetics. J. Cosmet. Dermatol. (2022) 21 1216-1223. DOI: 10.1111/jocd.14197 14. Lu H., Yang K., Zhan L., Lu T., Chen X., Cai X., Zhou C., Li H., Qian L., Lv G.. Optimization of Flavonoid Extraction in Dendrobium officinale Leaves and Their Inhibitory Effects on Tyrosinase Activity. Int. J. Anal. Chem. (2019) 2019 7849198. DOI: 10.1155/2019/7849198 15. Wang Q., Zi C.T., Wang J., Wang Y.N., Huang Y.W., Fu X.Q., Wang X.J., Sheng J.. Dendrobium officinale Orchid Extract Prevents Ovariectomy-Induced Osteoporosis in Vivo and Inhibits RANKL-Induced Osteoclast Differentiation in Vitro. Front. Pharmacol. (2017) 8 966. DOI: 10.3389/fphar.2017.00966 16. Ma H., Zhang K., Jiang Q., Dai D., Li H., Bi W., Chen D.D.Y.. Characterization of plant polysaccharides from Dendrobium officinale by multiple chromatographic and mass spectrometric techniques. J. Chromatogr. A (2018) 1547 29-36. DOI: 10.1016/j.chroma.2018.03.006 17. Zhou C., Xie Z., Lei Z., Huang Y., Wei G.. Simultaneous identification and determination of flavonoids in Dendrobium officinale. Chem. Cent. J. (2018) 12 40. DOI: 10.1186/s13065-018-0403-8 18. Yoo S.R., Jeong S.J., Lee N.R., Shin H.K., Seo C.S.. Simultaneous determination and anti-inflammatory effects of four phenolic compounds in Dendrobii Herba. Nat. Prod. Res. (2017) 31 2923-2926. DOI: 10.1080/14786419.2017.1300798 19. Liu J.L., Yu N.J., Xing L.H., Wang R., Xu J., Peng D.Y., Tong X.H., Ou J.M., He X.L.. Simultaneous determination and analysis of amino acids and nucleosides in Dendrobium by UHPLC-QTRAP-MS/MS. Zhongguo Zhong Yao Za Zhi = Zhongguo Zhongyao Zazhi = China J. Chin. Mater. Med. (2020) 45 3890-3899. DOI: 10.19540/j.cnki.cjcmm.20200513.201 20. He L., Su Q., Bai L., Li M., Liu J., Liu X., Zhang C., Jiang Z., He J., Shi J.. Recent research progress on natural small molecule bibenzyls and its derivatives in Dendrobium species. Eur. J. Med. Chem. (2020) 204 112530. DOI: 10.1016/j.ejmech.2020.112530 21. Song C., Zhang Y., Manzoor M.A., Li G.. Identification of alkaloids and related intermediates of Dendrobium officinale by solid-phase extraction coupled with high-performance liquid chromatography tandem mass spectrometry. Front. Plant Sci. (2022) 13 952051. DOI: 10.3389/fpls.2022.952051 22. Yang J., Kuang M.T., Yang L., Huang W., Hu J.M.. Modern interpretation of the traditional application of Shihu—A comprehensive review on phytochemistry and pharmacology progress of Dendrobium officinale. J. Ethnopharmacol. (2023) 302 115912. DOI: 10.1016/j.jep.2022.115912 23. Chen W., Lu J., Zhang J., Wu J., Yu L., Qin L., Zhu B.. Traditional Uses, Phytochemistry, Pharmacology, and Quality Control of Dendrobium officinale Kimura et. Migo. Front. Pharmacol. (2021) 12 726528. DOI: 10.3389/fphar.2021.726528 24. Chen W.-H., Wu J.-J., Li X.-F., Lu J.-M., Wu W., Sun Y.-Q., Zhu B., Qin L.-P.. Isolation, structural properties, bioactivities of polysaccharides from Dendrobium officinale Kimura et. Migo: A review. Int. J. Biol. Macromol. (2021) 184 1000-1013. DOI: 10.1016/j.ijbiomac.2021.06.156 25. Hou B., Luo J., Zhang Y., Niu Z., Xue Q., Ding X.. Iteration expansion and regional evolution: Phylogeography of Dendrobium officinale and four related taxa in southern China. Sci. Rep. (2017) 7 43525. DOI: 10.1038/srep43525 26. Fu L.K., Jin J.M.. China Plant Red Data Book-Rare and Endangered Plants (1992) Volume 1 27. Si J.P., Yu Q.X., Song X.S., Shao W.J.. Artificial cultivation modes for Dendrobium officinale. Zhongguo Zhong Yao Za Zhi = China J. Chin. Mater. Med. (2013) 38 481-484 28. Cheng J., Dang P.-P., Zhao Z., Yuan L.-C., Zhou Z.-H., Wolf D., Luo Y.-B.. An assessment of the Chinese medicinal Dendrobium industry: Supply, demand and sustainability. J. Ethnopharmacol. (2019) 229 81-88. DOI: 10.1016/j.jep.2018.09.001 29. Du G., Yang R., Yan F., Wei S., Ren D., Li X.. Use of Microscopic Characteristics and Multielemental Fingerprinting Analysis to Trace Three Different Cultivation Modes of Medicinal and Edible Dendrobium officinale in China. Biol. Trace Elem. Res. (2022) 201 1006-1018. DOI: 10.1007/s12011-022-03196-5 30. Hornyák M., Dziurka M., Kula-Maximenko M., Pastuszak J., Szczerba A., Szklarczyk M., Płażek A.. Photosynthetic efficiency, growth and secondary metabolism of common buckwheat (Fagopyrum esculentum Moench) in different controlled-environment production systems. Sci. Rep. (2022) 12 257. DOI: 10.1038/s41598-021-04134-6 31. Zeng L., Zhou X., Liao Y., Yang Z.. Roles of specialized metabolites in biological function and environmental adaptability of tea plant (Camellia sinensis) as a metabolite studying model. J. Adv. Res. (2021) 34 159-171. DOI: 10.1016/j.jare.2020.11.004 32. Strzemski M., Dresler S., Sowa I., Czubacka A., Agacka-Mołdoch M., Płachno B.J., Granica S., Feldo M., Wójciak-Kosior M.. The Impact of Different Cultivation Systems on the Content of Selected Secondary Metabolites and Antioxidant Activity of Carlina acaulis Plant Material. Molecules (2019) 25. DOI: 10.3390/molecules25010146 33. Madhavi B.G.K., Khan F., Bhujel A., Jaihuni M., Kim N.E., Moon B.E., Kim H.T.. Influence of different growing media on the growth and development of strawberry plants. Heliyon (2021) 7 e07170. DOI: 10.1016/j.heliyon.2021.e07170 34. Alami M.M., Ouyang Z., Zhang Y., Shu S., Yang G., Mei Z., Wang X.. The Current Developments in Medicinal Plant Genomics Enabled the Diversification of Secondary Metabolites’ Biosynthesis. Int. J. Mol. Sci. (2022) 23. DOI: 10.3390/ijms232415932 35. Zhang X.L., Si J.P., Wu L.S., Guo Y.Y., Yu J., Wang L.H.. Field experiment of F1 generation and superior families selection of Dendrobium officinale. Zhongguo Zhong Yao Za Zhi (2013) 38 3861-3865. PMID: 24558865 36. Hou Z.H., Yin J.L., Lu Y.F., Song J.H., Wang S.P., Wei S.D., Liu Z.X., Zhang Y.X., Fang Z.W.. Transcriptomic analysis reveals the temporal and spatial changes in physiological process and gene expression in common buckwheat (Fagopyrum esculentum Moench) grown under drought stress. Agronomy (2019) 9. DOI: 10.3390/agronomy9100569 37. Zhang C., Chen J., Huang W., Song X., Niu J.. Transcriptomics and Metabolomics Reveal Purine and Phenylpropanoid Metabolism Response to Drought Stress in Dendrobium sinense, an Endemic Orchid Species in Hainan Island. Front. Genet. (2021) 12 692702. DOI: 10.3389/fgene.2021.692702 38. Silveira N.M., Ribeiro R.V., de Morais S.F.N., de Souza S.C.R., da Silva S.F., Seabra A.B., Hancock J.T., Machado E.C.. Leaf arginine spraying improves leaf gas exchange under water deficit and root antioxidant responses during the recovery period. Plant Physiol. Biochem. PPB (2021) 162 315-326. DOI: 10.1016/j.plaphy.2021.02.036 39. Parthasarathy A., Savka M.A., Hudson A.O.. The Synthesis and Role of β-Alanine in Plants. Front. Plant Sci. (2019) 10 921. DOI: 10.3389/fpls.2019.00921 40. Iwagami T., Ogawa T., Ishikawa T., Maruta T.. Activation of ascorbate metabolism by nitrogen starvation and its physiological impacts in Arabidopsis thaliana. Biosci. Biotechnol. Biochem. (2022) 86 476-489. DOI: 10.1093/bbb/zbac010 41. Nabavi S.M., Šamec D., Tomczyk M., Milella L., Russo D., Habtemariam S., Suntar I., Rastrelli L., Daglia M., Xiao J.. Flavonoid biosynthetic pathways in plants: Versatile targets for metabolic engineering. Biotechnol. Adv. (2020) 38 107316. DOI: 10.1016/j.biotechadv.2018.11.005 42. Zhao X., Guo S., Ma Y., Zhao W., Wang P., Zhao S., Wang D.. Ascorbic acid prevents yellowing of fresh-cut yam by regulating pigment biosynthesis and energy metabolism. Food Res. Int. (2022) 157 111424. DOI: 10.1016/j.foodres.2022.111424 43. Wołosiak R., Drużyńska B., Derewiaka D., Piecyk M., Majewska E., Ciecierska M., Worobiej E., Pakosz P.. Verification of the Conditions for Determination of Antioxidant Activity by ABTS and DPPH Assays—A Practical Approach. Molecules (2022) 27. DOI: 10.3390/molecules27010050 44. Wagner A.E., Huebbe P., Konishi T., Rahman M.M., Nakahara M., Matsugo S., Rimbach G.. Free radical scavenging and antioxidant activity of ascorbigen versus ascorbic acid: Studies in vitro and in cultured human keratinocytes. J. Agric. Food Chem. (2008) 56 11694-11699. DOI: 10.1021/jf802403d 45. Badalamenti N., Russi S., Bruno M., Maresca V., Vaglica A., Ilardi V., Zanfardino A., Di Napoli M., Varcamonti M., Cianciullo P.. Dihydrophenanthrenes from a Sicilian Accession of Himantoglossum robertianum (Loisel.) P. Delforge Showed Antioxidant, Antimicrobial, and Antiproliferative Activities. Plants (2021) 10. DOI: 10.3390/plants10122776 46. Liu C., Cao Z., Zhang W., Tickner J., Qiu H., Wang C., Chen K., Wang Z., Tan R., Dong S.. Lumichrome inhibits osteoclastogenesis and bone resorption through suppressing RANKL-induced NFAT activation and calcium signaling. J. Cell. Physiol. (2018) 233 8971-8983. DOI: 10.1002/jcp.26841 47. Kauss T., Moynet D., Rambert J., Al-Kharrat A., Brajot S., Thiolat D., Ennemany R., Fawaz F., Mossalayi M.D.. Rutoside decreases human macrophage-derived inflammatory mediators and improves clinical signs in adjuvant-induced arthritis. Arthritis Res. Ther. (2008) 10 R19. DOI: 10.1186/ar2372 48. Morquette A.J., Lee J.B., Grossman S.K., Hsu S.. Rutoside and Ascorbic Acid in the Treatment of Schamberg Pigmented Purpuric Dermatosis. Cureus (2021) 13 e14592. DOI: 10.7759/cureus.14592 49. Chen X., Gu N., Xue C., Li B.R.. Plant flavonoid taxifolin inhibits the growth, migration and invasion of human osteosarcoma cells. Mol. Med. Rep. (2018) 17 3239-3245. DOI: 10.3892/mmr.2017.8271 50. Razak S., Afsar T., Ullah A., Almajwal A., Alkholief M., Alshamsan A., Jahan S.. Taxifolin, a natural flavonoid interacts with cell cycle regulators causes cell cycle arrest and causes tumor regression by activating Wnt/ β -catenin signaling pathway. BMC Cancer (2018) 18. DOI: 10.1186/s12885-018-4959-4 51. Li J., Hu L., Zhou T., Gong X., Jiang R., Li H., Kuang G., Wan J., Li H.. Taxifolin inhibits breast cancer cells proliferation, migration and invasion by promoting mesenchymal to epithelial transition via β-catenin signaling. Life Sci. (2019) 232 116617. DOI: 10.1016/j.lfs.2019.116617 52. Wang R., Zhu X., Wang Q., Li X., Wang E., Zhao Q., Wang Q., Cao H.. The anti-tumor effect of taxifolin on lung cancer via suppressing stemness and epithelial-mesenchymal transition in vitro and oncogenesis in nude mice. Ann. Transl. Med. (2020) 8 590. DOI: 10.21037/atm-20-3329 53. Li Y., Kong D., Fu Y., Sussman M.R., Wu H.. The effect of developmental and environmental factors on secondary metabolites in medicinal plants. Plant Physiol. Biochem. (2020) 148 80-89. DOI: 10.1016/j.plaphy.2020.01.006
--- title: 'Type 2 Diabetes: Also a “Clock Matter”?' authors: - Annamaria Docimo - Ludovica Verde - Luigi Barrea - Claudia Vetrani - Pasqualina Memoli - Giacomo Accardo - Caterina Colella - Gabriella Nosso - Marcello Orio - Andrea Renzullo - Silvia Savastano - Annamaria Colao - Giovanna Muscogiuri journal: Nutrients year: 2023 pmcid: PMC10059837 doi: 10.3390/nu15061427 license: CC BY 4.0 --- # Type 2 Diabetes: Also a “Clock Matter”? ## Abstract Background: We investigated whether chronotype is associated with glycemic control, antidiabetic treatment, and risk of developing complications in patients with type 2 diabetes (T2DM). Methods: The diabetologists filled out an online questionnaire on the Google Form platform to collect the following parameters of subjects with T2DM: body mass index (BMI), fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), diabetes history, antidiabetic treatment, diabetic complications, and chronotype categories. Results: We enrolled 106 subjects with T2DM (M/F: $\frac{58}{48}$; age: 63.3 ± 10.4 years; BMI: 28.8 ± 4.9 kg/m2). Thirty-five point eight% of the subjects showed a morning chronotype (MC), $47.2\%$ an intermediate chronotype (IC), and $17\%$ an evening chronotype (EC). EC subjects reported significantly higher HbA1c ($p \leq 0.001$) and FPG ($$p \leq 0.004$$) values, and higher prevalence of cardiovascular complications (CVC) ($$p \leq 0.028$$) and of subjects taking basal ($p \leq 0.001$) and rapid insulin ($$p \leq 0.01$$) compared to MC subjects. EC subjects reported significantly higher HbA1c ($p \leq 0.001$) and FPG ($$p \leq 0.015$$) than IC subjects. An inverse association was found between chronotype score, HbA1c (r = −0.459; $p \leq 0.001$), and FPG (r = −0.269; $$p \leq 0.05$$), remaining significant also after adjustment for BMI, age, and disease duration. Conclusions: EC is associated with higher prevalence of CVC and poorer glycemic control independently of BMI and disease duration in subjects with T2DM. ## 1. Introduction Chronotype represents the behavioral expression of the circadian rhythm of a subject [1]. It is possible to classify the patients in three categories: morning chronotype or “lark”, if the subject is more active during the first part of the day, evening chronotype or “owl”, if the subject is more active during the last part of the day, and intermediate chronotype, if the subject is in an intermediate position between morning chronotype and evening chronotype subjects [1]. It has been demonstrated that evening chronotype subjects have more risk of developing type 2 diabetes mellitus, metabolic syndrome, and cardiovascular diseases, compared to morning chronotype subjects [2]. Moreover, a lower chronotype score in the Morningness–Eveningness Questionnaire (MEQ), which indicates an evening chronotype subject, has been inversely associated to body mass index (BMI) values [3] and directly associated to adherence to the Mediterranean diet (MD) [4]. These results could be partially explained by the fact that morning chronotype subjects tend to be more regular concerning the eating habits [5] and more physically active [6]. Furthermore, staying awake at night is associated to a higher frequency of night eating syndrome and a higher consumption of junk food [7]. In general, evening chronotype subjects are characterized by an unhealthy lifestyle, showing increased smoking and alcohol intake rates [8]. Evening chronotype subjects more often experience a worsened sleep quality expressed in terms of a higher Pittsburgh Sleep Quality Index (PSQI) [9], which, in turn, causes an increased caloric consumption, in part because of the energy expenditure due to the extended wakefulness [10]. It has already been demonstrated that evening chronotype subjects with poor quality of sleep tend to have a higher rate of insulin resistance (evaluated by HOMA index) and an increased post prandial glycemia, rather than morning and intermediate chronotype subjects [11]. In turn, short-duration sleepers are at higher risk of developing type 2 diabetes mellitus and impaired glucose tolerance (IGT). This was highlighted by Chaput et al. in 2007, who classified 740 patients among short, normal, or long sleepers groups based on oral glucose tolerance test (OGTT) response [12]. As demonstrated by Anothaisintawee et al. in 2017 in 1014 non-shift workers with prediabetes, evening chronotype is in fact associated with higher values of HbA1c [13]. Other environmental factors need to be taken into account when considering the impact on HbA1c in patients affected by type 2 diabetes mellitus, such as light and noise exposure, which indirectly influences chronotype categories, and walkability of the neighborhood [14]. Since being an evening chronotype subject is a risk factor of developing type 2 diabetes mellitus, it is reasonable to hypothesize that chronotype could also have a role in metabolic control in subjects that are already diagnosed with type 2 diabetes mellitus. Indeed, we aim to investigate the association of chronotype categories with metabolic control, the risk of developing diabetic-related complications, and treatment in subjects with type 2 diabetes mellitus. ## 2. Materials and Methods The participants of this study were adults aged 18 years and above with diagnosis of type 2 diabetes mellitus. The data were collected using an online questionnaire on the Google Form platform, which was filled out by the diabetologists, based on the data of their patients. The cross-sectional study was performed according to the ethical standards of the institutional and national research committee and to the Declaration of Helsinki. An informed consent was collected from all the study participants. ## 2.1. On Line Questionnaire Questions from 1 to 13 included the clinical parameters: gender (male/female), age (years), height (cm), weight (kg), type 2 diabetes mellitus duration (years), fasting blood glucose (mg/dL), HbA1c (%), comorbidities (arterial hypertension, dyslipidemia, and coronary heart disease), and diabetic complications (diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy). The questions from 14 to 21 were about the diabetic treatment (metformin; sodium glucose cotransporter inhibitors, SGLT-2i; glucagon-like peptide-1 receptor agonists, GLP1-RA; pioglitazone; dipeptidyl peptidase-4 inhibitor, DDP-4i; acarbose; rapid insulin and long-acting insulin). Questions about comorbidities, complications, and diabetic treatment were answered in a dichotomous format “yes” and “no” in the questionnaire. The chronotype was assessed through the MEQ, which has 19 questions about the subject’s favorite time to carry out habitual both physical and mental activities. This is a multiple choice questionnaire and it is a 4–5-point numerical scale. The MEQ score has a range from 16 to 86, in which a higher result indicates a more MC subject and vice versa. The 22° question indicated the chronotype score of the patient. The patients were then divided into three groups: morning chronotype subjects (“lark”: 59–86 score), intermediate chronotype subjects (42–58 score), and evening chronotype subjects (“owl”: 16–41 score). ## 2.2. Clinical Parameters The anthropometric measures (weight and height) were assessed with light clothes on and no shoes, and the BMI (weight (kg) divided by height squared (m2), kg/m2) was calculated. Body weight was determined to the nearest 0.1 kg while using a calibrated balance beam scale (Seca 711; Seca, Hamburg, Germany). BMI was classified according to the World Health Organization’s criteria with normal weight: 18.5–24.9 kg/m2; overweight, 25.0–29.9 kg/ m2; grade I obesity, 30.0–34.9 kg/ m2; grade II obesity, 35.0–39.9 kg/m2; and grade III obesity, ≥40.0 kg/m2. The information about comorbidities (arterial hypertension, dyslipidemia, and coronary heart disease), diabetic complications (diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy), and the antidiabetic drugs were collected. According the guidelines of Italian Association for the study of diabetes (SID) [15], the patients were classified into three groups: first-line treatment (metformin), second-line treatment (SGLT-2i, GLP1-RA), and third-line treatment (pioglitazone; DDP-4i; acarbose; insulin). The glycemic control was assessed by evaluating the fasting blood glucose (mg/dL) and the glycated hemoglobin (HbA1c) (%). The methodology used for assessing the fasting blood glucose was the hexokinase and glucose-6-phosphate dehydrogenase (G6PDH), while, for the HbA1c, high-performance liquid chromatography (HPLC) was employed. The methodologies applied were the same in all the centers included in the study. ## 2.3. Statistics SPSS software (PASW version 21.0, SPSS Inc., Chicago, IL, USA) was employed to analyze the collected data. Results have been described as mean ± standard deviation (SD) or number (%). Differences in multiple groups (gender; BMI class; presence of arterial hypertension, dyslipidemia, coronary heart disease, diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy; patients in treatment with metformin, SGLT-2i, GLP1-RA, pioglitazone, DDP-4i, acarbose, rapid insulin, and long-acting insulin; and line of treatment) among the 3 chronotype classes were assessed by the chi-square test. Differences in the mean values (average age, BMI, type 2 diabetes mellitus duration, fasting blood glucose, and HbA1c) between the three chronotype groups were analyzed by ANOVA test followed by the Bonferroni post-hoc test. Pearson correlation was employed to evaluate the correlation between chronotype score and age, BMI, type 2 diabetes mellitus duration, fasting blood glucose, and HbA1c. Linear regression was used to investigate the association of HbA1c and fasting blood glucose, with chronotype controlling for age, BMI, and type 2 diabetes mellitus. ## 3.1. Descriptive Statistics One-hundred and six patients were enrolled (age: 63.3 ± 10.4 years; weight: 81.9 ± 16.7 kg; BMI: 28.8 ± 4.9 kg/m2). They were fairly distributed for gender: 48 females ($45.3\%$) and 58 males ($54.7\%$). A total of $20.8\%$ of them showed a normal weight, while $45.3\%$ were overweight, $21.7\%$ suffered from grade I obesity, $9.4\%$ from grade II obesity, and $2.8\%$ from grade III obesity. Concerning type 2 diabetes mellitus, the mean duration was 10.3 ± 8.2 years; the average fasting blood glucose and HbA1c values were 149 ± 49 mg/dL and 7.5 ± 1.5 %, respectively. A total of $77.4\%$ of the patients were affected by arterial hypertension, $65.1\%$ by dyslipidemia, and $23.6\%$ by coronary heart disease. Some of the patients had already developed diabetic complications. In particular, $11.3\%$ of them suffered from diabetic retinopathy, $11.3\%$ from diabetic neuropathy, and $21.7\%$ from diabetic nephropathy. The diabetic treatment in $83\%$ of the cases included metformin, while $34\%$ included GLP1-RA, $37.7\%$ included SGLT-2i, $23.6\%$ included long-acting insulin, $10.4\%$ included rapid insulin, $7.5\%$ included DDP-4i, $3.8\%$ included pioglitazone, and only $0.9\%$ included acarbose. For $15.1\%$ of the participants, a first-line treatment was performed, whereas $49.1\%$ of them were following a second-line treatment and $35.8\%$ were following a third-line treatment. The mean chronotype score was 56 ± 14 and the results showed $17\%$ of evening chronotype subjects, $47.2\%$ of intermediate chronotype subjects, and $35.8\%$ of morning chronotype subjects. ## 3.2. Comparison between Chronotype Categories Morning chronotype subjects were $44.7\%$ females and $55.3\%$ males, with a mean age of 60.8 ± 10.3 years and a mean BMI of 27.6 ± 3.8 kg/m2. Intermediate chronotype subjects were $62\%$ females and $38\%$ males, with a mean age of 65 ± 11 years and a mean BMI of 29.3 ± 5.3 kg/m2. Evening chronotype subjects were $55.6\%$ females and $44.4\%$ males, with a mean age of 62 ± 8 years and a mean BMI of 29.9 ± 5.3 kg/m2. A total of $23.7\%$ of the morning chronotype subjects had a normal weight, $50\%$ were overweight, $18.4\%$ were affected by grade I obesity, and $7.9\%$ were affected by grade II obesity. None of these subjects were affected by grade III obesity. A total of $18\%$ of the intermediate chronotype subjects had a normal weight, $46\%$ were overweight, $24\%$ were affected by grade I obesity, $6\%$ were affected by grade II obesity, and $6\%$ were affected by grade III obesity. A total of $22.2\%$ of the evening chronotype subjects had a normal weight, $33.4\%$ were overweight, $22.2\%$ were affected by grade I obesity, and $22.2\%$ were affected by grade II obesity. None of these subjects were affected by grade III obesity. No significant difference was found in terms of distribution in the BMI classes between the three groups. The differences in terms of HbA1c (morning chronotype subjects: 6.8 ± $0.9\%$; intermediate chronotype subjects: 7.5 ± $1.4\%$; evening chronotype subjects: 8.9 ± $1.9\%$) ($p \leq 0.001$) and fasting blood glucose (morning chronotype subjects: 138 ± 31 mg/dL; intermediate chronotype subjects: 145 ± 48 mg/dL; evening chronotype subjects: 183 ± 71 mg/dL) ($$p \leq 0.005$$) between the three groups were significant. Although there were not statistically significant differences in terms of age and BMI among the three groups, evening chronotype subjects showed HbA1c levels significantly higher (8.9 ± $1.9\%$) compared to morning chronotype (6.8 ± $0.9\%$) ($p \leq 0.001$) and intermediate chronotype subjects (7.5 ± $1.4\%$) ($p \leq 0.001$). Furthermore, in evening chronotype subjects, fasting blood glucose levels were significantly higher (183 ± 71 mg/dL) when compared to the values of morning chronotype (138 ± 31 mg/dL) ($$p \leq 0.004$$) and intermediate chronotype (145 ± 48 mg/dL) ($$p \leq 0.015$$) subjects. No significant differences were demonstrated comparing HbA1c and fasting blood glucose between morning chronotype and intermediate chronotype subjects. The three groups did not differ for prevalence of dyslipidemia, diabetic retinopathy, neuropathy, and nephropathy. In fact, morning chronotype subjects had a $60.5\%$ prevalence of dyslipidemia, while intermediate chronotype subjects had a $62\%$ prevalence and evening chronotype subjects had an $83.3\%$ prevalence. Diabetic retinopathy was present in $7.9\%$ of the morning chronotype subjects, $10\%$ of the intermediate chronotype subjects, and $22.2\%$ of the evening chronotype subjects. Diabetic neuropathy was present in $7.9\%$ of the morning chronotype subjects, $10\%$ of the intermediate chronotype subjects, and $22.2\%$ of the evening chronotype subjects. Diabetic nephropathy was present in $13.2\%$ of the morning chronotype subjects, $26\%$ of the intermediate chronotype subjects, and $27.8\%$ of the evening chronotype subjects. Among the three groups, there was no significant difference in terms of type 2 diabetes mellitus duration. In fact, morning chronotype subjects had a mean duration of 10.7 ± 8.3 years, intermediate chronotype subjects of 10.1 ± 8 years, and evening chronotype subjects of 10.3 ± 9.1 years. There was no significant difference even in the prevalence of first, second, or third line of antidiabetic treatment. First-line treatment was employed in $15.8\%$ of the morning chronotype subjects, $16\%$ of the intermediate chronotype subjects, and $11.1\%$ of the evening subjects. Second-line treatment was employed in $60.5\%$ of the morning chronotype subjects, $44\%$ of the intermediate chronotype subjects, and $38.9\%$ of the evening subjects. A higher prevalence of subjects in third line of treatment was found in evening chronotype subjects ($50\%$) compared to morning chronotype ($23.7\%$) and intermediate chronotype ($40\%$) subjects, although this was a trend, and it did not reach statistical significance ($$p \leq 0.319$$). In addition, there were not significant differences among the three groups in terms of the prevalence of non-insulinic antidiabetic treatment. Metformin was employed in $81.6\%$ of the morning chronotype subjects, $78\%$ of the intermediate chronotype subjects, and $100\%$ of the evening chronotype subjects. GLP1-RA were employed in $28.9\%$ of the morning chronotype subjects, $36\%$ of the intermediate chronotype subjects, and $38.9\%$ of the evening chronotype subjects. SGLT-2i were employed in $34.2\%$ of the morning chronotype subjects, $36\%$ of the intermediate chronotype subjects, and $50\%$ of the evening chronotype subjects. DDP-4i were employed in $13.2\%$ of the morning chronotype subjects, $4\%$ of the intermediate chronotype subjects, and $5.6\%$ of the evening chronotype subjects. Pioglitazone was employed in $5.3\%$ of the morning chronotype subjects, $4\%$ of the intermediate chronotype subjects, and none of the evening chronotype subjects. Acarbose was administered to $2.6\%$ of the morning chronotype subjects and to none of the evening and intermediate chronotype subjects. The three groups showed different prevalence of treatment both with basal insulin ($p \leq 0.001$) and rapid insulin ($$p \leq 0.032$$). In particular, in evening chronotype subjects, there was a significant higher prevalence of subjects taking basal insulin ($p \leq 0.001$) and rapid insulin ($$p \leq 0.010$$) compared to morning chronotype subjects. In addition, the comparison between morning chronotype and evening chronotype subjects highlighted a significantly higher rate of arterial hypertension ($$p \leq 0.031$$) and coronary heart disease ($$p \leq 0.028$$). In fact, $68.4\%$ of the morning chronotype subjects were affected by arterial hypertension, while $94.4\%$ of the evening chronotype subjects were affected by it. A total of $13.2\%$ of the morning chronotype subjects were affected by coronary heart disease and $38.9\%$ of the evening chronotype subjects were affected by it (Table 1). ## 3.3. Correlation Studies A significant inverse correlation was found between chronotype score and HbA1c values (r = −0.459; $p \leq 0.001$) (Figure 1), showing that a decrease in chronotype score (more evening chronotype subjects) was associated with an increase in HbA1c values (less controlled type 2 diabetes mellitus). An inverse correlation was also found between chronotype score and fasting blood glucose values (r = −0.269; $$p \leq 0.005$$) (Figure 2). This correlation remained significant also correcting the analysis for BMI ($$p \leq 0.054$$), age ($$p \leq 0.112$$), and type 2 diabetes mellitus duration ($$p \leq 0.052$$). HbA1c was influenced mainly by chronotype score ($p \leq 0.001$) and less, but still significantly, by BMI ($$p \leq 0.015$$) and age ($$p \leq 0.015$$), while it was not duration of type 2 diabetes mellitus ($$p \leq 0.174$$) (Table 2). Fasting blood glucose was influenced mainly by chronotype score ($$p \leq 0.001$$) and then by age ($$p \leq 0.002$$). BMI ($$p \leq 0.343$$) and type 2 diabetes mellitus ($$p \leq 0.190$$) had no weight on fasting blood glucose (Table 3). ## 4. Discussion As demonstrated by our study, chronotype could play a role in metabolic control of the type 2 diabetes mellitus, expressed in term of HbA1c and fasting blood glucose. In particular, we have demonstrated that evening chronotype subjects had a worse control of type 2 diabetes mellitus. This result was independent from BMI, age, and type 2 diabetes mellitus duration. In our study, the worse glycemic control of evening chronotype subjects is also demonstrated by the fact that these subjects were more likely in treatment with insulin, both basal and rapid, compared to the morning chronotype subjects. Indeed, in the evening chronotype subjects, there is a trend showing higher prevalence of third line treatment. Our results are in accordance with the literature, which has already reported that evening chronotype subjects had a worse control of type 2 diabetes mellitus in terms of HbA1c values and the need of insulin treatment [16]. Reutrakul et al. have attributed this finding to the fact that these subjects consumed a higher percentage of the daily calories at dinner compared to the other chronotype categories, and thus experienced a chronodisruption in nutrition. This result was also supported from the evidence that it was independent from the sleep disturbances. The difference with our study is that they did not use a validated questionnaire such as the MEQ, but they have utilized, as an indicator of chronotype, a construct derived from mid-sleep time on weekends [16]. Moreover, Hashemipour et al. demonstrated that the association between evening chronotype subjects and poorer control of type 2 diabetes mellitus is independent from other sleep variables and even from BMI [17]. In addition, von Schnurbein et al., carrying out a study in young patients with type 1 diabetes mellitus, demonstrated the same association between evening chronotype subjects and the need of insulin that we found in our cohort [18]. Although, in our study, subjects belonging to the three categories of chronotype have similar BMI, thus suggesting that they potentially have similar calorie intake, we could hypothesize that evening chronotype subjects were more prone to consume higher percentage of calories at dinner and this could result in a worsened metabolic control as previously reported [19]. In addition, one of the main hormonal determinants of chronotype categories has been identified in cortisol, and evening chronotype subjects have been shown to have a delayed cortisol peak time. Of note, the presence of the cortisol peak at a time of day when it should not be could be responsible for derangements of glucose metabolism. In fact, circadian misalignment and sleep deprivation cause delay on the cortisol rhythm and impairment on the overall exposure to cortisol during the day [20]. In particular, cortisol undergoes a complete inverse pattern across the sleep/wake cycle, peaking after awakening and showing high levels at the end of the wake episode and beginning of the sleep episode, contributing to insulin resistance and hyperglycemia [21]. The circadian disruptions, indeed, cause an inadequate pancreatic beta cell insulin secretion after a standardized meal, explaining the consequent hyperglycemia [22]. Alterations in the cortisol rhythm are associated also with cardiovascular disease [23]. In particular, Muscogiuri et al. in 2021 demonstrated that among 172 middle-aged adults, cardiovascular diseases are more frequent in evening chronotype subjects [3]. In our study, an increased risk of cardiometabolic complications, such as arterial hypertension and coronary heart disease, was highlighted when comparing the evening chronotype subjects with the morning chronotype ones, and this outcome is independent of BMI. In addition, there are other cross-sectional studies demonstrating an association between chronotype categories and risk of developing type 2 diabetes mellitus [24]. For example, X. Tan et al. in 2020 examined the data from the UK Biobank of 337,083 white British people and an association between evening chronotype subjects and augmented risk of developing type 2 diabetes mellitus was found [24]. In fact, circadian rhythm disruptions are associated with a higher prevalence of hyperphagia, obesity, metabolic syndrome, hyperlipemia, and hyperglycemia, as demonstrated in a study performed in two American and European independent populations by Aguilar-Galarza et al., [ 25] and insulin-resistance, as proven by Barrea et al. in a 2022 study which investigated 300 women affected by polycystic ovary syndrome [26]. As this is a cross-sectional study, it is not possible to identify the causality and it could be plausible that it is hyperglycemia itself that influences and disrupts the circadian rhythm, causing a desynchronization in metabolically uncompensated patients with type 2 diabetes mellitus. Further studies are needed to evaluate the physiopathological mechanisms which link circadian disruption and poorer glycemic control. In fact, as prolonged and erratic daily eating patterns are associated with a higher risk of developing type 2 diabetes mellitus [27], a targeted dietary therapy for evening chronotype subjects, such as time-restricted eating could be considered to improve the glycemic control of these patients. ## References 1. Barrea L., Muscogiuri G., Pugliese G., Modica R., Laudisio D., Aprano S., Faggiano A., Colao A., Savastano S.. Chronotype: What role in the context of gastroenteropancreatic neuroendocrine tumors?. J. Transl. Med. (2021) 19 324. DOI: 10.1186/s12967-021-03010-1 2. Barrea L., Vetrani C., Altieri B., Verde L., Savastano S., Colao A., Muscogiuri G.. The Importance of Being a ‘Lark’ in Post-Menopausal Women with Obesity: A Ploy to Prevent Type 2 Diabetes Mellitus?. Nutrients (2021) 13. DOI: 10.3390/nu13113762 3. Muscogiuri G., Barrea L., Aprano S., Framondi L., Di Matteo R., Altieri B., Laudisio D., Pugliese G., Savastano S., Colao A.. Chronotype and cardio metabolic health in obesity: Does nutrition matter?. Int. J. Food Sci. Nutr. (2021) 72 892-900. DOI: 10.1080/09637486.2021.1885017 4. Muscogiuri G., Barrea L., Aprano S., Framondi L., Di Matteo R., Laudisio D., Pugliese G., Savastano S., Colao A., On Behalf Of The Opera Prevention P.. Chronotype and Adherence to the Mediterranean Diet in Obesity: Results from the Opera Prevention Project. Nutrients (2020) 12. DOI: 10.3390/nu12051354 5. Kivela L., Papadopoulos M.R., Antypa N.. Chronotype and Psychiatric Disorders. Curr. Sleep Med. Rep. (2018) 4 94-103. DOI: 10.1007/s40675-018-0113-8 6. Maukonen M., Kanerva N., Partonen T., Kronholm E., Konttinen H., Wennman H., Mannisto S.. The associations between chronotype, a healthy diet and obesity. Chronobiol. Int. (2016) 33 972-981. DOI: 10.1080/07420528.2016.1183022 7. Rodriguez-Cortes F.J., Morales-Cane I., Rodriguez-Munoz P.M., Cappadona R., De Giorgi A., Manfredini R., Rodriguez-Borrego M.A., Fabbian F., Lopez-Soto P.J.. Individual Circadian Preference, Eating Disorders and Obesity in Children and Adolescents: A Dangerous Liaison? A Systematic Review and a Meta-Analysis. Children (2022) 9. DOI: 10.3390/children9020167 8. Fabbian F., Zucchi B., De Giorgi A., Tiseo R., Boari B., Salmi R., Cappadona R., Gianesini G., Bassi E., Signani F.. Chronotype, gender and general health. Chronobiol. Int. (2016) 33 863-882. DOI: 10.1080/07420528.2016.1176927 9. Lim S.T., Kim D.Y., Kwon H.T., Lee E.. Sleep quality and athletic performance according to chronotype. BMC Sports Sci. Med. Rehabil. (2021) 13. DOI: 10.1186/s13102-020-00228-2 10. Chaput J.P.. Sleep patterns, diet quality and energy balance. Physiol. Behav. (2014) 134 86-91. DOI: 10.1016/j.physbeh.2013.09.006 11. Rawat A., Gangwar A.K., Tiwari S., Kant S., Garg R.K., Singh P.K.. Sleep quality and insulin resistance in adolescent subjects with different circadian preference: A cross-sectional study. J. Family Med. Prim. Care (2019) 8 2502-2505. DOI: 10.4103/jfmpc.jfmpc_400_19 12. Chaput J.P., Despres J.P., Bouchard C., Tremblay A.. Association of sleep duration with type 2 diabetes and impaired glucose tolerance. Diabetologia (2007) 50 2298-2304. DOI: 10.1007/s00125-007-0786-x 13. Anothaisintawee T., Lertrattananon D., Thamakaison S., Knutson K.L., Thakkinstian A., Reutrakul S.. Later chronotype is associated with higher hemoglobin A1c in prediabetes patients. Chronobiol. Int. (2017) 34 393-402. DOI: 10.1080/07420528.2017.1279624 14. Afroz-Hossain A., Dawkins M., Myers A.K.. Sleep and Environmental Factors Affecting Glycemic Control in People with Type 2 Diabetes Mellitus. Curr. Diab. Rep. (2019) 19 40. DOI: 10.1007/s11892-019-1159-9 15. Linea Guida della Società Italiana di Diabetologia (SID) e dell’Associazione dei Medici Diabetologi (AMD). La Terapia del Diabete Mellito di Tipo 2 (2021) 16. Reutrakul S., Hood M.M., Crowley S.J., Morgan M.K., Teodori M., Knutson K.L., Van Cauter E.. Chronotype is independently associated with glycemic control in type 2 diabetes. Diabetes Care (2013) 36 2523-2529. DOI: 10.2337/dc12-2697 17. Hashemipour S., Yazdi Z., Mahabad N.. Association of Evening Chronotype with Poor Control of Type 2 Diabetes: Roles of Sleep Duration and Insomnia Level. Int. J. Endocrinol. Metab. (2020) 18 e99701. DOI: 10.5812/ijem.99701 18. von Schnurbein J., Boettcher C., Brandt S., Karges B., Dunstheimer D., Galler A., Denzer C., Denzer F., Vollbach H., Wabitsch M.. Sleep and glycemic control in adolescents with type 1 diabetes. Pediatr. Diabetes (2018) 19 143-149. DOI: 10.1111/pedi.12538 19. Jakubowicz D., Wainstein J., Ahren B., Bar-Dayan Y., Landau Z., Rabinovitz H.R., Froy O.. High-energy breakfast with low-energy dinner decreases overall daily hyperglycaemia in type 2 diabetic patients: A randomised clinical trial. Diabetologia (2015) 58 912-919. DOI: 10.1007/s00125-015-3524-9 20. O’Byrne N.A., Yuen F., Butt W.Z., Liu P.Y.. Sleep and Circadian Regulation of Cortisol: A Short Review. Curr. Opin. Endocr. Metab. Res. (2021) 18 178-186. DOI: 10.1016/j.coemr.2021.03.011 21. Scheer F.A., Hilton M.F., Mantzoros C.S., Shea S.A.. Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc. Natl. Acad. Sci. USA (2009) 106 4453-4458. DOI: 10.1073/pnas.0808180106 22. Buxton O.M., Cain S.W., O’Connor S.P., Porter J.H., Duffy J.F., Wang W., Czeisler C.A., Shea S.A.. Adverse metabolic consequences in humans of prolonged sleep restriction combined with circadian disruption. Sci. Transl. Med. (2012) 4 129ra143. DOI: 10.1126/scitranslmed.3003200 23. Oster H., Challet E., Ott V., Arvat E., de Kloet E.R., Dijk D.J., Lightman S., Vgontzas A., Van Cauter E.. The Functional and Clinical Significance of the 24-Hour Rhythm of Circulating Glucocorticoids. Endocr. Rev. (2017) 38 3-45. DOI: 10.1210/er.2015-1080 24. Tan X., Ciuculete D.M., Schioth H.B., Benedict C.. Associations between chronotype, MTNR1B genotype and risk of type 2 diabetes in UK Biobank. J. Intern. Med. (2020) 287 189-196. DOI: 10.1111/joim.12994 25. Aguilar-Galarza A., Garcia-Gasca T., Mejia C., Diaz-Munoz M., Perez-Mendoza M., Anaya-Loyola M.A., Garaulet M.. Evening chronotype associates with increased triglyceride levels in young adults in two independent populations. Clin. Nutr. (2021) 40 2373-2380. DOI: 10.1016/j.clnu.2020.10.030 26. Barrea L., Verde L., Vetrani C., Savastano S., Colao A., Muscogiuri G.. Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome?. Nutrients (2022) 14. DOI: 10.3390/nu14050955 27. Schuppelius B., Peters B., Ottawa A., Pivovarova-Ramich O.. Time Restricted Eating: A Dietary Strategy to Prevent and Treat Metabolic Disturbances. Front. Endocrinol. (2021) 12 683140. DOI: 10.3389/fendo.2021.683140
--- title: Human Milk Oligosaccharides Variation in Gestational Diabetes Mellitus Mothers authors: - Yuqi Dou - Yuanli Luo - Yan Xing - Hui Liu - Botian Chen - Liye Zhu - Defu Ma - Jing Zhu journal: Nutrients year: 2023 pmcid: PMC10059845 doi: 10.3390/nu15061441 license: CC BY 4.0 --- # Human Milk Oligosaccharides Variation in Gestational Diabetes Mellitus Mothers ## Abstract Gestational diabetes mellitus (GDM) is a common disease of pregnancy, but with very limited knowledge of its impact on human milk oligosaccharides (HMOs) in breast milk. This study aimed to explore the lactational changes in the concentration of HMOs in exclusively breastfeeding GDM mothers and the differences between GDM and healthy mothers. A total of 22 mothers (11 GDM mothers vs. 11 healthy mothers) and their offspring were enrolled in the study and the levels of 14 HMOs were measured in colostrum, transitional milk, and mature milk. Most of the HMOs showed a significant temporal trend with decreasing levels over lactation; however, there were some exceptions for 2′-Fucosyllactose (2′-FL), 3-Fucosyllactose (3-FL), Lacto-N-fucopentaose II (LNFP-II), and Lacto-N-fucopentaose III (LNFP-III). Lacto-N-neotetraose (LNnT) was significantly higher in GDM mothers in all time points and its concentrations in colostrum and transitional milk were correlated positively with the infant’s weight-for-age Z-score at six months postnatal in the GDM group. Significant group differences were also found in LNFP-II, 3′-Sialyllactose (3′-SL), and Disialyllacto-N-tetraose (DSLNT) but not in all lactational periods. The role of differently expressed HMOs in GDM needs to be further explored by follow-up studies. ## 1. Introduction Breast milk contains all the nutrients needed to support infant growth and development; therefore, it is the ideal source of nutrition for infants in the first six months of life and is recognized as the gold standard for feeding babies [1]. There have been many studies showing both short- and long-term health benefits of human milk for both the mother and the offspring [2,3,4,5]. Human milk oligosaccharides (HMOs) are the third largest solid component of human milk after lactose and lipid, which are water-soluble oligosaccharides secreted by the mother’s mammary glands during pregnancy and lactation [6]. HMOs are composed of 3–23 monosaccharide molecules including D-glucose (Glc), D-galactose (Gal), N-acetyl-glucosamine (GlcNAc), L-fucose (Fuc), and sialic acid (Sia) [7], of which N-acetylneuraminic acid (Neu5Ac) is the most predominant form of sialic acid [8]. Based on the terminal monosaccharide, HMOs can be classified into four types: [1] fucosylated sialylated type, [2] non-fucosylated non-sialylated type, [3] fucosylated non-sialylated type, and [4] non-fucosylated sialylated type. Approximately 200 oligosaccharide structures have been identified in human milk [6,9,10]. HMOs have shown the ability to reduce infection, maintain intestinal micro-ecological balance, and be involved in immune regulation [2,11,12,13,14,15]. The concentration of HMOs is influenced by a variety of factors, including genetics (e.g., secretor and Lewis genes), geographic location, gestational age, parity, lactation stage, maternal age, weight, body mass index (BMI), mode of delivery, maternal diet, race, socioeconomic status, other environmental factors (e.g., seasonality), infant gestational age, and infant sex [16,17,18,19]. Gestational diabetes mellitus (GDM) is a common disease of pregnancy in which diabetes mellitus first occurs during pregnancy. The prevalence of GDM in China is about 18–$20\%$ [20]. GDM is associated with maternal metabolism and altered intestinal flora [21]. It has been found that GDM can alter the composition of maternal blood [22,23] and placenta [24], and therefore may also affect the composition of human milk. In addition, it may also alter the activity of glycosyltransferases and glycosidases [25], thereby altering the glycopolymers in human milk, such as HMOs. HMOs are already present in the maternal circulation during pregnancy and the increase of some sialylated HMOs under GDM was found, which may be linked to the higher sialylation status caused by chronic inflammation [26,27]. In a group of overweight and obese women, serum 3′-Sialyllactose (3′-SL) levels were found to be higher in early pregnancy in women who would develop GDM in the future [27]. In addition to changes in maternal blood HMOs, Hirschmugl et al. [ 28] demonstrated that the profile of HMOs in neonatal cord blood was similar to that in maternal blood at delivery, and Hoch et al. [ 29] also found GDM-related alterations in maternal HMOs in the neonatal circulation, with higher 3′-SL in GDM cord blood. Moreover, only two studies examined the differences in the levels of HMOs in human milk between lactating GDM mothers and healthy mothers, but the findings were not consistent. In 2013, Smilowitz et al. [ 30] found no difference in mean abundance of HMOs in the transitional milk (two weeks postpartum) of women with and without gestational diabetes. Later in 2022, Wang et al. [ 31] analyzed 13 HMOs in colostrum and demonstrated that GDM colostrum had lower levels of sialylated oligosaccharides, especially 3′-SL, compared to healthy secretor mothers. The findings of HMOs in breast milk were inconsistent. Moreover, how HMOs change over lactational time in GDM mothers’ milk and the difference from healthy mothers’ milk at the same time remains unclear. Currently, there are also many studies investigating the association between the composition of HMOs in breast milk and infant growth and development indicators, but the conclusions are not yet consistent [32,33,34,35]. In this study, 14 HMOs in colostrum, transitional milk, and mature milk from parturients with and without GDM were quantified by an ion chromatography-based approach and the lactational changes of HMOs were compared between GDM and healthy mothers. Moreover, the possible factors influencing the content of HMOs and the correlation with infants’ health and growth outcomes (disease status, bowel movements, infant length, head circumference, and weight) were explored. ## 2.1. Ethical Review and Project Registration The study was approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (Ethics Committee approval number: IRB00006761-M2021062) and registered (Registration number: ChiCTR2100045411). ## 2.2.1. Study Subjects This study is part of a prospective cohort study. In the cohort study, mother–infant pairs were enrolled at Peking University Third Hospital and Beijing Haidian Maternal and Child Health Hospital from June 2021 to April 2022. GDM was screened during each participant’s routine 24- to 28-week prepartum clinical visit using a 75 g, 2 h oral glucose tolerance test (OGTT). Participants whose fasting glucose exceeded 5.1 mmol/L or 1 h OGTT >10.0 mmol/L or 2 h OGTT >8.5 mmol/L were diagnosed as GDM [36]. Additionally, these mothers do not suffer from other pregnancy disorders besides GDM, such as gestational hypertension, hypothyroidism, and polycystic ovary syndrome. We included women with GDM who received only nutritional and exercise therapy to achieve glycemic control goals, without treatment with insulin and oral hypoglycemic agents. Good glycemic control was defined according to the recommended glycemic control goal by the American Diabetes Association (ADA) [37] and the American College of Obstetricians and Gynecologists (ACOG) [38], which is fasting and pre-meal glucose levels <95 mg/dL (5.3 mmol/L) and 2 h postprandial glucose level <120 mg/dL (6.7 mmol/L). Other inclusion criteria hold the same for both GDM and healthy groups. Mothers were included when aged 18–45 years and giving birth to a healthy term single baby. Mothers were excluded if they had a history of type 1 or type 2 diabetes or a history of abnormal glucose tolerance before pregnancy. Both groups were willing to exclusively breastfeed. We conducted a survey of feeding patterns at 42 days postpartum to find out the percentage of exclusive breastfeeding. In addition to the requirement of subject enrollment, for this study, the subjects were further selected to minimize the differences of possible confounders between GDM and healthy participants, such as maternal age, pre-pregnancy BMI, gestational age, parity, mode of delivery, and gender of the infant [16,17,18]. Pre-pregnancy BMI (weight (kg)/height (m)2) was classified as follows: underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2). All mothers selected in this study had a normal weight according to pre-pregnancy BMI. Therefore, 11 GDM and 11 healthy mothers and their offspring were selected in this study. ## 2.2.2. Determination of Milk Type The presence or absence of α1,2-fucosylated and α1,4-fucosylated HMOs could be utilized to classify the breast milk samples into different milk types. By adapting the classification method of Wang et al. [ 17], the level of 2′-FL was used to determine secretor (Se) type, while LNFP-II for Lewis (Le) type, as they represented the presence of α1,2-fucosylated and α1,4-fucosylated HMOs, respectively. Lactating mothers containing α1,2-fucosylated structures were classified as secretor-positive (Se+) and others were classified as non-secretor (Se−); for lactating mothers containing α1,4-fucosylated HMOs, they were classified as Lewis-positive (Le+) and others were Lewis-negative (Le−). Therefore, lactating mothers could be classified into 4 groups, namely Se+Le+, Se+Le−, Se−Le+, and Se−Le−. ## 2.2.3. Milk Sample Collection and Storage Milk samples were collected from different stages of postpartum lactation, including within 7 days (colostrum), 12–14 days (transitional milk), and 42 days (mature milk) postpartum. Human milk sampling was standardized for all participants. In short, after cleaning the right breast which was not used to feed the infant at least 4 h before collection, whole breast milk samples were collected between 9:00 a.m. and 11:00 a.m. using a breast pump, followed by mixing and transferring into 50 mL sterilized centrifuge tubes. Samples were immediately frozen at −20 °C, transferred in a cold chain to the lab within 6 h, aliquoted on ice when received, and stored at −80 °C until thawed for analysis. ## 2.2.4. Data Collection and Definition of Variables For mothers, basic information included sociodemographic characteristics (age, occupation, and income), obstetric characteristics (pre-pregnancy weight, gestational age, and mode of delivery), and self-reported allergy history were collected. The information was obtained from records in the medical record system and questionnaires. Gestational weight gain (GWG) was calculated by the difference between the weight at the pre-delivery examination and the pre-pregnancy weight. Based on the history of delivery, the mothers were divided into primiparous and multiparous. For infants, general demographic information was recorded using only measurements provided by community health centers or hospitals, such as gender, weight, length, and head circumference. We used the World Health Organization Anthro software anthropometric calculator [39] to calculate children’s length-for-age Z-score (LAZ), weight-for-age Z-score (WAZ), and BMI-for-age Z-score (BMIZ), respectively. The World Health Organization 2006 criteria [40] were used to determine the Z-score cut-offs for growth status of children under 5 years of age: BMIZ at age >3 for obesity, >2 for overweight, <−2 for wasting, and <−3 for severe wasting; WAZ <−2 for low weight and <−3 for severe low weight; and LAZ <−2 for growth retardation and <−3 for severe growth retardation. In addition to this, infant health conditions reported by parents were also collected, including infant eczema, respiratory and digestive disorders, as well as feeding and bowel movements. ## 2.3. Quantification of HMOs The 14 HMOs standards were purchased from GLYCOSCI (ZZBIO, Shanghai, China), which were Lacto-N-tetracose (LNT) (Purity: $95\%$), lacto-N-neotetraose (LNnT) (Purity: $99\%$), 2′-Fucosyllactose (2′-FL) (Purity: $91.3\%$), 3-Fucosyllactose (3-FL) (Purity: $91\%$), Lacto-N-fucopentaose I (LNFP-I) (Purity: $95\%$), Lacto-N-fucopentaose II (LNFP-II) (Purity: $95\%$), Lacto-N-fucopentaose III (LNFP-III) (Purity: $95\%$), Lacto-N-difucosylhexaose I (LNDFH-I) (Purity: $95\%$), Sialyllacto-N-tetraose a (LST a) (Purity: $95\%$), Sialyllacto-N-tetraose b (LST b) (Purity: $95\%$), Sialyllacto-N-tetraose c (LST c) (Purity: $95\%$), Disialyllacto-N-tetraose (DSLNT) (Purity: $98\%$), 3′-SL (Purity: $98\%$), and 6′-Sialyllactose (6′-SL) (Purity: $98.7\%$). We prepared the standard reserve solution of 14 HMOs by accurately weighing each HMOs standard, dissolving them in pure water, and fixing the volume in volumetric flasks. Next, we diluted the standard reserve solution to obtain a series of standard working solutions with different concentrations. Furthermore, we thawed the human milk sample at 4 °C and took 0.2 mL of it to mix with 1.8 mL of warm water (60 °C), and then centrifuged the mixture at 8000 r/min at room temperature for 10 min. Next, we took the supernatant and filtered it through a 0.45 µm microporous membrane (Tianjin Jinteng Company, Tianjin, China) before ion chromatography analysis. The HMOs were separated by an ion chromatograph LC System (ICS-3000 ion chromatograph, Thermo Fisher, San Jose, CA, USA) equipped with a DIONEX CarboPacTM PA1 protective column (4 mm × 50 mm, 10 µm, Thermo Fisher Scientific, Waltham, MA, USA) and a DIONEX CarboPacTM PA1 analytical column (4 mm × 50 mm, 10 µm, Thermo Fisher Scientific). The column temperature was 30 °C and the flow rate was 1.0 mL/min. Mobile-phase solvents A, B, and C consisted of deionized water, 200 mM sodium hydroxide, and 150 mM sodium hydroxide + 500 mM sodium acetate, respectively. A pulse amperometric detector (Thermo Fisher Scientific, San Jose, CA, USA) was used for signal detection. The series of standard working solutions of HMOs were injected into the ion chromatograph separately. The standard curve was plotted with the concentration of the standard working solution as the horizontal coordinate and the peak area as the vertical coordinate. The milk sample was analyzed by the same parameter as the standard working solution. The peak of each HMO was determined by retention time and the peak area was measured. The concentration of HMOs in the human milk sample was calculated by the standard curve. ## 2.4. Statistical Analysis Methods Before analysis, the distribution of continuous variables was assessed by histograms, skewness and kurtosis measures, and Shapiro–Wilk tests. Means and standard deviations (normal distribution measurement data), median and interquartile range (skewed distribution measurement data), and composition ratios (enumeration data) were used to describe maternal and infant sociodemographic, anthropometric characteristics, and HMOs concentration. To assess differences between mothers with and without GDM, we used two independent samples t-test for group comparisons when the measurement data were normally distributed, and Mann–Whitney U in non-parametric tests was used when the measurement data were skewed. When the data type was categorical data, Fisher’s exact test was used to compare the difference between the two groups. Generalized estimating equation analysis (GEE) was used to study changes and differences in HMOs between the healthy and GDM groups at different stages of lactation. $p \leq 0.05$ was the level of significance. If there was no time and group interaction, the interaction term was removed, and the main effects analysis continued. When the time main effects were statistically different, one-way generalized estimating equation analysis was performed within each of the two groups. When the group main effects were statistically significant, grouped univariate analyses were performed for each time point. If there was an interaction between group and time, separate effects analysis was performed for time and group. Moreover, we used GEE to explore whether there were differences in indicators of infant growth and development in the two groups. Exploratory analyses were performed using Spearman’s rank correlation analysis to investigate the correlation between maternal characteristics (age, gestational age, pre-pregnancy BMI, gestational weight gain, mode of delivery, and allergic diseases) and infant gender and individual HMO concentrations at different time points. For these analyses, a significance level of $p \leq 0.05$ was used, with correlation levels interpreted as 0 to 0.19—very weak correlation, 0.20 to 0.39—weak correlation, 0.40~0.69—moderate correlation, 0.70~0.89—strong correlation, and 0.90~1.00—very strong correlation. A stacked bar chart plot was used to represent the relative concentrations of HMOs. Histograms were used to graphically demonstrate the variation of HMO concentrations over time in the two groups. ## 3.1. Basic Information on Maternity and Infancy of Both Groups A total of 22 mother–infant pairs (11 pairs each in the GDM and healthy groups) enrolled in the study, with mothers aged between 28 and 40 years and delivery gestational weeks between 38+2 and 41+3 weeks. In each group, 9 out of 11 ($81.8\%$) were primiparous, and 8 ($72.7\%$) had a vaginal delivery. The mothers with or without GDM showed no difference in their basic information (Supplementary Table S1). For GDM mothers, they were diagnosed as GDM at 25.26 ± 0.68 weeks, and the OGTT test results were 5.14 ± 0.28 mmol/L on fasting, 9.83 ± 1.67 mmol/L at 1 h, and 8.23 ± 1.24 mmol/L at 2 h. According to the recommended values of glycemic control goals, three women with GDM had good glycemic control and seven had poor glycemic control in this study. Moreover, one woman lacked prenatal visit data. The blood glucose level of the GDM group at the follow-up is shown in Supplementary Table S2. For infants, no difference was noticed in the two groups when they were born, with birth weight ranging from 2790 g to 4060 g, birth length ranging from 48 cm to 52 cm, and birth head circumference ranging from 32 cm to 35 cm and five males in each group (Supplementary Table S1). For infants of GDM mothers, the neonatal blood glucose at birth was 3.81 ± 1.52 mmol/L (Supplementary Table S2). ## 3.2. Milk Type Based on the level of 2′-FL and LNFP-II, the mothers involved in this study were categorized into four phenotypes, namely Se+Le+, Se+Le−, Se−Le+, and Se−Le−. The distribution of the four milk types is shown in Supplementary Table S3. In our study, most of the mothers belonged to Se+Le+ phenotype, which accounted for $68.2\%$ of 22 mothers, and the rest were Se-Le+ phenotype. None of the mothers were Se+Le− or Se−Le−. In GDM mothers, seven ($63.6\%$) were Se+Le+ and four ($36.4\%$) were Se−Le+, while in healthy mothers, the numbers were eight ($72.7\%$) and three ($27.3\%$), respectively. Since the milk type has an unignorable effect on the concentration of HMOs, the comparison of GDM with healthy mothers should be within the same milk type. As Se+Le+ is the major phenotype of subjects in this study, the following analysis mainly focused on it. The basic information of the phenotype Se+Le+ of GDM and healthy subjects showed no significant differences. Detailed information is shown in Table 1. ## 3.3. Changes of Total HMOs in GDM and Healthy Se+Le+ Mothers during Lactation The total level of HMOs was represented by the sum of 14 HMOs. In Se+Le+ mothers, the total level of HMOs in colostrum, transitional milk, and mature milk were 8.90 ± 2.87 g/L, 7.78 ± 2.69 g/L, and 5.98 ± 1.55 g/L, respectively. There was a significant downward trend in total HMOs over lactation in all groups (Figure 1A). In colostrum, the total of HMOs was significantly higher in GDM than healthy mothers ($$p \leq 0.030$$); however, no significant difference was observed for transitional milk and mature milk (Figure 1A). ## 3.4. Changes of Individual HMOs in GDM and Healthy Se+Le+ Mothers during Lactation Stacked histograms were used to represent the relative abundances of HMOs (Figure 1B). The level of 2′-FL was the most abundant HMO in colostrum ($33.75\%$ in healthy group and $24.45\%$ in GDM group), transitional milk ($33.09\%$ in healthy group and $30.62\%$ in GDM group), and mature milk ($43.54\%$ in healthy group and $38.11\%$ in GDM group). In the healthy group, 3′-SL was the lowest HMO in colostrum ($1.00\%$) and transitional milk ($1.16\%$) and LST a ($0.34\%$) was the lowest in mature milk; in the GDM group, LNFP-II was the lowest HMO in colostrum ($0.46\%$) and LST a was the lowest in transitional milk ($0.45\%$) and mature milk ($0.29\%$). Time trend analysis within each group showed that non-fucosylated non-sialylated HMOs such as LNT and LNnT decreased with milk maturation in both groups; for fucosylated non-sialylated HMOs, LNFP-I in the GDM group and LNDFH-I in the healthy group decreased significantly from colostrum to mature milk. LNFP-III in both groups and LNFP-I in the healthy group underwent a fluctuation that had the lowest value in the transitional milk. LNDFH-I in GDM group reached the peak in the transitional milk. For non-fucosylated sialylated HMOs, 6′-SL, LST a, LST b, and LST c in both groups and 3′-SL and DSLNT in the GDM group decreased significantly from colostrum to mature milk. DSLNT in the healthy group reached the peak in the transitional milk (Table 2). For the same lactational period, LNnT continued to show a higher concentration in GDM mothers’ milk than in healthy mothers over lactation ($$p \leq 0.002$$, 0.005, and 0.007 in colostrum, transitional, and mature milk, respectively). LNFP-II in colostrum ($$p \leq 0.010$$) and transitional milk ($$p \leq 0.038$$) also differed significantly between the two groups, with higher concentrations in the healthy group than in the GDM group. There were significant differences of 3′-SL in colostrum ($$p \leq 0.040$$) and mature milk ($$p \leq 0.040$$) between the two groups, with higher levels in the GDM group than in the healthy group. Between-group differences were observed in DSLNT only in colostrum ($$p \leq 0.014$$), with the GDM group being higher than the healthy group (Table 2). ## 3.5. Relations between Basic Maternal and Infant Information and HMO Concentrations The basic conditions of mother and infant were analyzed to discover other factors related to the concentration of HMOs. Spearman correlation analysis showed that maternal age was negatively correlated with LNFP-II in colostrum in the healthy and GDM groups; pre-pregnancy BMI showed a significant negative correlation with LNnT in colostrum in the healthy group and a significant positive correlation with LNFP-II in mature milk in the GDM group; and delivery mode showed a positive correlation with DSLNT in colostrum in the healthy group. No association was found between parity, GWG, maternal presence of allergic diseases, and infant gender with HMOs levels (Figure 2). ## 3.6. Infant Growth and Development in Relation to HMO Concentration Data on infant growth at 42 days, 3 months, and 6 months after birth showed that no significant group differences were observed, either in the GDM versus healthy group (Table 3). Spearman correlation analysis showed that the total concentration of HMOs in colostrum in the GDM group had a positive correlation with WAZ at 6 months. Moreover, the concentration of LNnT in colostrum and transitional milk was significantly and positively correlated with WAZ at 6 months; LNFP-II had a significant negative correlation on the length and weight for age of infants in all periods. In the healthy group, the concentration of 3′-SL in colostrum was significantly and negatively correlated with WAZ at 3 months. DSLNT in transitional milk showed a negative correlation with WAZ at 3 months (Figure 3). No differences were observed in the number of bowel movements and stool properties of the infants at 42 days after delivery in the two groups. Regarding disease status, an infant in the healthy group had disease (respiratory disease) within the first 42 days after delivery and was hospitalized; regarding feeding status, two mothers in each group fed their infants by mixed feeding at 42 days postpartum, with a ratio of breast milk to formula feeding ranging from 6:1 to 1:1. Neither disease status nor feeding status was observed to differ between the two groups (Supplementary Table S4). ## 4. Discussion Breast milk contains a variety of HMO structures, of which so far over 200 were identified. However, the majority of the total HMO concentration was only contributed by the top 10~15 structures [9]. Specifically, 2′-FL, LNDFH-I, LNFP-I, LNFP-II, LNT, 3-FL, 6′-SL, DSLNT, LNnT, Difucosyllactose (DFL), Fucosyldisialyllacto-N-hexaose I (FDS-LNH-I), LNFP-III, 3′-SL, LST c, and Trifucosyllacto-N-hexaose (TF-LNH) appear to constitute the majority of the total HMO components (>$75\%$) of mature milk [9]. A total of 12 of these 15 individual HMOs were measured in this study, as well as 2 individual HMOs of interest to us. The 14 HMOs in this study could represent somewhat the majority of the total level of HMOs. HMOs fucosylation patterns were determined by secretor and Lewis status. The proportion of different phenotypes varies by race. Many studies have assessed the quantitative variation in HMO between secretor and non-secretor individuals. Surveys in Europe, Asia, and Africa [13] showed that Se+Le+ mothers were the most predominant group (45–$77\%$ of those surveyed); the second predominant group was Se−Le+ (7–$34\%$), followed by Se+Le− (4–$28\%$) and Se−Le− (1–$26\%$). Another study reported typical distributions of Se+Le+, Se−Le+, Se+Le−, and Se−Le− in the global population as $70\%$, $20\%$, $9\%$, and $1\%$ [9]. In our study, the number of Se+Le+ phenotype ($68.2\%$) was comparable to the global average. Most of the HMOs showed a significant temporal trend with decreasing levels with lactation time; however, there were some exceptions, where 2′-FL, 3-FL, and LNFP-II remained stable during lactational period in the GDM and healthy groups, and LNFP-III peaked in mature milk in the GDM group. Studies had shown that 2′-FL was associated with stimulation of brain development, improved cognitive outcomes, and rapid increases in infant weight gain [41]. A significant increase in 3-FL after colostrum was also reported by Wang et al. [ 17], Plows et al. [ 41], Gu et al. [ 42], and Soyyilmaz et al. [ 9]. However, we did not observe this trend, and 3-FL remained constant in our study. 3-FL and 2′-FL have both been found to bind to norovirus and naturally act as a decoy against norovirus infection. Thus, adequate concentrations of 3-FL and 2′-FL may provide ongoing immune support for growing infants [41]. Moreover an animal experiment by Bhargava et al. [ 43] showed that LNFP-III could affect glucose homeostasis in a mouse model. The application of immunomodulatory HMO LNFP-III has been shown to improve glucose tolerance and insulin sensitivity by stimulating IL-10 production in macrophages and dendritic cells, reducing white adipose tissue inflammation and sensitizing adipocytes to insulin responses. This may explain the increase in LNFP-III content with lactation time in the GDM group. After controlling for most of the factors reported in the literature that may affect HMOs, we found that HMOs were significantly different in the healthy and GDM groups in terms of the content of LNnT, LNFP-II, 3′-SL, and DSLNT in the Se+Le+ group. Compared with studies of the same type, the study by Smilowitz et al. [ 30] did not find a difference in HMOs in breast milk between the GDM group ($$n = 8$$) and the healthy group ($$n = 16$$) two weeks after delivery, which may be due to the imbalance in the number of participants between the two groups and the small sample size of participants in the GDM group. Moreover, the authors did not classify the secretory phenotypes. Another research that explored the difference between GDM and healthy donors showed that the concentration of 3′-SL in colostrum of mothers in the secretor GDM group ($$n = 18$$) was significantly lower than that of mothers in the healthy group ($$n = 43$$) (GDM: 144 ± 161 mg/L versus healthy: 252 ± 181 mg/L, $p \leq 0.05$), which was the only individual HMO that differed between the two groups [31]. Moreover, not only was the 3′-SL content significantly lower in the GDM group than in the healthy group, but also the total sialylated HMOs content was lower than in the healthy group. This is different from our results, which showed higher 3′-SL and sialylated HMOs in the secretor GDM group than in the healthy group. In order to compare with this study, we screened only Se+ phenotype mothers without considering Lewis gene and reached the same conclusion as the Se+Le+ mothers. The 3′-SL and sialic HMOs in the breast milk of GDM mothers with Se+ phenotype were higher than those in the healthy group. Since the authors did not control for other disease conditions (pregnancy-induced hypertension and hypothyroidism), weight, and other factors when analyzing the relationship between GDM and HMO, this may have contributed to the difference in our study results. The increase in sialylation may occur in patients with diabetes. A recent study found increased sialylation of plasma N-glycans in patients with type 2 diabetes [44]. The authors proposed inflammatory processes as a possible underlying mechanism. Inflammation is associated with altered glycosylation caused by external sialylation of extracellular sialyltransferases [45]. Whether increased sialylation is a general response to inflammation in pregnancy and whether these changes in sialylation have an ameliorating or worsening effect remains to be elucidated. Additionally, increased sialylation may alter the interactions between carbohydrates on N-glycans and lectins, such as selectins or galactose lectins, with potentially profound effects on immune regulation and metabolism [46]. HMOs have been circulating in the bloodstream of pregnant women since at least week 10, through the amniotic fluid and possibly into the placenta via the umbilical cord [47]. 3′-SL is one of the most abundant HMOs in maternal serum, especially in early gestation, when alpha 1–2 focusing has not yet begun [26]. It has been shown that 3′-SL is closely associated with fasting blood glucose concentrations (changes) in early pregnancy, and the higher the 3′-SL concentration, the greater the increase in fasting blood glucose [27]. This may explain the higher total sialylated HMOs and 3′-SL in the GDM group compared to the healthy group. In GDM cases, if treatment allows the normalization of glucose and insulin parameters, it may similarly restore 3′-SL concentrations [46]. However, the sample size of this study was too small to investigate whether good or poor glycemic control might also affect 3′-SL. In a larger cohort of GDM patients receiving insulin or dietary advice, it would be interesting to investigate the potential impact of the respective type of treatment and its effectiveness on maternal 3′-SL. Gestational diabetes mellitus is a risk factor for the development of serious diseases such as necrotizing enterocolitis (NEC) in newborns. A large retrospective cohort study showed that maternal gestational diabetes mellitus was associated with the incidence of severe neonatal disease compared to mothers without diabetes (OR = 1.16, $95\%$ confidence interval (CI) = 1.04–1.30) [48]. Sialylated HMO is associated with the protective effect of breast milk against pathogenic infections and NEC, especially DSLNT, which is known for its prevention of NEC [46,49] and antibacterial and anti-biofilm activity against group B Streptococcus [50]. The higher DSLNT content in the colostrum of the GDM group may help to give more protection under higher risk. In addition to this, one study found a lower abundance of Bifidobacteria and Bacteriodes in infants delivered by cesarean section, with Clostridium difficile predominance [51]. Since DSLNT can reduce pathogenic infections, this may explain the higher levels of DSLNT in maternal HMOs in the healthy group of cesarean section compared to vaginal deliveries in colostrum. Comparisons between groups revealed that LNFP-II levels were significantly higher in both colostrum and transitional milk of healthy mothers than in the GDM group, and that LNFP-II levels in the GDM group showed a significant negative correlation with infant WAZ and LAZ at each period, and that this effect lasted until 6 months postpartum. Although it is difficult to give a plausible explanation, this unique finding may reveal key HMOs affecting infant growth and development in the GDM group and needs to be further validated in non-secretor lactating mothers and in larger samples. Moreover, for Se+Le+, maternal age was negatively associated with LNFP-II. This result may be related to age-induced changes in the body. However, the extent of the effect remains uncertain. This is different from the influence factors derived from other studies [17,52]. HMOs are intrinsic components that influence the intestinal microbiota by providing a source of energy for beneficial intestinal bacteria. LNnT has been shown to alter the human intestinal flora [53]. Another study reported that infants fed formula containing 2′-FL and LNnT developed a gut microbiota by an increase in the abundance of beneficial bifidobacteria and a decrease in the abundance of taxa with potentially pathogenic members [54]. The microbiota of GDM offspring showed an abundance of pro-inflammatory taxa. In a comparison of breastfed infants in the GDM and healthy groups, the offspring of GDM mothers showed increased abundance of pro-inflammatory taxa [55]. LNnT was higher in the GDM group than in the healthy group at all three time points, possibly indicating a role for LNnT in maintaining the intestinal flora of the GDM group. Comparing our study with Alderete et al. [ 32] and Larsson et al. [ 34], the similarity is that LNnT is associated with growth; however, unlike our study, Alderete et al. found a negative association with fat mass (FM)%, with each 1 μg/mL increase in LNnT being associated with a $0.03\%$ decrease in body fat (β = −0.03, $p \leq 0.01$), and Larsson et al. found that the high weight-gain (HW) group LNnT values were lower and FM% was significantly higher in the HW group compared to the healthy weight-gain (NW) group. In addition, a negative correlation between LNnT and FM index at 5 months was also found. Our study showed that LNnT in colostrum and transitional milk showed a significant positive correlation with WAZ at six months in the GDM group. The levels of HMOs in the GDM and healthy groups also showed that LNnT was significantly different between the two groups, and both were higher in the GDM group than in the healthy group. The role of LNnT in regulating intestinal flora in the offspring of GDM may make its effect on infant growth different from that of the offspring of healthy mothers. In the study conducted by Davis et al. [ 33], they observed that a higher proportion of LST c in breast milk was associated with lower WAZ of infants and that a higher proportion of 3′-SL contributed positively to WAZ and LAZ; however, our study showed that 3′-SL as well as DSLNT showed a negative association with WAZ at 3 months of age in the healthy group of infants. We also found that the total HMOs content in colostrum showed a positive correlation with the infant’s WAZ at six months postnatal. The observed correlation between HMO concentrations and infant weight/length suggests a potential role of HMO on infant growth and metabolism, which deserves future study. In addition to this, it has been shown that the concentration of HMOs is also associated with infant disease status [17]. This may be related to the effect of HMOs on the health of the developing immune system and the establishment of the gut microbiota [17]. However, our study did not find this benefit as the majority of infants from both GDM and healthy mothers remained healthy during investigation. This study has several limitations. First, the small sample size did not allow us to explore the changes of non-secretor mothers. Second, HMOs have been shown to affect intestinal flora, which have not been explored in our study. Third, the literature has shown that the content of HMOs is also affected by diet [31]. Considering the special situation of the GDM group (all the women with GDM controlled their blood sugar through diet and exercise according to the dietary recommendation), our study did not investigate the relationship between diet and HMOs. It is hoped that subsequent studies can take these into account and carry out relevant design and exploration. Despite these limitations, the current study has important advantages. To our knowledge, this is the first study to comprehensively describe and compare the differences in HMOs between healthy and GDM mothers during lactation at three different stages (colostrum, transitional milk, and mature milk). The longitudinal cohort design makes it feasible to assess prospective changes in HMO concentrations throughout the postpartum period in Se+Le+ mothers. ## 5. Conclusions This study explored changes in human milk oligosaccharides over time in GDM and healthy lactating mothers and showed that in Se+Le+ mothers, the total concentration of HMOs was higher in colostrum of GDM mothers. For individual HMOs, the concentration of LNnT, LNFP-II, 3′-SL, and DSLNT were statistically different between the two groups. Those HMOs may be key factors affecting the growth of infants and may be protective under GDM conditions, which needs further investigation. ## References 1. Sundekilde U.K., Downey E., O’Mahony J.A., O’Shea C.A., Ryan C.A., Kelly A.L., Bertram H.C.. The Effect of Gestational and Lactational Age on the Human Milk Metabolome. Nutrients (2016.0) 8. DOI: 10.3390/nu8050304 2. Donovan S.M.. The Role of Lactoferrin in Gastrointestinal and Immune Development and Function: A Preclinical Perspective. J. Pediatr. (2016.0) 173 S16-S28. DOI: 10.1016/j.jpeds.2016.02.072 3. Eidelman A.I.. Breastfeeding and the Use of Human Milk: An Analysis of the American Academy of Pediatrics 2012 Breastfeeding Policy Statement. Breastfeed Med. (2012.0) 7 323-324. DOI: 10.1089/bfm.2012.0067 4. Lonnerdal B.. Bioactive Proteins in Human Milk: Health, Nutrition, and Implications for Infant Formulas. J. Pediatr. (2016.0) 173 S4-S9. DOI: 10.1016/j.jpeds.2016.02.070 5. Wu T.C., Chen P.H.. Health consequences of nutrition in childhood and early infancy. Pediatr. Neonatol. (2009.0) 50 135-142. DOI: 10.1016/S1875-9572(09)60051-6 6. Chen X.. Human Milk Oligosaccharides (HMOS): Structure, Function, and Enzyme-Catalyzed Synthesis. Adv. Carbohydr. Chem. Biochem. (2015.0) 72 113-190. DOI: 10.1016/bs.accb.2015.08.002 7. Triantis V., Bode L., van Neerven R.J.J.. Immunological Effects of Human Milk Oligosaccharides. Front. Pediatr. (2018.0) 6 190. DOI: 10.3389/fped.2018.00190 8. Kunz C., Rudloff S., Baier W., Klein N., Strobel S.. Oligosaccharides in human milk: Structural, functional, and metabolic aspects. Annu. Rev. Nutr. (2000.0) 20 699-722. DOI: 10.1146/annurev.nutr.20.1.699 9. Soyyilmaz B., Miks M.H., Rohrig C.H., Matwiejuk M., Meszaros-Matwiejuk A., Vigsnaes L.K.. The Mean of Milk: A Review of Human Milk Oligosaccharide Concentrations throughout Lactation. Nutrients (2021.0) 13. DOI: 10.3390/nu13082737 10. Totten S.M., Wu L.D., Parker E.A., Davis J.C., Hua S., Stroble C., Ruhaak L.R., Smilowitz J.T., German J.B., Lebrilla C.B.. Rapid-throughput glycomics applied to human milk oligosaccharide profiling for large human studies. Anal. Bioanal. Chem. (2014.0) 406 7925-7935. DOI: 10.1007/s00216-014-8261-2 11. Doherty A.M., Lodge C.J., Dharmage S.C., Dai X., Bode L., Lowe A.J.. Human Milk Oligosaccharides and Associations with Immune-Mediated Disease and Infection in Childhood: A Systematic Review. Front. Pediatr. (2018.0) 6 91. DOI: 10.3389/fped.2018.00091 12. Mezoff E.A., Hawkins J.A., Ollberding N.J., Karns R., Morrow A.L., Helmrath M.A.. The human milk oligosaccharide 2′-fucosyllactose augments the adaptive response to extensive intestinal. Am. J. Physiol. Liver Physiol. (2016.0) 310 G427-G438. DOI: 10.1152/ajpgi.00305.2015 13. Bode L.. Human milk oligosaccharides: Every baby needs a sugar mama. Glycobiology (2012.0) 22 1147-1162. DOI: 10.1093/glycob/cws074 14. Chichlowski M., German J.B., Lebrilla C.B., Mills D.A.. The influence of milk oligosaccharides on microbiota of infants: Opportunities for formulas. Annu. Rev. Food Sci. Technol. (2011.0) 2 331-351. DOI: 10.1146/annurev-food-022510-133743 15. Yu Z.T., Chen C., Kling D.E., Liu B., McCoy J.M., Merighi M., Heidtman M., Newburg D.S.. The principal fucosylated oligosaccharides of human milk exhibit prebiotic properties on cultured infant microbiota. Glycobiology (2013.0) 23 169-177. DOI: 10.1093/glycob/cws138 16. Ferreira A.L., Alves R., Figueiredo A., Alves-Santos N., Freitas-Costa N., Batalha M., Yonemitsu C., Manivong N., Furst A., Bode L.. Human Milk Oligosaccharide Profile Variation Throughout Postpartum in Healthy Women in a Brazilian Cohort. Nutrients (2020.0) 12. DOI: 10.3390/nu12030790 17. Wang M.C., Zhao Z.W., Zhao A., Zhang J., Wu W., Ren Z.X., Wang P.Y., Zhang Y.M.. Neutral Human Milk Oligosaccharides Are Associated with Multiple Fixed and Modifiable Maternal and Infant Characteristics. Nutrients (2020.0) 12. DOI: 10.3390/nu12030826 18. Eussen S.R.B.M., Mank M., Kottler R., Hoffmann X.K., Behne A., Rapp E., Stahl B., Mearin M.L., Koletzko B.. Presence and Levels of Galactosyllactoses and Other Oligosaccharides in Human Milk and Their Variation during Lactation and According to Maternal Phenotype. Nutrients (2021.0) 13. DOI: 10.3390/nu13072324 19. Han S.M., Derraik J.G.B., Binia A., Sprenger N., Vickers M.H., Cutfield W.S.. Maternal and Infant Factors Influencing Human Milk Oligosaccharide Composition: Beyond Maternal Genetics. J. Nutr. (2021.0) 151 1383-1393. DOI: 10.1093/jn/nxab028 20. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care (2022.0) 45 S17-S38. DOI: 10.2337/dc22-S002 21. Hou W., Meng X., Zhao A., Zhao W., Pan J., Tang J., Huang Y., Li H., Jia W., Liu F.. Development of Multimarker Diagnostic Models from Metabolomics Analysis for Gestational Diabetes Mellitus (GDM). Mol. Cell. Proteom. (2018.0) 17 431-441. DOI: 10.1074/mcp.RA117.000121 22. Ravnsborg T., Svaneklink S., Andersen L.L.T., Larsen M.R., Jensen D.M., Overgaard M.. First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus. PLoS ONE (2019.0) 14. DOI: 10.1371/journal.pone.0214457 23. Shen L., Zhao D., Chen Y., Zhang K., Chen X., Lin J., Li C., Iqbal J., Zhao Y., Liang Y.. Comparative Proteomics Analysis of Serum Proteins in Gestational Diabetes during Early and Middle Stages of Pregnancy. Proteom. Clin. Appl. (2019.0) 13 e1800060. DOI: 10.1002/prca.201800060 24. Burlina S., Banfi C., Brioschi M., Visentin S., Dalfra M.G., Traldi P., Lapolla A.. Is the placental proteome impaired in well-controlled gestational diabetes?. J. Mass. Spectrom. (2019.0) 54 359-365. DOI: 10.1002/jms.4336 25. Lee C.L., Chiu P.C., Pang P.C., Chu I.K., Lee K.F., Koistinen R., Koistinen H., Seppala M., Morris H.R., Tissot B.. Glycosylation failure extends to glycoproteins in gestational diabetes mellitus: Evidence from reduced alpha2-6 sialylation and impaired immunomodulatory activities of pregnancy-related glycodelin-A. Diabetes (2011.0) 60 909-917. DOI: 10.2337/db10-1186 26. Jantscher-Krenn E., Aigner J., Reiter B., Kofeler H., Csapo B., Desoye G., Bode L., van Poppel M.N.M.. Evidence of human milk oligosaccharides in maternal circulation already during pregnancy: A pilot study. Am. J. Physiol. Endocrinol. Metab. (2019.0) 316 E347-E357. DOI: 10.1152/ajpendo.00320.2018 27. Jantscher-Krenn E., Treichler C., Brandl W., Schonbacher L., Kofeler H., van Poppel M.N.M.. The association of human milk oligosaccharides with glucose metabolism in overweight and obese pregnant women. Am. J. Clin. Nutr. (2019.0) 110 1335-1343. DOI: 10.1093/ajcn/nqz202 28. Hirschmugl B., Brandl W., Csapo B., van Poppel M., Kofeler H., Desoye G., Wadsack C., Jantscher-Krenn E.. Evidence of Human Milk Oligosaccharides in Cord Blood and Maternal-to-Fetal Transport across the Placenta. Nutrients (2019.0) 11. DOI: 10.3390/nu11112640 29. Hoch D., Brandl W., Strutz J., Kofeler H.C., van Poppel M.N.M., Bode L., Hiden U., Desoye G., Jantscher-Krenn E.. Human Milk Oligosaccharides in Cord Blood Are Altered in Gestational Diabetes and Stimulate Feto-Placental Angiogenesis In Vitro. Nutrients (2021.0) 13. DOI: 10.3390/nu13124257 30. Smilowitz J.T., Totten S.M., Huang J., Grapov D., Durham H.A., Lammi-Keefe C.J., Lebrilla C., German J.B.. Human milk secretory immunoglobulin a and lactoferrin N-glycans are altered in women with gestational diabetes mellitus. J. Nutr. (2013.0) 143 1906-1912. DOI: 10.3945/jn.113.180695 31. Wang X., Liu J., Li C., Xu Y., Wang X., Lu Y., Zhang T., Cao H., Huang L., Wang Z.. Pregnancy-Related Diseases and Delivery Mode can Affect the Content of Human Milk Oligosaccharides: A Preliminary Study. J. Agric. Food Chem. (2022.0) 70 5207-5217. DOI: 10.1021/acs.jafc.2c00147 32. Alderete T.L., Autran C., Brekke B.E., Knight R., Bode L., Goran M.I., Fields D.A.. Associations between human milk oligosaccharides and infant body composition in the first 6 mo of life. Am. J. Clin. Nutr. (2015.0) 102 1381-1388. DOI: 10.3945/ajcn.115.115451 33. Davis J.C., Lewis Z.T., Krishnan S., Bernstein R.M., Moore S.E., Prentice A.M., Mills D.A., Lebrilla C.B., Zivkovic A.M.. Growth and Morbidity of Gambian Infants are Influenced by Maternal Milk Oligosaccharides and Infant Gut Microbiota. Sci. Rep. (2017.0) 7 40466. DOI: 10.1038/srep40466 34. Larsson M.W., Lind M.V., Laursen R.P., Yonemitsu C., Larnkjaer A., Molgaard C., Michaelsen K.F., Bode L.. Human Milk Oligosaccharide Composition Is Associated with Excessive Weight Gain During Exclusive Breastfeeding-An Explorative Study. Front. Pediatr. (2019.0) 7 297. DOI: 10.3389/fped.2019.00297 35. Lagstrom H., Rautava S., Ollila H., Kaljonen A., Turta O., Makela J., Yonemitsu C., Gupta J., Bode L.. Associations between human milk oligosaccharides and growth in infancy and early childhood. Am. J. Clin. Nutr. (2020.0) 111 769-778. DOI: 10.1093/ajcn/nqaa010 36. Tran T.S., Hirst J.E., Do M.A., Morris J.M., Jeffery H.E.. Early prediction of gestational diabetes mellitus in Vietnam: Clinical impact of currently recommended diagnostic criteria. Diabetes Care (2013.0) 36 618-624. DOI: 10.2337/dc12-1418 37. 15. Management of Diabetes in Pregnancy: Standards of Medical Care in Diabetes-2022. Diabetes Care (2022.0) 45 S232-S243. DOI: 10.2337/dc22-S015 38. ACOG Practice Bulletin No. 190: Gestational Diabetes Mellitus. Obstet. Gynecol. (2018.0) 131 e49-e64. DOI: 10.1097/AOG.0000000000002501 39. Child Growth Standards 40. WHO Child Growth Standards: Head Circumference-For-Age, Arm Circumference-For-Age, Triceps Skinfold-For-Age and Subscapular Skinfold-For-Age: Methods and Development 41. Plows J.F., Berger P.K., Jones R.B., Alderete T.L., Yonemitsu C., Najera J.A., Khwajazada S., Bode L., Goran M.I.. Longitudinal Changes in Human Milk Oligosaccharides (HMOs) Over the Course of 24 Months of Lactation. J. Nutr. (2021.0) 151 876-882. DOI: 10.1093/jn/nxaa427 42. Gu F., Wang S., Beijers R., de Weerth C., Schols H.A.. Structure-Specific and Individual-Dependent Metabolization of Human Milk Oligosaccharides in Infants: A Longitudinal Birth Cohort Study. J. Agric. Food Chem. (2021.0) 69 6186-6199. DOI: 10.1021/acs.jafc.0c07484 43. Bhargava P., Li C., Stanya K.J., Jacobi D., Dai L., Liu S., Gangl M.R., Harn D.A., Lee C.H.. Immunomodulatory glycan LNFPIII alleviates hepatosteatosis and insulin resistance through direct and indirect control of metabolic pathways. Nat. Med. (2012.0) 18 1665-1672. DOI: 10.1038/nm.2962 44. Dotz V., Lemmers R.F.H., Reiding K.R., Hipgrave Ederveen A.L., Lieverse A.G., Mulder M.T., Sijbrands E.J.G., Wuhrer M., van Hoek M.. Plasma protein N-glycan signatures of type 2 diabetes. Biochim. Biophys. Acta Gen. Subj. (2018.0) 1862 2613-2622. DOI: 10.1016/j.bbagen.2018.08.005 45. Manhardt C.T., Punch P.R., Dougher C.W.L., Lau J.T.Y.. Extrinsic sialylation is dynamically regulated by systemic triggers in vivo. J. Biol. Chem. (2017.0) 292 13514-13520. DOI: 10.1074/jbc.C117.795138 46. Jantscher-Krenn E., Zherebtsov M., Nissan C., Goth K., Guner Y.S., Naidu N., Choudhury B., Grishin A.V., Ford H.R., Bode L.. The human milk oligosaccharide disialyllacto-N-tetraose prevents necrotising enterocolitis in neonatal rats. Gut (2012.0) 61 1417-1425. DOI: 10.1136/gutjnl-2011-301404 47. Hundshammer C., Minge O.. In Love with Shaping You-Influential Factors on the Breast Milk Content of Human Milk Oligosaccharides and Their Decisive Roles for Neonatal Development. Nutrients (2020.0) 12. DOI: 10.3390/nu12113568 48. Battarbee A.N., Venkatesh K.K., Aliaga S., Boggess K.A.. The association of pregestational and gestational diabetes with severe neonatal morbidity and mortality. J. Perinatol. (2020.0) 40 232-239. DOI: 10.1038/s41372-019-0516-5 49. Autran C.A., Kellman B.P., Kim J.H., Asztalos E., Blood A.B., Spence E.C.H., Patel A.L., Hou J., Lewis N.E., Bode L.. Human milk oligosaccharide composition predicts risk of necrotising enterocolitis in preterm infants. Gut (2018.0) 67 1064-1070. DOI: 10.1136/gutjnl-2016-312819 50. Craft K.M., Thomas H.C., Townsend S.D.. Sialylated variants of lacto-N-tetraose exhibit antimicrobial activity against Group B. Org. Biomol. Chem. (2019.0) 17 1893-1900. DOI: 10.1039/C8OB02080A 51. Penders J., Thijs C., Vink C., Stelma F.F., Snijders B., Kummeling I., van den Brandt P.A., Stobberingh E.E.. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics (2006.0) 118 511-521. DOI: 10.1542/peds.2005-2824 52. McGuire M.K., Meehan C.L., McGuire M.A., Williams J.E., Foster J., Sellen D.W., Kamau-Mbuthia E.W., Kamundia E.W., Mbugua S., Moore S.E.. What’s normal? Oligosaccharide concentrations and profiles in milk produced by healthy women vary geographically. Am. J. Clin. Nutr. (2017.0) 105 1086-1100. DOI: 10.3945/ajcn.116.139980 53. Elison E., Vigsnaes L.K., Rindom Krogsgaard L., Rasmussen J., Sorensen N., McConnell B., Hennet T., Sommer M.O., Bytzer P.. Oral supplementation of healthy adults with 2′-O-fucosyllactose and lacto-N-neotetraose is well tolerated and shifts the intestinal microbiota. Br. J. Nutr. (2016.0) 116 1356-1368. DOI: 10.1017/S0007114516003354 54. Puccio G., Alliet P., Cajozzo C., Janssens E., Corsello G., Sprenger N., Wernimont S., Egli D., Gosoniu L., Steenhout P.. Effects of Infant Formula with Human Milk Oligosaccharides on Growth and Morbidity: A Randomized Multicenter Trial. J. Pediatr. Gastroenterol. Nutr. (2017.0) 64 624-631. DOI: 10.1097/MPG.0000000000001520 55. Ponzo V., Ferrocino I., Zarovska A., Amenta M.B., Leone F., Monzeglio C., Rosato R., Pellegrini M., Gambino R., Cassader M.. The microbiota composition of the offspring of patients with gestational diabetes mellitus (GDM). PLoS ONE (2019.0) 14. DOI: 10.1371/journal.pone.0226545
--- title: Inhibition Kinetics and Theoretical Studies on Zanthoxylum chalybeum Engl. Dual Inhibitors of α-Glucosidase and α-Amylase authors: - Njogu M. Kimani - Charles O. Ochieng - Mike Don Ogutu - Kevin Otieno Yamo - Joab Otieno Onyango - Cleydson B. R. Santos journal: Journal of Xenobiotics year: 2023 pmcid: PMC10059848 doi: 10.3390/jox13010009 license: CC BY 4.0 --- # Inhibition Kinetics and Theoretical Studies on Zanthoxylum chalybeum Engl. Dual Inhibitors of α-Glucosidase and α-Amylase ## Abstract Compounds from Zanthoxylum chalybeum Engl. were previously reported for inhibitory activities of amylase and glucosidase enzymatic action on starch as a preliminary study toward the establishment of a management strategy against postprandial hyperglycemia, however, the inhibitory kinetics and molecular interaction of these compounds were never established. A study was thus designed to establish the inhibitory kinetics and in silico molecular interaction of α-glucosidase and α-amylase with Z. chalybeum metabolites based on Lineweaver–Burk/Dixon plot analyses and using Molecular Operating Environment (MOE) software, respectively. Skimmianine [5], Norchelerythrine [6], 6-Acetonyldihydrochelerythrine [7], and 6-Hydroxy-N-methyldecarine [8] alkaloids showed mixed inhibition against both α-glucosidase and α-amylase with comparable Ki to the reference acarbose ($p \leq 0.05$) on amylase but significantly higher activity than acarbose on α-glucosidase. One phenolic 2,3-Epoxy-6,7-methylenedioxyconiferol [10] showed a competitive mode of inhibition both on amylase and glucosidase which were comparable ($p \leq 0.05$) to the activity of acarbose. The other compounds analyzed and displayed varied modes of inhibition between noncompetitive and uncompetitive with moderate inhibition constants included chaylbemide A [1], chalybeate B [2] and chalybemide C [3], fagaramide [4], ailanthoidol [9], and sesame [11]. The important residues of the proteins α-glucosidase and α-amylase were found to have exceptional binding affinities and significant interactions through molecular docking studies. The binding affinities were observed in the range of −9.4 to −13.8 and −8.0 to −12.6 relative to the acarbose affinities at −17.6 and −20.5 kcal/mol on α-amylase and α-glucosidase residue, respectively. H-bonding, π-H, and ionic interactions were noted on variable amino acid residues on both enzymes. The study thus provides the basic information validating the application of extracts of Z. chalybeum in the management of postprandial hyperglycemia. Additionally, the molecular binding mechanism discovered in this study could be useful for optimizing and designing new molecular analogs as pharmacological agents against diabetes. ## 1. Introduction Diabetes mellitus (DM), a metabolic condition displayed by extraordinary amounts of plasma glucose (hyperglycemia), is closely linked to death and morbidity worldwide. It is a condition brought on by insufficient or excessive insulin secretion, insulin resistance, or both [1,2]. Predisposing factors of DM can be traced to environmental and genetic factors, namely, changes in physical activity and dietary habits, age, resistance to insulin, and diabetes in the family medical history [3]. In spite of the failure to discover an all-around therapeutic remedy, a number of management options have been discovered alongside insulin which have enhanced the management of DM [4]. Different categories of antidiabetic drugs are available which exert their action by boosting the body’s insulin production, improving the body’s sensitivity to insulin or reducing insulin resistance, and reducing intestinal glucose absorption [5]. The later therapeutic route may entail modulation of the enzyme α-amylase and α-glucosidase to delay the glucose absorption rate so as to maintain an optimal blood glucose level in DM patients. However, the major drawback of these drugs is their non-specificity in targeting different glucosidases. Practical examples include miglitol and acarbose. They are effective at decreasing glucose absorption through the inhibition of the activity of the α-glucosidases found in the small intestinal brush barrier, although they often cause diarrhea, flatulence, and abdominal bloating [6]. Metformin has been demonstrated as a better medication for DM, although is not recommended for patients with decreased renal or hepatic function [7]. The aforementioned negative side effects of these medications have prompted researchers to look for alternate treatments with fewer severe drawbacks, especially those derived from natural drug reservoirs such as the metabolites of medicinal plants. In order to alleviate ailments and ease human suffering, herbal medicines and natural products have been employed as a source of medicine for a long time. As a result, interest in phytomedicine is rising; plant extracts have the potential to be safer, are more readily available, are less expensive, and have fewer negative side effects than synthetic antihyperglycemic medications [8]. However, the scope of discovering novel natural compounds with pharmacological importance in order to control type II diabetic mellitus (T2DM) is still constrained owing to the lack of a sufficient mechanism-based comprehensive investigation of these phytopharmaceuticals. Following the application of the root bark and stem of Z. chalybeum Engl. ( Rutaceae) by traditional healers, a thorough bioassay investigation showed the extracts have an antihyperglycemic potential against streptozotocin- and alloxan-induced diabetic rats [9,10,11]. Subsequently, bioactivity-guided isolation resulted in the identification of some of the bioactive compounds including chaylbemide A [1], chalybemide B [2] and chalybemide C [3], fagaramide [4]; skimmianine [5], norchelerythrine [6], 6-acetonyldihydrochelerythrine [7] and 6-hydroxy-N-methyldecarine [8], ailanthoidol [9], 2,3-epoxy-6,7-methylenedioxyconiferol [10] and sesamine [11] with the structures shown in Figure 1. These compounds have been shown to inhibit the enzymes α-glycosidase and α-amylase with IC50 values between 43.22 and 49.36 μM at comparable levels to ($p \leq 0.05$) the positive control acarbose which has IC50 values of 42.67 and 44.88 μM against α-amylase and α-glycosidase, respectively [12]. Such results established the ability of Z. chalybuem against DM; however, the study failed to establish the possible mechanism of interaction between the enzymes and the inhibitors (compounds), thus necessitating further investigation on the mode of actions via inhibition kinetics and molecular interaction studies. The lock-and-key mechanism, where the lock encrypts the protein and the ligand is the key, is analogous to protein–ligand interaction. Hydrophobic contact appears to be the main mechanism promoting binding. By using chemoinformatic/bioinformatics tools, in silico techniques assist in finding pharmacological targets. Additionally, they can be used to discover potential active sites in target structures, create candidate compounds, dock the target with these ligands, use resulting binding affinities to order the ligands, and to enhance binding capabilities, further modify the molecules [13]. In an effort to create novel anti-diabetic drugs, in silico molecular modeling and analysis has been used to establish the potential mode of interaction of therapeutic agents with molecular receptors [14] to confirm the classical experimental bioassays. In that respect, a study to establish the mechanism of action based on inhibition constants and the in silico molecular interaction analysis of these Z. chalybeum metabolites against α-glucosidase and α-amylase was completed and results are reported herewith. ## 2.1. Isolation of Study Compounds and Their Kinetic Analyses The compounds under study were isolated from the root barks of Z. chalybeum. Briefly, the root barks were chopped into small pieces separately, air-dried at room temperature under shade for 21 days, and ground into a fine powder using an electric pulverizer. The powdered root bark (0.8 kg) was exhaustively extracted with $95\%$ aqueous methanol (4 × 1.5 L) and filtered to afford a 30 g crude sample. The crude sample was partitioned into total alkaloid extraction and nonalkaloid fraction, followed by a series of chromatographic procedures that led to the isolation of eleven pure compounds as described by Ochieng et al., 2020 [12]. Furthermore, the structures of the eleven compounds were elucidated following spectroscopic techniques as described by Ocheing et al., 2020 [12]. Mode of compound inhibition against porcine pancreas α-amylase and yeast α-glucosidase were determined at increasing substrate, pNPG, concentrations (0.25, 0.5, 1, 2, and 5 mM), both when the pure compounds 1–11 and acarbose were present at 0, 0.5, 1, 2.5, 10, and 20 mM and in their absence. Using Lineweaver–Burk plots, the mode of inhibition was established, followed by secondary plots (Dixon plots) depending on the established mode of inhibition. The following equation was used to obtain the inhibition constants (Ki) [15]: v=Vmax ×SKm(1+[I]Ki)+S(1+[I]αKi) where S and I are the concentrations of the substrate and inhibitor, respectively; Vmax is the maximum velocity; Km is the Michaelis–Menten constant; Ki is the competitive inhibition constant; and αKi is the uncompetitive inhibition constant. ## 2.2. Statistical Analysis A computer application for nonlinear regressions on the MS-Excel-2019 version was used to evaluate the kinetic data. Lineweaver–Burk plots on monoreplicate tests were performed followed by Dixon secondary plots to determine the inhibition constants. The means of the observed triplicate inhibition constants were subjected to analysis of variance with Tukey HSD/Tukey Kramer post-analysis to compare means. The least significant difference was considered at $p \leq 0.05$ and the coefficient of determination (R2) was obtained as the average of the regression curves from Dixon plots of individual experiments. ## 2.3. In Silico Method The Molecular Operating Environment (MOE) software v. 2015.10 from the Chemical Computing Group, Montreal, QC, Canada and the incorporated Merck Molecular Force Field (MMFF94x) were used for all in silico studies [16]. ## 2.3.1. Ligands Preparation Compounds 1–11 were obtained from the literature, and their 2D molecular graphs were sketched in ChemDraw Ultra Ver. 12.0 and saved as MDL files (.sdf). The.sdf file format of acarbose, the reference molecule, was retrieved from NCBI PubChem [17]. All the molecules were then imported into MOE where three-dimensional (3D) molecular models of each were generated. The MMFF94x force field was then used to optimize the generated geometries and subsequently subjected to a low-mode molecular dynamics conformational search (LowModeMD) to obtain the most favorable conformers for each ligand. An energy threshold of 5 kcal/mol above the lowest energy conformation was applied and the conformation limit was set to 10 for each ligand. The resulting conformers with the lowest force field energy were minimized using the AM1 Hamiltonian (MOPAC module of MOE), and the minimized geometries then saved into a MOE database for further action. ## 2.3.2. Drug-Likeness Predictions and Structural Skeleton Similarity Analysis The 11 ligands and acarbose were evaluated using Lipinski’s rule of five, taking the following factors into consideration: lipophilicity, molecular weight, and the amount of hydrogen bond acceptors and donors [18]. To establish the structural skeleton similarity common to these molecules, further analysis of their physicochemical parameters was completed. This was executed in DataWarrior software [19], a flexible, interactive, and chemistry-aware tool for the viewing and interpretation of chemical data. The existence of eight structural properties—electronegative atoms, carbo rings, aromatic carbon atoms, H-donor atoms, H-acceptor atoms, heterorings, rotatable bonds, and ring closures—was enumerated and examined after the compounds were subjected to DataWarrior for structure analysis. ## 2.3.3. ADME/Tox Prediction The compounds’ absorption, distribution, metabolism, and excretion (as well as toxicological; ADME/Tox) characteristics were predicted by means of the PreADMET online server [20] and SwissADME server [21]. This server computes pharmacokinetic properties such as Human Intestinal Absorption (HIA), the permeability of Caco-2 cells in vitro (PCaco-2), skin permeability (PSkin), plasma protein-binding (PPB), and permeation through the blood–brain barrier (CBrain/CBlood). The toxicity was predicted using PreADMET and pro-Tox II servers [22]. ## 2.3.4. 3D Protein Structures Preparation To predict, in silico, the potential interactions of the ligands with the enzymes α-glucosidase and α-amylase (PDB-IDS: 2QMJ and 7TAA, respectively) [23,24], 3D structures of the receptors were obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) [25]. The protein models were first prepared by removing all water molecules (MOE: SEQ) and correcting all incomplete and omitted residual amino acids that could result from the X-ray crystallographic data identified through the MOE software (Compute, Prepare, Structure Preparation, Correct). At 27 °C and pH 7.0, the models were virtually titrated to modify the ionization step of acidic and basic side chains of the amino acid. The models were then protonated accordingly in MOE software. Following that, the energy of the enzyme models was decreased in order to minimize the structure of the protein within the confines of the allocated force field. The reduction was completed slowly by setting all heavy atom sites and only permitting a steady increase within a radius of 0.5 to 1.5 Å in MOE software following the steps Compute, Energy Minimize, and Tether Atoms. This was to ensure no major variation in the protein structure as determined by the experiment. Finally, without any constraints, an energy minimization was performed, resulting in totally relaxed protein structures for further investigation. ## 2.3.5. Docking Simulation Each molecule was simulated to determine its optimal orientation within each protein model binding site (MOE: Compute, Dock). Before docking, α-glucosidase and α-amylase amino-acid residues’ binding interactions with acarbose were investigated. The protein sites for each enzyme model consisted of Asp203, Asp542, Asp327, His600, and Arg526 residues for α-glucosidase and Trp83, Asp340, Arg344, Arg204, Glu230, and Lys209 for α-amylase were selected and occupied by dummy atoms. To explore the binding modes of the 11 compounds with α-glucosidase and α-amylase proteins, 10 conformations of each ligand were docked into the respective enzyme’s chosen binding pocket using MOE-Dock module with conditions, placement: triangle matcher, rescoring: London dG, refinement: forcefield, retain: 10. RMSD (root mean square deviation) values, and docking scores of ligands’ top-ranked conformers were utilized to investigate their binding mechanisms. The determined Gibbs energy (MOE: London DG) resulting from the produced complexes of the enzymes and ligands (docking poses) were applied as the scoring parameters in this regard. Each complex’s S-score relates to its virtual free energy in kcal/mol. As a spontaneous reaction is indicated by a larger negative result, all docking poses were arranged in increasing S-score order. Before conducting molecular docking studies on the two enzymes, the docking protocol employed in this work was validated. To accomplish this, the co-crystalized ligand (acarbose) was re-docked into the binding sites of the enzymes, commonly referred to as self-dock. The docking simulation was carried out with the lowest energy conformer of the ligand docked into the active sites of the two proteins in various conformations, with the proteins treated as rigid entities (MOE: Rigid Receptor). The top ten hits from the docking simulation were used for further analysis. The postures derived from the self-dock were compared with the experimental conformation (the crystallographic pose), yielding binding modes, and orientations that are as shown in Figure 2. Additionally, the re-docked data of the co-crystallized ligand–protein interactions strongly corresponded (see Table 1) with the original interactions from the crystal structure complexes; additionally, the co-crystallized ligands’ RMSD values 1.74 Å for α-amylase and 1.6 Å for α-glucosidase which are less than 2 Å, which is recommended [26,27,28,29,30,31], demonstrate that the docking approach was adequately validated. For each enzyme structure, the lowest S-score from each self-dock was chosen and used as the reference for all subsequent docking simulations with that protein. ## 3.1. Kinetic Analyses Kinetic analysis based on both Lineweaver–Burk plots and Dixon plots revealed the modes of inhibition and the enzymes–inhibitor inhibition constants (Table 1) revealed that compounds showing mixed inhibitory modes such as Skimmianine [5], Norchelerythrine [6], 6-Acetonyldihydrochelerythrine [7], and 6-Hydroxy-N-methyldecarine [8] showed comparable Ki to acarbose ($p \leq 0.05$) on amylase while the other six compounds which showed significantly ($p \leq 0.05$) low activity compared to acarbose displayed either noncompetitive or uncompetitive modes against α-amylase actions on starch. The same compounds showing mixed inhibition on amylase showed similar modes on α-glucosidase activities, with significantly ($p \leq 0.05$) higher Ki values compared to Ki of acarbose indicating a better inhibitory potential towards α-glucosidase than amylase. Conversely, the other compounds displaying non-competitive and uncompetitive inhibitions against α-glucosidase activities showed comparable Ki values ($p \leq 0.05$) relative to acarbose. One compound 2,3-Epoxy-6,7-methylenedioxyconiferol [10] showed a competitive mode of inhibition both on α-amylase and α-glucosidase with Ki of 5.54 ± 0.58 (R2 = 0.985) and 17.21 ± 0.15 (R2 = 0.8692), respectively, which were statistically comparable ($p \leq 0.05$) to that of acarbose thus indicative of the most active inhibitors from the Z. chalybeum extracts. Compound 10 and the alkaloids (5, 6, 7, and 8) displaying competitive and mixed inhibitions, respectively, were thus invariably noted as the most potent inhibitors of both amylase and glucosidase. On the other hand, the remaining compounds 1, 2, 3, 4, 9, and 11 showed varied modes of inhibition and associated dissociation constants towards the two enzymes which would be categorized as moderate inhibitory activities. Such preliminary kinetic results would thus be better confirmed with molecular interaction studies based on in silico experiments. ## 3.2. Drug-Likeness Predictions and Structural Skeleton Similarity Analysis Lipinski’s rule of five and the Veber rules are closely linked to drug-likeness properties. Molecules with characteristics that match these rules could be deemed promising therapeutic candidates with high oral bioavailability. If a drug molecule fails to fulfill more than one of the five rules, it will have poor oral absorption. Lipinski’s rule of five states that a compound is orally bioactive when it has a molecular weight (MW) of 500 or less, a cLogP (partition coefficient between n-octanol and water) of 5 or less, a number of hydrogen bond donors (HBD) of no more than 5, a number of hydrogen bond acceptors (HBA) equal to or less than 10, and a number of rotatable bonds (RB) of 10 or less [32,33]. Unlike Lipinski’s rule of five, the Veber rules only specify two requirements for drug candidates to have excellent oral bioavailability. These requirements are that there are no more than ten rotatable bonds and that the polar surface area is at most 140 Å. Drug likeness is a high degree of control of several molecular and structural characteristics that determine whether a given ligand is similar to approved drugs. These descriptors (molecule size, hydrogen bonding properties, hydrophobicity, flexibility, and electronic distribution, among other pharmacophore features) determine ligand conduct in a living organism such as bio-transportation, bioavailability, proteins’ affinity, metabolism, reactivity, and toxicity [34]. This screening procedure was executed to assess the drug-likeness of the molecules using DataWarrior to evaluate the physicochemical characteristics and subsequently comparing them to those of acarbose using Lipinski’s rule of five. Additionally, these molecules were evaluated as to whether they were mutagenic, tumorigenic, irritant, or whether they had reproductive effects. As shown in Table 2, some of the compounds are predicted to have no toxic properties. Compounds 7 and 8 are shown to be mutagenic and tumorigenic. Compounds 1, 4, and 9 are predicted to have a reproductive effect with compound 9 further being shown to be an irritant. All the other compounds are predicted as being non-mutagenic, non-irritant, non-tumorigenic, and have no reproductive effects. The acceptable TPSA values range from 0 to 140 Å as molecules with a greater value tend to be poor at permeating cell membranes [33]. The compounds 1–11 obeyed this rule. However, acarbose had a TPSA value > 140 Å. Log P, MW, and TPSA values indicate that the compounds have good membrane permeability and oral bioavailability. Indeed, hydrophobicity, membrane permeability, and drug molecule bioavailability are all affected by these variables, in addition to HBA and HBD. Acceptable RB values also reflect good compound intestinal permeation and oral absorption. TPSA is also useful in determining drug transport and biodistribution behavior [35]. The hydrophobicity of a molecule is directly proportional to log P. The LogP values of between −2 and 6.5 indicate that the molecule is sufficiently hydrophobic and will therefore permeate through cellular membranes as there is an appropriate balance of permeability and solubility [36,37]. The LogP values (see Table 2) indicate that these molecules are hydrophobic and will therefore have a higher affinity for the organic phase over water. The compounds have optimum logP values within the range (0.9–4.3). To determine the structural skeleton similarity, the compounds were subjected to DataWarrior software analysis, which was used to enumerate and classify eight structural skeleton variables and look for similarities (Table 2). We evaluated the count of intramolecular rotatable bonds to determine the compounds’ flexibility and discovered that only compound 7 had one rotatable bond while all others were composed of two–six rotatable bonds and acarbose with the highest number [9]. Small molecules’ electronegative atoms (N, O, S, F, Cl) play a very important role in the formation of hydrogen bonds with protein amino acid residues; seven of these atoms were counted in compound 7, while the other compounds had two–six electronegative atoms. The reference drug acarbose had 19 electronegative atoms. We calculated the number of H-bond acceptors and donors to establish the type of electronegative atoms. Six of the molecules presented 1–2 H-donor atoms. In contrast, H-acceptor atoms were found in all the compounds, which contained two–seven H-acceptor atoms. The ring closures, most of which consist of carbon atoms engaged in electrostatic and hydrophobic interactions, were tallied, and all compounds had one to six rings, including acarbose with four. We considered both carbo- and heteroring closures to establish the form of the ring closures. Additionally, we discovered all compounds contained one–three3 carbo-rings. Unlike carbo-rings, 1–4 heterorings were found in 10 compounds, including acarbose. Lastly, we calculated the number of aromatic carbon atoms, and all but acarbose had 6–18 aromatic atoms. We discovered no inverse or direct correlation between the factors we studied and the compounds’ binding affinities after characterizing and analyzing them. ## 3.3. ADME/Tox Prediction The goal of the in silico absorption, distribution, metabolism, and excretion (ADME) profiling is to reduce high, late drug attrition during drug development and optimizing testing by focusing only on the most promising candidates. The predicted values of ADME for compounds 1–11 and the reference, acarbose, are presented in Table 3. ADME/Tox properties are related to pharmacokinetic (absorption, distribution, metabolism, excretion) and pharmacodynamic (drug efficacy and toxicity) characteristics of drugs. The in silico prediction of these properties is important, particularly in the optimization of drug leads or new drug molecules. ADME properties affect drug membrane permeation, oral bioavailability, and drug metabolism [35]. In this study, the eleven compounds were predicted to have high gastrointestinal absorption with values ranging from 93.66 to $97.93\%$. However, the reference acarbose has very poor intestinal absorption. The data on human intestinal absorption are the total amount of drug absorbed and bioavailable as a percentage of aggregate excretion in feces, bile, and urine [38]. It is worth noting that, because acarbose is designed to work in the gut, a low level of oral bioavailability does seem to be therapeutically preferable. These compounds including the reference were predicted to have no permeation across the blood–brain barrier (BBB). The BBB penetration is calculated as the proportion of drug concentration levels in the brain and blood [39]. The predicted plasma protein binding (PPB) values indicated that the molecules are variedly bound to the protein plasma with values ranging from 40.15–$90.55\%$. However, acarbose is bound to a low extent to the plasma protein. Only the unbound drug is typically available for permeation across cell membranes in order to reach the pharmacological target and elicit the desired activity [40]. Therefore, this property can not be emphasized enough. The Caco-2 (human colon carcinoma cell line) penetrability for the estimation of orally administered drug absorption of compounds 1–11 ranged from 24.39 to $57.03\%$ with the reference, acarbose, showing no permeation through the Caco-2 cell membrane. This implied that these compounds can be administered orally, with considerable permeation across cell membranes. The transdermal efficacy of these molecules as demonstrated by their skin permeability including the reference indicates that they cannot penetrate through the skin except for compound 2. It is reported that drugs with logKp values higher than −2.5 cm/h will not penetrate through the skin with ease [41]. The BOILED-Egg plot between WLOGP and TPSA to predict gastrointestinal absorption and brain penetration of the selected molecules is shown in Figure 3. It can be seen from the plot that the molecules are predicted to possess BBB permeant properties and considerable GI absorption. The metabolism of a drug is another important pharmacokinetic property that should be evaluated during drug development. Cytochrome P450, a family of isozymes, is one of the enzymes taking part in the liver metabolism and biotransformation of drugs. The metabolism of drugs by the cytochrome P450 system is a vital factor in drug interactions that can result in toxicities and a decrease in pharmacological activity. Therefore, determining if the drug is a substrate, inducer, or inhibitor of cytochrome P450 is important. There are various cytochrome P450 isozymes such as CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP2E1, and CYP3A4 that are involved in drug metabolism [42]. According to the ADME prediction using SwissADME, ligands 1, 2, and 4 are non-inhibitors for CYP2C19, CYP2C9, CYP2D6, and CYP3A4. Compounds 5–11 are however inhibitors of CYP2C9 and CYP3A4. Compounds 5, 6, 9–11 are also inhibitors of CYP2C19. The reference acarbose inhibits CYP3A4 and CYP2D6. The ADME properties of these compounds show satisfactory drug qualities. To ensure that the compounds do not harm human cells and organs, in silico toxicity predictions were executed. This prediction is essential in the early stages of drug discovery because many drug candidates fail in clinical trials due to toxicity. The prediction of toxicological properties of the ligands was performed using the ProTox-II webserver (see Table 3). The ProTox-II predicts the oral toxicity of compounds based on 2D molecular graph similarities with 33,000 compounds and their associated LD50 values. Other properties predicted include toxicological endpoints (immunotoxicity, carcinogenicity, and cytotoxicity) and organ toxicity (hepatotoxicity) [43]. Compounds 1–11 were predicted to have LD50 values of 1000, 2031, 1990, 760, 600, 1000, 2000, 2000, 3919, 720, and 1500 mg/kg, respectively. Acarbose was predicted to have an LD50 value of 24,000 mg/kg. The ADMET properties indicate that these molecules pass the adsorption, distribution, metabolism, excretion, and toxicity parameters having shown acceptable bioavailability scores and are orally safe, properties that mostly determine the success of a drug lead [44]. ## 3.4. Molecular Docking The target of the isolation of any natural product is to discover biologically potent therapeutic molecules. The isolation of secondary metabolites and screening of their biological activity, on the other hand, is a tedious, lengthy, and expensive endeavor that can be eased by the use of in silico methods. Molecular docking, a well-documented and powerful in silico approach, can help sieve out inactive compounds. It estimates the modes of interaction between optimized conformations of various compounds and a protein structure. It also helps to predict the probable mode of action of observed biological activity. Given the possibility of this viewpoint, we elaborate on comprehensive molecular docking analyses of isolated compounds from Zanthoxylum chalybeum Engl. which have been demonstrated to have varied inhibitions against α-glucosidase and α-amylase. We investigate the binding modes between both the targeted proteins and the ligands using the MOE software. The optimized structures (1–11) of the active compounds from Zanthoxylum chalybeum Engl. were docked into the active site of the α-glucosidase protein (N-terminal glucoamylase PDB ID: 2QMJ). These compounds were discovered to have good docking scores (−8 to −13 kcal/mol) and binding interactions with the amino residues Asp203, Asp327, Asp542, Arg526, and His600 (Table 4, Figure 4, Figure 5, Figure 6 and Figure 7, and Figures S25–S28 (Supplementary Materials)). Human α-glucosidase has a structure similar to that of human glycoside hydrolase family GH311 homologues, maltase glucoamylase, and sucrase-isomaltase. A trefoil at the N-terminus, the Type-P domain is linked to the β-sheet domain. The catalytic (β/α)8 barrel then follows with its two inserts β3 (insert I) and β4 (insert II). This is then proceeded by proximal and distal β-sheet domains at the C-terminus. The narrow substrate-binding pocket is formed by a loop from the N-terminal-sheet domain and inserts I and II and is located near the C-terminal ends of the catalytic (β/α)8 domain’s β-strands. Asp518 function as the catalytic nucleophile while Asp616 is the acid/base catalyst [45]. Compounds 1 and 3 which showed noncompetitive inhibition on both enzymes, and compound 2 with uncompetitive inhibition and noncompetitive inhibition on α-glucosidase and α-amylase respectively, demonstrated binding affinities of between −13.8 and −9.3 (kcal/mol) against α-amylase and α-glucosidase. Compound 1 formed conventional hydrogen interactions with α-amylase interface residues ASP 297 and ARG 344 (see Table 4 and Figure 5 and Figure 7). In addition, it formed hydrogen bond interactions with ASP 542 residues in the case of α-glucosidase. On the other hand, compound 2 formed hydrogen bond interactions with ASP 340 and ARG 344 in addition to the ionic bond with ASP 340 with α-amylase. On analysis of this compound’s interactions with α-glucosidase, it was found that it forms hydrogen bond interactions with MET 444 and ASP 542 residues in addition to ionic interactions with ASP 443 and ASP 542 residues. Conversely, compound 3 formed hydrophobic interactions of the types H-π and π-π, with HIS 296 and TYR 82 residues of the α-amylase, respectively. Analysis of compound 3 in complex with α-glucosidase revealed the presence of a hydrogen bond interaction with ARG 526 residue. Compound 4, which had noncompetitive inhibition on both α-amylase and α-glucosidase but with Ki values significantly higher than those of the reference, formed a hydrogen bond-type interaction with the ASP 340 residue of the α-amylase and a binding affinity of −9.4 kcal/mol. Docking of this compound on the binding site of α-glucosidase, indicated a binding affinity of −9.0 kcal/mol and analysis of the complex interactions revealed the presence of hydrogen bond-type interactions with MET 444 and ASP 542 residues in addition to H-π-type hydrophobic interactions with the PHE 575 residue. Compounds 5–8 were established to have mixed inhibitions on both enzymes and from the docking simulations registered binding affinities of between −9.5 and −13.8 kcal/mol. Compound 5 formed a hydrophobic interaction of the π-H-type with the TRP 83 residue of the α-amylase. It also showed a hydrogen bond-type as well as hydrophobic interactions of the type π–H with ASP 542 and TRP 406 residues of the α-glucosidase. Compound 6 formed conventional hydrophobic interactions of the π-cation type with the α-amylase interface residue ARG 344. Compound 7 formed conventional hydrogen interactions and hydrophobic π-H-type interactions with α-amylase interface residues HIS 210 and LEU 232, respectively. In complex with α-glucosidase, it formed hydrogen bond interactions with ASP 443 and ASP 542 residues. Docking compound 8 onto the binding pocket of α-amylase and α-glucosidase showed hydrophobic interactions of the H-π-type with α-amylase interface residues HIS 296 and TYR 82 observed. With THR 204 residues of α-glucosidase, it formed π-H hydrophobic interactions. Compounds 9 and 11 showed uncompetitive inhibition while compound 10 had competitive inhibition on both proteins. Compound 9 demonstrated binding affinities of −11.4 and −10.3 kcal/mol against α-amylase and α-glucosidase, respectively. It displayed conventional hydrogen interactions and hydrophobic π-H-type interactions with α-amylase interface residues ASP, 340, GLN 35, and TYR 79, and HIS 296, respectively. On analysis of its interaction with α-glucosidase, π-H hydrophobic interactions with the ASP 327 residue was observed. With compound 10, binding scores of −10.1 and −10.0 kcal/mol with α-amylase and α-glucosidase were observed, respectively. The α-amylase ASP 340 and GLN 35 residues interacted with compound 10 forming hydrogen bonds. Hydrogen bond interaction was also observed in the case of α-glucosidase with the enzyme ASP 327 residue. Compound 11 formed hydrogen bond-type interactions with ARG 204 and TRP 83 residues of the α-amylase binding pocket. These resulted in a binding score of −12.3 kcal/mol. The molecule formed hydrogen and a π-H type hydrophobic bonds with ARG 526 and PHE 575 residues, respectively, of the α-glucosidase. The binding affinity, in this case, was −11.0 kcal/mol. Acarbose was used as the control, showing a binding score of −17.6 kcal/mol with α-amylase, and formed ordinally hydrogen bonds with residues ASP 206, GLU 230, ASP 340, ASP 168, ARG 204, and TRP 83. In addition, it formed attractive electrostatic forces with residue ASP 206. On the other hand, with α-glucosidase, a binding affinity of −20.5 kcal/mol was recorded. With ASP 542, ASP 327, ASP 203, MET 444, ASP 474, HαIS 600, and ARG 526 residues of the binding pocket it formed hydrogen bond interactions, in addition to an ionic bond with ASP 542. Apparently, the greater number of H-bond interactions at the binding pocket in acarbose was a result of the higher number of hydroxyl functional groups which formed hydrogen bonds with the active sites’ amino acid residues [46]. In previously reported in silico studies, a number of 1,2-benzothiazine and xanthone derivatives, 8-c-ascorbyl[-]-epigallocatechin, and Voglibose also showed α-glucosidase inhibition through interaction with Asp203, Asp542, and Arg526 pocket residues of the receptor protein [47,48,49,50]. Additionally, reported molecular docking studies of 1, 2-benzothiazine derivative against α-amylase indicated excellent binding affinities with the residues TRP83, ASP340, ARG 204, and GLU 230 [51]. In addition to the control molecules, the eleven ligands formed several other bonds with main amino acid residues as can be seen in Figures S25–S28 which may interrupt the normal physiological functions of these two enzymes. The natural products studied through the molecular docking simulations in this work revealed significant binding energies with the pocket residues for the two proteins. These compounds established networks of different types of interactions (although fewer than the reference) that played a part in the binding affinity of the calculated complexes and therefore affirmed their dual inhibitory activity. We demonstrate that compounds 1–11 are dual inhibitors, effective to both proteins with comparable potency, and the docking studies results show that the compounds bind to the active sites of the receptors with a good binding energy of interactions and RMSD values. Importantly, all the studied compounds had formerly been reported to inhibit α-glucosidase and α-amylase, with IC50 values of between 43.22 and 58.21 µM [12]. The dual inhibitors involved in this study, as disruptors of a critical carbohydrate metabolic process, provide a potential starting point for structural optimization in search of more efficacious and highly potent carbohydrate metabolism inhibitors. The newly acquired information on compounds 1–11 being some of the few compounds known to have a dual inhibitory activity against α-glucosidase and α-amylase could be helpful in the future to look for inhibitors of such enzyme systems. Furthermore, the current results add an important component to the knowledge of these natural products’ mechanism(s) of action in the management of diabetes. Lastly, it is important to mention that a virtual screening of natural product libraries to yield an assortment of more hits is at the moment being investigated and will be the subject of future communication. ## 4. Conclusions In conclusion, this study aimed to find a potential duo inhibitor for α-amylase and α-glucosidase from the phytochemicals of the medicinal plant Zanthoxylum chalybeum Engl., examined their mode of interaction with protein residues of the binding pocket, and profiled their ADMET and Toxicity properties in silico. In comparison to the standard control (acarbose), the compounds exhibited remarkable inhibition constants (Ki), binding affinities and strong interactions with crucial pocket amino acid residues of the α-amylase and α-glucosidase proteins. The results of this study indicate that these molecules have the potential to be antidiabetic drugs by inhibiting α-amylase and α-glucosidase which are responsible for the metabolism of carbohydrates into absorbable simple sugars. Through inhibition of these enzymes, the absorption of dietary sugars and the succeeding postprandial upsurge in blood glucose and insulin levels is limited. However, more experimental studies are required to confirm the antidiabetic activity of these compounds in vivo. ## References 1. Marchetti P., Lupi R., del Guerra S., Bugliani M., D’Aleo V., Occhipinti M., Boggi U., Marselli L., Masini M.. Goals of Treatment for Type 2 Diabetes: Beta-Cell Preservation for Glycemic Control. Diabetes Care (2009.0) 32 S178-S183. DOI: 10.2337/dc09-S306 2. Wild S., Roglic G., Green A., Sicree R., King H.. Global Prevalence of Diabetes: Estimates for the Year 2000 and Projections for 2030. Diabetes Care (2004.0) 27 1047-1053. DOI: 10.2337/diacare.27.5.1047 3. Mohamed S.F., Mwangi M., Mutua M.K., Kibachio J., Hussein A., Ndegwa Z., Owondo S., Asiki G., Kyobutungi C.. Prevalence and Factors Associated with Pre-Diabetes and Diabetes Mellitus in Kenya: Results from a National Survey. BMC Public Health (2018.0) 18. DOI: 10.1186/s12889-018-6053-x 4. Vecchio I., Tornali C., Bragazzi N.L., Martini M.. The Discovery of Insulin: An Important Milestone in the History of Medicine. Front. Endocrinol. (2018.0) 9 613. DOI: 10.3389/fendo.2018.00613 5. Chaudhury A., Duvoor C., Dendi V.S.R., Kraleti S., Chada A., Ravilla R., Marco A., Shekhawat N.S., Montales M.T., Kuriakose K.. Clinical Review of Antidiabetic Drugs: Implications for Type 2 Diabetes Mellitus Management. Front. Endocrinol. (2017.0) 8 6. DOI: 10.3389/fendo.2017.00006 6. Derosa G., Maffioli P.. α-Glucosidase Inhibitors and Their Use in Clinical Practice. Arch. Med. Sci. (2012.0) 8 899-906. DOI: 10.5114/aoms.2012.31621 7. Inzucchi S.E., Lipska K.J., Mayo H., Bailey C.J., McGuire D.K.. Metformin in Patients with Type 2 Diabetes and Kidney Disease: A Systematic Review. JAMA (2014.0) 312 2668-2675. DOI: 10.1001/jama.2014.15298 8. Swilam N., Nawwar M.A.M., Radwan R.A., Mostafa E.S.. Antidiabetic Activity and In Silico Molecular Docking of Polyphenols from. Plants (2022.0) 11. DOI: 10.3390/plants11030452 9. Olila D., Opuda-Asibo J.. Antibacterial and Antifungal Activities of Extracts of Zanthoxylum Chalybeum and Warburgia Ugandensis, Ugandan Medicinal Plants. Afr. Health Sci. (2001.0) 1 66. PMID: 12789119 10. Agwaya M.S., Vuzi P.C., Nandutu A.M.. Hypoglycemic Activity of Aqueous Root Bark Extract Zanthoxylum Chalybeum in Alloxan-Induced Diabetic Rats. J. Diabetes Res. (2016.0) 2016 8727590. DOI: 10.1155/2016/8727590 11. Agwaya M., Nandutu A., Vuzi P.. Protective Effects of Zanthoxylum Chalybeum in Diabetes-Induced Myocardial Dysfunction in Rats. European J. Med. Plants (2016.0) 12 1-10. DOI: 10.9734/EJMP/2016/22137 12. Ochieng C.O., Nyongesa D.W., Yamo K.O., Onyango J.O., Langat M.K., Manguro L.A.O.. α-Amylase and α-Glucosidase Inhibitors from. Fitoterapia (2020.0) 146 104719. DOI: 10.1016/j.fitote.2020.104719 13. Bharathi A., Roopan S.M., Vasavi C.S., Munusami P., Gayathri G.A., Gayathri M.. Antidiabetic Studies of Dihydropyrimido [4,5-a]Acridin-2-Amines. BioMed Res. Int. (2014.0) 2014 971569. DOI: 10.1155/2014/971569 14. Bharatam P., Patel D., Adane L., Mittal A., Sundriyal S.. Modeling and Informatics in Designing Anti-Diabetic Agents. Curr. Pharm. Des. (2007.0) 13 3518-3530. DOI: 10.2174/138161207782794239 15. Segel I.H.. Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems (1993.0) 16. Halgren T.A.. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem. (1996.0) 17 490-519. DOI: 10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P 17. PubChem 18. Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J.. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug. Deliv. Rev. (2001.0) 46 3-26. DOI: 10.1016/S0169-409X(00)00129-0 19. www.Openmolecules.Org 20. PreADMET\|Prediction of ADME/Tox—Just Another BMDRC Sites Site 21. SwissADME 22. ProTox-II—Prediction of TOXicity of Chemicals 23. Sim L., Quezada-Calvillo R., Sterchi E.E., Nichols B.L., Rose D.R.. Human Intestinal Maltase-Glucoamylase: Crystal Structure of the N-Terminal Catalytic Subunit and Basis of Inhibition and Substrate Specificity. J. Mol. Biol. (2008.0) 375 782-792. DOI: 10.1016/j.jmb.2007.10.069 24. Brzozowski A.M., Davies G.J.. Structure of the. Biochemistry (1997.0) 36 10837-10845. DOI: 10.1021/bi970539i 25. RCSB PDB: Homepage 26. Cruz J.V., Giuliatti S., Alves L.B., Silva R.C., Ferreira E.F.B., Kimani N.M., Silva C.H.T.P., de Souza J.S.N., Espejo-Román J.M., Santos C.B.R.. Identification of Novel Potential Cyclooxygenase-2 Inhibitors Using Ligand- and Structure-Based Virtual Screening Approaches. J. Biomol. Struct. Dyn. (2022.0) 40 5386-5408. DOI: 10.1080/07391102.2020.1871413 27. Silva R.C., Freitas H.F., Campos J.M., Kimani N.M., Silva C.H.T.P., Borges R.S., Pita S.S.R., Santos C.B.R.. Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3clpro. Int. J. Mol. Sci. (2021.0) 22. DOI: 10.3390/ijms222111739 28. Silva L.B., Ferreira E.F.B., Espejo-Román J.M., Costa G.V., Cruz J.V., Kimani N.M., Costa J.S., Bittencourt J.A.H.M., Cruz J.N., Campos J.M.. Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach. Molecules (2023.0) 28. DOI: 10.3390/molecules28031035 29. Rossino G., Rui M., Pozzetti L., Schepmann D., Wünsch B., Zampieri D., Pellavio G., Laforenza U., Rinaldi S., Colombo G.. Setup and Validation of a Reliable Docking Protocol for the Development of Neuroprotective Agents by Targeting the Sigma-1 Receptor (S1R). Int. J. Mol. Sci. (2020.0) 21. DOI: 10.3390/ijms21207708 30. Al-Khodairy F.M., Khan M.K.A., Kunhi M., Pulicat M.S., Akhtar S., Arif J.M.. In Silico Prediction of Mechanism of Erysolin-Induced Apoptosis in Human Breast Cancer Cell Lines. Am. J. Bioinform. Res. (2013.0) 3 62-71. DOI: 10.5923/J.BIOINFORMATICS.20130303.03 31. Hevener K.E., Zhao W., Ball D.M., Babaoglu K., Qi J., White S.W., Lee R.E.. Validation of Molecular Docking Programs for Virtual Screening against Dihydropteroate Synthase. J. Chem. Inf. Model (2009.0) 49 444-460. DOI: 10.1021/ci800293n 32. Lipinski C.A.. Lead- and Drug-like Compounds: The Rule-of-Five Revolution. Drug Discov. Today Technol. (2004.0) 1 337-341. DOI: 10.1016/j.ddtec.2004.11.007 33. Protti Í.F., Rodrigues D.R., Fonseca S.K., Alves R.J., de Oliveira R.B., Maltarollo V.G.. Do Drug-Likeness Rules Apply to Oral Prodrugs?. ChemMedChem (2021.0) 16 1446-1456. DOI: 10.1002/cmdc.202000805 34. Ertl P., Rohde B., Selzer P.. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J. Med. Chem. (2000.0) 43 3714-3717. DOI: 10.1021/jm000942e 35. Kalita J., Chetia D., Rudrapal M.. Molecular Docking, Drug-Likeness Studies and ADMET Prediction of Quinoline Imines for Antimalarial Activity. J. Med. Chem. Drug. Des. (2019.0) 2 208-218. DOI: 10.16966/2578-9589.113 36. Hansch C., Fujita T.. J. Am. Chem. Soc. (1964.0) 86 1616-1626. DOI: 10.1021/ja01062a035 37. Box K., Comer J.. Using Measured PKa, LogP and Solubility to Investigate Supersaturation and Predict BCS Class. Curr. Drug Metab. (2008.0) 9 869-878. DOI: 10.2174/138920008786485155 38. Zhao Y.H., Le J., Abraham M.H., Hersey A., Eddershaw P.J., Luscombe C.N., Boutina D., Beck G., Sherborne B., Cooper I.. Evaluation of Human Intestinal Absorption Data and Subsequent Derivation of a Quantitative Structure–Activity Relationship (QSAR) with the Abraham Descriptors. J. Pharm. Sci. (2001.0) 90 749-784. DOI: 10.1002/jps.1031 39. Ma X.L., Chen C., Yang J.. Predictive Model of Blood-Brain Barrier Penetration of Organic Compounds. Acta Pharmacol. Sin. (2005.0) 26 500-512. DOI: 10.1111/j.1745-7254.2005.00068.x 40. Gonzalez D., Schmidt S., Derendorf H.. Importance of Relating Efficacy Measures to Unbound Drug Concentrations for Anti-Infective Agents. Clin. Microbiol. Rev. (2013.0) 26 274-288. DOI: 10.1128/CMR.00092-12 41. Wairata J., Sukandar E.R., Fadlan A., Purnomo A.S., Taher M., Ersam T.. Evaluation of the Antioxidant, Antidiabetic, and Antiplasmodial Activities of Xanthones Isolated from. Biomedicines (2021.0) 9. DOI: 10.3390/biomedicines9101380 42. Ogu C.C., Maxa J.L.. Drug Interactions Due to Cytochrome P450. Bayl. Univ. Med. Cent. Proc. (2017.0) 13 421-423. DOI: 10.1080/08998280.2000.11927719 43. Banerjee P., Eckert A.O., Schrey A.K., Preissner R.. ProTox-II: A Webserver for the Prediction of Toxicity of Chemicals. Nucleic Acids Res. (2018.0) 46 W257-W263. DOI: 10.1093/nar/gky318 44. Zafar F., Gupta A., Thangavel K., Khatana K., Sani A.A., Ghosal A., Tandon P., Nishat N.. Physicochemical and Pharmacokinetic Analysis of Anacardic Acid Derivatives. ACS Omega (2020.0) 5 6021-6030. DOI: 10.1021/acsomega.9b04398 45. Rehman N.U., Rafiq K., Khan A., Halim S.A., Ali L., Al-Saady N., Al-Balushi A.H., Al-Busaidi H.K., Al-Harrasi A.. α-Glucosidase Inhibition and Molecular Docking Studies of Natural Brominated Metabolites from Marine Macro Brown Alga Dictyopteris Hoytii. Mar. Drugs (2019.0) 17. DOI: 10.3390/md17120666 46. Aminu K.S., Uzairu A., Umar A.B., Ibrahim M.T.. Salicylic Acid Derivatives as Potential α-Glucosidase Inhibitors: Drug Design, Molecular Docking and Pharmacokinetic Studies. Bull. Natl. Res. Cent. (2022.0) 46 162. DOI: 10.1186/s42269-022-00853-6 47. Kulkarni S., Gupta P., Pallavi A., Zeng W.. Investigation of Enzymes Binding to “Voglibose- an Antidiabetic Drug” and the Choice of Enzyme to Be Used for Biosensing. J. Pharm. Res. Int. (2016.0) 14 30369. DOI: 10.9734/BJPR/2016/30369 48. Mohapatra S., Prasad A., Haque F., Ray S., De B., Ray S.S.. In Silico Investigation of Black Tea Components on α-Amylase, α-Glucosidase and Lipase. J. Appl. Pharm. Sci. (2015.0) 5 042-047. DOI: 10.7324/JAPS.2015.501207 49. Lakehal S., Ferkous F., Kraim K., Attoui Yahia O., Saihi Y.. Molecular Docking Study on Xanthone Derivatives toward Alpha-Glucosidase. Res. J. Pharm. Biol. Chem. Sci. (2016.0) 7 1739-1750 50. Saddique F.A., Ahmad M., Ashfaq U.A., Mansha A., Gul Khan S.. Alpha-Glucosidase Inhibition and Molecular Docking Studies of 4-Hydroxy-N’-[Benzylidene/1-Phenylethylidene]-2H-1,2-Benzothiazine-3-Carbohydrazide 1,1-Dioxides. Chiang Mai J. Sci. (2021.0) 48 460-469 51. Saddique F.A., Ahmad M., Ashfaq U.A., Muddassar M., Sultan S., Zaki M.E.A.. Identification of Cyclic Sulfonamides with an. Pharmaceuticals (2022.0) 15. DOI: 10.3390/ph15010106
--- title: 'Medical-Grade Poly(Lactic Acid)/Hydroxyapatite Composite Films: Thermal and In Vitro Degradation Properties' authors: - Leonard Bauer - Anamarija Rogina - Marica Ivanković - Hrvoje Ivanković journal: Polymers year: 2023 pmcid: PMC10059894 doi: 10.3390/polym15061512 license: CC BY 4.0 --- # Medical-Grade Poly(Lactic Acid)/Hydroxyapatite Composite Films: Thermal and In Vitro Degradation Properties ## Abstract Production of biocompatible composite scaffolds shifts towards additive manufacturing where thermoplastic biodegradable polymers such as poly(lactic acid) (PLA) are used as matrices. Differences between industrial- and medical-grade polymers are often overlooked although they may affect properties and degradation behaviour as significantly as the filler addition. In the present research, composite films based on medical-grade PLA and biogenic hydroxyapatite (HAp) with 0, 10, and 20 wt$.\%$ of HAp were prepared by solvent casting technique. The degradation of composites incubated in phosphate-buffered saline solution (PBS) at 37 °C after 10 weeks showed that the higher HAp content slowed down the hydrolytic PLA degradation and improved its thermal stability. Morphological nonuniformity after degradation was indicated by the different glass transition temperatures (Tg) throughout the film. The Tg of the inner part of the sample decreased significantly faster compared with the outer part. The decrease was observed prior to the weight loss of composite samples. ## 1. Introduction Tissue engineering represents an alternative approach for treating bone defects where biodegradable and biocompatible scaffolds act as temporary supports for cell attachment, proliferation and differentiation leading to bone tissue regeneration. Additive manufacturing techniques such as fused deposition modelling (FDM) or fused filament fabrication (FFF) emerged as suitable methods for personalized and integrated bone tissue engineering [1]. As one of the most commonly used biodegradable polymers for bone tissue engineering, poly(lactic acid) (PLA) is considered a thermoplastic polymer well-suited for additive manufacturing of bone scaffolds due to its low melting temperature for melt extrusion [2]. PLA shows desirable properties for tissue-engineering applications, including nontoxicity, biocompatibility, and nontoxic in vitro biodegradation [3]; however, it is unable to form a direct bond with bone tissue in vivo. The addition of bioactive ceramics or bioactive glass is one way to enhance the osteogenic potential of PLA-based scaffolds [4,5,6]. Synthetic calcium phosphates, in particular hydroxyapatite (HAp), are the most commonly used bioactive ceramics in dentistry, orthopaedics, and bone tissue engineering. The fabrication of PLA-HAp filaments used in additive manufacturing is usually done by extrusion of raw materials or composite pellets previously obtained by solvent casting. The ideal bone scaffold should degrade at a controlled rate in vivo leaving space for new bone tissue growth [7]. PLA is classified as a slow-degrading polymer in vitro and in vivo. The difference in degradation kinetics of PLA-based materials is caused by molecular weight, crystallinity, sample dimensions, microstructure, porosity, filler addition, and purity of raw components [8]. The degradation of PLA is usually described as a two-stage process involving a decrease in molecular weight and the onset of weight loss. The first stage usually occurs at the beginning of degradation, which can be attributed to the random hydrolytic ester cleavage, until reaching the critical molecular weight. At this point, the second stage of degradation is initiated and characterized by the onset of weight loss [3]. Due to slow PLA degradation, in vitro degradation tests of PLA-based composites are performed under physiological conditions for a few months [9], while in vitro degradation experiments at higher temperatures (e.g., 70 °C) could give an insight into degradation kinetics during a few weeks of incubation [10]. Both stages of the PLA degradation mechanism could be accelerated by the incorporation of inorganic fillers of different shapes and sizes. Previous works on the biodegradation of PLA-HAp composites [10,11] indicated accelerated degradation of materials with a higher content of HAp nano- or microparticles. The hydrophobic nature of PLA is responsible for its longer stability under physiological conditions, while the addition of hydrophilic HAp particles enhances medium absorption leading to faster autocatalytic hydrolysis of the polymer. Blending PLA with bioactive ceramics is an easy way to adjust the degradation rate of potential bone scaffolds, which is one of the predominant scaffold properties during tissue regeneration. The majority of research on PLA-based materials used as potential bone scaffolds was conducted with technical-grade PLA which is a cost-effective feedstock material for 3D-printing applications [12]. Such PLA is characterized as a semi-crystalline polymer with longer degradation under physiological conditions, containing different impurities and additives for easier additive manufacturing [13]. In clinical practices, materials with a strictly defined composition and high purity are required [14]. More importantly, the compositional difference between technical-grade and medical-grade biodegradable polymers leads to different material behaviour in vitro and in vivo, in terms of bioresorption and biodegradation [15,16]. However, such polymers are too expensive to be used in materials science, which has resulted in a few papers on medical-grade PLA-based materials [15]. Here, we report on the in vitro degradation of PLA-HAp composites based on medical-grade PLA and hydroxyapatite synthesized from environmentally friendly biogenic waste materials that are available in large quantities in nature. In this study, the degradation behaviour of medical-grade poly(lactic acid) modified with hydroxyapatite was examined after a prolonged incubation period in phosphate-buffered saline solution at 37 °C. PLA-HAp composite films with different content of hydroxyapatite (0–20 wt$.\%$) were prepared in order to investigate the influence of the inorganic phase on the degradation properties of medical-grade PLA. The compositional and morphological changes and thermal properties of PLA-HAp composites were investigated during 10 weeks of incubation. ## 2.1. Preparation of Hydroxyapatite Hydroxyapatite powder was prepared via hydrothermal transformation of cuttlefish bone (Sepia officinalis L., Adriatic Sea) which was used as a source of calcium ions [17]. Small samples of cuttlefish bone were cleaned with sodium hypochlorite solution (NaClO, $13\%$ active chlorine; Gram-Mol, Zagreb, Croatia) for 48 h at room temperature and then extensively washed in distilled water. A specific amount of cleaned bone samples was transferred into autoclave reactors with an appropriate volume of 0.6 mol dm−3 solution of ammonium dihydrogen phosphate (NH4H2PO4, $99\%$, Scharlau, Barcelona, Spain), respecting the calcium and phosphorus (Ca/P) molar ratio of 1.67. The reaction was carried out at a temperature of 200 °C for 48 h under self-generated pressure. After 48 h, the obtained samples were washed with hot distilled water and dried at a temperature of 105 °C. Finally, dried samples were milled and sieved to obtain a particle size of 90–125 µm. ## 2.2. Preparation of Poly(Lactic Acid)/Hydroxyapatite Composite Films Medical-grade poly(lactic acid) (MW ≈ 60 000, FP158009, Biosynth, Compton, UK) was dissolved in ethyl acetate (HPLC grade, Fischer Chemical, Leicester, UK) resulting in a 10 w/v% polymer solution. After the PLA was completely dissolved, hydroxyapatite was added to the polymer solution. Ultrasonic probe Sonoplus HD4200 (Bandelin, Berlin, Germany) with a UW200 converter and TT 213 sonotrode at $40\%$ amplitude was used 3 times for 2 min to assist in dispersing HAp particles within the solution. Composite PLA-HAp samples were prepared by varying the HAp weight content: $\frac{100}{0}$ (PLA), $\frac{90}{10}$ (PLA-10-HAp), and $\frac{80}{20}$ (PLA-20-HAp). Polymer solutions were concentrated at 50 °C with mild magnetic stirring until $\frac{2}{3}$ of the ethyl acetate volume evaporated. Thus, prepared dense polymer solutions were poured into concave silicone moulds to obtain films with reproducible shape and weight. The prepared films were dried at 50 °C for 24 h to remove the solvent. The solvent-casted films with a maximum thickness of 0.5 mm, diameter of ~20 mm and weight of 105 ± 15 mg were obtained. ## 2.3. In Vitro Degradation Experiment The degradation behaviour of PLA-HAp composite films was investigated in phosphate-buffered saline solution (PBS, pH 7.4) according to ISO 13781:2017 (E) standard. Weighed samples were immersed in 10 mL of PBS supplemented with 0.2 mg mL−1 of biocide (NaN3, 99+% AnalR NORMAPUR, VWR Chemicals, Leuven, Belgium) for 10 weeks at 37 °C. Triplicates of PLA, PLA-10-HAp and PLA-20-HAp composites were prepared for incubation periods of 2, 4, 5, 6, 7, 8, 9, and 10 weeks. The incubation medium was replaced by fresh PBS solution twice a week. At a specific incubation time, samples were carefully collected from the degradation medium, washed with distilled water, and dried at 50 °C until constant mass was obtained. The degradation degree was estimated as a weight loss determined at a specific time point with respect to the initial weight. ## 2.4. Identification and Characterization of Poly(Lactic Acid)/Hydroxyapatite Composite Films The identification of materials was carried out by X-ray diffraction analysis using an XRD-6000 diffractometer (Shimadzu, Kyoto, Japan) with Cu Kα radiation operated at 40 kV and 30 mA, in the range of diffraction angles (2θ) 5–70° at a scan speed of 0.2° s−1. The reference cards for HAp, brushite and halite standards (9-432, 9-77, and 5-628, respectively), compiled by the International Centre for Diffraction Data (ICDD, Newton Square, PA; USA) were used for crystal phase identification. The morphology of materials was examined by scanning electron microscopy (SEM Tescan Vega III Easyprobe, Tescan Orsay Holding, Brno, Czech Republic). An energy-dispersive X-ray (EDX) spectrometer (Bruker B-Quantax, Bruker Nano GmbH, Berlin, Germany) connected to the SEM has been used to determine the elemental composition of scaffolds. Prior to the SEM and EDX analysis, the samples were sputtered with gold and palladium for 60 s. Thermogravimetric analysis (TGA) was performed on a Netzsch STA 409 (Netzsch Instruments, Selb, Germany) with a constant synthetic airflow of 30 cm3 min−1 from 40 °C to 1200 °C at a heating rate of 10 °C min−1. Differential scanning calorimetry was performed on a DSC 3500 Sirius Netzsch (Netzsch Instruments, Selb, Germany) equipped with a special cooler IC70 Netzsch with a constant nitrogen flow of 50 cm3 min−1. Two continuous heating—cooling cycles were conducted at a heating rate of 10 °C min−1. The first cycle from 20 °C to 220 °C and back to −20 °C was used to erase the sample thermal history, while the inflexion point of the endothermic peak in the second cycle from −20 °C to 220 °C was determined as the glass transition temperature. ## 3.1. Weight Loss The weight loss of PLA and PLA-HAp composite films is shown in Figure 1. In the first period of degradation, all samples showed a slight linear weight loss. Until week 6, PLA sample lost 3 wt$.\%$, while both composite samples’ weight loss reached 6 wt$.\%$. After week 7, weight loss for PLA-10-HAp and PLA-20-HAp samples remained close to 6 wt$.\%$, while PLA sample weight loss exponentially increased up to 46 wt$.\%$. ## 3.2. XRD Analysis XRD patterns of PLA and PLA-HAp composite films before the degradation (0 W) and after 10 weeks (10 W) of the simulated degradation test are shown in Figure 2. An amorphous diffraction pattern of the PLA sample at 0 W, characteristic of a noncrystalline polymer material, is present in all XRD patterns at 0 W and 10 W. The XRD data for composite samples were a good match to the line pattern for crystalline HAp (ICCD 9-432), which increased in intensity at higher hydroxyapatite content. After 10 weeks of simulated degradation, besides the expected diffraction patterns belonging to amorphous PLA and crystalline HAp, additional crystalline peaks were visible, as seen in Figure 2b. The XRD diffraction pattern of PLA at week 10 showed diffraction peaks at Bragg angles 2θ = 31.6, 45.4, and 56.4 attributed to the strongest crystallographic planes [2 0 0], [2 2 0], and [2 2 2] of sodium chloride (halite, NaCl). The presence of sodium and chlorine was confirmed by the elemental composition of the PLA 10 W sample with an energy-dispersive X-ray (EDX) analysis. The characteristic EDX spectra and the acquired elemental surface mapping are presented in Appendix A. PLA-10-HAp sample at week 10 showed the strongest diffraction peak of brushite at Bragg angles 2θ = 11.6. Brushite (CaHPO4 × 2 H2O) is a phosphate mineral that can appear as an intermediary or final mineral within the active calcium phosphate equilibrium [18,19]. As PBS mainly contains phosphate, sodium, and chloride ions, the formation of both halite and brushite is expected and explained by the dissolution and precipitation processes between the buffered solution and tested samples. ## 3.3. Thermogravimetric Analysis Thermogravimetric analysis was performed to determine the amount of HAp in the prepared composite films (Figure 3). Weight loss of the samples until 350 °C can be attributed to the single-step PLA thermal degradation. The total weight loss of the neat HAp sample is 5.0 wt$.\%$ [20]; thus, the addition of HAp in composites should result in a proportional increase in residual mass. Thermogravimetric results of samples before in vitro degradation (Figure 3a) show that the remaining weight for the PLA, PLA-10-HAp, and PLA-20-HAp samples are 3.8 wt$.\%$, 12.5 wt$.\%$, and 19.4 wt$.\%$, respectively. Total weight loss follows the assumptions of significant polymer weight loss and residue, which corresponds to the inorganic filler content. Results after 10 weeks of simulated biodegradation in the PBS follow the same behaviour (Figure 3b), although the remaining weight at 1200 °C for the PLA sample doubled due to the presence of an additional thermally stable component. The remaining weight for the PLA, PLA-10-HAp, and PLA-20-HAp after 10 weeks is 7.4 wt$.\%$, 13.0 wt$.\%$, and 19.1 wt$.\%$, respectively. The increase for PLA from 3.8 wt$.\%$ at 0 to 7.4 wt$.\%$ after 10 weeks is attributed to the NaCl residue. The NaCl presence originated from the PBS agrees with the results of XRD and EDX analysis (Appendix A). In addition to total weight loss, the thermogravimetric analysis has indicated changes in the thermal stability of prepared samples. Depending on the degree of crystallinity, molecular weight, the PLA enantiomer ratio and polymer purity, the degradation temperature of PLA polymer usually varies between 290–380 °C [21,22]. Each of the mentioned PLA properties can significantly influence thermal and degradation properties and cannot be considered in an isolated way. Because of this, contradictory conclusions on how different parameters impact polymer behaviour can be found in the literature [23,24,25,26]. Different formulations of the polymeric matrix with compounds—such as bioactive glasses, calcium phosphates, starch, and copolymers—made general conclusions almost invalid [27,28,29,30]. Thus, each research path must start with observing basic relations between different parameters towards a particular behaviour path. As seen in Figure 3a, the thermal degradation temperatures of prepared films before in vitro degradation are 300 °C, 335 °C, and 350 °C for PLA, PLA-10-HAp, and PLA-20-HAp, respectively. Rise in temperature indicates that the addition of HAp improves the thermal stability of the polymer in prepared composites. Similarly, Albano et al. [ 31] reported that the addition of $30\%$ HAp to the PLLA matrix improves the thermal stability of the polymer. In more detail, HAp increases the polymer activation energy and initial decomposition temperature, but once the degradation process is initiated, the degradation rate is higher. Recently, Tazibt et al. [ 32] reported that fine dispersion of HAp can increase the degradation temperature in PLA composite, while higher filler content with nonideal dispersion can have the opposite effect on the properties. Compared with the neat PLA, the thermal degradation curves of our composite samples are shifted to higher temperatures, which could indicate a good dispersion of HAp in the matrix. The onset degradation temperature of the 10 W samples is around 290 °C for PLA and PLA-10-HAp, and 320 °C for PLA-20-HAp. The lower thermal degradation temperatures, compared with the 0 W samples, indicate that samples went through the in vitro degradation process. PLA-20-HAp sample kept the highest thermal degradation temperature and can be considered the most stable one. Additionally, a slight change in curve shape in the temperature range of 40–300 °C is visible between TGA curves before and after the in vitro degradation (Figure 3a,b). All curves in Figure 3a show the shoulder around 210 °C, which is not present on curves in Figure 3b, and weight loss up to $5\%$ depending on the HAp content. The appearance of the shoulder is not caused by the hydrophilic HAp filler addition, as it appears both on the samples with and without HAp. Detailed studies describing pathways of PLA thermal degradation have been reported [22,33,34]. The most accepted study supposes that the main degradation of poly(lactide) at lower temperatures involves a non-radical backbiting ester interchange reaction, which leads mostly to cyclic oligomers [22]. At higher temperatures, ketones and carbon monoxide are the main degradation products obtained as a result of a radical chain scission mechanism. Albano et al. [ 31] investigated the degradation behaviour of a similar PLLA-HAp composite. While the main degradation step was around 360 °C, using the derivative graph of thermal degradation, they distinguished a shoulder around 340 °C. In our case, the weight loss obtained at temperatures below 300 °C might have originated from the solvent residue that remained after solvent casting at low temperatures. As 10 W samples went through 10 weeks of simulated in vitro degradation in PBS, hydrolysis of hydrophobic polymer into more hydrophilic nature oligomers should affect and enhance chemical and physical water binding inside the composite structure. The PLA-10-HAp sample shows a gradual weight loss before the main PLA thermal degradation, unlike the PLA and PLA-20-HAp samples that do not show it. ## 3.4. Glass Transition Temperatures and Sample Disintegration The glass transition temperature (Tg) of PLA usually ranges from 50 °C to 80 °C and is highly dependent on the molecular weight, purity, crystallinity ratio, and thermal history of the polymer [35]. PLA used in this research is specified as a medical-grade PLLA with Mw ≈ 60 000. Our preliminary analysis of the polymer material showed that it is highly amorphous. The XRD analysis (Figure 2a) and the DSC measurement curves (Figure 4) confirm that there is no crystallisation peak in the PLA sample, even after the addition of HAp. The glass transition temperature of poly(lactic acid) film is 52.4 ± 0.6 °C. The addition of HAp has not affected the glass transition temperature. Tg remains at 52.7 ± 0.6 °C for PLA-10-HAp and 52.0 ±1.6 °C for PLA-20-HAp, which is considered to be within the experimental error. As already mentioned in Section 2.2., samples were prepared by solvent casting into concave silicone moulds to obtain films with reproducible shape and mass. Every sample had a thicker middle part of the film (up to 0.5 mm) and a thinner outer part (down to 0.2 mm as it comes to the edge). Under the assumption of uniform hydrolytic degradation, the outer thinner part of a sample should reduce in thickness until its complete disappearance, while the inner thicker part should remain stable. After 10 weeks of in vitro hydrolytic degradation, the PLA sample becomes visually nonuniform and disintegrated, as presented in Figure 5. Possible disintegration behaviour was initially indicated by the rapid exponential weight loss in the last weeks of tested biodegradation (Figure 1). The DSC measurement of PLA after 8 weeks showed that the inner thicker part of the film attained a glass transition temperature of 25.8 ± 2.8 °C. The glass transition temperature of the outer part of the same film remained at 44.5 ± 2.5 °C. The difference in Tg may indicate that the hydrolytic degradation of PLA is an autocatalytic reaction that is faster at thicker parts of the film. Athanasiou et al. [ 36] reported that less porous materials tend to hold in the acidic breakdown products, leading to an acceleration of the hydrolytic process. Degradation products locally reduce the pH, which further promotes the polymer degradation process. On the thinner outer part of our prepared films, degradation products are easily washed out, but pH remains stable and the polymer material retains its properties for a longer time. The disintegration behaviour has led us to determine the glass transition temperatures of the inner (In, data presented in Figure 6a) and outer (Out, data presented in Figure 6b) parts of all samples. Summarized glass transition temperature results are presented as data tables in Appendix B. From Figure 6a it is obvious that as time increases, the Tg shifts to lower temperatures. During the hydrolysis process, the scission of the longer polymer chains occurs and leads to a decrease in PLA molecular weight, which results in a decrease in the glass transition temperature. The Tg of the inner part of the PLA sample decreased to 31.3 ± 0.7 °C and 24.4 ± 0.2 °C after 2 and 5 weeks, respectively. As the inner part of the sample degraded significantly, Tg measured for PLA after 8 weeks increased to 25.8 ± 2.8 °C. It can be assumed that the degradation products act as plasticizers for polymer molecules and that with the progress of the degradation, they come out of the sample more easily. This may be the reason why the sample after 8 weeks of degradation (with less “plasticizer”) shows a higher Tg compared with the Tg of the sample after 5 weeks of degradation. Due to the sample degradation, it was impossible to collect the inner part of PLA 10 W for DSC analysis. The same behaviour of the inner part during degradation is observed in the PLA-10-HAp sample with a slight time delay (Figure 6a). Significant visual degradation and overall sample weight loss of composite samples did not occur within the 10-week degradation period, but Tg measurements confirmed that the inner part of the sample degraded significantly faster than the outer part. In comparison with PLA 10 W sample, PLA-10-HAp 10 W did not show any significant weight loss, which could indicate composite degradation. The presence of bioresorbable HAp allows for a continuous process of dissolution and precipitation, which, in the end, affects the weight of the degraded composite film. Pitt et al. [ 37] and Li et al. [ 38] showed that the decrease in molecular weight, accompanied by a Tg decrease, occurs much earlier than the material weight loss is observed [37,38]. Our in vitro degradation results agree with these findings. The PLA-20-HAp sample visually remained almost uniform, which could indicate that polymer degradation had not yet occurred. The DSC analysis confirmed that until 8 weeks of in vitro degradation, the Tg of the inner and outer parts of the PLA-20-HAp sample remained constant, at around 52 °C. After 10 weeks of degradation, PLA-20-HAp inner part showed a significant decrease in Tg. Still, we can assume that a greater amount of HAp is responsible for the slower PLA degradation. Tg of the PLA sample decreased from the initial 52.4 ± 0.6 °C below the physiological temperature of 37 °C after a few weeks of the simulated in vitro degradation. It is straightforward that its degradation properties significantly affect the mechanical stability of a potential implant and should be carefully modified for biomedical applications. The analysis of DSC data (Figure 6) combined with the weight loss results (Figure 3) leads us to the unambiguous conclusion that a greater amount of hydroxyapatite in medical-grade PLA/HAp composite films slows down the degradation process. These results are, at least partially, in disagreement with many other studies where the degradation of different PLA-HAp composites was enhanced as HAp content was increased [39,40,41]. Zhang et al. [ 10] recently reported a comprehensive study in which the quality of the 3D-printed scaffold decreased significantly as the degradation time increased. It is assumed that highly hydrophilic HAp particles help water infiltration, which should result in faster degradation, and that the degradation of PLA is mainly related to the solvent environment and acidic products from a scaffold or in vivo response [42]. Xu et al. [ 43] studied grafted PLA-HAp composite fibres and hypothesised that low HAp content may delay degradation due to the reduction of autocatalytic degradation, but that enhanced wettability, with higher quantities of HAp, is the reason for the increased degradation rate. Alex et al. [ 44] studied the relative role of polymer chain scission and solvation in the reduction of mechanical properties of degrading PLA and concluded that solvation plays a more active role. Based on our in vitro degradation results, it might be assumed that the hydrophilic nature of the HAp nanoparticles could disrupt the solvation, i.e., there is less water available to interact with the polymer. In either case, further research with a 3D-structured composite material and dynamic in vitro conditions should bring new information about the behaviour of our medical-grade PLA/HAp composite. ## 3.5. Sample Morphology The degradation of the neat PLA film and the composite PLA/HAp films in PBS at 37 °C was characterised by SEM as well. All micrographs before degradation (0 W) show a smooth surface, with few aggregated HAp particles on the surface of the PLA-20-HAp film (Figure 7i). After 2 weeks of degradation, the PLA surface (Figure 7b) indicates the appearance of scattered microparticles, whose size and presence become more significant as degradation is prolongated (Figure 7c,d). Observed microparticles are assumed to be halite crystals precipitated from the PBS, identified by the XRD, TGA, and EDX analysis (Section 3.2 and Section 3.3 and Appendix A). The surface of composite films shows noticeable changes with the degradation time. The smooth surface of initial samples (Figure 7e,i) turns into a pitted structure after 2 weeks of degradation (Figure 7f,j), where the higher amount of HAp changes hole dimensions from a submicron size to a micrometre range. At 5 W surface becomes more closed and the aforementioned holes are filled with brighter material. After 10 weeks of degradation, PLA-20-HAp surface (Figure 7l) is composed of a significant amount of white regions than the PLA-10-HAp film surface (Figure 7h). An energy-dispersive X-ray (EDX) analysis of the PLA-20-HAp surface, presented in Appendix C, shows that brighter areas match the distribution of calcium and phosphorus. In addition to the surface analysis of the films, samples’ cross-sections are analysed by SEM as well. Figure 8 presents cross-sections of samples at two different magnifications (500× and 2000×) before and after degradation (0 W and 10 W). Micrographs in Figure 8 at both magnifications before degradation (0 W) reveal that HAp is relatively evenly distributed through the whole cross-section of the inner sample part. The biggest agglomerates in PLA-10-HAp and PLA-20-HAp samples remained under 5 and 10 µm, respectively, while most of the particles were successfully blended within the polymer matrix. In spite of the hydrophobic nature of PLA, hydrophilic HAp improved the thermal properties and slowed down the degradation of PLA. The cross-section of the outer part of the PLA film (Figure 8c) became very thin (approx. 0.1 mm), which agrees with the significant weight loss in the last weeks of degradation. Holes are dominant on one side of the PLA film, whereas the larger portion of the remaining film seems nonporous on the micrometric level. Cross-section micrographs of composites after 10 W degradation (Figure 8g,h,k,l) reveal that degradation occurs from the inside of composite samples. As active dissolution/precipitation of inorganic components (HAp, brushite, halite) pitted the sample surface, it caused a similar effect on the film interior. Cross-sections of PLA-10-HAp and PLA-20-HAp show micrometric pores that form a nonhomogeneous interconnected structure. It could be assumed that due to the degradation of PLA, there is also a redistribution of HAp particles within the matrix. It seems that particles settled down, and this effect is more obvious in composite films with a higher HAp content. This is especially noticeable with the sample PLA-20-HAp. It would be interesting to study how such significant changes in the composite structure during degradation, from bulky to porous, affect its mechanical properties. This issue will be covered in our upcoming research. ## 4. Conclusions Composite films based on medical-grade PLA and hydroxyapatite synthesized from a biogenic source were prepared by solvent casting technique. The addition of relatively evenly distributed inorganic bioactive filler in the PLA matrix improved the thermal stability of the prepared composites. The degradation behaviour of prepared composite films incubated in phosphate-buffered saline solution at 37 °C was examined during a 10-week period. The hydrolytic degradation of PLA is an autocatalytic reaction that is faster at thicker parts of the film. The inner thicker part of the material, with an initial Tg~50 °C, degraded to the state where the glass transition temperature was below the physiological temperature. The decrease in glass transition temperature was seen before the weight loss of composite samples. The degradation caused PLA sample nonuniformity and disintegration in the last weeks of degradation. Slowed autocatalytic degradation in composite samples is indicated by the more stable glass transition temperatures compared with the neat PLA samples. SEM analysis of cross-sections of composite films showed noticeable changes with degradation time. We can assume that a greater amount of HAp is responsible for the slower degradation of medical-grade PLA. It should be emphasized that great attention should be directed towards comparing the results of technical- and medical-grade PLA. ## References 1. Qu M., Wang C., Zhou X., Libanori A., Jiang X., Xu W., Zhu S., Chen Q., Sun W., Khademhosseini A.. Multi-dimensional printing for bone tissue engineering. Adv. Healthc. Mater. (2021) 10 2001986. DOI: 10.1002/adhm.202001986 2. Chacón J.M., Caminero M.A., García-Plaza E., Núñez P.J.. Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection. Mater. Des. (2017) 124 143-157. DOI: 10.1016/j.matdes.2017.03.065 3. Tang T.O., Simon G.P.. Biodegradation of 3D-printed polylactic acid milliprojections under physiological conditions. J. Appl. Polym. Sci. (2020) 137 49129. DOI: 10.1002/app.49129 4. Distler T., Fournier N., Grünewald A., Polley C., Seitz H., Detsch R., Boccaccini A.R.. Polymer-bioactive glass composite filaments for 3D scaffold manufacturing by fused deposition modeling: Fabrication and characterization. Front. Bioeng. Biotechnol. (2020) 8 552. DOI: 10.3389/fbioe.2020.00552 5. Alksne M., Kalvaityte M., Simoliunas E., Rinkunaite I., Gendviliene I., Locs J., Rutkunas V., Bukelskiene V.. In vitro comparison of 3D printed polylactic acid/hydroxyapatite and polylactic acid/bioglass composite scaffolds: Insights into materials for bone regeneration. J. Mech. Behav. Biomed. Mater. (2020) 104 103641. DOI: 10.1016/j.jmbbm.2020.103641 6. Amnael Orozco-Díaz C., Moorehead R., Reilly G.C., Gilchrist F., Miller C.. Characterization of a composite polylactic acid-hydroxyapatite 3D-printing filament for bone-regeneration. Biomed. Phys. Eng. Express (2020) 7 25007. DOI: 10.1088/2057-1976/ab73f8 7. Bose S., Roy M., Bandyopadhyay A.. Recent advances in bone tissue engineering scaffolds. Trends Biotechnol. (2012) 30 546. DOI: 10.1016/j.tibtech.2012.07.005 8. Chen H., Shi Q., Shui H., Wang P., Chen Q., Li Z.. Degradation of 3D-printed porous polylactic acid scaffolds under mechanical stimulus. Front. Bioeng. Biotechnol. (2021) 8 889. DOI: 10.3389/fbioe.2021.691834 9. Deplaine H., Acosta-Santamaría V.A., Vidaurre A., Gómez Ribelles J.L., Doblaré M., Ochoa I., Gallego Ferrer G.. Evolution of the properties of a Poly(L-lactic acid) scaffold with double porosity during in vitro degradation in a phosphate-buffered saline solution. J. Appl. Polym. Sci. (2014) 131 40956-40966. DOI: 10.1002/app.40956 10. Zhang B., Wang L., Song P., Pei X., Sun H., Wu L., Zhou C., Wang K., Fan Y., Zhang X.. 3D printed bone tissue regenerative PLA/HA scaffolds with comprehensive performance optimizations. Mater. Des. (2021) 201 109490. DOI: 10.1016/j.matdes.2021.109490 11. Pérez E.. Mechanical performance of in vitro degraded polylactic acid/hydroxyapatite composites. J. Mater. Sci. (2021) 56 19915-19935. DOI: 10.1007/s10853-021-06508-7 12. Khosravani M.R., Berto F., Ayatollahi M.R., Reinicke T.. Characterization of 3D-printed PLA parts with different raster orientations and printing speeds. Sci. Rep. (2022) 12 1016. DOI: 10.1038/s41598-022-05005-4 13. Rajakaruna R.A.D.N.V., Subeshan B., Asmatulu E.. Fabrication of hydrophobic PLA filaments for additive manufacturing. J. Mater. Sci. (2022) 57 8987-9001. DOI: 10.1007/s10853-022-07217-5 14. Vacaras S., Baciut M., Lucaciu O., Dinu C., Baciut G., Crisan L., Hedesiu M., Crisan B., Onisor F., Armencea G.. Understanding the basis of medical use of Poly-lactide-based resorbable polymers and composites—A review of the clinical and metabolic impact. Drug Metab. Rev. (2019) 51 570-588. DOI: 10.1080/03602532.2019.1642911 15. Ahlinder A., Fuoco T., Finne-Wistrand A.. Medical grade polylactide, copolyesters and polydioxanone: Rheological properties and melt stability. Polym. Test. (2018) 72 214-222. DOI: 10.1016/j.polymertesting.2018.10.007 16. Farah S., Anderson D.G., Langer R.. Physical and mechanical properties of PLA, and their functions in widespread applications—A comprehensive review. Adv. Drug Deliv. Rev. (2016) 107 367-392. DOI: 10.1016/j.addr.2016.06.012 17. Ivankovic H., Gallego Ferrer G., Tkalcec E., Orlic S., Ivankovic M.. Preparation of highly porous hydroxyapatite from cuttlefish bone. J. Mater. Sci. Mater. Med. (2009) 20 1039-1046. DOI: 10.1007/s10856-008-3674-0 18. Johnsson M.S.A., Nancollas G.H.. The role of brushite and octacalcium phosphate in apatite formation. Crit. Rev. Oral Biol. Med. (2016) 3 61-82. DOI: 10.1177/10454411920030010601 19. Tas A.C., Bhaduri S.B.. Chemical processing of CaHPO. J. Am. Ceram. Soc. (2004) 87 2195-2200. DOI: 10.1111/j.1151-2916.2004.tb07490.x 20. Bauer L., Antunović M., Rogina A., Ivanković M., Ivanković H.. Bone-mimetic porous hydroxyapatite/whitlockite scaffolds: Preparation, characterization and interactions with human mesenchymal stem cells. J. Mater. Sci. (2021) 56 3947-3969. DOI: 10.1007/s10853-020-05489-3 21. Carrasco F., Pérez O.S., Maspoch M.L.. Kinetics of the thermal degradation of Poly(lactic acid) and polyamide bioblends. Polymers (2021) 13. DOI: 10.3390/polym13223996 22. McNeill I.C., Leiper H.A.. Degradation studies of some polyesters and polycarbonates—2. Polylactide: Degradation under isothermal conditions, thermal degradation mechanism and photolysis of the polymer. Polym. Degrad. Stab. (1985) 11 309-326. DOI: 10.1016/0141-3910(85)90035-7 23. Tsuji H., Mizuno A., Ikada Y.. Properties and morphology of Poly(L-lactide). III. Effects of initial crystallinity on long-term in vitro hydrolysis of high molecular weight Poly(L-lactide) film in phosphate-buffered solution. J. Appl. Polym. Sci. (2000) 77 1452-1464. DOI: 10.1002/1097-4628(20000815)77:7<1452::AID-APP7>3.0.CO;2-S 24. Göpferich A.. Mechanisms of polymer degradation and erosion. Biomater. Silver Jubil. Compend. (1996) 17 117-128. DOI: 10.1016/B978-008045154-1.50016-2 25. Vasanthan N., Ly O.. Effect of microstructure on hydrolytic degradation studies of Poly(L-lactic acid) by FTIR spectroscopy and differential scanning calorimetry. Polym. Degrad. Stab. (2009) 94 1364-1372. DOI: 10.1016/j.polymdegradstab.2009.05.015 26. Asti A., Gioglio L.. Natural and synthetic biodegradable polymers: Different scaffolds for cell expansion and tissue formation. Int. J. Artif. Organs (2014) 37 187-205. DOI: 10.5301/ijao.5000307 27. Blaker J.J., Nazhat S.N., Maquet V., Boccaccini A.R.. Long-term in vitro degradation of PDLLA/Bioglass® bone scaffolds in acellular simulated body fluid. Acta Biomater. (2011) 7 829-840. DOI: 10.1016/j.actbio.2010.09.013 28. Kang Y., Yao Y., Yin G., Huang Z., Liao X., Xu X., Zhao G.. A study on the in vitro degradation properties of Poly(L-lactic acid)/β-tricalcuim phosphate(PLLA/β-TCP) scaffold under dynamic loading. Med. Eng. Phys. (2009) 31 589-594. DOI: 10.1016/j.medengphy.2008.11.014 29. Yu M., Zheng Y., Tian J.. Study on the biodegradability of modified starch/polylactic acid (PLA) composite materials. RSC Adv. (2020) 10 26298-26307. DOI: 10.1039/D0RA00274G 30. Shuai C., Yang W., Feng P., Peng S., Pan H.. Accelerated degradation of HAP/PLLA bone scaffold by PGA blending facilitates bioactivity and osteoconductivity. Bioact. Mater. (2020) 6 490-502. DOI: 10.1016/j.bioactmat.2020.09.001 31. Albano C., González G., Palacios J., Karam A., Castillo R.V., Covis M.. Characterization of poly L-Lactide/Hydroxyapatite composite: Chemical, thermal and thermomechanical properties. Rev. Fac. Ing. Univ. Cent. Venez. (2013) 28 97-107 32. Tazibt N., Kaci M., Dehouche N., Ragoubi M., Atanase L.I.. Effect of filler content on the morphology and physical properties of Poly(Lactic Acid)-hydroxyapatite composites. Materials (2023) 16. DOI: 10.3390/ma16020809 33. Södergård A., Stolt M.. Properties of lactic acid based polymers and their correlation with composition. Prog. Polym. Sci. (2002) 27 1123-1163. DOI: 10.1016/S0079-6700(02)00012-6 34. Kopinke F.D., Remmler M., Mackenzie K., Möder M., Wachsen O.. Thermal decomposition of biodegradable polyesters—II. Poly(lactic acid). Polym. Degrad. Stab. (1996) 53 329-342. DOI: 10.1016/0141-3910(96)00102-4 35. Lim L.T., Auras R., Rubino M.. Processing technologies for Poly(lactic acid). Prog. Polym. Sci. (2008) 33 820-852. DOI: 10.1016/j.progpolymsci.2008.05.004 36. Athanasiou K.A., Schmitz J.P., Agrawal C.M.. The effects of porosity on in vitro degradation of Polylactic Acid–Polyglycolic acid implants used in repair of articular cartilage. Tissue Eng. (1998) 4 53-63. DOI: 10.1089/ten.1998.4.53 37. Pitt G.G., Gratzl M.M., Kimmel G.L., Surles J., Sohindler A.. Aliphatic polyesters II. The degradation of poly (DL-lactide), poly (ε-caprolactone), and their copolymers in vivo. Biomaterials (1981) 2 215-220. DOI: 10.1016/0142-9612(81)90060-0 38. Li S.M., Garreau H., Vert M.. Structure-property relationships in the case of the degradation of massive aliphatic poly-(α-hydroxy acids) in aqueous media—Part 1: Poly(dl-lactic acid). J. Mater. Sci. Mater. Med. (1990) 1 123-130. DOI: 10.1007/BF00700871 39. Szczepanska P., Szymanowski H., Suwalska M., Rosinska K., Skrodzki M., Uznanski P., Bociaga D., Bubko I., Drozd E., Gruber-Bzura B.. Influence of ceramic phosphate powders on the physicochemical and biological properties of Poly(L-lactide). Ceram. Int. (2023) 49 7692-7709. DOI: 10.1016/j.ceramint.2022.10.263 40. Behera K., Sivanjineyulu V., Chang Y.H., Chiu F.C.. Thermal properties, phase morphology and stability of biodegradable PLA/PBSL/HAp composites. Polym. Degrad. Stab. (2018) 154 248-260. DOI: 10.1016/j.polymdegradstab.2018.06.010 41. Kaavessina M., Chafidz A., Ali I., Al-Zahrani S.M.. Characterization of Poly(Lactic acid)/hydroxyapatite prepared by a solvent-blending technique: Viscoelasticity and in vitro hydrolytic degradation. J. Elastomers Plast. (2015) 47 753-768. DOI: 10.1177/0095244314557973 42. Weng W., Song S., Cao L., Chen X., Cai Y., Li H., Zhou Q., Zhang J., Su J.. A comparative study of bioartificial bone tissue Poly-L-lactic Acid/Polycaprolactone and PLLA scaffolds applied in bone regeneration. J. Nanomater. (2015) 2014 935149. DOI: 10.1155/2014/935149 43. Xu X., Chen X., Liu A., Hong Z., Jing X.. Electrospun Poly(L-lactide)-grafted hydroxyapatite/Poly(L-lactide) nanocomposite fibers. Eur. Polym. J. (2007) 43 3187-3196. DOI: 10.1016/j.eurpolymj.2007.05.024 44. Alex A., Ilango N.K., Ghosh P.. Comparative role of chain scission and solvation in the biodegradation of Polylactic Acid (PLA). J. Phys. Chem. B (2018) 122 9516-9526. DOI: 10.1021/acs.jpcb.8b07930
--- title: A Spatial Analysis of Food Insecurity and Body Mass Index with Income and Grocery Store Density in a Diverse Sample of Adolescents and Young Adults authors: - Joanna Buscemi - Alexander O’Donnell - Mary Takgbajouah - Paige Patano journal: Nutrients year: 2023 pmcid: PMC10059929 doi: 10.3390/nu15061435 license: CC BY 4.0 --- # A Spatial Analysis of Food Insecurity and Body Mass Index with Income and Grocery Store Density in a Diverse Sample of Adolescents and Young Adults ## Abstract Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minority populations. While there has been a proliferation of research linking food insecurity to obesity, these findings are mixed. It may be helpful to consider some additional geographic factors that may be associated with both factors including socioeconomic status and grocery store density. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults across two studies in a large, urban city. GIS analysis revealed that participants with the highest food insecurity tend to live in the zip codes with the lowest median income. There did not appear to be clear a relationship between food insecurity and store density. Participants with the highest BMI tend to live in zip codes with lower median income and participants with higher BMI tended to live in the south and west sides of Chicago, which have a relatively lower concentration of grocery stores in the city. Our findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence. ## 1. Introduction Food insecurity occurs when a household lacks consistent access to food. It affects more than 13.5 million households across the U.S., and close to 33.8 million individuals nationwide [1,2]. Rates of food insecurity have increasingly grown since 2005, but it does not affect all Americans equally [3]. Individuals with food insecurity are more likely to have completed less formal education, be female, come from racial or ethnic minority groups, live in a household with children, and have a lower income when compared to individuals without food insecurity [1,3,4]. In particular, Black individuals are almost $70\%$ more likely to be food insecure in comparison to white individuals, while Hispanic individuals are $24\%$ more likely to be food insecure than white individuals [3]. This is concerning, as food insecurity is associated with numerous negative health outcomes in both children and adults, including increased rates of depression, anxiety, anemia, metabolic and cardiovascular diseases, and obesity [1,4,5]. Obesity is defined as an excess of body fat that has the potential to lead to a number of adverse health outcomes, such as diabetes, heart disease, and several cancers, including breast, ovarian, prostate, and colon cancer [6]. Since the 1970s, global obesity rates have quadrupled, and obesity is currently the number two cause of early death in the United States and Europe [2,6]. In the U.S, more than a third of adults have obesity, and almost $20\%$ of children have obesity [6,7]. A large body of research has linked obesity to food insecurity; it is posited that those with food insecurity may engage in patterns of consumption that can increase the storage of fat [2,4,5]. For example, individuals may restrict their diet due to a lack of access to food, and then as a result overeat when there is an availability of food [2,4,5]. This binge and restrict cycle negatively impacts metabolic functioning and promotes the storage of fat in the body [2]. Additionally, food insecurity may also increase consumption of low-cost foods that are high in calories but low in nutrients, which can also promote obesity [2,4,5,8,9]. While there is empirical support for the obesity/food insecurity relationship, some results are mixed. For example, some studies have found differential associations across minoritized racial and ethnic groups, while other studies have found no differences [2,5]. When looking at sex differences, some studies have found no significant effects of food insecurity on obesity in men or children while finding a significant effect in women [1,9], with one finding that this significant effect only occurred among women experiencing short term food insecurity [1]. Furthermore, another study found that the relationship between food insecurity and obesity has been shown to be stronger in females with lower incomes when compared to males with lower incomes, suggesting a differential susceptibility to obesity by sex in individuals with lower incomes [3]. Additionally, a commentary on gaps in research on the relationship between the above-mentioned constructs also found that the association between food insecurity and obesity is particularly strong in women, compared to men [4], while another study found that food insecurity was significantly linked to obesity among women [2]. Overall, previous research seems to suggest that food insecurity may be associated with obesity among women, but not men. Given the equivocal findings regarding associations between obesity and food insecurity, it may be helpful to consider some additional geographic factors that may be associated with both factors, including socioeconomic status and grocery store density. Some research has shown that increased access to nutritious food options is associated with increased diet quality, and that living a shorter distance to the nearest supermarket is associated with better dietary behaviors and lower body mass index (BMI) [2,10,11]. On the other hand, research has also shown that grocery shopping at discount stores, grocery shopping at stores situated in low-income neighborhoods, and being Black or African American are associated with increased BMI and poorer diet quality [12]. Other research has found no significant results when investigating these relationships [12]. Geographic information systems (GIS) approaches to analyzing data can be helpful in mapping where inequities lie in terms of obesity and food insecurity and how these factors may cluster by region and SES/grocery store density. Identifying neighborhood-level inequities and patterns may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence. Previous GIS analyses have revealed significant associations between food swamps and deserts, income, and health outcomes, including obesity, but have not examined the role of self-reported food insecurity [13]. This specific factor may be important as individuals living in food oases may have limited access to food for a number of reasons, and vice versa. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults in the Chicagoland area. Given findings from previous research, we also explored these relationships by race/ethnicity and sex. Given that minoritized racial and ethnic groups are typically under-represented in research [4], it is important to study these constructs in a diverse sample. GIS analysis may reveal concentrated areas of higher BMI and/or food insecurity and may reveal patterns of these concentrations by income/grocery store density. In addition to the GIS analysis, we conducted regression analyses to determine whether there were any statistically significant relationships between our variables of interest. Based on previous literature, we hypothesized that there would be a visible pattern of higher food insecurity in areas of poverty and lower store density. We also hypothesized that there would be a visible pattern of higher BMIs in areas of poverty and lower store density. We hypothesized that these patterns would be exacerbated for Black and Latinx participants and that food insecurity would be more pronounced in females than males. Finally, we hypothesized that these patterns would be confirmed with our inferential statistical analyses. ## 2.1. Participants Data included in this study came from two separate studies [14,15]. We included data from both studies to increase our sample size and to be able to include participants from across the adolescent—early adult developmental period. The Study 1 sample was high school students across 4 schools and was a mixed methods study including quantitative and qualitative data. Study 2 was a community sample of young adults and utilized a cross-sectional, quantitative design. Both samples were diverse in terms of race/ethnicity and income and representative of the city and surrounding suburbs. Study 1: The participants were high school students across 4 public schools in Chicago. Of those who responded ($$n = 47$$), $44.7\%$ were female, $68.1\%$ were Black, $6.4\%$ were Latinx, $8.5\%$ were white, $8.5\%$ were Asian/Pacific Islander, and $8.5\%$ were multiracial. The average age was 15.8 years old (SD = 1.1). Eighty percent of the participants’ caregivers reported a household income of less than $50,000. The sample analyzed in this study ($$n = 20$$) included only participants who had complete data on measures of food insecurity and BMI. Study 2: One hundred-nine adults across the Chicagoland area participated in the study. The sample was $70\%$ female, $40\%$ Black, $40\%$ Latinx, $15\%$ white, and $5\%$ other. The average age of the larger sample was 33.12 (SD = 11.99). One participant identified as nonbinary. Almost $60\%$ of the participants had a household income of less than $50,000. The sample included in this analysis ($$n = 44$$) included participants who were under 30 years of age (meeting the criteria for young adults) and had complete data on measures of food insecurity and BMI. ## 2.2. Procedure Study 1: Students and caregivers were recruited through a convenience sampling approach. They were approached at school during times when both caregivers and students were typically present, such as orientations and open houses. Students participated in focus groups at school in which they were administered a questionnaire assessing demographic information and health behaviors. Caregivers were administered a similar questionnaire, but gave more information pertaining to socioeconomic status, food availability and home composition. All participants received a $50 Amazon gift card upon completion of the study. Study 2: Participants were recruited through contact tracers at Brother’s Health Collective, a health clinic in the Bronzeville neighborhood of Chicago, and through flyers posted around the Bronzeville area. After completing the informed consent process, participants completed a Qualtrics survey that asked demographic questions and health-related questions, including food insecurity. They were given a $50 gift card for their participation. ## 2.3. Measures For the purposes of the current study, we were interested in viewing the geospatial relationship between food insecurity and BMI related to proximity to grocery stores and income levels in Chicagoland zip codes. In both studies, we assessed food insecurity, BMI, and several other sociodemographic variables. However, food insecurity and BMI were assessed differently in each study. Study 1: Food security was measured through a 4-item subset of the home food availability questions from the National Health and Nutrition Examination Survey (NHANES). This measure includes statements such as “I don’t buy fruits because they cost too much” and “At the store where I buy my groceries, the variety of fresh fruits and vegetables is limited” and participants were asked to respond on a 4-point Likert scale the extent to which they agreed or disagreed with those statements. “ Strongly agree” and “agree” responses were grouped into one category (recoded as “1”) and responses including “disagree” or “strongly disagree” were grouped into a separate category and recoded as “0.” All of the items answered in the affirmative were summed (i.e., agree or strongly agree) and scores ranged from 1–4. Participants with a score of 1 had some food insecurity but it was low overall, and 4 indicated higher levels of food insecurity. Study 1 participants were weighed using a digital scale in a private area in the school wearing no shoes and light clothing. BMI was calculated using the following formula: weight in kilograms divided by height in meters squared. Study 2: Food security was measured with the 10-item USDA food security scale. Examples of questions included “I worried whether my food would run out before I got money to buy more,” and “In the last 12 months, were you ever hungry but didn’t eat because there wasn’t enough money for food?”. The measure was scored by summing all of the items answered in the affirmative (i.e., the number of questions to which participants responded with often true or sometimes true). Our score categories for this variable map onto the USDA’s guidelines for categorizing food insecurity with this measure. For example, a score of 0 was consistent with being food secure, 1–2 was food insecure without hunger, 3–5 was food insecure with hunger (moderate), and 5–7 was food insecure with hunger (severe). In Study 2, due to restrictions from the COVID-19 pandemic, participants self-reported their weight on the questionnaire, which was then used to calculate BMI using the same formula as Study 1. ## 2.4. Creation of Maps Maps were created using ArcGIS Pro, a desktop geographic information system software [16]. Figure 1 displays food insecurity overlaid on zip codes of the Chicago, IL metropolitan area that were shaded by median household income, and Figure 2 displays food insecurity overlaid on zip codes shaded according to density of grocery stores per square kilometer. Figure 3 displays BMI overlaid on zip codes of the Chicago, IL metropolitan area that were shaded by median household income. Figure 4 displays BMI shaded by grocery stores per square kilometer. Details regarding variable categories and visual representations can be found in each figure’s corresponding legend and figure caption. Although the Chicago metropolitan area consists of more zip codes than were displayed in the figures, only the suburban zip codes that were represented in our datasets were included in the analyses for ease of interpretation. All zip codes contained in the city of Chicago were included. Geographic boundaries for each zip code were extracted from geospatial data files downloaded from the U.S. Government’s open data repository [17]. Data for each zip code’s median household income and land area were drawn from the U.S. Census Bureau [18]. Grocery store density in each zip code was calculated by dividing the number of grocery stores by the land area in square kilometers. Grocery stores were defined as stores selling primarily a range of food products, including whole fruits and vegetables, and excluded gas stations, convenience stores, or liquor stores. Data on the frequency of grocery stores per zip code were drawn from the City of Chicago’s data portal [19]. Each participant is represented by a single symbol on the maps. Participants from the high school student participants (Study 1, $$n = 20$$) were represented by squares, and participants from the community sample of young adults (Study 2, $$n = 44$$) were represented by circles. ## 2.5. Regression Analyses While the purpose of this paper was to display spatial relationships between food insecurity and BMI and median income and store density, we conducted linear regression and logistic regression analyses, respectively, to determine whether statistically significant relationships exist between these variables. For the logistic regression analysis, we recoded the food insecurity variable into 0 (low food insecurity) and 1 (high food insecurity) based on the median split for each measure. ## 3.1. Food Insecurity Results Figure 1 displays food insecurity overlaid on a map of median income. The map shows that participants with the highest food insecurity (larger symbols) tend to live in the zip codes with the lowest median income. In contrast, participants with low food insecurity were dispersed broadly across zip codes, with participants represented in all income categories. Figure 2 displays food insecurity overlaid on a map of store density. There did not appear to be clear a relationship between food insecurity and store density. Logistic regression reveals that store density (X2 (1, $$n = 65$$) = 1.01, $$p \leq 0.32$$) and median income (X2 (1, $$n = 65$$) = 0.037, $$p \leq 0.85$$) did not significantly increase the likelihood of having higher levels of food insecurity. ## 3.2. BMI Results Figure 3 displays BMI overlaid on a map of median income. Participants with the highest BMI tend to live in zip codes with lower median income. Similarly, participants with lower BMIs were dispersed across zip codes with varying levels of median income. Figure 4 displays BMI overlaid on a map of store density. A pattern emerged such that participants with higher BMI tended to live in the south and west sides of Chicago, which have a relatively lower concentration of grocery stores in the city. Participants who lived in suburban zip codes tended to have lower BMI. Thus, city-dwelling participants with fewer grocery stores in their zip codes tended to have high BMIs. Linear regression analysis revealed that BMI was significantly and positively associated with median income (r[63] = −0.353 *, $$p \leq 0.004$$) but not with store density (r[63] = −0.151, $$p \leq 0.230$$). Of note, we did also map the data for both food insecurity and BMI based on race/ethnicity and sex but did not see any patterns in terms of our variables of interest, so we dropped these demographic variables to facilitate the interpretation of the maps. ## 4. Discussion Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minoritized populations. While there has been a proliferation of research linking food insecurity to obesity [1,2,4,5,8,9], these findings are mixed. Given the mixed findings from traditional quantitative approaches, we used GIS approaches to visualize additional geographic factors in Chicago and the surrounding area that may be associated with food insecurity and BMI. GIS analysis revealed that participants with the highest food insecurity tend to live in the zip codes with the lowest median income. This finding is consistent with broader national findings that food insecurity is closely linked to lower income levels [20]. In contrast, participants with low food insecurity were dispersed broadly across zip codes, with participants represented in all income categories. Those with high food insecurity tended to live in areas with lower median income. These findings are interesting and suggests that the relationship between income and high food insecurity may be stronger for those living in poverty whereas the relationship between income and low food insecurity may be less clear. Consistent with previous literature [21,22], there did not appear to be a clear pattern between food insecurity and store density. Some of the smaller shapes, indicating lower levels of food insecurity, do tend to be concentrated on the north side, which has a high concentration of stores, but the patterns are less clear in other areas of the city and suburbs. It may also be that the north side also has a higher level of income, which is really driving the picture of food insecurity above and beyond the concentration of stores. Our study only included grocery stores and excluded other types of stores where individuals living in food deserts may go to shop such as convenience stores and gas stations. While we wanted to limit this analysis to stores where fresh fruits and vegetables are more likely to be sold, there may be a wide range of accessibility to nutritious foods across these convenience stores that is not captured in our data. Regarding BMI, participants with the highest BMI tend to live in zip codes with lower median incomes, which is consistent with previous literature [23]. Similar to our food insecurity findings, however, participants with lower BMIs were dispersed across zip codes with varying levels of median income. This finding may suggest that the relationship between income and high BMI may be stronger for those living in poverty, whereas the relationship between income and lower BMI may be less clear. Additionally, a pattern emerged such that participants with higher BMI tended to live in the south and west sides of Chicago, which have a relatively lower concentration of grocery stores in the city. Participants who lived in suburban zip codes tended to have lower BMI. Thus, city-dwelling participants with fewer grocery stores in their zip codes tended to have higher BMIs. While we also mapped sex and race/ethnicity on each of these maps, we did not find any clear patterns as these sociodemographic variables related to BMI/food insecurity as they relate to income and store density. This was surprising given previous research; however, while our maps did highlight areas of segregation and economic inequities across the city, there were no visible patterns between race/ethnicity and our variables of interest (BMI/Food insecurity), and income showed more of a clear pattern among our participants across studies. Regarding inferential statistics findings, BMI was significantly and positively associated with median income but not with store density. Store density and median income did not significantly increase the likelihood of having higher levels of food insecurity. The finding that BMI was significantly associated with income has been demonstrated consistently in previous research. It is difficult to interpret the other findings given the small size of our sample. In other words, it is difficult to determine if these relationships do not exist, or that we were underpowered to find differences. Thus, it is important to replicate these methods with a larger sample of participants. Our GIS findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence. Through our mapping, we can see the health inequities that exist, particularly in the south and west sides. These areas are high poverty areas with known food deserts. Our findings suggest that it may be helpful to develop community-engaged interventions in these geographic regions to help address obesity and food insecurity related inequities. One strength of GIS analysis is that it creates a clear picture of where inequities lie that is easily interpretable for community members. Building relationships with community partners in high-risk regions and utilizing community-engaged methods to develop and implement interventions given our findings may be an important next step to addressing neighborhood-level social determinants of health driving inequities in obesity and food insecurity. Given that we found patterns between food insecurity and BMI on the income maps, it suggests that income-related inequalities have broader health consequences that should be addressed. In Cook County, a guaranteed income pilot was recently launched to determine whether economic and other inequities in Chicago may be mitigated by universal income [24]. Future research should look at how this initiative impacts BMI/food insecurity as well as other economic and social factors. Our findings may also suggest that it may be important to address food and nutrition deserts across large cities to ensure that those residing outside of the most congested and wealthy areas of the city have close access to healthful foods. Infrastructure building may also mitigate the harms of food deserts by facilitating transit to and from stores. Future research may also consider quantitative methods to determine whether links between income food insecurity/BMI may differ for those with high and low levels of each. Our study should be considered within the context of some important limitations. First, our GIS analysis is limited to one US metropolitan and surrounding area. While it may be generalizable to other similar-sized cities, it may not be generalizable to other smaller-sized cities in other geographic regions of the US. Further, we had a relatively small sample size overall. This limited our ability to conduct regression analyses to determine definitively if there was a statistically significant relationship between BMI and food insecurity and store density/median income. Future research should replicate our methods with larger samples of participants. Additionally, we did not have exact locations of our participants (only zip codes) which limited our ability to conduct a spatial logistic regression. Our sample was also largely comprised of minoritized adolescents and young adults of low-income which is important given the aims of our study but limit the generalizability to other incomes and racial and ethnic groups. Another limitation is that food insecurity was measured using two different measures across studies. While our maps depict the range of scores on each, it is possible that there is some measurement variability across scales. Finally, Study 2 calculated BMI from self-reported height and weight rather than objective measures. Despite its limitations, our study contributes to the literature by using GIS methods rather than typical quantitative measurement strategies so that geographic variations in patterns of these relations are not overlooked. As we see with our results, there are wide ranges of findings across relatively small geographic regions highlighting areas of highest inequity. Average scores mask these geographic differences making it difficult to develop tailored intervention and policy approaches to addressing inequities in areas of the greatest need. We also shed some light on the complicated relationship between food insecurity and BMI by looking at how these vary by income and grocery store density. ## 5. Conclusions Our study suggests that there are geographic inequities across neighborhoods that exist, pointing to a need for greater nuance in the development of intervention and policy approaches to address obesity and food insecurity. Future research may replicate this approach in other cities to see if findings are consistent and may call for more federal level policy approaches to address disparities across neighborhoods in cities who have high levels of health inequity. ## References 1. Coleman-Jensen A., Rabbitt M.P., Gregory C.A., Singh A.. Household Food Security in the United States in 2021* (2022.0) 14-16 2. Testa A., Jackson D.B.. Food Insecurity, Food Deserts, and Waist-to-Height Ratio: Variation by Sex and Race/Ethnicity. J. Community Health (2019.0) 44 444-450. DOI: 10.1007/s10900-018-00601-w 3. Walker R.J., Garacci E., Dawson A.Z., Williams J.S., Ozieh M., Egede L.E.. Trends in Food Insecurity in the United States from 2011–2017: Disparities by Age, Sex, Race/Ethnicity, and Income. Popul. Health Manag. (2021.0) 24 496-501. DOI: 10.1089/pop.2020.0123 4. Brown A.G.M., Esposito L.E., Fisher R.A., Nicastro H.L., Tabor D.C., Walker J.R.. Food insecurity and obesity: Research gaps, opportunities, and challenges. Transl. Behav. Med. (2019.0) 9 980-987. DOI: 10.1093/tbm/ibz117 5. Rasmusson G., Lydecker J.A., Coffino J.A., White M.A., Grilo C.M.. Household food insecurity is associated with binge-eating disorder and obesity. Int. J. Eat. Disord. (2019.0) 52 28-35. DOI: 10.1002/eat.22990 6. Obesity 7. Overweight & Obesity 8. Dhurandhar E.J.. The food-insecurity obesity paradox: A resource scarcity hypothesis. Physiol. Behav. (2016.0) 162 88-92. DOI: 10.1016/j.physbeh.2016.04.025 9. Morales M.E., Berkowitz S.A.. The Relationship between Food Insecurity, Dietary Patterns, and Obesity. Curr. Nutr. Rep. (2016.0) 5 54-60. DOI: 10.1007/s13668-016-0153-y 10. Fiechtner L., Kleinman K., Melly S.J., Sharifi M., Marshall R., Block J., Cheng E.R., Taveras E.M.. Effects of Proximity to Supermarkets on a Randomized Trial Studying Interventions for Obesity. Am. J. Public Health (2016.0) 106 557-562. DOI: 10.2105/AJPH.2015.302986 11. Wedick N.M., Ma Y., Olendzki B.C., Procter-Gray E., Cheng J., Kane K.J., Ockene I.S., Pagoto S.L., Land T.G., Li W.. Access to Healthy Food Stores Modifies Effect of a Dietary Intervention. Am. J. Prev. Med. (2015.0) 48 309-317. DOI: 10.1016/j.amepre.2014.08.020 12. Ghosh-Dastidar B., Cohen D., Hunter G., Zenk S.N., Huang C., Beckman R., Dubowitz T.. Distance to Store, Food Prices, and Obesity in Urban Food Deserts. Am. J. Prev. Med. (2014.0) 47 587-595. DOI: 10.1016/j.amepre.2014.07.005 13. Almalki A., Gokaraju B., Mehta N., Doss D.A.. Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities. ISPRS Int. J. Geo-Inf. (2021.0) 10. DOI: 10.3390/ijgi10110745 14. O’Donnell A.J., Mansfield D., Loiacono B., Withington M.H.C., Cory M., Sagan D., Buscemi J.. Barriers and Facilitators to High School Meal Program Participation: A Qualitative Study of Students and Their Caregivers. J. Sch. Health (2022.0) 92 1186-1193. DOI: 10.1111/josh.13229 15. Buscemi J., Arteaga N., O’Donnell A., Takgbajouah M., Bushnell A., Mirich C., Sagan D., Riley E., Harley P., Cabbler A.. Factors associated with COVID-19 vaccination uptake in community health center patients. Health Psychol. (2022.0). DOI: 10.1037/hea0001241 16. ArcGIS Pro; Version 3.0; 2022 17. The Home of the U.S.. Government’s Open Data 18. List of 67 Zip Codes in Chicago, Illinois 19. City of Chicago Data Portal 20. USDA Economic Research Service 21. Bonanno A., Li J.. Food Insecurity and Food Access in U.S. Metropolitan Areas. Appl. Econ. Perspect. Policy (2015.0) 37 177-204. DOI: 10.1093/aepp/ppu032 22. Herbert B., Christian C.S., Izeogu C.V., Babalola O.. Food Insecurity and the Food Store Environment in the Southern United States: A Case Study of Alabama Counties. Afr. J. Agric. (2017.0) 5 193-201 23. Kim T.J., Knesebeck O.V.D.. Income and obesity: What is the direction of the relationship? A systematic review and meta-analysis. BMJ Open (2018.0) 8 e019862. DOI: 10.1136/bmjopen-2017-019862 24. Cook County Promise Guaranteed Income Pilot
--- title: 'Age-Related Effects of COVID-19 Pandemic on Mechanical Reperfusion and 30-Day Mortality for STEMI: Results of the ISACS-STEMI COVID-19 Registry' authors: - Giuseppe De Luca - Magdy Algowhary - Berat Uguz - Dinaldo C. Oliveira - Vladimir Ganyukov - Oliver Busljetik - Miha Cercek - Lisette Okkels Jensen - Poay Huan Loh - Lucian Calmac - Gerard Roura i Ferrer - Alexandre Quadros - Marek Milewski - Fortunato Scotto D’Uccio - Clemens von Birgelen - Francesco Versaci - Jurrien Ten Berg - Gianni Casella - Aaron Wong Sung Lung - Petr Kala - José Luis Díez Gil - Xavier Carrillo - Maurits Dirksen - Victor Becerra Munoz - Michael Kang-yin Lee - Dafsah Arifa Juzar - Rodrigo de Moura Joaquim - Roberto Paladino - Davor Milicic - Periklis Davlouros - Nikola Bakraceski - Filippo Zilio - Luca Donazzan - Adriaan Kraaijeveld - Gennaro Galasso - Lux Arpad - Lucia Marinucci - Vincenzo Guiducci - Maurizio Menichelli - Alessandra Scoccia - Aylin Hatice Yamac - Kadir Ugur Mert - Xacobe Flores Rios - Tomas Kovarnik - Michal Kidawa - Josè Moreu - Vincent Flavien - Enrico Fabris - Iñigo Lozano Martínez-Luengas - Marco Boccalatte - Francisco Bosa Ojeda - Carlos Arellano-Serrano - Gianluca Caiazzo - Giuseppe Cirrincione - Hsien-Li Kao - Juan Sanchis Forés - Luigi Vignali - Helder Pereira - Stephane Manzo-Silberman - Santiago Ordoñez - Alev Arat Özkan - Bruno Scheller - Heidi Lehitola - Rui Teles - Christos Mantis - Ylitalo Antti - João António Brum Silveira - Cesar Rodrigo Zoni - Ivan Bessonov - Giuseppe Uccello - George Kochiadakis - Dimitrios Alexopulos - Carlos E. Uribe - John Kanakakis - Benjamin Faurie - Gabriele Gabrielli - Alejandro Gutierrez Barrios - Juan Pablo Bachini - Alex Rocha - Frankie C. C. Tam - Alfredo Rodriguez - Antonia Anna Lukito - Veauthyelau Saint-Joy - Gustavo Pessah - Andrea Tuccillo - Alfonso Ielasi - Giuliana Cortese - Guido Parodi - Mohammed Abed Burgadha - Elvin Kedhi - Pablo Lamelas - Harry Suryapranata - Matteo Nardin - Monica Verdoia journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10059932 doi: 10.3390/jcm12062116 license: CC BY 4.0 --- # Age-Related Effects of COVID-19 Pandemic on Mechanical Reperfusion and 30-Day Mortality for STEMI: Results of the ISACS-STEMI COVID-19 Registry ## Abstract Background: The constraints in the management of patients with ST-segment elevation myocardial infarction (STEMI) during the COVID-19 pandemic have been suggested to have severely impacted mortality levels. The aim of the current analysis is to evaluate the age-related effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI within the registry ISACS-STEMI COVID-19. Methods: This retrospective multicenter registry was performed in high-volume PPCI centers on four continents and included STEMI patients undergoing PPCI in March–June 2019 and 2020. Patients were divided according to age (< or ≥75 years). The main outcomes were the incidence and timing of PPCI, (ischemia time longer than 12 h and door-to-balloon longer than 30 min), and in-hospital or 30-day mortality. Results: We included 16,683 patients undergoing PPCI in 109 centers. In 2020, during the pandemic, there was a significant reduction in PPCI as compared to 2019 (IRR 0.843 ($95\%$-CI: 0.825–0.861, $p \leq 0.0001$). We found a significant age-related reduction ($7\%$, $$p \leq 0.015$$), with a larger effect on elderly than on younger patients. Furthermore, we observed significantly higher 30-day mortality during the pandemic period, especially among the elderly ($13.6\%$ vs. $17.9\%$, adjusted HR ($95\%$ CI) = 1.55 [1.24–1.93], $p \leq 0.001$) as compared to younger patients ($4.8\%$ vs. $5.7\%$; adjusted HR ($95\%$ CI) = 1.25 [1.05–1.49], $$p \leq 0.013$$), as a potential consequence of the significantly longer ischemia time observed during the pandemic. Conclusions: The COVID-19 pandemic had a significant impact on the treatment of patients with STEMI, with a $16\%$ reduction in PPCI procedures, with a larger reduction and a longer delay to treatment among elderly patients, which may have contributed to increase in-hospital and 30-day mortality during the pandemic. ## 1. Background Over 100 million cases of COVID-19, and more than 2 million deaths have been reported worldwide, leading to a severe commitment for the healthcare systems [1]. The conversion and occupation of many clinical units for COVID-19 patients led to the suspension of elective procedures and treatment of chronic conditions, whilst the maintenance of services for the management of urgent conditions, such as acute coronary syndromes, required to be preserved. Nevertheless, several previous reports showed a reduction in the number of treated acute coronary cases, accounted for by the fear of contagion preventing patients’ presentation at hospital [2,3,4,5,6,7,8,9,10]. An additional observation was the prolonged time from symptom onset to treatment [11,12,13], secondary to the oversaturation of the emergency departments, that contributed to explaining the higher mortality among STEMI patients observed in 2020. Elderly patients, due to the higher prevalence of comorbidities, are those mostly fragile patients who could have been more largely affected by the pandemic, especially when presenting with ST-segment elevation myocardial infarction. The International Study on Acute Coronary Syndromes–ST-elevation myocardial infarction (ISACS-STEMI) COVID-19 registry provided a snapshot of the treatment and outcomes of STEMI patients treated by primary angioplasty during the COVID-19 pandemic. The current analysis aimed to evaluate the age-related effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI within the registry. ## 2. Study Design and Population This is a large-scale retrospective multicenter registry promoted by the Eastern Piedmont University, Novara, Italy. The initial planning was to include European primary PCI centers [9] but the study was subsequently extended to several other regions on different continents (Latin America, Southeast Asia and North Africa). Included centers were required to perform more than 120 primary PCI/year (with expected average > 10/month), with the STEMI caseload not expected to undergo a planned reorganization of the STEMI network. The initial inclusion period was of 2 months (from 1 March to 30 April) but was subsequently prolonged to 30 June 2020. The data were compared with those retrospectively collected during the same months of 2019 (from 1 March to 30 June). Inclusion criteria: STEMI treated by primary angioplasty (including mechanical reperfusion for failed thrombolysis). Data Collection: Anonymized data were collected through a dedicated CRF. Each center identified a local Principal Investigator. Demographic, clinical and procedural data, including total ischemia and door-to-balloon time, referral to primary PCI facility, COVID-19 positivity, PCI procedural data, and in-hospital mortality were recorded. Data were centralized and managed at Eastern Piedmont University. Statistics. Data were analyzed using SPSS Statistics Software 23.0 (IBM SPSS Inc., Chicago, IL, USA) and R software (version 3.6.2, R Core Team, http://www.R-project.org, accessed on 24 June 2021) by an independent statistician (GC). Quantitative variables were described using median and interquartile range. Mean and confidence intervals were obtained assuming Poisson distributions for count data. Incidence rate ratio (IRR) was defined as the ratio between count data in 2020 and count data in 2019. Data were normalized for the different sizes of the national populations and for the possibly different time period of observation, and we considered the number of STEMI per million of residents in the corresponding population in a year (https://knoema.com/atlas/topics/Demographics/Age/Population-aged-75-years, accessed on 24 June 2021). Poisson regression models (with log link function) were applied to compare the incidence rates of primary PCI per million residents per year in 2020 with the same rate in 2019, correcting for possible impact of major risk factors [14]. Details are described in the Supplementary Materials (Section S1.1). Analyses were also conducted according to major European geographic areas (see Supplementary Materials) and subgroups of patients, according to age, gender, diabetes and hypertension. A subsequent analysis was based on individual patient data, which were grouped according to the year of the intervention (2019 vs. 2020). Absolute frequencies and percentages were used for qualitative variables. ANOVA or Mann–Whitney and chi-square tests were used for continuous and categorical variables, respectively. Normal distribution of continuous variables was tested by the Kolmogorov–Smirnov test. Multivariable logistic regression analyses were performed to identify the impact of the year of intervention on time delays and mortality after adjustment for baseline confounding factors between the two groups. All significant variables (set at a p-value < 0.1) were entered “in block” into the model. A $p \leq 0.05$ was considered statistically significant. The data coordinating center was established at the Eastern Piedmont University. Sample size calculation. In view of the observational nature of this registry, no sample size calculations or statistical power analysis were performed. ## 3. Results A total of 109 centers from four continents (Europe = 90; Latin America = 10; Southeast Asia = 7; North Africa = 2) participated (Table S1), leading to the inclusion of 16,674 STEMI patients, of whom 9044 patients were admitted in 2019 and 7630 patients in 2020. A total of 3178 patients were elderly ($19.1\%$ of the total population), with a similar proportion in both 2019 and 2020. The number of STEMI patients treated percutaneously per million residents showed a consistent reduction, on average, from 559 ($95\%$ CI 514–607) in 2019 to 477 ($95\%$ CI 435–522) in 2020. ( Figure 1 and Figures S1–S3). The incidence rate ratio (IRR) was 0.843 ($95\%$ CI 0.825–0.861, $p \leq 0.0001$), showing a significant reduction of $15.7\%$ in the number of STEMI cases from 2019 to 2020. We found a significant age-related reduction ($7\%$, $$p \leq 0.015$$), with a larger effect in the elderly than in younger patients. Among elderly patients, the number of STEMI cases treated percutaneously per million residents had a consistent reduction, on average, from 1384 ($95\%$ CI 1312–1459) in 2019 to 1099 ($95\%$ CI 1035–1166) in 2020 (incidence rate ratio (IRR) 0.80 ($95\%$ CI 0.73–0.87), $p \leq 0.001$) (Figure 1 and Figure S1). A significant heterogeneity was observed across the centers (IRR had high variability between centers measured by std error = 0.35, ANOVA chi-square test for random vs. fixed effect Poisson model: $p \leq 0.001$) (Figure 1). The number of STEMI cases treated percutaneously per million residents had a consistent reduction, on average, from 484 ($95\%$ CI 442–529) in 2019 to 420 ($95\%$ CI 381–462) in 2020 in younger patients, a less marked reduction (IRR was 0.856 ($95\%$ CI 0.82–0.90, $p \leq 0.0001$) as compared to elderly patients (Figure 1 and Figure S2). A significant heterogeneity was observed across centers (IRR had high variability between centers measured by a std error = 0.22, ANOVA chi-square test for random vs. fixed effect Poisson model: $p \leq 0.001$) (Figure 1). The heterogeneity across centers was not related to the incidence of COVID-19 disease, nor to COVID-19-related mortality (Figures S3–S6). In fact, in both elderly and young patients, the reduction in STEMI procedures was not associated with the national number of COVID-19-positive patients, at either 30th of April (elderly: r = −0.075, p value = 0.438; young: $r = 0.027$, p value = 0.784) or 30th of June (elderly r = −0.028, p value = 0.773, Figure S3; young: $r = 0.111$, p value 0.25, Figure S4), nor with the national number of COVID-19-related deaths at 30th of April (elderly: r = −0.070, p value = 0.467; young: r = −0.002, p value = 0.98) or 30th of June (elderly: r = −0.120, p value = 0.221, Figure S5; young r = −0.017, p value = 0.863, Figure S6). Almost all participating continents had a reduction in STEMI cases (Figures S7–S10), that was significant only for European centers, whereas a larger reduction was observed in the young rather than elderly patients in North Africa. Furthermore, we used Poisson regression to investigate the reduction in STEMI in subgroups of subjects in both elderly and young patients, by gender, hypertension, diabetes and smoking. We found a significant difference in this reduction between smokers (IRR = 0.85 ($95\%$ CI 0.80, 0.90), $p \leq 0.0001$) and non-smokers in young (IRR 0.78 ($95\%$ CI 0.73, 0.82) < 0.0001) (Figure S11) (p int = 0.024) but not in elderly patients (IRR 0.78 ($95\%$ CI 0.73, 0.82) < 0.0001) (Figure S12). No significant interaction was found for other variables (Figures S11–S14). ## 4. Baseline Demographic and Clinical Characteristics Individual data analysis was restricted to 16,083 patients with complete demographic, clinical procedural and outcome data (complete cases: $96.4\%$), 8698 in 2019 and 7385 in 2020. Table 1 shows the baseline characteristics of elderly and young patients according to the year of intervention. No difference was observed in baseline characteristics. As shown in Table 1, the COVID-19 pandemic was associated with a longer ischemia time, in both elderly and young patients, whereas a significantly longer door-to-balloon time was observed only in young patients (Figure 2). The association between the COVID-19 pandemic and ischemia time longer than 12 h was confirmed, after correction for baseline clinical confounders in both the elderly (adjustment for geographic area, family history for CAD, radial access, door-to-balloon > 30 min and in-hospital RASI therapy; adjusted OR = 1.27 (1.02–1.59), $$p \leq 0.034$$), and young patients (adjustment for smoking, geographic area, previous PCI, door-to-balloon time > 30 min, DES, bivalirudin, mechanical support, in-hospital RASI therapy; adjusted OR = 1.35 (1.2–1.51, $p \leq 0.001$). No significant interaction was observed for major risk factors between young (gender, $$p \leq 0.19$$; diabetes, $$p \leq 0.25$$; hypertension, $$p \leq 0.89$$; smoking, $$p \leq 0.4$$), and elderly patients (gender, $$p \leq 0.36$$; diabetes, $$p \leq 0.12$$; hypertension, $$p \leq 0.57$$; smoking, $$p \leq 0.21$$). The association between the COVID-19 pandemic and a door-to-balloon time longer than 30 min was confirmed after correction for baseline clinical confounders in young patients (adjustment for smoking, geographic area, previous PCI, ischemia time > 12 h, DES, bivalirudin, mechanical support, in-hospital RASI therapy; adjusted OR =1.11 (1.03–1.19), $$p \leq 0.006$$). No significant interaction was observed for major risk factors among young patients (gender, $$p \leq 0.46$$; diabetes, $$p \leq 0.32$$; hypertension, $$p \leq 0.12$$; smoking, $$p \leq 0.46$$). No difference was observed in the rate of cardiogenic shock at presentation, infarct location, out-of-hospital cardiac arrest, or rescue procedures after failed thrombolysis. The prevalence of SARS-CoV 2 positivity was low in both young and elderly patients (81 cases, $0.6\%$ vs. 28 cases, $0.9\%$, $$p \leq 0.071$$). ## 5. Procedural Characteristics Concerning procedural characteristics (Table 2), the use of DES and radial access were more frequent in 2020 ($92.7\%$ vs. $90.6\%$, $$p \leq 0.003$$) among young patients, whereas no differences were observed for other procedural variables. ## 6. In-Hospital and 30-Day Mortality A significantly higher in-hospital mortality was observed in 2020 as compared to 2019 in both elderly (180 deaths, $10.7\%$ vs. 200 deaths, $14.7\%$, OR ($95\%$ CI) = 1.43 (1.15–1.78), $p \leq 0.001$) and young patients (277 deaths, $3.9\%$ vs. 281 deaths, $4.7\%$, OR ($95\%$ CI) = 1.19 (1.01–1.41), $$p \leq 0.043$$) (Figure 2). The significantly poorer outcomes observed in STEMI patents treated in 2020 persisted after correction for all potential confounding factors in both elderly (adjustment for family history for CAD, geographic area, ischemia time, time, radial access, and in-hospital RASI) (adjusted OR ($95\%$ CI) = 1.64 (1.31–2.06), $p \leq 0.001$), and young patients (adjustment for smoking, geographic area, previous PCI, ischemia time, door-to-balloon time, DES, bivalirudin, mechanical support, in-hospital RASI therapy; adjusted OR ($95\%$ CI) = 1.22 (1.01–1.46), $$p \leq 0.036$$) (p interaction 0.12). Data on 30-day mortality were available in 14,303 ($88.9\%$). Patients treated in 2020 had a significantly higher mortality in both elderly (201 deaths, $13.6\%$ vs. 215 deaths $17.9\%$, adjusted HR ($95\%$ CI) = 1.55 (1.24–1.93), $p \leq 0.001$) and young patients (303 deaths, $4.8\%$ vs. 308 death, $5.7\%$; adjusted HR ($95\%$ CI) = 1.25 (1.05–1.49), $$p \leq 0.013$$) (p interaction 0.24) (Figure 3). SARS-CoV2 positivity was similarly associated with high mortality in both young (in hospital: $18.5\%$ vs. $4.2\%$, OR ($95\%$ CI) = 5.2 (2.95–9.2), $p \leq 0.001$; 30-day: $26.5\%$ vs. $5.1\%$, OR ($95\%$ CI) = 4.79 (2.92–7.85), $p \leq 0.001$) and elderly patients (in-hospital: $46.4\%$ vs. $12.2\%$, OR ($95\%$ CI) = 6.3 (2.96–13.3), $p \leq 0.001$; 30-day: $58.3\%$ vs. $15.2\%$, OR ($95\%$ CI) = 4.22 (2.47−7.18), $p \leq 0.001$). ## 7. Discussion The ISACS-STEMI COVID-19 represents the largest registry worldwide, including more than 16,000 patients STEMI patients undergoing primary PCI during the COVID-19 pandemic, treated from March to June 2019 and 2020, and the first to provide data on 30-day mortality. This is the first report investigating the age-related impact of the COVID-19 pandemic on the management of STEMI. We found a significant reduction in the number of primary PCI procedures during the pandemic (in 2020) as compared to 2019, that was more marked in elderly patients. Although there was significant heterogeneity across the centers, it was not explained by the rate of either local or national deaths due to COVID-19. Furthermore, in-hospital and 30-day mortality were higher during the pandemic period, especially among elderly patients, likely reflecting the significantly longer ischemia time associated with impaired logistics and treatment during this challenging period. Direct and indirect effects COVID-19 on cardiovascular disease and mortality have been identified [15]. Reports about the presence of inflammatory pathophysiological mechanisms, triggering plaque disruption and generating a pro-thrombotic milieu [16,17,18] supported an expected rise in the number of patients presenting with ACS during the pandemic. Conversely, initial reports from small-sized registries showed a remarkable reduction in the number of acute coronary patients. These data were subsequently confirmed in a larger Chinese registry [8] and in European cohorts, including patients treated in March and April 2019–2020 [9,10]. Various factors are likely to have contributed to such a finding, with huge national and regional differences that could vary from −20 to −$70\%$ compared to pre-pandemic times [2,3,4,5,6,7,8,9,10]. It has been speculated that the need to shift healthcare resources for the treatment of COVID-19 patients, the isolation induced by the lock-down and the fear of contamination or burdening already overwhelmed clinical services could have prevented their presentation at hospital. Patients’ behavior may have contributed to increase morbidity and mortality, especially in STEMI patients in whom prolonged ischemia negatively impacted myocardial salvage, left-ventricular function, and both short and long-term survival [11,12,13]. Challenges in logistics for the ambulance system and emergency departments and the potential need to rule out potential COVID-19 positivity before admission may have contributed to the overall delay in treating patients with STEMI during the pandemic. Furthermore, effects associated with social distancing and isolation may also have played a role, including emotional stress, depression, and more sedentary lifestyle. However, so far, no study has investigated the age-related impact of the COVID-19 pandemic on STEMI. In fact, elderly patients represent a fragile, high-risk population, with known atypical symptoms and more prolonged timing to diagnosis and treatment, higher thrombotic and bleeding risk and worse periprocedural outcome; factors that are expected to contribute to a higher susceptibility of this population to the deleterious direct and indirect effects of COVID-19 [19,20,21,22,23]. The data from the ISACS-STEMI COVID-19 registry, conducted in high-volume primary PCI centers on several continents (Europe, Latin America, Southeast Asia and North Africa provide relevant, reliable information for this controversial debate. Consistent with other small-sized registries and our previous report, we found a significant reduction in the number of STEMI patients undergoing mechanical reperfusion. However, the reduction was significantly higher in elderly patients as compared to young patients. A major explanation for this finding is certainly the larger risk profile and presence of comorbidities among elderly patients. In fact, their frailty, lack of support from family members and the higher risk of mortality in the case of SARS-CoV2 infection, have certainly increased the fear of infection restraining them from contacting the emergency system even in the case of chest pain. Moreover, the initial misclassification of patients with dyspnea may have delayed access to the reperfusion therapies. Notably, in step with previous reports, the reduction in STEMI patients undergoing mechanical revascularization was not consistent across all the centers. Additionally, it was not related to the local or national incidence of COVID-19 or rates of death due to COVID-19 in both groups of patients. We cannot exclude local disparities among health care organizations and management of cardiovascular emergencies during the COVID-19 pandemic, which may have impacted on both the fear of contagion and the risk of out-of-hospital sudden death. Both factors may have contributed to the observed heterogeneity across centers. We found that the COVID-19 pandemic was associated with a significantly longer ischemia time and rates of late presentation similarly occurring in young and elderly patients, whereas a higher rate of door-to-balloon time beyond 30 min was observed in both groups but statistically significant only in younger patients. This finding was presumably due to the larger sample size and statistical power of the young patients’ group. The longer door-to-balloon time may certainly be explained by organizational delays due to the specific COVID-19 protocols for screening patients and preparing equipment and personnel in the catheterization laboratory. Several additional factors may have played a role in the observed longer ischemia time during the COVID-19 pandemic, including both direct patients’ and emergency system-related delays, as previously described [24]. The longer delay to treatment contributes to the significantly overall higher mortality observed during this pandemic, as compared to 2019, that was confirmed after correction for major differences and, additionally, for COVID-19 positivity, in both young and elderly patients. We observed a more remarkable increase in mortality during the pandemic in the elderly, potentially explained by the larger thrombotic risk profile and fragility of elderly patients, but not ischemia time. However, we did not find a significant statistical interaction between the two groups in terms of mortality. Importantly, the COVID-19 positive population represented a very high-risk subgroup in both age groups, confirming recent reports by a smaller-sized study and our own group [9,25]. In light of the large vaccine campaign recently started worldwide and based on available data, it is extremely important that scientific societies and health authorities promote public campaigns in order to highlight the importance of the prompt recognition and response to the characteristic symptoms of acute myocardial infarction and the positive impact on the outcomes, especially among elderly patients. ## 8. Limitations This study is limited by its retrospective design. It was conducted during a challenging pandemic emergency, and we expected to encounter missing data. Nevertheless, our main data analysis and conclusions are based on counts and, therefore, the overall cohort of patients was included. Furthermore, even in the analysis based on full individual patient data, this limitation and the potential risk of type II error was largely overcome by the high rate of complete cases (>$95\%$) and the high statistical power due to the size of the study population. Finally, even though in the present registry of patients undergoing mechanical reperfusion, we did not find any difference in out-of-hospital cardiac arrest, we cannot exclude the possibility that the reduction in STEMI patients observed in 2020 may partly have resulted from higher rates of pre-hospital death due to longer delays to first medical contact, as was described during the COVID-19 pandemic [18,25]. Finally, primary PCI being the major reperfusion strategy worldwide, our registry was restricted to primary PCI centers. Therefore, we could not provide data on STEMI patients treated by thrombolysis. ## 9. Conclusions The COVID-19 pandemic had a relevant impact on the treatment of patients with STEMI, with a significant reduction in primary PCI procedures, especially in elderly patients. We observed longer delays to treatment, which may have contributed to the increased in-hospital and 30-day mortality during this pandemic. Our data suggest that health authorities, supported by scientific societies, should take vigorous action to prevent patients from neglecting characteristic symptoms of an acute myocardial infarction, especially among elderly patients. ## References 1. Li Q., Guan X., Wu P., Wang X., Zhou L., Tong Y., Ren R., Leung K.S.M., Lau E.H.Y., Wong J.Y.. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N. Engl. J. Med. (2020.0) 26 1199-1207. DOI: 10.1056/NEJMoa2001316 2. Wood S.. The Mystery of the Missing STEMIs during the COVID-19 Pandemic. tctMD: 2020 3. Garcia S., Albaghdadi M.S., Meraj P.M., Schmidt C., Garberich R., Jaffer F.A., Dixon S., Rade J.J., Tannenbaum M., Chambers J.. Reduction in ST-Segment Elevation Cardiac Catheterization Laboratory Activations in the United States during COVID-19 Pandemic. J. Am. Coll. Cardiol. (2020.0) 75 34913-34915. DOI: 10.1016/j.jacc.2020.04.011 4. Tam C.F., Cheung K.S., Lam S., Wong A., Yung A., Sze M., Lam Y.M., Chan C., Tsang T.C., Tsui M.. Impact of Coronavirus Disease 2019 (COVID-19) Outbreak on ST-Segment-Elevation Myocardial Infarction Care in Hong Kong, China. Circ. Cardiovasc. Qual. Outcomes (2020.0) 13 e006631. DOI: 10.1161/CIRCOUTCOMES.120.006631 5. Piccolo R., Bruzzese D., Mauro C., Aloia A., Baldi C., Boccalatte M., Bottiglieri G., Briguori C., Caiazzo G., Calabrò P.. Population Trends in Rates of Percutaneous Coronary Revascularization for Acute Coronary Syndromes Associated with the COVID-19 Outbreak. Circulation (2020.0) 141 2035-2037. DOI: 10.1161/CIRCULATIONAHA.120.047457 6. Roffi M., Guagliumi G., Ibanez B.. The Obstacle Course of Reperfusion for STEMI in the COVID-19 Pandemics. Circulation (2020.0) 141 1951-1953. DOI: 10.1161/CIRCULATIONAHA.120.047523 7. De Rosa S., Spaccarotella C., Basso C., Calabrò M.P., Curcio A., Filardi P.P., Mancone M., Mercuro G., Muscoli S., Nodari S.. Reduction of hospitalizations for myocardial infarction in Italy in the COVID-19 era. Società Italiana di Cardiologia and the CCU Academy investigators group. Eur. Heart J. (2020.0) 41 2083-2088. DOI: 10.1093/eurheartj/ehaa409 8. Xiang D., Xiang X., Zhang W., Yi S., Zhang J., Gu X., Xu Y., Huang K., Su X., Yu B.. Management and Outcomes of Patients with STEMI during the COVID-19 Pandemic in China. J. Am. Coll. Cardiol. (2020.0) 76 1318-1324. DOI: 10.1016/j.jacc.2020.06.039 9. De Luca G., Verdoia M., Cercek M., Jensen L.O., Vavlukis M., Calmac L., Johnson T., Ferrer G.R., Ganyukov V., Wojakowski W.. Impact of COVID-19 Pandemic on Mechanical Reperfusion for Patients With STEMI. J. Am. Coll. Cardiol. (2020.0) 76 2321-2330. DOI: 10.1016/j.jacc.2020.09.546 10. De Luca G., Cercek M., Jensen L.O., Vavlukis M., Calmac L., Johnson T., Roura I Ferrer G., Ganyukov V., Wojakowski W., von Birgelen C.. Impact of COVID-19 pandemic and diabetes on mechanical reperfusion in patients with STEMI: Insights from the ISACS STEMI COVID 19 Registry. Cardiovasc. Diabetol. (2020.0) 19 215. DOI: 10.1186/s12933-020-01196-0 11. De Luca G., Suryapranata H., Ottervanger J.P., Antman E.M.. Time delay to treatment and mortality in primary angioplasty for acute myocardial infarction: Every minute of delay counts. Circulation (2004.0) 109 1223-1225. DOI: 10.1161/01.CIR.0000121424.76486.20 12. De Luca G., van’t Hof A.W., de Boer M.J., Ottervanger J.P., Hoorntje J.C., Gosselink A.T., Dambrink J.H., Zijlstra F.. Suryapranata HTime-to-treatment significantly affects the extent of ST-segment resolution and myocardial blush in patients with acute myocardial infarction treated by primary angioplasty. Eur. Heart J. (2004.0) 25 1009-1013. DOI: 10.1016/j.ehj.2004.03.021 13. Ibanez B., James S., Agewall S., Antunes M.J., Bucciarelli-Ducci C., Bueno H., Caforio A.L.P., Crea F., Goudevenos J.A., Halvorsen S.. ESC Scientific Document Group.2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur. Heart J. (2018.0) 39 119-177. PMID: 28886621 14. Gelman A., Hill J.. Data Analysis Using Regression and Multilevel/Hierarchical Models (2007.0) 15. Madjid M., Safavi-Naeini P., Solomon S.D., Vardeny O.. Potential Effects of Coronaviruses on the Cardiovascular System: A Review. JAMA Cardiol. (2020.0) 5 831-840. DOI: 10.1001/jamacardio.2020.1286 16. Madjid M., Vela D., Khalili-Tabrizi H., Casscells S.W., Litovsky S.. Systemic infections cause exaggerated local inflammation in atherosclerotic coronary arteries: Clues to the triggering effect of acute infections on acute coronary syndromes. Tex. Heart Inst. J. (2007.0) 34 11-18. PMID: 17420787 17. Tang N., Bai H., Chen X., Gong J., Li D., Sun Z.. Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy. J. Thromb. Haemost. (2020.0) 18 1094-1099. DOI: 10.1111/jth.14817 18. Baldi E., Sechi G.M., Mare C., Canevari F., Brancaglione A., Primi R., Klersy C., Palo A., Contri E., Ronchi V.. Lombardia CARe Researchers. Out-of-Hospital Cardiac Arrest during the COVID-19 Outbreak in Italy. N. Engl. J. Med. (2020.0) 383 496-498. DOI: 10.1056/NEJMc2010418 19. De Luca G., van’t Hof A.W., Ottervanger J.P., Hoorntje J.C., Gosselink A.T., Dambrink J.H., de Boer M.J., Suryapranata H.. Ageing, impaired myocardial perfusion, and mortality in patients with ST-segment elevation myocardial infarction treated by primary angioplasty. Eur. Heart J. (2005.0) 26 662-666. DOI: 10.1093/eurheartj/ehi110 20. De Luca G., van’t Hof A.W., Huber K., Gibson C.M., Bellandi F., Arntz H.R., Maioli M., Noc M., Zorman S., Secco G.G.. Impact of advanced age on myocardial perfusion, distal embolization, and mortality patients with ST-segment elevation myocardial infarction treated by primary angioplasty and glycoprotein IIb-IIIa inhibitors. Heart Vessel. (2014.0) 29 15-20. DOI: 10.1007/s00380-013-0323-4 21. Verdoia M., Pergolini P., Nardin M., Rolla R., Tonon F., Kedhi E., Suryapranata H., Carriero A., De Luca G.. Novara Atherosclerosis Study Group (NAS). Impact of aging on platelet reactivity in diabetic patients receiving dual antiplatelet therapy. J. Thromb. Thrombolysis (2019.0) 48 413-421. DOI: 10.1007/s11239-019-01873-2 22. Verdoia M., Pergolini P., Rolla R., Nardin M., Schaffer A., Barbieri L., Marino P., Bellomo G., Suryapranata H., De Luca G.. Advanced age and high-residual platelet reactivity in patients receiving dual antiplatelet therapy with clopidogrel or ticagrelor. J. Thromb. Haemost. (2016.0) 14 57-64. DOI: 10.1111/jth.13177 23. Silverio A., Di Maio M., Citro R., Esposito L., Iuliano G., Bellino M., Baldi C., De Luca G., Ciccarelli M., Vecchione C.. Cardiovascular risk factors and mortality in hospitalized patients with COVID-19: Systematic review and meta-analysis of 45 studies and 18,300 patients. BMC Cardiovasc. Disord. (2021.0) 21. DOI: 10.1186/s12872-020-01816-3 24. Stefanini G.G., Montorfano M., Trabattoni D., Andreini D., Ferrante G., Ancona M., Metra M., Curello S., Maffeo D., Pero G.. ST-Elevation Myocardial Infarctionin Patients with COVID-19: Clinical and Angiographic Outcomes. Circulation (2020.0) 141 2113-2116. DOI: 10.1161/CIRCULATIONAHA.120.047525 25. Marijon E., Karam N., Jost D., Perrot D., Frattini B., Derkenne C., Sharifzadehgan A., Waldmann V., Beganton F., Narayanan K.. Out-of-hospital cardiac arrest during the COVID-19 pandemic in Paris, France: A population-based, observational study. Lancet Public Health (2020.0) 5 e437-e443. DOI: 10.1016/S2468-2667(20)30117-1
--- title: 2 Hydroxybutyric Acid-Producing Bacteria in Gut Microbiome and Fusobacterium nucleatum Regulates 2 Hydroxybutyric Acid Level In Vivo authors: - Fujian Qin - Jiankang Li - Tianxiao Mao - Shuo Feng - Jing Li - Maode Lai journal: Metabolites year: 2023 pmcid: PMC10059959 doi: 10.3390/metabo13030451 license: CC BY 4.0 --- # 2 Hydroxybutyric Acid-Producing Bacteria in Gut Microbiome and Fusobacterium nucleatum Regulates 2 Hydroxybutyric Acid Level In Vivo ## Abstract 2-hydroxybutyric acid (2HB) serves as an important regulatory factor in a variety of diseases. The circulating level of 2HB in serum is significantly higher in multiple diseases, such as cancer and type 2 diabetes (T2D). However, there is currently no systematic study on 2HB-producing bacteria that demonstrates whether gut bacteria contribute to the circulating 2HB pool. To address this question, we used BLASTP to reveal the taxonomic profiling of 2HB-producing bacteria in the human microbiome, which are mainly distributed in the phylum Proteobacteria and Firmicutes. In vitro experiments showed that most gut bacteria ($\frac{21}{32}$) have at least one path to produce 2HB, which includes Aspartic acid, methionine, threonine, and 2-aminobutyric acid. Particularly, Fusobacterium nucleatum has the strongest ability to synthesize 2HB, which is sufficient to alter colon 2HB concentration in mice. Nevertheless, neither antibiotic (ABX) nor Fusobacterium nucleatum gavage significantly affected mouse serum 2HB levels during the time course of this study. Taken together, our study presents the profiles of 2HB-producing bacteria and demonstrates that gut microbiota was a major contributor to 2HB concentration in the intestinal lumen but a relatively minor contributor to serum 2HB concentration. ## 1. Introduction 2-hydroxybutyric acid (2HB) is a metabolite biomarker in various diseases. For example, several studies have indicated that 2HB is a major risk factor for insulin resistance and type 2 diabetes (T2D) [1,2,3,4,5]. The underlying biochemical mechanisms may involve increased lipid oxidation and oxidative stress [5]. Furthermore, 2HB is a valid marker of dysregulation associated with cognitive decline [6] and mitochondrial disease [7]. Increased serum 2HB level is often present in lung cancer [8,9], colorectal cancer [10,11], and hepatocellular carcinoma [12,13]. In contrast, 2HB fell significantly during the first three months of hormonal therapy for patients with prostate cancer [14]. Recent research shows that serum 2HB was enriched in COVID-19 patients versus healthy controls [15]. Importantly, circulating 2HB levels are a robust marker of an elevated hepatic cytosolic NADH/NAD+ ratio, and this high NADH/NAD+ ratio is causally related to hepatic insulin resistance and glucose tolerance [16]. Previous work suggests that of the 11 organs tested, only the liver and skin released 2HB in fasted pigs [17]. It was recently shown that when localized to the nucleus, lactate dehydrogenase A (LDHA) gains a non-canonical enzyme activity to produce 2HB [18]. Notably, LDH has already been identified in several microbial genera, including Lactobacillus, Fusobacterium, Clostridium, and Desulfovibrio [19,20,21,22]. Previous research has shown that 2HB concentration in the intestinal lumen of CRC patients was negatively associated with pyramidobacter piscolens and coprobacter fastidiosus [23]. Intestinal 2HB level was strongly negatively correlated with Ruminococcus, Lachnospiraceae_UCG.001, and Lachnospiraceae_UCG.006 in ischemic stroke rats treated with Naomaitong [24]. Additionally, vancomycin pretreatment increased cecum and serum levels of 2HB [25]. The above studies indicate that aside from the host, gut microbiota could also potentially influence 2HB levels. Whereas, to date, no studies have systematically identified 2HB-producing bacteria and whether gut bacteria can directly affect the level of 2HB in serum. In this study, we identified potential bacteria that can synthesize 2HB in human gut microbiota by BLASTP (v 2.10.1+). These bacteria are mainly distributed in Proteobacteria and Firmicutes. Further in vitro fermentation experiments were conducted to test the ability of representative human gut bacteria to produce 2HB, and the results show that most of them can synthesize 2HB. We then discovered significantly reduced intestinal lumen and fecal 2HB levels in antibiotic-treated mice. In contrast, oral gavage of Fusobacterium nucleatum was effective in augmenting 2HB levels in colon contents. However, serum concentrations of 2HB did not differ significantly in ABX or Fusobacterium nucleatum treatment group. In conclusion, this study is the first systematic study of 2HB-producing bacteria and provides important insights into the relationship between intestinal bacteria and the level of 2HB. ## 2.1. Chemicals Antibiotic cocktails for preparation of pseudo sterile mice: ampicillin sodium (Aladdin, CAS: 69-52-3), metronidazole (Shanghai yuan ye, CAS: 443-48-1), vancomycin hydrochloride (Shanghai yuan ye, CAS: 1404-93-9), neomycin sulfate (Shanghai yuan ye, CAS: 1405-10-3), gentamicin sulfate (Aladdin, CAS: 1405-41-0). B vitamins for preparation of culture media: folic acid (Macklin, CAS: 59-30-3), pyridoxine hydrochloride (Macklin, CAS: 58-56-0), riboflavin (Macklin, CAS: 83-88-5), biotin (Macklin, CAS: 58-85-5), thiamine (Macklin, CAS: 59-43-8), nicotinic acid (Macklin, CAS: 59-67-6), calcium pantothenate (Macklin, CAS: 137-08-6), Vitamin B12 (Macklin, CAS: 68-19-9), p-aminobenzoic acid (Macklin, CAS: 150-13-0), thioctic acid (Macklin, CAS: 62-46-4). Standards, internal standards, and derivatization reagents used for 2HB quantification: Sodium DL-2-Hydroxybutyrate (TCI Shanghai, CAS 5094-24-6), (2,3,3-2H3)-Sodium(±)-2-Hydroxybutyrate (ZZBIO, CAS: 1219798-97-6), 3-Nitrophenylhydrazine hydrochloride (Aladdin, CAS: 636-95-3), 1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (Macklin, CAS: 1892-57-5). ## 2.2. Sequence and BLASTP of Enzyme It is generally believed that the more similar the protein sequence is, the more similar its structure is, and it may have similar functions. BLASTP is a protein primary structure sequence alignment program developed and managed by NCBI (National Center for Biotechnology Information). The input protein sequence can be compared with the known sequence in the database to obtain the sequence similarity. In this study, sequence acquisition and comparison were essential, as described previously [26]. Briefly, the query enzyme sequences were manually collected from the Refseq database of NCBI. After filtering by peptide length, all sequences were compared against reference sequences from the HMP database (https://www.hmpdacc.org, accessed on 2 March 2021). Performing a BLASTP search and applying a cutoff of either $45\%$ or $40\%$ (LDH) for sequence identity along with an e-value threshold of 1 × 10−5 (Figure 1). ## 2.3. Bacterial Strains and Culture Conditions Bacterial strains were purchased from the American Tissue Culture Collection (ATCC, Manassas, VA, USA), DSMZ, JCM, BNCC, or biobw. Detailed information on the bacteria strains used in this study is provided in Supplementary Table S1. Aerobic bacteria were cultured at 37 °C in a liquid medium (MPYG, FT, BHI) or in a medium solidified by the addition of $1.5\%$ agar, whereas anaerobic bacteria were cultured under strictly anaerobic conditions ($80\%$ N2, $10\%$ H2, $10\%$ CO2) in an anaerobic chamber. The colony-forming units (CFU) of bacteria were estimated in a spectrophotometer by recording the absorbance at 600 nm. Bacterial strains were identified based on the sequence of their 16S rRNA gene. In short, 16S sequences were amplified using universal PCR primers: 27F (AGAGTTTGATCCTGGCTCAG) and 1492R (GGTTACCTTGTTACGACTT), and the PCR product was subsequently sequenced at Sangon Biotech (Shanghai, China). ## 2.4. Fermentation Studies To resuscitate the strains from glycerol stocks, they were streaked onto plates, and single colonies were chosen to inoculate a liquid medium. The cultures were then incubated at 37 °C for 24 h, and this growth was used to establish the first seed culture. Stocks solution of the individual substrate (Asp, Met, Thr, and 2AB) by dissolving them in PBS at 20 mg/mL. Then filter and sterilize using a 0.22 µm filter. For determination of 2HB production, bacteria were inoculated at $4\%$ of the total volume in flasks and cultured for 24 h at 37 °C, and then cultured in the presence of substrate (950 µL bacterial culture plus 50 µL stocks solution of the substrate) solution at a final concentration of 1 mg/mL for another 4 h. 950 µL blank medium plus 50 µL PBS was used as a blank control to test the ability to synthesize 2HB, and 950 µL bacterial culture medium plus 50 µL PBS was used as background control to screen the substrate preference of bacteria. ## 2.5. Animal Experiments SPF female C57BL/6J mice (5 weeks) were purchased from GemPharmatech Co. Ltd. (Nanjing, China). All animals were housed in a 12 h/12 h light-dark cycle environment and had full access to food and water. After 1 week of adaptation, mice were randomly divided into different groups. ABX mice were prepared following the same method as before [27]. Briefly, mice were treated with a combination of various antibiotics (Ampicillin, 5 mg/mL; Gentamicin, 5 mg/mL; Neomycin, 5 mg/mL; Metronidazole, 5 mg/mL; Vancomycin, 2.5 mg/mL) via oral gavage at 0.2 mL per day. After 7 days, serum and fecal were dissected from control and antibiotic-treated (ABX) mice for 2HB quantitative. Meanwhile, fecal samples were also collected, resuspended in sterile water, filtered with a 70 µm strainer, and applied on antibiotic-free agar plates prepared with brain heart infusion (BHI) medium. Both anaerobic (37 °C for 3 days) and aerobic cultures (37 °C for 2 days) were performed to confirm microbiota depletion [28]. For cultured bacteria transfer, Fusobacterium nucleatum (Fn) was resuspended in PBS and orally administered doses of 2 × 108 CFU per mouse. Specifically, the 2HB group mice were treated with 100 mg/kg 2HB in 0.2 mL of PBS by oral gavage daily; the Fn group mice received 2 × 108 Fn per mouse daily; the Fn plus Met group mice that received 2 × 108 Fn and 100 mg/kg methionine per mouse daily; the Fn plus Thr group mice that received 2 × 108 Fn and 100 mg/kg Threonine per mouse daily; the Fn plus 2AB group mice that received 2 × 108 Fn and 100 mg/kg 2-aminobutyric acid per mouse daily; the control group received only the carrier, PBS, by gavage in an equal volume. All the protocols for animal experiments were reviewed and approved by the Animal Ethics Committee of China Pharmaceutical University (27 January 2022, Approval Code: 2022-01-027). ## 2.6. Quantitative PCR Analysis for Fusobacterium Nucleatum Abundance Fn quantification was performed as described before. Briefly, genomic DNA (gDNA) was extracted from frozen mice feces samples using the QIAamp Fast DNA Stool Kit (51604, QIAGEN, Hilden, Germany), according to the manufacturer’s instructors [29,30]. Fn quantification was performed by using ChamQ SYBR qPCR Master Mix (Vazyme) on a QuantStudio3 qPCR machine. Relative abundance was calculated by the 2−ΔCt method. Universal Eubacteria (Eu)16S was used as a reference gene. The following primer sets were used: gDNA from mouse stool was examined. Fn-F: 5′-CAACCATTACTTTAACTCTACCATGTTCA-3′,Fn-R: 5′-GTTGACTTTACAGAAGGAGAT TATGTAAAAATC-3′,Eu-F: 5′-CGGCAACGAGCGCAACCC-3′,Eu-R: 5′-CCATTGTAGCACGTGTGTAGCC-3′. ## 2.7. Publicly Available Metagenomic Sequence Data The relative abundance of the top 20 genera obtains from publicly available metagenomic sequence data. The metagenomic sequence data of individuals were collected from European Nucleotide Archive (https://www.ebi.ac.uk/ena/browser/view/ (accessed on 5 May 2021), including the populations from Austria (PRJEB7774), Australia (PRJEB7774), China (PRJEB21528, PRJEB10878, PRJNA422434, PRJEB6337, PRJEB12123, PRJEB13870, PRJNA422434, PRJNA422434), Germany (PRJEB27928, PRJEB6070), Denmark (PRJEB2054), France (PRJEB6070), Tanzania [278393], Italy (PRJNA278393), Japan (PRJDB3601), Republic of Korea (PRJEB1690), Peru (PRJNA268964), Sweden (PRJEB1786) and USA (PRJNA177201, PRJEB12449, PRJNA275349, PRJNA48479, PRJNA389280, PRJNA398089, PRJNA268964) ## 2.8. 2HB Quantitation by Liquid Chromatography-Mass Spectrometry (QQQ LC/MS) Quantitative analysis of 2HB was performed with an Agilent 6495 mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) in negative mode, and multiple reaction monitoring (MRM) was used. For the analysis, a Waters BEH Amide column was employed (50 mm × 2.1 mm inner Q16 diameter, 1.7-mm particle size). The system received 2 μL of the sample through an autosampler, which had been conditioned at 4 °C, while the column temperature was held at 40 °C. An isocratic flow of $85\%$ mobile phase A ($0.1\%$ formic acid in water) and $15\%$ mobile phase B ($0.1\%$ formic acid in acetonitrile) at a flow rate of 0.3 mL/min was used. The ion source parameters were set as follows: the gas temperature at 300 °C; gas flow at 10 L/min; sheath gas temperature at 350 °C; sheath gas flow at 11 L/min, capillary at 3000 V, and nozzle voltage at 1000 V. Serum and the supernatant of the bacterial culture were extracted with three times the volume of acetonitrile. Fecal samples (mg) were extracted with ten times the volume of $50\%$ acetonitrile in water (μL). After centrifugation at 12,000 rpm 10 min 4 °C, the supernatant was collected, followed by derivatization as described earlier [31]. Briefly, 40 μL of supernatant, 20 μL 150 mM 3-NPH, and 20 μL 240 mM EDC were added to a 1.5 mL EP tube and sealed with parafilm. Next, a brief vortex of the mixture was for 1 min, and samples were derivatized at 30 °C 60 rpm for 40 min. After the derivatization, an additional 420 μL of $10\%$ acetonitrile was added to the sample, vortexed, centrifuged, and the 80 μL supernatant was transferred to the glass autosampler vial for quantitation. The quantitation of 2HB was performed concerning their corresponding isotope-labeled internal standards (2HB-D2). The m/z of monitored ions are as follows: $\frac{241}{152}$ (2HB-D2), $\frac{239}{152}$ (2HB). Collision energies were 15 for 2HB-D2 and 14 for 2HB. Calibration curves of 2HB were drafted with 2HB standards for absolute quantitation of the biological concentration of 2HB in samples. ## 2.9. Statistical Analysis The graphical abstract is produced by Figdraw. Data are expressed as mean ± s.e.m. Statistical analysis was performed via either R version 4.0 or GraphPad Prism 8. Pairwise comparisons were conducted using the two-tailed paired Student’s t-test, with a significance threshold of $p \leq 0.05.$ ## 3.1. Identification of 2HB-Producing Bacteria in Human Gut Bacteria As an important endogenous metabolite, 2HB-producing bacteria have not been systematically studied, and in this study, we first identified 2HB-producing bacteria in the human microbiome. The proposed biosynthetic pathway of 2HB in gut bacteria was conducted according to the KEGG database (www.genome.jp/kegg, accessed on 10 February 2021) (Figure 1A). As shown in Figure 1A, aspartate, methionine, threonine, and 2-aminobutyric can be used as substrates by bacteria to synthesize 2-ketobutyric acid, a precursor of 2HB, which can be catalyzed by LDH to generate 2HB. We collected synthetic enzymes in the 2HB pathway from the NCBI Reference Sequence Database and compared them with the HMP database (Figure S1 and Table S2). The results showed that enzymes related to 2HB synthesis are widely distributed in the gut microbiota. Specifically, 373,366,550,836 species can use Aspartic acid (Asp), Methionine (Met), Threonine (Thr), and 2-aminobutyric acid (2AB) for the synthesis of 2HB, respectively (Figure 1B–E). Furthermore, these bacteria were mainly distributed in Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Cyanobacteria, an) d Fusobacteria (Figure S2). And the top 20 most abundant genera have the potential to use at least one of Asp, Met, Thr, and 2AB to synthesize 2HB (Figure 1F). ## 3.2. Determination of 2HB Biosynthesis In Vitro We next tested the ability of 32 strains of bacteria to produce 2HB in vitro by liquid chromatography-mass spectrometry/mass (LC-MS/MS). After 24 h of growth (A. muciniphila were cultured for 72 h). 2HB content in supernatant significantly increased in 21 bacterial strains, reduced in 4 bacterial strains, and 7 bacterial strains had no significant changes (Figure 2A and Table S3). Although 21 strains of bacteria have the ability to synthesize 2HB, their yields vary greatly. Moreover, different bacteria strains have different substrate preferences. For example, Fusobacterium nucleatum performs the highest synthetic ability and preferentially uses threonine as the substrate for 2HB production. ( Figure 2B and Table S3). ## 3.3. Gut Bacteria in Mice Are Significantly Positively Correlated with Intestinal Lumen 2HB Level To determine whether gut bacteria are associated with 2HB levels in C57BL/6J mice, a cocktail of antibiotics (ABX) was used to remove gut bacteria [27], and the successful elimination of symbiotic bacteria was verified by colony loss (Figure 3A,B). ABX treatment did not significantly affect food intake or body weight (Figure 3C,D, and Table S4). 2HB levels were significantly decreased in the intestinal lumen and fecal, but no significant changes were observed in the serum in ABX-treated mice compared to mice with sterile water treatment (Figure 3E–I and Table S4). Therefore, these data indicate that gut bacteria in C57BL/6J mice is directly linked to the host intestinal lumen and fecal 2HB level. ## 3.4. Fn Elevates 2HB in the Colon The in vitro experiments shown in Figure 2 indicate that Fusobacterium nucleatum (Fn) has a significantly higher capacity to synthesize 2HB than other bacteria, which use Met, Thr, or 2AB as substrates. Therefore, we investigated whether Fn can affect the 2HB levels in vivo (Figure 4A). With Fn or Fn plus substrate gavage, we observed a marked increase of Fn in feces through qRT-PCR (Figure 4B and Table S5). Nevertheless, there is no significant change in serum 2HB concentration after 2 weeks of continuous oral gavage of Fn (Figure 4C and Table S5). 2HB levels were significantly increased in the cecum and colon content of 2HB-treatment versus control mice (Figure 4E,F). In Fn, Fn + Thr, and Fn + 2AB groups, we observed elevated levels of 2HB in the colon only (Figure 4D–G and Table S5). Altogether, our results suggest that 2HB-producing bacteria can affect the concentration of 2HB locally in the intestinal tract but have no significant effect on serum. ## 4. Discussion and Conclusions Multiple evidence suggests that numerous gut microbes metabolites may contribute to host metabolism, such as trimethylamine (TMA) [32], inosine [33], indole propionic acid (IPA) [34], phenylacetylglutamine(PAGln) [35], N,N,N-trimethyl-5-aminovaleric acid (TMAVA) [36]. Previous research has shown that, in addition to endogenous sources, 2HB could be produced by bacteria via lactate dehydrogenase (LDH) enzymes [19,21]. Therefore, we guess that gut bacteria may contribute to 2HB metabolism. Yet, there exists no systematic study on 2HB-producing bacteria. An initial objective of the study was to identify the taxonomic and abundance profiling of 2HB-producing bacteria in the human gut microbiome. We found four pathways for 2HB synthesis in the KEGG database and identified the distribution of pathway enzymes in HMP by BLASTP. Our results have demonstrated that potential 2HB-producing bacteria are ubiquitous in the human gut microbiome (Figure 1). Furthermore, bacteria-carrying enzymes related to 2HB synthesis are mainly distributed in Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Cyanobacteria in the genus level (Figure S2). Consistent with our BLASTP result, in vitro fermentation experiments suggest that most bacteria can synthesize 2HB, although yields vary widely (Figure 2A). What’s more, our data phenocopy findings that different bacterial have different preferred substrates (Figure 2B). The most substantial increase occurred in Fusobacterium nucleatum supernatant by about 281 times compared with the blank media. Earlier studies have demonstrated that LDH of Fusobacterium nucleatum uniquely exhibits a broad substrate preference for 2-ketobutyric acid (2-KB), a key precursor for the synthesis of 2HB [37]. In addition, Clostridium sporogenes and Peptostreptococcus anaerobius also have a strong ability to promote 2HB synthesis, which increased by more than ten times in their culture supernatant. Interestingly, Lactobacillus and Bifidobacterium, which are known for producing lactic acid via LDH, have a weak ability to produce 2HB. These findings collectively intimated that gut microbiota might play an essential role in contributing to the 2HB level. Here, we demonstrate that ABX-mediated microbiota depletion reduces the accumulation of 2HB in the intestinal lumen and fecal (Figure 3F–I), suggesting a potential direct effect of gut bacteria on 2HB in vivo. By contrast, colon 2HB concentrations are significantly increased after 2HB, Fusobacterium nucleatum, and Fusobacterium nucleatum plus Thr or 2AB gavage (Figure 4F). The concentration of 2HB in the control group in Figure 3 and Figure 4 showed a gradual increase along the digestive tract, which was consistent with the change in gut bacterial abundance. Strikingly, neither ABX nor Fusobacterium nucleatum treatment influenced serum 2HB (Figure 3E and Figure 4C). The concentration of 2HB in the intestinal lumen may be too low to cause a significant change in the serum 2HB pool. Thus, we speculate that the main origin of 2-HB may be derived from the host metabolism. Aside from its potential role as an early biomarker of disease [38], 2HB has also demonstrated high potential to be an important regulatory factor. Earlier research shows that 2HB derived from commensal bacteria ameliorated acetaminophen-induced cell damage and liver injury in mice [25]. Previous studies indicated that 2HB acts as an important antioxidant metabolite for HPV-induced cervical tumor growth [18]. Interestingly, Fusobacterium nucleatum [30,39,40,41,42] and Peptostreptococcus anaerobius [43,44] have also been implicated in the development of colorectal cancer. Moreover, Studies have shown that supplementation with 2-KB extends the lifespan in wild-type worms [45]. Elderly mice fed with autophagy-induced metabolite 2-KB can prevent hair loss [46]. Collectively, 2HB-producing bacteria may represent a useful therapeutic target in a diverse range of human diseases. The present study has several limitations. First of all, our research was carried out in normal mice, but whether similar effects will be observed in the state of the disease remains to be evaluated. Because the intestinal barrier may be damaged under the condition of disease, which may help 2HB enter the blood. Secondly, given the positive correlation between the level of 2HB and insulin resistance [16], which is a risk factor for type 2 diabetes and cancer patients, the role of 2HB and 2HB-producing bacteria in disease deserves further study. Finally, although 32 strains of bacteria have been studied in this study, they are still far from reflecting the real bacterial situation in vivo. Even so, this study reveals, for the first time, the potential 2HB-producing bacteria in the human gut microbiome. In conclusion, we identified the taxonomic profiling of 2HB-producing bacteria in the human gut microbiome. Furthermore, we demonstrated that intestinal bacteria could significantly affect the content of 2HB in the intestinal lumen and feces but have no significant effect on the concentration in serum. ## References 1. Ferrannini E.N.A., Camastra S., Nannipieri M., Mari A., Adam K.-P., Milburn M.V., Kastenmüller G., Adamski J., Tuomi T., Lyssenko V.. Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes (2013) 62 1730-1737. DOI: 10.2337/db12-0707 2. Cobb J.E.A., Motsinger-Reif A., Carr B., Groop L., Ferrannini E.. α-Hydroxybutyric Acid Is a Selective Metabolite Biomarker of Impaired Glucose Tolerance. Diabetes Care (2016) 39 988-995. DOI: 10.2337/dc15-2752 3. Peddinti G., Cobb J., Yengo L., Froguel P., Kravić J., Balkau B., Tuomi T., Aittokallio T., Groop L.. Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia (2017) 60 1740-1750. DOI: 10.1007/s00125-017-4325-0 4. Tricò D., Prinsen H., Giannini C., De Graaf R., Juchem C., Li F., Caprio S., Santoro N., Herzog R.I.. Elevated α-Hydroxybutyrate and Branched-Chain Amino Acid Levels Predict Deterioration of Glycemic Control in Adolescents. J. Clin. Endocrinol. Metab. (2017) 102 2473-2481. DOI: 10.1210/jc.2017-00475 5. Gall W.E., Beebe K., Lawton K.A., Adam K.-P., Mitchell M., Nakhle P.J., Ryals J.A., Milburn M.V., Nannipieri M., Camastra S.. α-Hydroxybutyrate Is an Early Biomarker of Insulin Resistance and Glucose Intolerance in a Nondiabetic Population. PLoS ONE (2010) 5. DOI: 10.1371/journal.pone.0010883 6. Peterson M.J., Geoghegan S., Lawhorne L.W.. An Exploratory Analysis of Potential New Biomarkers of Cognitive Function. J. Gerontol. A Biol. Sci. Med. Sci. (2019) 74 299-305. DOI: 10.1093/gerona/gly122 7. Legault J.T., Strittmatter L., Tardif J., Sharma R., Tremblay-Vaillancourt V., Aubut C., Boucher G., Clish C.B., Cyr D., Daneault C.. A Metabolic Signature of Mitochondrial Dysfunction Revealed through a Monogenic Form of Leigh Syndrome. Cell Rep. (2015) 13 981-989. DOI: 10.1016/j.celrep.2015.09.054 8. Klupczynska A., Plewa S., Dyszkiewicz W., Kasprzyk M., Sytek N., Kokot Z.J.. Determination of low-molecular-weight organic acids in non-small cell lung cancer with a new liquid chromatography–tandem mass spectrometry method. J. Pharm. Biomed. Anal. (2016) 129 299-309. DOI: 10.1016/j.jpba.2016.07.028 9. Zheng Y., He Z., Kong Y., Huang X., Zhu W., Liu Z., Gong L.. Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway. J. Proteome Res. (2021) 20 3444-3454. DOI: 10.1021/acs.jproteome.0c01019 10. Qiu Y., Cai G., Su M., Chen T., Zheng X., Xu Y., Ni Y., Zhao A., Xu L.X., Cai S.. Serum Metabolite Profiling of Human Colorectal Cancer Using GC−TOFMS and UPLC−QTOFMS. J. Proteome Res. (2009) 8 4844-4850. DOI: 10.1021/pr9004162 11. Nishiumi S., Kobayashi T., Ikeda A., Yoshie T., Kibi M., Izumi Y., Okuno T., Hayashi N., Kawano S., Takenawa T.. A Novel Serum Metabolomics-Based Diagnostic Approach for Colorectal Cancer. PLoS ONE (2012) 7. DOI: 10.1371/journal.pone.0040459 12. Fitian A.I., Nelson D.R., Liu C., Xu Y., Ararat M., Cabrera R.. Integrated metabolomic profiling of hepatocellular carcinoma in hepatitis C cirrhosis through GC/MS and UPLC/MS-MS. Liver Int. (2014) 34 1428-1444. DOI: 10.1111/liv.12541 13. Zeng J., Yin P., Tan Y., Dong L., Hu C., Huang Q., Lu X., Wang H., Xu G.. Metabolomics Study of Hepatocellular Carcinoma: Discovery and Validation of Serum Potential Biomarkers by Using Capillary Electrophoresis–Mass Spectrometry. J. Proteome Res. (2014) 13 3420-3431. DOI: 10.1021/pr500390y 14. Saylor P.J., Karoly E.D., Smith M.R.. Prospective Study of Changes in the Metabolomic Profiles of Men during Their First Three Months of Androgen Deprivation Therapy for Prostate Cancer. Clin. Cancer Res. (2012) 18 3677-3685. DOI: 10.1158/1078-0432.CCR-11-3209 15. Shi D., Yan R., Lv L., Jiang H., Lu Y., Sheng J., Xie J., Wu W., Xia J., Xu K.. The serum metabolome of COVID-19 patients is distinctive and predictive. Metabolism (2021) 118 154739. DOI: 10.1016/j.metabol.2021.154739 16. Goodman R.P., Markhard A.L., Shah H., Sharma R., Skinner O.S., Clish C.B., Deik A., Patgiri A., Hsu Y.-H.H., Masia R.. Hepatic NADH reductive stress underlies common variation in metabolic traits. Nature (2020) 583 122-126. DOI: 10.1038/s41586-020-2337-2 17. Jang C., Hui S., Zeng X., Cowan A.J., Wang L., Chen L., Morscher R.J., Reyes J., Frezza C., Hwang H.Y.. Metabolite Exchange between Mammalian Organs Quantified in Pigs. Cell Metab. (2019) 30 594-606.e3. DOI: 10.1016/j.cmet.2019.06.002 18. Liu Y., Guo J.-Z., Liu Y., Wang K., Ding W., Wang H., Liu X., Zhou S., Lu X.-C., Yang H.-B.. Nuclear lactate dehydrogenase A senses ROS to produce α-hydroxybutyrate for HPV-induced cervical tumor growth. Nat. Commun. (2018) 9 4429. DOI: 10.1038/s41467-018-06841-7 19. Chaillou S., Champomier-Vergès M.-C., Cornet M., Crutz-Le Coq A.-M., Dudez A.-M., Martin V., Beaufils S., Darbon-Rongère E., Bossy R., Loux V.. The complete genome sequence of the meat-borne lactic acid bacterium. Nat. Biotechnol. (2005) 23 1527-1533. DOI: 10.1038/nbt1160 20. Heidelberg J., Seshadri R., Haveman S.A., Hemme C.L., Paulsen I., Kolonay J.F., Eisen J.A., Ward N., Methe B., Brinkac L.M.. The genome sequence of the anaerobic, sulfate-reducing bacterium. Nat. Biotechnol. (2004) 22 554-559. DOI: 10.1038/nbt959 21. Kapatral V., Anderson I., Ivanova N., Reznik G., Los T., Lykidis A., Bhattacharyya A., Bartman A., Gardner W., Grechkin G.. Genome Sequence and Analysis of the Oral Bacterium. J. Bacteriol. (2002) 184 2005-2018. DOI: 10.1128/JB.184.7.2005-2018.2002 22. Monot M., Boursaux-Eude C., Thibonnier M., Vallenet D., Moszer I., Medigue C., Martin-Verstraete I., Dupuy B.. Reannotation of the genome sequence of. J. Med. Microbiol. (2011) 60 1193-1199. DOI: 10.1099/jmm.0.030452-0 23. Coker O.O., Liu C., Wu W.K.K., Wong S.H., Jia W., Sung J.J.Y., Yu J.. Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers. Microbiome (2022) 10 35. DOI: 10.1186/s40168-021-01208-5 24. Lin H., Chen S., Shen L., Hu T., Cai J., Zhan S., Liang J., Huang M., Xian M., Wang S.. Integrated Analysis of the Cecal Microbiome and Plasma Metabolomics to Explore NaoMaiTong and Its Potential Role in Changing the Intestinal Flora and Their Metabolites in Ischemic Stroke. Front. Pharmacol. (2022) 12 773722. DOI: 10.3389/fphar.2021.773722 25. Zheng N., Gu Y., Hong Y., Sheng L., Chen L., Zhang F., Hou J., Zhang W., Zhang Z., Jia W.. Vancomycin pretreatment attenuates acetaminophen-induced liver injury through 2-hydroxybutyric acid. J. Pharm. Anal. (2019) 10 560-570. DOI: 10.1016/j.jpha.2019.11.003 26. Song Z., Cai Y., Lao X., Wang X., Lin X., Cui Y., Kalavagunta P.K., Liao J., Jin L., Shang J.. Taxonomic profiling and populational patterns of bacterial bile salt hydrolase (BSH) genes based on worldwide human gut microbiome. Microbiome (2019) 7 9. DOI: 10.1186/s40168-019-0628-3 27. Jimeno R., Brailey P.M., Barral P.. Quantitative Polymerase Chain Reaction-based Analyses of Murine Intestinal Microbiota After Oral Antibiotic Treatment. J. Vis. Exp. (2018) 141 e58481. DOI: 10.3791/58481 28. Xing C., Wang M., Ajibade A.A., Tan P., Fu C., Chen L., Zhu M., Hao Z.-Z., Chu J., Yu X.. Microbiota regulate innate immune signaling and protective immunity against cancer. Cell Host Microbe (2021) 29 959-974.e7. DOI: 10.1016/j.chom.2021.03.016 29. Gao Y., Bi D., Xie R., Li M., Guo J., Liu H., Guo X., Fang J., Ding T., Zhu H.. Signal Transduct. Target. Ther. (2021) 6 398. DOI: 10.1038/s41392-021-00795-x 30. Yang Y., Weng W., Peng J., Hong L., Yang L., Toiyama Y., Gao R., Liu M., Yin M., Pan C.. Gastroenterology (2017) 152 851-866.e24. DOI: 10.1053/j.gastro.2016.11.018 31. Meng X., Pang H., Sun F., Jin X., Wang B., Yao K., Yao L., Wang L., Hu Z.. Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage. Anal. Chem. (2021) 93 10075-10083. DOI: 10.1021/acs.analchem.1c00767 32. Rath S., Rud T., Pieper D.H., Vital M.. Potential TMA-Producing Bacteria Are Ubiquitously Found in Mammalia. Front. Microbiol. (2020) 10 2966. DOI: 10.3389/fmicb.2019.02966 33. Mager L.F., Burkhard R., Pett N., Cooke N.C.A., Brown K., Ramay H., Paik S., Stagg J., Groves R.A., Gallo M.. Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy. Science (2020) 369 1481-1489. DOI: 10.1126/science.abc3421 34. Menni C., Hernandez M.M., Vital M., Mohney R.P., Spector T.D., Valdes A.M.. Circulating levels of the anti-oxidant indoleproprionic acid are associated with higher gut microbiome diversity. Gut Microbes (2019) 10 688-695. DOI: 10.1080/19490976.2019.1586038 35. Nemet I., Saha P.P., Gupta N., Zhu W., Romano K.A., Skye S.M., Cajka T., Mohan M.L., Li L., Wu Y.. A Cardiovascular Disease-Linked Gut Microbial Metabolite Acts via Adrenergic Receptors. Cell (2020) 180 862-877.e22. DOI: 10.1016/j.cell.2020.02.016 36. Zhao M., Zhao L., Xiong X., He Y., Huang W., Liu Z., Ji L., Pan B., Guo X., Wang L.. TMAVA, a Metabolite of Intestinal Microbes, Is Increased in Plasma from Patients with Liver Steatosis, Inhibits γ-Butyrobetaine Hydroxylase, and Exacerbates Fatty Liver in Mice. Gastroenterology (2020) 158 2266-2281.e27. DOI: 10.1053/j.gastro.2020.02.033 37. Furukawa N., Miyanaga A., Nakajima M., Taguchi H.. Structural Basis of Sequential Allosteric Transitions in Tetrameric d-Lactate Dehydrogenases from Three Gram-Negative Bacteria. Biochemistry (2018) 57 5388-5406. DOI: 10.1021/acs.biochem.8b00557 38. Sousa A.P., Cunha D.M., Franco C., Teixeira C., Gojon F., Baylina P., Fernandes R.. Which Role Plays 2-Hydroxybutyric Acid on Insulin Resistance?. Metabolites (2021) 11. DOI: 10.3390/metabo11120835 39. Tahara T., Yamamoto E., Suzuki H., Maruyama R., Chung W., Garriga J., Jelinek J., Yamano H.-O., Sugai T., An B.. Cancer Res (2014) 74 1311-1318. DOI: 10.1158/0008-5472.CAN-13-1865 40. Abed J., Emgård J.E., Zamir G., Faroja M., Almogy G., Grenov A., Sol A., Naor R., Pikarsky E., Atlan K.A.. Fap2 Mediates. Cell Host Microbe (2016) 20 215-225. DOI: 10.1016/j.chom.2016.07.006 41. Chen S., Su T., Zhang Y., Lee A., He J., Ge Q., Wang L., Si J., Zhuo W., Wang L.. Gut Microbes (2020) 11 511-525. DOI: 10.1080/19490976.2019.1695494 42. Guo P., Tian Z., Kong X., Yang L., Shan X., Dong B., Ding X., Jing X., Jiang C., Jiang N.. FadA promotes DNA damage and progression of. J. Exp. Clin. Cancer Res. (2020) 39 202. DOI: 10.1186/s13046-020-01677-w 43. Tsoi H., Chu E.S., Zhang X., Sheng J., Nakatsu G., Ng S.C., Chan A., Chan F.K., Sung J.J.Y., Yu J.. Gastroenterology (2017) 152 1419-1433.e5. DOI: 10.1053/j.gastro.2017.01.009 44. Long X., Wong C.C., Tong L., Chu E.S.H., Szeto C.H., Go M.Y.Y., Coker O.O., Chan A.W.H., Chan F.K.L., Sung J.J.Y.. Nat. Microbiol. (2019) 4 2319-2330. DOI: 10.1038/s41564-019-0541-3 45. Wu N., Ma Y.-C., Gong X.-Q., Zhao P.-J., Jia Y.-J., Zhao Q., Duan J.-H., Zou C.-G.. The metabolite alpha-ketobutyrate extends lifespan by promoting peroxisomal function in. Nat. Commun. (2023) 14 240. DOI: 10.1038/s41467-023-35899-1 46. Chai M., Jiang M., Vergnes L., Fu X., de Barros S.C., Doan N.B., Huang W., Chu J., Jiao J., Herschman H.. Stimulation of Hair Growth by Small Molecules that Activate Autophagy. Cell Rep. (2019) 27 3413-3421.e3. DOI: 10.1016/j.celrep.2019.05.070
--- title: Adropin Predicts Chronic Kidney Disease in Type 2 Diabetes Mellitus Patients with Chronic Heart Failure authors: - Tetiana A. Berezina - Zeljko Obradovic - Elke Boxhammer - Alexander A. Berezin - Michael Lichtenauer - Alexander E. Berezin journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10059962 doi: 10.3390/jcm12062231 license: CC BY 4.0 --- # Adropin Predicts Chronic Kidney Disease in Type 2 Diabetes Mellitus Patients with Chronic Heart Failure ## Abstract Adropin is a multifunctional secreted protein, which is involved in the metabolic modulation of the heart-brain-kidney axis in heart failure (HF). The aim of the study was to detect the plausible predictive value of serum levels of adropin for chronic kidney disease (CKD) grades 1–3 in type 2 diabetes mellitus (T2DM) patients with chronic HF. We enrolled 417 T2DM individuals with chronic HF and subdivided them into two groups depending on the presence of CKD. The control group was composed of 25 healthy individuals and 30 T2DM patients without HF and CKD. All eligible patients underwent an ultrasound examination. Adropin was detected by ELISA in blood samples at the study baseline. We found that adropin levels in T2DM patients without HF and CKD were significantly lower than in healthy volunteers, but they were higher than in T2DM patients with known HF. The optimal cut-off point for adropin levels was 2.3 ng/mL (area under the curve [AUC] = 0.86; $95\%$ CI = 0.78–0.95; sensitivity = $81.3\%$, specificity = $77.4\%$). The multivariate logistic regression adjusted for albuminuria/proteinuria showed that serum levels of adropin <2.30 ng/mL (OR = 1.55; $$p \leq 0.001$$) independently predicted CKD. Conclusions: Low levels of adropin in T2DM patients with chronic CH seem to be an independent predictor of CKD at stages 1–3. ## 1. Introduction Patients with type 2 diabetes mellitus (T2DM) and chronic heart failure (HF) are at increased risk of newly developing chronic kidney disease (CKD), which interferes with mortality and quality of life [1]. On the other hand, T2DM and/or HF are common comorbidities in patients with pre-existent CKD [2,3]. These conditions are closely overlapped in conventional cardiovascular (CV) and unique kidney-specific (anaemia, malnutrition, altered bone mineral metabolism, etc.) risk factors and strongly pathophysiologically interrelated by the complex relationship between myocardial, vascular, and renal injury [4]. These risk factors are found to be sufficient in contributing to the decline of kidney function, which is frequently noticed in patients with any phenotype of HF [5]. Indeed, there are a large number of common pathways, such as systemic and microvascular inflammation, altered cellular immune reactions, oxidative stress and mitochondrial dysfunction, neurohormonal activation, skeletal muscle and adipose tissue dysfunction, which are sustained by glucose and lipid toxicity, impaired nutritional status, altered acid-base, and fluid condition [4,5]. Despite the implementation of conventional management, the incidence rate and, consequently, the prevalence of CKD among HF patients continue to increase [6]. This growth is provoked by the age and increased life span of HF patients as well as a signature of comorbidities, which in particular include T2DM as a global factor that has reached pandemic levels worldwide [7]. The presence of CKD often intervenes in the decision to initiate and maintain life-saving HF therapies among T2DM patients with HF [8]. In addition to that, the synthesis, clearance, peak diagnostic values, and predictive capabilities of the majority of conventional cardiac biomarkers, including natriuretic peptides, are affected by CKD [9]. Yet, new management of HF with sodium-glucose cotransporter 2 (SGLT2) inhibitors was found to be effective in improving clinical outcomes regardless of the circulating levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP) [10,11]. Interestingly, kidney injury biomarkers are not validated to change HF management, whereas continuous monitoring of NPs in HF patients with concomitant T2DM and CKD has limited evidence of its efficacy [12]. In this context, the discovery of novel biomarker-guided approaches to predict CKD and its evolution among HF patients with T2DM with any concentrations of natriuretic peptides (NPs) seems to be promising. Adropin is a multifunctional peptide, which, being primarily secreted by the liver and brain, modulates the metabolic homeostasis of the heart, vasculature, kidney, and skeletal muscles in connection with nutrition status [13]. Adropin exerts its biological effects through binding with three distinct membrane receptors, which seem to be responsible for various modulations of target tissue metabolism. In fact, the Nb-3/Notch signalling pathway is suppressed by adropin, which thereby promotes a central inhibitory effect on water deprivation-induced drinking [14]. Through a canonical cascade including the G-coupled protein receptor 19 (GPR19)–mitogen-activated protein kinase (MAPK)–pyruvate dehydrogenase lipoamide kinase isozyme 4 (PDK4) pathway, adropin downregulates the expression of PDK-4 and consequently mediates metabolic homeostasis of cardiac cells [15]. Finally, favourable effects of adropin on vascular structure and function are mediated by vascular endothelial growth factor (VEGF) via binding with VEGF receptor 2 (VEGFR2) [16]. Yet, adropin may activate the glucose transporter 4 receptor through its Akt phosphorylation and improve glucose metabolism [16]. There is strong evidence regarding the fact that adropin is able to inhibit inflammation by suppressing the production of several pro-inflammatory cytokines (tumour necrosis factor alpha, C-reactive protein, and interleukin-6), improve cardiac function and coronary blood flow, reduce the levels of serum triglycerides, total cholesterol, and low-density lipoprotein cholesterol, and increase the level of high-density lipoprotein cholesterol [17,18,19,20]. The clinical significance of adropin levels in different patient populations remains controversial. There is numerous evidence that overweight/obese/T2DM patients had lower adropin levels than healthy volunteers and that a decreased adropin level was associated with a risk of renal dysfunction in patients with T2DM [21,22,23,24]. Another study reported that elevated serum levels of adropin correlated with a low risk of carotid atherosclerosis in T2DM patients [25]. Yet, elevated serum adropin levels after treatment with sitagliptin or SGLT2 inhibitors were strongly associated with improvements in fasting blood glucose, glycosylated haemoglobin (HbA1c), insulin sensitivity, and NP levels [26,27]. Along with it, aerobic exercise training was able to increase plasma levels of adropin in connection with blood pressure reduction by increasing nitric oxide production and bioavailability [28]. On the contrary, in patients with cardiac dysfunction, the serum levels of adropin increased significantly according to the New York Heart Association (NYHA) class of HF and demonstrated a tendency to decrease during treatment with hydralazine combined with sodium nitroprusside and SGLT2 inhibitors [27,29,30,31]. Although low levels of adropin predicted CKD in T2DM and high levels of adropin were associated with HF, there is no certain evidence that adropin has a discriminative value for CKD in HF patients with T2DM [32]. The aim of the study was to detect the plausible predictive value of serum levels of adropin for CKD 1–3 grades in T2DM patients with chronic HF. ## 2.1. Research Object and Patient Characteristics This is a clinical cohort study in which patients with T2DM were enrolled from the local database of the private hospital “Vita-Center” (Zaporozhye, Ukraine). A total of 612 patients with T2DM were selected according to inclusion and exclusion criteria, which are indicated in Figure 1. We excluded patients who had end-stage target organ disease (4 patients with end-stage ischemia-induced cardiomyopathy, 4 patients with CKD 4–5 stages), severe symptoms of hypoglycemia (5 patients), or hypotension (4 patients), and those who were listed as candidates for surgical procedures (7 patients who require CABG). Finally, we enrolled 417 individuals with T2DM who had chronic HF and subdivided them into two groups depending on the presence of CKD 1–3 grades (estimated GFR > 30 mL/min/1.73 m2). At the same time, 25 healthy individuals and 30 patients with T2DM without HF and CKD were included in the study as controls. The diagnosis of CKD was established according to the definition of CKD in the Kidney Disease Improving Global Outcomes (KDIGO) Consensus Report [33]. Albuminuria/proteinuria were defined as urine albumin to creatinine ratio = 30–300 mg/g and urine total protein to creatinine ratio > 300 mg/g [34]. T2DM and HF were established according to conventional clinical recommendations [35,36]. We determined HF with reduced EF (HFrEF) as HF with a left ventricular ejection fraction (LVEF) of ≤$40\%$; HF with mildly reduced EF (HFmrEF) as HF with an LVEF of 41–$49\%$; and HF with preserved EF (HFpEF) as HF with an LVEF of ≥$50\%$ [36]. The European Society of Cardiology (ESC) clinical guidelines were used to determine concomitant diseases and CV risk factors, such as hypertension [37], dyslipidemia [38], and coronary artery disease/chronic coronary syndrome [39]. ## 2.2. Determination of Anthropometric Parameters, Co-Morbidities, and Concomitant Diseases Standard anthropometric features including height (cm), weight (kg), waist circumference (cm), hip-to-waist ratio (WHR), body mass index (BMI), and body surface area (BSA) were measured according to current recommendations [40]. ## 2.3. Hemodynamic Features All eligible patients underwent B-mode echocardiography and impulse/tissue Doppler examinations obtained with a commercially available diagnostic system, Vivid T8 (“GE Medical Systems”, Freiburg, Germany), by a blinded ultrasonographer in compliance with current guidelines [41]. Left ventricular (LV) end-diastolic (LVEDV) and end-systolic (LVESV) volumes, left atrial volume (LAV), and LV ejection fraction (LVEF) were calculated using the modified Simpson’s technique from the apical 2- and 4-chamber images. The LAV index (LAVI) was estimated as a ratio of LAV to BSA. The tricuspid annular plane systolic excursion (TAPSE) and basal right ventricular diameter were detected in the conventional right ventricular (RV)-focused apical four-chamber view obtained with medial transducer orientation [42]. Tissue Doppler recordings and impulse Doppler captures were received from three consecutive beats at the end of expiration from standard apical 2- and 4-chamber views, and average values were used for the final analyses. We evaluated early diastolic blood filling (E), longitudinal strain ratio (e’) and estimated the E/e’ ratio, which was expressed as the ratio equation of E wave velocity to averaged medial and lateral e’ velocities [41]. Left ventricular hypertrophy (LVH) was detected when LV myocardial mass index (LVMMI) was >115 g/m2 or >95 g/m2 in males and females, respectively [42]. ## 2.4. Blood Sampling and Determination of Glomerular Filtration Rate and Insulin Resistance The collection of blood samples from fasting patients was performed at the same time (from 7:00 to 8:00 a.m.). The blood was collected from an antecubital vein (3–5 mL) and maintained at 4 °C. After centrifugation (3000 r/min, 30 min), polled serum aliquots were immediately stored at ≤−70 °C until analysis. Conventional biochemistry parameters were routinely measured at the local biochemical laboratory of the “Vita-Center” (Zaporozhye, Ukraine) using a Roche P800 analyser (Basel, Switzerland). We used the CKD-EPI formula to estimate the glomerular filtration rate (GFR) [43]. Insulin resistance was evaluated using the Homeostatic Assessment Model of Insulin Resistance (HOMA-IR) [44]. ## 2.5. Biomarker Determination Serum concentrations of NT-proBNP, high-sensitivity C-reactive protein (hs-CRP), and adropin were determined using commercially available enzyme-linked immunosorbent assay (ELISA) kits (Elabscience, Houston, TX, USA) according to the manufacturer’s instructions. All ELISA data were analysed according to the standard curve, and each sample was measured in duplicate as the mean value was finally analysed. Both the intra- and inter-assay coefficients of variability for each biomarker were <$10\%$. ## 2.6. Statistics Statistical analysis was executed using SPSS 21.0 for Windows (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 9 (GraphPad Software, San Diego, CA, USA). Continuous variables were expressed as means (M) ± standard deviation (SD) or median (Me) and interquartile range [IQR] depending on the presence or absence of a normal distribution, respectively. The Kolmogorov–Smirnov test was used as an assessment for normal distribution. The distribution of dichotomous values was analysed using the chi-square test. Data between two groups were compared with an unpaired t-test and a Mann–Whitney U test, while those among multiple groups were assessed by one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. The Spearman r coefficient was used for correlations between the levels of adropin and other parameters. The Receive Operation Characteristics (ROC) curve analysis was used to assess predictive performance. We detected the optimal cut-off point for adropin with the Jouden index and evaluated the model’s area under the curve (AUC), confidence interval (CI), sensitivity, and specificity. Predictors of CKD were determined by univariate and multivariate logistic regression analysis. We reported the odds ratio (OR) and the $95\%$ confidence interval ($95\%$ CI) for each predictor. Differences were considered significant at the level of statistical significance, $p \leq 0.05.$ ## 3.1. General Characteristics of the Patients The entire number of T2DM patients with known HF consists of 231 male ($55.4\%$) and 186 female ($44.6\%$) patients with an average age of 53 (41–64) years who have a large spectrum of comorbidities and concomitant diseases, including dyslipidaemia ($83.0\%$), hypertension ($84.4\%$), stable coronary artery disease ($33.8\%$), smoking ($40.3\%$), abdominal obesity ($42.9\%$), left ventricular (LV) hypertrophy ($80.1\%$), atrial fibrillation ($13.7\%$), microalbuminuria ($18.0\%$), and macroalbuminuria/proteinuria ($3.8\%$) (Table 1). The control groups were age- and gender-matched groups of 25 healthy volunteers and 30 T2DM non-HF patients without CKD. Among the entire number of T2DM HF patients, HFpEF, HFmrEF, and HFrEF were detected in $31.7\%$, $33.6\%$, and $34.8\%$ of patients, respectively. We established I/II HF New York Heart Association (NYHA) class in $67.6\%$ of patients; others ($32.4\%$) had III HF NYHA class. All patients were hemodynamically stable and had average values of LVEF of 46 (37–55)%, LVMMI of 154 ± 5 g/m2, LAVI of 43 (37–52) mL/m2, E/e’ of 13.5 ± 0.3 units, basal RV diameter of 24 (12–36) mm, and TAPSE of 25 (21–28) mm. Fasting levels of creatinine, glucose, and NT-proBNP were 108.6 ± 8.5 µmol/L, 6.12 ± 1.3 mmol/L, and 2615 (1380–3750) pmol/mL, respectively. All HF patients received conventional therapy, depending on their HF phenotype. The majority of them were treated with SGLT2 inhibitors, metformin, and statins. We did not find any significant differences between CKD and non-CKD groups in age, gender, BMI, waist circumference, WHR, a presentation of concomitant diseases and risk factors, apart from albuminuria, proteinuria, and atrial fibrillation (AF), which were detected more frequently in CKD group patients than those with non-CKD. Therefore, there were no significant differences between CKD and non-CKD patients in HF phenotypes, NYHA classes, and main haemodynamic performances, including LVEF, basal RV diameter, TAPSE, as well as the levels of circulating biomarkers. In fact, non-CKD patients had lower E/e’ and more often received mineralocorticoid receptor antagonists when compared with patients from the CKD group. ## 3.2. Circulating Levels of Adropin in T2DM HF Patients with and without CKD Compared with Healthy Volunteers and Non-HF/Non-CKD Diabetics The levels of circulating adropin in T2DM patients without HF and CKD were significantly lower (4.15 ng/mL, $95\%$ confidence interval (CI) = 3.72–4.60 ng/mL) than in healthy volunteers (5.88 ng/mL, $95\%$ CI = 4.90–7.10 ng/mL, $$p \leq 0.012$$), but they were higher than in T2DM patients with known HF (2.37 ng/mL, $95\%$ CI = 1.90–2.75 ng/mL, $$p \leq 0.001$$) (Figure 2). Therefore, there was a significant difference between adropin levels in T2DM HF patients with and without CKD (2.08 ng/mL, $95\%$ CI = 1.82–2.33 ng/mL, and 2.65 ng/mL, $95\%$ CI = 2.06–3.11 ng/mL, respectively, $$p \leq 0.001$$). The levels of adropin in these groups of T2DM HF patients were significantly lower than in healthy volunteers and T2DM without HF and CKD. ## 3.3. Spearman’s Correlation between Circulating Levels of Myokines and Other Parameters We found that in the entire T2DM HF patient population the levels of adropin were found to be negatively correlated with LVEF (r = −0.58, $$p \leq 0.001$$), NYHA class (r = −0.30, $$p \leq 0.012$$), BMI (r = −0.29, $$p \leq 0.012$$), hs-CRP (r = −0.28, $$p \leq 0.001$$), triglycerides (r = −0.23; $$p \leq 0.044$$), fasting plasma glucose (r = −0.22; $$p \leq 0.042$$), HOMA-IR (r = −0.27; $$p \leq 0.001$$), and HbA1c (r = −0.24; $$p \leq 0.010$$), while positively correlated with NT-proBNP levels ($r = 0.36$; $$p \leq 0.001$$), LAVI ($r = 0.32$; $$p \leq 0.001$$), high-density lipoprotein cholesterol ($r = 0.26$; $$p \leq 0.001$$), and eGFR ($r = 0.30$; $$p \leq 0.001$$). Aligned with it, adropin levels were significantly associated with HFrEF (r = −0.34; $$p \leq 0.001$$) in the CKD group, whereas in the non-CKD group we did not notice such a correlation. There was a positive correlation between the levels of adropin and the use of SGLT2 inhibitors ($r = 0.38$, $$p \leq 0.001$$), whereas other concomitant medications did not exert any significant associations with this parameter. Adropin levels did not correlate with albuminuria and proteinuria. ## 3.4. ROC Curve Analysis of the Predictive Value of Adropin for CKD in T2DM Patients with HF The Receive Operation Curve (ROC) analysis (Figure 3) yielded the optimal cut-off point for serum levels of adropin (versus non-CKD HF T2DM) at 2.3 ng/mL (area under the curve (AUC) = 0.86; $95\%$ CI = 0.78–0.95; sensitivity = $81.3\%$, specificity = $77.4\%$; likelihood ratio = 3.623; $$p \leq 0.0001$$). ## 3.5. Predictive Models for CKD in T2DM Patients with HF: Univariate and Multivariate Logistic Regression Analysis Adjusted to Albuminuria/Proteinuria We used univariate logistic regression variables (BMI, age, E/e’, and LAVI), which were structured depending on their median value in the entire T2DM HF population, the cut-off point level of adropin, and the presence versus absence of several conditions, including left ventricular (LV) hypertrophy, atrial fibrillation, and the use of SGLT2 inhibitors (Table 2). The univariate logistic regression adjusted for albuminuria/proteinuria revealed that CKD was predicted by the following variables: serum levels of adropin <2.30 ng/mL (OR = 1.84; $$p \leq 0.001$$), LV hypertrophy (OR = 1.10; $$p \leq 0.022$$), age ≥ 53 years (OR = 1.05; $$p \leq 0.044$$); E/e’ (OR = 1.08; $$p \leq 0.010$$), and LAVI ≥ 43 mL/m2 (OR = 1.06; $$p \leq 0.001$$). The multivariate logistic regression showed that serum levels of adropin < 2.30 ng/mL (OR = 1.55; $$p \leq 0.001$$) retained their independence as a predictor for CKD. ## 4. Discussion The results of our study showed that the circulating levels of adropin < 2.30 ng/mL independently predicted CKD 1–3 grades in T2DM patients with chronic HF. This finding may shed new light on the use of biomarker-guided management in HF patients with metabolic comorbidities, including T2DM. Indeed, the presence of CKD in HF patients sufficiently constrains the proof-of-decision for therapies, while the risk of poor clinical outcomes in CKD patients is reported to be higher than that of those who do not have CKD [39]. There is restrictive evidence of the reproducibility and predictability of conventional kidney injury biomarkers, including albuminuria, proteinuria, albumin/creatinine ratio, eGFR, cystatin C, as well as cardiac biomarkers (natriuretic peptides, cardiac troponins) for CKD in HF patients with concomitant T2DM [45,46]. Thus, our results seem to show novel potency for adropin in a selective group of patients with HF. Although adropin has been previously investigated in numerous animal and clinical studies as a biomarker of T2DM-induced nephropathy, endothelial dysfunction, arterial stiffening, and atherosclerosis [24,47,48,49], there are scarce studies regarding its predictive ability in the HF population with several concomitant diseases, including T2DM and obesity [50]. Accumulating data suggest that adropin exerts metabolic regulation of gluconeogenesis, ketone production, and lipid oxidation in the myocardium, liver, and skeletal muscle [51,52]. Yet, adropin ameliorates the flexibility of metabolic homeostasis through increasing glycolytic flux via both oxidative and non-oxidative pathways and downregulating skeletal muscle fatty acid uptake by an expression of the sarcolemmal fatty acid translocase [51,53,54]. Finally, adropin improves insulin sensitivity, suppresses oxidative stress and mitochondrial dysfunction, and attenuates the worsening repair potency of precursors via the activation of Akt phosphorylation, transcription 3 (STAT3) signalling, the glucose transporter 4 receptor, and tyrosine protein kinase JAK2 (JAK2)/signal transducer pathways [55,56]. In addition to that, adropin was able to directly promote microangiogenesis, increase microvessel density, suppress oxidative stress, and inhibit myocardial fibrosis and apoptosis regardless of its capability of ameliorating glucose and lipid metabolism [57]. These effects are promoted by adropin through downregulation of the expression levels of transforming growth factor β1, NADPH oxidase 4, and cleaved caspase 3, and upregulation of the expression of phosphor-endothelial nitric oxide synthase [57]. In a clinical context, all these mean that adropin exerts tissue protective capability via numerous distinguished mechanisms and that a decreased pool of this circulating peptide is considered a powerful marker of CV risk. Indeed, low levels of adropin were found in patients with arterial hypertension and atherosclerosis [22,58,59,60]. Moreover, patients with overweight/obesity and known T2DM and CKD exhibited lower levels of adropin when compared with healthy volunteers [23,61,62,63]. Yet, low levels of adropin were found in patients with acute myocardial infarction [64] and stable coronary artery disease (CAD) [65]. In fact, decreased circulating concentrations of adropin in peripheral blood corresponded to a higher risk of T2DM, CAD, and CKD, but not for HF [66]. The results of our study showed that the levels of adropin in chronic HF patients with T2DM were lower than in healthy volunteers and T2DN individuals without HF, whereas in other studies [31,66] elevated levels of adropin were detected in HF patients. We, however, suggested that the use of adropin could be practically useful in patients with T2DM and HF regardless of CKD to stratify the patients at risk of CKD and provide continuous monitoring for risk modification during treatment, including a prediction of target organ damages and a response to the therapy. Perhaps our findings will result in the implementation of a guideline-recommended HF treatment programme. Indeed, all individuals were clinically stable and treated with optimal combinations, including SGLT2 inhibitors, renin-angiotensin-aldosterone antagonists, and beta-blockers. The main causes of changeable levels of adropin in HF might be a suppression of adropin synthesis and release due to inflammatory and neurohumoral activation in connection with concomitant circulatory deficiency and subsequent tissue ischemia/hypoxia, poor kidney clearance of adropin, declining skeletal muscle mass and adipose mass tissue, malabsorption and failed digestion of nutrients, and psychological problems [67]. It is reasonable to suggest that the dynamics of the adropin level might depend on the complex interplay of these factors. Indeed, recently Xu W et al. [ 2021] [30] reported that the levels of adropin were found to be increased during effective treatment of chronic HF, whereas others noticed that pre-existing increased levels of the peptide were strongly associated with the severity of HF [31,65]. However, all investigators confirmed that the concentrations of adropin correlated with BMI, NT-proBNP, the lipid profile, HOMA-IR, and biomarkers of inflammation such as hs-CRP, interleukin-6, and LVEF. However, positive or negative relations between adropin levels and other parameters are still controversial. We established that adropin levels were negatively associated with LVEF, NYHA class, BMI, hs-CRP, and some parameters of glucose (fasting plasma glucose, HOMA-IR, and HbA1c) and lipid metabolism (serum levels of triglycerides), and positively correlated with NT-proBNP levels, LAVI, high-density lipoprotein cholesterol, and eGFR. In fact, these factors are considered plausible variables for univariate logistic analysis to verify possible predictors for CKD. We adjusted the analysis to account for albuminuria/proteinuria and found that adropin independently predicted CKD in HF patients with T2DM. One possible explanation of the role of adropin in the prediction of CKD is its close relationship to the severity of endothelial dysfunction and microvascular dysregulation, which have been previously disputed as triggers of kidney injury resulting in CKD [68]. Perhaps the concept of the kidney vascular endothelium playing a key role in supporting effective kidney perfusion in HF is more promising to explain the restrictive tissue protective impact of low levels of adropin on this target organ. The next explanation is based on the idea of a presence of persistence injury to tubular epithelial cells in the kidney during the natural evolution of HF [69]. The metabolic capabilities of adropin seem to show a protective influence on tubular apparatus regardless of fluctuations in eGFR. Thus, persistent ischemia in the kidney may be ameliorated by increased production of adropin, so that elevated levels of the peptide might be a marker of adaptive mechanisms, by which a risk of target organ damage is diminished. On the contrary, low levels of adropin characterise a maladaptive shift in energy homeostasis due to overexpression of pro-inflammatory genes and overproduction of inflammatory cytokines, which are supported by T2DM-induced oxidative stress and mitochondrial dysfunction [69]. Plausible diagnostic and predictive values of adropin are illustrated in Figure 4. Whether a low level of adropin retains its discriminative value for CKD in HF patients with T2DM depending on HF phenotype, nutrient status, the spectrum of comorbidities, and fluid retention/overload is under scientific discussion and requires more investigations in the future. ## 5. Study Limitations This study has several limitations. The first is the small number of healthy volunteers and T2DM patients without CKD and HF. The secondary limitation relates to a lack of nutrient status data for the patients, while we did not change their diet we recommended enhancing their optimal food plan. At last, but not least, we did not investigate a link between changes in adipose tissue mass/skeletal muscle mass and ketogenesis in the context of the possible impact of these processes on adropin. Therefore, we did not have clear evidence of kidney perfusion. We believe these ideas may be taken into consideration for planning new investigations in the future. Therefore, we think these limitations will not interfere with the acceptance of our hypothesis and data interpretation. ## 6. Conclusions We established that circulating levels of adropin in HF patients with T2DM were significantly lower than those in healthy volunteers and T2DM individuals without HF. Moreover, adropin levels < 2.30 ng/mL independently predicted CKD 1–3 grades in T2DM patients with chronic HF, which may be considered a novel biomarker-based approach for stratifying HF patients at CKD risk during management. ## References 1. Haynes R., Zhu D., Judge P.K., Herrington W.G., Kalra P.A., Baigent C.. Chronic kidney disease, heart failure and neprilysin inhibition. Nephrol Dial. Transpl. (2020) 35 558-564. DOI: 10.1093/ndt/gfz058 2. Beldhuis I.E., Lam C.S., Testani J.M., Voors A.A., Van Spall H.G., ter Maaten J.M., Damman K.. Evidence-Based Medical Therapy in Patients with Heart Failure With Reduced Ejection Fraction and Chronic Kidney Disease. Circulation (2022) 145 693-712. DOI: 10.1161/CIRCULATIONAHA.121.052792 3. Tuegel C., Bansal N.. Heart failure in patients with kidney disease. Heart (2017) 103 1848-1853. DOI: 10.1136/heartjnl-2016-310794 4. Mitsas A.C., Elzawawi M., Mavrogeni S., Boekels M., Khan A., Eldawy M., Stamatakis I., Kouris D., Daboul B., Gunkel O.. Heart Failure and Cardiorenal Syndrome: A Narrative Review on Pathophysiology, Diagnostic and Therapeutic Regimens-From a Cardiologist’s View. J. Clin. Med. (2022) 11. DOI: 10.3390/jcm11237041 5. Smith D.H., Thorp M.L., Gurwitz J.H., McManus D.D., Goldberg R.J., Allen L.A., Hsu G., Sung S.H., Magid D.J., Go A.S.. Chronic kidney disease and outcomes in heart failure with preserved versus reduced ejection fraction: The Cardiovascular Research Network PRESERVE Study. Circ. Cardiovasc. Qual. Outcomes (2013) 6 333-342. DOI: 10.1161/CIRCOUTCOMES.113.000221 6. House A.A., Wanner C., Sarnak M.J., Piña I.L., McIntyre C.W., Komenda P., Kasiske B.L., Deswal A., Defilippi C.R., Cleland J.G.F.. Conference Participants. Heart failure in chronic kidney disease: Conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. (2019) 95 1304-1317. DOI: 10.1016/j.kint.2019.02.022 7. Schefold J.C., Filippatos G., Hasenfuss G., Anker S.D., von Haehling S.. Heart failure and kidney dysfunction: Epidemiology, mechanisms and management. Nat. Rev. Nephrol. (2016) 12 610-623. DOI: 10.1038/nrneph.2016.113 8. Ceriello A., Catrinoiu D., Chandramouli C., Cosentino F., Dombrowsky A.C., Itzhak B., Nebojsa M.L., Francesco P., Schnell O.. Heart failure in type 2 diabetes: Current perspectives on screening, diagnosis and management. Cardiovasc. Diabetol. (2021) 20 218. DOI: 10.1186/s12933-021-01408-1 9. Han X., Zhang S., Chen Z., Adhikari B.K., Zhang Y., Zhang J., Sun J., Wang Y.. Cardiac biomarkers of heart failure in chronic kidney disease. Clin. Chim. Acta. (2020) 510 298-310. DOI: 10.1016/j.cca.2020.07.040 10. Maltês S., Cunha G.J., Rocha B.M., Presume J., Guerreiro R., Henriques C., Rodrigues C., Araújo I., Fonseca C.. Dapagliflozin in a Real-World Chronic Heart Failure Population: How Many Are Actually Eligible?. Cardiology (2021) 146 201-206. DOI: 10.1159/000512432 11. Butt J.H., Adamson C., Docherty K.F., de Boer R.A., Petrie M.C., Inzucchi S.E., Kosiborod M.N., Langkilde A.M., Lindholm D., Martinez F.A.. Efficacy and Safety of Dapagliflozin in Heart Failure with Reduced Ejection Fraction According to N-Terminal Pro-B-Type Natriuretic Peptide: Insights From the DAPA-HF Trial. Circ. Heart Fail. (2021) 14 e008837. DOI: 10.1161/CIRCHEARTFAILURE.121.008837 12. Berezin A.E., Berezin A.A.. Biomarkers in heart failure: From research to clinical practice. Ann. Lab. Med. (2023) 43 225-236. DOI: 10.3343/alm.2023.43.3.225 13. Mushala B.A.S., Scott I.. Adropin: A hepatokine modulator of vascular function and cardiac fuel metabolism. Am. J. Physiol. Heart Circ. Physiol. (2021) 320 H238-H244. DOI: 10.1152/ajpheart.00449.2020 14. Stein L.M., Yosten G.L., Samson W.K.. Adropin acts in brain to inhibit water drinking: Potential interaction with the orphan G protein-coupled receptor, GPR19. Am. J. Physiol. Regul. Integr. Comp. Physiol. (2016) 310 R476-R480. DOI: 10.1152/ajpregu.00511.2015 15. Thapa D., Stoner M.W., Zhang M., Xie B., Manning J., Guimaraes D., Shiva S., Jurczak M.J., Scott I.. Adropin regulates pyruvate dehydrogenase in cardiac cells via a novel GPCR-MAPK-PDK4 signaling pathway. Redox Biol. (2018) 18 25-32. DOI: 10.1016/j.redox.2018.06.003 16. Lovren F., Pan Y., Quan A., Singh K.K., Shukla P.C., Gupta M., Al-Omran M., Teoh H., Verma S.. Adropin is a novel regulator of endothelial function. Circulation (2010) 122 S185-S192. DOI: 10.1161/CIRCULATIONAHA.109.931782 17. Ali I.I., D’Souza C., Singh J., Adeghate E.. Adropin’s Role in Energy Homeostasis and Metabolic Disorders. Int. J. Mol. Sci. (2022) 23. DOI: 10.3390/ijms23158318 18. Ghoshal S., Stevens J.R., Billon C., Girardet C., Sitaula S., Leon A.S., Rao D., Skinner J.S., Rankinen T., Bouchard C.. Adropin: An endocrine link between the biological clock and cholesterol homeostasis. Mol. Metab. (2018) 8 51-64. DOI: 10.1016/j.molmet.2017.12.002 19. Jasaszwili M., Billert M., Strowski M.Z., Nowak K.W., Skrzypski M.. Adropin as A Fat-Burning Hormone with Multiple Functions-Review of a Decade of Research. Molecules (2020) 25. DOI: 10.3390/molecules25030549 20. Skrzypski M., Kołodziejski P.A., Pruszyńska-Oszmałek E., Wojciechowicz T., Janicka P., Krążek M., Małek E., Strowski M.Z., Nowak K.W.. Daily Treatment of Mice with Type 2 Diabetes with Adropin for Four Weeks Improves Glucolipid Profile, Reduces Hepatic Lipid Content and Restores Elevated Hepatic Enzymes in Serum. Int. J. Mol. Sci. (2022) 23. DOI: 10.3390/ijms23179807 21. Jurrissen T.J., Ramirez-Perez F.I., Cabral-Amador F.J., Soares R.N., Pettit-Mee R.J., Betancourt-Cortes E.E., McMillan N.J., Sharma N., Rocha H.N.M., Fujie S.. Role of adropin in arterial stiffening associated with obesity and type 2 diabetes. Am. J. Physiol. Heart Circ. Physiol. (2022) 323 H879-H891. DOI: 10.1152/ajpheart.00385.2022 22. Li B., Tian X., Guo S., Zhang M., Li J., Zhai N., Wang H., Zhang Y.. Pentraxin-3 and adropin as inflammatory markers of early renal damage in type 2 diabetes patients. Int. Urol. Nephrol. (2020) 52 2145-2152. DOI: 10.1007/s11255-020-02568-x 23. Es-Haghi A., Al-Abyadh T., Mehrad-Majd H.. The Clinical Value of Serum Adropin Level in Early Detection of Diabetic Nephropathy. Kidney Blood Press Res. (2021) 46 734-740. DOI: 10.1159/000519173 24. Zang H., Jiang F., Cheng X., Xu H., Hu X.. Serum adropin levels are decreased in Chinese type 2 diabetic patients and negatively correlated with body mass index. Endocr. J. (2018) 65 685-691. DOI: 10.1507/endocrj.EJ18-0060 25. Wei W., Liu H., Qiu X., Zhang J., Huang J., Chen H., Qiu S., Lin R., Li S., Tu M.. The association between serum adropin and carotid atherosclerosis in patients with type 2 diabetes mellitus: A cross-sectional study. Diabetol. Metab. Syndr. (2022) 14 27. DOI: 10.1186/s13098-022-00796-y 26. Wang Q., An Y., Zhang L., Zhang Y., Wang G., Liu J.. Regulation of Adropin by Sitagliptin monotherapy in participants with newly diagnosed type 2 Diabetes. BMC Endocr. Disord. (2022) 22. DOI: 10.1186/s12902-022-01233-x 27. Berezin A.A., Obradovic Z., Novikov E.V., Boxhammer E., Lichtenauer M., Berezin A.E.. Interplay between Myokine Profile and Glycemic Control in Type 2 Diabetes Mellitus Patients with Heart Failure. Diagnostics (2022) 12. DOI: 10.3390/diagnostics12122940 28. Davoodi M., Hesamabadi B.K., Ariabood E., Izadi M.R., Afousi A.G., Bigi M.A.B., Asvadi-Fard M., Gaeini A.A.. Improved blood pressure and flow-mediated dilatation via increased plasma adropin and nitrate/nitrite induced by high-intensity interval training in patients with type 2 diabetes. Exp. Physiol. (2022) 107 813-824. DOI: 10.1113/EP089371 29. Lian W., Gu X., Qin Y., Zheng X.. Elevated plasma levels of adropin in heart failure patients. Intern. Med. (2011) 50 1523-1527. DOI: 10.2169/internalmedicine.50.5163 30. Xu W., Qian L., Yuan X., Lu Y.. Combined effects of hydralazine and nitrate on serum biochemistry and left ventricular remodeling in chronic heart failure patients. Pak. J. Pharm. Sci. (2021) 34 381-386. PMID: 34275783 31. Kalkan A.K., Cakmak H.A., Erturk M., Kalkan K.E., Uzun F., Tasbulak O., Diker V.O., Aydin S., Celik A.. Adropin and Irisin in Patients with Cardiac Cachexia. Arq. Bras. Cardiol. (2018) 111 39-47. DOI: 10.5935/abc.20180109 32. Yosaee S., Soltani S., Sekhavati E., Jazayeri S.. Adropin- A Novel Biomarker of Heart Disease: A Systematic Review Article. Iran. J. Public Health (2016) 45 1568-1576. PMID: 28053922 33. de Boer I.H., Khunti K., Sadusky T., Tuttle K.R., Neumiller J.J., Rhee C.M., Rosas S.E., Rossing P., Bakris G.. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care (2022) 45 dci220027. DOI: 10.2337/dci22-0027 34. Toto R.D.. Microalbuminuria: Definition, detection, and clinical significance. J. Clin. Hypertens. (2004) 6 2-7. DOI: 10.1111/j.1524-6175.2004.4064.x 35. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care (2021) 44 S15-S33. DOI: 10.2337/dc21-S002 36. McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Bohm M., Burri H., Butler J., Celutkiene J., Chioncel O.. ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. (2021) 42 3599-3726. DOI: 10.1093/eurheartj/ehab368 37. Williams B., Mancia G., Spiering W., Agabiti Rosei E., Azizi M., Burnier M., Clement D.L., Coca A., de Simone G., Dominiczak A.. ESC Scientific Document Group. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur. Heart J. (2018) 39 3021-3104. DOI: 10.1093/eurheartj/ehy339 38. Mach F., Baigent C., Catapano A.L., Koskinas K.C., Casula M., Badimon L., Chapman M.J., De Backer G.G., Delgado V., Ference B.A.. ESC Scientific Document Group. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur. Heart J. (2020) 41 111-188. DOI: 10.1093/eurheartj/ehz455 39. Knuuti J., Wijns W., Saraste A., Capodanno D., Barbato E., Funck-Brentano C., Prescott E., Storey R.F., Deaton C., Cuisset T.. ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur. Heart J. (2020) 41 407-477. DOI: 10.1093/eurheartj/ehz425 40. Piepoli M.F., Hoes A.W., Agewall S., Albus C., Brotons C., Catapano A.L., Cooney M.T., Corra U., Cosyns B., Deaton C.. ESC Scientific Document Group. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur. Heart J. (2016) 37 2315-2381. DOI: 10.1093/eurheartj/ehw106 41. Yamamoto J., Wakami K., Muto K., Kikuchi S., Goto T., Fukuta H., Seo Y., Ohte N.. Verification of Echocardiographic Assessment of Left Ventricular Diastolic Dysfunction in Patients with Preserved Left Ventricular Ejection Fraction Using the American Society of Echocardiography and European Association of Cardiovascular Imaging 2016 Recommendations. Circ. Rep. (2019) 1 525-530. DOI: 10.1253/circrep.CR-19-0094 42. Lang R.M., Badano L.P., Mor-Avi V., Afilalo J., Armstrong A., Ernande L., Flachskampf F.A., Foster E., Goldstein S.A., Kuznetsova T.. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur. Heart J.-Cardiovasc. Imaging (2015) 16 233-270. DOI: 10.1093/ehjci/jev014 43. Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.L., Castro A.F., Feldman H.I., Kusek J.W., Eggers P., Van Lente F.. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. (2009) 150 604-612. DOI: 10.7326/0003-4819-150-9-200905050-00006 44. Matthews D.R., Hosker J.P., Rudenski A.S., Naylor B.A., Treacher D.F., Turner R.C.. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia (1985) 28 412-419. DOI: 10.1007/BF00280883 45. Castiglione V., Aimo A., Vergaro G., Saccaro L., Passino C., Emdin M.. Biomarkers for the diagnosis and management of heart failure. Heart Fail Rev. (2022) 27 625-643. DOI: 10.1007/s10741-021-10105-w 46. Berezin A.E.. Circulating Biomarkers in Heart Failure. Adv. Exp. Med. Biol. (2018) 1067 89-108. DOI: 10.1007/5584_2017_140 47. Niepolski L., Grzegorzewska A.E.. Salusins and adropin: New peptides potentially involved in lipid metabolism and atherosclerosis. Adv. Med. Sci. (2016) 61 282-287. DOI: 10.1016/j.advms.2016.03.007 48. Fujie S., Hasegawa N., Horii N., Uchida M., Sanada K., Hamaoka T., Padilla J., Martinez-Lemus L.A., Maeda S., Iemitsu M.. Aerobic Exercise Restores Aging-Associated Reductions in Arterial Adropin Levels and Improves Adropin-Induced Nitric Oxide-Dependent Vasorelaxation. J. Am. Heart Assoc. (2021) 10 e020641. DOI: 10.1161/JAHA.120.020641 49. Yazgan B., Avcı F., Memi G., Tastekin E.. Inflammatory response and matrix metalloproteinases in chronic kidney failure: Modulation by adropin and spexin. Exp. Biol. Med. (2021) 246 1917-1927. DOI: 10.1177/15353702211012417 50. Memi G., Yazgan B.. Adropin and spexin hormones regulate the systemic inflammation in adenine-induced chronic kidney failure in rat. Chin. J. Physiol. (2021) 64 194-201. DOI: 10.4103/cjp.cjp_13_21 51. Bozic J., Kumric M., Kurir T.T., Males I., Borovac J.A., Martinovic D., Vilovic M.. Role of Adropin in Cardiometabolic Disorders: From Pathophysiological Mechanisms to Therapeutic Target. Biomedicines (2021) 9. DOI: 10.3390/biomedicines9101407 52. Jensen-Cody S.O., Potthoff M.J.. 52. Jensen-Cody SO, Potthoff MJ. Hepatokines and metabolism: Deciphering communication from the liver. Mol. Metab. (2021) 44 101138. DOI: 10.1016/j.molmet.2020.101138 53. Yang C., DeMars K.M., Hawkins K.E., Candelario-Jalil E.. Adropin reduces paracellular permeability of rat brain endothelial cells exposed to ischemia-like conditions. Peptides (2016) 81 29-37. DOI: 10.1016/j.peptides.2016.03.009 54. Dodd W.S., Patel D., Lucke-Wold B., Hosaka K., Chalouhi N., Hoh B.L.. Adropin decreases endothelial monolayer permeability after cell-free hemoglobin exposure and reduces MCP-1-induced macrophage transmigration. Biochem. Biophys. Res. Commun. (2021) 582 105-110. DOI: 10.1016/j.bbrc.2021.10.032 55. Sato K., Yamashita T., Shirai R., Shibata K., Okano T., Yamaguchi M., Mori Y., Hirano T., Watanabe T.. Adropin Contributes to Anti-Atherosclerosis by Suppressing Monocyte-Endothelial Cell Adhesion and Smooth Muscle Cell Proliferation. Int. J. Mol. Sci. (2018) 19. DOI: 10.3390/ijms19051293 56. Wang L., Jin F., Wang P., Hou S., Jin T., Chang X., Zhao L.. Adropin Inhibits Vascular Smooth Muscle Cell Osteogenic Differentiation to Alleviate Vascular Calcification via the JAK2/STAT3 Signaling Pathway. BioMed Res. Int. (2022) 2022 9122264. DOI: 10.1155/2022/9122264 57. Li B., Wang Z., He Y., Chen T., Zhang Y., Yuan X., Li P.. Adropin Improves Radiation-Induced Myocardial Injury via VEGFR2/PI3K/Akt Pathway. Oxid. Med. Cell Longev. (2022) 2022 8230214. DOI: 10.1155/2022/8230214 58. Wu L., Fang J., Chen L., Zhao Z., Luo Y., Lin C., Fan L.. Low serum adropin is associated with coronary atherosclerosis in type 2 diabetic and non-diabetic patients. Clin. Chem. Lab. Med. (2014) 52 751-758. DOI: 10.1515/cclm-2013-0844 59. Gulen B., Eken C., Kucukdagli O.T., Serinken M., Kocyigit A., Kılıc E., Uyarel H.. Adropin levels and target organ damage secondary to high blood pressure in the ED. Am. J. Emerg. Med. (2016) 34 2061-2064. DOI: 10.1016/j.ajem.2016.04.014 60. Gu X., Li H., Zhu X., Gu H., Chen J., Wang L., Harding P., Xu W.. Inverse Correlation Between Plasma Adropin and ET-1 Levels in Essential Hypertension: A Cross-Sectional Study. Medicine (2015) 94 e1712. DOI: 10.1097/MD.0000000000001712 61. Hu W., Chen L.. Association of Serum Adropin Concentrations with Diabetic Nephropathy. Mediators. Inflamm. (2016) 2016 6038261. DOI: 10.1155/2016/6038261 62. Li N., Xie G., Zhou B., Qu A., Meng H., Liu J., Wang G.. Serum Adropin as a Potential Biomarker for Predicting the Development of Type 2 Diabetes Mellitus in Individuals witah Metabolic Dysfunction-Associated Fatty Liver Disease. Front. Physiol. (2021) 12 696163. DOI: 10.3389/fphys.2021.696163 63. Beigi A., Shirzad N., Nikpour F., Nasli Esfahani E., Emamgholipour S., Bandarian F.. Association between serum adropin levels and gestational diabetes mellitus; a case-control study. Gynecol. Endocrinol. (2015) 31 939-941. DOI: 10.3109/09513590.2015.1081681 64. Yu H.Y., Zhao P., Wu M.C., Liu J., Yin W.. Serum adropin levels are decreased in patients with acute myocardial infarction. Regul. Pept. (2014) 190–191 46-49. DOI: 10.1016/j.regpep.2014.04.001 65. Zhao L.P., Xu W.T., Wang L., You T., Chan S.P., Zhao X., Yang X.J.. Serum adropin level in patients with stable coronary artery disease. Heart Lung Circ. (2015) 24 975-979. DOI: 10.1016/j.hlc.2015.03.008 66. Berezin A.A., Obradovic Z., Fushtey I.M., Berezina T.A., Novikov E.V., Schmidbauer L., Lichtenauer M., Berezin A.E.. The Impact of SGLT2 Inhibitor Dapagliflozin on Adropin Serum Levels in Men and Women with Type 2 Diabetes Mellitus and Chronic Heart Failure. Biomedicines (2023) 11. DOI: 10.3390/biomedicines11020457 67. Czerwińska M., Czarzasta K., Cudnoch-Jędrzejewska A.. New Peptides as Potential Players in the Crosstalk Between the Brain and Obesity, Metabolic and Cardiovascular Diseases. Front. Physiol. (2021) 12 692642. DOI: 10.3389/fphys.2021.692642 68. Verma S.K., Molitoris B.A.. Renal endothelial injury and microvascular dysfunction in acute kidney injury. Semin. Nephrol. (2015) 35 96-107. DOI: 10.1016/j.semnephrol.2015.01.010 69. Berezin A.E., Berezin A.A., Lichtenauer M.. Myokines and Heart Failure: Challenging Role in Adverse Cardiac Remodeling, Myopathy, and Clinical Outcomes. Dis. Markers. (2021) 2021 6644631. DOI: 10.1155/2021/6644631
--- title: Relationship between Muscle Mass, Bone Density and Vascular Calcifications in Elderly People with SARS-CoV-2 Pneumonia authors: - Rossella Del Toro - Francesco Palmese - Francesco Feletti - Gianluca Zani - Maria Teresa Minguzzi - Ernesto Maddaloni - Nicola Napoli - Giorgio Bedogni - Marco Domenicali journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10059976 doi: 10.3390/jcm12062372 license: CC BY 4.0 --- # Relationship between Muscle Mass, Bone Density and Vascular Calcifications in Elderly People with SARS-CoV-2 Pneumonia ## Abstract Background: Little is known about the changes in organs and tissues that may make elder patients more vulnerable to acute stressors such as SARS-CoV-2 infection. Methods: In 80 consecutive elderly patients with SARS-CoV-2 infection, we evaluated the association between the descending thoracic aorta calcium score, L1 bone density and T12 skeletal muscle density measured on the same scan by high-resolution computed tomography. Results: At median regression, the ln-transformed DTA calcium score was inversely associated with L1 bone density (−0.02, $95\%$CI −0.04 to −0.01 ln-Agatston units for an increase of 1 HU) and with T12 muscle density (−0.03, −0.06 to −0.001 ln-Agatston units for an increase of 1 HU). At penalized logistic regression, an increase of 1 ln-Agatston unit of DTA calcium score was associated with an OR of death of 1.480 (1.022 to 2.145), one of 1 HU of bone density with an OR of 0.981 (0.966 to 0.996) and one of 1 HU of muscle density with an OR of 0.973 (0.948 to 0.999). These relationships disappeared after correction for age and age was the stronger predictor of body composition and death. Conclusions: Age has a big effect on the relationship between vascular calcifications, L1 bone density and T12 muscle density and on their relationship with the odds of dying. ## 1. Introduction Frailty is a condition characterized by a decline in several homeostatic systems, which makes a person more vulnerable to stressors and puts them at risk for health problems [1,2]. Evidence-practice gaps in the clinical care of frailty have been addressed by several strategies. Some studies explore links between biological changes and changes in organs and tissues that cause frailty, and others aim at identifying frailty biomarkers [3]. The SARS-CoV-2 pandemic has opened a new chapter, with several studies indicating that SARS-CoV-2 mortality is linked to frailty in patients aged ≥ 65 [4,5,6,7,8,9]. Nowadays, vaccines have made the clinical picture of SARS-CoV-2 infection much better [10]. However, much of the world population is still unvaccinated, and future variants might cause severe forms of pneumonia [11]. We had never seen such a large-scale exposure of the elderly population of industrialized countries to an infectious agent capable of generating such high levels of inflammation. Understanding what happened in the fragile population may allow for new strategies beyond the treatment of SARS-CoV-2. A recent study that looked at 63 hospitals in 11 European countries and included 2434 patients found that the Clinical Frailty Scale is a good risk marker for hospital mortality in adults with dementia [12]. However, there remains a gap in the literature regarding pre-existing changes in organs and tissues, which may drive vulnerability and reduce resistance to stressors as acute as SARS-CoV-2. With this in mind, researchers should investigate body composition and sarcopenia as markers of frailty in the elderly and as a predictor of poor prognosis in SARS-CoV-2 pneumonia. Recent data show that low muscle mass and high visceral fat are predictors of negative outcomes in SARS-CoV-2 patients [13,14]. Arterial calcification is strongly associated with atherosclerosis and has been proposed as a biological marker of aging. Calcium efflux from bone increases with age-related bone loss, which reduces bone mineral density. Age-related increases in calcium efflux in the arterial wall progressively stiffen blood vessels, but the relations between these processes have to be further explored [15]. Thomas et al. found that the calcium content of the descending thoracic aorta (DTAC) was associated with non-cardiovascular disease (CVD) mortality, including chronic obstructive pulmonary disease, hip fracture and pneumonia [16]. Furthermore, cardiovascular calcification and osteoporosis follow the same pathogenic pathway, and animal models confirm the existence of abnormalities linked to aging. Mice with defects in klotho gene expression show a short lifespan, emphysema, osteoporosis, and the calcification of the medial layer of the aorta [17,18]. We hypothesized that frailty in the elderly involves pathways common to muscle mass, bone density and blood vessel calcification. On that basis, the aim of this study was to evaluate the relationship between paravertebral skeletal muscle mass, lumbar vertebral bone density and thoracic aortic calcifications measured on the same high-resolution computed tomography (HRCT) scan and their association with death in patients with SARS-CoV-2 pneumonia. ## 2.1. Study Design This is a cross-sectional study of patients aged ≥ 65 years with SARS-CoV-2 who were admitted to the Department of Internal Medicine and the Intensive Care Unit of Santa Maria delle Croci Hospital (Ravenna, Italy) from 1 February to 31 March 2021 during the second wave of the SARS-CoV-2 pandemic. To be eligible for the study, the patients had to have SARS-CoV-2-related pneumonia [19], including swab tests positive for coronavirus and radiological evidence of interstitial pneumonia; furthermore, each patient had to have undergone an HRCT of the chest comprising all the regions of interest for the present study. Oncological disease and immunosuppressive therapy were reasons for exclusion from the study. The study was conducted in accordance with the Declaration of Helsinki and ethical approval was obtained from Comitato Etico della Romagna (CEROM protocol number $\frac{10263}{2021}$ I$\frac{.5}{305}$ approved on 10 December 2021); informed consent was obtained from all subjects. ## 2.2. Laboratory Assessment Blood was collected on the first day of hospitalization for routine biochemical analysis, including a complete blood count, high-sensitivity C-reactive protein, alanine aminotransferase (ALT), bilirubin, lactic dehydrogenase (LDH) and prothrombin time. ## 2.3. HRCT HRCT was performed with a Philips Brilliant CT 64-slice system at a resolution of 0.977 × 0.977 mm. The images were taken from the PACS archive and transferred to the Philips Intellispace Portal 9.0 console and were preliminarily post-processed through the Multi-Modality Advanced Vessels Analysis protocol and specific MPR reconstructions. ## 2.3.1. HRCT—Calcium Content of Descending Thoracic Aorta DTAC was defined as the amount of calcium within the wall of the descending thoracic aorta as measured by HRCT [16,20]. The calcified atheromas of interest were selected manually. We included the aortic tract located between an upper plane passing through the bifurcation of the pulmonary artery and a lower one passing through the apex of the heart [16]. The system automatically calculated the Agatson score by using a threshold of 130 HU [21] (Figure 1). ## 2.3.2. HRCT—T12 Paravertebral Muscle Area and Density The T12 skeletal muscle area was defined as muscle tissue located posterior to the T12 spine and ribs and lateral to the lateral borders of the erector spinal muscles. The T12 muscle density was measured using Hounsfield units (HU) [14,22]. A single slice passing through the body of T12 was used to measure the paravertebral muscles. The T12 skeletal muscle mass was calculated as the sum of the right and left dorsal areas, whereas the T12 skeletal muscle density was calculated as the mean of the right and left dorsal radiodensities. The muscles’ perimeter was drawn on both sides using the multi-modality viewer’s “ellipse” function (Figure 2). Low skeletal muscle area is linked to poor prognosis in patients with various types of cancer. For instance, sarcopenia is an independent predictor of poor postoperative survival in patients with lung cancer [22]. An association has been reported between skeletal muscle density and mortality in mechanically ventilated patients [23]. Additionally, a low T12 skeletal muscle area has been reported as an independent predictor of in-hospital mortality and long-term survival among patients with community-acquired pneumonia [24]. ## 2.3.3. HRCT—L1 Bone Mineral Density The L1 vertebra is usually included in chest and abdominal CT scans, is easily identified and is useful for retrospective studies [25]. The L1 vertebral bone mineral density was measured using CT attenuation in HU by drawing a circle with a diameter of 1 cm in the center of the vertebral body of L1 [26] (Figure 3). ## 2.4. Statistical Analysis Most continuous variables were not Gaussian distributed, and all are reported as median (50th percentile) and interquartile intervals (IQI, 25th and 75th percentiles). Discrete variables are reported as the number and proportion of subjects with the characteristic of interest. The DTAC Agatston score was transformed for analysis by adding 0.01 to its value, ranging from 0 to 14,526, and log-transformed by taking the natural logarithm (ln) of the ensuing value. This reduced its skewness and allowed DTAC to meet the assumptions made by the median or logistic regression models where it was used as a response or predictor variable. We used median regression with heteroscedasticity-robust standard errors to quantify the association between: [1] Ln DTAC score and bone density (continuous predictor); [2] Ln DTAC score and muscle density (continuous predictor); [3] muscle density and bone density (continuous predictor); and [4] muscle area and bone density (continuous predictor) [27,28]. We pre-specified three median regression models for each predictor as follows: M1 containing only the continuous predictor of interest; M2 adding age (continuous, years/10) as a predictor to M1; and M3 adding gender (discrete, 0 = female; 1 = male) as predictors to M2 [29]. Such models were pre-specified because of the known effects of age and gender on DTAC score, bone density and muscle density. Univariable and multivariable fractional polynomials were used to evaluate whether the relationship of the response variable with the continuous predictors was linear, which was found to be so in all models [29]. We used penalized logistic regression to evaluate the association between the occurrence of death during the hospital stay and the predictors of interest (DTAC score, muscle density and bone density). Penalized logistic regression was used because of the low absolute number of deaths [30,31]. We pre-specified three logistic regression models for each predictor as follows: M1 containing only the predictor (continuous) of interest; M2 adding age (continuous, years/10) as the predictor to M1; and M3 adding gender (discrete, 0 = female; 1 = male) as predictors to M2. Such models were pre-specified because of the known effect of age and gender on the risk of death. Univariable and multivariable fractional polynomials were used to evaluate whether the relationship of the logit of death with continuous predictors was linear, which was found to be so in all models [29]. Statistical analysis was performed using Stata 17.0 (Stata Corporation, College Station, TX, USA). ## 3.1. Baseline Features of the Patients Table 1 gives the baseline features of the patients. The patients had normal median values of blood count, electrolytes, renal function, prothrombin time and lactate dehydrogenase. Nevertheless, the frequent presence of lymphopenia is an indicator of the severity of the underlying SARS-CoV-2 infection [32]. ## 3.2. Association between DTAC Score, Bone Density, Muscle Density and Muscle Area Table 2 gives the univariable and multivariable median regression models used to evaluate the associations of interest. At median regression, an increase of 1 HU of bone density was associated with a decrease of 0.02 ln-Agatston units of DTAC score (Model M1a), but this relationship disappeared after age (Model M1b) and age and sex (Model 1c) were taken into account. An increase of 1 HU of muscle density was associated with a decrease of 0.03 ln-Agatston units of DTA calcium score (Model M2a) but this relationship disappeared after age (Model M2b) and age and sex (Model 2c) were taken into account. An increase of 1 HU of bone density was associated with an increase of 0.13 HU of muscle density (Model 3a) but this relationship disappeared after age (Model M3b) and age and sex (Model M3c) were taken into account. There was no association between muscle area and bone density (Models 4a–4c). Importantly, age was the strongest predictor of all outcomes. Sex added only to the prediction of muscle density from bone density. ## 3.3. Association between Death and DTAC Score, Bone Density, Muscle Density and Muscle Area Sixteen out of 80 patients ($20\%$) died during the hospital stay. Table 3 gives the univariable and multivariable penalized logistic regression models used to evaluate the association between death and ln DTA calcium score, bone density, and muscle density. An increase of 1 ln-Agatston unit of DTA calcium score was associated with an OR of death of 1.480 ($95\%$CI 1.022 to 2.145, Model M1a); however, the addition of age (Model M1b) and age and sex (Model M1c) to the model caused this association to disappear. An increase of 1 HU of bone density was associated with an OR of death of 0.981 ($95\%$CI 0.966 to 0.996, Model 2a); however, the addition of age (Model M2b) and age and sex (Model M2c) to the model caused this association to disappear. An increase of 1 HU of muscle density was associated with an OR of death of 0.973 ($95\%$CI 0.948 to 0.999, Model M3a); however, the addition of age (Model M3b) and age and sex (Model M3c) to the model caused this association to disappear. Importantly, age was the strongest predictor of death in all models and sex did not add to it. ## 4. Discussion This study analyzed for the first time three different tissues on the same HRCT scan in patients with SARS-CoV-2 pneumonia. We found inverse relationships between DTAC score and bone density and DTAC score and muscle density. This relationship disappeared, however, after the contribution of age was taken into account. Importantly, the direct association between DTAC and death and the inverse associations between muscle density, bone density and death also disappeared after correction for age. Even if this study is the first to employ the same HRCT scan to obtain measures of vascular calcifications, bone density and muscle density, it is not without limitations. The main limitation is that it was performed on a relatively low sample of subjects ($$n = 80$$) and, even if the death rate ($20\%$, $$n = 16$$) was in keeping with the expectations, the absolute number of events was too low to obtain precise estimates of the effects sizes. However, we found clear evidence of a big effect of age on the relationship between vascular calcifications, bone density and muscle density, as well as their association with death. Moreover, the small sample size prevented us from performing a mediation analysis aimed at establishing the effect of age on the association between body composition measurements and between them and death. There is increasing evidence that there is a link between vascular calcification and bone metabolism; coincidentally, aging is characterized by the development of osteoporosis and vascular disease. The seemingly contradictory association between bone demineralization and vascular mineralization is commonly referred to as the bone–vascular axis [33]. Several studies have demonstrated an inverse association between bone mineral density and vascular calcification. Furthermore, high bone turnover is associated with increased CV mortality in elderly individuals, regardless of age, gender, PTH serum levels or previous hip fractures [34]. The results of the present study show that the density of L1 trabecular bone is inversely associated with the calcium content of the descending thoracic aorta in elderly patients, but this association disappears after correction for age. Giannini et al. reported that coronary, aortic and thoracic aortic calcium can be used to assess the risk of death in SARS-CoV-2 patients using non-gated CT [35]. In their study, total thoracic calcium, which included coronary, aortic valve and thoracic aortic calcium, was a stronger predictor of mortality than coronary artery calcium. Instead, we focused our investigation on assessing DTAC for two reasons. First, CT can help identify individuals with increased vulnerability to non-CVD-related morbidity and mortality, especially chronic obstructive pulmonary disease, hip fracture and pneumonia. Second, there is a prognostic difference between medial and intimal calcifications. Both atherosclerotic and non-atherosclerotic processes were demonstrated in the thoracic aorta by Abramowitz et al. [ 36]. Medial non-atherosclerotic calcification may reflect biological aging and is more frequently reported in the aorta than in the coronary arteries. This may explain the different associations of thoracic aortic and coronary artery calcifications with non-CVD mortality. By selecting only the descending segment of the thoracic aorta, we attempted to reduce the impact of known risk factors on CVD. Older people often have bone loss which suggests the presence of osteoporosis, which is defined as a systemic skeletal disease with an increase in bone fragility and susceptibility to fractures [37]. The interaction between bone and muscle has been the focus of scientists’ attention for decades, not only because of its importance for the musculoskeletal system but also because of the complex chemical and metabolic interactions [38]. Genetic, endocrine and environmental factors are recognized to be the basis of sarcopenia and osteoporosis, both of which are associated with aging. The loss of bone mineral density appears to be coincidental with decreased muscle mass, strength and function, and it is accepted as a single disease called osteosarcopenia. An increased risk of falls, fractures, frailty, and mortality is associated with osteosarcopenia. Our results are consistent with the literature, which supports a direct association between bone density and muscle mass. We found, however, that the relationship disappeared after correction for age. Recent studies have shown that muscle density is related to muscle strength, and its measurement could be important for diagnosing and screening for sarcopenia [39]. In a subset of the obese population, osteopenia and osteoporosis can be found simultaneously, giving rise to osteosarcopenic obesity, which has worse health outcomes [40]. Both sarcopenia and obesity have been identified as risk factors for mortality in SARS-CoV-2 infection. In agreement with the hypothesized synchronous trend of bone loss and sarcopenia, we found an inverse correlation between DTAC score and T12 muscle density in the present study. After correcting for age, however, the correlation was lost. ## 5. Conclusions In conclusion, we examined the association between descending aorta calcifications, L1 bone mineral density and T12 muscle density on the same HRCT scan in elderly people with SARS-CoV-2 pneumonia. Vascular calcifications were inversely related to bone mineral density and muscle density, and bone and muscle density were directly related, but none of these findings stayed after adjusting for age. Importantly, the odds of death were directly associated with DTAC and inversely associated with L1 bone mineral density and T12 muscle density, but this association was lost after correction for age. Therefore, age is an important factor that should be taken into consideration by further studies in this area. ## References 1. Fried L.P., Tangen C.M., Walston J., Newman A.B., Hirsch C., Gottdiener J., Seeman T., Tracy R., Kop W.J., Burke G.. Frailty in Older adults: Evidence for a phenotype. J. Gerontol. Ser. A Biol. Sci. Med. Sci. (2001) 56 M146-M156. DOI: 10.1093/gerona/56.3.M146 2. Rockwood K., Mitnitski A.. Frailty in Relation to the Accumulation of Deficits. J. Gerontol. Ser. A (2007) 62 722-727. DOI: 10.1093/gerona/62.7.722 3. Dent E., Martin F.C., Bergman H., Woo J., Romero-Ortuno R., Walston J.D.. Management of frailty: Opportunities, challenges, and future directions. Lancet (2019) 394 1376-1386. DOI: 10.1016/S0140-6736(19)31785-4 4. Miles A., Webb T.E., Mcloughlin B.C., Mannan I., Rather A., Knopp P., Davis D.. Outcomes from COVID-19 across the range of frailty: Excess mortality in fitter older people. Eur. Geriatr. Med. (2020) 11 851-855. DOI: 10.1007/s41999-020-00354-7 5. Owen R.K., Conroy S.P., Taub N., Jones W., Bryden D., Pareek M., Faull C., Abrams K.R., Davis D., Banerjee J.. Comparing associations between frailty and mortality in hospitalised older adults with or without COVID-19 infection: A retrospective observational study using electronic health records. Age Ageing (2020) 50 307-316. DOI: 10.1093/ageing/afaa167 6. Aw D., Woodrow L., Ogliari G., Harwood R.. Association of frailty with mortality in older inpatients with Covid-19: A cohort study. Age Ageing (2020) 49 915-922. DOI: 10.1093/ageing/afaa184 7. Bellelli G., Rebora P., Valsecchi M.G., Bonfanti P., Citerio G.. Frailty index predicts poor outcome in COVID-19 patients. Intensiv. Care Med. (2020) 46 1634-1636. DOI: 10.1007/s00134-020-06087-2 8. De Smet R., Mellaerts B., Vandewinckele H., Lybeert P., Frans E., Ombelet S., Lemahieu W., Symons R., Ho E., Frans J.. Frailty and Mortality in Hospitalized Older Adults With COVID-19: Retrospective Observational Study. J. Am. Med Dir. Assoc. (2020) 21 928-932. DOI: 10.1016/j.jamda.2020.06.008 9. Hewitt J., Carter B., Vilches-Moraga A., Quinn T.J., Braude P., Verduri A., Pearce L., Stechman M., Short R., Price A.. The effect of frailty on survival in patients with COVID-19 (COPE): A multicentre, European, observational cohort study. Lancet Public Health (2020) 5 e444-e451. DOI: 10.1016/S2468-2667(20)30146-8 10. Graña C., Ghosn L., Evrenoglou T., Jarde A., Minozzi S., Bergman H., Buckley B.S., Probyn K., Villanueva G., Henschke N.. Efficacy and safety of COVID-19 vaccines. Cochrane Database Syst. Rev. (2022) 12 CD015477. DOI: 10.1002/14651858.cd015477 11. Mathieu E., Ritchie H., Ortiz-Ospina E., Roser M., Hasell J., Appel C., Giattino C., Rodés-Guirao L.. A global database of COVID-19 vaccinations. Nat. Hum. Behav. (2021) 5 947-953. DOI: 10.1038/s41562-021-01122-8 12. Sablerolles R.S.G., Lafeber M., Kempen J.A.L.V., A van de Loo B.P., Boersma E., Rietdijk W.J.R., A Polinder-Bos H., Mooijaart S.P., van der Kuy H., Versmissen J.. Association between Clinical Frailty Scale score and hospital mortality in adult patients with COVID-19 (COMET): An international, multicentre, retrospective, observational cohort study. Lancet Health Longev. (2021) 2 e163-e170. DOI: 10.1016/S2666-7568(21)00006-4 13. Battisti S., Pedone C., Napoli N., Russo E., Agnoletti V., Nigra S.G., Dengo C., Mughetti M., Conte C., Pozzilli P.. Computed Tomography Highlights Increased Visceral Adiposity Associated With Critical Illness in COVID-19. Diabetes Care (2020) 43 e129-e130. DOI: 10.2337/dc20-1333 14. Schiaffino S., Albano D., Cozzi A., Messina C., Arioli R., Bnà C., Bruno A., Carbonaro L.A., Carriero A., Carriero S.. CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19. Radiology (2021) 300 E328-E336. DOI: 10.1148/radiol.2021204141 15. Shaw L.J., Raggi P., Berman D.S., Callister T.Q.. Coronary artery calcium as a measure of biologic age. Atherosclerosis (2006) 188 112-119. DOI: 10.1016/j.atherosclerosis.2005.10.010 16. Thomas I.C., Thompson C.A., Yang M., Allison M.A., Forbang N.I., Michos E.D., McClelland R.L., Budoff M.J., Criqui M.H.. Thoracic Aorta Calcification and Noncardiovascular Disease–Related Mortality. Arter. Thromb. Vasc. Biol. (2018) 38 1926-1932. DOI: 10.1161/ATVBAHA.118.310850 17. Kuro-O M., Matsumura Y., Aizawa H., Kawaguchi H., Suga T., Utsugi T., Ohyama Y., Kurabayashi M., Kaname T., Kume E.. Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature (1997) 390 45-51. DOI: 10.1038/36285 18. Lin C.-C., Yang C.-H., Kuo W.-T., Chen C.-Y.. Evaluation of Anti-aging Compounds Using the Promoters of Elastin and Fibrillin-1 Genes Combined with a Secreted Alkaline Phosphatase Reporter in Normal Human Fibroblasts. Curr. Pharm. Biotechnol. (2015) 16 1053-1062. DOI: 10.2174/1389201016666150826115814 19. Bhimraj A., Morgan R.L., Shumaker A.H., Lavergne V., Baden L., Cheng V.C.-C., Edwards K.M., Gandhi R., Muller W.J., O’Horo J.C.. Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients with (COVID-19). Clin. Infect. Dis. (2020) 27 ciaa478. DOI: 10.1093/cid/ciaa478 20. Nishimura J.M., Ansari A.Z., D’Souza D.M., Moffatt-Bruce S.D., Merritt R.E., Kneuertz P.J.. Computed Tomography-Assessed Skeletal Muscle Mass as a Predictor of Outcomes in Lung Cancer Surgery. Ann. Thorac. Surg. (2019) 108 1555-1564. DOI: 10.1016/j.athoracsur.2019.04.090 21. Looijaard W.G.P.M., Dekker I.M., Stapel S.N., Girbes A.R.J., Twisk J.W.R., Straaten H.M.O.-V., Weijs P.J.M.. Skeletal muscle quality as assessed by CT-derived skeletal muscle density is associated with 6-month mortality in mechanically ventilated critically ill patients. Crit. Care (2016) 20 386. DOI: 10.1186/s13054-016-1563-3 22. Sun L., Ma H., Du G., Fan D., Liu J., Wang X., Zhang W., Liu B., Yin F.. Low Skeletal Muscle Area at the T12 Paravertebral Level as a Prognostic Marker for Community-Acquired Pneumonia. Acad. Radiol. (2022) 29 e205-e210. DOI: 10.1016/j.acra.2021.12.026 23. Hermann D.M., Lehmann N., Gronewold J., Bauer M., Mahabadi A.A., Weimar C., Berger K., Moebus S., Jöckel K.-H., Erbel R.. Thoracic aortic calcification is associated with incident stroke in the general population in addition to established risk factors. Eur. Heart J.-Cardiovasc. Imaging (2015) 16 684-690. DOI: 10.1093/ehjci/jeu293 24. Agatston A.S., Janowitz W.R., Hildner F.J., Zusmer N.R., Viamonte M., Detrano R.. Quantification of coronary artery calcium using ultrafast computed tomography. J. Am. Coll. Cardiol. (1990) 15 827-832. DOI: 10.1016/0735-1097(90)90282-T 25. Hendrickson N.R., Pickhardt P.J., Del Rio A.M., Rosas H.G., Anderson P.A.. Bone Mineral Density T-Scores Derived from CT At-tenuation Numbers (Hounsfield Units): Clinical Utility and Correlation with Dual-energy X-ray Absorptiometry. Iowa Orthop. J. (2018) 38 25-31. PMID: 30104921 26. Pickhardt P.J., Pooler B.D., Lauder T., del Rio A.M., Bruce R.J., Binkley N.. Opportunistic Screening for Osteoporosis Using Abdominal Computed Tomography Scans Obtained for Other Indications. Ann. Intern. Med. (2013) 158 588-595. DOI: 10.7326/0003-4819-158-8-201304160-00003 27. Koenker R., Chernozhukov V., He X., Peng L.. Handbook of Quantile Regression (2018) 28. Machado J.A., Parente P., Santos Silva J.. QREG2: Stata Module to Perform Quantile Regression with Robust and Clustered Standard Errors (2021) 29. Royston P., Sauerbrei W.. Multivariable Model—Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polyno-Mials for Modelling Continuous Variables (2008) 30. Heinze G., Puhr R.. Bias-reduced and separation-proof conditional logistic regression with small or sparse data sets. Stat. Med. (2010) 29 770-777. DOI: 10.1002/sim.3794 31. Puhr R., Heinze G., Nold M., Lusa L., Geroldinger A.. Firth’s logistic regression with rare events: Accurate effect estimates and predictions?. Stat. Med. (2017) 36 2302-2317. DOI: 10.1002/sim.7273 32. Tan L., Wang Q., Zhang D., Ding J., Huang Q., Tang Y.-Q., Wang Q., Miao H.. Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study. Signal Transduct. Target. Ther. (2020) 5 33. DOI: 10.1038/s41392-020-0148-4 33. Pieralice S., Vigevano F., Del Toro R., Napoli N., Maddaloni E.. Lifestyle Management of Diabetes: Implications for the Bone-Vascular Axis. Curr. Diabetes Rep. (2018) 18 84. DOI: 10.1007/s11892-018-1060-y 34. Fadini G.P., Rattazzi M., Matsumoto T., Asahara T., Khosla S.. Emerging Role of Circulating Calcifying Cells in the Bone-Vascular Axis. Circulation (2012) 125 2772-2781. DOI: 10.1161/CIRCULATIONAHA.112.090860 35. Giannini F., Toselli M., Palmisano A., Cereda A., Vignale D., Leone R., Nicoletti V., Gnasso C., Monello A., Manfrini M.. Coronary and total thoracic calcium scores predict mortality and provides pathophysiologic insights in COVID-19 patients. J. Cardiovasc. Comput. Tomogr. (2021) 15 421-430. DOI: 10.1016/j.jcct.2021.03.003 36. Abramowitz Y., Jilaihawi H., Chakravarty T., Mack M.J., Makkar R.R.. Porcelain Aorta. Circulation (2015) 131 827-836. DOI: 10.1161/CIRCULATIONAHA.114.011867 37. Nuti R., Brandi M.L., Checchia G., Di Munno O., Dominguez L., Falaschi P., Fiore C.E., Iolascon G., Maggi S., Michieli R.. Guidelines for the management of osteoporosis and fragility fractures. Intern. Emerg. Med. (2019) 14 85-102. DOI: 10.1007/s11739-018-1874-2 38. Hirschfeld H.P., Kinsella R., Duque G.. Osteosarcopenia: Where bone, muscle, and fat collide. Osteoporos. Int. (2017) 28 2781-2790. DOI: 10.1007/s00198-017-4151-8 39. Wang L., Yin L., Zhao Y., Su Y., Sun W., Chen S., Liu Y., Yang M., Yu A., Guglielmi G.. Muscle Density, but Not Size, Correlates Well With Muscle Strength and Physical Performance. J. Am. Med. Dir. Assoc. (2020) 22 751-759. DOI: 10.1016/j.jamda.2020.06.052 40. Ormsbee M.J., Prado C.M., Ilich J.Z., Purcell S., Siervo M., Folsom A., Panton L.. Osteosarcopenic obesity: The role of bone, muscle, and fat on health. J. Cachex-Sarcopenia Muscle (2014) 5 183-192. DOI: 10.1007/s13539-014-0146-x
--- title: Elucidation of OSW-1-Induced Stress Responses in Neuro2a Cells authors: - Kentaro Oh-hashi - Hibiki Nakamura - Hirotaka Ogawa - Yoko Hirata - Kaori Sakurai journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10059990 doi: 10.3390/ijms24065787 license: CC BY 4.0 --- # Elucidation of OSW-1-Induced Stress Responses in Neuro2a Cells ## Abstract OSW-1, a steroidal saponin isolated from the bulbs of Ornithogalum saundersiae, is a promising compound for an anticancer drug; however, its cytotoxic mechanisms have not been fully elucidated. Therefore, we analyzed the stress responses triggered by OSW-1 in the mouse neuroblastoma cell line Neuro2a by comparing it with brefeldin A (BFA), a Golgi apparatus-disrupting reagent. Among the Golgi stress sensors TFE3/TFEB and CREB3, OSW-1 induced dephosphorylation of TFE3/TFEB but not cleavage of CREB3, and induction of the ER stress-inducible genes GADD153 and GADD34 was slight. On the other hand, the induction of LC3-II, an autophagy marker, was more pronounced than the BFA stimulation. To elucidate OSW-1-induced gene expression, we performed a comprehensive gene analysis using a microarray method and observed changes in numerous genes involved in lipid metabolism, such as cholesterol, and in the regulation of the ER–Golgi apparatus. Abnormalities in ER–Golgi transport were also evident in the examination of secretory activity using NanoLuc-tag genes. Finally, we established Neuro2a cells lacking oxysterol-binding protein (OSBP), which were severely reduced by OSW-1, but found OSBP deficiency had little effect on OSW-1-induced cell death and the LC3-II/LC3-I ratio in Neuro2a cells. Future work to elucidate the relationship between OSW-1-induced atypical Golgi stress responses and autophagy induction may lead to the development of new anticancer agents. ## 1. Introduction OSW-1, the steroidal saponin isolated from Ornithogalum caudatum, has been reported to cause growth arrest and cell death in several cancer cells [1,2,3]. OSW-1 is cytotoxic at very low concentrations and is considered one of the compounds promising as an antitumor agent. However, its exact mechanisms of cell death have not been fully elucidated, although it has been reported that its steroidal backbone allows it to bind to oxysterol-binding protein (OSBP) and oxysterol-binding protein-related protein (ORP) [2]. The ORP family has evolutionally conserved sterol-binding domains and controls the transport and sensing of lipid/cholesterol transport. OSBP is reported to form a complex with the VAP protein at the membrane contact sites between the ER and Golgi apparatus and to be involved in the exchange and transport of cholesterol and phosphatidyl inositol 4-phosphate [4,5]. Thus, OSW-1-targeting OSBPs may affect the homeostasis of the ER–Golgi apparatus, and it was recently reported that OSW-1 triggers Golgi stress responses in the human cervix adenocarcinoma cell line, HeLa [6]. In the ER in close proximity to the Golgi apparatus, its stress response signals, IRE1, ATF6, and PERK, have been identified [7,8,9]. Their downstream factors, such as ATF4 and GADD153 (CHOP), have been suggested to act as proapoptotic factors [10,11,12], and they have been reported to be involved in many diseases, such as tumors and neurodegenerative disorders [13,14]. On the contrary, the concept of the Golgi stress response remains obscure. Even under such circumstances, TFE3, CREB3, and Hsp47 have been reported as factors involved in Golgi stress [15,16,17]. Several TFE3 target genes (e.g., GM130 and SIAT4A), and an Arf4 gene as a target factor for CREB3, have been reported [16,18]. However, the endogenous stimuli that activate these Golgi sensors are not well understood. Recently, we reported on the activation and regulation of ER stress responses and CREB3 using the mouse neuroblastoma cell line Neuro2a [12,19,20,21]. In particular, we have shown that CREB3 activation in Neuro2a cells is distinct from ER stress stimulation (e.g., thapsigargin and tunicamycin), and that its targets are not the known Herp or Edem1 [19,21]. Based on our previous findings [12,19,20,21], we investigated the cytotoxicity of OSW-1 on Neuro2a cells and its ER and Golgi stress responses in detail. We further established OSBP-deficient Neuro2a cells, one of the targets of OSW-1, by a genome-editing approach and analyzed their characteristics and OSW-1 sensitivity. ## 2.1. OSW-1 Stimulation in Neuro2a Induced Cell Death, Accompanied by Autophagy OSW-1, a steroidal saponin, is a promising compound for cancer treatment; however, the precise signaling pathways have not been fully elucidated [1,2,3] (Figure 1A). Since we have evaluated ER stress responses using a mouse neuroblastoma cell line, Neuro2a, by pharmacological and CRISPR/Cas9 approaches [12,20], we attempted to characterize the cytotoxic actions of OSW-1 on this cell line in detail. The measurement of cell viability using a WST-1 reagent showed that OSW-1 induced cell death in a dose-dependent manner (Figure 1B). In the following studies, we treated the cells with 10 nM OSW-1. In this study, we used BFA, a well-known ER/Golgi stress inducer, in parallel, and compared the signaling pathways between the two [22]. First, based on previous studies [12,20], we examined the expression of an ER stress-inducible proapoptotic factor, GADD153, in Neuro2a cells. We also analyzed the expression of LC3, since it was recently reported that OSW-1 induces autophagy in cancer cell lines [23]. As shown in Figure 1C, 8 h of treatment with OSW-1 increased the amount of LC3-II, which is an autophagy marker, more profoundly, but the induction of GADD153, a well-known ER stress-inducible factor, was, to a lesser extent, compared with 8 h of treatment with BFA. On the other hand, 24 h of treatment with BFA dramatically increased the GADD153 and LC3-II levels, which were more profound than those in the OSW-1-treated cells. These results imply that OSW-1 activates the autophagy pathway rather than the typical ER stress cascades in Neuro2a cells. ## 2.2. OSW-1 Affected Genes Involved in Lipid Metabolism, ER/Golgi Homeostasis, and Autophagy in Neuro2a Cells To comprehensively understand the gene fluctuation induced by the OSW-1 treatment, we performed a microarray analysis using cells with or without OSW-1 for 12 h and found that OSW-1 changed many types of genes in Neuro2a cells. Since OSW-1 is known to influence lipid metabolism, especially cholesterol metabolism [4,5,24], we listed some OSW-1-responsive genes associated with cholesterol metabolism and lipid transfer (Figure 2A). We also focused on the genes regulating ER–Golgi homeostasis and autophagy that were influenced by the OSW-1 treatment. Among them, we chose five genes, ABCA1, CREBRF, ZDHHC22, Arfgap3, and Mxd4, which were ranked as the most OSW-1-inducible genes, and their induction was verified by RT-PCR (Figure 2B). We also investigated the gene expression of some ER and Golgi stress-related factors. OSW-1 treatment for 8 h induced five genes that we selected; however, their induction by the BFA treatment differed. Both GADD153 and GADD34, which are ER stress-inducible factors, were dramatically induced by treatment with BFA but not OSW-1. On the other hand, TFE3 and TFEB mRNA are reported as not only Golgi stress sensors but also autophagy-related transcription factors [25,26,27]; however, their mRNA expression was not significantly induced by the OSW-1 treatment. Arf4, which is reported to be a CREB3-regulated gene, was induced by BFA but not OSW-1 [16]. ## 2.3. OSW-1 Downregulated ER–Golgi Transport in Neuro2a Cells OSBP has been reported as one of the primary targets of OSW-1 [2]. OSBP forms a complex with VAP proteins between the ER and the Golgi apparatus and is involved in cholesterol transport [4,5]. Therefore, we investigated the effects of OSW-1 or BFA treatment on the expression of these factors. The OSW-1 already reduced OSBP expression at 8 h of treatment and, after 16 h of treatment with the OSW-1, the OSBP expression was reduced by less than half compared to the unstimulated control cells (Figure 3). On the other hand, the BFA treatment for 16 h accumulated OSBP, and its molecular size was slightly lower. In contrast, the OSW-1 treatment increased the amount of VAPA and VAPB proteins by 1.5- to 2-fold, but the BFA did not alter their expression. Next, we investigated whether OSW-1 affected protein transport via ER to Golgi transport using two NanoLuc-tagged constructs: SP-NanoLuc-myc/His (SP-NL-MH) and angiogenin-myc-NanoLuc (hANG-myc-NL). As shown in Figure 4, the OSW-1 treatment decreased extracellular NanoLuc activity inversely but proportionally to the increase in intracellular NanoLuc activity, which was similar for the two NanoLuc-tagged proteins. Since BFA is well known to disrupt the Golgi apparatus structure [22], its treatment dramatically dampened the secretion of each NanoLuc-tagged protein. ## 2.4. OSW-1 Induced Atypical Golgi Stress Responses, and Its Cytotoxicity Was OSBP-Independent in Neuro2a Cells Since it has been reported that OSW-1 triggers Golgi stress responses [6], we examined whether the OSW-1 induced Golgi stress responses in this cell line. As the ER stress responses by the OSW-1 and BFA were different (Figure 1C and Figure 2B), their effects on CREB3 cleavage and the expression of TFE3/TFEB were also quite different. The treatment with BFA for 8 h clearly induced cleavage of full-length CREB3, but the OSW-1 showed little CREB3 cleavage after 16 h of treatment (Figure 5). On the other hand, the OSW-1 treatment for 8 h shifted a portion of the TFE3 and TFEB to the lower molecular weight side, which has been reported to be the dephosphorylation of TFE3/TFEB upon Golgi stress [25]. The effects of the BFA stimulation on the TFE3 and TFEB were slight at 8 h of stimulation but were similar to those of the OSW-1 at 16 h of stimulation. TFE3 and TFEB have also been reported to regulate some autophagy-related genes [26,27]. Thus, the LC3-II, one of the autophagic markers, was similar to the changes in TFE3/TFEB with the OSW-1 and BFA treatments (Figure 1C and Figure 5B). Finally, we established OSBP-deficient Neuro2a cells using the CRISPR/Cas9 system and evaluated their features. Unexpectedly, the OSBP deficiency hardly influenced the expression of VAPA, VAPB, TFE3, and TFEB proteins, although treatment with OSW-1 influenced the expression of these proteins (Supplementary Figure S1). Then, we compared cell viability and OSW-1-stimulated changes in the LC3-II/LC3-I ratio between wild-type and OSBP-deficient cells. The OSW-1 stimulation caused significant cytotoxicity against wild-type and two OSBP-deficient cell lines (#1 and #2), but there was little effect of OSBP deficiency on OSW-1 toxicity (Figure 6A). In addition, the loss of OSBP in Neuro2a hardly changed the LC3-II/LC3-I ratio in the presence or absence of OSW-1 (Figure 6B). ## 3. Discussion OSW-1, a natural product, has been reported to be a promising tumoricidal compound against several kinds of cancerous cells [1,2,3,6]. OSW-1 has been reported to target oxysterol-binding proteins, especially OSBP and ORP4L [2]; however, the precise cytotoxic mechanisms have not been fully elucidated. Recently, OSW-1 was shown to trigger Golgi stress responses in HeLa cells [6]. The Golgi stress response is a relatively recently proposed stress signal that is less well understood than ER stress, which is caused by dysfunction of the nearby organelle, the ER [7,8,9,10,11,12,13,14]. Until now, Golgi stress has been classified by several signaling pathways, including TFE3, CREB3, and Hsp47 [15,16,17]; however, there remain many unresolved issues, such as the endogenous stimuli that cause Golgi stress and the downstream factors of these stress sensors. We previously characterized the processing and degradation of CREB3 in a mouse neuroblastoma cell line, Neuro2a [19,21]. We, therefore, attempted to analyze the effects of OSW-1 on Neuro2a cells. In particular, we compared the effects of OSW-1 with those of BFA, which induces cytotoxicity through Golgi apparatus disruption [22]. As we previously reported [21], BFA rapidly upregulated the mRNA expression of the ER stress-inducible genes GADD153 and GADD34 concomitant with CREB3 cleavage in Neuro2a cells. On the other hand, dephosphorylation of TFE3 and TFEB occurred at a later phase. In contrast, OSW-1 dephosphorylated TFE3 and TFEB at an early phase but did not trigger CREB3 cleavage at all. Although it has been reported that OSW-1 causes Golgi stress [6], this is the first report comparing TFE3, TFEB, and CREB3 simultaneously. In the case of ER stress responses, the three stress sensors, the IRE1, PERK, and ATF6 pathways, are most likely activated in almost the same way. Thus, the OSW-1-induced atypical Golgi stress in Neuro2a cells may help us understand its signaling regulation. Further analysis of the mechanisms of TFE3/TFEB activation by OSW-1 and its relationships with autophagy and cell death will be needed. To understand the mechanism of OSW-1-induced cytotoxicity, we comprehensively analyzed the genes that are altered by OSW-1 stimulation. In fact, we observed a variety of gene changes upon OSW-1 stimulation, and, in particular, we have listed the major changes in those involved in ER–Golgi apparatus homeostasis and lipid metabolism, including cholesterol. OSW-1 stimulation significantly decreased OSBP protein expression, which has been reported as one of its targets [2]. Interestingly, it was also accompanied by an increase in VAP proteins, which form a complex in cholesterol transport between the ER and the Golgi apparatus [4,5]. This may be reflected in the induction of the ABCA1 gene involved in cholesterol transport by OSW-1 stimulation [28]. Since the ABCA1 gene is also induced by BFA treatment, both stimuli are thought to cause impaired lipid transport between the ER and Golgi apparatus. On the other hand, mutations in the VAPB gene have been implicated in familial amyotrophic lateral sclerosis, and it has been reported that mutant VAPB proteins aggregate abnormally in neuronal cells [29,30,31]. It has also been reported that overexpression of wild-type VAPB protein or knockdown of endogenous VAPB triggers cellular disorders, such as dysfunction of the proteasome, ER, and Golgi apparatus [30,31]. Therefore, it is possible that the accumulation of VAP proteins associated with the transient loss of OSBP from OSW-1 treatment is responsible for the cellular disorder in Neuro2a cells, as observed by the OSW-1 treatment attenuating the secretory activity of NanoLuc-tagged proteins. In contrast, the ZDHHC22 gene, a palmitoyltransferase localized to the ER and Golgi apparatus, was significantly increased by treatment with OSW-1 but not BFA [32,33]. Similarly, the effects of OSW-1 on OSBP and VAP proteins are different from those of BFA stimulation, suggesting that their stress pathways are different. There are only a few reports on the function of ZDHHC22 in tumor development and progression, and the results are discordant [32,33]. Therefore, the relationship between ZDHHC22 mRNA induction by OSW-1 and cytotoxicity requires further investigation. The treatment with BFA but not OSW-1 apparently induced CREB3 cleavage, accompanied by a strong induction of ER stress responses. Although the target genes of CREB3 are not well understood, the induction of Arf4 mRNA only by the BFA is thought to coincide well with the CREB3 cleavage [16]. On the other hand, CREBRF was initially identified as a negative regulator of CREB3 [34]. However, the relationship between CREB3 and CREBRF is not clear, since our studies using the GAL4 reporter system have reported that CREBRF promotes CREB3-mediated transcriptional activity [35]. Since OSW-1 stimulation did not induce CREB3 cleavage, the action of CREBRF induced by OSW-1 appears to be CREB3-independent. Interestingly, a large-scale genomic analysis has reported that a single nucleotide variant in CREBRF is associated with obesity [36,37]. Therefore, it is likely that metabolic abnormalities are caused by OSW-1-induced CREBRF mRNA. However, it is unclear whether CREBRF is involved in OSW-1-induced cell death since transient overexpression of CREBRF in Neuro2a cells did not show marked cytotoxicity. Mxd4 belongs to the bHLH-ZIP transcription factor Myc family, which is well known to control numerous genes involved in proliferation, energy metabolism, and translation [38]. The Myc gene is considered a major oncogene, and the Myc protein acts as a Myc network by forming heterodimers with Max and Mxd. The Mxd family, including Mxd4, has been reported to act in an inhibitory manner against Myc [38]. It is unclear how OSW-1 increased Mxd4 mRNA expression in Neuro2a cells, but it is thought that this increase might contribute to OSW-1 cytotoxicity. In addition to the above genes, OSW-1 also fluctuated the expression of various genes in Neuro2a cells (Figure 2A), and a detailed analysis of these genes may provide clues for the development of cancer therapies. OSBP is one of a large family and is homologous to ORP4 [4]. Since previous reports have shown that OSBP is more strongly repressed by OSW-1 than ORP4 [39], we established OSBP-deficient Neuro2a cells and analyzed their characteristics. Unexpectedly, there were no apparent morphological abnormalities in the OSBP-deficient cells. In addition, there were no changes in the TFE3, TFEB, or VAP proteins observed with the OSW-1 treatment. We further examined OSW-1-induced cell death since the knockdown of OSBP in HeLa cells using shRNA significantly increased OSW-1 sensitivity [2]. However, the effect of OSBP deficiency was observed only to a small extent in this study using Neuro2a cells, and there was no difference in the increase in the LC3-II/LC3-I ratio between the wild-type and OSBP-deficient Neuro2a cells. This discrepancy may be due to differences in the cell types and experimental conditions used, but the reasons for this difference are unclear. In addition, it remains to be determined what types of genes are affected by OSBP deficiency, and whether they overlap with genes that are altered by OSW-1 stimulation in Neuro2a cells. Future detailed comparative analysis may lead to a better understanding of OSW-1-induced cytotoxicity. Since Mimaki et al. reported the anti-tumor effect of OSW-1 [1], the mechanism of its action has been elucidated from various aspects [40]. However, despite its very potent cytotoxicity, it has not become an actual therapeutic agent because the mechanism of its anticancer effect is unclear. As for apoptosis signaling, a typical pathway in which OSW-1 impairs mitochondrial structure and function, causing cytochrome c leakage and activating caspase-3, has been reported [41], and, in our current conditions, we observed an increase in cleaved caspase-3, although much weaker than with BFA stimulation. On the contrary, a mitochondria-independent pathway of OSW-1 action has also been observed [42]. The disruption of intracellular calcium homeostasis by inhibition of sodium-calcium exchanger 1 by OSW-1 has also been reported [43]. In addition, cleavage of the anti-apoptosis factor Bcl-2 by OSW-1-induced activation of caspase-8 has been reported [44]. Thus, the mechanisms of action of OSW-1 in vitro are diverse, and although it is effective in vivo [1,40,41,45], the details of its anticancer activity have not been clarified. The present study revealed that OSBP is not a major target in OSW-1-induced atypical Golgi stress, autophagy, and cell death in Neuro2a cells. Therefore, further clarification of the relationship between this atypical Golgi stress and various gene changes and autophagy in Neuro2a cells will lead to an analysis of the mechanism of action and in vivo efficacy of OSW-1. The ChemMapper server was used to search the potential molecular targets for OSW-1. Some of the most frequently retrieved proteins were involved in steroid metabolism or served as receptors for steroid hormones. Possible OSW-1-binding factors include metabolic enzymes and receptors for compounds with a cholesterol backbone. Therefore, the multiple factors that were altered by the OSW-1 treatment may be involved in OSW-1-induced cell death in Neuro2a cells. In the future, an analysis of each gene specifically altered by OSW-1 stimulation and the characterization of other OSW-1-binding factors, including ORP4, will lead to the elucidation of Golgi stress mechanisms and the development of new cancer therapies. ## 4.1. Construction of Plasmids gRNAs against mouse OSBP #1 (5′-GCCGGGCCCGGCAGCCATCG-3′) or #2 (5′-GATGGCGGCGACCGAGCTGAG-3′) aligned with tracer RNA were inserted into a pcDNA3.1-derived vector with a U6 promoter [12]. To prepare the donor gene construct, the IRES sequence and a puromycin resistance gene were, respectively, amplified by PCR using a pIRES-GFP vector (Clontech) and a pEBMulti vector (Wako) as templates. Those genes were, in turn, inserted into a pGL3-derived vector (IRES-puro in a pGL3-derived vector). A DNA fragment encoding the N-terminal region of OSBP (103 bp from the translation start site) was then inserted into the IRES-puro pGL3-derived vector. The hCas9 construct (#41815) used in this study was obtained from Addgene [46]. The NanoLuc (NL) gene was provided by Promega. The NL gene having the signal peptide sequence (SP) from the mouse MANF gene was amplified by PCR and inserted into a pcDNA3.1 myc/His vector (SP-NL-MH). A human angiogenin (hANG) gene was synthesized by Eurofins. The hANG, having a myc-epitope at the 3′end, was amplified by PCR and inserted into a pcDNA3.1 vector. The NL gene was then fused to the 3′end of the hANG-myc gene (hANG-myc-NL). The details of each primer and construct preparation are described in the Supplementary Materials and Methods. ## 4.2. Cell Culture and Treatment The Neuro2a cells were maintained in Dulbecco’s modified Eagle’s minimum essential medium containing $5\%$ fetal bovine serum (Invitrogen, Waltham, MA, USA). To establish OSBP-deficient cells, the Neuro2a cells were transfected with the indicated constructs, the cells were transfected with gRNA (#1 or #2), the hCas9, the donor genes were cultured with puromycin at the appropriate concentration, and the resultant cells were used in this study. During selection, the normal parental cells were maintained with a normal culture medium and were used as wild-type control cells for the following experiments. In each experiment, the parental and deficient cells were seeded in 96-, 48- or 6-well plates with a non-puromycin-containing culture medium. Then, the cells were treated with or without brefeldin A (BFA, 0.5 μg/mL) or OSW-1 at the indicated concentration. ## 4.3. Measurement of Cell Viability For the measurement of cell viability using the Cell Counting Kit (a WST-1 reagent) (Dojindo, Tokyo, Japan) [12,20], the same number of Neuro2a cells in the 96-well plate were treated with BFA (0.5 μg/mL) or OSW-1 at the indicated concentration and cultured for 24 h. During the last two hours, WST-1 solution was added to each well and incubated at 37 °C, according to the manufacturer’s instructions. The difference between the absorbance at 450 and 620 nm was measured as an indicator of cell viability. Each absorbance in the untreated cells was respectively defined as 1.0. ## 4.4. Microarray Analysis For the microarray analysis, total RNA (miRNA) was isolated from the Neuro2a cells using Triagent (Molecular Research Center, Cincinnati, OH, USA), and the quality was evaluated by a 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA). Cy3-labeled probes were prepared from 200 ng of total RNA using the Low Input Quick Amp Labeling Kit, one-color, (Agilent Technologies, #5190-2305) and hybridized with a microarray slide (Whole Mouse Genome DNA microarray 4x44K Ver. 2.0; Agilent Technologies) for 17 h at 65 °C. The microarray slide was washed and scanned with a microarray scanner (ArrayScan, Agilent Technologies) to obtain the fluorescent signal of the probes. The signal was processed for digitization using Feature Extraction software 10.7 (Agilent Technologies) and analyzed with GeneSpring GX software 14. 9 (Agilent Technologies) for gene expression. ## 4.5. Reverse Transcription Polymerase Chain Reaction To estimate the expression level of each gene by RT-PCR, total RNA was extracted from cells lysed with TRI reagent (Molecular Research Center), and equal amounts of total RNA from each sample were converted to cDNA by reverse transcription using Random 9-mer Primer with SuperScript III Reverse Transcriptase (RT) (Life Technologies, Carlsbad, CA, USA), as previously described [12,21]. Each cDNA was added to a PCR mixture for amplification (Taq PCR Kit, Takara, Shiga, Japan). The PCR primers used in this study were as follows: ABCA1 sense primer, 5′-AGCAGAGGCAATGACCAGTT-3′, ABCA1 antisense primer, 5′-GGACTTGTTGATGAGCCTGA-3′; Arfgap3 sense primer, 5′-TTTTTGCTTCTCACGCCTCTCT-3′, Arfgap3 antisense primer, 5′-TCCTGCTCCTTCCTCTTATC-3′; Arf4 sense primer, 5′-GTCACCACCATTCCTACCAT-3′, Arf4 antisense primer, 5′-CTAGCTTGTCTGTCATCTCA-3′; CREBRF sense primer, 5′-AAGTCAAGATCAACCCTGTG-3′, CREBRF antisense primer, 5′-TTGGTTGGCTGTTCTCTCAT-3′; GADD34 sense primer, 5′-GAATCACCTTGGGCTGCACCTA-3′, GADD34 antisense primer, 5′-GGAATCAGGGGTAAGGTAGGGA-3′; GADD153 sense primer 5′-GAATAACAGCCGGAACCTGA-3′, GADD153 antisense primer 5′-GGACGCAGGGTCAAGAGTAG-3′; Mxd4 sense primer, 5′-TAAACTCAGGCTCTACTTGG-3′, Mxd4 antisense primer, 5′-TCTACACTCTGCACTGATAG-3′; TFE3 sense primer, 5′-AACCCTACACGCTACCACCT-3′, TFE3 antisense primer, 5′-TAGATTTCCAGACACCGGCAGC-3′; TFEB sense primer, 5′-GAATGCTGATCCCCAAGGCCAA-3′, TFEB antisense primer, 5′-TCGGCCATATTCACACCCGA-3′; ZDHHC22 sense primer, 5′-CCATCACTGTTTCTTCACCG-3′, ZDHHC22 antisense primer, 5′-CGGAGAAGAACTGGCTGATT-3′; G3PDH sense primer, 5′-ACCACAGTCCATGCCATCAC-3′, G3PDH antisense primer, 5′-TCCACCACCCTGTTGCTGTA-3′. The typical reaction cycling conditions were as follows: 30 s at 96 °C, 30 s at 58 °C and 30 s at 72 °C. The results represent 21 or 28 cycles of amplification. The products were separated by electrophoresis on $2.0\%$ agarose gels and visualized using ethidium bromide. The expression level of each mRNA was analyzed using the ImageJ software 1.53 (National Institutes of Health), and the relative amount of each mRNA was calculated based on the G3PDH value obtained from the identical cDNA [12,21]. ## 4.6. Western Blotting Analysis We detected the amounts of each protein in the cell lysates, as previously described [12,19,20,21]. The cells were lysed with homogenization buffer (20 mM Tris-HCl (pH 8.0) containing 137 mM NaCl, 2 mM EDTA, $10\%$ glycerol, $1\%$ Triton X-100, 1 mM PMSF, 10 μg/mL leupeptin and 10 μg/mL pepstatin A, 1 mM sodium vanadate and 1 mM NaF). After the protein concentration was determined using Bradford Protein Assay Dye Reagent (Bio-Rad, Hercules, CA, USA), each cell lysate was dissolved in an equal amount of 2× sodium dodecyl sulfate (SDS)–Laemmli sample buffer (62.5 mM Tris-HCl (pH 6.8) containing $2\%$ SDS and $10\%$ glycerol), and equal amounts of cell lysate were prepared. Equal amounts of protein were separated on 10 or $12.5\%$ SDS-polyacrylamide gels, transferred onto polyvinylidene difluoride membranes (GE Healthcare, Chicago, IL, USA), and identified by enhanced chemiluminescence (GE Healthcare) using antibodies against GADD153 (Santa Cruz Biotechnology, Dallas, TX, USA), TFEB (Cell Signaling Technology, Danvers, MA, USA), TFE3 (Cell Signaling Technology), OSBP (Proteintech, Rosemont, IL, USA), VAPA (Proteintech), VAPB (Proteintech), CREB3 (Proteintech) and G3PDH (Proteintech). The expression level of each protein was analyzed using ImageJ software 1.53 (National Institutes of Health), and the relative amount of each protein was calculated based on the G3PDH value obtained from the same lysate. The protein expression levels of each lysate were normalized as described in each Figure legend [12,21]. ## 4.7. Measurement of Protein Secretion The cells in the 48-well plate transfected with the SP-NL-MH or hANG-myc-NL gene were incubated for 42 h. After that, the culture medium was replaced with OPTI-MEM medium and treated with OSW-1 (10 nM), BFA (0.5 μg/mL), or vehicle for an additional 6 h. The culture medium and cells were lysed with Passive Lysis Buffer (Promega, Madison, MI, USA) and harvested. An equal amount of each sample was mixed with the diluted NanoLuc substrate (Promega), and the luciferase activity was measured by a Glo-MAX luminometer (Promega). ## 4.8. Statistical Analysis The results are expressed as the means ± SEM. The statistical analyses were carried out using one-way ANOVA followed by the Tukey–Kramer test. $p \leq 0.05$ was considered statistically significant. ## References 1. Mimaki Y., Kuroda M., Kameyama A., Sashida Y., Hirano T., Oka K., Maekawa R., Wada T., Sugita K., Beutler J.A.. Cholestane glycosides with potent cytostatic activities on various tumor cells from Ornithogalum saundersiae bulbs. Bioorganic Med. Chem. Lett. (1997) 7 633-636. DOI: 10.1016/S0960-894X(97)00071-1 2. Burgett A.W.G., Poulsen T.B., Wangkanont K., Anderson D.R., Kikuchi C., Shimada K., Okubo S., Fortner K.C., Mimaki Y., Kuroda M.. Natural products reveal cancer cell dependence on oxysterol-binding proteins. Nat. Chem. Biol. (2011) 7 639-647. DOI: 10.1038/nchembio.625 3. Zhou Y., Garcia-Prieto C., Carney D.A., Xu R.-H., Pelicano H., Kang Y., Yu W., Lou C., Kondo S., Liu J.. OSW-1: A Natural Compound with Potent Anticancer Activity and a Novel Mechanism of Action. J. Natl. Cancer Inst. (2005) 97 1781-1785. DOI: 10.1093/jnci/dji404 4. Nakatsu F., Kawasaki A.. Functions of Oxysterol-Binding Proteins at Membrane Contact Sites and Their Control by Phosphoinositide Metabolism. Front. Cell Dev. Biol. (2021) 9 664758. DOI: 10.3389/fcell.2021.664788 5. Mesmin B., Bigay J., Polidori J., Jamecna D., Lacas-Gervais S., Antonny B.. Sterol transfer, PI4P consumption, and control of membrane lipid order by endogenous OSBP. EMBO J. (2017) 36 3156-3174. DOI: 10.15252/embj.201796687 6. Kimura M., Sasaki K., Fukutani Y., Yoshida H., Ohsawa I., Yohda M., Sakurai K.. Anticancer saponin OSW-1 is a novel class of selective Golgi stress inducer. Bioorganic Med. Chem. Lett. (2019) 29 1732-1736. DOI: 10.1016/j.bmcl.2019.05.022 7. Yoshida H., Matsui T., Yamamoto A., Okada T., Mori K.. XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor. Cell (2001) 107 881-891. DOI: 10.1016/S0092-8674(01)00611-0 8. Zhu C., E Johansen F., Prywes R.. Interaction of ATF6 and serum response factor. Mol. Cell. Biol. (1997) 17 4957-4966. DOI: 10.1128/MCB.17.9.4957 9. Harding H.P., Zhang Y., Ron D.. Protein translation and folding are coupled by an endoplasmic-reticulum-resident kinase. Nature (1999) 397 271-274. DOI: 10.1038/16729 10. Marciniak S.J., Yun C.Y., Oyadomari S., Novoa I., Zhang Y., Jungreis R., Nagata K., Harding H.P., Ron D.. CHOP induces death by promoting protein synthesis and oxidation in the stressed endoplasmic reticulum. Genes Dev. (2004) 18 3066-3077. DOI: 10.1101/gad.1250704 11. Galehdar Z., Swan P., Fuerth B., Callaghan S.M., Park D.S., Cregan S.P.. Neuronal Apoptosis Induced by Endoplasmic Reticulum Stress Is Regulated by ATF4–CHOP-Mediated Induction of the Bcl-2 Homology 3-Only Member PUMA. J. Neurosci. (2010) 30 16938-16948. DOI: 10.1523/JNEUROSCI.1598-10.2010 12. Oh-Hashi K., Sugiura N., Amaya F., Isobe K.-I., Hirata Y.. Functional validation of ATF4 and GADD34 in Neuro2a cells by CRISPR/Cas9-mediated genome editing. Mol. Cell. Biochem. (2017) 440 65-75. DOI: 10.1007/s11010-017-3156-0 13. Bi M., Naczki C., Koritzinsky M., Fels D., Blais J., Hu N., Harding H., Novoa I., Varia M., Raleigh J.. ER stress-regulated translation increases tolerance to extreme hypoxia and promotes tumor growth. EMBO J. (2005) 24 3470-3481. DOI: 10.1038/sj.emboj.7600777 14. Kim I., Xu W., Reed J.C.. Cell death and endoplasmic reticulum stress: Disease relevance and therapeutic opportunities. Nat. Rev. Drug Discov. (2008) 7 1013-1030. DOI: 10.1038/nrd2755 15. Taniguchi M., Yoshida H.. TFE3, HSP47, and CREB3 Pathways of the Mammalian Golgi Stress Response. Cell Struct. Funct. (2017) 42 27-36. DOI: 10.1247/csf.16023 16. Reiling J.H., Olive A.J., Sanyal S., Carette J.E., Brummelkamp T.R., Ploegh H.L., Starnbach M.N., Sabatini D.M.. A CREB3–ARF4 signalling pathway mediates the response to Golgi stress and susceptibility to pathogens. Nat. Cell Biol. (2013) 15 1473-1485. DOI: 10.1038/ncb2865 17. Gao J., Gao A., Liu W., Chen L.. Golgi stress response: A regulatory mechanism of Golgi function. Biofactors (2021) 47 964-974. DOI: 10.1002/biof.1780 18. Sasaki K., Yoshida H.. Golgi stress response and organelle zones. FEBS Lett. (2019) 593 2330-2340. DOI: 10.1002/1873-3468.13554 19. Oh-Hashi K., Soga A., Naruse Y., Takahashi K., Kiuchi K., Hirata Y.. Elucidating post-translational regulation of mouse CREB3 in Neuro2a cells. Mol. Cell. Biochem. (2018) 448 287-297. DOI: 10.1007/s11010-018-3333-9 20. Oh-Hashi K., Kohno H., Kandeel M., Hirata Y.. Characterization of IRE1α in Neuro2a cells by pharmacological and CRISPR/Cas9 approaches. Mol. Cell. Biochem. (2019) 465 53-64. DOI: 10.1007/s11010-019-03666-w 21. Oh-Hashi K., Hasegawa T., Mizutani Y., Takahashi K., Hirata Y.. Elucidation of brefeldin A-induced ER and Golgi stress responses in Neuro2a cells. Mol. Cell. Biochem. (2021) 476 3869-3877. DOI: 10.1007/s11010-021-04187-1 22. Helms J.B., Rothman J.E.. Inhibition by brefeldin A of a Golgi membrane enzyme that catalyses exchange of guanine nucleotide bound to ARF. Nature (1992) 360 352-354. DOI: 10.1038/360352a0 23. Wu M., Huang Q., Liao M., Wu X., Xi H., Ma H., Li S., Zhang Y., Xia Y.. OSW-1 induces apoptosis and cyto-protective autophagy, and synergizes with chemotherapy on triple negative breast cancer metastasis. Cell. Oncol. (2022) 45 1255-1275. DOI: 10.1007/s13402-022-00716-2 24. Lim C.-Y., Davis O.B., Shin H.R., Zhang J., Berdan C.A., Jiang X., Counihan J.L., Ory D.S., Nomura D.K., Zoncu R.. ER-lysosome contacts enable cholesterol sensing by mTORC1 and drive aberrant growth signalling in Niemann-Pick type C. Nat. Cell Biol. (2019) 21 1206-1218. DOI: 10.1038/s41556-019-0391-5 25. Taniguchi M., Nadanaka S., Tanakura S., Sawaguchi S., Midori S., Kawai Y., Yamaguchi S., Shimada Y., Nakamura Y., Matsumura Y.. TFE3 is a bHLH-ZIP-type transcription factor that regulates the mammalian Golgi stress response. Cell Struct. Funct. (2015) 40 13-30. DOI: 10.1247/csf.14015 26. Raben N., Puertollano R.. TFEB and TFE3: Linking lysosomes to cellular adaptation to stress. Annu. Rev. Cell Dev. Biol. (2016) 32 255-278. DOI: 10.1146/annurev-cellbio-111315-125407 27. Martina J.A., Diab H.I., Lishu L., Jeong-A L., Patange S., Raben N., Puertollano R.. The Nutrient-Responsive Transcription Factor TFE3 Promotes Autophagy, Lysosomal Biogenesis, and Clearance of Cellular Debris. Sci. Signal. (2014) 7 ra9. DOI: 10.1126/scisignal.2004754 28. Plummer A.M., Culbertson A.T., Liao M.. The ABCs of Sterol Transport. Annu. Rev. Physiol. (2021) 83 153-181. DOI: 10.1146/annurev-physiol-031620-094944 29. Nishimura A.L., Mitne-Neto M., Silva H.C., Richieri-Costa A., Middleton S., Cascio D., Kok F., Oliveira J.R., Gillingwater T., Webb J.. A Mutation in the Vesicle-Trafficking Protein VAPB Causes Late-Onset Spinal Muscular Atrophy and Amyotrophic Lateral Sclerosis. Am. J. Hum. Genet. (2004) 75 822-831. DOI: 10.1086/425287 30. Teuling E., Ahmed S., Haasdijk E., Demmers J., Steinmetz M.O., Akhmanova A., Jaarsma D., Hoogenraad C.C.. Motor Neuron Disease-Associated Mutant Vesicle-Associated Membrane Protein-Associated Protein (VAP) B Recruits Wild-Type VAPs into Endoplasmic Reticulum-Derived Tubular Aggregates. J. Neurosci. (2007) 27 9801-9815. DOI: 10.1523/JNEUROSCI.2661-07.2007 31. Moumen A., Virard I., Raoul C.. Accumulation of Wildtype and ALS-Linked Mutated VAPB Impairs Activity of the Proteasome. PLoS ONE (2011) 6. DOI: 10.1371/journal.pone.0026066 32. Tang F., Yang C., Li F.-P., Yu D.-H., Pan Z.-Y., Wang Z.-F., Li Z.-Q.. Palmitoyl transferases act as potential regulators of tumor-infiltrating immune cells and glioma progression. Mol. Ther.-Nucleic Acids (2022) 28 716-731. DOI: 10.1016/j.omtn.2022.04.030 33. Huang J., Li J., Tang J., Wu Y., Dai F., Yi Z., Wang Y., Li Y., Wu Y., Ren G.. ZDHHC22-mediated mTOR palmitoylation restrains breast cancer growth and endocrine therapy resistance. Int. J. Biol. Sci. (2022) 18 2833-2850. DOI: 10.7150/ijbs.70544 34. Audas T.E., Li Y., Liang G., Lu R.. A Novel Protein, Luman/CREB3 Reruitment Factor, Inhibits Luman Activation of the Unfolded Protein Response. Mol. Cell. Biol. (2008) 28 3952-3966. DOI: 10.1128/MCB.01439-07 35. Oh-Hashi K., Hasegawa T., Naruse Y., Hirata Y.. Molecular characterization of mouse CREB3 regulatory factor in Neuro2a cells. Mol. Biol. Rep. (2021) 48 5411-5420. DOI: 10.1007/s11033-021-06543-2 36. Minster R.L., Hawley N., Su C.-T., Sun G., E Kershaw E., Cheng H., Buhule O.D., Lin J., Reupena M.S., Viali S.. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat. Genet. (2016) 48 1049-1054. DOI: 10.1038/ng.3620 37. Loos R.J.F.. CREBRF variant increases obesity risk and protects against diabetes in Samoans. Nat. Genet. (2016) 48 976-978. DOI: 10.1038/ng.3653 38. Prochownik E.V., Wang H.. Normal and Neoplastic Growth Suppression by the Extended Myc Network. Cells (2022) 11. DOI: 10.3390/cells11040747 39. Bensen R.C., Gunay G., Finneran M.C., Jhingan I., Acar H., Burgett A.W.G.. Small Molecule Targeting of Oxysterol-Binding Protein (OSBP)-Related Protein 4 and OSBP Inhibits Ovarian Cancer Cell Proliferation in Monolayer and Spheroid Cell Models. ACS Pharmacol. Transl. Sci. (2021) 4 744-756. DOI: 10.1021/acsptsci.0c00207 40. Zhan Z., Liu Z., Zhang C., Gao H., Lai J., Chen Y., Huang H.. Anticancer effects of OSW-1 on glioma cells via regulation of the PI3K/AKT signal pathway: A network pharmacology approach and experimental validation in vitro and in vivo. Front. Pharmacol. (2022) 13 967141. DOI: 10.3389/fphar.2022.967141 41. Zhang Y., Fang F., Fan K., Zhang Y., Zhang J., Guo H., Yu P., Ma J.. Effective cytotoxic activity of OSW-1 on colon cancer by inducing apoptosis in vitro and in vivo. Oncol. Rep. (2017) 37 3509-3519. DOI: 10.3892/or.2017.5582 42. Iguchi T., Kuroda M., Naito R., Watanabe T., Matsuo Y., Yokosuka A., Mimaki Y.. Cholestane glycosides from Ornithogalum saundersiae bulbs and the induction of apoptosis in HL-60 cells by OSW-1 through a mitochondrial-independent signaling pathway. J. Nat. Med. (2018) 73 131-145. DOI: 10.1007/s11418-018-1252-4 43. Garcia-Prieto C., Riaz Ahmed K.B., Chen Z., Zhou Y., Hammoudi N., Kang Y., Lou C., Mei Y., Jin Z., Huang P.. Effective killing of leukemia cells by the natural product OSW-1 through disruption of cellular calcium homeostasis. J. Biol. Chem. (2013) 288 3240-3250. DOI: 10.1074/jbc.M112.384776 44. Zhu J., Xiong L., Yu B., Wu J.. Apoptosis Induced by a New Member of Saponin Family Is Mediated through Caspase-8-Dependent Cleavage of Bcl-2. Mol. Pharmacol. (2005) 68 1831-1838. DOI: 10.1124/mol.105.015826 45. Ding X., Li Y., Li J., Yin Y.. OSW-1 inhibits tumor growth and metastasis by NFATc2 on triple-negative breast cancer. Cancer Med. (2020) 9 5558-5569. DOI: 10.1002/cam4.3196 46. Esvelt K.M., Yang L., Aach J., Guell M., DiCarlo J.E., Norville J.E., Church G.M.. RNA-Guided Human Genome Engineering via Cas9. Science (2013) 339 823-826. DOI: 10.1126/science.1232033
--- title: 'A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction' authors: - Ivanoe De Falco - Antonio Della Cioppa - Tomas Koutny - Martin Ubl - Michal Krcma - Umberto Scafuri - Ernesto Tarantino journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10059991 doi: 10.3390/s23062957 license: CC BY 4.0 --- # A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction ## Abstract In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about $3.03\%$ for precision, $1.56\%$ for recall, $3.17\%$ for F1, and $1.56\%$ for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place. ## 1. Introduction In Machine Learning (ML) [1], the problem of data privacy, i.e., the existence of data private to the owning subject, has become relevant in many application fields in these last years, as well shown in the recent survey in [2]. In ML, this data privacy issue was explicitly tackled for the first time in 2015 with the introduction of the concept of Federated Learning (FL) [3,4]; in the basic form of this approach, a server starts its execution by creating a random solution under the form of a model that is sent to a set of clients, one for each set of data that should be kept private. Learning on any local set of private data only takes place on the local client associated with those specific data, so it is never sent elsewhere. Learning on a node results in the model being modified locally to best adhere to the local data. The locally modified model is sent back to the server at the end of the local learning. Once the latter has received all the modified models, it aggregates them to create a new model that considers all the local learning. After the aggregation phase, this latter model is sent again to the clients, and the process continues until it reaches a termination criterion on the server. A good survey on recent advances in FL can be found in [5]. Not all the existing ML techniques can be used within FL, because only some can undergo a meaningful aggregation process. For example, there is no such aggregation process available for methods based on Deep Neural Networks (DNNs) [6], logistic regression [7] and Radial Basis Functions [8]. Another important issue relevant to ML is the interpretability of the proposed solutions, as well evidenced in [9]. This means that a proposed solution should be understandable by any user. This is clearly a problem with the recent and numerically well-performing ML techniques, which DNNs [10,11] are. The latter build an internal model that, although capable of excellent numerical performance, is unintelligible to the user, be it a physician or a patient, which is why they are called black boxes. To somehow get rid of this problem, in 2017, Explainable Artificial Intelligence (XAI) (https://sites.google.com/view/fl-tutorial/?pli=1 accessed on 8 February 2023) [12] was introduced, which tries to endow DNNs with mechanisms allowing the creation of an external model that can somehow explain the behavior of the algorithm in making its decisions [13]. The problem with this is that there cannot be any guarantee that the external model is the same as the internal one over the data domain, which could lead to serious errors that could even be fatal in the medical domain. This paper proposes a general ML framework that can satisfy the issues related to data privacy and interpretability. Namely, our approach is based on the use of Evolutionary Algorithms (EAs), a widely used class of ML methodologies [14,15,16], and consists of the use of a distributed version of an EA (dEA) [17,18]. The proposed methodology is close to the classical FL approach, yet it is simultaneously different from it. It is similar because it allows each client to work on local data only and because global knowledge of the problem is obtained by aggregating the different local knowledge. The difference with the classical FL is that this aggregation is performed implicitly rather than explicitly, as in the typical FL scheme. A thorough explanation of this difference is given later in the paper. From an architectural viewpoint, the dEA framework we put forward contains both a master acting as the server and, thus, managing the algorithm, and a set of nodes, each of which represents a client and only contains local data to be kept private. Grammatical Evolution (GE) [19,20] is used as the specific EA: in it, each proposed solution is a model constituted by an expression linking (some of) the problem variables, so it represents explicit knowledge that users can immediately understand. Given this choice, we make use of a distributed GE scheme. In our proposed framework, learning takes place locally on each node, on which good local knowledge specific to the local data is gained in terms of a local model. Moreover, at given times, these good local models are sent to the master. This latter evaluates the global quality of each of these models over all the data, thanks to the help of all the nodes, without any need to transmit data from any node; then, it sends to all of the nodes all these models. These arrived solutions enter the local learning process; in this way, the local knowledge in each node can be augmented thanks to that arriving from other nodes. As a consequence of this exchange of information, better global knowledge can be obtained. This kind of implicit aggregation process ties local models into a global one. Said otherwise, during the execution of our algorithm, global knowledge emerges from the data contained in the various local sets without any need to physically exchange or make them visible. At the end of the execution, the model performing the best globally, i.e., over all the local sets of private data, is obtained as desired. Moreover, as a very interesting byproduct of our framework, apart from the global knowledge of the problem, for each set of private data, personalized knowledge is obtained that is specific to each of them. The proposed framework does not deal with the security issue in the current implementation. For a thorough description of the problems of privacy guarantees for users and detection against possible attacks, interested readers can refer to [21,22]. The framework is applicable in different domains [23,24]: healthcare, bank loans, advertising, financial fraud, and insurance, among others. In this paper, we focus our attention on the medical field, where there is a high need for data privacy and interpretability of the solutions. Regarding privacy, medical data are highly sensitive and strictly personal to the patient, so they should not be disclosed to anybody else, meaning both any other patient participating in the study and any person involved in the handling of the data or the learning process. In the European Union, this issue is regulated by the General Data Protection Regulation (GDPR) ($\frac{2016}{679}$ law) (https://eur-lex.europa.eu/eli/reg/$\frac{2016}{679}$/oj accessed on 3 February 2023) [25] that concerns data protection and privacy within the Union. For interpretability, in medicine, a solution should be understandable by any subject participating in the study. This holds for a physician wishing to evaluate from a medical viewpoint the soundness and the interest of the knowledge proposed by the ML system, or a patient wishing to receive a diagnosis that is clear and well explains the reasons for that decision. This also follows the GDPR law, specifically Article 12, which states that the data controller gives information to the ‘data subject in a concise, transparent, intelligible and easily accessible form, using clear and plain language’. Moreover, Article 25 recognizes subjects’ right to contest any automated decision making that was solely algorithmic. Within the medical field, we chose to take into account diabetes [26] disease, with specific reference to the prediction of future glucose values for subjects suffering from Type-1 diabetes mellitus (T1DM). Diabetes is a chronic disease, and its T1DM version is characterized by the fact that the subject’s pancreas produces practically no insulin, which calls for a life-lasting treatment consisting in the daily administration of amounts of insulin. In fact, if not treated, diabetes determines hyperglycemia, a condition of increased blood glucose values that with time may yield relevant damage to several parts of the body, among which are the eyes, kidneys, nerves, heart, lower limbs, and blood vessels [27]. As the data set to conduct our experiments, we avail ourselves of the well-known and publicly available Ohio T1DM data set [28]. In the experiments, rather than attempting to predict the exact future glucose values, as would be the case in multivariable regression, we treat prediction as a classification problem. This is an approach already taken in the scientific literature through various methods [29]. To follow this way of operating, we divide the glucose range into seven intervals, and for each future value, we aim at predicting the interval it lies within. This is a good way to predict if a future glucose value will lie in high-risk intervals, such as those associated with very low or very high values. In this case, immediate recovery actions can be taken to eliminate or reduce risks to the subject’s health. In clinical practice, Time-in-Range represents the time spent within a safe glucose-level range [30]. Within the safe range, the patient may avoid unnecessary actions to correct the blood glucose level, which may accidentally trigger an undesired outcome. In principle, the patient needs to know whether he/she is staying within the safe range or deviating from it. The proposed prediction addresses this need, while relieving the patient from the stress of operating with exact glucose levels, which may lead to diabetes burnout [31]. As a side effect, the resulting models can be simpler, thus reducing the computational complexity for lower-power devices and for possible cloud processing for thousands of patients. Moreover, the proposed method has the future potential to be applied as a watchdog over an insulin pump’s controller activity. As it is a prediction method, it could detect the controller’s failure to keep glucose levels in the safe range ahead of time. As the outcome of our experiments, we expect to obtain an explicit global model able to perform generalization. This means that such a model should perform acceptably well on all the subjects involved in this learning process and on others not involved in creating the model. This would be highly important in real-world situations where we have to start monitoring diabetic subjects for which we do not have specific knowledge. We would need a general model to use on them to predict their future glucose values, and we could use the one obtained through our framework. To evaluate the effectiveness of the mechanism of information exchange among nodes, our algorithm is experimentally compared against a distributed EA differing only in the absence of exchange. This comparison is effected both in terms of numerical performance achieved in the classification and from the statistical analysis perspective. The rest of this paper is structured as follows. Section 2 presents a brief state-of-the-art review. Section 3 describes the proposed collaborative approach and the data set used. The experimental framework and the discussion about findings are reported in Section 4. In the same section, the results of the statistical analysis test, performed over the twelve subjects of the complete Ohio T1DM data set, are outlined. The last section exposes the conclusions and provides some indications on future work. ## 2. State of the Art An important issue when dealing with ML applications is data privacy related to the protection of sensible personal information. This issue is increasing with the usage of online platforms collecting private data to provide services. A privacy-preservation framework must ensure high protection to let individuals share their information. FL represents the most employed technology to accomplish the privacy task [3,4,32]. This federated technique facilitates distributed collaborative learning by multiple clients under the coordination of a server. Data privacy is assured by training a prediction model through decentralized data, locally associated with different clients and not exchanged or transferred. Federated Learning is applied to support privacy-sensitive applications in several fields [24]. Another important issue of ML lies in its ability to discover underlying explanatory structures. The most performing techniques, i.e., deep learning neural networks, can be regarded as black boxes lacking an explicit knowledge representation. Utilizing black box learning models involves difficulty in understanding what model inputs drive the decisions (explainability) and, above all, prevents specialists from understanding the reason for a prediction (interpretability) [9,33]. The demand for transparent decisions pushes towards explainable and interpretable systems [34]. Explainable systems are black box learning models endowed with external XAI tools, without guarantee that these external tools allow capturing the internal model behavior. Interpretable models are models able to explain themselves by providing explicit models. From now on, the term interpretability is employed with the above meaning. Both the above issues assume noticeable importance in the medical domain, e.g., diabetes management. Several techniques have been investigated to discover data-driven glucose forecasting models, ranging from approaches based on regression [35,36,37,38,39] to those that handle the prediction as a classification problem [29,40,41,42]. These techniques can be classified as explainable or interpretable based on the techniques employed for discovering the learning model. Leaving aside the regression-based models, a brief literature survey on the state-of-the-art works on diabetes classification using data-driven ML models is conducted for the explainable and interpretable models described above. The review is related to recent articles that explore different techniques for dealing with glucose prediction formulated as a classification problem. The first category concerns explainable techniques, most based on neural models, that exhibit outstanding performance at the expense of the difficulty of comprehending the aspects that can explain the decision, even when enriched with external XAI explanation tools [43,44,45,46,47,48,49,50,51,52,53,54,55]. As already illustrated in the introduction, the lack of explanation could yield the usage of these classification techniques problematic in the medical domain [56]. In fact, these learning models’ inner workings are too complicated to understand for physicians. The second category includes interpretable models characterized by explicit prediction models. Most of these models rely on decision trees [57,58,59,60,61,62,63,64,65,66,67,68,69]. Although the methods based on these trees [70] could provide explicit knowledge, in many cases, it is challenging to linearize the resulting acyclic decision graphs into simple decision rules. Other attempts have been carried out to make predictions through classification rules based on if–then–else conditions induced by an evolutionary approach [71,72]. Independently of the belonging category, none of the above-examined approaches consider the problem of data privacy, which remains a critical concern when handling sensitive information such as diabetic data [73]. FL technology has been utilized in the medical domain to train a prediction model through decentralized data for dealing with different problems [74,75,76,77]. Only some recent papers contemplate the problem of implementing privacy-protected diabetes prediction systems relying on FL approaches and encryption with different training processes [78,79]. However, instead of employing data related to a single patient, the training concerns data collected in each hospital [78] or grouped by defining cohorts associated with diabetes-related complications [79]. Therefore, while ensuring data protection, these approaches do not permit the development of personalized models that are important from the point of view of precision medicine. This review makes us confident that, at least in the recent scientific literature, data privacy has not been considered for tuning interpretable glucose forecasting models for diabetic patients. Indeed, most reviewed predictive models rely on centralized training data, or refer to decentralized training clients associated with data not referred to single patients, and thus are unable to allow personal disease treatment and prevention strategies. Table 1 summarizes the results of the review. As evinced from this table, the limitation of the current FL approaches is related to solution interpretability. We aim to overcome this limitation by dealing with a data-privacy paradigm able to discover interpretable Machine Learning models for glucose prediction. This paradigm is based on some collaborative concepts inspired by FL. Specifically, this collaboration is pursued by training a Federated Learning-inspired global model, relying on a dEA that evolves multiple decentralized clients, each representing a single patient, holding local data samples without exchanging them. The collaborative training consists in sharing the model discovered by each local patient to be aggregated in a global model. The personalized models emerged at the end of evolution can be exploited for personal diabetes treatment or aggregated and used as global models. Regarding interpretability, we concentrated on a grammar-based Evolutionary Algorithm to discover explicit classification models. The following section illustrates the devised framework and its specific application to the glucose forecasting problem. ## 3.1. The Proposed Approach This paper introduces a novel Federated Learning-inspired Evolutionary Algorithm (FLEA). The proposed methodology is a master/slave dEA [18,80] in which each slave runs a canonical sequential EA, and individuals, i.e., predictive models, can synchronously migrate between populations with a given frequency [81]. Specifically, each slave evolves a population of predictive models using learning data from a specific slave exclusively. In particular, the proposed method works as follows. During the evolution:at specific instants of time (migration interval), the best model evolved so far on each slave is sent to the master node;the master node returns the collected models to all the slaves, which evaluate them on the local data, thus preserving data privacy;each slave uses the immigrant models within its population by replacing as many local individuals as possible with the lowest performance if better. In this way, the evolved models on each slave node receive information about the evolving models on the other slave nodes. Thanks to the mechanisms of selection, replacement, and genetic variation, the slave nodes can integrate the incoming information into their own population. The above steps are graphically outlined in Figure 1. At the end of evolution, each slave sends the best predictive model found to the master node that collects all of them. Then, the master node sends to each slave the list of all the best local models just received. Each slave evaluates such models on its local data and sends the list of their performance to the master node. This last step is particularly important when the proposed method is inserted in a system that continuously optimizes models on local data. In fact, when new local data are added to the system, the master node could provide the initial population of a new slave node with the predictive models coming from the other individuals, thus allowing a boost in the search for the specific local model on these new data. Algorithms 1 and 2 report the pseudocode for the master and slave, respectively. It is worth noting that the proposed methodology is very close to an FL approach [5]. The differences lie in the fact that, in the federated approach, the integration of patterns is explicitly performed by the master node, and the communication of learned patterns is direct. In contrast, in the proposed approach, the integration of patterns is implicitly performed by the slave nodes, in that it is demanded of the mechanisms of selection and genetic variation, i. e., crossover [82], that, eventually, perform the integration. The communication is indirectly effected through the master node. ## 3.2. The Data Set The FLEA framework is investigated to forecast future glycemic trends for T1DM patients. The experiments are conducted on the Ohio T1DM data set, released in 2020 [28], which gathers data of T1DM patients. This data set was collected at the Ohio University and contains data related to twelve subjects, each of whom was monitored with a Continuous Glucose Monitoring (CGM) system for about eight weeks while being on insulin pump therapy. Given the availability of data in the dataset related to, among others, measured subcutaneous glucose, injected insulin (basal plus boluses), and carbohydrate ingested during the day (time and estimated size of all meals), future glucose values can be predicted on the basis of the sets of current and recent values available for these three parameters. The sampling interval of glucose measurements achieved by the CGM system is equal to Δt=5 min. Each slave only contains the private data associated with a single patient. To allow supervised learning, the data series of each patient are partitioned into training and testing sets, respectively, used to extract the model during the learning phase and assess its quality over unseen samples. The supervised learning phase is carried out on the six patients related to the data set released in 2018 [83], while a successive validation phase is performed on the testing sets of the remaining six patients added to the data set in 2020. The information about the number of training and testing samples for each patient is reported in [28]. Algorithm 1 Pseudocode of FLEA on the master nodeset global stopping condition to FALSEwhile not global stopping condition do for each slave do receive the best local model end for for each slave do send the list of the received best local models end for for each slave do receive the local stopping condition end for if all local stopping conditions are TRUE then set global stopping condition to TRUE end ifend whilefor each slave do receive the final best local modelend forfor each slave do send the list of the final best local models receive the list of the fitness of the final best local modelsend for ## Data Preprocessing As regards the data preprocessing, we performed the following arrangement:Samples with missing glucose readings in training and testing sets are thrown away to avoid that the predicting model can be the result of artificial observations;Insulin and carbohydrates data were aligned to the closest CGM glucose reading time;No outlier detection and no data normalization were effected. It is pointed out that the discrete signals of administered insulin, i. e., insulin boluses plus insulin basal, and the assumed carbohydrates are to convert into continuous signals to estimate their impact on the glucose values over time. The Hovorka model [84], simulating the absorption rate of the injected insulin through a two-compartment chain, is employed for preprocessing the injected insulin boluses. This model permits adding the signal delineating the absorption rate of the boluses to the signal representing the absorption rate of subcutaneously administered long-acting insulin. Let us assume that the glucose level G(t), the injected insulin U(t), and the consumed carbohydrates Dg(t) are available. The model for insulin absorption is [1]dS1dt=U(t)−S1tmaxI [2]dS2dt=S1−S2tmaxI in which S1 and S2 are the two compartments making up the chain for modeling the absorption of subcutaneously infused short-acting insulin, U(t) [mU min−1] is the amount of injected insulin, tmaxI=55 [min] is the constant indicating the time-to-maximum insulin absorption, and S1(t) [mU] and S2(t) [mU] are the amounts of insulin in the two compartments. Then, the plasma insulin concentration I [mU L−1] is described as [3]dIdt=S2VI·tmaxI−ke·I where ke=0.138 [min−1] is the fractional elimination rate of the insulin from plasma and VI=0.12 [L kg−1] is the insulin distribution volume. The constant values are derived from Hovorka’s model [85]. Regarding the carbohydrate intake, in the presence of a meal, the gut absorption rate is modeled in accord with [84] as [4]C(t)=Dg·Ag·t·e−t/tmaxtmax2 where tmax=40 [min] is the time-of-maximum appearance rate of glucose in the accessible compartment, Dg is the amount of digested carbohydrates, and Ag=0.8 is the carbohydrates bioavailability [86]. This function rapidly increases after the meal and then lowers to 0 in 2–3 h. Outside such a period, the values of missing carbohydrate are filled with zeroes. At the end of the preprocessing, by integrating Equation [3] and exploiting Equation [4], we have two signals, discretized every Δt minutes, for the absorbed insulin and carbohydrates, i.e., I(t) and C(t), respectively. More specifically, when at time t there is an insulin release or carbohydrate intake event, their absorbed quantities are propagated over time through Equations [3] and [4] from the current time t ahead and, if needed, summed to the residual quantity evaluated by the compartment model in the past. Typically, the variation range of G(t) is about [2÷25][mmolL−1], of I(t) is about [0÷10][mUL−1], and of C(t) is [0÷3][g]. ## 3.3. FLEA to Forecast Future Glycemic Trends Forecasting future glycemic trends for T1DM patients can be regarded as a multivariate time series regression problem, falling within the learning of data-driven models exploiting information extracted from CGM systems. To apply the FLEA framework to the above problem, we exploit the capability of the Grammatical Evolution [82] to automatically evolve interpretable regression models. Moreover, differently from other EAs, GE explicitly makes use of the context-free grammars that are able to design a specific form for the evolved models. To do this, we need to define a suitable grammar and a fitness function. Moreover, rather than attempting to predict the exact future glucose values, we transform the time series regression into a classification problem. ## 3.3.1. The Grammar The context-free grammar in Figure 2 depicts the syntax of the GE-based expressions evolved on each slave, where 〈gluc〉 represents the glucose levels in the past, 〈ins〉 and 〈cho〉 indicate insulin and carbohydrates to be absorbed in the future, and 〈dg〉 is the difference between the current and past glucose levels, respectively. In our grammar, the protected psqrt and plog functions return the square root of the absolute value of the argument, the logarithm of the summation of 1, and the absolute value of the argument, respectively, while aq stands for the protected analytic quotient operator [87]. Table 2 outlines the protected functions utilized in the grammar. By considering the values of G(t) every Δt minutes in a time window of kΔt minutes before the current instant t, as well as the values of I(t) and C(t) every Δt minutes in a time window of hΔt minutes after the current instant t, we search for an explicit regression model to predict the future glucose value G^(t+hΔt) at a forecasting horizon hΔt:[5]G^(t+hΔt)=(ΓG(t),G(t−Δt),…,G(t−kΔt)−Θ(I(t),I(t+Δt),…,I(t+hΔt))+ΩC(t),C(t+Δt),…,C(t+hΔt))◊ΦdG(t,t−Δt),…,dG(t,t−kΔt) where the symbol ◊ represents an algebraic operation in the set: {+,−,·}, and Γ,Θ, Ω, and Φ are expressions on G, I, C, and dG, respectively. ## 3.3.2. From Regression to Classification Glucose prediction is typically performed through multiseries regression to predict glucose values as accurately as possible. Nonetheless, in the literature, the use of classification to forecast glucose ranges rather than exact values is becoming more and more popular [47,48,49,88]. This is of great help when high-risk situations such as hyperglycemic events or, even more crucially, hypoglycemic ones should be forecasted with good advance. In these cases, it is more important to predict the occurrence of such an event than the precise glucose values. To perform classification, the continuous glucose values were mapped into seven intervals, leading to a seven-class problem. More precisely, two classes make reference to hypoglycemia, three relate to euglycemia (normal values), and two refer to hyperglycemia. The decision to consider two hypo- and two hyperclasses is based on the outcome of the international consensus held in 2017 and reported in [89]. Following that consensus, we used the same bounds as in that document; the corresponding bounds are reported in Table 3. As concerns the euglycemic range, unlike [89], we prefer instead to consider a division into three classes, as reported in Table 3. The rationale for this is that if we had just one normal/target range, then we would not be able to track possibly dangerous, out-of-target-range deviating glucose development: we would directly pass from a series of normal values to the occurrence of a hypoglycemic event, without any warning. Instead, by using three classes, the middle one being larger and the two border zones towards hypo- and hyper-being ‘thin’, we would obtain warnings before a hypo- or a hyperglycemic event took place. In fact, for hypoglycemia, we would have a series of normal values, followed by a (series of) normal-closing-to-hypovalues, followed by hypovalues. In Table 3, for each class, the ID we assigned to it is displayed, the corresponding glucose value range is shown both in mmol/L and in mg/dL, and the action(s) required during the monitoring are reported. In this way, the problem is transformed into a classification task, and the aim is to predict the class of any glucose value in the future, starting from the available values for glucose, absorbed insulin, and carbohydrates, as expressed in Equation [5]. Figure 3 shows, for the testing set of each subject, the transformation of the continuous glucose signal into the corresponding set of items for the seven-class classification task considered in this paper. Table 4 reports the number of samples in the training (Tr) and testing (Ts) sets for each patient and class. From the table, it can be easily seen that, for all six subjects, the three classes related to normal values and the two for the hyperglycemic values are much more populated than the two corresponding to ’very low’ and ’low’ glucose values. The latter two often only contain few values, and it is worth noting that for subjects 563, 570, and 588, the ’very low’ class is even empty. This means that all the six data sets are highly unbalanced, which is a complication in classification [90]. ## 3.3.3. Fitness Function To evaluate the quality of any solution proposed, a suitable fitness function should make reference to the specific metrics typically used for this kind of problem, as, e.g., accuracy, sensitivity, specificity, area under the ROC curve, F1 score, Matthews correlation coefficient, and so on. We decided to use the F1 score, since the data sets corresponding to each of the six subjects investigated here are highly unbalanced; especially, their classes 0 (very low glucose values), 1 (low glucose values), and, for some subjects, 6 (very high glucose values) contain very few items with respect to the other four classes. It is well known that, whenever a data set is highly unbalanced in terms of number of items contained in the different classes, as it is the case here, metrics such as accuracy, sensitivity, and specificity are not suitable: good performance on the most populated class(es) could lead to numerically good results without actual learning taking place on the least populated class(es). This could mean that every time an item belonging to one of the minority classes has to be classified, the algorithm could wrongly assign it to one of the majority class(es). For unbalanced data sets, instead, metrics such as F1 score or Matthews correlation coefficient can more effectively take this problem into account. For a two-class problem in which we have a positive class and a negative one, F1 score is computed as [6]F1=tptp+0.5·(fp+fn) where:tp: the number of true positives, i.e., the items in the positive class that are correctly assigned to that class;fp: the number of false positives, i.e., the items in the negative class that are incorrectly assigned to the positive class;fn: the number of false negatives, i.e., the items in the positive class that are incorrectly assigned to the negative class. When, instead, there are more than two classes, as in this case, the definition of F1 score can be generalized in several ways. Within this paper, we used the method of weighted averaging, This means that the resulting F1 score value accounts for the contribution of the F1 score computed for each class and weighted by the number of items of that given class. In formula:[7]F1=∑$$n = 1$$ncpi·F1inc where nc is the number of classes in the data set, pi is the percentage of items in the i-th class, and F1i is the F1 score value computed on the i-th class. The admissible range for F1 score is [0.0–1.0], and higher values represent better classifications. By choosing this metric, the classification problem becomes a maximization one. ## 4.1. Experimental Framework Setting Our approach was implemented by exploiting PonyGE2, a freely downloadable and patent-free GE implementation in Python [82]. PonyGE2 has a number of GE-specific parameters to set, the meaning of which can be found in [82]. After a preliminary tuning, the parameters used for all the experiments were set as follows: population size and maximum generations equal to 200 and 500, respectively; codon size equal to 100,000, tournament selection with size 4, mutation probability equal to $10\%$, one-point crossover probability equal to $90\%$, int flip per codon mutation with one mutation event, and Position Independent Grow method for the individual initialization. For the slaves, a single local stopping conditionwas considered and set to the fulfillment of the maximum number of generations. We set the communication between the master and the slaves to take place every 100 generations. This value was chosen because of two motivations. The first reason comes from the field of dEAs: it is known that any subpopulation should not receive immigrating individuals too frequently, because this would perturb the local evolution at each communication time. The local search must be given sufficient time to suitably integrate the arrived individuals into the local subpopulation, so as to exploit their good features. The second reason is related to the FL principles themselves in terms of security: an FL algorithm should involve the least possible amount of information being transmitted, because any possible communication could be attacked, possibly resulting in a subject’s relevant information being disclosed or in an injection of fake data by attackers, which could lead to totally wrong learning. Hence, based on our experience, we feel the value of 100 implies the lowest amount of communication that allows improvement in the learning process while, at the same time, not excessively exposing the process to external attacks. In fact, this value of 100, together with the number of generations being set to 500, means that, during the whole execution, only five communication phases between the master and the slaves take place. The forecasting horizon is hΔ(t)=30 min, because the forecasting accuracy becomes worse and less reliable as the prediction horizon augments [91,92]. A horizon longer than 30 min, e.g., 2 or 4 h, is only practical for time spans that refer to almost steady-state situations as nocturnal predictions when sleeping. Any external event can cause a substantial and unpredictable glucose-level variation during these long intervals. The considered past time window is kΔ(t)=60 min for the historical samples leveraged for the forecasting. The time span for the historical data is chosen considering that 30-min data in the past are enough to perform an effective prediction [93]. Given that the values are taken at 5-min intervals, the values for k and h are equal to 12 and 6, respectively. This implies that both in the grammar and in Equation [5], at each time t, we consider twelve glucose values in the past and six insulin and carbohydrate values in the future. To assess the effectiveness of the proposed approach, we conducted two experiments:In the first experiment, we used a non-FL approach consisting of FLEA with no communication between the master and the slave nodes during the evolution. In other words, we executed a separate optimization for all the patients, thus obtaining for each of them a personalized model. At the end of the executions, we collected all the models and selected among all of them the model with the best average performance on all the patients;In the second experiment, we used FLEA. The average outcomes for each run were evaluated at the end of the evolution by considering all the best local models received by the master node from all the slaves and measuring their performance on all the patients to evaluate how they perform on average when adopted as global models. For each patient, indicated with the identifier ID, twenty runs were carried out to reduce the randomness in the GE algorithm initialization. The evaluation is performed on all instances for which a glucose measurement is available over the prediction period. ## 4.2. Findings and Discussion Table 5 and Table 6 report the F1 score of the best models on each slave of both experiments, and the last row and column show their averages and standard deviations. By looking at Table 5, it can be evidenced that, when adopted as a global model, each personalized model exhibits F1 score values that are quite different on a specific patient (rows), and the same is true also when the performance of the models is measured on a specific patient (columns). On the contrary, inspecting the results of Table 6 related to our approach, the scenario changes. Independently of the adopted global model, the average F1 score of the evolved models is always better than the previous case, except for subject 570, and very close to each other (rows). A similar consideration also holds for all the models on a specific patient (columns), thus evidencing that the proposed approach can evolve generalized models exhibiting better performance. Moreover, if we look at the best models evolved on local data (diagonals in the tables), it is evident that communication helps improve the performance on local nodes too. Analogous reflections can be made by comparing the corresponding panes of Figure 4 and Figure 5 reporting the confusion matrices on the testing set for both experiments. This comparison documents that FLEA frequently increases the number of items correctly assigned to the classes. This can be verified by looking at the cells along the diagonals, which in most cases contain higher values. Once we found the two global models proposed by the two different approaches, we wished to investigate their generalization capability to ascertain whether or not they have similar performance for this issue. To this aim, we executed them on six more patients in the Ohio data set. Table 7 reports the corresponding results in terms of F1 score for the two models over the testing sets of these six additional patients. The last column in the table reports the average and the standard deviation of these F1 values. The table reports that, over all six subjects, the model obtained by FLEA always performs better than that achieved by the non-FL approach. This is very important, because it allows concluding that the model provided by our FLEA framework is more general; therefore, it can be used for new subjects not participating in the learning process. The confusion matrices corresponding to these experiments are reported in Figure 6 and Figure 7 for the two algorithms without and with communication, respectively. The comparison between the corresponding panes of the two figures evidences that the items are more frequently assigned correctly when FLEA is considered: the cells along the diagonals contain higher values when communication takes place. This is of crucial importance for the two classes corresponding to hypoglycemic events. In fact, the latter are high-risk situations, therefore correctly predicting them well in advance is a major issue for subjects’ health. Moreover, in this case, the results confirm that the model produced by the FLEA algorithm is more general and can be useful when new subjects are to be monitored from scratch. Table 8 reports a comprehensive view of the six subjects’ numerical scores. This table further confirms that the model achieved by FLEA, on average, performs better than that obtained by the non-FL approach. Specifically, the former shows an improvement of about $3.03\%$ for precision, $1.56\%$ for recall, $3.17\%$ for F1, and $1.56\%$ for accuracy. ## 4.3. Statistical Analysis A statistical analysis test was executed to assess whether or not the best model proposed by the FLEA algorithm performs better than that obtained by the non-FL one over the complete set of the twelve subjects making up the Ohio T1DM data set. This analysis was carried out on the online web platform ‘Statistical Tests for Algorithms Comparison’ [94] (STAC) (https://tec.citius.usc.es/stac/ accessed on 15 January 2023). From among the several statistical tests available, the Quade test was chosen because it considers the higher difficulty of some problems and the larger differences that may be shown by the various algorithms over them; hence, the Quade test is different from the Friedman and Aligned Friedman ones, in which all the problems are considered to be of equal importance. For more details on the statistical analysis shown here in general, and on the Quade test in particular, interested readers can refer to the widely cited paper by [95]. Before running a statistical test, a null hypothesis H0 must be chosen; we set it as the fact that the two models proposed by the two algorithms are statistically equivalent. Moreover, a significance level α must be chosen; we set its value as 0.05, which means that, if the null hypothesis is rejected by the test, there is a $5\%$ probability of incorrectly rejecting it. Table 9 reports the results of this test: the first column contains the algorithms compared, and the second the corresponding rank value; better algorithms are characterized by lower ranking values. The table shows that FLEA performs better than non-FL on this test. Yet, as the computed p-value is 0.16680, which is higher than 0.05, this test cannot exclude the statistical equivalence between the two algorithms. To further investigate this issue, we must make reference to post hoc procedures, also described in [95]. Table 10 reports the results, in terms of adjusted p-values, for the complete set of post hoc procedures available in STAC, i.e., Bonferroni–Dunn, Holm, Hochberg, Finner, and Li. The FLEA algorithm was chosen as the control method because it is the algorithm with the lowest ranking value in the Quade test. To understand the results in the table, each post hoc procedure returns an adjusted p-value for non-FL. For all the procedures, this value is lower than the significance level 0.05. This means that the null hypothesis H0 of equivalence is rejected by all of them. Hence, FLEA is statistically better than non-FL. ## 5. Conclusions and Future Work To suitably deal with the issues of data privacy and interpretability, in this paper, we proposed a distributed framework that constitutes an innovative approach to Federated Learning. The framework consists in a master process and a set of slaves: On each of the latter, a Grammatical Evolution algorithm is run that only learns from local private data and generates explicit models that humans can interpret. At given times, a migration process occurs between the slaves through the master, the result of which is that each slave receives the local best models found by the other slaves. This results in an exchange of knowledge between the different nodes merging locally gained knowledge. This process of knowledge exchange allows obtaining local models that can work effectively over all the local sets of private data, i.e., they can be used as global models. As data privacy and interpretability are highly relevant to the medical field, we applied this framework to a medical problem, i.e., that of the prediction of future glucose values for T1DM patients. This problem is transformed into a seven-class classification task. To assess the importance of this process of knowledge exchange, the framework was experimentally compared with another that only differs in the fact that knowledge exchange does not take place between the slaves. The results show the importance of this exchange process to create a set of personalized models, each of which can be used as the global model. Moreover, the model obtained by our federated approach showed higher generalization capability than that achieved by the non-FL approach when the two were applied to the data of subjects who did not participate in the learning process. A statistical analysis evidenced the superiority of the FLEA algorithm. In our future work, the results and behavior shown by our framework on this specific problem must be further investigated on other data sets from the medical domain in which data privacy and interpretable solutions are hard constraints. Another important step to take in our future work is to perform an experimental investigation to evaluate if an optimal frequency for information exchange exists that allows improving the results without causing too many risks in terms of security for the whole framework. This analysis can be crucial considering that it is well in the field of dEAs that the numerical quality of the results may depend, even more highly, on communication frequency. This could lead to improvement in the results provided by our approach, which could, in this way, perform much better than the model without communication. Regarding security, it should be evidenced that, currently, the framework we proposed does not deal with the data security problem during the phases of information exchange. As we exploit a distributed approach to evolve a global model, it is highly appropriate to discuss data transmission security. The proposed approach does not propagate patient data (e.g., his/her measured glucose) but rather a compression of his/her metabolic responses in the form of a best-suited model at the given time. Solely, this does not raise a concern; nonetheless, in connection with the known treatment setup, it may give an opportunity to use such representation to build a targeted attack. To avoid an eavesdropping attack, an encrypted communication link between the master and slave nodes has to be established. Data transmission security is not a concern of this paper and will be a subject of future work. In our future work, we will have to suitably address this problem to define a complete Federated Learning framework that could help in real-world medical trials where data privacy must be guaranteed. ## References 1. Mitchell T.M.. Machine Learning (1997.0) Volume 1 2. Liu B., Ding M., Shaham S., Rahayu W., Farokhi F., Lin Z.. When machine learning meets privacy: A survey and outlook. ACM Comput. Surv. (CSUR) (2021.0) 54 1-36. DOI: 10.1145/3436755 3. Konečnỳ J., McMahan B., Ramage D.. Federated optimization: Distributed optimization beyond the datacenter. arXiv (2015.0) 4. Konečnỳ J., McMahan H.B., Ramage D., Richtárik P.. Federated optimization: Distributed machine learning for on-device intelligence. arXiv (2016.0) 5. Li Q., Wen Z., Wu Z., Hu S., Wang N., Li Y., Liu X., He B.. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Trans. Knowl. Data Eng. (2023.0) 35 3347-3366. DOI: 10.1109/TKDE.2021.3124599 6. McMahan H.B., Moore E., Ramage D., Agüera y Arcas B.. Federated learning of deep networks using model averaging. arXiv (2016.0) 7. Yang Q., Liu Y., Chen T., Tong Y.. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) (2019.0) 10 1-19. DOI: 10.1145/3298981 8. Xu J., Jin Y., Du W., Gu S.. A federated data-driven evolutionary algorithm. Knowl.-Based Syst. (2021.0) 233 107532. DOI: 10.1016/j.knosys.2021.107532 9. Rudin C.. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. (2019.0) 1 206-215. DOI: 10.1038/s42256-019-0048-x 10. Bengio Y.. Learning deep architectures for AI. Found. Trends® Mach. Learn. (2009.0) 2 1-127. DOI: 10.1561/2200000006 11. Schmidhuber J.. Deep learning in neural networks: An overview. Neural Netw. (2015.0) 61 85-117. DOI: 10.1016/j.neunet.2014.09.003 12. Gunning D.. Explainable artificial intelligence (xai). Def. Adv. Res. Proj. Agency (DARPA) Nd Web (2017.0) 2 1. DOI: 10.1126/scirobotics.aay7120 13. Montavon G., Samek W., Müller K.R.. Methods for interpreting and understanding deep neural networks. Digit. Signal Process. (2018.0) 73 1-15. DOI: 10.1016/j.dsp.2017.10.011 14. Back T.. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (1996.0) 15. Back T., Fogel D.B., Michalewicz Z.. Handbook of Evolutionary Computation (1997.0) 16. Whitley D.. An overview of evolutionary algorithms: Practical issues and common pitfalls. Inf. Softw. Technol. (2001.0) 43 817-831. DOI: 10.1016/S0950-5849(01)00188-4 17. Tomassini M.. Parallel and distributed evolutionary algorithms: A review. Evolutionary Algorithms in Engineering and Computer Science (1999.0) 113-133 18. Gong Y.J., Chen W.N., Zhan Z.H., Zhang J., Li Y., Zhang Q., Li J.J.. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl. Soft Comput. (2015.0) 34 286-300. DOI: 10.1016/j.asoc.2015.04.061 19. Ryan C., Collins J.J., O’Neill M.. Grammatical evolution: Evolving programs for an arbitrary language. Proceedings of the European Conference on Genetic Programming 83-96 20. O’Neill M., Ryan C.. Grammatical evolution. IEEE Trans. Evol. Comput. (2001.0) 5 349-358. DOI: 10.1109/4235.942529 21. Blanco-Justicia A., Domingo-Ferrer J., Martínez S., Sánchez D., Flanagan A., Eeik Tan K.. Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Eng. Appl. Artif. Intell. (2021.0) 106 104468. DOI: 10.1016/j.engappai.2021.104468 22. Mothukuri V., Parizi R.M., Pouriyeh S., Huang Y., Dehghantanha A., Srivastava G.. A survey on security and privacy of federated learning. Future Gener. Comput. Syst. (2021.0) 115 619-640. DOI: 10.1016/j.future.2020.10.007 23. Li L., Fan Y., Tse M., Lin K.Y.. A review of applications in federated learning. Comput. Ind. Eng. (2020.0) 149 106854. DOI: 10.1016/j.cie.2020.106854 24. Banabilah S., Aloqaily M., Alsayed E., Malik N., Jararweh Y.. Federated learning review: Fundamentals, enabling technologies, and future applications. Inf. Process. Manag. (2022.0) 59 103061. DOI: 10.1016/j.ipm.2022.103061 25. Regulation P.. General data protection regulation. Intouch (2018.0) 25 1-5 26. Sun H., Saeedi P., Karuranga S., Pinkepank M., Ogurtsova K., Duncan B.B., Stein C., Basit A., Chan J.C., Mbanya J.C.. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. (2022.0) 183 109119. DOI: 10.1016/j.diabres.2021.109119 27. Papatheodorou K., Banach M., Bekiari E., Rizzo M., Edmonds M.. Complications of diabetes 2017. J. Diabetes Res. (2018.0) 2018 3086167. DOI: 10.1155/2018/3086167 28. Marling C., Bunescu R.. The OhioT1DM dataset for blood glucose level prediction: Update 2020. NIH Public Access (2020.0) 2675 71 29. Woldaregay A., Arsand E., Botsis T., Albers D., Mamykina L., Hartvigsen G.. Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in type 1 diabetes. J. Med. Internet Res. (2019.0) 21 e11030. DOI: 10.2196/11030 30. Battelino T., Danne T., Bergenstal R.M., Amiel S.A., Beck R., Biester T., Bosi E., Buckingham B.A., Cefalu W.T., Close K.L.. Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care (2019.0) 42 1593-1603. DOI: 10.2337/dci19-0028 31. Abdoli S., Hessler D., Smither B., Miller-Bains K., Burr E.M., Stuckey H.L.. New insights into diabetes burnout and its distinction from diabetes distress and depressive symptoms: A qualitative study. Diabetes Res. Clin. Pract. (2020.0) 169 108446. DOI: 10.1016/j.diabres.2020.108446 32. Rodríguez-Barroso N., Jiménez-López D., Luzón M.V., Herrera F., Martínez-Cámara E.. Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges. Inf. Fusion (2023.0) 90 148-173. DOI: 10.1016/j.inffus.2022.09.011 33. Rudin C., Chen C., Chen Z., Huang H., Semenova L., Zhong C.. Interpretable machine learning: Fundamental principles and 10 grand challenges. Stat. Surv. (2022.0) 16 1-85. DOI: 10.1214/21-SS133 34. Mengnan D., Ninghao L., Xia H.. Techniques for interpretable machine learning. Commun. ACM (2020.0) 63 68-77 35. De Falco I., Della Cioppa A., Koutny T., Krcma M., Scafuri U., Tarantino E.. Genetic programming-based induction of a glucose-dynamics model for telemedicine. J. Netw. Comput. Appl. (2018.0) 119 1-13. DOI: 10.1016/j.jnca.2018.06.007 36. De Falco I., Della Cioppa A., Giugliano A., Marcelli A., Koutny T., Krcma M., Scafuri U., Tarantino E.. A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives. Appl. Soft Comput. (2019.0) 77 316-328. DOI: 10.1016/j.asoc.2019.01.020 37. Tyler N.S., Jacobs P.G.. Artificial intelligence in decision support systems for type 1 diabetes. Sensors (2020.0) 20. DOI: 10.3390/s20113214 38. Felizardo V., Garcia N.M., Pombo N., Megdiche I.. Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction—A systematic literature review. Artif. Intell. Med. (2021.0) 118 1-15. DOI: 10.1016/j.artmed.2021.102120 39. Della Cioppa A., De Falco I., Koutny T., Ubl M., Scafuri U., Tarantino E.. Reducing high-risk glucose forecasting errors by evolving interpretable models for type 1 diabetes. Appl. Soft Comput. (2023.0) 134 110012. DOI: 10.1016/j.asoc.2023.110012 40. Maniruzzaman M., Rahman M., Ahammed B.. Classification and prediction of diabetes disease using machine learning paradigm. Health Inf. Sci. Syst. (2020.0) 8 1-14. DOI: 10.1007/s13755-019-0095-z 41. Sharma T., Shah M.. A comprehensive review of machine learning techniques on diabetes detection. Vis. Comput. Ind. Biomed. Art (2021.0) 4 30. DOI: 10.1186/s42492-021-00097-7 42. Rastogi R., Bansal M.. Diabetes prediction model using data mining techniques. Meas. Sensors (2023.0) 25 100605. DOI: 10.1016/j.measen.2022.100605 43. Cuesta-Frau D., Miró–Martínez P., Oltra–Crespo S., Jordán–Núñez J., Vargas B., Vigil L.. Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Comput. Methods Programs Biomed. (2018.0) 165 197-204. DOI: 10.1016/j.cmpb.2018.08.018 44. Lekha S., Suchetha M.. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J. Biomed. Health Inform. (2018.0) 22 1630-1636. DOI: 10.1109/JBHI.2017.2757510 45. Wan S., Liang Y., Zhang Y.. Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput. Electr. Eng. (2018.0) 72 274-282. DOI: 10.1016/j.compeleceng.2018.07.042 46. Kannadasan K., Damodar Reddy E.. andVenkatanareshbabu, K. Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clin. Epidemiol. Glob. Health (2019.0) 7 530-535. DOI: 10.1016/j.cegh.2018.12.004 47. Zhu T., Li K., Herrero P., Chen J., Georgiou P.. A Deep Learning Algorithm for Personalized Blood Glucose Prediction. Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data (KDH), within the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), CEUR Workshop Proceedings 64-78 48. Bevan R., Coenen F.. Experiments in non-personalized future blood glucose level prediction. Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH), within the 24th European Conference on Artificial Intelligence (ECAI2020), CEUR Workshop Proceedings Volume 2675 100-104 49. Mayo M., Koutny T.. Neural multi-class classification approach to blood glucose level forecasting with prediction uncertainty visualisation. Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH), within the 24th European Conference on Artificial Intelligence (ECAI2020), CEUR Workshop Proceedings Volume 2675 80-84 50. Liu Y., Liu W., Chen H., Cai X., Zhang R., An Z., Shi D., Ji L.. Graph convolutional network enabled two-stream learning architecture for diabetes classification based on flash glucose monitoring data. Biomed. Signal Process. Control (2021.0) 69 102896. DOI: 10.1016/j.bspc.2021.102896 51. Yin H., Mukadam B., Dai X., Jha N.K.. DiabDeep: Pervasive diabetes diagnosis based on wearable medical sensors and efficient neural networks. IEEE Trans. Emerg. Top. Comput. (2021.0) 9 1139-1150. DOI: 10.1109/TETC.2019.2958946 52. Kumari S., Kumar D., Mittal M.. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int. J. Cogn. Comput. Eng. (2021.0) 2 40-46. DOI: 10.1016/j.ijcce.2021.01.001 53. Ganie S., Malik M.. An ensemble machine learning approach for predicting type-II diabetes mellitus based on lifestyle indicators. Healthc. Anal. (2022.0) 2 100092. DOI: 10.1016/j.health.2022.100092 54. Gupta H., Varshney H., Sharma T.K., Pachauri N., Verma O.P.. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell. Syst. (2022.0) 8 3073-3087. DOI: 10.1007/s40747-021-00398-7 55. Joseph L.P., Joseph E.A., Ramendra P.. Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture. Comput. Biol. Med. (2022.0) 151 106178. DOI: 10.1016/j.compbiomed.2022.106178 56. Lundberg S.M., Nair B., Vavilala M.S., Horibe M., Eisses M.J., Adams T., Liston D.E., Low D.K.W., Newman S.F., Kim J.. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. (2018.0) 2 749-760. DOI: 10.1038/s41551-018-0304-0 57. Sisodia D., Sisodia D.S.. Prediction of diabetes using classification algorithms. Procedia Comput. Sci. (2018.0) 132 1578-1585. DOI: 10.1016/j.procs.2018.05.122 58. Cuesta H.A., Coffman D.L., Branas C., Murphy H.M.. Using decision trees to understand the influence of individual- and neighborhood-level factors on urban diabetes and asthma. Health Place (2019.0) 58 102119. DOI: 10.1016/j.healthplace.2019.04.009 59. Sneha C., Gangil T.. Analysis of diabetes mellitus for early prediction using optimal features selection. J. Big Data (2019.0) 6 13. DOI: 10.1186/s40537-019-0175-6 60. Hasan K., Alam A., Das D., Hossain E., Hasan M.. Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access (2020.0) 8 76516-76531. DOI: 10.1109/ACCESS.2020.2989857 61. Choubey D.K., Kumar P., Tripathi S., Kumar S.. Performance evaluation of classification methods with PCA and PSO for diabetes. Netw. Model. Anal. Health Inform. Bioinform. (2020.0) 9 1-30. DOI: 10.1007/s13721-019-0210-8 62. Özmen E.P., Özcan T.. Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm. J. Forecast. (2020.0) 39 661-670. DOI: 10.1002/for.2652 63. Naveen Kishore G., Rajesh V., Vamsi Akki Reddy A., Sumedh K., Rajesh Sai Reddy T.. Prediction of diabetes using machine learning classification algorithms. Int. J. Sci. Technol. Res. (2020.0) 1 1805-1808 64. Torkey H., Ibrahim E., El-Din Hemdan E., El-Sayed A., Shouman M.A.. Diabetes classification application with efficient missing and outliers data handling algorithms. Complex Intell. Syst. (2022.0) 8 237-253. DOI: 10.1007/s40747-021-00349-2 65. Hassan M.M., Mollick S., Yasmin F.. An unsupervised cluster-based feature grouping model for early diabetes detection. Healthc. Anal. (2022.0) 2 10o112 66. Ahmedi U., Issa G.F., Khan M.A., Aftab S., Khang M.F., Said R.A.T., Ghazal T.M., Ahmad M.. Prediction of diabetes empowered with fused machine learning. IEEE Access (2022.0) 10 8529-8538. DOI: 10.1109/ACCESS.2022.3142097 67. Chang V., Ganatra M.A., Hall K., Golightly L., Xu Q.A.. An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthc. Anal. (2022.0) 2 100118. DOI: 10.1016/j.health.2022.100118 68. Thotad P.N., Bharamagoudar G.R., Anami B.S.. Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods. Diabetes Metab. Syndr. Clin. Res. Rev. (2023.0) 17 102690. DOI: 10.1016/j.dsx.2022.102690 69. Theerthagiri P., Ruby A.U., Vidya J.. Diagnosis and classification of the diabetes using machine learning algorithms. SN Comput. Sci. (2023.0) 4 1-10. DOI: 10.1007/s42979-022-01485-3 70. Fürnkranz J.. Decision Tree. Encyclopedia of Machine Learning (2010.0) 263-267 71. Alvarado J., Velasco J.M., Hidalgo J.I., Fernández de Vega F.. Blood Glucose Prediction Using a Two Phase TSK Fuzzy Rule Based System. Proceedings of the IEEE Congress on Evolutionary Computation (CEC) 1-8 72. De La Cruz M., Cervigón C., Alvarado J., Botella-Serrano M., Hidalgo J.I.. Evolving Classification Rules for Predicting Hypoglycemia Events. Proceedings of the IEEE Congress on Evolutionary Computation (CEC) 1-8 73. Sun C., Ippel L., Dekker A., Dumontier M., van Soes J.. A systematic review on privacy-preserving distributed data mining. Data Sci. (2021.0) 4 121-150. DOI: 10.3233/DS-210036 74. Xu J., Glicksberg B.S., Su C., Walker P., Bian J., Wang F.. Federated learning for healthcare informatics. J. Healthc. Inform. Res. (2021.0) 5 1-19. DOI: 10.1007/s41666-020-00082-4 75. Rahman A., Hossain M.S., Muhammad G., Kundu D., Debnath T., Rahman M., Khan M.S.I., Tiwari P., Band S.S.. Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues. Cluster Comput. (2022.0) 1-41. DOI: 10.1007/s10586-022-03658-4 76. Wang W., Li X., Qiu X., Zhang X., Brusic V., Zhao J.. A privacy preserving framework for federated learning in smart healthcare systems. Inf. Process. Manag. (2023.0) 60 103167. DOI: 10.1016/j.ipm.2022.103167 77. Lai J., Song X., Wang R., Li X.. Edge intelligent collaborative privacy protection solution for smart medical. Cyber Secur. Appl. (2023.0) 1 100010. DOI: 10.1016/j.csa.2022.100010 78. Liu J., Xi L., Yang H., Zhuang L.. A diabetes prediction system based on federated learning. Proceedings of the IEEE International Conference on Big Data, Information and Computer Network (BDICN) 486-491 79. Islam H., Mosa A.. A federated mining approach on predicting diabetes-related complications: Demonstration using real-world clinical data. Proceedings of the AMIA Annual Symposium Proceedings 556-564 80. Dubreuil M., Gagn e C., Parizeau M.. Analysis of a master-slave architecture for distributed evolutionary computations. IEEE Trans. Syst. Man Cybern.-Part B (2001.0) 36 229-235. DOI: 10.1109/TSMCB.2005.856724 81. Cantú-Paz E.. Migration policies, selection pressure, and parallel evolutionary algorithms. J. Heuristics (2001.0) 7 311-334. DOI: 10.1023/A:1011375326814 82. O’Neill M., Ryan C.. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language (2003.0) 83. Marling C., Bunescu R.. The OhioT1DM dataset for blood glucose level prediction. Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data (KDH) 60-63 84. Hovorka R., Canonico V., Chassin L.J., Haueter U., Massi-Benedetti M., Orsini Federici M., Pieber T.R., Schaller H.C., Schaupp L., Vering T.. Nonlinear model predictive control of glucose concentration in subjects with Type 1 diabetes. Physiol. Meas. (2004.0) 25 905-920. DOI: 10.1088/0967-3334/25/4/010 85. Hovorka R., Powrie J.K., Smith G.D., Sönksen P.H., Carson E.R., Jones R.H.. Five-compartment model of insulin kinetics and its use to investigate action of chloroquine in NIDDM. Am. J. Physiol. (1993.0) 265 162-1752. DOI: 10.1152/ajpendo.1993.265.1.E162 86. Livesey G., Wilson P.D., Dainty J.R., Brown J.C., Faulks R.M., Roe M.A., Newman T.A., Eagles J., Mellon F.A., Greenwood R.H.. Simultaneous time-varying systemic appearance of oral and hepatic glucose in adults monitored with stable isotopes. Am. J. Physiol. (1998.0) 275 717-728. DOI: 10.1152/ajpendo.1998.275.4.E717 87. Ni J., Drieberg R., Rockett P.. The use of an analytic quotient operator in Genetic Programming. IEEE Trans. Evol. Comput. (2013.0) 17 146-152. DOI: 10.1109/TEVC.2012.2195319 88. Varela Lorenzo A., Delgado Gutierrez A.. Glucose Classification and Prediction System with Neural Networks. Ph.D. Thesis (2020.0) 89. Danne T., Nimri R., Battelino T., Bergenstal R.M., Close K.L., DeVries J.H., Garg S., Heinemann L., Hirsch I., Amiel S.A.. International consensus on use of continuous glucose monitoring. Diabetes Care (2017.0) 40 1631-1640. DOI: 10.2337/dc17-1600 90. Sun Y., Wong A.K., Kamel M.. Classification of imbalanced data: A review. Int. J. Pattern Recognit. Artif. Intell. (2009.0) 23 687-719. DOI: 10.1142/S0218001409007326 91. Hidalgo J.I., Colmenar J.M., Kronberger G., Winkler S.M., Garnica O., Lanchares J.. Data based prediction of blood glucose concentrations using evolutionary methods. J. Med. Syst. (2017.0) 41 1-20. DOI: 10.1007/s10916-017-0788-2 92. Contador S., Colmenar J.M., Garnica O., Velasco J.M., Hidalgo J.I.. Blood glucose prediction using multi-objective grammatical evolution: Analysis of the “agnostic” and “what-if” scenarios. Genet. Program. Evolvable Mach. (2022.0) 23 161-192. DOI: 10.1007/s10710-021-09424-6 93. De Falco I., Della Cioppa A., Koutny T., Krcma M., Scafuri U., Tarantino E.. A Grammatical Evolution approach for estimating blood glucose levels. Proceedings of the 11th IEEE Global Communications Conf.-Int. Workshop on AI-driven Smart Healthcare (AIdSH) 1-6 94. Rodríguez-Fdez I., Canosa A., Mucientes M., Bugarín A.. STAC: A web platform for the comparison of algorithms using statistical tests. Proceedings of the IEEE international conference on fuzzy systems (FUZZ-IEEE) 1-8 95. Derrac J., García S., Molina D., Herrera F.. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. (2011.0) 1 3-18. DOI: 10.1016/j.swevo.2011.02.002
--- title: Physicochemical Characterization, Biocompatibility, and Antibacterial Properties of CMC/PVA/Calendula officinalis Films for Biomedical Applications authors: - Wen-Hsin Huang - Chia-Yi Hung - Pao-Chang Chiang - Hsiang Lee - I-Ting Lin - Pin-Chuang Lai - Ya-Hui Chan - Sheng-Wei Feng journal: Polymers year: 2023 pmcid: PMC10059992 doi: 10.3390/polym15061454 license: CC BY 4.0 --- # Physicochemical Characterization, Biocompatibility, and Antibacterial Properties of CMC/PVA/Calendula officinalis Films for Biomedical Applications ## Abstract This study reports a carboxymethyl cellulose (CMC)/polyvinyl alcohol (PVA) composite film that incorporates Calendula officinalis (CO) extract for biomedical applications. The morphological, physical, mechanical, hydrophilic, biological, and antibacterial properties of CMC/PVA composite films with various CO concentrations ($0.1\%$, $1\%$, $2.5\%$, $4\%$, and $5\%$) are fully investigated using different experiments. The surface morphology and structure of the composite films are significantly affected by higher CO concentrations. X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FTIR) analyses confirm the structural interactions among CMC, PVA, and CO. After CO is incorporated, the tensile strength and elongation upon the breaking of the films decrease significantly. The addition of CO significantly reduces the ultimate tensile strength of the composite films from 42.8 to 13.2 MPa. Furthermore, by increasing the concentration of CO to $0.75\%$, the contact angle is decreased from 15.8° to 10.9°. The MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide] assay reveals that the CMC/PVA/CO-$2.5\%$ and CMC/PVA/CO-$4\%$ composite films are non-cytotoxic to human skin fibroblast cells, which is favorable for cell proliferation. Remarkably, $2.5\%$ and $4\%$ CO incorporation significantly improve the inhibition ability of the CMC/PVA composite films against Staphylococcus aureus and Escherichia coli. In summary, CMC/PVA composite films containing $2.5\%$ CO exhibit the functional properties for wound healing and biomedical engineering applications. ## 1. Introduction Wounds in skin tissues are usually caused by thermal, chemical, and traumatic injuries. Wound healing is one of the most complex and dynamic processes in the human body, involving homeostasis, inflammation, proliferation, and the remodeling of skin tissue [1]. However, wound healing is easily affected and delayed in patients with diabetes mellitus and complicated systematic health conditions [2,3]. In addition, delays to wound healing significantly increase the risk of wound infection, which results in immune responses, tissue inflammation, and damage. Thus, the development of wound dressings with biocompatible and antimicrobial properties is important to accelerate the wound healing process [4]. Recently, biopolymer-based wound dressings prepared by combining natural and synthetic polymers have attracted considerable attention. Carboxymethyl cellulose (CMC) is a natural polymer and water-soluble derivative of cellulose. CMC has been widely used in the pharmaceutical, cosmetic, and food industries because of its excellent film-forming ability, high swelling ability, biocompatibility, biodegradability, hydrophilicity, cost-effectiveness, and stable internal network structure properties [5,6]. The hydrophilic groups (CH2COO−) of CMC enhance hydrogen bonding and bind to the hydroxyl groups of the glucopyranose chain of cellulose that constitutes its backbone [6,7]. Furthermore, CMC is a pH- and ionic strength-sensitive polysaccharide. As a polyanionic polymer, CMC has been found to possess bioadhesive properties and can strongly attach to the mucosal surfaces of the oral cavity and gastrointestinal tract [7]. The mucoadhesive properties of CMC allow for prolonged contact time in the specific tissues, thus enhancing bioavailability and preventing degradation for drug delivery applications [8]. In addition, CMC has also been shown to adhere to the mucous membrane and skin easily, which is beneficial for wound healing and skin regeneration applications [9]. These properties are crucial for the preparation of CMC scaffolds, hydrogels, and films for biomedical applications. However, the weaknesses of CMC include its poor mechanical strength, stability, and barrier properties [10,11]. To achieve significant effects, blending with other water-soluble polymers is a promising solution to overcome the disadvantages [12]. Among the most common synthetic polymers, poly(vinyl alcohol) (PVA) is a water-soluble semi-crystalline polymer that is predominantly composed of carbon chains. PVA is used in numerous biomedical and pharmaceutical applications because it possesses many useful characteristics, including biodegradability, flexibility, hydrophilicity, cell-adhesive properties, controlled tensile strength, non-toxicity, and an excellent film-forming ability [12,13]. PVA is prepared through the polymerization of vinyl acetate followed by partial hydrolysis. PVA is a non-ionic polymer with an odorless and translucent nature, and it has the ability to form an oxygen barrier [14]. Both CMC and PVA are biodegradable and biocompatible. After blending CMC with PVA, the strong hydrogen bonding between the hydroxyl groups results in a remarkable improvement in the mechanical properties [12,15,16]. In addition, recent studies have suggested that the incorporation of bio-compounds can improve the bioactivity and antimicrobial activity of composite films [12,16]. Essential oils and extracts of herbal materials are known to be powerful antioxidant and antimicrobial agents in biomedical applications [17,18,19]. The surface modification of metallic materials with peppermint essential oils was demonstrated to have bacteriostatic effects on Gram-positive bacteria and prevent biofilm formation [20]. Calendula officinalis (CO) is a medicinal herb also known as pot marigold [21,22]. CO flower extract consists of carotenes, flavonoids, terpenoids, triterpenoids, polyphenols, phenolic acids, quinines, coumarins, and other constituents [23,24]. It has been demonstrated to possess anti-inflammatory, antioxidant, antimicrobial, antifungal, free radical inhibitory, and wound-healing activities [25,26,27,28]. In comparison to cinnamon and clove essential oils, CO flower extracts have additional biological effects, including the enhancement of cell proliferation/migration and the promotion of collagen metabolism and angiogenesis at the wound site [27,28]. Furthermore, CO flower extracts have been applied for the treatment of skin burns, ulcerations, inflammation, and wounds [22,26]. The pharmacological activity of CO extract can be enhanced via incorporation into a polymeric matrix with controlled release over time for wound healing [21,27]. However, CMC/PVA/CO composite films have not yet been investigated. In addition, the optimal contents and proportions of CMC/PVA/CO composite films to achieve better mechanical, bioactive, and antimicrobial properties remain unclear. Thus, the aims of the present study are to develop novel CMC/PVA/CO composite films using the solution casting method and investigate the morphological, physical, mechanical, biological, and antimicrobial properties of the composite films. ## 2.1. Materials Sodium CMC (high viscosity; MW: 700 kDa) and PVA (MW: 9000 Da) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All other chemicals and solvents used in the present study were of analytical grade and did not require further purification. Bacterial strains of Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC 25922) were acquired from the American Type Culture Collection. ## 2.2. Preparation of CMC/PVA/CO Films CMC ($2\%$; w/v) and PVA ($5\%$; w/v) were dissolved in distilled water via continuous magnetic stirring for 60 min at 80 °C. The obtained CMC and PVA solutions were mixed (1:1; v/v) under mechanical stirring for 30 min. Different ratios ($0\%$, $0.1\%$, $1\%$, $2.5\%$, $4\%$, and $5\%$) of CO extract (Cheng Yi Chemical Co, Taiwan) containing glycerol and TWEEN-80 relative to the CMC/PVA mixture were prepared. The composition ratio of CO extract, glycerol, and TWEEN-80 for the $5\%$ group was 95:1:4 (v/v), respectively. By adjusting the composition ratios of CO extract, the other experimental groups with different ratios ($4\%$, $2.5\%$, $1\%$, $0.1\%$, and $0\%$) of CO extract containing glycerol and TWEEN-80 relative to the CMC/PVA mixture could be obtained. The CMC/PVA/CO films were developed using the solution casting method [17]. Briefly, the CMC/PVA/CO mixed solutions were magnetically stirred for 30 min and placed under a vacuum for 10 min. After obtaining a homogeneous solution, the final solutions were poured into a Petri dish with a diameter of 10 cm and dried in a vacuum oven at 40 °C for 24 h to obtain the CMC/PVA/CO composite films. ## 2.3. Scanning Electron Microscopy The surface morphology of the CMC/PVA/CO composite films was examined using scanning electron microscopy (SEM; Hitachi SU-3500, Hitachi High Technologies, Minato-ku, Tokyo, Japan) at an accelerating voltage of 5–20 kV and a working distance of 5 mm [29]. Prior to observation, all of the CMC/PVA/CO composite films were coated with a thin layer of gold for 10 min under a high vacuum at 10 kV for 90 s. Moreover, the cross-sectional viewpoints of the composite films were captured at 90°, relative to the electron beam. ## 2.4. X-ray Diffraction (XRD) and Fourier Transform Infrared Spectrometry (FTIR) The crystalline nature of the CMC/PVA/CO films was characterized using X-ray diffraction (XRD; Empyrean, PANalytical BV, Almelo, The Netherlands) with Cu Kα radiation (λ = 0.154 nm) in the range of 2θ = 5°–90° and a step size and scan speed of 0.05° and 2°/min, respectively. The chemical compositions of the CMC/PVA/CO films were characterized using Fourier transform infrared (FTIR) spectroscopy (Spotlight 200i Sp2 with an AutoATR System, PerkinElmer, Waltham, MA, USA). Spectra in the range of 4000–400 cm–1 were recorded in transmission mode with a resolution of 0.5 cm–1. ## 2.5. Thickness and Mechanical Properties The thickness of the CMC/PVA/CO composite films was measured using a hand-held electronic digital micrometer (Mitutoyo, Japan) with an accuracy of 0.01 mm. Composite films with smooth and uniform thickness were selected for measurement. The mean values were calculated from five random positions on each film. The mechanical properties of the CMC/PVA/CO composite films, including the ultimate tensile strength (TS) and elongation rate at break (EB), were determined using a universal testing machine (AGX-V, SHIMADZU, Kyoto, Japan). Prior to testing, the composite films were cut into rectangular strips (5 × 50 mm2) and mounted in the machine. The initial grip separation and cross-head speed were set at 25 mm and 1 mm/min, respectively. Finally, the TS (MPa) and EB (%) were measured for each composite film according to previous studies [17,30]. ## 2.6. Contact Angle The differences in the surface wettability of the CMC/PVA/CO composite films were measured using optical measurements of the static contact angle of water. Briefly, a 5 μL water drop was slowly added to the film surface and recorded using a video contact angle system (FTA-32, First Ten Angstroms, Portsmouth, VA, USA). After drawing the height and width of the drops on the film surface, the contact angle value was automatically calculated in the system [29]. ## 2.7. In Vitro Cell Culture and Cell Viability A human skin fibroblast cell line (CCD-966SK) was used for the cytocompatibility test. CCD-966SK cells were cultured and maintained in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS), 2 mM L-glutamine, 100 U/mL penicillin, 100 mg/mL streptomycin mixed antibiotics, and 1 mM sodium pyruvate under standard growth conditions in a $5\%$ CO2 incubator. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) (Sigma-Aldrich, St. Louis, MO, USA) assay was performed to determine the CCD-966SK cell viability [31]. CCD-966SK cells were seeded at an initial density of 5 × 104 cells/mL in a 24-well plate. After culturing for 24 h, the growth medium was replaced with normal medium (control), $5\%$ dimethyl sulfoxide (DMSO), and the extract medium from CMC/PVA, CMC/PVA/CO-$0.1\%$, CMC/PVA/CO-$1\%$, CMC/PVA/CO-$2.5\%$, CMC/PVA/CO-$4\%$, and CMC/PVA/CO-$5\%$ groups. The extract media were prepared from the UV-sterilized composite membranes in the DMEM media supplemented with $10\%$ FBS for 24 h. After 48 h of incubation, the morphology of CCD-966SK cells was observed under an optical microscope (Eclipse TS100; Nikon Corporation, Tokyo, Japan) and captured using a CMOS camera with SPOT Advance imaging software (SPOT Idea, Diagnostic Instruments, Sterling Heights, MI, USA). Cell viability was assessed by adding MTT (5 mg/mL) (Sigma-Aldrich, Burlington, MA, USA) to the cultured cells and incubating for 4 h. The supernatant was then removed, and 500 µL of DMSO was added to dissolve the formazan crystals. The absorbance (optical density, OD) of the purple formazan was quantified using a microplate reader (Model 2020, Anthos Labtec Instruments, Eugendorf, Wals, Austria) at wavelengths of $\frac{570}{690}$ nm. Higher OD values indicated higher cell metabolic activity and hence better biocompatibility. ## 2.8. Antibacterial Activity Evaluation The antibacterial activities of the CMC/PVA/CO composite films were assessed against Gram-positive bacteria (S. aureus, ATCC 25923) and Gram-negative bacteria (E. coli, ATCC 25922) using the disc agar diffusion method, as previously described [32]. The films were cut into 6 mm diameter round discs and disinfected under a UV lamp for 1 h. After disinfection, the films were gently placed onto the agar plates, which had been previously spread with bacterial broth cultures (100 μL). All of the agar plates were subsequently incubated at 37 °C for 24 h, and the diameters of the clear zones around the CMC/PVA/CO films were measured and recorded in millimeters. ## 2.9. Statistical Analysis The experimental data were analyzed using one-way analysis of variance (ANOVA) (SPSS Inc., Chicago, IL, USA) and Tukey’s honest post hoc test. The difference was considered to be statistically significant when the p value < 0.05 () and the p value < 0.01 (). ## 3.1. Surface Characterization Figure 1 shows the visual appearances of the CMC/PVA composite films containing different CO concentrations ($0\%$, $0.1\%$, $1\%$, $2.5\%$, $4\%$, and $5\%$). All of the films show an integral and transparent appearance. With an increasing ratio of CO, rougher surfaces could be observed in the composite films, especially in the CMC/PVA/CO-$4\%$ and CMC/PVA/CO-$5\%$ groups. A similar tendency is also observed in the SEM experiments (Figure 2). The surface morphology and structure of the CMC/PVA composite films is affected by the addition of CO ($0\%$, $0.1\%$, $1\%$, $2.5\%$, $4\%$, and $5\%$). As shown in Figure 2, a smooth, continuous, and homogenous surface with fewer pores in the structure is observed in the CMC/PVA composite films with lower CO concentrations ($0\%$, $0.1\%$, $1\%$, and $2.5\%$). This is because good compatibility and interactions exist between biopolymers and lower CO concentrations via hydrogen bonding between the –OH and –COOH groups, as confirmed by previous FTIR results [17,30]. However, the CMC/PVA composite films with higher CO concentrations ($4\%$ and $5\%$) exhibit surfaces with a certain unevenness and wrinkles. Moreover, the cross-sectional structure exhibits complex and irregular configurations, indicating that the film structure is significantly affected by the incorporation of higher CO concentrations and amounts of TWEEN-80 surfactant. Similar tendencies and results were also demonstrated in previous studies using higher levels of essential oils and surfactants [11,17,30]. ## 3.2. XRD and FTIR Spectroscopy The X-ray diffraction (XRD) patterns show characteristic peaks, which identify the crystalline properties and phases of the prepared composite polymer films. Figure 3A shows the results of the XRD analysis. The diffraction pattern of pure CMC features a peak at 2θ = 22.20°, whereas that of pure PVA shows a significant peak at approximately 2θ = 19.5° [15,33]. The diffraction patterns of CMC/PVA composite films exhibit a peak at 2θ = 20°–25°, indicating that the strong interaction between CMC and PVA forms a composite [34]. These results are similar to those of previous studies [15,33]. Interestingly, with the addition of CO extract ($0.1\%$, $1\%$, and $2.5\%$) to the CMC/PVA composite films, the crystallinity decreases and the amorphous phase increases because the diffraction peak becomes broad and weak. Moreover, this phenomenon may be due to the complete dissociation and successful miscibility between CMC and PVA after the incorporation of CO extract ($0.1\%$, $1\%$, and $2.5\%$) [35]. However, the left shift of the broad peak in the CMC/PVA/CO-$1\%$ group may be associated with the changes in structure and the influence of pure PVA [15,33,34]. In addition, no new diffraction peaks are observed in the CMC/PVA/CO composite films, indicating that no chemical interaction occurs between CMC/PVA and CO. The FTIR spectra of CMC/PVA composite films containing different CO concentrations ($0\%$, $0.1\%$, $1\%$, $2.5\%$, $4\%$, and $5\%$) with their characteristic peaks are presented in Figure 3B. Wide bands located at 3000–3600 cm−1 are observed for all of the CMC/PVA/CO composite films. These bands are characteristic –OH stretching groups, which arise mainly from the strong hydrogen bonding between CMC and PVA. Sharp bands at 2900 cm−1 are also observed for all films, which are possibly related to the symmetric and asymmetric stretching of the CH2 groups of CMC and PVA [36,37]. Moreover, the peak at 1750 cm−1 is assigned to the carbonyl stretching of organic acids and phenolic compounds, which are the major components of CO [23,37]. Moreover, in a previous study, the major peaks of CO extract in FTIR spectra corresponded to hydroxyl groups (3488 cm−1), alkyl and C-H groups (2940 cm−1), methyl groups (1463 cm−1), ether groups (1230 cm−1), and terpenoid and flavone compounds (1049 cm−1), which also appeared in the CMC/PVA/CO composite films [28]. The –CH vibrational band is observed at 1330 cm−1, and the C-C stretching band is found at 835 cm−1. At 1650, 1473, and 1082 cm−1, symmetric and asymmetric stretching vibrations of C=O groups for CMC/PVA are observed [36,37]. Overall, similar major sharp peaks but varying peak amplitudes are observed in the FTIR spectra of the composite films, indicating that the incorporation of CO does not change the structure of the film matrix. ## 3.3. Thickness and Mechanical Properties The thicknesses of the CMC/PVA, CMC/PVA/CO-$0.1\%$, CMC/PVA/CO-$1\%$, CMC/PVA/CO-$2.5\%$, CMC/PVA/CO-$4\%$, and CMC/PVA/CO-$5\%$ composite films are 58 ± 8, 58 ± 8, 50 ± 7, 88 ± 8, 128 ± 30, and 226 ± 32 μm, respectively. The thickness of the composite films increases with an increased CO concentration. Similar results were also reported in previous studies, where the thickness of composite films increased markedly with increasing concentrations of essential oils and extracts of polymer composite films [11,12,38]. The TS and EB of the composite films are shown in Figure 4. As shown in Figure 4A, the TS of the pure CMC/PVA composite film is 42.8 ± 7.0 MPa. The TS decreases with an increasing CO concentration up to $5\%$, reaching a value of 13.2 ± 0.5 MPa. The TS of the CMC/PVA/CO-$0.1\%$, CMC/PVA/CO-$1\%$, CMC/PVA/CO-$2.5\%$, and CMC/PVA/CO-$4\%$ composite films are 13.5 ± 3.3, 18.2 ± 5.9, 28.9 ± 3.8, and 20.4 ± 1.7 MPa, respectively. Among the CMC/PVA/CO composite films, CMC/PVA/CO-$2.5\%$ has the TS closest to that of skin tissue (28 MPa) [33]. This result is in agreement with previous studies, which indicated that the more essential oils or extracts were incorporated, the greater the decrease in the mechanical properties of the polymer composite films would be [11,12,38]. A similar tendency is also observed for CMC/PVA/CO composite films in the EB analysis (Figure 4B). The EB of the CMC/PVA is 7.0 ± 1.1 MPa. With the addition of CO, the EB of the CMC/PVA/CO composite film decreases. The EB of the CMC/PVA/CO-$5\%$ is 2.8 ± 0.1 MPa, while CMC/PVA/CO-$2.5\%$ maintains a higher EB value (5.6 ± 0.3 MPa). Multiple reasons, including the viscosity, thickness, intra-molecular bonding force, and microstructure, are related to this reduction in mechanical properties [11,12,38]. The observed changes are also in agreement with the SEM, XRD, and FTIR results. In* general, the CMC/PVA composite films exhibit significantly decreased mechanical properties when CO is added. However, CMC/PVA/CO-$2.5\%$ can maintain approximately $65\%$ and $80\%$ of the TS and EB values, respectively, of CMC/PVA composite films. A possible reason for this increase may be that a CO concentration of $2.5\%$ can maintain the interfacial interaction of the CMC/PVA composite without weakening the structure compared with the other concentrations. ## 3.4. Surface Wettability Surface wettability is an important material property for enhancing protein adsorption, the cellular response, and tissue repair. An improvement in surface wettability can promote cellular interactions between the prepared composites and living tissues [39,40]. Contact angle measurements of water droplets on the surface of the CMC/PVA/CO composite films were performed to evaluate the wettability. CMC and PVA are both highly hydrophilic polymers. Figure 5A shows digital images of the CMC/PVA/CO composite films. The contact angles of the CMC/PVA/CO composite films are 15.8° ± 3.4°, 16.8° ± 3.5°, 15.7° ± 0.6°, 15.1 ± 2.3°, 10.9° ± 2.9°, and 13.4° ± 1.8°, respectively (Figure 5B). In general, the contact angles of the CMC/PVA/CO composite films decrease with increasing CO concentration. The CMC/PVA/CO-$4\%$ exhibits the highest wettability of the composite films, whereas no significant differences are observed between CMC/PVA/CO-$4\%$ and CMC/PVA/CO-$5\%$. Thus, the increased hydrophilicity of the CMC/PVA/CO composite films correlates with increasing CO concentrations. The surface roughness, structure, chemistry composition, and topographic design of the polymer composite films have great influence on wettability. In the present study, the improvement in surface wettability for CMC/PVA/CO-$4\%$ may be partially due to the heterogeneous surface geometries and structure [38,40]. Based on previous results, the increase in surface wettability of composite films would provide a desirable microenvironment for cell response, tissue engineering, and wound-healing applications [1,4,41]. ## 3.5. Biocompatibility of the CMC/PVA/CO Composite Films Biocompatibility is essential for biomaterial applications [16]. Thus, the cytotoxicity of the prepared CMC/PVA/CO composite films was evaluated using the MTT assay. The cell morphology and cell viability of human dermal fibroblasts (CCD966SK) grown for 48 h under different culture conditions (control group, $5\%$ DMSO, and CMC/PVA/CO composite film immersion medium) are presented in Figure 6. As shown in Figure 6A, human dermal fibroblasts cultured in the CMC/PVA/CO composite film immersion medium show good growth and healthy morphologies with minimal cell apoptosis compared to the $5\%$ DMSO group. Based on the MTT assay data, all of the CMC/PVA/CO composite films exhibit comparable biocompatibility and non-toxicity. Thus, these composite films are suitable candidates for further tissue engineering and wound-healing applications. It has been reported that CMC/PVA films can absorb wound fluid through ion exchange, which enhances tissue formation, regeneration, and rapid epithelialization [5]. ## 3.6. Antibacterial Properties of CMC/PVA/CO Films S. aureus and E. coli are the major pathogenic bacterial strains in wound sites experiencing inflammation and chronic infection. Thus, S. aureus and E. coli are usually selected as representative bacteria to evaluate the antibacterial properties of composite films [34,37,42]. Figure 7 shows the antibacterial activity of the CMC/PVA/CO composite films against Gram-positive (S. aureus) and Gram-negative (E. coli) bacteria. In the absence of CO, the CMC/PVA composite film has lower antibacterial activity, while the CMC/PVA composite films containing various concentrations of CO exhibit antibacterial activity. With increasing CO concentration, the area of the inhibition zone increases. The diameters of the inhibition zones for CMC/PVA/CO-$0.1\%$, CMC/PVA/CO-$1\%$, CMC/PVA/CO-$2.5\%$, CMC/PVA/CO-$4\%$, and CMC/PVA/CO-$5\%$ composite films against S. aureus are 17.5 ± 0.7, 17.6 ± 0.3, 18.6 ± 0.8, 20.4 ± 0.6, and 18.4 ± 0.7 mm, respectively. Moreover, the diameters of the inhibition zones for CMC/PVA/CO-$0.1\%$, CMC/PVA/CO-$1\%$, CMC/PVA/CO-$2.5\%$, CMC/PVA/CO-$4\%$, and CMC/PVA/CO-$5\%$ composite films against E. coli are 17.2 ± 0.6, 17.6 ± 0.3, 18.4 ± 0.3, 18.4 ± 0.3, and 17.8 ± 0.9 mm, respectively. Overall, the ability of the CMC/PVA/CO composite film to inhibit S. aureus was better than its ability to inhibit E. coli. This is because the cell wall structure with a layer of peptidoglycan located between the outer membrane and the cytoplasmic membrane of Gram-negative bacteria is more complex than that of Gram-positive bacteria, which can protect bacteria from external stimulation [42]. These results are consistent with those of previous studies investigating CMC/PVA composite films incorporating nanoparticles, antibiotics, and essential oils [4,12,34,37,42]. The antibacterial effect of the pure CMC/PVA films is limited as shown in the present study and previous studies [36]. Thus, multiple strategies were developed to improve the antibacterial activity of the pure CMC/PVA films [4,12,33,37,42]. The antioxidant, antibacterial, and antifungal effects of CMC/PVA have been reported to be enhanced by the cooperation with $1.5\%$ and $3.0\%$ cinnamon essential oil [17,25]. This is the first study to show that the antibacterial effects of CMC/PVA composite films can be efficiently promoted by the addition of CO extract. According to previous studies, CO extract alone or in combination with polymer films or scaffolds can efficiently inhibit bacteria and enhance wound healing [27,28,43]. This is because polyphenols (especially flavonoids), which are the major constituents of CO, have excellent antimicrobial properties [21]. In addition, the major components of CO include carotenes, flavonoids, terpenoids, triterpenoids, polyphenols, phenolic acids, quinines, coumarins, carbohydrates, essential oils, minerals, and fatty acids [23,24,28]. The antimicrobial activity of CO has been attributed to flavonoids, triterpenoids, and essential oils. Possible mechanisms have been suggested for the antibacterial effects of CO, such as damaging the cytoplasmic membrane, denaturing proteins, disrupting the enzyme system, and reducing ATP synthesis [20,23,24,28,44]. Overall, the multifunctional characteristics of CMC/PVA/CO composite films are useful for wound-dressing applications because they contribute to wound healing at the stages of hemostasis, inflammation, protein adhesion, and cellular proliferation. ## 4. Conclusions In the present study, CMC/PVA composite films containing various CO concentrations ($0.1\%$, $1\%$, $2.5\%$, $4\%$, and $5\%$) were developed using a simple solution casting technique. The morphological, physical, mechanical, hydrophilic, biological, and antibacterial properties of the CMC/PVA composite films were affected by CO incorporation. The pure CMC/PVA composite film had smooth morphology and homogenous appearance, but CO incorporation resulted in a rough surface and irregular structure, especially regarding the CMC/PVA/CO-$5\%$ composite film. XRD and FTIR results indicate that CO incorporation does not change the structure of the film matrix. Results regarding the mechanical properties revealed that tensile strength and elongation rate upon the breaking of the composite films were influenced by CO incorporation. The addition of CO caused a decrease in mechanical strength, while the CMC/PVA/CO-$2.5\%$ composite films maintained approximately $65\%$ of tensile strength and $80\%$ of elongation rate at break. All composite films showed good hydrophilicity and that they were biocompatible towards the proliferation and growth of fibroblast cells. All concentrations of CO were effective against Staphylococcus aureus and Escherichia coli when compared to the pure CMC/PVA film. Finally, the improved physicochemical, biological, and antibacterial properties of the CMC/PVA/CO-$2.5\%$ composite films were found to be beneficial for further biomedical and wound-healing applications. ## References 1. Brumberg V., Astrelina T., Malivanova T., Samoilov A.. Modern Wound Dressings: Hydrogel Dressings. Biomedicines (2021) 9. DOI: 10.3390/biomedicines9091235 2. Freytag C., Odermatt E.K.. Standard Biocompatibility Studies Do Not Predict All Effects of PVA/CMC Anti-Adhesive Gel in vivo. Eur. Surg. Res. (2016) 56 109-122. DOI: 10.1159/000443235 3. Feng R., Fu R., Duan Z., Zhu C., Ma X., Fan D., Li X.. Preparation of sponge-like macroporous PVA hydrogels via n-HA enhanced phase separation and their potential as wound dressing. J. Biomater. Sci. Polym. Ed. (2018) 29 1463-1481. DOI: 10.1080/09205063.2018.1466469 4. Falbo F., Spizzirri U.G., Restuccia D., Aiello F.. Natural Compounds and Biopolymers-Based Hydrogels Join Forces to Promote Wound Healing. Pharmaceutics (2023) 15. DOI: 10.3390/pharmaceutics15010271 5. Kanikireddy V., Varaprasad K., Jayaramudu T., Karthikeyan C., Sadiku R.. Carboxymethyl cellulose-based materials for infection control and wound healing: A review. Int. J. Biol. Macromol. (2020) 164 963-975. DOI: 10.1016/j.ijbiomac.2020.07.160 6. Zennifer A., Senthilvelan P., Sethuraman S., Sundaramurthi D.. Key advances of carboxymethyl cellulose in tissue engineering & 3D bioprinting applications. Carbohydr. Polym. (2021) 256 117561. PMID: 33483063 7. Javanbakht S., Shaabani A.. Carboxymethyl cellulose-based oral delivery systems. Int. J. Biol. Macromol. (2019) 133 21-29. DOI: 10.1016/j.ijbiomac.2019.04.079 8. Pornpitchanarong C., Rojanarata T., Opanasopit P., Ngawhirunpat T., Bradley M., Patrojanasophon P.. Maleimide-functionalized car boxymethyl cellulose: A novel mucoadhesive polymer for transmucosal drug delivery. Carbohydr. Polym. (2022) 288 119368. DOI: 10.1016/j.carbpol.2022.119368 9. Basu P., Narendrakumar U., Arunachalam R., Devi S., Manjubala I.. Characterization and Evaluation of Carboxymethyl Cellulose-Based Films for Healing of Full-Thickness Wounds in Normal and Diabetic Rats. ACS Omega (2018) 3 12622-12632. DOI: 10.1021/acsomega.8b02015 10. Wang B., Yang X., Qiao C., Li Y., Li T., Xu C.. Effects of chitosan quaternary ammonium salt on the physicochemical properties of sodium carboxymethyl cellulose-based films. Carbohydr. Polym. (2018) 184 37-46. DOI: 10.1016/j.carbpol.2017.12.030 11. Lin D., Zheng Y., Wang X., Huang Y., Ni L., Chen X., Wu Z., Huang C., Yi Q., Li J.. Study on physicochemical properties, antioxidant and antimicrobial activity of okara soluble dietary fiber/sodium carboxymethyl cellulose/thyme essential oil active edible composite films incorporated with pectin. Int. J. Biol. Macromol. (2020) 165 1241-1249. DOI: 10.1016/j.ijbiomac.2020.10.005 12. Bahrami A., Fattahi R.. Biodegradable carboxymethyl cellulose-polyvinyl alcohol composite incorporated with. Food Sci. Nutr. (2021) 9 4974-4985. DOI: 10.1002/fsn3.2449 13. Gaaz T.S., Sulong A.B., Akhtar M.N., Kadhum A.A., Mohamad A.B., Al-Amiery A.A.. Properties and Applications of Polyvinyl Alcohol, Halloysite Nanotubes and Their Nanocomposites. Molecules (2015) 20 22833-22847. DOI: 10.3390/molecules201219884 14. Kumar A., Sood A., Han S.S.. Poly(vinyl alcohol)-alginate as potential matrix for various applications: A focused review. Carbohydr. Polym. (2022) 277 118881. DOI: 10.1016/j.carbpol.2021.118881 15. Zhu J., Li Q., Che Y., Liu X., Dong C., Chen X., Wang C.. Effect of Na. Polymers (2020) 12. DOI: 10.3390/polym12020453 16. Alsahag M., Alisaac A., Al-Hazmi G.A.A., Pashameah R.A., Attar R.M.S., Saad F.A., El-Metwaly N.M.. Preparation of carboxymethyl cellulose/polyvinyl alcohol wound dressing composite immobilized with anthocyanin extract for colorimetric monitoring of wound healing and prevention of wound infection. Int. J. Biol. Macromol. (2023) 224 233-242. DOI: 10.1016/j.ijbiomac.2022.10.119 17. Fasihi H., Fazilati M., Hashemi M., Noshirvani N.. Novel carboxymethyl cellulose-polyvinyl alcohol blend films stabilized by Pickering emulsion incorporation method. Carbohydr. Polym. (2017) 167 79-89. DOI: 10.1016/j.carbpol.2017.03.017 18. Parham S., Kharazi A.Z., Bakhsheshi-Rad H.R., Nur H., Ismail A.F., Sharif S., RamaKrishna S., Berto F.. Antioxidant, Antimicrobial and Antiviral Properties of Herbal Materials. Antioxidants (2020) 9. DOI: 10.3390/antiox9121309 19. Zubair M., Shahzad S., Hussain A., Pradhan R.A., Arshad M., Ullah A.. Current Trends in the Utilization of Essential Oils for Polysaccharide- and Protein-Derived Food Packaging Materials. Polymers (2022) 14. DOI: 10.3390/polym14061146 20. Cazzola M., Ferraris S., Allizond V., Bertea C.M., Novara C., Cochis A., Geobaldo F., Bistolfi A., Cuffini A.M., Rimondini L.. Grafting of the peppermint essential oil to a chemically treated Ti6Al4V alloy to counteract the bacterial adhesion. Surf. Coat. Technol. (2019) 378 125011. DOI: 10.1016/j.surfcoat.2019.125011 21. Nicolaus C., Junghanns S., Hartmann A., Murillo R., Ganzera M., Merfort I.. In vitro studies to evaluate the wound healing properties of. J. Ethnopharmacol. (2017) 196 94-103. DOI: 10.1016/j.jep.2016.12.006 22. Balciunaitiene A., Puzeryte V., Radenkovs V., Krasnova I., Memvanga P.B., Viskelis P., Streimikyte P., Viskelis J.. Sustainable-Green Synthesis of Silver Nanoparticles Using Aqueous Hyssopus officinalis and. Molecules (2022) 27. DOI: 10.3390/molecules27227700 23. Miguel M., Barros L., Pereira C., Calhelha R.C., Garcia P.A., Castro M., Santos-Buelga C., Ferreira I.C.. Chemical characterization and bioactive properties of two aromatic plants:. Food Funct. (2016) 7 2223-2232. DOI: 10.1039/C6FO00398B 24. Butnariu M., Coradini C.Z.. Evaluation of Biologically Active Compounds from. Chem. Cent. J. (2012) 6 35. DOI: 10.1186/1752-153X-6-35 25. Alexandre J.T.M., Sousa L.H.T., Lisboa M.R.P., Furlaneto F.A.C., do Val D.R., Marques M., Vasconcelos H.C., de Melo I.M., Leitão R., Castro Brito G.A.. Anti-inflammatory and antiresorptive effects of. Clin. Oral Investig. (2018) 22 2175-2185. DOI: 10.1007/s00784-017-2308-7 26. Cruceriu D., Balacescu O., Rakosy E.. Integr. Cancer Ther. (2018) 17 1068-1078. DOI: 10.1177/1534735418803766 27. Givol O., Kornhaber R., Visentin D., Cleary M., Haik J., Harats M.. A systematic review of. Wound Repair Regen. (2019) 27 548-561. DOI: 10.1111/wrr.12737 28. Pedram Rad Z., Mokhtari J., Abbasi M.. Int. J. Biol. Macromol. (2019) 135 530-543. DOI: 10.1016/j.ijbiomac.2019.05.204 29. Chan Y.H., Lew W.Z., Lu E., Loretz T., Lu L., Lin C.T., Feng S.W.. An evaluation of the biocompatibility and osseointegration of novel glass fiber reinforced composite implants: In vitro and in vivo studies. Dent. Mater. (2018) 34 470-485. DOI: 10.1016/j.dental.2017.12.001 30. Chu Y., Xu T., Gao C., Liu X., Zhang N., Feng X., Liu X., Shen X., Tang X.. Evaluations of physicochemical and biological properties of pullulan-based films incorporated with cinnamon essential oil and Tween 80. Int. J. Biol. Macromol. (2019) 122 388-394. DOI: 10.1016/j.ijbiomac.2018.10.194 31. Chan Y.H., Ho K.N., Lee Y.C., Chou M.J., Lew W.Z., Huang H.M., Lai P.C., Feng S.W.. Melatonin enhances osteogenic differentiation of dental pulp mesenchymal stem cells by regulating MAPK pathways and promotes the efficiency of bone regeneration in calvarial bone defects. Stem Cell Res. Ther. (2022) 13 73. DOI: 10.1186/s13287-022-02744-z 32. El Fawal G.F., Abu-Serie M.M., Hassan M.A., Elnouby M.S.. Hydroxyethyl cellulose hydrogel for wound dressing: Fabrication, characterization and in vitro evaluation. Int. J. Biol. Macromol. (2018) 111 649-659. DOI: 10.1016/j.ijbiomac.2018.01.040 33. Verma N., Pramanik K., Singh A.K., Biswas A.. Design of magnesium oxide nanoparticle incorporated carboxy methyl cellulose/poly vinyl alcohol composite film with novel composition for skin tissue engineering. Mater. Technol. (2022) 37 706-716. DOI: 10.1080/10667857.2021.1873634 34. Sethi V., Kaur M., Thakur A., Rishi P., Kaushik A.. Unravelling the role of hemp straw derived cellulose in CMC/PVA hydrogel for sustained release of fluoroquinolone antibiotic. Int. J. Biol. Macromol. (2022) 222 844-855. DOI: 10.1016/j.ijbiomac.2022.09.212 35. Saadiah M.A., Zhang D., Nagao Y., Muzakir S.K., Samsudin A.S.. Reducing crystallinity on thin film based CMC/PVA hybrid polymer for application as a host in polymer electrolytes. J. Non-Cryst. Solids (2019) 511 201-211. DOI: 10.1016/j.jnoncrysol.2018.11.032 36. Deshmukh R.K., Akhila K., Ramakanth D., Gaikwad K.K.. Guar gum/carboxymethyl cellulose based antioxidant film incorporated with halloysite nanotubes and litchi shell waste extract for active packaging. Int. J. Biol. Macromol. (2022) 15 1-13. DOI: 10.1016/j.ijbiomac.2021.12.198 37. Amaregouda Y., Kamanna K., Gasti T., Kumbar V.. Enhanced Functional Properties of Biodegradable Polyvinyl Alcohol/Carboxymethyl Cellulose (PVA/CMC) Composite Films Reinforced with L-alanine Surface Modified CuO Nanorods. J. Polym. Environ. (2022) 30 2559-2578. DOI: 10.1007/s10924-022-02377-6 38. Song X., Zuo G., Chen F.. Effect of essential oil and surfactant on the physical and antimicrobial properties of corn and wheat starch films. Int. J. Biol. Macromol. (2018) 107 Pt A 1302-1309. DOI: 10.1016/j.ijbiomac.2017.09.114 39. Hashmi M., Ullah S., Ullah A., Akmal M., Saito Y., Hussain N., Ren X., Kim I.S.. Optimized Loading of Carboxymethyl Cellulose (CMC) in Tri-component Electrospun Nanofibers Having Uniform Morphology. Polymers (2020) 12. DOI: 10.3390/polym12112524 40. Rao K.M., Sudhakar K., Suneetha M., Won S.Y., Han S.S.. Fungal-derived carboxymethyl chitosan blended with polyvinyl alcohol as membranes for wound dressings. Int. J. Biol. Macromol. (2021) 190 792-800. DOI: 10.1016/j.ijbiomac.2021.09.034 41. Fathi A., Khanmohammadi M., Goodarzi A., Foroutani L., Mobarakeh Z.T., Saremi J., Arabpour Z., Ai J.. Fabrication of chitosan-polyvinyl alcohol and silk electrospun fiber seeded with differentiated keratinocyte for skin tissue regeneration in animal wound model. J. Biol. Eng. (2020) 14 27. DOI: 10.1186/s13036-020-00249-y 42. Darbasizadeh B., Fatahi Y., Feyzi-Barnaji B., Arabi M., Motasadizadeh H., Farhadnejad H., Moraffah F., Rabiee N.. Crosslinked-polyvinyl alcohol-carboxymethyl cellulose/ZnO nanocomposite fibrous mats containing erythromycin (PVA-CMC/ZnO-EM): Fabrication, characterization and in-vitro release and anti-bacterial properties. Int. J. Biol. Macromol. (2019) 141 1137-1146. DOI: 10.1016/j.ijbiomac.2019.09.060 43. Kharat Z., Amiri Goushki M., Sarvian N., Asad S., Dehghan M.M., Kabiri M.. Chitosan/PEO nanofibers containing. Int. J. Pharm. (2021) 609 121132. DOI: 10.1016/j.ijpharm.2021.121132 44. Barbieri R., Coppo E., Marchese A., Daglia M., Sobarzo-Sánchez E., Nabavi S.F., Nabavi S.M.. Phytochemicals for human disease: An update on plant-derived compounds antibacterial activity. Microbiol. Res. (2017) 196 44-68. DOI: 10.1016/j.micres.2016.12.003
--- title: Characterization of Virulence Factors in Candida Species Causing Candidemia in a Tertiary Care Hospital in Bangkok, Thailand authors: - Natnaree Saiprom - Thanwa Wongsuk - Worrapoj Oonanant - Passanesh Sukphopetch - Narisara Chantratita - Siriphan Boonsilp journal: Journal of Fungi year: 2023 pmcid: PMC10059995 doi: 10.3390/jof9030353 license: CC BY 4.0 --- # Characterization of Virulence Factors in Candida Species Causing Candidemia in a Tertiary Care Hospital in Bangkok, Thailand ## Abstract Candidemia is often associated with high mortality, and Candida albicans, Candida tropicalis, Candida glabrata, and Candida parapsilosis are common causes of this disease. The pathogenicity characteristics of specific Candida spp. that cause candidemia in Thailand are poorly understood. This study aimed to characterize the virulence factors of Candida spp. Thirty-eight isolates of different Candida species from blood cultures were evaluated for their virulence properties, including exoenzyme and biofilm production, cell surface hydrophobicity, tissue invasion, epithelial cell damage, morphogenesis, and phagocytosis resistance; the identity and frequency of mutations in ERG11 contributing to azole-resistance were also determined. C. albicans had the highest epithelial cell invasion rate and phospholipase activity, with true hyphae formation, whereas C. tropicalis produced the most biofilm, hydrophobicity, protease activity, and host cell damage and true hyphae formation. ERG11 mutations Y132F and S154F were observed in all azole-resistant C. tropicalis. C. glabrata had the most hemolytic activity while cell invasion was low with no morphologic transition. C. glabrata was more easily phagocytosed than other species. C. parapsilosis generated pseudohyphae but not hyphae and did not exhibit any trends in exoenzyme production. This knowledge will be crucial for understanding the pathogenicity of Candida spp. and will help to explore antivirulence-based treatment. ## 1. Introduction Candidemia caused by blood-borne Candida is the most common fungal bloodstream infection (BSI) in hospitalized patients [1] and has a high mortality rate of almost $50\%$ [2]. Candida albicans is the main cause of candidemia globally, but the incidence of non-C. albicans Candida (NAC) species such as Candida glabrata, Candida tropicalis, and Candida parapsilosis has also increased [3,4]. These four species, C. albicans ($42.1\%$), C. glabrata ($26.7\%$), C. parapsilosis ($15.9\%$), and C. tropicalis ($8.7\%$) are the most common causes of invasive candidemia and account for approximately $90\%$ of all Candida BSIs [5]. In Thailand, C. tropicalis was the most frequent species isolated from blood samples [6,7]. The NAC spp. represent a therapeutic challenge given the different antifungal susceptibility profiles of different Candida species [1]. The azole antifungal drugs are most commonly used to fight infections caused by Candida spp., and long-term fluconazole treatments for chronic infections have enabled Candida spp. to develop resistance to these, with C. tropicalis, C. glabrata, and C. krusei frequently resistant to azole antifungals [8,9]. Various azole-resistance mechanisms in Candida species have been described, including overexpression of cytochrome P450 lanosterol 14alpha-demethylase (Erg11) and different drug efflux transporters (CDR and MDR) [10,11,12]. In addition, the mutation in the ERG11 gene reduces azole affinity for the target enzyme CYP51A1 and is one of the mechanisms contributing to azole resistance in clinical isolates. Greater understanding of specific point mutations in ERG11 linked to azole resistance could help identify resistant strains, adjust treatment strategies, and rationally design new drugs. The propensity for Candida species to cause disseminated invasive infections may be linked to potential virulence factors [13]. C. albicans, C. tropicalis, and C. glabrata are the more virulent species, and infections with them are more likely to result in death [14]. Candida pathogenicity is facilitated by various virulence factors that enable adherence and invasion to the target cell surface, formation of biomass, yeast-to-hyphae transition, production of tissue-damaging hydrolytic enzymes (e.g., proteases, phospholipases, and hemolysins), and evasion of immune cells [15,16,17]. Virulence factors can differ depending on the infecting species, geographical origin, infection type, infection site, and host reaction. Previously, we discovered that more than half of the genotyped isolates causing BSIs in patients in our hospital had new genotypes as defined using multilocus sequence typing (MLST) [6]. These novel genotypes may result in different virulence phenotypes for isolates from Thailand. Despite extensive research into identifying pathogenic factors in Candida species, particularly C. albicans, little is known about virulence factors of the current Candida species causing BSIs in Thailand. Knowledge of these virulence factors is crucial for understanding the pathogenesis of candidemia and can help identify new targets for antivirulence-based therapeutics to treat infecting Candida spp. Therefore, this study aimed to characterize the virulence factors, including extracellular enzymatic activities, cell surface hydrophobicity (CSH), and biofilm formation, host-pathogen interaction, and the mutation pattern of ERG11 genes in Candida species isolated from patients with candidemia in Bangkok, Thailand. ## 2.1. Yeast Isolates and Ethical Statement The 38 clinical isolates of C. albicans, C. glabrata, C. parapsilosis and C. tropicalis were obtained in our previous study and were isolated from patients with candidemia between June 2018, and July 2019, at the Department of Central Laboratory and Blood Bank, Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand. Isolate information is listed in Table S1. Briefly, isolates were identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (Bruker Microflex, Bremen, Germany) and internal transcribed spacer sequence. Antifungal susceptibility testing was evaluated using Sensititre YeastOneTM (Trek Diagnostic Systems, Cleveland, OH, USA) [6]. Candida yeast cells were cultured on Sabouraud dextrose agar (SDA) (Oxoid, Cambridge, UK) and incubated overnight at 30 °C. A single colony was inoculated into Sabouraud dextrose broth (SDB) (Oxoid, Cambridge, UK) and incubated at 30 °C for 24 h for virulence factor assessment. This work was approved by The Ethics Committee of the Faculty of Medicine Vajira Hospital, Navamindradhiraj University (COA $\frac{072}{2565}$, Study code $\frac{315}{64}$E) and Institutional Biosafety Committee, Faculty of Tropical Medicine, Mahidol University (FTM-IBC-22-01). ## 2.2. Hydrolytic Enzyme Activity Assays Extracellular protease activity was tested by inoculating Candida isolates on bovine serum albumin (BSA) agar with some modifications from previous studies [18,19]. The BSA agar contained $0.1\%$ KH2PO4, $0.05\%$ MgSO4, $2\%$ agar, $0.01\%$ yeast extract, and $0.2\%$ BSA adjusted to pH 4.5. A suspension (5 μL of 106 cells/mL) of each strain was inoculated onto BSA medium, and plates were incubated at 37 °C for up to 5 days. A white precipitation zone around the colonies represented the cleavage of BSA and positive protease activity. The diameters of the yeast colony and the white precipitation zone were recorded. To evaluate hemolytic activity, Candida yeast cells were adjusted to an inoculum size of 1 × 106 cells/mL in SDB medium. The cell suspension (5 μL) was inoculated in duplicate on the surface of SDA supplemented with $3\%$ glucose and $7\%$ fresh sheep blood [20]. Plates were dried at room temperature and incubated for 48 h at 37 °C. After the incubation period, the presence of a translucent halo indicated positive hemolysis, and the diameters of the yeast colony and the clear zone were measured. Candida phospholipase activity was tested on egg yolk medium containing 13.0 g/L SDA, 11.7 g/L NaCl, 1.48 g/L CaCl2.2H2O2, $2\%$ agar, and $10\%$ sterile egg yolk [19]. Yeast cells were grown overnight in SDB before adjustment to a McFarland standard of 0.5 (106 cells/mL). A standard inoculum (5 μL) of the test strain was aseptically inoculated onto egg yolk agar. Plates were dried at room temperature and then incubated at 37 °C for 48 h. A white precipitation zone surrounding colonies was considered positive for extracellular phospholipase activity, and the diameters of the white precipitation zone and the yeast colony were measured. The levels of phospholipase, proteinase and hemolytic activity were represented by the proteolytic zone (Pz value), which was the ratio of the diameter of the yeast colony to the overall diameter of the colony plus that of the white precipitation, clear halo, or translucent halo zones, respectively [21]. All hydrolytic enzyme activity experiments were performed in duplicate and repeated at least twice times. The isolate Pz values were established by measuring in duplicate the diameter of the colony and enzyme activity zone. The Pz values were averaged from two separate experiments, each carried out in duplicate. ## 2.3. CSH Assay CSH was assessed using the microbial hydrocarbon adhesion assay [22]. Yeast cells were grown overnight in Sabouraud dextrose broth (SDB) at 37 °C and then harvested and washed twice with phosphate-buffered saline (PBS). A yeast cell suspension with an optical density at 600 nm (OD600) between 0.4 and 0.5 was prepared in PBS (A0). The yeast suspension (3 mL) was overlaid by 0.4 mL of the hydrophobic hydrocarbon n-hexadecane (Sigma-Aldrich, Burlington, MA, USA). Cells were vortexed for 2 min and incubated for 10 min at 30°C to allow phase separation. The aqueous phase was measured (A1) at OD600. The decrease in absorbance was used to calculate the percentage of hydrophobicity of the cell surface (% hydrophobicity): hydrophobicity (%) = [1 − (A1/A0)] × 100. CSH tests were performed in at least three independent experiments, each carried out in duplicate to obtain an average value of % CSH value. ## 2.4. Biofilm Formation Yeast cells were grown overnight in SDB at 37 °C and then were adjusted to a 0.5 McFarland standard with SDB medium. Yeast suspension (100 μL) was seeded into wells in a 96-well flat bottom plate and incubated at 30 °C for 48 h. Wells were then washed twice with PBS to remove non-adhering cells. Empty wells were allowed to dry for 30 min, and then 200 μL of $0.4\%$ crystal violet was added to each well and incubated for 45 min at room temperature. The plate was washed gently twice using distilled water, and 200 μL of absolute ethanol was added to destain the biofilm. The plate was then incubated for 45 min at room temperature. A 150 μL volume of eluted crystal violet was transferred to a new 96-well plate, and the OD at 590 nm was measured using SunriseTM ELISA reader (Tecan Group Ltd., Männedorf, Switzerland). Sterile SDB without yeast cells was used as negative control [23]. Each isolate was performed in two independent experiments and each experiment was repeated at least eight times. The biomass of each isolate was presented as the average OD value based on two independent experiments. ## 2.5. Candida spp. Invasion Assay The invasion properties of different Candida species were analyzed using an invasion assay with adenocarcinomic human alveolar basal epithelial cell (A459 cell) monolayers in 24-well plates. A549 cells were cultured in cell culture flasks at 37 °C in $5\%$ CO2 in RPMI 1640 medium (GibcoTM, New York, NY, USA) containing $10\%$ fetal bovine serum (FBS) (HyCloneTM, Pasching, Austria) until $80\%$ confluence was achieved. The A549 cell monolayer was washed twice with 5 mL of Dulbecco’s phosphate-buffered saline (DPBS). Cells were dissociated using 1 mL of $0.25\%$ Trypsin-EDTA solution (GibcoTM, New York, NY, USA) and incubated at 37 °C for 2 min. Trypsin was inactivated by adding RPMI 1640 medium containing $10\%$ FBS. The cell suspension was centrifuged for 5 min at 123× g. The pellet was resuspended in 1 mL of RPMI 1640 medium containing $1\%$ FBS. Cell number and cell viability were counted using $0.4\%$ Trypan Blue staining. A549 cells were seeded at 1 × 105 cells per well on sterile 12-mm diameter glass round coverslips placed in wells in a 24-well plate and incubated overnight. Candida yeast cells were suspended in RPMI containing $1\%$ FBS to a 0.5 McFarland standard (approximately 1 × 106 CFU/mL). A549 cells were infected with Candida species at a multiplication of infection (MOI) of 5 and incubated for 4 h at 37 °C in $5\%$ CO2. An uninoculated cell line served as a negative control. After 4 h, cultures were washed twice with PBS to remove non-adhering yeast and fixed with absolute ethanol. Candida internalized into cells was observed using Gram’s staining. The percentage of invading yeast cells was determined by dividing the number of (partially) internalized cells by the total number of adherent cells and multiplying by 100 [24]. At least 100 yeast cells were counted. Candida cells with small daughter cells were regarded as one cell. Each isolate was performed in duplicates on at least two separate occasions. The percentage of invading yeast cell values was averaged from two separate experiments. ## 2.6. Cytotoxicity Assay To determine the ability of Candida species to damage human epithelial cells, the release of lactate dehydrogenase (LDH) was determined after 18 h of infection at MOI of 5. A549 epithelial cells were cultured at 37 °C in $5\%$ CO2 in RPMI containing $10\%$ FBS until $80\%$ confluence. A549 cells were seeded at 1.5 × 104 cells per well in RPMI containing $1\%$ FBS in 96-well plates and incubated overnight. Candida yeast cells grown overnight in SDB were washed three times, suspended in PBS, and adjusted to a 0.5 McFarland standard in RPMI plus $1\%$ FBS. Yeast cell suspension (75 μL) in RPMI medium plus $1\%$ FBS was transferred onto cultured A549 cells and incubated for 18 h at 37 °C in $5\%$ CO2. Cell supernatants were collected after 18 h for LDH testing using the CytoTox-96 nonradioactive cytotoxicity assay (Promega, Madison, WI, USA). Initially, 50 μL of each control (RPMI, RPMI with lysis buffer, cell lysate, and A549 cell supernatant), and the coculture supernatant samples were added to a 96-well plate. Next, 50 μL of the CytoTox 96 reagent was added, and the plate was incubated for 30 min in the dark. Subsequently, 50 μL of a stop solution containing 1 M acetic acid was added to each well, and the absorbance was measured at 492 nm. Culture media were used as blanks for subtracting the culture medium background from all absorbance values. The percentage of the LDH release in the coculture medium was calculated according to the following formula:% LDH release = (Experimental supernatant – A549 cell supernatant) × 100 (A549 cell lysate – A549 cell supernatant) Cytotoxicity assays were performed at least twice in triplicate separate experiments. The cytotoxic activity of each isolate was presented as the average %LDH release derived from three independent experiments. ## 2.7. Fluorescent Staining and Confocal Microscopy Strains VJR H2246, VJR H1584, VJR H0668, and VJR H0235 were selected as representative strains of C. albicans, C. glabrata, C. tropicalis, and C. parapsilosis, respectively, for describing the Candida–epithelial cell interaction. Candida spp. were cultured on SDA at 37 °C overnight. Candida spp. were resuspended in 2 mL of PBS and adjusted to a 0.5 McFarland standard solution. Fluorescent staining was performed as previously described with modifications [25]. Briefly, A549 cells suspended in RPMI containing $1\%$ FBS were seeded at 5 × 105 cells/well on a sterile glass coverslip placed in a 6-well tissue culture plate and incubated overnight at 37 °C with $5\%$ CO2. The A549 cell monolayer was infected with Candida spp. At MOI of 5 for 1 or 4 h, after which cells were washed three times with PBS. Cells were fixed with $4\%$ paraformaldehyde in PBS for 30 min at room temperature and permeabilized with $0.5\%$ Triton X-100 (Sigma-Aldrich, Waltham, MA, USA) for 30 min at room temperature. After washing three times with PBS, yeast cells were stained by incubating with $10\%$ Calcofluor white solution (Sigma-Aldrich, Markham, ON, Canada) in PBS for 15 min at 37 °C, then washing three times with PBS [26]. The actin cytoskeleton was stained with phalloidin conjugated with Alexa Fluor 647 (1:1000; Invitrogen, Buffalo, NY, USA) by incubation at 37 °C for 1 h. Stained cells were washed three times with PBS, and then the coverslips were mounted on glass slides using 8 μL of ProLong Gold antifade reagent (Invitrogen, NY, USA). Confocal microscopy was performed with a laser scanning confocal microscope (LSM 700; Carl Zeiss, Germany) using a 100× objective lens with oil-immersion and Zen software (2010 edition, Zeiss, Germany). The excitation and emission wavelengths were $\frac{352}{461}$ for Calcofluor white and $\frac{594}{633}$ for Alexa Fluor 647. Each isolate of the representative strains was performed in duplicate on at least two separate occasions. The confocal microscopy images were taken from two independent experiments. ## 2.8. Human Monocyte Uptake of Candida Cells Candida spp. were cultured on SDA at 37 °C overnight and were then resuspended with 2 mL of PBS and adjusted to a McFarland standard solution of 3 (approximately 1.5 × 108 CFU/mL). A total of 1 × 106 cells were stained with 10 µg/mL carboxyfluorescein succinimidyl ester (CFSE) (BD company, Franklin Lakes, NJ, USA) and incubated for 30 min at 37 °C. Candida spp. was washed three times with 200 µL of Dulbecco’s PBS (DPBS) (HyCloneTM, Pasching, Austria), followed by centrifugation for 5 min at 1109× g; the pellet was resuspended with 20 µL DPBS. THP-1 (ATCC TIB-22) human monocyte line in RPMI 1640 medium supplemented with $10\%$ FBS, 100 units/mL penicillin, and 100 μg/mL streptomycin cultured at 37 °C in $5\%$ CO2. Media was changed every two days [27]. Cells were washed with 5 mL of DPBS by centrifugation for 5 min at 123× g. Cells were resuspended with RPMI 1640 supplemented with $1\%$ FBS and adjusted to 3 × 106 cells/mL. The cell suspension (3 × 105 cells/100 μL) was seeded into each well of a 96-well plate. THP-1 was coincubated with CFSE-labeled yeast suspensions at MOI of 1 for 1 h at 37 °C. After incubation, plates were kept on ice for 5 min to stop phagocytosis, and wells were washed three times with 200 μL of DPBS by centrifugation at 123× g, 4 °C for 5 min. Cocultured cells were incubated on ice with $0.004\%$ trypan blue solution for 15 min to quench extracellular fluorescence, then washed twice with DPBS. Fluorescence was evaluated using flow cytometry (FACSCalibur, Becton Dickinson), and data were analyzed using FlowJo software v10 (FlowJo LLC, Ashland, OR, USA). The fluorescence was determined for each isolate on at least three separate occasions. The Phagocytic Index (PI) was calculated as the product of the percentage of positive cells (% CFSE Positive THP-1 cell) multiplied by the mean of the fluorescence intensity (MFI) of the positive population (MFI of CFSE Positive THP-1 cell). The PI was calculated according to the following formula [28]. PI values were averaged from three separate experiments. Phagocytosis Index (PI) = % CFSE Positive THP-1 cell × MFI of CFSE Positive THP-1 cell ## 2.9. Amplification and Sequencing of ERG11 Gene Genomic DNA samples from all isolates were obtained from a previous study [6]. The full-length ERG11 gene was PCR amplified using primers and amplification conditions detailed in Table S2. The final volume 50 μL PCR reaction mixture contained 0.8 µM of each primer and 25 µL of 2× GoTaq Green master mix (Promega, Madison, WI, USA) containing buffer, nucleotides, and Taq polymerase. All reactions were run on a T100 Thermal Cycler (Bio-Rad Laboratories Inc, Hercules, CA, USA). Amplification products were purified and sequenced in both directions by Macrogen Inc. The entire ERG11 open reading frame sequence from each Candida species was aligned with C. albicans ATCC 90028 (GU371851), C. glabrata ATCC 90030 (KR998002), C. tropicalis ATCC 750 (M23673), and C. parapsilosis ATCC 22019 (GQ302972) using Clustal W implemented in the MEGA X software package (v1.0). ## 2.10. Statistical Analysis Data were analyzed using the statistical software Stata version 11. The Mann–Whitney test was used to calculate statistical differences among isolates of C. albicans, C. parapsilosis, C. glabrata, and C. tropicalis. In all tests, a $p \leq 0.05$ was considered significant. ## 3.1. Virulence Assessment of Invasive Candida spp. Isolates We investigated the production of extracellular enzymes, including, phospholipase, protease, and hemolysin in Candida spp. that cause BSIs. All the tested isolates demonstrated potent hemolytic activity on sheep blood SDA plates with Pz values < 0.69 (Table S3). The mean Pz values for the hemolytic activity of C. albicans, C. parapsilosis, C. tropicalis, and C. glabrata were 0.39 ± 0.07, 0.55 ± 0.03, 0.38 ± 0.02, and 0.32 ± 0.03, respectively. Figure 1 depicts enzyme activity as a 1-Pz value on the y-axis, with a lower Pz value indicating greater enzyme activity. The average hemolytic activity for C. glabrata was significantly higher than that of the other Candida spp., indicating C. glabrata had the strongest enzyme activity (Figure 1a). Proteinase activity was found in $57.9\%$ of all isolates. Proteinase production was detected in $100\%$ of the C. tropicalis isolates followed by $75\%$ of C. parapsilosis, and $50\%$ of C. albicans isolates but was not found in any of the C. glabrata isolates (Table S3). The mean Pz values for proteinase-positive C. tropicalis, C. parapsilosis, and C. albicans isolates were 0.38 ± 0.03, 0.90 ± 0.10, and 0.86 ± 0.17, respectively, indicating that C. tropicalis had a higher proteinase activity than that of C. albicans or C. parapsilosis. The protease activity of C. tropicalis was significantly higher than that of each of the other species (Figure 1b). Phospholipase activity was detected in $26.3\%$ of the isolates and was present in all C. albicans isolates but was undetectable in other Candida species (Table S3). The average Pz value in phospholipase-positive C. albicans was 0.53 ± 0.05, indicating that C. albicans had potent phospholipase activity, which significantly differed from that of the other species (Figure 1c). ## 3.2. Adhesion of Invasive Candida spp. Isolates To evaluate the virulence factors involved in the adhesion process, the CSH and biofilm formation of different Candida species were examined. CSH has a considerable influence on interactions between the Candida and the host surface. A high CSH is considered to be a virulence factor for numerous fungal species. The determination of CSH on the yeast cell surface is usually related to biofilm formation. CSH was found in varying degrees in all four Candida species. The highest average CSH value was found in C. tropicalis (41.0 ± $12.20\%$), followed by C. parapsilosis (35.5 ± $15.66\%$), C. glabrata (34.9 ± $9.75\%$), and C. albicans (26.7 ± $5.75\%$). The %CSH significantly differed between C. albicans and C. tropicalis as well as between C. albicans and C. glabrata (Figure 2a). The biomass of the Candida biofilm was quantified at 48 h with a crystal violet assay. C. tropicalis isolates produced greater total biofilm formation than that in C. albicans, C. glabrata, or C. parapsilosis (average OD at 590 nm: 0.25 ± 0.2, 0.1 ± 0.01, 0.12 ± 0.01, and 0.11 ± 0.02, respectively), When each Candida species was compared, C. tropicalis produced significantly more biofilm than that in the other species (Figure 2b). ## 3.3. Ability to Invade and Damage Epithelial Cells We compared the invasion potential of each Candida species using the A459 epithelial cell monolayer infection model. C. albicans ($33.37\%$) had the highest percentage of invasion after 4 h of infection, followed by C. tropicalis ($29.64\%$), C. parapsilosis ($25.68\%$), and C. glabrata ($12.60\%$). C. glabrata demonstrated a much lower invasion rate compared with that of other Candida species. The degree of invasion between C. albicans, C. tropicalis, or C. parapsilosis was not significantly different (Figure 3a). The cell damage caused by each Candida species was tested in A549 monolayer culture by measuring the amount of LDH released in culture supernatants during 18 h of infection. The amount of LDH released due to C. tropicalis ($34.8\%$) was significantly higher than that released due to another Candida spp. The amount of LDH released due to C. albicans ($3.77\%$), C. glabrata ($3.85\%$), or C. parapsilosis ($11.75\%$) (Figure 3b) was not significantly different among these species. ## 3.4. Morphologic Transition in Candida spp. Isolates Confocal microscopy of A549 epithelial cells cocultured with each Candida species was used to assess the morphological changes of different Candida spp., after 1 and 4 h of co-culturing. After the first hour of infection, all Candida species adhered to the epithelial cell surface as budding yeast cells, although only C. albicans had formed germ tubes. At 4 h after infection, both C. albicans and C. tropicalis exhibited pseudohyphae and hyphae that pierced the epithelial host cell, whereas C. glabrata was only present as the yeast form on the epithelial surface, and C. parapsilosis had changed morphologically into pseudohyphae (Figure 4). ## 3.5. Phagocytosis Assay with Candida spp. Isolates To compare the level of immune cell phagocytosis of different Candida species, human monocytes (THP-1) were infected with live Candida spp. at an MOI of 1, and the level of phagocytosis was measured via flow cytometry. C. glabrata had the highest phagocytic index followed by C. albicans, C. tropicalis, and C. parapsilosis. C. glabrata was significantly more phagocytosed by THP-1 cells than C. tropicalis and C. parapsilosis were (Figure 5). ## 3.6. Mutation in ERG11 Gene The amino acid substitution in the ERG11 protein sequence contributes to azole resistance in clinical isolates. The complete sequence of the ERG11 gene in each of the 38 Candida isolates was amplified and sequenced to identify amino acid polymorphisms. The susceptibility profiles of the isolates from our previous study are shown in Table S1. All C. albicans isolates were susceptible to all azole antifungal drugs tested, although $50\%$ of C. albicans isolates had ERG11 substitutions. Four distinct ERG11 substitutions in C. albicans (D116E, D153E, K342R, and E226D) were identified. For C. tropicalis, four ($28.6\%$) of the 14 C. tropicalis isolates were resistant to fluconazole, while ten were susceptible. Two ERG11 substitutions (Y132F and S154F) were commonly found in fluconazole-resistant C. tropicalis. No ERG11 mutations were identified in isolates of C. parapsilosis or C. glabrata (Table S3). ## 4. Discussion C. albicans is the most common cause of candidemia worldwide, and NAC spp. are becoming more common, particularly in the Asia–Pacific region [6,29,30]. Insufficient information exists on the virulence properties of Candida spp. that cause candidemia in Thailand; therefore, we characterized the virulence factors of Candida spp. isolated from patients with BSIs as well as the presence of ERG11 mutations that contribute to azole-resistance. We found ERG11 mutations in C. albicans and C. tropicalis isolates but not in C. glabrata or C. parapsilosis. Thus, the ERG11 mutation may not be a major resistant mechanism in C. glabrata and C. parapsilosis isolates in Thailand. In C. albicans, four missense mutations (D116E, D153E, K342R, and E226D) were found in fluconazole-susceptible isolates. Although a previous study showed that the majority of the fluconazole-susceptible isolates had only one ERG11 missense mutation [31], we discovered the coexistence of D116E and D153E in a fluconazole-susceptible C. albicans isolate; however, the coexistence of D116E and D153E has been demonstrated in azole-resistant isolates [32]. The ERG11 mutation in C. albicans did not cause azole resistance in our isolate. For C. tropicalis, $28.5\%$ of the isolates contained two distinct amino acid alterations (Y132F and S154F), which commonly coexist and were detected in all isolates resistant to fluconazole. This is a similar result to that in previous studies where the Y132F and S154F mutations in ERG11 were strongly associated with azole-resistant phenotype and suggested that these mutations could be potential predictive markers of azole resistance [33,34]. The Y132F mutation is responsible for the loss of the normal hydrogen bonding between tyrosine and heme. This alteration affects drug binding because heme is a binding cofactor of azole for ERG11 [35,36,37]. Substitutions of both Y132F and S154F prevented the development of Pi-Pi and Pi-cation interactions between the cofactor and the ligand molecules, which lowers the overall binding energy of the altered docked complex [35]. As a pathogen, Candida spp. must first attach to various host cells. We evaluated the virulence factors involved in the adhesion process, such as CSH and biofilm formation in 38 strains of Candida species isolated from patients with a BSI. CSH is an important virulence factor that contributes to fungal pathogenicity and is essential for cell adhesion to human epithelial cells as well as to catheters or medical devices implanted in patients [38,39]. Fungal adhesion to surfaces and subsequent biofilm formation is critical because this can lead to antifungal drug resistance. A high CSH is a common feature of disease-causing yeast isolates [40,41]. C. tropicalis had the highest hydrophobicity values in the current study, followed by those of C. parapsilosis, C. glabrata, and C. albicans. Consistent with previous research, NAC, as ordered by C. tropicalis, C. parapsilosis, and C. glabrata, have a greater CSH value than that of C. albicans [42]. Biofilm formation is also important in Candida pathogenesis because this protects pathogenic yeast from host immune cells and causes resistance to antifungal treatment. Additionally, biofilm development facilitates Candida adhering to indwelling medical equipment such as cardiac devices, artificial joints, and vascular catheters [43]. Our results revealed that C. tropicalis had the highest biomass production followed by C. parapsilosis, C. glabrata, and C. albicans, respectively. This result agrees with previous studies where NAC strains could produce more biofilm than C. albicans could [42,44,45]. Biofilm formation in this study was related to the presence of CSH. Following adherence, the pathogen must invade the cells. The invasion and damage to the epithelium are mostly regarded as pathogenic properties [46]. To enable penetration of the epithelial cell barrier, Candida secretes a series of hydrolytic enzymes such as hemolysin, protease, and phospholipase that digest the host cell membrane and defend against the immune response of the host [47]. Hemolysin production is an important virulence factor for Candida because hemolysin lyses red blood cells to obtain iron sourced from hemoglobin, supporting hyphal penetration and yeast dissemination in the host [48,49]. In our study, all the clinical isolates were able to produce hemolysins. These findings corroborate those in a previous study that evaluated the hemolysin activity of 70 clinical isolates from patients with BSIs [50]. Phospholipases hydrolyze phospholipids as substrates, causing host cell membranes to rupture, and the secretion of phospholipases from Candida species is involved in tissue invasion [51]. C. albicans has strong phospholipase activity, but NAC spp. can exhibit lower or negative phospholipase production [15,52]. Corroborating these studies, all C. albicans isolates in this study produced strong phospholipase activity, whereas none of the NAC spp. exhibited this activity. However, a previous study identified that the majority of phospholipase producers were NAC strains, with C. tropicalis and C. parapsilosis exhibiting marked phospholipase activity [53]. Proteases can degrade host epithelial and mucosal barrier proteins, including albumin, collagen, and mucin, aiding Candida in resisting host immune system attacks by degrading antibodies, complement factors, and cytokines [17,54]. Candida-secreted aspartyl proteases (SAPs) can enhance hypha formation, epithelial cell damage, and the maintenance of biomass as well as increase yeast invasiveness in in vivo models [55,56]. We found that the detection rate of proteinase production in NAC isolates from blood ($60.7\%$) was higher than in C. albicans ($50\%$), which contradicts previous studies where the proteinase activity was present at a lower rate in NAC isolates than that in C. albicans [57,58]. Among the NAC isolates in this study, $100\%$ of C. tropicalis and $25\%$ of C. parapsilosis isolates were protease producers. Individual isolates of C. tropicalis in our investigation showed strong levels of proteinase activity compared with that in other Candida spp. isolates. No strains of C. glabrata were protease-positive strains. Our results do not agree with a previous study that reported that $80\%$ of C. glabrata isolates from blood were protease producers [57]. The differences in hydrolytic enzyme patterns may vary depending on the strains present in geographical regions and the host populations. After adherence and hydrolytic enzyme secretion, Candida spp. can invade and damage the host epithelial cells. Candida invades epithelial cells via two distinct mechanisms: inducing endocytosis and active penetration [59]. The induction of endocytosis contributes to an early point of the invasion process, whereas active penetration is the main epithelial invasion mechanism [60]. The morphologic transition switching between the yeast and hyphal forms represents a virulence factor that facilitates the active invasion process. Our findings support previous research that C. albicans and C. tropicalis can form hyphal filamentous structures that penetrate epithelial cells. In this study, C. parapsilopsis strains were unable to form true hyphae but did undergo a morphologic shift to the pseudohyphal form. C. glabrata cannot form elongated pseudohyphae or true hyphae structures, which is consistent with previous research [61,62]. We demonstrated that the formation of true hyphal filamentous or pseudohyphae in Candida isolates such as C. albicans and C. tropicalis correlates with their invasion ability in a host epithelium model where C. albicans had the highest level of invasion, followed by C. tropicalis, C. parapsilosis, and C. glabrata. Using the release of the intracellular marker enzyme LDH from the epithelial cells into the culture media, we evaluated the damage to epithelial cells caused by different Candida species. C. tropicalis infection significantly increased epithelial cell injury within 18 h of infection in comparison with the effect of other Candida species. Monocytes are blood-borne phagocytic cells that encounter and eliminate microbes and yeast cells. We demonstrated that monocytes could more efficiently phagocytose C. glabrata, as indicated by a higher phagocyte index than that of other Candida spp.; C. tropicalis and C. parapsilosis were more resistant to phagocytosis, with the lowest phagocyte index. The differences in phagocyte-uptake efficiency of Candida spp. corresponded well with C. glabrata being less virulent than other species in this study. The difference in phagocyte recognition of the four Candida spp. depends on the cell wall composition of pathogen-associated molecular patterns, including the presence of mannans, mannoproteins, chitin, and β-glucans. The cell wall of C. glabrata contains $50\%$ more mannose than C. albicans [63], and C. glabrata is more likely to be more easily recognized by phagocyte mannose-specific receptors (mannose receptor, TLR4, dectin-2, and galectine-3) [64]. C. albicans modifies the glycan component on the cell surface and does not expose β-glucans in the true hyphae state, which prevents activation of the dectin-1 receptor involved in phagocytosis [65,66,67]. However, this study only demonstrated the level of uptake of different Candida species into the phagocytic cell but not killing activity. Further study is needed to elucidate the rate of host cell killing and intracellular survival mechanisms among Candida species. With the increasing prevalence of C. tropicalis infections in our country and the occurrence of drug resistance, anti-virulence therapy in combination with antifungal drug treatment may be beneficial in the fight against C. tropicalis. Considering the proteases involved in biofilm formation, hyphal production, and epithelial cell damage, this study observed a statistically significant increase in the production of proteases, biofilm formation, and epithelial cell damage among C. tropicalis isolates compared to other species. Among these virulent features, proteases may play a critical role in the pathogenesis of C. tropicalis infection in our study. Biofilms formed by C. tropicalis are resistant to many antifungal agents, which makes them very difficult to treat. Therefore, strategies for overcoming this problem may include the use of protease inhibitors in combination with antifungal drugs. Aspartyl protease inhibitors can reduce Candida-induced tissue damage, proliferation, and virulence in vivo as well as reduce Candida adherence to the materials commonly used in medical devices [68,69,70,71,72]. Previous reports showed that treatment with aspartyl protease inhibitors reduced the occurrence of oral candidiasis in immunocompromised patients [73,74]. Optimistically, information from the current study will be useful for understanding the pathogenicity process of Candida spp. in Thailand and assisting physicians in making treatment decisions to target virulence factors in combination with antifungal drugs against Candida infections. ## 5. Conclusions This study provides vital information regarding the pathogenicity of Candida spp. causing BSIs in Thailand. The current study demonstrates that Candida spp. in Thailand display distinct characteristics of virulence factor expression. C. tropicalis exhibits biofilm, hydrophobicity, protease activity, host cell damage, and true hyphae formation, whereas C. albicans has the highest epithelial cell invasion rate, phospholipase activity, and true hyphae formation. C. glabrata produces the most hemolysis activity, whereas C. parapsilosis produces pseudohyphae and is resistant to monocyte phagocytosis. Knowledge of these virulence factors will be crucial for understanding the pathogenicity of Candida species and helping explore treatment. The findings in this study raise awareness concerning the deleterious effects of C. tropicalis, the most common cause of BSI in Thailand, which harbor several virulence features such as dimorphism, biofilm formation, high hydrophobicity, and phagocytosis resistance. ## References 1. Kullberg B.J., Arendrup M.C.. Invasive candidiasis. N. Engl. J. Med. (2015) 373 1445-1456. DOI: 10.1056/NEJMra1315399 2. Li Y., Du M., Chen L.A., Liu Y., Liang Z.. Nosocomial bloodstream infection due to. Mycopathologia (2016) 181 485-495. DOI: 10.1007/s11046-016-9997-3 3. Kothalawala M., Jayaweera J., Arunan S., Jayathilake A.. The emergence of non-albicans candidemia and evaluation of HiChrome. BMC Microbiol. (2019) 19. DOI: 10.1186/s12866-019-1518-3 4. Xiao Z., Wang Q., Zhu F., An Y.. Epidemiology, species distribution, antifungal susceptibility and mortality risk factors of candidemia among critically ill patients: A retrospective study from 2011 to 2017 in a teaching hospital in China. Antimicrob. Resist. Infect. Control. (2019) 8 89. DOI: 10.1186/s13756-019-0534-2 5. Pfaller M., Neofytos D., Diekema D., Azie N., Meier-Kriesche H.U., Quan S.P., Horn D.. Epidemiology and outcomes of candidemia in 3648 patients: Data from the prospective antifungal therapy (PATH Alliance(R)) registry, 2004–2008. Diagn Microbiol. Infect. Dis. (2012) 74 323-331. DOI: 10.1016/j.diagmicrobio.2012.10.003 6. Boonsilp S., Homkaew A., Phumisantiphong U., Nutalai D., Wongsuk T.. Species distribution, antifungal susceptibility, and molecular epidemiology of. J. Fungi (2021). DOI: 10.3390/jof7070577 7. Tulyaprawat O., Pharkjaksu S., Chongtrakool P., Ngamskulrungroj P.. An Association of an eBURST group with triazole resistance of. Front. Microbiol. (2020) 11 934. DOI: 10.3389/fmicb.2020.00934 8. Doi A.M., Pignatari A.C., Edmond M.B., Marra A.R., Camargo L.F., Siqueira R.A., da Mota V.P., Colombo A.L.. Epidemiology and microbiologic characterization of nosocomial candidemia from a Brazilian national surveillance program. PLoS ONE (2016) 11. DOI: 10.1371/journal.pone.0146909 9. Khairat S.M., Sayed A.M., Nabih M., Soliman N.S., Hassan Y.M.. Prevalence of. Infect. Drug Resist. (2019) 12 2409-2416. DOI: 10.2147/IDR.S196972 10. Whaley S.G., Berkow E.L., Rybak J.M., Nishimoto A.T., Barker K.S., Rogers P.D.. Azole antifungal resistance in. Front. Microbiol. (2016) 7 2173. DOI: 10.3389/fmicb.2016.02173 11. Zuza-Alves D.L., Silva-Rocha W.P., Chaves G.M.. An Update on. Front. Microbiol. (2017) 8 1927. DOI: 10.3389/fmicb.2017.01927 12. Jin L., Cao Z., Wang Q., Wang Y., Wang X., Chen H., Wang H.. MDR1 overexpression combined with. BMC Infect. Dis. (2018) 18. DOI: 10.1186/s12879-018-3082-0 13. Negri M., Silva S., Henriques M., Oliveira R.. Insights into. Eur. J. Clin. Microbiol. Infect. Dis. (2012) 31 1399-1412. DOI: 10.1007/s10096-011-1455-z 14. Mora Carpio A.L., Climaco A.. Fungemia Candidiasis. StatPearls (2022) 15. Chin V.K., Foong K.J., Maha A., Rusliza B., Norhafizah M., Ng K.P., Chong P.P.. Trop. Biomed. (2013) 30 654-662. PMID: 24522136 16. Mattei A.S., Alves S.H., Severo C.B., Guazzelli Lda S., Oliveira Fde M., Severo L.C.. Determination of germ tube, phospholipase, and proteinase production by bloodstream isolates of. Rev. Soc. Bras. Med. Trop. (2013) 46 340-342. DOI: 10.1590/0037-8682-0045-2013 17. Neji S., Hadrich I., Trabelsi H., Abbes S., Cheikhrouhou F., Sellami H., Makni F., Ayadi A.. Virulence factors, antifungal susceptibility and molecular mechanisms of azole resistance among. J. Biomed. Sci. (2017) 24 67. DOI: 10.1186/s12929-017-0376-2 18. Chakrabarti A., Nayak N., Talwar P.. In vitro proteinase production by. Mycopathologia (1991) 114 163-168. DOI: 10.1007/BF00437209 19. Tsang C.S.P., Chu F.C.S., Leung W.K., Jin L.J., Samaranayake L.P., Siu S.C.. Phospholipase, proteinase and haemolytic activities of. J. Med. Microbiol. (2007) 56 1393-1398. DOI: 10.1099/jmm.0.47303-0 20. Luo G., Samaranayake L.P., Yau J.Y.. J. Clin. Microbiol. (2001) 39 2971-2974. DOI: 10.1128/JCM.39.8.2971-2974.2001 21. Sachin C.R.K., Santosh S.. In vitro evaluation of proteinase, phospholipase and haemolysin activities of. Int. J. Med. Biomed. Res. (2012) 1 153-157. DOI: 10.14194/ijmbr.1211 22. Rosenberg M.. Bacterial adherence to hydrocarbon: A useful technique for studying cell surface hydrophobicity. FEMS Microbiol. Lett. (1984) 22 289-295. DOI: 10.1111/j.1574-6968.1984.tb00743.x 23. Ramage G., Vande Walle K., Wickes B.L., Lopez-Ribot J.L.. Standardized method for in vitro antifungal susceptibility testing of. Antimicrob. Agents Chemother. (2001) 45 2475-2479. DOI: 10.1128/AAC.45.9.2475-2479.2001 24. Correia I., Prieto D., Roman E., Wilson D., Hube B., Alonso-Monge R., Pla J.. Cooperative role of MAPK pathways in the interaction of. Microorganisms (2019) 8. DOI: 10.3390/microorganisms8010048 25. Saiprom N., Sangsri T., Tandhavanant S., Sengyee S., Phunpang R., Preechanukul A., Surin U., Tuanyok A., Lertmemongkolchai G., Chantratita W.. Genomic loss in environmental and isogenic morphotype isolates of. PLoS Negl. Trop. Dis. (2020) 14. DOI: 10.1371/journal.pntd.0008590 26. Dagher Z., Xu S., Negoro P.E., Khan N.S., Feldman M.B., Reedy J.L., Tam J.M., Sykes D.B., Mansour M.K.. Fluorescent tracking of yeast division clarifies the essential role of spleen tyrosine kinase in the intracellular control of. Front. Immunol. (2018) 9 1058. DOI: 10.3389/fimmu.2018.01058 27. Tsuchiya S., Yamabe M., Yamaguchi Y., Kobayashi Y., Konno T., Tada K.. Establishment and characterization of a human acute monocytic leukemia cell line (THP-1). Int. J. Cancer (1980) 26 171-176. DOI: 10.1002/ijc.2910260208 28. Villasenor-Cardoso M.I., Frausto-Del-Rio D.A., Ortega E.. Aminopeptidase N (CD13) is involved in phagocytic processes in human dendritic cells and macrophages. Biomed. Res. Int. (2013) 2013 562984. DOI: 10.1155/2013/562984 29. Paul S., Kannan I., Mohanram K.. Extensive. Curr. Med. Mycol. (2019) 5 1-6. DOI: 10.18502/cmm.5.3.1739 30. Ngamchokwathana C., Chongtrakool P., Waesamaae A., Chayakulkeeree M.. Risk factors and outcomes of non-albicans. J. Fungi (2021) 7. DOI: 10.3390/jof7040269 31. Ying Y., Zhao Y., Hu X., Cai Z., Liu X., Jin G., Zhang J., Zhang J., Liu J., Huang X.. In vitro fluconazole susceptibility of 1,903 clinical isolates of. Microb. Drug Resist. (2013) 19 266-273. DOI: 10.1089/mdr.2012.0204 32. Morio F., Loge C., Besse B., Hennequin C., Le Pape P.. Screening for amino acid substitutions in the. Diagn. Microbiol. Infect. Dis. (2010) 66 373-384. DOI: 10.1016/j.diagmicrobio.2009.11.006 33. Jiang C., Dong D., Yu B., Cai G., Wang X., Ji Y., Peng Y.. Mechanisms of azole resistance in 52 clinical isolates of. J. Antimicrob. Chemother. (2013) 68 778-785. DOI: 10.1093/jac/dks481 34. Chew K.L., Cheng J.W., Jureen R., Lin R.T., Teo J.W.. Mycoscience (2017) 58 111-115. DOI: 10.1016/j.myc.2016.11.001 35. Paul S., Shaw D., Joshi H., Singh S., Chakrabarti A., Rudramurthy S.M., Ghosh A.K.. Mechanisms of azole antifungal resistance in clinical isolates of. PLoS ONE (2022) 17. DOI: 10.1371/journal.pone.0269721 36. Forastiero A., Mesa-Arango A.C., Alastruey-Izquierdo A., Alcazar-Fuoli L., Bernal-Martinez L., Pelaez T., Lopez J.F., Grimalt J.O., Gomez-Lopez A., Cuesta I.. Antimicrob. Agents Chemother. (2013) 57 4769-4781. DOI: 10.1128/AAC.00477-13 37. Tan J., Zhang J., Chen W., Sun Y., Wan Z., Li R., Liu W.. The A395T mutation in ERG11 gene confers fluconazole resistance in. Mycopathologia (2015) 179 213-218. DOI: 10.1007/s11046-014-9831-8 38. Klotz S.A., Drutz D.J., Zajic J.E.. Factors governing adherence of. Infect. Immun. (1985) 50 97-101. DOI: 10.1128/iai.50.1.97-101.1985 39. Panagoda G.J., Ellepola A.N., Samaranayake L.P.. Adhesion of. Mycoses (2001) 44 29-35. DOI: 10.1046/j.1439-0507.2001.00611.x 40. Galan-Ladero M.A., Blanco-Blanco M.T., Hurtado C., Perez-Giraldo C., Blanco M.T., Gomez-Garcia A.C.. Determination of biofilm production by. Yeast (2013) 30 331-339. DOI: 10.1002/yea.2965 41. Dabiri S., Shams-Ghahfarokhi M., Razzaghi-Abyaneh M.. Comparative analysis of proteinase, phospholipase, hydrophobicity and biofilm forming ability in. J. Mycol. Med. (2018) 28 437-442. DOI: 10.1016/j.mycmed.2018.04.009 42. Silva-Dias A., Miranda I.M., Branco J., Monteiro-Soares M., Pina-Vaz C., Rodrigues A.G.. Adhesion, biofilm formation, cell surface hydrophobicity, and antifungal planktonic susceptibility: Relationship among. Front. Microbiol. (2015) 6 205. DOI: 10.3389/fmicb.2015.00205 43. Cavalheiro M., Teixeira M.C.. Front. Med. (2018) 5 28. DOI: 10.3389/fmed.2018.00028 44. Sriphannam C., Nuanmuang N., Saengsawang K., Amornthipayawong D., Kummasook A.. Anti-fungal susceptibility and virulence factors of. J. Mycol. Med. (2019) 29 325-330. DOI: 10.1016/j.mycmed.2019.08.001 45. Nouraei H., Pakshir K., ZareShahrabadi Z., Zomorodian K.. High detection of virulence factors by. Microb. Pathog. (2020) 149 104574. DOI: 10.1016/j.micpath.2020.104574 46. Moyes D.L., Richardson J.P., Naglik J.R.. Virulence (2015) 6 338-346. DOI: 10.1080/21505594.2015.1012981 47. Schaller M., Borelli C., Korting H.C., Hube B.. Hydrolytic enzymes as virulence factors of. Mycoses (2005) 48 365-377. DOI: 10.1111/j.1439-0507.2005.01165.x 48. Linares C.E., de Loreto E.S., Silveira C.P., Pozzatti P., Scheid L.A., Santurio J.M., Alves S.H.. Enzymatic and hemolytic activities of. Rev. Inst. Med. Trop. Sao Paulo (2007) 49 203-206. DOI: 10.1590/S0036-46652007000400001 49. Furlaneto M.C., Favero D., Franca E.J., Furlaneto-Maia L.. Effects of human blood red cells on the haemolytic capability of clinical isolates of. J. Biomed. Sci. (2015) 22 13. DOI: 10.1186/s12929-015-0120-8 50. Vieira de Melo A.P., Zuza-Alves D.L., da Silva-Rocha W.P., Ferreira Canario de Souza L.B., Francisco E.C., Salles de Azevedo Melo A., Maranhao Chaves G.. Virulence factors of. J. Mycol. Med. (2019) 29 132-139. DOI: 10.1016/j.mycmed.2019.02.002 51. Ghannoum M.A.. Potential role of phospholipases in virulence and fungal pathogenesis. Clin. Microbiol. Rev. (2000) 13 122-143. DOI: 10.1128/CMR.13.1.122 52. Canela H.M.S., Cardoso B., Vitali L.H., Coelho H.C., Martinez R., Ferreira M.. Prevalence, virulence factors and antifungal susceptibility of. Mycoses (2018) 61 11-21. DOI: 10.1111/myc.12695 53. Sharma Y., Chumber S.K., Kaur M.. Studying the prevalence, species distribution, and detection of in vitro production of phospholipase from. J. Glob. Infect. Dis. (2017) 9 8-11. DOI: 10.4103/0974-777X.199995 54. Borst A., Fluit A.C.. High levels of hydrolytic enzymes secreted by. J. Med. Microbiol. (2003) 52 971-974. DOI: 10.1099/jmm.0.05228-0 55. Naglik J.R., Challacombe S.J., Hube B.. Microbiol. Mol. Biol. Rev. (2003) 67 400-428. DOI: 10.1128/MMBR.67.3.400-428.2003 56. Silva S., Hooper S.J., Henriques M., Oliveira R., Azeredo J., Williams D.W.. The role of secreted aspartyl proteinases in. Clin. Microbiol. Infect. (2011) 17 264-272. DOI: 10.1111/j.1469-0691.2010.03248.x 57. Tay S.T., Abidin I.A., Hassan H., Ng K.P.. Proteinase, phospholipase, biofilm forming abilities and antifungal susceptibilities of Malaysian. Med. Mycol. (2011) 49 556-560. PMID: 21254967 58. Sariguzel F.M., Berk E., Koc A.N., Sav H., Demir G.. Investigation of the relationship between virulence factors and genotype of. J. Infect. Dev. Ctries (2015) 9 857-864. DOI: 10.3855/jidc.5359 59. Dalle F., Wachtler B., L’Ollivier C., Holland G., Bannert N., Wilson D., Labruere C., Bonnin A., Hube B.. Cellular interactions of. Cell Microbiol. (2010) 12 248-271. DOI: 10.1111/j.1462-5822.2009.01394.x 60. Wachtler B., Citiulo F., Jablonowski N., Forster S., Dalle F., Schaller M., Wilson D., Hube B.. PLoS ONE (2012) 7. DOI: 10.1371/journal.pone.0036952 61. Silva S., Henriques M., Oliveira R., Azeredo J., Malic S., Hooper S.J., Williams D.W.. Characterization of. Eur. J. Oral Sci. (2009) 117 669-675. DOI: 10.1111/j.1600-0722.2009.00677.x 62. Zupancic M.L., Frieman M., Smith D., Alvarez R.A., Cummings R.D., Cormack B.P.. Glycan microarray analysis of. Mol. Microbiol. (2008) 68 547-559. DOI: 10.1111/j.1365-2958.2008.06184.x 63. De Groot P.W., Kraneveld E.A., Yin Q.Y., Dekker H.L., Gross U., Crielaard W., de Koster C.G., Bader O., Klis F.M., Weig M.. The cell wall of the human pathogen. Eukaryot. Cell (2008) 7 1951-1964. DOI: 10.1128/EC.00284-08 64. Netea M.G., Brown G.D., Kullberg B.J., Gow N.A.. An integrated model of the recognition of. Nat. Rev. Microbiol. (2008) 6 67-78. DOI: 10.1038/nrmicro1815 65. Heilmann C.J., Sorgo A.G., Siliakus A.R., Dekker H.L., Brul S., de Koster C.G., de Koning L.J., Klis F.M.. Hyphal induction in the human fungal pathogen. Microbiology (2011) 157 2297-2307. DOI: 10.1099/mic.0.049395-0 66. Ebanks R.O., Chisholm K., McKinnon S., Whiteway M., Pinto D.M.. Proteomic analysis of. Proteomics (2006) 6 2147-2156. DOI: 10.1002/pmic.200500100 67. Gantner B.N., Simmons R.M., Underhill D.M.. Dectin-1 mediates macrophage recognition of. EMBO J. (2005) 24 1277-1286. DOI: 10.1038/sj.emboj.7600594 68. Borg-von Zepelin M., Meyer I., Thomssen R., Wurzner R., Sanglard D., Telenti A., Monod M.. HIV-protease inhibitors reduce cell adherence of. J. Investig. Dermatol. (1999) 113 747-751. DOI: 10.1046/j.1523-1747.1999.00747.x 69. Bektic J., Lell C.P., Fuchs A., Stoiber H., Speth C., Lass-Florl C., Borg-von Zepelin M., Dierich M.P., Wurzner R.. HIV protease inhibitors attenuate adherence of. FEMS Immunol. Med. Microbiol. (2001) 31 65-71. DOI: 10.1111/j.1574-695X.2001.tb01588.x 70. Tsang C.S., Hong I.. HIV protease inhibitors differentially inhibit adhesion of. Mycoses (2010) 53 488-494. DOI: 10.1111/j.1439-0507.2009.01743.x 71. De Bernardis F., Mondello F., Scaravelli G., Pachi A., Girolamo A., Agatensi L., Cassone A.. High aspartyl proteinase production and vaginitis in human immunodeficiency virus-infected women. J. Clin. Microbiol. (1999) 37 1376-1380. DOI: 10.1128/JCM.37.5.1376-1380.1999 72. Cassone A., De Bernardis F., Torosantucci A., Tacconelli E., Tumbarello M., Cauda R.. In vitro and in vivo anticandidal activity of human immunodeficiency virus protease inhibitors. J. Infect. Dis. (1999) 180 448-453. DOI: 10.1086/314871 73. Cassone A., Tacconelli E., De Bernardis F., Tumbarello M., Torosantucci A., Chiani P., Cauda R.. Antiretroviral therapy with protease inhibitors has an early, immune reconstitution-independent beneficial effect on. J. Infect. Dis. (2002) 185 188-195. DOI: 10.1086/338445 74. Cauda R., Tacconelli E., Tumbarello M., Morace G., De Bernardis F., Torosantucci A., Cassone A.. Role of protease inhibitors in preventing recurrent oral candidosis in patients with HIV infection: A prospective case-control study. J. Acquir. Immune. Defic. Syndr. (1999) 21 20-25. DOI: 10.1097/00126334-199905010-00003
--- title: Comparison of Local Metabolic Changes in Diabetic Rodent Kidneys Using Mass Spectrometry Imaging authors: - Xin Zhang - Yanhua Liu - Shu Yang - Xin Gao - Shuo Wang - Zhaoying Wang - Chen Zhang - Zhi Zhou - Yanhua Chen - Zhonghua Wang - Zeper Abliz journal: Metabolites year: 2023 pmcid: PMC10060001 doi: 10.3390/metabo13030324 license: CC BY 4.0 --- # Comparison of Local Metabolic Changes in Diabetic Rodent Kidneys Using Mass Spectrometry Imaging ## Abstract Understanding the renal region-specific metabolic alteration in different animal models of diabetic nephropathy (DN) is critical for uncovering the underlying mechanisms and for developing effective treatments. In the present study, spatially resolved metabolomics based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to compare the local metabolic changes in the kidneys of HFD/STZ-induced diabetic rats and db/db mice. As a result, a total of 67 and 59 discriminating metabolites were identified and visualized in the kidneys of the HFD/STZ-induced diabetic rats and db/db mice, respectively. The result showed that there were significant region-specific changes in the glycolysis, TCA cycle, lipid metabolism, carnitine metabolism, choline metabolism, and purine metabolism in both DN models. However, the regional levels of the ten metabolites, including glucose, AMP, eicosenoic acid, eicosapentaenoic acid, Phosphatidylserine (36:1), Phosphatidylserine (36:4), Phosphatidylethanolamine (34:1), Phosphatidylethanolamine (36:4), Phosphatidylcholine (34:2), Phosphatidylinositol (38:5) were changed in reversed directions, indicating significant differences in the local metabolic phenotypes of these two commonly used DN animal models. This study provides comprehensive and in-depth analysis of the differences in the tissue and molecular pathological features in diabetic kidney injury in HFD/STZ-induced diabetic rats and db/db mice. ## 1. Introduction Diabetes mellitus (DM) is a chronic metabolic disease with a growing global incidence [1,2,3,4,5]. It is estimated that the number of diabetic patients will increase to 700 million by 2045 [6], with an estimated incidence rate of $10\%$ in China [7]. Type 2 diabetes mellitus (T2DM) is the most prevalent form of DM, affecting over $90\%$ of all diagnosed patients. It is estimated that 30–$40\%$ of diabetic patients will develop diabetic nephropathy (DN), which is one of the major complications of DM and the leading cause of end-stage renal disease (ESRD) [8]. Despite its widespread impact, the etiology and pathogenesis of DN is still not fully understood, and the current treatments are limited. Animal models have been crucial in uncovering the pathophysiology of diseases, in identifying new targets for treatment, and in testing potential therapeutic agents. Numerous spontaneous and experimentally induced animal models of DN have been established for various research purposes [9]. Diabetic db/db mice are a good representation of the early changes in human DN, including albuminuria, podocyte loss, and mesangial matrix expansion, but do not show the later, more advanced morphological changes [10]. The HFD/STZ-induced DN model, which involves a high-fat diet feeding and a low-dose streptozotocin injection, is a commonly used model and exhibits elevated albuminuria, increased kidney index, and glomerular hypertrophy and mesangial matrix accumulation. However, the similarity of this model to human DN has been questioned [11,12]. As none of the current animal models can perfectly replicate all the pathological features of human DN, the selection of animal models for DN research must be conducted with caution. Understanding the phenotypic features of different animal models of DN can aid in the effective and targeted use of these models and enhance our understanding of DN. Metabolomics is an important molecular profiling technology that can comprehensively capture the biological consequences of disease by identifying the metabolic biomarkers that correlate with disease phenotypes [13,14]. Several researchers have identified a large number of metabolic biomarkers related to DN using nuclear magnetic resonance (NMR), gas chromatography–mass spectrometry (GC–MS), and liquid chromatography–mass spectrometry (LC–MS)-based metabolomic approaches. For example, Salek et al. [ 15] investigated the metabolic similarities between mice, rats, and humans with T2DM using 1H-NMR-based metabolomics and identified some metabolic perturbations common to all three species in urine samples. These metabolomics studies have greatly advanced our understanding of the phenotypes of DN, but the local metabolic changes in the diabetic kidney can only be partially evaluated through the analysis of homogeneous samples, such as urine, serum, and tissue homogenates. Mass spectrometry imaging (MSI) is a highly advanced molecular imaging technology that allows the spatially resolved metabolic profiling of a variety of functional metabolites in tissue sections, which is important for gaining a more accurate understanding of the tissue-specific molecular pathological features underlying diseases [16]. A variety of MSI techniques, such as matrix-assisted laser desorption ionization (MALDI)-MSI, desorption electrospray ionization (DESI)-MSI, and secondary ion mass spectrometry (SIMS), have been developed and frequently used in the biomedical field. For example, Satoshi Miyamoto et al. [ 17] revealed an increase in the glomerular ATP/AMP ratio in the diabetic kidney using MALDI-MSI and identified sphingomyelin (d18:$\frac{1}{16}$:0) as the key regulator for ATP production in mesangial cells. Additionally, our research group developed a spatially resolved metabolomic approach based on air flow-assisted desorption electrospray ionization (AFADESI)-MSI and MALDI-MSI and discovered the tissue-specific metabolic reprogramming processes in the kidney of a rat model of DN 12 weeks after the induction of diabetes by HFD/STZ [18]. In this study, we described the application of AFADESI-MSI-based spatially resolved metabolomics to investigate the region-specific metabolic changes in a rat model of DN produced 20 weeks after the induction of diabetes by HFD/STZ and a spontaneous DN model of 28-week-old db/db mice. Local metabolic changes in the kidneys of the two animal models were compared with an aim to determine the key discriminating metabolites that were common or different to the two species. The research workflow is summarized in Figure 1. ## 2.1. Chemicals and Reagents HPLC-grade acetonitrile (ACN) and methanol (MeOH) were purchased from Merck (Muskegon, MI, USA). Purified water was obtained from Wahaha (Hangzhou, China). STZ and citrate were purchased from Sigma-Aldrich (St. Louis, MO, USA). ## 2.2. Animal Models DN model of HFD/STZ-induced rat: Six-week-old male Wistar rats weighing 175–210 g were purchased from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China); they were housed in a constant environment maintained at 23 ± 3 °C with a 12 h day/night cycle. The rats were randomly divided into control ($$n = 6$$) and DN model ($$n = 6$$) groups. A normal pellet diet and an HFD (5.24 Kcal/g, $60\%$ fat; HFD12492, high-fat feed, Beijing HFK Bioscience Co., Ltd, Beijing, China) were given to the rats in the control and model groups, respectively. After 4 weeks, the rats in the model group were induced to develop insulin resistance (IR) and then injected intraperitoneally (ip) with STZ at a dose of 35 mg/kg to induce hyperglycemia, while the rats in the control group were injected ip with citrate buffer (PH = 4.4, vehicle). The fasting blood glucose (FBG) level was measured 5–7 days after the STZ or buffer injection. An FBG level of ≥16.7 mmol/L for three consecutive days was used as the standard for establishing the model. After 20 weeks of feeding, 24 h urine was collected from each rat using metabolic cages. Blood was collected from the abdominal artery. The kidneys were collected and snap-frozen in liquid nitrogen. DN model of db/db mouse: Four-week-old male BKS.DB mice (6 db/db homozygotes and 6 db/m littermate controls) weighing 16.5–35.6 g were purchased from gempharmatech Co., Ltd. (Jiangsu, China); they were housed at a constant temperature of 23 ± 3 °C with a 12 h dark/12 h light cycle. The db/m littermate controls were set as the control group, and the db/db homozygotes were set as the diabetes model group. After 24 weeks of feeding, the urine, blood, and kidney samples were collected in the same way as those used in the HFD/STZ-induced rats. All the samples were stored at −80 °C until analysis. The animal experiments were approved by the animal welfare ethics committee of Beijing Union-Genius Pharmaceutical Technology Development Co., Ltd. (Beijing, China) and conducted in accordance with the NIH Guide for the Care and Use of Laboratory Animals. ## 2.3. Biochemical and Histopathological Analysis The FBG was determined using a blood glucose meter (Roche, Basel, Switzerland). Glycosylated hemoglobin (HbA1c) was detected with a Quo-Test HbA1c Analyzer (QUOTIENT Diagnostics Ltd. Walton-on-Thames, Surrey, UK). The urine creatinine and urea nitrogen were measured using an AU480 automatic chemistry analyzer (Beckman Coulter Inc., Brea, CA, USA). The frozen kidney tissues were cut into 8 μm serial sections at −22 °C using a Leica CM1860 cryostat (Leica Microsystems Ltd., Wetzlar, Germany) and mounted onto adhesion microscope slides (Thermo Scientific, CA, USA). Hematoxylin and eosin (H&E) staining of kidney sections was performed to reveal the histopathological lesions. ## 2.4. AFADESI−MSI Analysis The MSI experiments were performed on an AFADESI-MSI platform, which consisted of a home-built AFADESI ion source and a Q-OT-qIT hybrid mass spectrometer (Orbitrap Fusion Lumos; Thermo Fisher Scientific, San Jose, CA, USA) [19,20]. Mass spectra were obtained in positive and negative full MS modes with a scan range of 100–1000 Da at a mass resolution of 120,000. Additional details on the experimental parameters can be found in the Supporting Information (Table S1). ## 2.5. Data Processing and Analysis Raw files obtained from the analysis in the positive and negative AFADESI-MSI ion modes were converted to.cdf format by Xcalibur 4.0.2 (Thermo Scientific, San Jose, CA USA). The converted file was then imported into MassImager (MassImager 2.0, Beijing, China) for the background subtracting, normalizing, and reconstructing of the ion image. Regions of interest (ROIs) were selected by matching the H&E stain image to generate individual.txt format data. The datasets were imported into Markerview™ software 1.2.1 (AB SCIEX Toronto, ON, Canada) for peak picking and alignment. Relative intensities were calculated for each ROI using total ion current (TIC) normalization, and further compared using Student’s t-tests to find the differentiating metabolites associated with DN ($p \leq 0.05$). ## 2.6. LC-MS/MS Analysis of Kidney Homogenates The kidney homogenates were analyzed by high-resolution LC-MS/MS to obtain structural information. Detailed procedures for the LC-MS/MS experiments are provided in the Supporting Information. ## 2.7. Venn Diagram and Pathway Enrichment Analysis of Discriminating Metabolites Venn diagrams were drawn using Venn 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html, accessed on 5 April 2022), and then, the theoretical m/z of the discriminating metabolites involved in each group was entered into the corresponding list to automatically refresh the presented results. Pathway analysis was performed using the Pathway Analysis module in MetaboAnalyst 3.0 (https://www.metaboanalyst.ca, accessed on 7 April 2022). The HMDB IDs of the discriminatory metabolites were entered into the compound list, and the species was selected as rat. An enrichment analysis of the pathways was then conducted. The results of the “Match Status” were saved as a.csv file, and pathway enrichment analysis graphs were generated using the R version 3.6.1 programming language. ## 3.1. Assessment of Renal Injury in HFD/STZ-Induced Diabetic Rats and db/db Mice The results for the body weight, water consumption, food intake, and biochemical parameters of the rats in the control and the HFD/STZ-induced DN model group are summarized in Supporting Information Figures S2A–C and S3A–F. As compared to the control group, the body weight of the rats in the model group significantly decreased ($p \leq 0.01$) (Figure 2A), The urea nitrogen was slightly increased (Figure 3C), while the level of urine creatinine was significantly decreased ($p \leq 0.01$) (Figure 3D), The kidney weight and the kidney/body weight ratio in the HFD/STZ-induced DN group were remarkably higher than those in the control group ($p \leq 0.05$) (Figure 3E,F). Representative photomicrographs of the H&E-stained kidney tissues of the rats in the control and HFD/STZ-induced DN model group are shown in the Figure S1A,B. The histological examinations of the kidneys revealed swelling, mild cortical tubular epithelial space-like changes, and protein flocculation in the tubular lumen in the DN group. These biological and pathological changes indicate that diabetes causes mild pathological changes in the chronic progressive nephropathy in rats. The results for the body weight, water consumption, food intake, and biochemical parameters of the db/m and db/db mice are shown in Supporting Information Figures S2D–F and S3G–L. The change trends of the water consumption, FBG, HbA1c, urea nitrogen, urine creatinine, and kidney weight in the db/db mice were similar to those observed in the HFD/STZ-induced diabetic rats. However, the change trends of the body weight and kidney/body weight ratios were reversed in the db/db mice as compared to the HFD/STZ-induced diabetic rats, which may be due to the reversed food intake changes in the two diabetic models. The results of the H&E staining of the kidney tissues in the db/db mice were also examined and showed similar signs of renal hypertrophy, morphological changes, and vacuolar degeneration of the renal tubular epithelial cells, as observed in the HFD/STZ-induced diabetic rats. However, a more significant sign of glomerular damage, represented by the sieve-like dilatation of the glomerular capillaries, was observed in the kidneys of the db/db mice in DN model of the db/db mice compared to the HFD/STZ-induced DN rats. ## 3.2. AFADESI-MSI Analysis of Kidneys of HFD/STZ-Induced DN Rats and db/db DN Mice The frozen renal sections of the control and model groups were profiled by AFADESI-MSI to investigate the spatially resolved metabolic alteration in the HFD/STZ-induced DN rats and the db/db DN mice. The renal section was roughly divided into four regions, i.e., whole kidney (W), cortex (C), outer medulla (OM) and inner medulla (IM), based on H&E staining. In the positive AFADESI-MSI mode, 1750, 1642, 1569, and 1846 ion features were detected in the W, C, OM, and IM regions in the HFD/STZ-induced DN rats, respectively, while 1481, 1328, 1442, and 1691 ion features were detected in the W, C, OM, and IM regions in the db/db DN mice, respectively. In the negative AFADESI-MSI mode, 554, 528, 493, and 515 versus 580, 572, 529, and 585 peaks in the W, C, OM, and IM regions from the HFD/STZ-induced DN rats versus the db/db DN mice, respectively. A Student’s t-test was performed to compare the metabolic profiles of each region of the kidneys collected from the control and model groups. Based on a threshold of a p value < 0.05, a total of 274, 291, 318, and 224 differentiating variables were selected in the W, C, OM, and IM regions of the HFD/STZ-induced DN rats, and 299, 233, 326, and 294 differentiating variables were selected in the corresponding regions of the db/db DN mice, respectively. The structures of the differentiating metabolites were provisionally identified based on our previously described protocols [18]. Briefly, the possible element composition of the ion was calculated using Xcalibur 4.0 (Thermo Fisher Scientific), based on the exact mass and isotope pattern, and it was then searched in HMDB (http://hmdb.ca/, accessed on 20 April 2022) and METLIN (http://metlin.scripps.edu/, accessed on 21 April 2022) to find the possible metabolites. Finally, high-resolution on-tissue AFADESI−MS/MS analysis of the kidney sections and LC-MS/MS analysis of the kidney homogenates were performed to confirm the structures of the metabolites by interpretating their fragmentation characteristics. As result, 12 and 55 discriminating metabolites were finally identified in the HFD/STZ-induced DN rats in the positive and negative ion modes, respectively. 17 and 42 metabolites were identified in db/db DN mice in the positive and negative ion modes, respectively. The results of the metabolite identifications, including the fold changes (FC) of the differentiating metabolites, are presented in Tables S2–S5 and Figure S2. As depicted in the Venn diagram (Figure 4), the majority of the differentiating metabolites have significant changes in the entire kidney. However, certain metabolites showed changes only in specific regions. In the HFD/STZ-induced diabetic rats, 8, 10, and 1 distinctive discriminating metabolites were detected in the cortex, outer medulla, and inner medulla, respectively. In the db/db mouse model, 4, 13, and 5 unique discriminating metabolites were detected in the cortex, outer medulla, and inner medulla, respectively. The fan charts (Figure 4C,D) depict the proportion of differential metabolites found in the different regions of the kidney. The analysis revealed that the ranking of the number of discriminating metabolites identified in the different regions of the HFD/STZ-induced rat kidneys was OM/C > W > IM, while in the db/db mice, the ranking was OM > W > C > IM, indicating that the outer medulla region may be more vulnerable to diabetic renal damage in comparison to the other regions [21,22]. ## 3.3. Pathway Enrichment Analysis The pathway enrichment analysis of the discriminating metabolites was performed using MetaboAnalyst 3.0 (https://www.metaboanalyst.ca, accessed on 10 May 2022) to identify the metabolic pathways involved in the HFD/STZ-induced diabetic rats and db/db mice. As shown in Figure 5, a total of 17 altered metabolic pathways were identified in the HFD/STZ-induced diabetic rats, with 12 items having impact values greater than 0. The top five affected pathways were linoleic acid metabolism, taurine and hypotaurine metabolism, α-linolenic acid metabolism, glycerophospholipid metabolism, and the TCA cycle. Similarly, a total of 19 metabolic pathways were associated with the db/db mice. As shown in the Figure 5, there are nine items having impact values greater than 0. The top five affected pathways in db/db were glycerophospholipid metabolism, arachidonic acid metabolism, purine metabolism, the TCA cycle, and glycine, serine, and threonine metabolism. ## 4. Discussion The findings suggest that there are similarities and differences in the metabolic alteration in kidneys of HFD/STZ-induced DN rats and db/db DN mice. A comparison of the discriminatory metabolites associated with DN between the two models is shown in Figure S3. Only 15 ($14.6\%$) discriminatory metabolites showed the same trend of change, with 7 metabolites being down-regulated and 8 being up-regulated in both DN models. Forty-four ($42.7\%$) discriminatory metabolites were found to be specifically altered in the HFD/STZ-induced rats, while thirty-six ($35\%$) discriminatory metabolites were uniquely changed in the db/db mice. The classes of the discriminatory metabolites, which had common or specific variance in the two models, are summarized in Figure S3, with most of the discriminating metabolites being lipids. The comparison of the metabolic alterations associated with DN in the two models is shown in Figure S4. The results indicated that both the HFD/STZ-induced DN rats and the db/db DN mice had alterations in their metabolic pathways, including glycerophospholipid metabolism, purine metabolism, the TCA cycle, and glycosylphosphatidylinositol (GPI)-anchored biosynthesis metabolism. However, the findings suggest that linoleic acid metabolism was more affected by the HFD/STZ-induced DN, while the changes in the glycerophospholipid metabolism and the arachidonic acid metabolism were more pronounced in the db/db-induced DN. ## 4.1. Alteration of Lipid Metabolism in HFD/STZ-Induced Diabetic Rats and db/db Mice A total of 49 and 41 discriminating lipids were identified in the HFD/STZ-induced diabetic rats and db/db mice, respectively. The fan chart of the discriminating lipid species is provided in Figure S5. The spatial distribution and changes of these lipids in the HFD/STZ-induced diabetic rats and db/db mice are summarized in Figure 6 and Figure 7, respectively. These metabolites showed unique distributions across the kidney sections, indicating extensive alteration in the lipid metabolism occurred in a region-specific manner in both models of DN. ## 4.1.1. Fatty Acid Metabolism In the HFD/STZ-induced DN rats, three monounsaturated fatty acids (MUFAs), including oleic acid, eicosenoic acid, and nervonic acid were up-regulated, and four polyunsaturated fatty acids (PUFAs), including linolenic acid, linoleic acid, eicosapentaenoic acid, and docosahexaenoic acid were down-regulated. Additionally, the fatty acid esters of the hydroxy fatty acids FAHFA (34:1) were decreased, whereas the FAHFA (34:0) was increased. In the db/db DN mice, similar change trends were observed for palmitelaidic acid, eicosenoic acid, linolenic acid, and arachidonic acid compared to the HFD/STZ-induced diabetic rats. However, the change trends were found to be the opposite for eicosenoic acid and eicosapentaenoic acid. Fatty acids (FAs) are known to play a role in a wide range of diseases, including T2DM, inflammatory diseases, and cancer [23]. Linolenic acid has been shown to reduce inflammation by inhibiting the accumulation of the extracellular matrix (ECM) in DN [24]. On the other hand, oleic acid and palmitelaidic acid are the most abundant dietary and plasma fatty acids and have different effects on insulin resistance [25]. Oleic acid has been linked to the production of reactive oxygen species (ROS), leading to oxidative damage in proximal renal tubular cells [26]. In contrast, palmitelaidic acid has been shown to regulate glucose homeostasis, lipid metabolism, and cytokine production, thereby improving metabolic disorders [27]. A new family of synthetic fatty acid esters of hydroxy fatty acids (FAHFAs) secreted by adipose tissue has been found to have beneficial metabolic effects, such as enhanced insulin-stimulated glucose transport and glucose-stimulated GLP1 and insulin secretion, and to exhibit anti-inflammatory effects [28,29,30]. These FAHFAs are considered to be promising endogenous lipids with therapeutic potential for T2DM. ## 4.1.2. Phospholipid Metabolism Significant changes were observed in the levels of 5 phosphatidylcholine (PC), 8 phosphatidylethanolamine (PE), 5 phosphatidylserine (PS), 5 phosphatidylinositol (PI), 1 phosphatidic acid (PA), 9 phosphatidylglycerol (PG), 4 lysophospholipids, and 4 sphingomyelin (SM) metabolites in the HFD/STZ-induced DN rats. PC(34:1), PC(36:1), PC(36:2), PC(36:4), PE(p-18:$\frac{0}{20}$:4(5Z,8Z,11Z, 14Z)), PG(36:2), PG(36:3), PG(40:6), LysoPG(18:1), and SM(d18:$\frac{1}{16}$:0) were found to be up-regulated, while PC(34:2), PE(34:1), PE(36:2), PE(36:4), PE(38:4), PE(38:6), PE(p-38:6), phosphorylethanolamine, LysoPE(16:0), LysoPE(20:4), PS(34:1), PS(36:1), PS(36:2), PS(36:4), PS(40:6), PG(32:0), PG(34:2), PG(36:4), PG(40:8), PG(42:10), PG(44:12), PA(34:2), PI(34:2), PI(18:1(9Z)/18:1(9Z)), PI(36:4), PI(38:5), PI(40:6), LysoPI(16:0), and SM(42:2) were down-regulated. In the db/db DN mice, 8 PC, 10 PE, 2 PS, 4 PG, 3 PA, 4 PI, 3 lysophospholipid, and 2 SM metabolites were altered significantly. PC(34:0), PC(34:1), PC(34:2), PC(36:2), PC(36:3), PC(36:4), PC(38:4), LysoPC(18:0), PE(34:1), PE(p-34:1), PE(36:4), PE(p-36:4), PE(p-38:4), PE(40:1), PE(42:1), PS(36:1), PS(36:4), PG(34:1), LysoPG(18:1), LysoPG(18:2), PA(16:$\frac{0}{18}$:1(9Z)), PA(38:4), PI(34:0), PI(38:4), PI(38:5), SM(d18:$\frac{1}{16}$:0), and SM(d18:$\frac{1}{24}$:1(15Z)) were found to be up-regulated, while PC(p-38:5), PE(36:6), PE(p-40:4), PE(p-40:7), PG(32:0), PG(38:4), PG(40:8), PA(41:5), and PI(40:6) were down-regulated. PC and PE are the most abundant phospholipids in mammalian cell membranes. The PC/PE ratios in various tissues are associated with atherosclerosis, insulin resistance, and obesity [31]. In the present study, we calculated the PC/PE ratios in different regions of the kidneys in both models. The results showed that the PC/PE ratios were elevated in the W, C, OM and IM regions of the HFD/STZ-induced DN rats as compared to the control group. In the db/db mice, the PC/PE ratio was elevated in the W, OM, and IM regions, but decreased in the C region in the model group as compared to the control group. These changes in the PC/PE ratio may alter the fluidity and permeability of the cell membrane, leading to cellular damage [32,33]. The PS metabolites were found to have opposite change trends in the two DN models. In the cortex and outer medulla of the HFD/STZ-induced rats, most of the PS metabolites decreased, whereas they accumulated in the db/db mice (as shown in Figure 7E and Figure S7E,F). The decrease in PS metabolites may be a result of increased glycation in the diabetic state. Under diabetes, PS can be glycosylized to the form Amadori compounds and advanced glycation end products (AGEs), which are known to accumulate in diabetic patients [34]. The changes in the PG metabolites were consistent between the two DN model groups, with most of the PG metabolites showing a decrease in the model group and LysoPG being generally elevated in the cortical and outer medullary regions in both model groups (Figure 6F,G, Figure 7E,F, Figure S6F–H and S7F,G). Meanwhile, PA was found to be decreased in the HFD/STZ-induced rats (Figure 6G) but elevated in the db/db mice (Figure 7F and Figure S7G). These alterations in PA levels may lead to the pathological remodeling of cardiolipin (CL) and mitochondrial dysfunction in diabetes [18]. The trends in PI metabolites differed between the two DN models. The PI levels decreased in the cortical and outer medulla of the HFD/STZ-induced rats (Figure 6G,H and Figure S6I), while they showed an upward trend in the db/db mice (Figure 7G and Figure S7H). A decrease in PI biomarkers is commonly observed in patients with T2DM and DN, which may be linked to activation of the sorbitol pathway (SP). These findings were consistent with the previous studies on HFD/STZ-induced rats [35]. SM plays critical structural and functional roles in cells and has been linked to podocyte injury and atherosclerotic plaque inflammation [36,37,38]. The results of this study showed an accumulation of SM(d18:$\frac{1}{16}$:0) in the kidneys of both the DN models (Figure 6H, Figure 7G, Figure S6J and S7H,I). Increased levels of SM (d18:$\frac{1}{16}$:0) may suppress the activity of AMP-activated protein kinase (AMPK) and decrease PGC-1α protein expression, leading to an increase in glycolytic pathway activity [39]. ## 4.2. Alteration of Glycolysis and the TCA Cycle in HFD/STZ-Induced Diabetic Rats and db/db Mice The trends and spatial distributions of the metabolites involved in the glycolysis and TCA cycle pathways are illustrated in Figure 8 and Figure S8. Increased glucose levels were observed in the renal cortex in both of the DN models. However, the metabolic fate of glucose varied between the two models, with a slight decrease observed in the outer medulla in the HFD/STZ-induced DN rats, but a significant increase was seen in the outer medulla and inner medulla regions in the db/db DN mice. This difference may be attributed to differences in the expression of the sodium-glucose cotransporters (SGLTs), which are predominantly distributed in the outer medulla and play a crucial role in the resorption and utilization of glucose in the kidney. The lower expression of SGLT2 in the outer medulla in the HFD/STZ-induced DN rats and the increased expression of SGLT2 in the outer medulla in the db/db mice may have contributed to the observed trends [40]. The trends of the metabolites associated with the glycolysis and TCA cycle pathways showed differences between the two DN models. Citrate, glutamate, and aspartate were found to decrease in the HFD/STZ-induced DN rats, while malate and glutamate showed a decrease in the db/db DN mice. On the other hand, succinate was significantly increased in the cortex and outer medulla in the db/db DN mice. These results were consistent with the previous findings [18]. The alteration of these TCA intermediates indicates a reduction in the succinate dehydrogenase (SDH) activity and a disruption of the mitochondrial homeostasis and function in the kidneys; these have been associated with the progression of DN [41,42]. ## 4.3. Alteration of Purine Metabolism in HFD/STZ-Induced Diabetic Rats and db/db Mice The change trends and spatial distributions of the metabolites related to the purine metabolism in the two model groups are depicted in Figure 9 and Figure S9. Adenosine monophosphate (AMP) showed a slight decrease in the outer medulla in the HFD/STZ-induced DN rats, but it was significantly increased in all the renal regions in the db/db DN mice. AMP acts as the primary regulator of AMPK, a crucial player in the regulation of metabolism and energy balance [43]. The activity of AMPK was reportedly decreased in both STZ-induced diabetic rats and db/db mice [44]. These opposing change trends in the AMP levels suggest that the renal energy metabolism may be different in the two DN models. ## 4.4. Alteration of Carnitine Metabolism in HFD/STZ-Induced Diabetic Rats and db/db Mice The change trends and spatial distributions of the metabolites associated with carnitine metabolism in the two model groups are displayed in Figure 10 and Figure S10. A significant reduction in L-carnitine levels was observed in the HFD/STZ-induced DN rats. Both DN models showed an accumulation of long-chain acylcarnitines, with increased levels of stearoylcarnitine in the HFD/STZ-induced DN rats and elevated concentrations of palmitoylcarnitine, linoleylcarnitine, and octadecenoylcarnitine in the db/db DN mice. Carnitines play an important role in several intermediate metabolic processes, especially in oxidative lipid metabolism, where they facilitate the transportation of long-chain fatty acids through the mitochondrial membrane to promote the β-oxidation of fatty acids [45]. L-Carnitine has been demonstrated to have anti-inflammatory and antioxidant properties, as well as to improve insulin sensitivity and dyslipidemia [46,47]. Acylcarnitines are intermediate metabolites of fatty acid oxidation, and their accumulation has been associated with insulin resistance in diabetes [48,49]. These altered levels of carnitine metabolites indicate a disruption in the mitochondrial oxidative lipid metabolism in the kidneys in both DN models. ## 4.5. Alteration of Choline Metabolism in HFD/STZ-Induced Diabetic Rats and db/db Mice Decreased levels of choline were observed in both the DN models, while increased levels of betaine were observed in the db/db mice (Figure 10). Choline is crucial in the synthesis of PC, PE, and SM and plays a key role in the regulation of cell membrane fluidity and osmoregulation. As an oxidized metabolite of choline, betaine is an important osmoprotectant in the kidney and contributes to the formation of the axial osmolality. The altered levels of choline and betaine indicated that the axial osmolality of the kidney may be impacted in DN. The changes in the metabolites and metabolic pathways associated with DN are summarized in Figure 11. Our findings showed that although both the HFD/STZ-induced diabetic rats and the db/db mice exhibit dysregulations of glycolysis, the TCA cycle, lipid metabolism, carnitine metabolism, choline metabolism, and purine metabolism, the specific changes in the levels of these metabolites and their regional distribution vary between the two DN models. This was highlighted by the opposite glucose and AMP alteration in the medulla. Additionally, eight lipids, including eicosenoic acid, eicosapentaenoic acid, PS(36:1), PS(36:4), PE(34:1), PC(34:2), PE(36:4), and PI(38:5), also showed contrasting change trends between the two DN models. These differences in metabolic phenotype should be considered when selecting animal models for drug development. For example, HFD/STZ-induced diabetic rats may be not appropriate for testing SGLT2-targeted drugs due to the low expression of the target in the renal medulla. The present study has some limitations that should be noted. One of the main limitations is that the number size ($$n = 6$$) for each group is relatively small, which may impact the statistical power of the results and affect the robustness of the findings. To account for inter-animal variability, it would be ideal to have a larger sample size. Another limitation is that only male animals were used in this study, and it remains unclear whether these findings can be generalized to female animals or humans. Further research is needed to examine the metabolic changes in female DN animal models and to determine the translational relevance of these findings to human populations. Despite these limitations, this study provides valuable insights into the region-specific metabolic changes in different DN animal models and can guide future research on the pathophysiology and treatment of DN. ## 5. Conclusions To summarize, this study compared the renal metabolic disturbances in an HFD/STZ-induced DN rat model and a db/db DN mouse model using spatially resolved metabolomics-based AFADESI-MSI. A wide range of discriminating metabolites were identified in the HFD/STZ-induced DN rats and the db/db DN mice by comparing their corresponding controls, respectively. The results showed that glycolysis, lipid metabolism, the TCA cycle, carnitine metabolism, choline metabolism, and purine metabolism were altered in a region-specific manner in the kidneys of both DN models. However, the regional levels of glucose, AMP, eicosenoic acid, eicosapentaenoic acid, PS(36:1), PS(36:4), PE(34:1), PE(36:4), PC(34:2), and PI(38:5) were changed in opposite directions, suggesting a significant difference in the metabolic phenotypes in the HFD/STZ-induced DN rat model and the db/db DN mouse model. This study provides more comprehensive and detailed information on the differences in the molecular pathological features in the two frequently used animal models of DN. ## References 1. Feldman E.L., Callaghan B.C., Pop-Busui R., Zochodne D.W., Wright D.E., Bennett D.L., Bril V., Russell J.W., Viswanathan V.. Diabetic Neuropathy. Nat. Rev. Dis. Prim. (2019) 5 42. DOI: 10.1038/s41572-019-0092-1 2. Patel S., Srivastava S., Singh M.R., Singh D.. Mechanistic Insight into Diabetic Wounds: Pathogenesis, Molecular Targets and Treatment Strategies to Pace Wound Healing. Biomed. Pharmacother. (2019) 112 108615. DOI: 10.1016/j.biopha.2019.108615 3. Danne T., Garg S., Peters A.L., Buse J.B., Mathieu C., Pettus J.H., Alexander C.M., Battelino T., Ampudia-Blasco F.J., Bode B.W.. International Consensus on Risk Management of Diabetic Ketoacidosis in Patients With Type 1 Diabetes Treated With Sodium–Glucose Cotransporter (SGLT) Inhibitors. Diabetes Care (2019) 42 1147-1154. DOI: 10.2337/dc18-2316 4. Sabanayagam C., Banu R., Chee M.L., Lee R., Wang Y.X., Tan G., Jonas J.B., Lamoureux E.L., Cheng C.-Y., Klein B.E.K.. Incidence and Progression of Diabetic Retinopathy: A Systematic Review. Lancet Diabetes Endocrinol. (2019) 7 140-149. DOI: 10.1016/S2213-8587(18)30128-1 5. Tan Y., Zhang Z., Zheng C., Wintergerst K.A., Keller B.B., Cai L.. Mechanisms of Diabetic Cardiomyopathy and Potential Therapeutic Strategies: Preclinical and Clinical Evidence. Nat. Rev. Cardiol. (2020) 17 585-607. DOI: 10.1038/s41569-020-0339-2 6. Cho N.H., Shaw J.E., Karuranga S., Huang Y., da Rocha Fernandes J.D., Ohlrogge A.W., Malanda B.. IDF Diabetes Atlas: Global Estimates of Diabetes Prevalence for 2017 and Projections for 2045. Diabetes Res. Clin. Pract. (2018) 138 271-281. DOI: 10.1016/j.diabres.2018.02.023 7. Wang L., Gao P., Zhang M., Huang Z., Zhang D., Deng Q., Li Y., Zhao Z., Qin X., Jin D.. Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013. JAMA (2017) 317 2515. DOI: 10.1001/jama.2017.7596 8. Umanath K., Lewis J.B.. Update on Diabetic Nephropathy: Core Curriculum 2018. Am. J. Kidney Dis. (2018) 71 884-895. DOI: 10.1053/j.ajkd.2017.10.026 9. Srinivasan K., Ramarao P.. Animal Models in Type 2 Diabetes Research: An Overview. Indian J. Med. Res. (2007) 125 451-472. PMID: 17496368 10. Brosius F.C., Alpers C.E., Bottinger E.P., Breyer M.D., Coffman T.M., Gurley S.B., Harris R.C., Kakoki M., Kretzler M., Leiter E.H.. Mouse Models of Diabetic Nephropathy. J. Am. Soc. Nephrol. (2009) 20 2503-2512. DOI: 10.1681/ASN.2009070721 11. Kitada M., Ogura Y., Koya D.. Rodent Models of Diabetic Nephropathy: Their Utility and Limitations. Int. J. Nephrol. Renovasc. Dis. (2016) 9 279-290. DOI: 10.2147/IJNRD.S103784 12. Noshahr Z.S., Salmani H., Khajavi Rad A., Sahebkar A.. Animal Models of Diabetes-Associated Renal Injury. J. Diabetes Res. (2020) 2020 9416419. DOI: 10.1155/2020/9416419 13. Zhang A., Sun H., Wang P., Han Y., Wang X.. Modern Analytical Techniques in Metabolomics Analysis. Analyst (2012) 137 293-300. DOI: 10.1039/C1AN15605E 14. Abbiss H., Maker G., Trengove R.. Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases. Metabolites (2019) 9. DOI: 10.3390/metabo9020034 15. Salek R.M., Maguire M.L., Bentley E., Rubtsov D.V., Hough T., Cheeseman M., Nunez D., Sweatman B.C., Haselden J.N., Cox R.D.. A Metabolomic Comparison of Urinary Changes in Type 2 Diabetes in Mouse, Rat, and Human. Physiol. Genom. (2007) 29 99-108. DOI: 10.1152/physiolgenomics.00194.2006 16. Caprioli R.M., Farmer T.B., Gile J.. Molecular Imaging of Biological Samples: Localization of Peptides and Proteins Using MALDI-TOF MS. Anal. Chem. (1997) 69 4751-4760. DOI: 10.1021/ac970888i 17. Miyamoto S., Hsu C.-C., Hamm G., Darshi M., Diamond-Stanic M., Declèves A.-E., Slater L., Pennathur S., Stauber J., Dorrestein P.C.. Mass Spectrometry Imaging Reveals Elevated Glomerular ATP/AMP in Diabetes/Obesity and Identifies Sphingomyelin as a Possible Mediator. EBioMedicine (2016) 7 121-134. DOI: 10.1016/j.ebiom.2016.03.033 18. Wang Z., Fu W., Huo M., He B., Liu Y., Tian L., Li W., Zhou Z., Wang B., Xia J.. Spatial-Resolved Metabolomics Reveals Tissue-Specific Metabolic Reprogramming in Diabetic Nephropathy by Using Mass Spectrometry Imaging. Acta Pharm. Sin. B (2021) 11 3665-3677. DOI: 10.1016/j.apsb.2021.05.013 19. He J., Tang F., Luo Z., Chen Y., Xu J., Zhang R., Wang X., Abliz Z.. Air Flow Assisted Ionization for Remote Sampling of Ambient Mass Spectrometry and Its Application: Air Flow Assisted Ionization for Remote Sampling. Rapid Commun. Mass Spectrom. (2011) 25 843-850. DOI: 10.1002/rcm.4920 20. Lv Y., Li T., Guo C., Sun C., Tang F., Huang L., Luo Z., Li X., Zhang R., Zang Q.. A High-Performance Bio-Tissue Imaging Method Using Air Flow-Assisted Desorption Electrospray Ionization Coupled with a High-Resolution Mass Spectrometer. Chin. Chem. Lett. (2019) 30 461-464. DOI: 10.1016/j.cclet.2018.06.006 21. Zhang P., Chen J.-Q., Huang W.-Q., Li W., Huang Y., Zhang Z.-J., Xu F.-G.. Renal Medulla Is More Sensitive to Cisplatin than Cortex Revealed by Untargeted Mass Spectrometry-Based Metabolomics in Rats. Sci. Rep. (2017) 7 44804. DOI: 10.1038/srep44804 22. Linnan B., Yanzhe W., Ling Z., Yuyuan L., Sijia C., Xinmiao X., Fengqin L., Xiaoxia W.. In Situ Metabolomics of Metabolic Reprogramming Involved in a Mouse Model of Type 2 Diabetic Kidney Disease. Front. Physiol. (2021) 12 779683. DOI: 10.3389/fphys.2021.779683 23. Calder P.C.. Functional Roles of Fatty Acids and Their Effects on Human Health. JPEN J. Parenter. Enter. Nutr. (2015) 39 18S-32S. DOI: 10.1177/0148607115595980 24. Kim D.-H., Yoo T.-H., Lee S.H., Kang H.Y., Nam B.Y., Kwak S.J., Kim J.-K., Park J.T., Han S.H., Kang S.-W.. Gamma Linolenic Acid Exerts Anti-Inflammatory and Anti-Fibrotic Effects in Diabetic Nephropathy. Yonsei Med. J. (2012) 53 1165. DOI: 10.3349/ymj.2012.53.6.1165 25. Palomer X., Pizarro-Delgado J., Barroso E., Vázquez-Carrera M.. Palmitic and Oleic Acid: The Yin and Yang of Fatty Acids in Type 2 Diabetes Mellitus. Trends Endocrinol. Metab. (2018) 29 178-190. DOI: 10.1016/j.tem.2017.11.009 26. Arany I., Clark J.S., Reed D.K., Juncos L.A., Dixit M.. Role of P66shc in Renal Toxicity of Oleic Acid. Am. J. Nephrol. (2013) 38 226-232. DOI: 10.1159/000354357 27. Miklankova D., Markova I., Hüttl M., Stankova B., Malinska H.. The Different Insulin-Sensitising and Anti-Inflammatory Effects of Palmitoleic Acid and Oleic Acid in a Prediabetes Model. J. Diabetes Res. (2022) 2022 458790. DOI: 10.1155/2022/4587907 28. Smith U., Kahn B.B.. Adipose Tissue Regulates Insulin Sensitivity: Role of Adipogenesis, de Novo Lipogenesis and Novel Lipids. J. Intern. Med. (2016) 280 465-475. DOI: 10.1111/joim.12540 29. Tan D., Ertunc M.E., Konduri S., Zhang J., Pinto A.M., Chu Q., Kahn B.B., Siegel D., Saghatelian A.. Discovery of FAHFA-Containing Triacylglycerols and Their Metabolic Regulation. J. Am. Chem. Soc. (2019) 141 8798-8806. DOI: 10.1021/jacs.9b00045 30. Yore M.M., Syed I., Moraes-Vieira P.M., Zhang T., Herman M.A., Homan E.A., Patel R.T., Lee J., Chen S., Peroni O.D.. Discovery of a Class of Endogenous Mammalian Lipids with Anti-Diabetic and Anti-Inflammatory Effects. Cell (2014) 159 318-332. DOI: 10.1016/j.cell.2014.09.035 31. van der Veen J.N., Kennelly J.P., Wan S., Vance J.E., Vance D.E., Jacobs R.L.. The Critical Role of Phosphatidylcholine and Phosphatidylethanolamine Metabolism in Health and Disease. Biochim. Et Biophys. Acta BBA Biomembr. (2017) 1859 1558-1572. DOI: 10.1016/j.bbamem.2017.04.006 32. Allen H.G., Allen J.C., Boyd L.C., Alston-Mills B.P., Fenner G.P.. Determination of Membrane Lipid Differences in Insulin Resistant Diabetes Mellitus Type 2 in Whites and Blacks. Nutrition (2006) 22 1096-1102. DOI: 10.1016/j.nut.2006.07.007 33. Xu T., Xu X., Zhang L., Zhang K., Wei Q., Zhu L., Yu Y., Xiao L., Lin L., Qian W.. Lipidomics Reveals Serum Specific Lipid Alterations in Diabetic Nephropathy. Front. Endocrinol. (2021) 12 781417. DOI: 10.3389/fendo.2021.781417 34. Maciel E., da Silva R.N., Simões C., Melo T., Ferreira R., Domingues P., Domingues M.R.M.. Liquid Chromatography–Tandem Mass Spectrometry of Phosphatidylserine Advanced Glycated End Products. Chem. Phys. Lipids (2013) 174 1-7. DOI: 10.1016/j.chemphyslip.2013.05.005 35. Zhu C., Liang Q., Hu P., Wang Y., Luo G.. Phospholipidomic Identification of Potential Plasma Biomarkers Associated with Type 2 Diabetes Mellitus and Diabetic Nephropathy. Talanta (2011) 85 1711-1720. DOI: 10.1016/j.talanta.2011.05.036 36. Le Barz M., Boulet M.M., Calzada C., Cheillan D., Michalski M.-C.. Alterations of Endogenous Sphingolipid Metabolism in Cardiometabolic Diseases: Towards Novel Therapeutic Approaches. Biochimie (2020) 169 133-143. DOI: 10.1016/j.biochi.2019.10.003 37. Ussher J.R., Koves T.R., Cadete V.J.J., Zhang L., Jaswal J.S., Swyrd S.J., Lopaschuk D.G., Proctor S.D., Keung W., Muoio D.M.. Inhibition of De Novo Ceramide Synthesis Reverses Diet-Induced Insulin Resistance and Enhances Whole-Body Oxygen Consumption. Diabetes (2010) 59 2453-2464. DOI: 10.2337/db09-1293 38. Mitrofanova A., Mallela S.K., Ducasa G.M., Yoo T.H., Rosenfeld-Gur E., Zelnik I.D., Molina J., Varona Santos J., Ge M., Sloan A.. SMPDL3b Modulates Insulin Receptor Signaling in Diabetic Kidney Disease. Nat. Commun. (2019) 10 2692. DOI: 10.1038/s41467-019-10584-4 39. Zhang G., Darshi M., Sharma K.. The Warburg Effect in Diabetic Kidney Disease. Semin. Nephrol. (2018) 38 111-120. DOI: 10.1016/j.semnephrol.2018.01.002 40. Vallon V., Rose M., Gerasimova M., Satriano J., Platt K.A., Koepsell H., Cunard R., Sharma K., Thomson S.C., Rieg T.. Knockout of Na-Glucose Transporter SGLT2 Attenuates Hyperglycemia and Glomerular Hyperfiltration but Not Kidney Growth or Injury in Diabetes Mellitus. Am. J. Physiol.-Ren. Physiol. (2013) 304 F156-F167. DOI: 10.1152/ajprenal.00409.2012 41. Forbes J.M., Thorburn D.R.. Mitochondrial Dysfunction in Diabetic Kidney Disease. Nat. Rev. Nephrol. (2018) 14 291-312. DOI: 10.1038/nrneph.2018.9 42. Hallan S., Sharma K.. The Role of Mitochondria in Diabetic Kidney Disease. Curr. Diab. Rep. (2016) 16 61. DOI: 10.1007/s11892-016-0748-0 43. Garcia D., Shaw R.J.. AMPK: Mechanisms of Cellular Energy Sensing and Restoration of Metabolic Balance. Mol. Cell (2017) 66 789-800. DOI: 10.1016/j.molcel.2017.05.032 44. Guo Y., Ran Z., Zhang Y., Song Z., Wang L., Yao L., Zhang M., Xin J., Mao X.. Marein Ameliorates Diabetic Nephropathy by Inhibiting Renal Sodium Glucose Transporter 2 and Activating the AMPK Signaling Pathway in Db/Db Mice and High Glucose–Treated HK-2 Cells. Biomed. Pharmacother. (2020) 131 110684. DOI: 10.1016/j.biopha.2020.110684 45. McCann M.R., George De la Rosa M.V., Rosania G.R., Stringer K.A.. L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine. Metabolites (2021) 11. DOI: 10.3390/metabo11010051 46. Komlósi K., Havasi V., Bene J., Süle N., Pajor L., Nicolai R., Benatti P., Calvani M., Melegh B.. Histopathologic Abnormalities of the Lymphoreticular Tissues in Organic Cation Transporter 2 Deficiency: Evidence for Impaired B Cell Maturation. J. Pediatr. (2007) 150 109-111.e2. DOI: 10.1016/j.jpeds.2006.09.042 47. Schreiber B.. Levocarnitine and Dialysis: A Review. Nutr. Clin. Pract. (2005) 20 218-243. DOI: 10.1177/0115426505020002218 48. Hoppel C.L., Genuth S.M.. Carnitine Metabolism in Normal-Weight and Obese Human Subjects during Fasting. Am. J. Physiol.-Endocrinol. Metab. (1980) 238 E409-E415. DOI: 10.1152/ajpendo.1980.238.5.E409 49. Mihalik S.J., Goodpaster B.H., Kelley D.E., Chace D.H., Vockley J., Toledo F.G.S., DeLany J.P.. Increased Levels of Plasma Acylcarnitines in Obesity and Type 2 Diabetes and Identification of a Marker of Glucolipotoxicity. Obesity (2010) 18 1695-1700. DOI: 10.1038/oby.2009.510
--- title: Effect of Protective Measures Adopted in the COVID-19 Pandemic on Hemodialysis Patients journal: Cureus year: 2023 pmcid: PMC10060006 doi: 10.7759/cureus.35552 license: CC BY 3.0 --- # Effect of Protective Measures Adopted in the COVID-19 Pandemic on Hemodialysis Patients ## Abstract Introduction The use of masks and other preventive measures is nowadays an essential measure to prevent COVID-19 infections, particularly in hemodialysis patients. The aim of this study was to understand whether these protective measures adopted during the COVID-19 pandemic reduced or somehow contained the number of respiratory infections in a population of hemodialysis patients. Methods This was a longitudinal retrospective single-center study of hemodialysis patients with at least six months of follow-up in a central hospital. A total of 103 patients were evaluated for the study. Two groups were defined: a control group that was followed in the year before the beginning of the pandemic and a group that followed in the year after its beginning. Results Patients in the pandemic group had a higher prevalence of previous major cardiovascular events ($48.9\%$ vs $8.6\%$) and heart failure ($31.3\%$ vs $12.1\%$) than those in the control group. Vaccination rates for influenza and pneumococcus as well as the monthly analytical results were similar in both groups. There were no significant differences in lower respiratory infections, hospitalizations caused by lower respiratory infections, and mortality between both groups. However, not accounting for aspiration pneumonias, the pandemic group had half the mortality due to respiratory infections ($2.2\%$ vs $5.2\%$). Conclusion Despite patients in the pandemic group having a similar prevalence of respiratory infections and hospitalizations motivated by lower respiratory infections, they presented about half the mortality of the control group. This suggests that although there was no decrease in the number of infections, protective measures may have contributed to a decreased mortality. ## Introduction The utilization of preventive measures, such as masks, by staff and patients, greater distance between each patient during hemodialysis, and the use of individual protection equipment by nurses was not a common practice in hemodialysis units. However, there is now evidence that these preventive measures could decrease the incidence of other respiratory infections beyond COVID-19 [1]. With the beginning of the COVID-19 pandemic, several measures were adopted. Madeira Island, due to its geographical characteristics and particularities, such as having its airport closed at the beginning of the pandemic, represented an excellent place to reach conclusions without external constraints. Along with the lesser circulation of airborne COVID-19 in the local population, these measures could also represent a protective factor against other respiratory infections besides COVID-19. Furthermore, during hemodialysis sessions, the patients started to be placed at least two meters away from each other, wearing a surgical mask became mandatory, frequent hand hygiene was promoted, breaks for food were suspended during sessions, patients were contacted before each session in order to track any symptoms or high-risk contacts, and transportation to the hospital was guaranteed individually for each patient. Isolation measures were established for patients confirmed to be infected or for those signaled as high-risk contacts. Besides, despite being asymptomatic, patients and all the staff working in the hospital unit were submitted to a PCR SARS-CoV2 screening every 15 days. The main goal of this study was to understand whether the protective measures adopted with COVID-19 could bring any benefit in terms of preventing other respiratory infections in hemodialysis patients. Thus, the objective was to comprehend whether, even after the end of the pandemic, these measures should be maintained in order to improve the outcomes of these patients. ## Materials and methods This is a longitudinal retrospective, single-center study of hemodialysis patients followed in a central hospital. Two groups were defined, a control group with hemodialysis patients followed in a hospital environment in the year before the beginning of the pandemic (April 2019 to March 2020) and a group with hemodialysis patients followed in the year after the beginning of the pandemic (April 2020 to March 2021). These time limits were established since COVID-19 cases only started to be reported by the end of March 2020, given the geographical specificity of Madeira Island and the public health measures assumed. Only patients with a hospital follow-up of more than six months were considered. All those who did not meet this requirement were excluded. We used demographic variables including sex and age, and evaluated comorbidities such as hypertension, diabetes, and dyslipidemia; major cardiovascular events history (CVH) accounting for nonfatal stroke and nonfatal myocardial infarction; heart failure; chronic liver disease; malignancy diseases; human immunodeficiency virus (HIV); or concomitant hepatotropic virus infection (hepatitis B virus [HBV] and hepatitis C virus [HCV]). Previous vaccination for influenza, pneumococcus, or COVID-19 (the latter in the post-pandemic group) was documented. Monthly analytical data from these patients were also collected, with particular emphasis on hemoglobin, calcium, phosphorus, parathyroid hormone (PTH), albumin, and ferritin as part of the routine assessment of hemodialysis patients. Finally, the existence of lower respiratory infections, those leading to hospital admissions, morbidity, and mortality due to respiratory infections, was documented. Data were analyzed using Statistical Package for Social Sciences (SPSS), version 27.0 (IBM Corp., Armonk, NY). ## Results A total of 103 patients were evaluated for the study, with 58 in the control group and 45 in the post-pandemic group. There were no significant differences in gender distribution (p-value = 0.611) or age (p-value = 0.624). Regarding comorbidities (Table 1), patients in the pandemic group had an almost six-fold higher prevalence of CVH than those in the control group ($48.9\%$ vs $8.6\%$; p-value < 0.0001) and had an almost three-fold higher heart failure ($31.3\%$ vs $12.1\%$; p-value = 0.017). Malignancy was also higher in the pandemic group ($22.2\%$ vs $13.8\%$), but the difference was not statistically significant. As for the remaining comorbidities, such as diabetes and arterial hypertension, they were similar in both groups. Table 1 \| Unnamed: 0 \| Control group \| Post-pandemic group \| P-value \| Unnamed: 4 \| \| --- \| --- \| --- \| --- \| --- \| \| Number of patients \| 58 \| 45 \| \| \| \| Age (years) \| 61.1 (± 17.9) \| 62.8 (± 15.1) \| 0.624 \| \| \| Sex \| Sex \| Sex \| Sex \| \| \| Male \| 30 (51.7%) \| 21 (46.7%) \| 0.611 \| \| \| Female \| 28 (48.3%) \| 24 (53.3%) \| 0.611 \| \| \| Chronic kidney disease etiology \| Chronic kidney disease etiology \| Chronic kidney disease etiology \| Chronic kidney disease etiology \| \| \| Diabetes \| 27 (46.6%) \| 20 (44.4%) \| \| \| \| Arterial hypertension \| 6 (10.3%) \| 2 (4.4%) \| \| \| \| Obstructive nephropathy \| 6 (10.3%) \| 5 (11.1%) \| \| \| \| Reflux nephropathy \| 2 (3.4%) \| 0 (0.0%) \| \| \| \| Chronic pyelonephritis \| 2 (3.4%) \| 2 (4.4%) \| \| \| \| Cardio-renal syndrome \| 1 (1.7%) \| 1 (2.2%) \| \| \| \| Autosomal dominant polycystic kidney disease \| 2 (3.4%) \| 1 (2.2%) \| \| \| \| IgA nephropathy \| 2 (3.4%) \| 2 (4.4%) \| \| \| \| Membranous glomerulonephritis \| 1 (1.7%) \| 1 (2.2%) \| \| \| \| Focal segmental glomerulosclerosis \| 0 (0.0%) \| 1 (2.2%) \| \| \| \| ANCA vasculitis \| 0 (0.0%) \| 1 (2.2%) \| \| \| \| Thrombotic microangiopathy \| 1 (1.7%) \| 0 (0.0%) \| \| \| \| Lupus nephritis \| 1 (1.7%) \| 1 (2.2%) \| \| \| \| Cast nephropathy \| 1 (1.7%) \| 1 (2.2%) \| \| \| \| Non-steroidal anti-inflammatory drugs nephropathy \| 0 (0.0%) \| 1 (2.2%) \| \| \| \| Unknown \| 6 (10.3%) \| 6 (13.3%) \| \| \| \| Vascular access (%) \| Vascular access (%) \| Vascular access (%) \| Vascular access (%) \| \| \| AV fistula \| 21 (36.2%) \| 11 (24.4%) \| \| \| \| AV graft \| 2 (3.4%) \| 2 (4.4%) \| \| \| \| Catheter \| 35 (60.4%) \| 32 (71.1%) \| \| \| \| Comorbid conditions \| Comorbid conditions \| Comorbid conditions \| Comorbid conditions \| \| \| Diabetes \| 30 (51.7%) \| 26 (57.8%) \| 0.541 \| \| \| Hypertension \| 32 (55.2%) \| 28 (62.2%) \| 0.472 \| \| \| Dyslipidemia \| 6 (10.3%) \| 2 (4.4%) \| 0.461 \| \| \| CVH \| 5 (8.6%) \| 22 (48.9%) \| <0.0001 \| \| \| Heart failure \| 7 (12.1%) \| 14 (31.1%) \| 0.017 \| \| \| Chronic liver disease \| 0 \| 2 (4.4%) \| 0.188 \| \| \| Neoplastic diseases \| 8 (13.8%) \| 10 (22.2%) \| 0.264 \| \| \| HBV \| 1 (1.7%) \| 0 (0.0%) \| - \| \| \| HCV \| 3 (5.2%) \| 2 (4.4%) \| - \| \| \| HIV \| 1 (1.7%) \| 1 (2.2%) \| - \| \| \| HIV + HBV \| 0 (0.0%) \| 0 (0.0%) \| - \| \| \| HIV + HCV \| 1 (1.7%) \| 1 (2.2%) \| - \| \| \| Vaccination \| Vaccination \| Vaccination \| Vaccination \| \| \| Influenza \| 25 (43.1%) \| 23 (51.1%) \| 0.419 \| \| \| Pneumococcus \| 4 (6.9%) \| 4 (8.9%) \| 0.727 \| \| \| COVID-19 \| - \| 35 (77.8%) \| - \| \| \| Analytical data \| Analytical data \| Analytical data \| Analytical data \| Reference values \| \| Hemoglobin (g/dL) \| 11.1 (± 1.0) \| 10.8 (± 1.3) \| 0.282 \| 13.7-17.3 \| \| Calcium (mg/dL) \| 8.8 (± 0.7) \| 8.8 (± 0.8) \| 0.984 \| 8.9-10.3 \| \| Phosphor (mg/dL) \| 4.3 (± 1.5) \| 4.6 (± 1.5) \| 0.328 \| 2.4-4.7 \| \| Parathyroid hormone (pg/mL) \| 528.6 (± 824.7) \| 379.6 (± 711.6) \| 0.482 \| 130-550 \| \| Albumin (g/L) \| 35.7 (± 4.7) \| 35.7 (± 4.5) \| 0.709 \| 35-48 \| \| Ferritin (ng/mL) \| 583.9 (± 1383) \| 558.1 (± 477.7) \| 0.935 \| 30-400 \| \| Lower respiratory infections \| 6 (10.3%) \| 8 (17.8%) \| 0.386 \| \| \| Lower respiratory infections leading to hospital admissions \| 5 (8.6%) \| 8 (17.8%) \| 0.233 \| \| \| Deaths \| 11 (19%) \| 8 (17.8%) \| 0.877 \| \| \| Deaths due to respiratory infections \| 3 (5.2%) \| 3 (6.7%) \| 1.000 \| \| Vaccination rates (Table 1) for influenza and pneumococcus were also identical, and it should be noted that by the end of the time period studied, $77.8\%$ of patients in the pandemic group had already been vaccinated for COVID-19. In terms of analytical results, the values were similar between both groups regarding hemoglobin, ferritin, albumin, calcium, phosphorus, and PTH concentration (Table 1). None of the above variables remained in the logistic regression equation, so none of them independently influenced mortality. There were no significant differences in lower respiratory infections between the groups ($17.8\%$ vs $10.3\%$, p-value = 0.386) and in hospitalizations motivated by lower respiratory infections (17.8 vs $8.6\%$; p-value = 0.233). None of these respiratory infections was due to COVID-19. Mortality was also similar between both groups ($17.8\%$ vs $19\%$, p-value = 0.877), with $6.7\%$ of deaths caused by respiratory infections in the pandemic group and $5.2\%$ in the control group (p-value = 1). Nevertheless, it should be noted that in the control group, two of the deaths by respiratory infections were due to aspiration pneumonia. In this sense, if these deaths are not accounted for (since they effectively do not reflect an airborne infection and therefore are not preventable with airway protection measures), the mortality in the pandemic group was about half the mortality of the control group ($2.2\%$ vs $5.2\%$). ## Discussion Patients on hemodialysis have an increased risk of infections. This can be explained by the higher burden of comorbidities, the intrinsic frailty presented, the existence of vascular accesses, and the frequent exposure to hospital settings and other infected patients [2]. For all these reasons, hemodialysis is an important risk factor for mortality from COVID-19 [2,3]. Most patients receive in-center hemodialysis, which ensures optimal physical isolation and infection control. With the beginning of the COVID-19 pandemic, measures such as social distancing, mask-wearing, hand washing, triage, and isolation of suspected/confirmed cases had to be implemented in dialysis care delivery in order to prevent the spread of the infection. Madeira Island has certain characteristics that make it unique for this study, not only for its geography but also for the magnitude of the protective measures taken. Besides mask-wearing, the lockdown was imposed, and the airport closed right after the first cases were detected in Portugal (in March 2020). After the airport reopened (in July 2020), all travelers had to undergo PCR tests (before boarding or on arrival), and they also had to stay in prophylactic isolation for two weeks after arrival in the initial months of the pandemic. As intended, these measures culminated in a low incidence of new COVID-19 cases, and the incidence of the disease never exceeded 50 cases per million inhabitants per day until mid-October 2020. All of this context reinforces the comparative power of this study. Since there was no significant COVID-19 circulation (and a complete absence of cases in hemodialysis patients), it is possible to analyze the protective effects of these measures on respiratory infections other than COVID-19. These exceptional measures will hardly be imposed in the same magnitude in the near future, rendering this a unique opportunity to reflect on these issues. However, contrary to what was expected, there were no significant differences between the pandemic and control groups regarding the prevalence of respiratory infections and hospitalizations motivated by lower respiratory infections. This is the opposite of what has been documented in the literature, with reports of lower incidences of influenza and pneumococcus infections [1,4-8]. This could be explained by the greater surveillance for respiratory symptoms in a period with still reduced cases of COVID-19, which may have contributed to the maximization of the identification of respiratory infections, a more aggressive therapeutic approach, and eventually more hospitalizations. Another important factor is the higher prevalence of important comorbid conditions in the pandemic group, such as CVH and heart failure. This could be explained by the fact that some patients with major comorbidities were transferred from other dialysis clinics to the hospital center for stricter surveillance and more rigorous protective measures. There are several risk factors for lower respiratory infections in adults, such as older age, chronic or structural pulmonary disease, previous lower respiratory infections, poor nutritional status, or immunosuppression [9]. On the other hand, there seems to be no influence of other factors, such as being overweight, passive smoking, and chronic renal disease [9]. The latter may support the extrapolation of these results to the remaining population, not limiting them to this specific population. Moreover, some comorbidities are associated with worse outcomes, despite not being related to an increased incidence of pneumonia. These comorbidities have been exhaustively described recently in the COVID-19 pandemic. A relevant set of comorbidities reported are cardiovascular diseases, which are associated with worse outcomes and increased risk of death [10,11]. Therefore, previous myocardial infarction, hypertension, diabetes, renal disease, and smoking are associated with an increased likelihood of severe COVID-19 and worse outcomes [11-14]. It is also important to point out the particular case of cerebrovascular disease, which is also associated with worse outcomes in COVID-19. However, in the literature, it is generally unclear whether the stroke occurred before or after the respiratory infection [14]. Liver disease and obesity were also associated with higher mortality in COVID-19 [14]. This higher prevalence of patients with greater comorbidities in the post-pandemic group might be due to the transfer of many of the other patients with fewer comorbidities to outpatient hemodialysis units so that cohorts for COVID-19 patients could be opened in the hospital unit, and greater safety and effectiveness of protective measures for the remaining patients could be guaranteed. This selection of patients with higher comorbidities and consequent lower physiological reserve may have been a strong reason for the absence of a clear and massive difference in respiratory infections and hospitalizations motivated by lower respiratory infections. Nevertheless, neither CVH nor heart failure independently influenced mortality. The mortality in the pandemic group was about half the mortality of the control group (if deaths from aspiration pneumonias are not accounted for). This could suggest that, although there was no decrease in the number of infections, protective measures may have contributed to a decrease in mortality. However, the low numbers and relatively low mortality in this sample do not allow us to draw definitive conclusions. Considering that we have now more information about the virus behavior and vaccination is part of our daily practice, more studies are needed to understand the real impact of these measures and their effectiveness in this scenario. It is also worth mentioning that it is still possible to optimize vaccination against influenza and pneumococcus. Finally, it is pertinent to highlight some of the limitations of the study, such as the limited sample size for statistical measurements, as mentioned above, and the retrospective nature of the study. ## Conclusions Madeira Island has some characteristics that make it unique for this study. All of the exceptional measures that were adopted will hardly be imposed in the same magnitude in the near future, allowing a unique analysis of this subject. Despite patients in the pandemic group having a similar prevalence of respiratory infections and hospitalizations motivated by lower respiratory infections, they presented about half the mortality of the control group. This suggests that although there was no decrease in the number of infections, protective measures may have contributed to a decreased mortality. ## References 1. Prasertbun R, Mori H, Mahittikorn A, Siri S, Naito T. Pneumonia, influenza, and dengue cases decreased after the COVID-19 pandemic in Thailand. Trop Med Health (2022) 50 27. PMID: 35337390 2. Combe C, Kirsch AH, Alfano G. At least 156 reasons to prioritize COVID-19 vaccination in patients receiving in-centre haemodialysis. Nephrol Dial Transplant (2021) 36 571-574. PMID: 33475137 3. Valeri AM, Robbins-Juarez SY, Stevens JS. Presentation and outcomes of patients with ESKD and COVID-19. J Am Soc Nephrol (2020) 31 1409-1415. PMID: 32467113 4. Yan Y, Tomooka K, Naito T, Tanigawa T. Decreased number of inpatients with community-acquired pneumonia during the COVID-19 pandemic: a large multicenter study in Japan. J Infect Chemother (2022) 28 709-713. PMID: 35115238 5. Tang HJ, Lai CC, Chao CM. Changing epidemiology of respiratory tract infection during COVID-19 pandemic. Antibiotics (Basel) (2022) 11 1-13 6. Teng JL, Fok KM, Lin KP, Chan E, Ma Y, Lau SK, Woo PC. Substantial decline in invasive pneumococcal disease during coronavirus disease 2019 pandemic in Hong Kong. Clin Infect Dis (2022) 74 335-338. PMID: 33907808 7. Chan KF, Ma TF, Ip MS, Ho PL. Invasive pneumococcal disease, pneumococcal pneumonia and all-cause pneumonia in Hong Kong during the COVID-19 pandemic compared with the preceding 5 years: a retrospective observational study. BMJ Open (2021) 11 0 8. Chiu NC, Chi H, Tai YL. Impact of wearing masks, hand hygiene, and social distancing on influenza, enterovirus, and all-cause pneumonia during the coronavirus pandemic: retrospective national epidemiological surveillance study. J Med Internet Res (2020) 22 0 9. Almirall J, Serra-Prat M, Bolíbar I, Balasso V. Risk factors for community-acquired pneumonia in adults: a systematic review of observational studies. Respiration (2017) 94 299-311. PMID: 28738364 10. Maddaloni E, D'Onofrio L, Siena A. Impact of cardiovascular disease on clinical outcomes in hospitalized patients with Covid-19: a systematic review and meta-analysis. Intern Emerg Med (2021) 16 1975-1985. PMID: 34273056 11. Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat Rev Cardiol (2020) 17 543-558. PMID: 32690910 12. Lim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Nat Rev Endocrinol (2021) 17 11-30. PMID: 33188364 13. Nassar M, Daoud A, Nso N. Diabetes mellitus and COVID-19: review article. Diabetes Metab Syndr (2021) 15 102268. PMID: 34562865 14. Harrison SL, Buckley BJ, Rivera-Caravaca JM, Zhang J, Lip GY. Cardiovascular risk factors, cardiovascular disease, and COVID-19: an umbrella review of systematic reviews. Eur Heart J Qual Care Clin Outcomes (2021) 7 330-339. PMID: 34107535
--- title: New Smart Bioactive and Biomimetic Chitosan-Based Hydrogels for Wounds Care Management authors: - Simona-Maria Tatarusanu - Alexandru Sava - Bianca-Stefania Profire - Tudor Pinteala - Alexandra Jitareanu - Andreea-Teodora Iacob - Florentina Lupascu - Natalia Simionescu - Irina Rosca - Lenuta Profire journal: Pharmaceutics year: 2023 pmcid: PMC10060009 doi: 10.3390/pharmaceutics15030975 license: CC BY 4.0 --- # New Smart Bioactive and Biomimetic Chitosan-Based Hydrogels for Wounds Care Management ## Abstract Wound management represents a continuous challenge for health systems worldwide, considering the growing incidence of wound-related comorbidities, such as diabetes, high blood pressure, obesity, and autoimmune diseases. In this context, hydrogels are considered viable options since they mimic the skin structure and promote autolysis and growth factor synthesis. Unfortunately, hydrogels are associated with several drawbacks, such as low mechanical strength and the potential toxicity of byproducts released after crosslinking reactions. To overcome these aspects, in this study new smart chitosan (CS)-based hydrogels were developed, using oxidized chitosan (oxCS) and hyaluronic acid (oxHA) as nontoxic crosslinkers. Three active product ingredients (APIs) (fusidic acid, allantoin, and coenzyme Q10), with proven biological effects, were considered for inclusion in the 3D polymer matrix. Therefore, six API-CS-oxCS/oxHA hydrogels were obtained. The presence of dynamic imino bonds in the hydrogels’ structure, which supports their self-healing and self-adapting properties, was confirmed by spectral methods. The hydrogels were characterized by SEM, swelling degree, pH, and the internal organization of the 3D matrix was studied by rheological behavior. Moreover, the cytotoxicity degree and the antimicrobial effects were also investigated. In conclusion, the developed API-CS-oxCS/oxHA hydrogels have real potential as smart materials in wound management, based on their self-healing and self-adapting properties, as well as on the benefits of APIs. ## 1. Introduction The management of acute and chronic wounds represents a challenge and a continuous concern of the health system worldwide. The prevalence of all types of injuries is increasing every year due to an aging population and the growing incidence of wound-related comorbidities, such as diabetes, high blood pressure, obesity, autoimmune diseases, and peripheral vascular dysfunctions [1,2,3]. Among the patients hospitalized for acute conditions, 25–$50\%$ present or develop wounds during hospitalization, with high risks of infection and chronicity [4,5]. Nowadays, various products are available in the wound care market, such as dressings, creams, ointments, gels, sprays, and powders [6]. After performing the debridement, choosing the appropriate dressing is the next step in wound management, and it is crucial for the final outcome of the healing process [7,8]. It requires a balance between benefits, safety for the patient, and cost effectiveness [9]. The dressing should also be adapted to patient preferences and clinical conditions in order to ensure compliance and treatment success [10]. Topical treatment for wound management requires the fulfillment of several conditions to ensure and maintain a favorable environment for the entire healing process [11,12]. The ideal wound dressing should be biocompatible and biodegradable, and it should fit perfectly to the wound’s shape, though not too adherent to the wound surface, provide protection against mechanical and thermal stress, prevent microbial contamination, and optimize the moisture in the affected tissue in order to promote effective, rapid, and painless healing [13,14]. Biopolymers are a good substitute for traditional wound healing agents due to their benefits such as biocompatibility, biodegradability, bioactivity, bioresorptivity, no-toxicity, enhanced antithrombin activity, and anticoagulant activity, protection from mechanical stress, drying, infections, and radiation, and effective gelling and swelling ability [15,16]. So, various biopolymers-based dressings, such as creams, films, powders, sponges, electrospun fibers, and hydrogels were developed according to the requirements for the healing process [17,18]. In many aspects, hydrogels are considered ideal for wound care since they mimic the skin structure, promote growth factor synthesis, and the autolysis process [19]. Defined by their 3D networks, hydrogels are capable of encapsulating large amounts of water or biological fluids, which makes them suitable for the management of draining painful wounds, radiation wounds, minor burns, or dry wounds [20,21]. Due to their hydrophilic character, oxygen permeability, the ability of diffusion, cell adhesion, ability to incorporate and release a wide variety of therapeutic agents, and ease in topical application, hydrogels represent a very important alternative in wound treatment [20,22]. Along with the positive aspects, some drawbacks reduce the applicability of hydrogels in medical practice. These include poor mechanical stability (the viscosity decreases over time), high risk for microbial contamination (due to the high amount of water content), toxicity potential (due to unreacted molecules when crosslinking agents are used), degradation, and high variability in release profile of the incorporated active substances [20,23]. To overcome these disadvantages, a wide variety of strategies were proposed for hydrogel development. Different types of polymers (synthetic, semisynthetic, or naturals) and physical or chemical crosslinking methods were proposed in order to enhance hydrogel performance and extend their applications as tissue (bone and cartilage) engineering, cell therapeutics, wound healing, controlled drug release, biosensors, and medical devices [21,24]. If, in the past, the main role of hydrogels was to protect the damaged tissue, at present, extensive research is being conducted for “smart” materials that not only protect the wounds but have the ability to influence all stages of healing [25]. Crosslinking is mandatory in hydrogel development in order to achieve a stable-over-time 3D matrix. The traditionally used physical and chemical crosslinking methods have demonstrated over time some undesirable effects on polymer functionality (structure rigidity) or tissue cytotoxicity (inhibition of epidermal cell proliferation and adhesion) [26]. The dynamic crosslinking methods have gained more attention lately as studies suggest that using chemical interactions and versatile hydrogels may be developed [27]. These chemical interactions are capable of uncoupling and recoupling if the dynamic equilibrium of the reaction is achieved or exceeded [24,28]. For example, oxidized alginate, polyethylene glycol dibenzaldehyde, and oxidized polysaccharides (hyaluronic acid and chitosan) were investigated for their crosslinking capacity via Schiff bases linkage in order to develop innovative nontoxic self-healing hydrogels for different medical applications [27,29]. The presence of a Schiff bases linkage (dynamic imine bonds) allows hydrogels to modify their 3D matrix, so as to adapt perfectly to the shape and depth of a wound (self-adapting ability). In this way, intimate contact with the damaged tissue is ensured, the healing processes are accelerated, and the protection against harmful environmental factors is higher [28,30]. The self-healing capability allows hydrogels to reorganize their structure according to the environmental conditions to which they are subjected. As a result, the hydrogels will have superior stability over time and exposure to various stressors (pH changes, temperature variations, and mechanical shear), compared to hydrogels obtained by conventional crosslinking methods [31,32]. Chitosan (CS) is currently one of the most studied biopolymers, having a polysaccharide structure that gives it versatility for numerous practical applications. It is used in the formulation of solutions, suspensions, hydrogels, micro- and nanoparticles, nanofibers, and sponges [33,34,35]. Among the uses of CS in biomedicine are synthesis of artificial skin, surgical sutures, artificial blood vessels, controlled release of drugs, tumor inhibition, and acceleration of wound healing [26,36]. The properties that recommend CS for these uses are biocompatibility, biodegradability, nontoxicity, mucoadhesiveness, and the ability to modulate physiological processes and antimicrobial properties [35,37]. CS also accelerates the wound-healing process by stimulating the migration of inflammatory cells, macrophages, and fibroblasts [37]. In this way, the inflammatory phase is reduced and the proliferative phase starts earlier in the wound-healing process [38]. The in vitro and in vivobehavior of CS depends on its molecular weight, deacetylation degree (DDA), and viscosity [39]. In vitro wound healing studies concluded that the higher the DDA, the more activated fibroblasts were founded in the proliferative phase of healing [40]. In vivo studies on animals have proven superior effects of CS in the treatment of burn wounds compared to heparin, and superior effects of CS with high DDA and molecular weight compared to CS batches with small DDA ($75\%$) and low molecular weight (˂100 kDa) [41,42]. The study of infected postburn wounds in mice treated with topical antibiotics embedded in a CS-based hydrogel showed a significant decrease in mortality from $90\%$ to $14\%$, as well as an acceleration of the healing process [43]. In order to improve its physicochemical properties and biological effects, different functionalized CS derivatives (glycol-CS, tannic acid-CS, carboxymethyl/β-tricalcium phosphate-CS, quaternized CS-graft-polyaniline, CS-grafted-dyhidrocaffeic acid, and agarose-CS) were obtained, using various chemical pathways (alkylation, acylation, quaternization, thiolation, sulfation, graft copolymerization, etc) [33,39,44,45]. Moreover, by crosslinking functionalized CS derivatives using oxidized polymers, versatile hydrogels for a variety of medical purposes (cartilage repair, bone regeneration, and wound healing) were developed [46,47,48,49,50,51,52]. Hyaluronic acid (HA) is a key molecule in the medical, pharmaceutical, nutrition, and cosmetic fields. Medical studies have demonstrated that HA is involved in wound regeneration processes, cell proliferation and migration, and tissue hemodynamics [53]. The effects on the wound healing process depend on its molecular weight. HA with a low molecular weight (50–200 kDa) has been demonstrated to be responsible for extracellular matrix regenerations through the stimulation of proteoglycans and fibronectin synthesis by fibroblasts [54,55,56]. Also, cytokine motility, angiogenesis, inflammatory effects, and oxidative stress may be modulated using products with HA with low molecular weight in wound treatments [57,58]. The oxidation of polysaccharides, such as CS, HA, cellulose, pectin, and alginates, was reported to enhance their water solubility and backbone flexibility due to the opening of the glucopyranose ring [59]. Moreover, the Schiff bases obtained through the reaction of carbonyl groups introduced in their structure proved superior antifungal and antibacterial properties [60]. These aspects show a promising perspective for using oxCS and oxHA as crosslinkers in the design of innovative wound-healing hydrogels based on bio-polymers. The aim of this study was to develop new 3D bioactive and biomimetic CS-based hydrogels, with self healing and self adapting properties for wound care, using oxCS and oxHA as nontoxic crosslinkers. Three active product ingredients (APIs), fusidic acid, allantoin, and coenzyme Q10 (Figure 1), with proven biological effects, were considered for inclusion in the 3D polymer matrix. Fusidic acid (FA) is a traditional antimicrobial agent recommended in the treatment of skin infections such as impetigo, infected wounds, folliculitis, abscesses, and erythrasma, being active both on aerobic and anaerobic germs [61,62]. This antibiotic has regained the interest of medical professionals as a result of the accelerated increase in antibiotic resistance used currently in topical infections. It acts on microbial metabolism as an inhibitor of protein synthesis at the level of the bacterial cell by disrupting the turnover of the elongate factor in ribosomes [63]. Topical products containing fusidic acid are available as ointments with $2\%$ sodium fusidate, creams with $2\%$ fusidic acid, and ophthalmic gels with $1\%$ fusidic acid [62,63]. Allantoin (Ala) acts as a skin regeneration factor, contributes to collagen synthesis, accelerates the healing processes, and has a protective action against irritating factors, being included in the composition of topical products in concentrations of 0.5–$2\%$ [60,64,65,66]. The use of CoQ10 in topical treatments has proven beneficial by activating energy production in mitochondria and reducing the level of free radicals, resulting in antioxidant effects [67,68,69]. In the scientific literature, there are presented data that support CoQ10 exhibiting anti-inflammatory effects and favoring the wound healing processes [67]. Moreover, CoQ10 also showed significant antioxidant activity in vivo on malondialdehyde and superoxide dismutase levels, stimulating collagen synthesis (by scavenging collagenases), which promotes faster extracellular matrix recovery [68,69]. The novelty of our study is supported by the original design of the polymer matrix, CS-based hydrogels crosslinked with oxCS or oxHA, and having embedded FA, Ala, and CoQ10 as APIs, not being reported in the literature. The structure of the developed hydrogels, as well as the embedding of APIs, was proven using spectral methods (FTIR and NMR). The developed hydrogels were characterized by SEM, swelling degree, pH, and the internal organization of the 3D matrix was studied by rheological behavior. In addition, the cytotoxicity degree and the antimicrobial effects were also investigated. Based on their self-healing and self-adapting properties, as well as biological effects, these hydrogels have great potential to be used for a wide range of applications, including wound healing. ## 2.1. Materials Chitosan with a medium molecular weight (200–300 kDa, DDA > $85\%$, viscosity of 200–800 cP) and hyaluronic acid sodium salt from Streptococcus equi, with low molecular weight (100–230 kDa) were pharmaceutical grade; lactic acid ($99\%$), sodium periodate ($99.8\%$), ethylenglicol anhydrous ($98\%$) were analytical grade; allantoin (Ala), fusidic acid (FA) and coenzyme Q10 (CoQ10) were micronized pharmaceutical grade powders. All these chemicals were purchased from Sigma-Aldrich (Merck Group, Schnelldorf, Germany) and were used without any further purification. Dialysis tubing cellulose membrane with a molecular weight cut off of 14,000 Da was also purchased from Sigma-Aldrich. Human fibroblasts (HGF, CLS Cell Lines Service GmbH, Eppelheim, Germany), MEMα medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), fetal bovine serum (FBS, Gibco, Thermo Fisher Scientific, Waltham, MA, USA), and $1\%$ Penicillin-Streptomycin-Amphotericin B mixture (Lonza, Basel, Switzerland) were also used. Gram-negative (Escherichia coliATCC 25922) and Gram-positive (Staphylococcus aureusATCC 25923) bacterial strains aswell as pathogenic yeast (Candida albicansATCC 90028) were provided by Mecconti, Poland. ## 2.2.1. Preparation of CS-oxCS/oxHA and API-CS-oxCS/oxHA Hydrogels Using the oxidized polymers (oxCS, oxHA) as crosslinking agents, which were obtained according to the method reported in the literature [46,48] (Supplementary Materials), CS-based hydrogels were prepared. First, CS (1.0 g, 1.5 g, and 2.0 g) was hydrated in a sufficient amount of distilled water, at 50 °C for 15 min, under magnetic stirring (200 rpm), and then lactic acid (0.7 mL in 10 mL distilled water) was poured slowly and stirred again for other 10 min. Distilled water up to 100 g was added and stirring (200 rpm) and heating (50 °C) were continued for another 10 mi, when different CS solutions ($1\%$, $1.5\%$, $2\%$, w/w) were obtained. Second, oxCS and oxHA solutions ($1\%$, $1.5\%$, $2\%$, w/w) were prepared by mixing corresponding amounts of oxidized polymer with the appropriate quantity of distilled water and heated at 50 °C, under magnetic stirring (200 rpm). Finally, the oxidized polymer solution (oxCS/oxHA) was added gradually over the CS solution, in different ratios (Table 1), followed by stirring (300 rpm) until gelation occurred. Therefore, six CS-oxCS/oxHA hydrogels were prepared. For the inclusion of APIs into the 3D polymer matrix, two CS-based hydrogels were selected: CS1.5-oxCS1.5 and CS1.0-oxHA2.0. The selection was based on the results of physicochemical tests, morphological characterization, and rheological behavior. The best morphological network was defined with the highest rate of structural recovery after exposure to very high shear stress (thixotropic test). The selected APIs were inglobated directly to the CS-based hydrogels because they are slightly soluble in water. So, the bioactive CS-based hydrogels (API-CS-oxCS/oxHA) were prepared similarly to the CS-based hydrogels. Briefly, the corresponding APIs (FA, Ala, and, CoQ10, respectively) were dispersed by magnetic stirring (10 min, 300 rpm) in a sufficient amount of distilled water (10–15 mL), at room temperature (23 ± 2 °C) and then was added over the CS-oxCS/oxHA hydrogels and stirred again for an extra 10 min. As result, six API-CS-oxCS/oxHA hydrogels (FA-CS-oxCS, Ala-CS-oxCS, CoQ10-CS-oxCS, FA-CS-oxHA, Ala-CS-oxHA, and CoQ10-CS-oxHA) were prepared (Table 2). ## 2.2.2. Physicochemical Characterization of CS-oxCS/oxHA and API-CS-oxCS/oxHA Hydrogels The developed CS-based hydrogels (CS-oxCS/oxHA and API-CS-oxCS/oxHA) were characterized by a spectral analysis (FT-IR) in order to confirm the Schiff base formation (CS-oxCS/oxHA) and API inclusion (API-CS-oxCS/oxHA). Their morphology was highlighted using scanning electron microscopy (SEM), while the microstructure and the self-healing ability were evaluated by their rheological behavior. In addition, the swelling degree, pH, and 3D matrix appearances were also studied and correlated. ## Macroscopic Aspect and Microscopic Analysis The macroscopic aspect was evaluated by a visual observation of the hydrogels in natural light, while for the microscopic analysis, a Zeiss A Scope optical microscope (VIB, Gent, Belgium) with polarized light and objective ×40 was used. ## Fourier-Transform Infrared Spectroscopy (FT-IR) The FT-IR characterization of the CS-oxCS/oxHA and API-CS-oxCS/oxHA was carried out using the ABB-MB 3000 FT-IR MiracleTM Single Bounce ATR-crystal ZnSe system, in the wavelength range 500–4000 cm−1. Sixteen scans were performed at a resolution of 4 cm−1 for each determination. The obtained spectra were interpreted with Horizon MBTM FT-IR software version number-3.1.29.5. ## Scanning Electron Microscopy (SEM) The SEM images of the CS-oxCS/oxHA and API-CS-oxCS/oxHA hydrogels (lyophilized samples) were recorded using a HITACHI mass microscope (Nitech, Krefeld, Germany) at an acceleration voltage of 5–15 kV and under SE (secondary electron) detector. Secondary electrons are generated near the surface regions of the samples and carry information about the surface characteristics, being suitable to study the morphology and topography of a material by providing high-resolution images [70]. ## Swelling Degree Test The swelling degree (SD) of CS-oxCS/oxHA and API-CS-oxCS/oxHA hydrogels was evaluated in the same conditions, using lyophilized samples. The samples (1 cm in diameter) were weighed before and after immersion in a phosphate buffer solution (PBS), pH = 7.4. The samples were taken off from the PBS every 1 h, lightly dabbed with filter paper to remove excess solution, and reweighed. The operation was repeated until the mass of the hydrated sample was constant. The SD (%) was calculated using the following equation:SD (%) = (Mt − M0)/M0 × 100[1] where: M0 is the weight of the sample before immersion and Mt is the weight of the sample at a different time. The experiments were performed in triplicate. ## Determination of pH The pH of the hydrogels (CS-oxCS/oxHA and API-CS-oxCS/oxHA) was determined using a Melter Toledo pH-meter (Themo Fisher Scientific, Vienna, Austria), calibrated, and verified in the range of pH 1.0–14.0. The pH value was calculated as the average of three successive measurements. The pH meter was calibrated and verified with standard solutions, after each set of measurements. The values obtained for API-CS-oxCS/oxHA were compared with the corresponding CS-oxCS/oxHA hydrogels. ## Rheological Behavior The rheological measurements were performed using an Anton Paar MCR 302 rheometer (Anton Parr, Graz, Austria). To keep the working temperature constant, a Peltier system was used, with direct control over the sample temperature. The samples (CS-oxCS/oxHA and API-CS-oxCS/oxHA) were prepared 24 h before and kept at 2–8 °C. Two hours before starting the experiment, hydrogels were kept at room temperature (23 ± 2 °C) and then placed directly on a plate-plate system with striations (diameter 35 mm) to make the measurements. A solvent trap was used to avoid sample drying during the measurements. Data were interpreted with RheoCompasssoftware version V1.25.373. The working temperature was 32 °C and the measuring distance (gap) was settled at 0.5 mm for all samples. ## Amplitude Sweep Test This test is used to determine the limit of the linear viscoelastic range (LVE), the maximum limit of deformation tolerated by the sample without the internal structure being destroyed and to characterize the microstructure of the semisolids materials [63]. During the measurements, the amplitude is ramped (with controlled shear deformation) while the frequency is maintained constant. The set parameters are presented in Table 3. During the amplitude sweep test, the LVE (straight line on the diagram), accumulation and loss modulus (G′ and G″), and yield point were determined for each sample. Oscillatory rheological measurements are used to obtain information on the stability of multiphase systems. The results are presented as a diagram with shear strain plotted on the x-axis and accumulation modulus G′ and loss modulus G″ plotted on the y-axis with both axes on a logarithmic scale [88]. The G′ provides information about the amount of internal deformation energy stored in the structure, while G″ characterizes the deformation energy lost from the system during exposure of the sample to shear forces [89]. The yield point (τ) or yield stress is the value of the shear stress at the limit of the LVE region. Larger yield stress may indicate a more stable structure and better sedimentation stability over time [90]. The diagrams for CS-oxCH/oxHA and API-CS-oxCS/oxHA are presented in Figure 9. The amplitude sweep test gives information on the viscoelastic behavior of hydrogels. So, if the accumulation modulus (G′) is higher than the loss modulus (G″), the hydrogel exhibits a solidlike behavior while if G″ exceeds the G′, it behaves like a liquid [91,92]. Based on recorded results (Table 6), it appreciates that CS-oxCS hydrogels present a structured liquid behavior, having G” higher than G′. It was noted that G″ increases with the CS:oxCS ratio, so the CS2.0-oxCS1.0 (CS:oxCS = 2:1) exhibits the higher internal energy lost. In turn, the CS1.0-oxCS2.0 (CS:oxCS = 1:2) has the lowest internal energy lost, however, microcracks were observed on the G′ evolution in the LVE range, indicating a gradual breakdown of the microstructure. The yield points and LVE ranges for all of the CS-oxCS hydrogels are similar as a result of the liquid-like behavior. On the other hand, CS-oxHA hydrogels have a gel structure, since the G′ modulus is much higher than the G” modulus. The best gel structure has CS1.5-oxHA1.5 in which the CS:oxHA is 1:1. For this hydrogel, the G′ modulus is approximately 2.2 times higher than CS1.0-oxHA2.0 (CS:oxHA = 1:2) and around 12 times higher than CS2.0-oxHA1.0 (CS:oxHA = 2:1), respectively. This suggests a stronger reticulation in the 3D matrix of CS1.5-oxHA1.5 and a higher stiffness of the hydrogel. The highest yield point (452.440 Pa) of CS1.5-oxHA1.5 indicates a superior resistance to shear stress, though the LVE is the smallest ($77\%$), which means the hydrogel has the lowest stability over time. In the case of API-CS-oxHA hydrogels, it was observed that the LVE range increases, which suggests an improvement in the stability over time. This could be explained by the new molecular interactions (e.g., hydrogen bonds) which may have occurred between free hydroxyl, amino or carboxyl groups, and reactive groups of APIs (as primary or secondary amines in Ala and carboxyl or hydroxyl in FA). In the case of API-CS-oxCS hydrogels, the range of LVE remains similar to that of the CS-oxCS hydrogels. The G′ modules decreased in all API-CS-oxCS/oxHA, compared to CS-oxCS/oxHA, as a result of the internal friction between the API particles during exposure to shear forces. The G″ increases in API-CS-oxHA (with solid-like behavior) while it decreases in API-CS-oxCS (liquidlike behavior) hydrogels. In the hydrogels with structured liquid rheology (API-CS-oxCS), the APIs particles will slide over each other easily due to the fluid consistency of the hydrogel base, and the energy losses under shear stress are reduced. In the API-CS-oxHA hydrogels, the APIs particles will face the strength of the 3D matrix when exposed to various shear rates and the internal structures will lose more energy. For the same reason, the yield points remain similar to the API-CS-oxCS. In API-CS-oxHA, the yield points’ values decrease 5–12 times compared to the CS-oxHA hydrogels. ## Thixotropic Test The test consists in three stages: in the first one, very small deformation forces are applied (within the LVE range determined in the amplitudes sweep test) to simulate the hydrogel behavior at resting conditions. In the second step, the sample is subjected to high shear—very high deformation forces (outside the LVE) in order to simulate the breakdown of the sample. In the last stage, the minimum shear is returned with very small deformation forces to simulate the recovery of the structure [71,72]. At the end of the test, the samples recover their structure in variable percentages depending on the intrinsic physical properties. The set parameters are presented in Table 4. The thixotropic behavior was quantified by loss factor or damping factor, tan δ, on each step of the test, which expresses the ratio between the loss (G″) and accumulation (G′) modules at rest conditions, on deformation, and after the sample recovered their internal structure, using the following formula:Tan δ = G″/G′[2] The recovery rate was determined with respect to the G′ restoration after the sample was exposed to $700\%$ shear strain. The formulations designed for wound management may be exposed to various mechanical forces when an application on the damaged tissue is performed (e.g., tube extrusion, pumping, and spreading). If the product has the ability of self-healing, its structure will recover after the stress and the benefits of the healing process will be maximized [93]. The thixotropy provides information on the behavior of the analyzed samples when they were subjected to mechanical stress, and imposes the analysis of the sample before application, during the application, and after the removal of shear forces [94,95]. By applying a high shear force, over the LVE range, the hydrogel network was broken and the value of the G′ modulus suddenly decreased. In the case of hydrogels with self-healing capacity, G′ returned to the initial values (total restoration of the network) or close to the initial values (partial restoration of the 3D structure), when the shearing force was removed [93,95]. In our experiments, the LVE for all samples, CS-oxCA/oxHA and API-CS-oxCS/oxHA were under $102\%$. So, to assess the self-healing ability, we exposed the sample to $700\%$ shear strain. The diagrams and the results are presented in Figure 10 and Table 7, respectively. All analyzed samples show thixotropic behavior and recover their mechanical properties when the applied deformation forces are removed. These results support the self-healing capacity of the hydrogels crosslinked through dynamic Schiff base-type imine bonds and their potential for wound healing. The behavior is explained by the loosening of the dynamic bonds under the application of shear. When the mechanical stress is removed, the covalent interactions are restored and the gel regains its 3D structure. For CS-oxCS/oxHA, the breaking of the internal structure occurred immediately when the shear rate was increased from ɣ = $0.1\%$ to ɣ = $700\%$. The loss factor, tan δ, multiplied from 1.7 to 285 times in the deformation stage compared to the rest conditions, depending on the rheological behavior (“liquid or solid like”) and crosslinker (oxCS/oxHA) concentrations. The CS-oxCS hydrogels had recovered almost completely (>$95\%$) their internal structure after the high shear forces were removed and the hydrogels were able to relax under a very low shear rate. Although the recovery was not significantly influenced by the amount of crosslinking agent, rate recovery was higher as the ratio between CS-oxCS increased, CS1.0-oxCS2.0 (1:2) recovered $95.338\%$, while CS1.5-oxCS1.5 (1:1) and CS2.0-oxCS1.0 (2:1) recovered more than $99\%$ from their structure. As the difference in recovery for the last two hydrogels was around $0.2\%$ we could conclude that after the optimal ratio between CS and oxCS is achieved (1:1), the increase in the crosslinker agent does not provide significant advantages in the self-healing ability of the hydrogels. The tan δ value in the deformation stage varied at an inverse proportionality with the CS:oxCS ratio, increasing 285 times for CS1.0-oxCS2.0, almost 104 times for CS1.5-oxCS1.5, and only 9.54 times for CS2.0-oxCS1.0. This is explained by the presence of a higher amount of crosslinker agent which enriches the hydrogels with numerous dynamic Schiff bonds. These interactions are broken under mechanical stress, however, in being dynamic, they were restored when the stress was removed. So, the lower the loss factor in the second step of the thixotropic test, the recovery rate increased for the CS-oxCS hydrogels. In the case of the CS-oxHA hydrogels, it was noted that there was an important decrease in their recovery as the CS:oxHA ratio was increasing (that means the decrease in the oxHA concentration). CS1.0-oxHA2.0 recovered $99.618\%$ from its structure while CS1.5-oxHA1.5 recovered $36.456\%$ and CS2.0-oxHA1.0 regained only $13.691\%$ from their structures. So, in cases of these hydrogels with “solid-like” rheological behavior, the concentration of crosslinker (oxHA) plays a crucial role in the self-healing ability. In the deformation step of CS-oxHA hydrogels, the tan δ had the same evolution with CS-oxCS, decreasing with the crosslinker concentration. Although the “gel-like” hydrogel was more stable under mechanical stress, tan δ increased only 34 times for CS1.0-oxHA2.0 and around 12 times for CS1.5-oxHA1.5 and 8 times for CS2.0-oxHA1.0, which means the energy losses were higher (G″ is higher than G′) under stress conditions in hydrogels with an increased concentration of crosslinker since there are more dynamic bonds subjected to breakage. The CS-oxCS/oxHA hydrogels (CS1.5-oxCS1.5 and CS1.0-oxHA2.0) selected for embedding the APIs (FA, Ala, and CoQ10) have demonstrated more than $99\%$ self-healing ability. For the API-CS/oxCS hydrogels, a higher sensitivity at the application of deformation forces was observed and the recovery was down to around $30\%$. In addition to these, microcracks in the internal structure were observed at very low shear strains ($0.1\%$) as a result of the frictions occurring between the API particles. The recovery rate was not influenced by the nature of the APIs. All three API-CS-oxCS hydrogels (FA-CS-oxCA, Ala-CS-oxCS, and CoQ10-CS-oxCS) recovered more than $70\%$ after exposure to extremely high shear forces. However, the loss factor (tan δ) in the deformation stage was around two times higher for CoQ10-CS-oxCS compared to FA-CS-oxCA and Ala-CS-oxCS. This could be related to the particle size distribution of CoQ10 or different kinds of interactions between its molecule and hydrogel matrix. The internal structure was broken immediately after $700\%$ shear strain was applied to the API-CS-oxHA hydrogels. More differences in the recovery rate for these hydrogels were noted, in reference to API-CS-oxCS. Thus, Ala-CS-oxCS recovered $81.755\%$ from its structure while FA-CS-oxCA and CoQ10-CS-oxCS have a restoration of around $70\%$. The loss factor (tan δ) was almost 10 times higher in the CoQ10-CS-oxHA hydrogels than the other two (Ala-CS-oxHA and FA-CS-oxHA), being similar to CoQ10-CS-oxCS. The sensitivity to the application of very low deformation forces was not observed, as no microcracks occurred. ## Cell Viability Assay The cytotoxicity degree of CS-oxCS/oxHA and API-CS-oxCS/oxHA was assessed by an MTS assay using the CellTiter 96®AQueous One Solution Cell Proliferation Assay (Promega, Madison, WI USA), according to the manufacturers’ instructions and extract dilution method [73]). For this purpose, the hydrogel samples’ extracts were done in a complete cell culture medium at $1\%$ (v/v), for 24 h at 37 °C. Human gingival fibroblasts were seeded at 0.4 × 105 cells/mL into 96-well tissue culture-treated plates in an MEMα medium with $10\%$ fetal bovine serum and $1\%$ penicillin–streptomycin–amphotericin B mixture. The next day, the cells were incubated in triplicate for 72 h with different concentrations (v/v) of hydrogel samples’ extracts ($0.25\%$, $0.5\%$, $0.75\%$, and $1\%$). MTS reagent was added and absorbance readings were done 3 h later at 490 nm on a FLUOstar® Omega microplate reader (BMG LABTECH, Ortenberg, Germany). Cell viability was expressed as a percentage of the control cells’ viability (means ± SD). ## Antimicrobial Assay The antimicrobial screening of the API-CS-oxCS/oxHA hydrogels was determined using a disk diffusion assay [74,75] against different reference strains, namely Staphylococcus aureus ATCC 25923, Escherichia coli ATCC25922, and Candida albicans ATCC90028. All microorganisms were stored at −80 °C in $20\%$ glycerol. The bacterial strains were refreshed on nutrient agar, and the yeast strain was refreshed on Sabouraud dextrose agar at 37 °C. Microbial suspensions were prepared with these cultures in a sterile solution to obtain turbidity that is optically comparable to that of 0.5 McFarland standards. Volumes of 0.1 mL from each inoculum were spread on the Petri dishes. The sterilized paper disks (6 mm) were placed on the plates and aliquots (100 μL) of the samples were added. To evaluate the antimicrobial effects, the growth inhibition was measured under standard conditions after 24 h of incubation at 37 °C. All tests were carried out in triplicate. After incubation, the samples were visualized with SCAN1200®, version 8.6.10.0 (Interscience, Saint Nom la Brétèche, France), and the results were analyzed using XLSTAT Ecology version 2019.4.1 software and expressed as the mean ± SD [76]. ## 3.1.1. Macroscopic and Microscopic Features CS-oxCS/oxHA hydrogels are transparent or semitransparent and incorporate a large amount of air by stirring. The hydrogels have a yellow color that intensifies with the increase in the amount of crosslinking agent. The consistency of CS-oxHA hydrogels increases as the concentration of oxHA increases, while for CS-oxCS, an opposite effect was observed. The macroscopic aspect of API-CS-oxCS/oxHA based hydrogels is dependent on API embedded in a 3D polymer matrix, slightly white for Ala-CS-oxCS/oxHA, white to slightly yellow for FA-CS-oxCS/oxHA, and yellow for CoQ10-CS-oxCS/oxHA. After preparation, the API-CS-based hydrogels were stored at 2–8 °C. In polarized light, CS-oxCS/oxHA hydrogels show a homogeneous aspect (Figure 2). In the case of CS-oxHA hydrogels, the microscopic analysis suggests a “gel-like” behavior, while in the case of CS-oxCS hydrogels, a “liquid-like” behavior was observed. In similar conditions, the API-CS-based hydrogels show a quasihomogeneous aspect with APIs uniformly dispersed (appearing as sparkling dots) in a 3D polymer matrix (Figure 3). ## 3.1.2. Scanning Electron Microscopy (SEM) The SEM images of the CS-oxCS/oxHA hydrogels revealed a 3D structure, dependent on the CS:oxCS/oxHA ratio. Interestingly, in the case of CS-oxCS hydrogels, the hydrogel with the highest content of crosslinker (CS1.0-oxCH2.0) displayed the least organized microstructure with few large pores (>600 µm). The CS1.5-oxCS1.5 and CS2.0-oxCS1.0 hydrogels show a better organized morphological aspect, however, the micropores are not very well shaped. CS-oxHA hydrogels show a better-defined matrix as the amount of crosslinker increases (Figure 4). As expected, CS1.0-oxHAox2.0, containing $2\%$ oxHA, shows the quasiuniform distribution of 100–300 µm pores, while CS1.5-oxHA1.5 ($1.5\%$ oxHA) and CS2.0-oxHA1.0 ($1.0\%$ oxHA) revealed a less uniform defined pores. This is explained by a reduced number of carbonyl groups available for Schiff base imino interactions when the concentration of oxHA decreases. The morphology of APIs-CS-based hydrogels is similar to corresponding CS-oxCS/oxHA with relatively a uniform distribution of pores and homogenous dispersion of APIs (Figure 5). ## 3.1.3. FT-IR Spectroscopy The crosslinking of CS-oxCS/oxHA hydrogels through the formation of dynamic Schiff base-type covalent bonds between the carbonyl group of oxidized polymers (oxCS, oxHA) and the free amine group of CS, was highlighted by identifying the specific peak of the vibration of the C=N bond at 1651 cm−1 [77]. In addition, the decrease in the peak intensity of the -C=O (1732 cm−1) and -NH2 (2924–2854 cm−1) groups confirms their interaction. A new peak was observed at 1720 cm−1 which is characteristic of the vibrations generated by the –COOH group from the lactic acid, used as a solvent (Figure 6) [78]. On the FA-CS-oxCS/oxHA spectra (Figure 7a,b), more broad and intense peaks were observed, compared with CS-oxCS/oxHA spectra. These differences are explained by the addition of the stretching vibrations and overlapping of signals of functional groups as O-H (3550–3200 cm−1), C=O from COOH (around 1750 cm−1), double bonds C=C (1620–1560 cm−1), and C-O bonds (around 1100 cm−1) from both the FA and polysaccharide backbone of CS, oxCS, and oxHA [79]. Ala-CS-oxCS/oxHA shows also a broad peak in the 3300–2854 cm−1 range as a result of the overlapping of the N-H and C-H signals from the Ala structure and the CS-oxCS/oxHA hydrogels matrix (Figure 7c,d). Moreover, an intense peak at 1180–1000 cm−1 resulted from the overlapping of the C-O stretching of polymers with the C-N vibrations of Ala [65]. In the CoQ10-oxCS/oxHA spectra (Figure 7e,f) there appeared a new peak at the 2980–2930 cm−1 range which may be assigned to the alkenil radical in the CoQ10 structure. More intense peaks at 1730 cm−1 and between 2000–1180 cm−1 are also highlighted due the presence of the carbonyl group and –C-O-C- of CoQ10, respectively [67]. ## 3.1.4. Swelling Degree SD is an important parameter of hydrogels used in the treatment of wounds. The penetration of liquids into the pores of the 3D polymer matrix gives hydrogels the optimal hydration property necessary to heal a dry or wet wound [80,81]. For all samples, around $70\%$ from the entire value of SD was recorded after 1 h at the start of the experiment (Figure 8). The maximum value of SD was achieved after 3 h for CS-oxHA, and after 4 h for CS-oxCS, respectively (Figure 8a). Although the SD for CS-oxCS was higher (up to $568\%$) than CS-oxHA (up to $440\%$), the hydration rate was similar for both types of hydrogels. It was also noted that for CS-oxHA, the SD decreases proportionally with the decrease in the crosslinking agent concentration. So, CS1.0-oxHA2.0 recorded an SD up to $440\%$, while for CS1.5-oxHA1.5 and CS2.0-oxHA1.0, the values recorded were $282\%$ and only $86\%$, respectively. This may be explained by different morphologies, as observed in the SEM images. CS1.0-oxHA2.0 has more defined pores that make water uptake more efficient. For CS-oxCS, an opposite effect was observed, the SD increasing with the decreasing of the oxCS concentration in the polymer matrix. The SD of API-CS-oxCS/oxHA (Figure 8b) is not significantly different from the values recorded for CS-oxCS/oxHA although a decrease of 10–$25\%$ was observed at each time. This could be explained by the different interactions between APIs (Ala, FA, or CoQ10) and the free hydroxyl, amino, or carboxyl groups in the hydrogel’s matrix. The SD seems to be not influenced by the type of APIs embedded in the 3D polymer matrix. A good SD is mandatory for a wound healing dressing, as the proper moisture content is decisive for each phase of wound healing. Epithelial migration, angiogenesis, collagen synthesis, and autolytic debridement occur in optimal moisture content [22,82]. Also, the pain perception and scar surface are reduced in optimally hydrated wounds [83]. After 6 h from the start of the experiment, the API-CS-oxCS/oxHA hydrogels have an SD of more than $400\%$ which make them perfect to absorb the exudate in the damaged tissue, as well as a perfect medium for different kinetic release of therapeutic agents to wound sites. ## 3.1.5. pH Value A healthy skin has a slightly acidic pH in the range of 4.5–6.5, which inhibits pathogenic microbial development while the beneficial microbial flora is favored (the so-called “acid mantle of the skin” is formed) [84]. In the chronic wounds exudate, the pH values are shifted towards the alkaline values (6.9–8.9) and start to decrease once the signs of healing are visible [85]. pH values higher than 6.5 have been shown to increase the development of Staphylococcus aureus, Staphilococcus epidermides, Pseudomonas aeruginosa, Klebsiella spp., and other species commonly found in infected wounds. Secondary compounds resulting from microbial metabolism, such as ammonia, interfere with optimal oxygenation of the injured tissue and favor its necrosis, prolonging the healing process [86,87]. Also, bacterial metabolites contribute to maintaining the pH in the alkaline range, the formation of the biofilm, and, finally, the disruption in the physiological process of recovering the integrity and functionality of the injured tissue [85]. For these reasons, in wound healing, the pH of topical products is preferred to be in the physiological range. The pH values recorded for CS-oxCS/oxHA and API-CS-oxCS/oxHA are in the 5.77–6.012 (Table 5), which means they are within the physiological range of healthy skin and so support their wound application. It seems that the concentration and type of the crosslinking agent do not influence the pH value of hydrogels. This is because, in each hydrogel, there is the same concentration of polymer (CS and oxCS/oxHA) and of lactic acid ($0.7\%$ w/w). Moreover, the recorded pH values ensure the stability of the APIs embedded in the 3D polymer matrix. It was reported that Ala is stable at pH 4–9 while hydrolytic decomposition occurs with strong acids and bases, FA is stable around pH 5–6.2, and CoQ10 is stable under pH 6 [62,66,69]. ## 3.2.1. Cell Viability Assay In vitro biocompatibility of CS-oxCS/oxHA and API-CS-oxCS/oxHA was assessed by the extract dilution method and MTS assay after 72 h incubation. All samples were biocompatible (>$70\%$ cell viability) at the tested concentrations ($0.25\%$, $0.5\%$, $0.75\%$, and $1\%$) except FA-CS-oxHA which induced a dose-dependent decrease in cell viability and determined $60\%$ cell vialility at $1\%$ (v/v) concentration (Figure 11). ## 3.2.2. Antimicrobial Assay The API-CS-oxCS/oxHA hydrogels did not present antimicrobial activity against Escherichia coli as a Gram-negative bacterial strain and against Candida albicans as a yeast strain. Referring to the activity on the Gram-positive bacterial strain Staphylococus aureus, it was noted that the hydrogels containing FA (FA-CS-oxCS and FA-CS-oxHA) were very efficient, with up to 36 mm of inhibition zone (36.52 ± 0.28 mm for FA-CS-oxCS and 36.04 ± 0.49 mm for FA-CS-oxHA) (Figure 12). This effect was a bit more intense than that of the FA (31.45 ± 0.35 mm) and was frequently used to treat skin infections as well as chronic bone and joint infections [63]. This difference could be due to CS, which is known to have both antibacterial and fungicidal activities against different microorganisms [96,97,98]. ## 4. Conclusions The aim of our study was to design and characterize new smart self-healing and self-adapting CS-base hydrogels for wound care management using oxCS and oxHA as nontoxic crosslinkers agents. Different ratios between CS and oxidized polymers (CS:oxCS/oxHA) were used in order to study the physicochemical characteristics of hydrogels, according to their 3D structure, based on Schiff base-type covalent bonds. The dynamic imino bonds confer hydrogels self-healing ability, which means that the bonds could break and rebuild themselves in different environmental conditions. In addition, the hydrogel matrix obtained via dynamic Schiff bases is capable of adapting to the shape and depth of the damaged tissue which accelerates healing and ensures better protection. Three APIs (FA, Ala, and CoQ10) were selected to be embedded into the polymer matrix of CS-based hydrogels. The structure of the CS-oxCS/oxHA, the success of imine bond formation between free amine groups of CS and carbonyl groups of oxCS/oxHA, more exactly, was proven using spectral methods (FT-IR and 1H/13C-NMR). The inclusion of APIs into the polymer matrix was proven through FT-IR spectra. The hydrogels (CS-oxCS/oxHA and API-CS-oxCS/oxHA) were analyzed for their SD (%), and pH and the values recorded recommend them as suitable for use on damaged tissue. The SEM analysis showed differences in 3D networks depending on the type and concentration of the crosslinker. The rheological tests confirmed the capacity of all hydrogels to restore their internal structural organization after exposure to high shear forces which endowed them with the self-healing ability and high mechanical stability to various stressors. The embedding of APIs into the hydrogel matrix does not interfere with their mechanical performance which makes these smart hydrogels promising for the management of wounds as a wide variety of drugs may be included in their matrix to modulate the targeted therapeutic outcomes. Except for FA-CS-oxHA, API-CA-oxCS/oxHA hydrogels are not cytotoxic and, moreover, FA-CS-oxHA showed important antibacterial effects on the Staphylococcus aureus bacterial strain. In conclusion, the results of our study confirm that smart nontoxic hydrogels could be developed using oxidized biopolymers as crosslinker agents, with real potential in managing the treatment of irregular deep shaped wounds as a result of their self-healing and self-adapting properties. ## References 1. Yao Z., Niu J., Cheng B.. Prevalence of Chronic Skin Wounds and Their Risk Factors in an Inpatient Hospital Setting in Northern China. Adv. Ski. Wound Care (2020.0) 33 1-10. DOI: 10.1097/01.ASW.0000694164.34068.82 2. Järbrink K., Ni G., Sönnergren H., Schmidtchen A., Pang C., Bajpai R., Car J.. Prevalence and Incidence of Chronic Wounds and Related Complications: A Protocol for a Systematic Review. Syst. Rev. (2016.0) 5 152. DOI: 10.1186/s13643-016-0329-y 3. Sen C.K.. Human Wounds and Its Burden: An Updated Compendium of Estimates. Adv. Wound Care (2019.0) 8 39-48. DOI: 10.1089/wound.2019.0946 4. Las Heras K., Igartua M., Santos-Vizcaino E., Hernandez R.M.. Chronic Wounds: Current Status, Available Strategies and Emerging Therapeutic Solutions. J. Control. Release (2020.0) 328 532-550. DOI: 10.1016/j.jconrel.2020.09.039 5. Olsson M., Järbrink K., Divakar U., Bajpai R., Upton Z., Schmidtchen A., Car J.. The Humanistic and Economic Burden of Chronic Wounds: A Systematic Review. Wound Repair Regen. (2019.0) 27 114-125. DOI: 10.1111/wrr.12683 6. Voljč T., Semenič D., Aghaei S.. Contribution of Topical Agents to Wound Healing. Recent Advances in Wound Healing (2022.0) 7. Powers J.G., Higham C., Broussard K., Phillips T.J.. Wound Healing and Treating Wounds: Chronic Wound Care and Management. J. Am. Acad. Dermatol. (2016.0) 74 607-625. DOI: 10.1016/j.jaad.2015.08.070 8. Bowers S., Franco E.. Chronic Wounds: Evaluation and Management. Am. Fam. Physician (2020.0) 101 159-166. PMID: 32003952 9. Wound-Management-Guidelines 10. Jones R.E., Foster D.S., Longaker M.T.. Management of Chronic Wounds-2018. JAMA (2018.0) 320 1481-1482. DOI: 10.1001/jama.2018.12426 11. Doughty D.. Dressings and More: Guidelines for Topical Wound Management. Nurs. Clin. N. Am. (2005.0) 40 217-231. DOI: 10.1016/j.cnur.2004.09.012 12. Öhnstedt E., Lofton Tomenius H., Vågesjö E., Phillipson M.. The Discovery and Development of Topical Medicines for Wound Healing. Expert Opin. Drug Discov. (2019.0) 14 485-497. DOI: 10.1080/17460441.2019.1588879 13. Moore Z.E., Webster J.. Dressings and Topical Agents for Preventing Pressure Ulcers. Cochrane Database Syst. Rev. (2018.0) 12 CD009362. DOI: 10.1002/14651858.CD009362.pub3 14. Derwin R., Patton D., Avsar P., Strapp H., Moore Z.. The Impact of Topical Agents and Dressing on pH and Temperature on Wound Healing: A Systematic, Narrative Review. Int. Wound J. (2022.0) 19 1397-1408. DOI: 10.1111/iwj.13733 15. Cheng S., Wang H., Pan X., Zhang C., Zhang K., Chen Z., Dong W., Xie A., Qi X.. Dendritic Hydrogels with Robust Inherent Antibacterial Properties for Promoting Bacteria-Infected Wound Healing. ACS Appl. Mater. Interfaces (2022.0) 14 11144-11155. DOI: 10.1021/acsami.1c25014 16. Sethuram L., Thomas J.. Therapeutic Applications of Electrospun Nanofibers Impregnated with Various Biological Macromolecules for Effective Wound Healing Strategy—A Review. Biomed. Pharmacother. (2023.0) 157 113996. DOI: 10.1016/j.biopha.2022.113996 17. Moholkar D.N., Sadalage P.S., Peixoto D., Paiva-Santos A.C., Pawar K.D.. Recent Advances in Biopolymer-Based Formulations for Wound Healing Applications. Eur. Polym. J. (2021.0) 160 110784. DOI: 10.1016/j.eurpolymj.2021.110784 18. Mo C., Xiang L., Chen Y.. Advances in Injectable and Self-Healing Polysaccharide Hydrogel Based on the Schiff Base Reaction. Macromol. Rapid Commun. (2021.0) 42 e2100025. DOI: 10.1002/marc.202100025 19. Xiang J., Shen L., Hong Y.. Status and Future Scope of Hydrogels in Wound Healing: Synthesis, Materials and Evaluation. Eur. Polym. J. (2020.0) 130 109609. DOI: 10.1016/j.eurpolymj.2020.109609 20. Bahram M., Mohseni N., Moghtader M.. An Introduction to Hydrogels and Some Recent Applications. Emerging Concepts in Analysis and Applications of Hydrogels (2016.0) 21. Caló E., Khutoryanskiy V.V.. Biomedical Applications of Hydrogels: A Review of Patents and Commercial Products. Eur. Polym. J. (2015.0) 65 252-267. DOI: 10.1016/j.eurpolymj.2014.11.024 22. Brumberg V., Astrelina T., Malivanova T., Samoilov A.. Modern Wound Dressings: Hydrogel Dressings. Biomedicines (2021.0) 9. DOI: 10.3390/biomedicines9091235 23. Ghasemiyeh P., Mohammadi-Samani S.. Hydrogels as Drug Delivery Systems; Pros and Cons. Trends Pharm. Sci. (2019.0) 5 7-24 24. Bordbar-Khiabani A., Gasik M.. Smart Hydrogels for Advanced Drug Delivery Systems. Int. J. Mol. Sci. (2022.0) 23. DOI: 10.3390/ijms23073665 25. Farahani M., Shafiee A.. Wound Healing: From Passive to Smart Dressings. Adv. Healthc. Mater. (2021.0) 10 e2100477. DOI: 10.1002/adhm.202100477 26. Berger J., Reist M., Mayer J.M., Felt O., Peppas N.A., Gurny R.. Structure and Interactions in Covalently and Ionically Crosslinked Chitosan Hydrogels for Biomedical Applications. Eur. J. Pharm. Biopharm. (2004.0) 57 19-34. DOI: 10.1016/S0939-6411(03)00161-9 27. Han Y., Cao Y., Lei H.. Dynamic Covalent Hydrogels: Strong yet Dynamic. Gels (2022.0) 8. DOI: 10.3390/gels8090577 28. Li Y., Wang X., Fu Y., Wei Y., Zhao L., Tao L.. Self-Adapting Hydrogel to Improve the Therapeutic Effect in Wound-Healing. ACS Appl. Mater. Interfaces (2018.0) 10 26046-26055. DOI: 10.1021/acsami.8b08874 29. Talebian S., Mehrali M., Taebnia N., Pennisi C.P., Kadumudi F.B., Foroughi J., Hasany M., Nikkhah M., Akbari M., Orive G.. Self-Healing Hydrogels: The Next Paradigm Shift in Tissue Engineering?. Adv. Sci. (2019.0) 6 1801664. DOI: 10.1002/advs.201801664 30. Barroso N., Guaresti O., Pérez-Álvarez L., Ruiz-Rubio L., Gabilondo N., Vilas-Vilela J.L.. Self-Healable Hyaluronic Acid/Chitosan Polyelectrolyte Complex Hydrogels and Multilayers. Eur. Polym. J. (2019.0) 120 109268. DOI: 10.1016/j.eurpolymj.2019.109268 31. Samal S.K., Dash M., Dubruel P., Van Vlierberghe S.. Smart Polymer Hydrogels: Properties, Synthesis and Applications. Smart Polymers and Their Applications (2014.0) 237-270 32. Tian S., Wang M., Wang X., Wang L., Yang D., Nie J., Ma G.. Smart Hydrogel Sensors with Antifreezing, Antifouling Properties for Wound Healing. ACS Biomater. Sci. Eng. (2022.0) 8 1867-1877. DOI: 10.1021/acsbiomaterials.1c01599 33. Crini G.. Historical Review on Chitin and Chitosan Biopolymers. Environ. Chem. Lett. (2019.0) 17 1623-1643. DOI: 10.1007/s10311-019-00901-0 34. Kulkarni N., Shinde S.D., Jadhav G.S., Adsare D.R., Rao K., Kachhia M., Maingle M., Patil S.P., Arya N., Sahu B.. Peptide-Chitosan Engineered Scaffolds for Biomedical Applications. Bioconjugate Chem. (2021.0) 32 448-465. DOI: 10.1021/acs.bioconjchem.1c00014 35. Seidi F., Khodadadi Yazdi M., Jouyandeh M., Dominic M., Naeim H., Nezhad M.N., Bagheri B., Habibzadeh S., Zarrintaj P., Saeb M.R.. Chitosan-Based Blends for Biomedical Applications. Int. J. Biol. Macromol. (2021.0) 183 1818-1850. DOI: 10.1016/j.ijbiomac.2021.05.003 36. Patrulea V., Ostafe V., Borchard G., Jordan O.. Chitosan as a Starting Material for Wound Healing Applications. Eur. J. Pharm. Biopharm. (2015.0) 97 417-426. DOI: 10.1016/j.ejpb.2015.08.004 37. Azad A.K., Sermsintham N., Chandrkrachang S., Stevens W.F.. Chitosan Membrane as a Wound-Healing Dressing: Characterization and Clinical Application. J. Biomed. Mater. Res. B Appl. Biomater. (2004.0) 69 216-222. DOI: 10.1002/jbm.b.30000 38. Dai T., Tanaka M., Huang Y.-Y., Hamblin M.R.. Chitosan Preparations for Wounds and Burns: Antimicrobial and Wound-Healing Effects. Expert Rev. Anti Infect. Ther. (2011.0) 9 857-879. DOI: 10.1586/eri.11.59 39. Minagawa T., Okamura Y., Shigemasa Y., Minami S., Okamoto Y.. Effects of Molecular Weight and Deacetylation Degree of Chitin/Chitosan on Wound Healing. Carbohydr. Polym. (2007.0) 4 640-644. DOI: 10.1016/j.carbpol.2006.07.007 40. Cao R., Yu H., Long H., Zhang H., Hao C., Shi L., Du Y., Jiao S., Guo A., Ma L.. Low Deacetylation Degree Chitosan Oligosaccharide Protects against IL-1β Induced Inflammation and Enhances Autophagy Activity in Human Chondrocytes. J. Biomater. Sci. Polym. Ed. (2022.0) 33 517-531. DOI: 10.1080/09205063.2021.1996962 41. Jin Y., Ling P.-X., He Y.-L., Zhang T.-M.. Effects of Chitosan and Heparin on Early Extension of Burns. Burns (2007.0) 33 1027-1031. DOI: 10.1016/j.burns.2006.12.002 42. Alsarra I.A.. Chitosan Topical Gel Formulation in the Management of Burn Wounds. Int. J. Biol. Macromol. (2009.0) 45 16-21. DOI: 10.1016/j.ijbiomac.2009.03.010 43. Ong S.-Y., Wu J., Moochhala S.M., Tan M.-H., Lu J.. Development of a Chitosan-Based Wound Dressing with Improved Hemostatic and Antimicrobial Properties. Biomaterials (2008.0) 29 4323-4332. DOI: 10.1016/j.biomaterials.2008.07.034 44. Satitsri S., Muanprasat C.. Chitin and Chitosan Derivatives as Biomaterial Resources for Biological and Biomedical Applications. Molecules (2020.0) 25. DOI: 10.3390/molecules25245961 45. Ardean C., Davidescu C.M., Nemeş N.S., Negrea A., Ciopec M., Duteanu N., Negrea P., Duda-Seiman D., Musta V.. Factors Influencing the Antibacterial Activity of Chitosan and Chitosan Modified by Functionalization. Int. J. Mol. Sci. (2021.0) 22. DOI: 10.3390/ijms22147449 46. Amirian J., Zeng Y., Shekh M.I., Sharma G., Stadler F.J., Song J., Du B., Zhu Y.. In-Situ Crosslinked Hydrogel Based on Amidated Pectin/Oxidized Chitosan as Potential Wound Dressing for Skin Repairing. Carbohydr. Polym. (2021.0) 251 117005. DOI: 10.1016/j.carbpol.2020.117005 47. Bao Z., Yu A., Shi H., Hu Y., Jin B., Lin D., Dai M., Lei L., Li X., Wang Y.. Glycol Chitosan/Oxidized Hyaluronic Acid Hydrogel Film for Topical Ocular Delivery of Dexamethasone and Levofloxacin. Int. J. Biol. Macromol. (2021.0) 167 659-666. DOI: 10.1016/j.ijbiomac.2020.11.214 48. Kuth S., Karakaya E., Reiter N., Schmidt L., Paulsen F., Teßmar J., Budday S., Boccaccini A.R.. Oxidized Hyaluronic Acid-Gelatin-Based Hydrogels for Tissue Engineering and Soft Tissue Mimicking. Tissue Eng. Part C Methods (2022.0) 28 301-313. DOI: 10.1089/ten.tec.2022.0004 49. Ma L., Tan Y., Chen X., Ran Y., Tong Q., Tang L., Su W., Wang X., Li X.. Injectable Oxidized Alginate/Carboxylmethyl Chitosan Hydrogels Functionalized with Nanoparticles for Wound Repair. Carbohydr. Polym. (2022.0) 293 119733. DOI: 10.1016/j.carbpol.2022.119733 50. Weng L., Chen X., Chen W.. Rheological Characterization of in Situ Crosslinkable Hydrogels Formulated from Oxidized Dextran and N-Carboxyethyl Chitosan. Biomacromolecules (2007.0) 8 1109-1115. DOI: 10.1021/bm0610065 51. Qi X., Xiang Y., Cai E., You S., Gao T., Lan Y., Deng H., Li Z., Hu R., Shen J.. All-in-one: Harnessing Multifunctional Injectable Natural Hydrogels for Ordered Therapy of Bacteria-Infected Diabetic Wounds. Chem. Eng. J. (2022.0) 439 135691. DOI: 10.1016/j.cej.2022.135691 52. Wang Y., Xie R., Li Q., Dai F., Lan G., Shang S., Lu F.. A Self-Adapting Hydrogel Based on Chitosan/Oxidized Konjac Glucomannan/AgNPs for Repairing Irregular Wounds. Biomater. Sci. (2020.0) 8 1910-1922. DOI: 10.1039/C9BM01635J 53. Yang H., Song L., Zou Y., Sun D., Wang L., Yu Z., Guo J.. Role of Hyaluronic Acids and Potential as Regenerative Biomaterials in Wound Healing. ACS Appl. Bio Mater. (2021.0) 4 311-324. DOI: 10.1021/acsabm.0c01364 54. Fallacara A., Baldini E., Manfredini S., Vertuani S.. Hyaluronic Acid in the third Millennium. Polymers (2018.0) 10. DOI: 10.3390/polym10070701 55. Ke C., Sun L., Qiao D., Wang D., Zeng X.. Antioxidant Acitivity of low Molecular Weight Hyaluronic Acid. Food Chem. Toxicol. (2011.0) 49 2670-2675. DOI: 10.1016/j.fct.2011.07.020 56. de Paiva W.K.V., de Medeiros W.R.D.B., de Assis C.F., Dos Santos E.S., de Sousa Júnior F.C.. Physicochemical Characterization and in Vitro Antioxidant Activity of Hyaluronic Acid Produced by Streptococcus Zooepidemicus CCT 7546. Prep. Biochem. Biotechnol. (2022.0) 52 234-243. DOI: 10.1080/10826068.2021.1929320 57. Nyman E., Henricson J., Ghafouri B., Anderson C.D., Kratz G.. Hyaluronic Acid Accelerates Re-Epithelialization and Alters Protein Expression in a Human Wound Model. Plast. Reconstr. Surg. Glob. Open (2019.0) 7 e2221. DOI: 10.1097/GOX.0000000000002221 58. Litwiniuk M., Krejner A., Speyrer M.S., Gauto A.R., Grzela T.. Hyaluronic Acid in Inflammation and Tissue Regeneration. Wounds (2016.0) 28 78-88. PMID: 26978861 59. Sen S.O., Nayak A.K., Devbhuti P., Ghosh A.. Modification of Gums by Periodate Oxidation: A Natural Cross-Linker. Int. J. Pharm. Pharm. Sci. (2019.0) 11 1-6. DOI: 10.22159/ijpps.2019v11i7.31058 60. Koosha M., Aalipour H., Sarraf Shirazi M.J., Jebali A., Chi H., Hamedi S., Wang N., Li T., Moravvej H.. Physically Crosslinked Chitosan/PVA Hydrogels Containing Honey and Allantoin with Long-Term Biocompatibility for Skin Wound Repair: An In Vitro and In Vivo Study. J. Funct. Biomater. (2021.0) 12. DOI: 10.3390/jfb12040061 61. Sharma M., Malik G., Gulati D., Kaushik P., Arora S.. Formulation and evaluation of fusidic acid based transferosome for burn wound infection. Mater. Today Proc. (2022.0) 68 836-841. DOI: 10.1016/j.matpr.2022.06.260 62. Lemaire S., Van Bambeke F., Pierard D., Appelbaum P.C., Tulkens P.M.. Activity of Fusidic Acid Against Extracellular and Intracellular. Clin. Infect. Dis. (2011.0) 52 S493-S503. DOI: 10.1093/cid/cir165 63. Fernandes P.. Fusidic Acid: A Bacterial Elongation Factor Inhibitor for the Oral Treatment of Acute and Chronic Staphylococcal Infections. Cold Spring Harb. Perspect. Med. (2016.0) 6 a025437. DOI: 10.1101/cshperspect.a025437 64. Nokoorani Y.D., Shamloo A., Bahadoran M., Moravvej H.. Fabrication and Characterization of Scaffolds Containing Different Amounts of Allantoin for Skin Tissue Engineering. Sci. Rep. (2021.0) 11 16164. DOI: 10.1038/s41598-021-95763-4 65. Dinica R.M., Sandu C., Dediu Botezatu A.V., Cazanevscaia Busuioc A., Balanescu F., Ionica Mihaila M.D., Dumitru C.N., Furdui B., Iancu A.V.. Allantoin from Valuable Romanian Animal and Plant Sources with Promising Anti-Inflammatory Activity as a Nutricosmetic Ingredient. Sustainability (2021.0) 13. DOI: 10.3390/su131810170 66. Becker L.C., Bergfeld W.F., Belsito D.V., Klaassen C.D., Marks J.G., Shank R.C., Slaga T.J., Snyder P.W., Andersen F.A.. Final Report of the Safety Assessment of Allantoin and Its Related Complexes. Int. J. Toxicol. (2010.0) 29 84S-97S. DOI: 10.1177/1091581810362805 67. Liu Q., Sun Y., Cui Q., Cheng J., Killpartrik A., Kemp A.H., Guo M.. Characterization, antioxidant capacity, and bioaccessibility of Coenzyme Q10 loaded whey protein nanoparticles. LWT (2022.0) 160 113258. DOI: 10.1016/j.lwt.2022.113258 68. Temova Rakuša Ž., Kristl A., Roškar R.. Stability of Reduced and Oxidized Coenzyme Q10 in Finished Products. Antioxidants (2021.0) 10. DOI: 10.3390/antiox10030360 69. Fir M.M., Smidovnik A., Milivojevic L., Zmitek J., Prosek M.. Studies of CoQ10 and Cyclodextrin Complexes: Solubility, Thermo- and Photo-Stability. J. Incl. Phenom. Macrocycl. Chem. (2009.0) 64 225-232. DOI: 10.1007/s10847-009-9555-4 70. Suri A., Pratt A., Tear S., Walker C., El-Gomati M.. Next Generation Secondary Electron Detector with Energy Analysis Capability for SEM. J. Microsc. (2020.0) 279 207-211. DOI: 10.1111/jmi.12867 71. Rheology: Basics of Rheometry-Oscillation: Anton Paar Wiki 72. Yilmaz M.T., Vatansever C.. Three Interval Thixotropy Test to Determine Structural Regeneration of a Glucomannan Based Hydrocolloid Film at Air/Water Interface: Interfacial, Molecular, Thermal and Surface Characterization. Food Hydrocoll. (2016.0) 61 458-468. DOI: 10.1016/j.foodhyd.2016.06.004 73. 73.ISO 10993-5: 2009 (E)Biological Evaluation of Medical Devices—Part 5: Tests for In Vitro CytotoxicityISOGeneva, Switzerland2009. Biological Evaluation of Medical Devices—Part 5: Tests for In Vitro Cytotoxicity (2009.0) 74. Bauer A.W., Perry D.M., Kirby W.M.. Single disc antibiotic sensitivity testing of Staphylococci. AMA Arch. Intern. Med. (1959.0) 104 208-216. DOI: 10.1001/archinte.1959.00270080034004 75. 75. Clinical and Laboratory Standards Institute (CLSI) Performance Standards for Antimicrobial Susceptibility Testing32nd ed.CLSI Supplement M100Clinical and Laboratory Standards Institute (CLSI)San Antonio, TX, USA2022978-1-68440-134-5/978-1-68440-135-2. Performance Standards for Antimicrobial Susceptibility Testing (2022.0) 76. XLSTAT\|Statistical Software for Excel n.d. 77. Metzler C.M., Cahill A., Metzler D.E.. Equilibriums and Absorption Spectra of Schiff Bases. J. Am. Chem. Soc. (1980.0) 102 6075-6082. DOI: 10.1021/ja00539a017 78. Mauricio-Sánchez R.A., Salazar R., Luna-Bárcenas G.L., Mendoza-Galván A.. FTIR Spectroscopy Studies on the Spontaneous Neutralization of Chitosan Acetate Films by Moisture Conditioning. Vib. Spectrosc. (2018.0) 9 1-6 79. Riaz U., Ashraf S.M., Thomas S., Grohens Y., Jyotishkumar P.. Chapter 20. Characterization of Polymer Blends with FTIR Spectroscopy. Characterization of Polymer Blends with FTIR Spectroscopy (2014.0) 80. Op’t Veld R.C., Walboomers X.F., Jansen J.A., Wagener F.A.D.T.G.. Design Considerations for Hydrogel Wound Dressings: Strategic and Molecular Advances. Tissue Eng. Part B Rev. (2020.0) 26 230-248. DOI: 10.1089/ten.teb.2019.0281 81. Nuutila K., Eriksson E.. Moist Wound Healing with Commonly Available Dressings. Adv. Wound Care (2021.0) 10 685-698. DOI: 10.1089/wound.2020.1232 82. Cui L., Yao Y., Yim E.K.F.. The Effects of Surface Topography Modification on Hydrogel Properties. APL Bioeng. (2021.0) 5 031509. DOI: 10.1063/5.0046076 83. Junker J.P.E., Kamel R.A., Caterson E.J., Eriksson E.. Clinical Impact Upon Wound Healing and Inflammation in Moist, Wet, and Dry Environments. Adv. Wound Care (2013.0) 2 348-356. DOI: 10.1089/wound.2012.0412 84. Proksch E.. PH in Nature, Humans and Skin. J. Dermatol. (2018.0) 45 1044-1052. DOI: 10.1111/1346-8138.14489 85. Rippke F., Berardesca E., Weber T.M.. PH and Microbial Infections. Curr. Probl. Dermatol. (2018.0) 54 87-94. PMID: 30130777 86. Wang Y., Li X., Zhao W., Gao Z.Z., Zheng M.J., Pang Y.L., Chen Z.Y.. Advances in the Research of Influence of pH Value on Healing of Chronic Wounds. Zhonghua Shao Shang Za Zhi (2019.0) 35 474-476. PMID: 31280545 87. Wallace L.A., Gwynne L., Jenkins T.. Challenges and Opportunities of pH in Chronic Wounds. Ther. Deliv. (2019.0) 10 719-735. DOI: 10.4155/tde-2019-0066 88. Rheology: Basics of Rheometry-Rotation: Anton Paar Wiki 89. Mezger T.. Applied Rheology: With Joe Flow on Rheology Road (2021.0) 90. Jons C.K., Grosskopf A.K., Yan J., Klich J.H., Saouaf O.M., Appel E.A.. Yield-Stress and Creep Control Depot Formation and Persistence of Injectable Hydrogels Following Subcutaneous Administration. Adv. Funct. Mater. (2022.0) 32 40-42. DOI: 10.1002/adfm.202203402 91. Amplitude Sweeps: Anton Paar Wiki 92. Baby D.K., Sabu T., Sarathchandran C., Chandran N.. Chapter 9. Rheology of hydrogels In Micro and Nano Technologies in Rheology of Polymer Blends and Nanocomposites (2020.0) 193-204 93. Larson R.G., Wei Y.. A Review of Thixotropy and its Rheological Modeling. J. Rheol. (2019.0) 63 477. DOI: 10.1122/1.5055031 94. Mezger T.G.. Applied Rheology (2017.0) 95. Sánchez-Cid P., Jiménez-Rosado M., Alonso-González M., Romero A., Perez-Puyana V.. Applied Rheology as Tool for the Assessment of Chitosan Hydrogels for Regenerative Medicine. Polymers (2021.0) 13. DOI: 10.3390/polym13132189 96. Kong M., Chen X.G., Xing K., Park H.J.. Antimicrobial properties of chitosan and mode of action: A state of the art review. Int. J. Food Microbiol. (2010.0) 144 51-63. DOI: 10.1016/j.ijfoodmicro.2010.09.012 97. Ceramella J., Iacopetta D., Catalano A., Cirillo F., Lappano R., Sinicropi M.S.. A Review on the Antimicrobial Activity of Schiff Bases: Data Collection and Recent Studies. Antibiotics (2022.0) 11. DOI: 10.3390/antibiotics11020191 98. Goy R.C., Morais S., Assis O.. Evaluation of the antimicrobial activity of chitosan and its quaternized derivative on. Rev. Bras. Farmacogn. (2016.0) 26 122-127. DOI: 10.1016/j.bjp.2015.09.010
--- title: 'Consensus Paper: Latent Autoimmune Cerebellar Ataxia (LACA)' authors: - Mario Manto - Marios Hadjivassiliou - José Fidel Baizabal-Carvallo - Christiane S Hampe - Jerome Honnorat - Bastien Joubert - Hiroshi Mitoma - Sergio Muñiz-Castrillo - Aasef G. Shaikh - Alberto Vogrig journal: Cerebellum (London, England) year: 2023 pmcid: PMC10060034 doi: 10.1007/s12311-023-01550-4 license: CC BY 4.0 --- # Consensus Paper: Latent Autoimmune Cerebellar Ataxia (LACA) ## Abstract Immune-mediated cerebellar ataxias (IMCAs) have diverse etiologies. Patients with IMCAs develop cerebellar symptoms, characterized mainly by gait ataxia, showing an acute or subacute clinical course. We present a novel concept of latent autoimmune cerebellar ataxia (LACA), analogous to latent autoimmune diabetes in adults (LADA). LADA is a slowly progressive form of autoimmune diabetes where patients are often initially diagnosed with type 2 diabetes. The sole biomarker (serum anti-GAD antibody) is not always present or can fluctuate. However, the disease progresses to pancreatic beta-cell failure and insulin dependency within about 5 years. Due to the unclear autoimmune profile, clinicians often struggle to reach an early diagnosis during the period when insulin production is not severely compromised. LACA is also characterized by a slowly progressive course, lack of obvious autoimmune background, and difficulties in reaching a diagnosis in the absence of clear markers for IMCAs. The authors discuss two aspects of LACA: [1] the not manifestly evident autoimmunity and [2] the prodromal stage of IMCA’s characterized by a period of partial neuronal dysfunction where non-specific symptoms may occur. In order to achieve an early intervention and prevent cell death in the cerebellum, identification of the time-window before irreversible neuronal loss is critical. LACA occurs during this time-window when possible preservation of neural plasticity exists. Efforts should be devoted to the early identification of biological, neurophysiological, neuropsychological, morphological (brain morphometry), and multimodal biomarkers allowing early diagnosis and therapeutic intervention and to avoid irreversible neuronal loss. ## Introduction Immune-mediated cerebellar ataxias (IMCAs) have diverse etiologies [1–6] (Table 1). IMCAs are divided into two groups: [1] those in which the trigger of autoimmunity leading to cerebellar damage are known, such as infection (e.g., post-infectious cerebellar syndrome, PiCS, post-infectious cerebellitis), neoplasm (e.g., paraneoplastic cerebellar degeneration, PCD), and gluten sensitivity (gluten ataxia, GA); and [2] those with no clear triggers but with serological markers strongly suggestive of IMCAs (e.g., anti-GAD ataxia). When immune-mediated mechanisms leading to cerebellar damage are strongly suspected, but a serological profile does not match any of the known etiologies, the patients are categorized as having primary autoimmune cerebellar ataxia (PACA) [7].Table 1List of diverse etiologies in immune-mediated cerebellar ataxias (IMCAs)1 The etiologies in which cerebellar ataxia is the predominant (isolated or main) clinical phenotype1.1 Well-established independent etiologiesCommon clinical profiles (symptoms, clinical courses, and therapeutic responses) are observed. Mostly, well-characterized autoantibodies are associated. The trigger of the autoimmunity is clear, except anti-GAD ataxia.• Gluten ataxia(gluten sensitivity)• Post-infectious cerebellitis(infection)• Miller Fisher syndrome(infection)• Paraneoplastic cerebellar degeneration(malignancy)• Opsoclonus myoclonus syndrome (infection or malignancy)• Anti-GAD ataxia(unknown)1.2. Clinical spectrum encompassing diverse unknown etiologiesAutoimmunity is suspected, but lack of any specific well-characterized or pathogenic antibodies.• Primary autoimmune cerebellar ataxia (PACA)2 The etiologies in which cerebellar ataxia can be one of various neurological presentationsThis category encompasses various etiologies characterized by a more global neurological dysfunction where cerebellar ataxia can be one of many neurological features. Mostly, extra-cerebellar symptoms (e.g., seizures, memory deficits, behavioral changes, cognitive changes, sleep disturbances, rigidity, myoclonus, brainstem symptoms, peripheral nerve symptoms, and autonomic dysfunction) are main phenotypes. The prevalence of these etiologies is rare among patients with cerebellar ataxia.• Cerebellar ataxia associated with autoantibodies toward ion channels/related proteinsAnti-VGCC, Caspr2, DPPX• Cerebellar ataxia associated with autoantibodies toward synaptic adhesion moleculesAnti-LGI1, IgLON5, mGluR delta• Cerebellar ataxia associated with autoantibodies toward transmitter receptorsAnti-NMDAR, AMPAR, mGluR1, mGluR2, mGluR5, GABAAR, GABABR, GlycineR• Autoimmunities toward myelin-related proteinsAnti-MAG• Autoimmunities toward glial cellsGFAP astrocytopathy• Perivascular T cell inflammation in the brainstemChronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids Patients with IMCAs generally develop a cerebellar motor syndrome, characterized mainly by gait ataxia, with an acute or subacute clinical course. Identification of well-characterized antibodies (Abs) is essential in the diagnosis of some IMCAs: for example, onconeural Abs in PCD, anti-gliadin and anti-TG6 Abs in GA, and high-titer of anti-GAD antibodies (anti-GAD Abs) in anti-GAD ataxia [1–6]. In contrast, some patients exhibit ataxia with a slowly progressive time-course, without obvious autoimmune background [8–10]. Neurological symptoms other than ataxia sometimes precede the development of ataxia, suggestive of the existence of a prodromal stage in IMCAs [9–11]. The condition is analogous to latent autoimmune diabetes in adults (LADA), which is characterized by an atypical presentation of autoimmune type 1 diabetes mellitus (DM), slow and progressive course, and sometimes fluctuating association of anti-GAD Abs, the sole autoimmune biomarker [12–15]. Patients are initially diagnosed with type 2 DM and gradual decompensation due to islet autoimmunity and insulin deficiency leading to insulin dependency [16]. In analogy with LADA, we propose a novel clinical concept of latent autoimmune cerebellar ataxia (LACA) to underline two conditions: “not manifestly evident autoimmune pathologies” and “prodromal stage of IMCAs.” This novel clinical concept, LACA, will provide a framework for early intervention during a period when cerebellar reserve, capacities for compensation and restoration, is preserved [17]. The current consensus paper aims to discuss the validity of the LACA hypothesis. From this point of view, we revisit clinical profiles of CA associated with low-titer of anti-GAD Abs CA, GA, and PCD. ## Slowly Progressive Cerebellar Ataxia and “Not Manifestly Evident” Autoimmunity There are reports of patients showing slowly progressive ataxia without definite autoimmune triggers or well-characterized Abs responding positively to immunotherapies [8–10]. Some researchers questioned if these patients have IMCAs. They argue that it is uncertain whether their autoimmune tendency is directly or indirectly responsible for the insult to the cerebellum based on the following two problems [18]. The first problem is the significance of the presence of autoantibodies. If such antibodies are not pathogenic, then autoantibodies and ataxia coexist, and the ataxia is caused by metabolic/toxic or degenerative mechanisms other than autoimmunity. It should also be noted that some autoantibodies such as anti-thyroid Abs and low-titer anti-GAD Abs can be found in healthy subjects, albeit at low frequencies [18]. In addition, some antibodies, e.g., anti-NH2-terminal of α-enolase (NAE) Abs, co-exist in patients with multiple system atrophy, suggesting degenerative changes might induce secondarily autoimmune processes [19]. The second problem is the lack of autoimmune features. For example, in diagnosis criteria of PACA, clinical features that suggest autoimmune etiology were proposed, including acute or subacute time course, midline cerebellar atrophy, CSF pleocytosis and/or positive CSF restricted IgG oligoclonal bands, history of other autoimmune disorders, or family history of autoimmune disorders [7]. However, some of these patients may not exhibit many of the autoimmunity-pointing features defined by PACA criteria. Thus, the reported cases were diagnosed as IMCAs for the first time as a result of response to immunotherapies or intrathecal Ab synthesis [8–10] and the exclusion of other etiologies. Faced with such cases, clinicians may have difficulty making a diagnosis of IMCAs. ## “Prodromal Stage” in Immune-Mediated Cerebellar Ataxias Degenerative diseases, including Alzheimer’s, idiopathic Parkinson’s disease, progressive supranuclear palsy, usually develop gradually, over the course of months to years. It is well known that some non-neurological and neurological symptoms (e.g., anosmia in Parkinson’s disease) can be present at the prodromal stage [20, 21]. Consistently, a pre-symptomatic or prodromal stage was observed in an animal model of degenerative ataxia [22]. In patients with degenerative ataxia, pre-ataxic symptoms have recently been reported in gait stability [23] and ocular movements [24]. Time-course of development of ataxia in spinocerebellar ataxia (SCA) types 1, 2, 3, and 6 mutation carriers was traced longitudinally and revealed marginal progression in the prodromal period, followed by increasing progression once ataxia is established [25]. On the other hand, it should be acknowledged that particular neurological symptoms such as brainstem attacks preceded the manifestation of ataxia in some patients with IMCAs [9–11]. Some organ-specific autoimmune disorders can also be present as prodromal [9]. ## The Analogy with Latent Autoimmune Diabetes in Adults/Slowly Progressive Insulin-Dependent Diabetes Mellitus Anti-GAD Abs are specifically associated with type 1 DM, characterized by acute onset with an immediate requirement for insulin therapy and immune-mediated destruction of pancreatic beta-cells. Interestingly, anti-GAD Abs are also detected in ~$10\%$ of patients with adult-onset DM initially diagnosed as type 2 DM [13–16]. Thyroid and gastric auto-immunity are also frequently associated. These patients, who initially do not require insulin treatment, gradually become insulin-dependent. This atypical subset of autoimmune DM is referred to as slowly progressive insulin-dependent DM (SPIDDM) [26] or latent autoimmune diabetes in adults (LADA) [12, 27]. The presence of anti-GAD Abs reflects the autoimmune-mediated inflammation in the islet. LADA shows a lack of manifestly evident autoimmunity. In addition to atypical clinical courses (slow progression to insulin dependency), anti-GAD Abs are not repeatedly detected. It was reported that after 3 years of follow-up, $37\%$ of LADA patients remained anti-GAD Ab positive, $20\%$ fluctuated between positivity and negativity, and the remaining $43\%$ became anti-GAD Ab negative [15]. The time course of LADA is now classified into three stages [16]. At the first stage of the disease, genetically triggered autoimmunity slowly destroys the beta-cell and reduces insulin secretion. At a second stage, exposure to an unhealthy lifestyle causes insulin resistance and overload of beta-cells. Eventually, any compensations by beta-cells fail to meet the increasing insulin need, resulting in hyperglycemia and, finally, an insulin-dependent status. In other words, the slowly progressive immune-mediated inflammation ultimately disrupts beta-cells’ reserve capacities [13]. Thus, clinicians often struggle to reach an early diagnosis. Careful estimations on insulin deficiency (e.g., C-peptide test) are recommended in LADA/SPIDDM [13, 14]. Recent studies using ELISA methods show that the titer of anti-GAD Abs has no relevance to insulin-requiring diabetes [14]. The progression to a stage of beta-cell destruction occurs in patients with high-titer and low-titer. Furthermore, an early intervention targeting anti-GAD Ab-positive individuals without manifest diabetes is proposed [16]. In conclusion, a clinical notion of LADA/SPIDDM argues for the importance of early intervention by stressing two critical characteristics: [1] autoimmune etiology is present but easily overlooked, [2] beta-cell deficiency is potential but not active /symptomatic in the early stage (a prodromal stage). ## Definition of Latent Autoimmune Cerebellar Ataxia (LACA) We propose the concept of latent autoimmune cerebellar ataxia (LACA) analogous to LADA. The term LACA should be used in the following situations (see also Fig. 1):An autoimmune etiology is present but not easily detectable since it is not associated with personal history of autoimmune diseases or well-characterized autoantibodies. The ataxia is subclinical or so mild that is difficult to detect on clinical examination, and non-specific symptoms or other non-cerebellar neurological manifestations may precede the manifestation of ataxia. This stage can be retrospectively identified as prodromal. LACA by definition is likely to follow a course of slow progression. Ultimately, the autoimmune mechanisms will affect the cerebellum, resulting in clinical ataxia and eventually marked cerebellar atrophy. The notion of LACA is introduced to encourage clinicians to carefully examine the possibility of slow-evolving IMCA, as well as to stress the importance of the early intervention of immunotherapies during a period when there is cerebellar reserve. Fig. 1A definition of LACA. Prodromal IMCAs and IMCAs are classified based on the manifestation of two factors: cerebellar ataxias and autoimmunity. LACA can be utilized to describe situations in which cerebellar ataxias are latent or the autoimmunity is latent. IMCAs, immune-mediated cerebellar ataxias; LACA, latent autoimmune cerebellar ataxia; MFS, Miller Fisher syndrome; PCD, paraneoplastic cerebellar degeneration; OMS, opsoclonus myoclonus syndrome; PACA, primary autoimmune cerebellar ataxia A clinical course is shown in Fig. 2 and the comparisons between LADA and LACA are shown in Table 2.Fig. 2A clinical course from presymptomatic stage and prodromal stage to ataxic stage. LACA can be used to describe the prodromal stage in IMCAs. During a period of the presymptomatic and prodromal stages, the internal model is preserved, leading to normal predictive operations, whereas, in ataxic stage, the internal model is impaired, resulting in a development of cerebellar ataxias. As the disease progresses, cerebellar reserve is lost. IMCAs, immune-mediated cerebellar ataxias; LACA, latent autoimmune cerebellar ataxia; MFS, Miller Fisher syndrome; PCD, paraneoplastic cerebellar degeneration; OMS, opsoclonus myoclonus syndrome; PACA, primary autoimmune cerebellar ataxiaTable 2Comparisons between latent autoimmune diabetes in adults (LADA) and latent autoimmune cerebellar ataxia (LACA)Latent autoimmune diabetes in adults (LADA)Latent autoimmune cerebellar ataxia (LACA)AutoimmunityPresent, but not easily detectable because often subclinicalProdromal stage• Present• Compensations for islet autoimmunity and insulin deficiency are disrupted, LADA becomes manifest• Present• Brainstem attacks were reported• A disruption of cerebellar reserve leads to a clinical manifestation of LACAClinical course• Adult onset (30–50 years), slowly progressive to insulin dependency• Anti-GAD antibodies positive• Different from auto-immune diabetes type 1 and ketosis prone diabetes• Mostly, slowly progressive• Possible detection of circulating antibodies• May evolve into IMCAOutcome of autoimmune insultβ cell destructionCerebellar atrophy• β cell destruction and cerebellar atrophy is potential but may be not active/symptomatic• Early intervention warranted• Possible preventive therapiesAbbreviations: IMCAs, immune-mediated cerebellar ataxias ## “Not Manifestly Evident” Autoimmunity Observed in LACA This section highlights “not manifestly evident” autoimmunity in LACA through examples of CA associated with anti-GAD Abs and paraneoplastic cerebellar degeneration (PCD). In the “Slowly Progressive Cerebellar Ataxia and “Not Manifestly Evident” Autoimmunity” section, we proposed two factors of “not manifestly evident” autoimmunity: [1] the presence of poorly characterized autoantibodies and [2] the lack of autoimmune features. Here we will discuss these two features in CA associated with anti-GAD Abs and PCD, respectively. The significance of poorly characterized Abs associated with cerebellar ataxia needs to be carefully followed in conjunction with the manifestation of other autoimmunity features. ## High-Titer and Low-Titer Anti-GAD Ab and CA (Hiroshi Mitoma, Christiane S Hampe, Marios Hadjivassiliou) High-titer anti-GAD65 Ab in serum (often also found in the CSF) leads to the diagnosis of anti-GAD ataxia [2, 4]. The titer is usually above10,000 U/mL (or 10- to 100-fold higher compared to those of patients with type 1 DM) [2, 4]. In patients with ataxia and serum anti-GAD Abs exceeding 2000 U/mL, one can safely consider anti-GAD ataxia [6]. In contrast, the significance of low-titer anti-GAD Ab is unclear. However, it should be acknowledged that a portion of patients with low-titer anti-GAD Ab and CA must have immune-mediated etiologies since immunotherapies improved are effective. We will use the terms “high-titer” and “low-titer” as defined in the respective studies rather than reporting specific values due to the use of different anti-GAD Ab assays. The practical problem is that the autoimmune nature may not be easily detectable in CA associated with low-titer anti-GAD Ab [8–10, 28–35]. Only half of such patients have a history of autoimmune diseases, and very few showed intrathecal production of anti-GAD Abs [8–10, 28–35]. Moreover, all patients showed pan-cerebellar ataxia with insidious clinical courses [8–10, 28–35]. Due to these clinical presentations atypical for IMCAs, the presence of low-titer anti-GAD Ab does not provide obvious evidence that immune-mediated mechanisms insult the cerebellum. The autoimmune significance of low-titer anti-GAD Ab was determined only after confirming the responsiveness to immunotherapies that relied on these few clues [8–10, 28–35]. Thus, it is likely that the majority of patients with high-titer of anti-GAD Ab, and not, as yet, any clinical evidence of ataxia, may well have LACA, and that a smaller proportion of patients with low-titer anti-GAD Ab may also have LACA. Notably, the epitope specificities of anti-GAD Abs in type 1 DM differ significantly from those in neurological diseases associated with anti-GAD Ab. In addition, it was suggested that high-titer anti-GAD Ab decreased GABA release, while low-titer anti-GAD Ab had no such pathogenic actions [32]. These characteristics of low-titer anti-GAD Ab and CA show the following principle: “The presence of auto-Abs alone may not support the diagnosis of IMCAs due to multiple epitopes, while low-titer auto-Abs are not necessarily correlated with only low-level insults on the cerebellum.” This principle should be considered when diagnosing “not manifestly evident” autoimmunity. Since epitope specificities have not been considered so far in the diagnosis of neuroimmune diseases, we will discuss methodological problems in defining epitopes in the next section. ## Importance of Epitope Mapping of Anti-GAD Abs (Christiane S Hampe) Epitope mapping of disease-specific anti-GAD Abs can support the correct diagnosis and prediction of disease, understanding of the underlying autoimmune response, identification of antibodies with pathologic potential, and development of therapeutics. Disease-specific anti-GAD Ab epitopes in type 1 DM and neurological disorders, such as Stiff Person Syndrome (SPS), were evident already in the early 90s, when Baekkeskov et al. found that anti-GAD Abs in patients with SPS recognized both linear and conformational GAD epitopes, while those in patients with type 1 DM were dependent on the conformation integrity of the molecule [36]. Subsequent analyses utilized fusion proteins substituting regions of GAD65 with those of the slightly larger isoform GAD67. GAD$\frac{65}{67}$ fusion proteins [37] helped to identify two major regions in the middle and the C-terminus that contained conformational epitopes relevant to type 1 DM, while anti-GAD Ab in patients with SPS recognized epitopes located also at the N-terminus [38] (Fig. 3). However, there were considerable shortcomings associated with this epitope mapping approach. The use of GAD67 as a “scaffold” for GAD65 epitope regions depends on the relative lack of antigenicity of GAD67. Indeed, antibodies in type 1 DM only occasionally recognize GAD67 [39]. However, anti-GAD Ab in SPS and other neurological disorders frequently react with GAD67, and GAD$\frac{65}{67}$ fusion proteins are therefore of limited use for epitope analyses, especially in neurological disorders [31].Fig. 3Anti-GAD Ab epitope domains recognized by antibodies in different diseases. Linear anti-GAD Ab epitopes recognized by patients with SPS (rectangular boxes) have been identified using both peptide mapping and ES-RBA. These epitopes are dispersed across the entire GAD molecule. Conformational anti-GAD Ab epitopes recognized by patients with SPS (yellow ovals) or patients with type 1 DM (blue circles) have been identified by the use of GAD$\frac{65}{67}$ fusion proteins and ES-RBA An epitope mapping assay (epitope-specific radioligand binding assay (ES-RBA)) allows the detection of conformational and linear GAD65-specific antibody epitopes and was able to distinguish between anti-GAD Abs in sera of patients with type 1 DM, SPS, limbic encephalitis, CA, and anti-GAD Ab-positive epilepsy [40, 41] (Fig. 3). This epitope recognition was independent of anti-GAD Ab titer, a crucial aspect as anti-GAD Ab titers often vary considerably. Importantly, other studies using GAD65 fragments for epitope mapping in patients with the same neurological autoimmune diseases could not identify these disease-specific epitopes [42, 43], underlining the relevance of this approach for the identification of disease-specific anti-GAD Ab epitopes. Therefore, the presence of anti-GAD Abs in itself is not sufficient for a precise diagnosis. Furthermore, low anti-GAD Ab titers do not always suggest a more benign pathogenesis. Other factors, such as anti-GAD Ab epitopes (linear and conformational), location of anti-GAD Ab production (systemic and intrathecal), and IgG subclasses, need to be part of a careful evaluation of the underlying autoimmune response. Future studies to identify anti-GAD Ab epitopes in LACA are needed to establish a disease-specific pattern, which may aide clinicians in the diagnosis and targeted therapy. ## “Not Manifestly Evident” Autoimmunity in PCD (Sergio Muñiz-Castrillo, Alberto Vogrig, Bastien Joubert, Jérôme Honnorat) Paraneoplastic cerebellar degeneration (PCD), often manifesting as rapidly progressive cerebellar syndrome, is defined as a severe subacute pancerebellar syndrome triggered by cancer [44, 45]. PCD has been reported as one of the most common paraneoplastic neurological syndromes (PNS), accounting for nearly 20–$30\%$ of those fulfilling the criteria for definite PNS [46–49]. ## Well and Poorly Characterized Onconeural Antibodies A characteristic of PCD, as in other PNS, is the association with autoantibodies that target a great diversity of neural antigens (Table 3). Most of these antigens are located at the intracellular level; hence, the antibodies are thought not to be directly involved in the pathogenesis of the disease that is instead mostly mediated by cytotoxic T cells [50]. Nevertheless, most of these antibodies are reliable biomarkers of an underlying cancer and therefore useful to guide the tumor screening [44], as nearly $65\%$ of these patients are not known to have cancer at the time of diagnosis [51, 52]. In two PCD series, the most common anti-neural Abs identified were, in decreasing order of frequency, Yo, Hu, CV2/CRMP5, Tr/DNER (delta/notch-like epidermal growth factor-related receptor), Ri, and Ma2 [51, 52]. Furthermore, the oncological accompaniments considerably differ between these Abs [53–58] (Table 3). Recently, anti-Kelch-like protein 11 (KLHL11) Abs have also been described in association with testicular tumors and brainstem-cerebellar involvement [59].Table 3Main antibody and cancer associations with PCDAntibodyPredominant sex, median age (y)Frequency of cerebellar involvement (%)Other neurological phenotypesUsual tumorsFrequency of cancer (%)Ref. Yo/PCA-1>$95\%$ F, 60>90Brainstem, cranial nerve involvement, or peripheral neuropathy may co-occur with PCD ($10\%$)Ovary and breast>90[62, 77]Tr/DNER$70\%$ M, 55>90Uncommonly LE or co-occurring encephalopathy or optic neuritisHodgkin lymphoma90[53, 54]KLHL11>$95\%$ M, 4585Co-occurring brainstem involvement is almost constant, LE, myelitisTesticular80[59]Ri/ANNA-$270\%$ F, 6550–70Brainstem, OMS, movement disordersBreast > lung>70[56, 57,]MAP1B/PCA-$250\%$ M/F, 7040Sensorimotor neuropathy, EMSCLC, NSCLC, breast80[117]Ma$270\%$ M, 5520–30LE, diencephalitis, brainstem encephalitisTesticular, NSCLC>75[58]CV2/CRMP$560\%$ M, 6020–25EM, SNN, chorea, uveo-retinal involvementSCLC, malignant thymoma>80[118, 119]SOX-1/AGNA$60\%$ M, 6020LEMSSCLC>90[120]Amphiphysin$60\%$ F, 6515Polyradiculoneuropathy, SNN, EM, SPSSCLC, breast80[121]Hu/ANNA-$170\%$ M, 6510–20SNN, chronic gastrointestinal pseudo-obstruction, EM, LESCLC >> NSCLC85[55]P/Q VGCC>$95\%$ M, 65<2Co-occurring LEMS ($50\%$)SCLC90[122, 123]Abbreviations: AGNA, anti-glial nuclear antibody; ANNA, antineuronal nuclear antibody; CRMP5, collapsin response-mediator protein-5; DNER, delta/notch-like epidermal growth factor-related receptor; EM, encephalomyelitis; F, female; KLHL11, Kelch-like protein 11; LE, limbic encephalitis; LEMS, Lambert-Eaton myasthenic syndrome; MAP1B, microtubule associated protein 1B; M, male; NSCLC, non-small-cell lung cancer; OMS, opsoclonus-myoclonus syndrome; PCA, Purkinje cell antibody; PCD, paraneoplastic cerebellar degeneration; SCLC, small-cell lung cancer; SNN, sensory neuronopathy; SPS, stiff-person syndrome; VGCC, voltage-gated calcium channels; y, years Besides, several additional antibodies have been identified in only a few PCD patients (Table 4), such as Abs directed against TRIM$\frac{9}{67}$ and metabotropic glutamate receptor 2 [60, 61]; their characterization therefore warrants further investigation. Finally, seronegative cases may account for approximately $20\%$ of PCD, being associated with gynecological cancers, lymphomas (non-Hodgkin and Hodgkin), and lung cancer in women, and with lung, Hodgkin lymphoma, and genitourinary cancers in men [52]. Conversely, cancer is not found in about $10\%$ of Ab-positive suspected PCD [51, 52].Table 4Poorly characterized antibodies associated with PCDAntibodyNumber of cases reportedFrequency of cerebellar involvement (%)Other neurological phenotypesAssociated tumorsFrequency of cancer (%)Ref. TRIM$\frac{9}{672100}$-NSCLC100[60]mGluR22100-Neuroendocrine cancer, alveolar rhabdomyosarcoma100[61]Protein kinase Cγ2100-NSCLC, hepatic adenocarcinoma100[124, 125]CARP VIII3100-Melanoma, ovarian and breast cancer100[126–128]ARHGAP261070Cognitive impairment, hyperekplexiaDiverse carcinomas, B-cell lymphoma, melanoma50[129]Neuronal intermediate filament (light chain)2150Encephalopathy, myelopathyNeuroendocrine carcinomas80[130]ITPR12550Peripheral neuropathy, encephalitis, myelopathyBreast and others carcinomas30[131, 132]ANNA-31130Sensorimotor neuropathy, LE, myelopathySCLC80[133]Abbreviations: ANNA-3, anti-nuclear antibody 3; ARHGAP26, Rho GTPase-activating protein 26; CARP VIII, carbonic anhydrase-related protein VIII; ITPR1, inositol 1,4,5-triphosphate receptor 1; LE, limbic encephalitis; mGluR2, metabotropic glutamate receptor 2; NSCLC, non-small-cell lung cancer; PCD, paraneoplastic cerebellar degeneration; SCLC, small-cell lung cancer; TRIM, tripartite motif-containing protein ## Main Clinical Features and Temporal Patterns PCD commonly presents as a truncal and appendicular ataxia developed over a matter of weeks [51]. Additionally, horizontal nystagmus is an almost constant feature, often with some vertical and torsional component, whereas down-beat nystagmus in particular has been proposed as a hallmark of PCD [55, 62]. Hyperacute presentations in less than 24 h have been described in patients with anti-Yo and anti-Hu Abs, though they only account for approximately $5\%$ of all PCDs [56, 62–64]. Less commonly, the cerebellar presentation can be slowly progressive over months, especially in patients with anti-Ri and anti-Ma2 Abs [57, 65]. ## LACA: the Prodromal Stage There has been cumulating evidence suggesting that some neurological symptoms precede the manifestation of ataxia in degenerative/genetic ataxias. Alteration of gait and postural sway control was detected in subjects with prodromal SCA2 using a wearable sensor-based system [23]. Pre-ataxic changes in SCA3 patients were reported in vestibulo-ocular reflex gain, main sequence of vertical volitional saccades, and slow-phase velocity of central and gaze-evoked (SPV-GE) nystagmus in SCA3 [24]. We argue the existence of a “prodromal stage” that precedes the manifestation of CAs also in IMCAs. We retrospectively review prodromal symptoms in low-titer anti-GAD ataxia (section “Prodromal Stage in Patients with Anti-GAD Antibodies (José Fidel Baizabal-Carvallo)”), PCD (section “Prodromal Stage in PCD (Sergio Muñiz-Castrillo, Alberto Vogrig, Bastien Joubert, Jérôme Honnorat)”), gluten ataxia (GA) (section “Prodromal Stage in Gluten Ataxia (Marios Hadjivassiliou)”), and post-infectious cerebellar syndrome (section “Prodromal Stage in Post-infectious Cerebellar Syndrome (PiCS) (José Fidel Baizabal-Carvallo)”). ## Prodromal Stage in Patients with Anti-GAD Antibodies (José Fidel Baizabal-Carvallo) The clinical course of LACA is largely unknown. Emerging evidence suggests that patients with LACA may present with systemic or organ-specific autoimmune disorders before the onset of progressive ataxia, including neurological and systemic manifestations (Table 5). Moreover, a prodromal stage characterized by nonspecific symptoms including malaise, fatigue, or even cognitive complaints may be seen before the onset of neurological symptoms. Table 5Summary of clinical manifestations preceding or accompanying the onset of LACASystemic or organ-specific autoimmune disorder Latent autoimmune diabetes mellitus Overt type 1 diabetes mellitus Polyglandular syndrome type II (Schmidt syndrome) Autoimmune thyroiditis Vitiligo Pernicious anemia Myasthenia gravis Gluten sensitivityNeurologicalOculomotor Horizontal nystagmus Downbeat nystagmus Upbeat nystagmus Multidirectional nystagmus Oculomotor paresis Opsoclonus Abnormal smooth pursuit Increased latency of saccades Decreased velocity of saccadesOther Fluctuating vertigo Fluctuating limb or gait ataxia Axial stiffness Limb stiffness Epileptic seizuresAbbreviations: LACA, latent autoimmune cerebellar syndrome Oculomotor manifestations may precede the development of overt ataxia. Multidirectional, horizontal, upbeat, and downbeat nystagmus may present in patients with low-titer anti-GAD Ab [28, 66, 67]. These patients should be evaluated carefully as they may initially show relatively minor signs of ataxia, such as difficulties with tandem gait or mild dysmetria. Such relatively subtle cerebellar manifestations may progress with time, leading to overt CA [68]. A clinical picture with more diffuse brainstem manifestations may also be a presentation of LACA [11]. Emerging evidence suggests that about $25\%$ and $35\%$ of patients with ataxia and high anti-GAD Ab develop episodes of transient neurological dysfunction involving brainstem nuclei and cerebellar connections [11]. These so-called brainstem attacks manifest with horizontal and/or vertical diplopia, nystagmus, vertigo, nausea, vomiting, dysarthria, paralysis of the posterior pharyngeal wall, gait, and limb ataxia [11, 31, 34]. These prodromal symptoms may last from several minutes to days but even weeks or months; their frequency may also be extremely variable, and the episodes may be isolated or with a paroxysmal character. Although these episodes usually resolve spontaneously and patients initially do not present with evident CA, an insidious progressive cerebellar syndrome usually appears within the following 3 months, but longer latencies of up to 2 years have also been reported [11, 31]. The reversibility of such symptoms suggests a transient autoimmune impairment apart from conspicuous neuronal loss that will eventually lead to progressive cerebellar damage. Besides brainstem manifestations, axial or appendicular muscle rigidity, sometimes with superimposed muscle spasms may antedate the onset of a progressive CA [31, 69]. Such manifestations are consistent with the “classic” or “focal/segmental” subtypes of SPS antedating the cerebellar syndrome, sometimes underlying, by years [33, 70]. The coexistence of systemic or organ-specific autoimmune disorders [11, 35] with LACA suggests the presence of an active multi-organ autoimmune response with different degrees of active antibody production. Low and high-titer anti-GAD Abs may be identified in patients with gluten ataxia, which is a form of sporadic ataxia associated with anti-gliadin and anti-transglutaminase 6 Abs [71]. Forty percent of patients with gluten ataxia were anti-GAD Ab positive with a mean titer of 25 U/mL [71]. Whether such low-titers of anti-GAD Abs contributed to the cerebellar damage is difficult to define; however, the titer of anti-GAD Abs decreased in a third of these patients following a gluten-free diet, a phenomenon related to clinical improvement [71]. Similarly, $70\%$ of patients with anti-GAD ataxia were found to have positive serology for gluten sensitivity, some of which responded well to a gluten-free diet without requiring immunosupression. This suggests a significant overlap between gluten ataxia and anti-GAD ataxia [72]. Current literature does not associate LACA with underlying neoplasia; however, few patients with CA and high-titer of anti-GAD Abs have been found with occult neoplasia and may exceptionally exists with low anti-GAD Ab titer [31, 33, 66]. The morphological appearance of the cerebellum assessed by neuroimaging seems a poor predictor of anti-GAD Ab titer, as up to $43\%$ of patients with high Ab titer do not have cerebellar atrophy on MRI [73]. In patients with high titer anti-GAD antibodies, improvement of oculomotor abnormalities may follow treatment with IVIg, corticosteroids, or plasma exchange; however, the response may be incomplete or selective to specific form of ocular motor deficits. Cyclophosphamide may provide benefit in those who are refractory to treatment with IVIg, plasma exchange, or steroids [66]. However, it is unclear what proportion of these patients will eventually evolve to overt CA with high-titer anti-GAD Ab. Miller Fisher syndrome with negative anti-GQ1b and relatively low anti-GAD Ab (<2000 IU/mL) has been reported to improve clinically with IVIg alongside with a decrease of anti-GAD Ab titer [74]. Despite the low-titer of anti-GAD Abs, improvement with monthly courses of IVIg has been registered in selected patients coupled with improvement in cerebellar perfusion [75, 76]. Intravenous methylprednisolone 1000 mg/day for 5 days followed by oral prednisone for 2 months or oral corticosteroids alone may also provide clinical benefit on different ataxia scales [28, 29]. In summary, the brainstem manifestations either as transient deficits, i.e., “brainstem attacks” or as oculomotor dysfunction are among the most notable preceding symptoms in patients with ataxia associated with high-titer and low-titer anti-GAD Abs. SPS-like, systemic, or organ-specific autoimmune disease may also be present. Immunotherapy usually relates with good outcomes and clinical improvement associates with decrease in anti-GAD Ab titer, even if they are low at baseline. Whether this is explained by publication bias through successfully treated patients should be clarified in further studies. ## Prodromal Stage in PCD (Sergio Muñiz-Castrillo, Alberto Vogrig, Bastien Joubert, Jérôme Honnorat) PCD may be accompanied by extracerebellar involvement, the frequency, and severity of which largely depends on the associated antibody. In general, patients with anti-Yo Abs and those with anti-Tr/DNER Abs tend to manifest PCD as isolated or predominant neurological manifestation [51]. Nevertheless, a thorough neurological examination in patients with anti-Yo Abs can disclose mild signs or symptoms suggesting involvement of corticospinal tract (30–$58\%$) [62, 77], brainstem disorders or cranial neuropathies ($13\%$) [77], peripheral disorders ranging from hyporeflexia and mild sensory disturbances ($54\%$) [62] to a clear diagnosis of peripheral neuropathy ($10\%$) [77], or even gastrointestinal dysmotility in rare circumstances [77]. Sometimes, the cerebellar syndrome is so severe that it prevents the optimal mental status evaluation. Robust dysarthria and motor dysfunction interfere with the evaluation of the cognitive impairment. Despite these difficulties in the examination, approximately one-fifth of patients with anti-Yo Abs has clinical evidence of cognitive dysfunction, typically in the form of emotional lability and memory deficits [62]. It remains unclear if these deficits relate to the cognitive functions of the cerebellum (Schmahmann syndrome) or reflect the involvement of extra-cerebellar structures [78]. In a series of 28 patients with anti-Tr/DNER Abs, all but one patient had cerebellar involvement, which was isolated except for 2 cases ($7\%$) who showed encephalopathy and sensory neuropathy [53]. PCD patients with other anti-neural Abs show distinctive associations with both central and peripheral neurological disorders (Table 3). Importantly, the extracerebellar involvement can be a clue for the associated antibody. For example, sensory neuronopathy with or without encephalomyelitis is typically associated with anti-Hu Abs [55], hearing loss and/or tinnitus is frequently associated with anti-KLHL11 Abs [59], opsoclonus-myoclonus in adults typically associate with anti-Ri antibodies [56, 57], while narcolepsy-cataplexy and hypopituitarism are common in patients with anti-Ma2 Abs [58]. Notably, these extra-cerebellar symptoms are sometimes subtle, and precede the manifestations of CA. PCD is sometimes preceded by prodromal clinical symptoms such as nausea, vomiting, dizziness, and vertigo [79, 80]. In addition, some patients with anti-Ri Abs may present with an isolated action tremor that evolves into an overt PCD late in the disease [57]. Similarly, hearing loss or tinnitus antedate the development of cerebellar/brainstem dysfunction in nearly $25\%$ of the patients with anti-KLHL11 Abs [59]. We observed that up to $37\%$ of patients with ataxia and anti-KLHL11 Abs experienced transient, paroxysmal, episodes of vertigo, unbalance, and vomiting, which lasted from months to years prior to the development of a permanent cerebellar dysfunction [81]. Furthermore, the genetic abnormalities observed in ovary cancer of patients with anti-Yo PCD and leading to the immune breakdown responsible of PCD immune activation are present in some women many years before the development of the cerebellar symptoms [82]. These data suggest that in some patients the cerebellar immune reaction can be present many months and years before the clinical symptoms. In conclusion, even in patients with PCD, many arguments suggest that the immune reaction is present in the cerebellum many weeks or months before the development of clinical symptoms suggesting that the concept of LACA can be extended to patients with PCD. ## Prodromal Stage in Gluten Ataxia (Marios Hadjivassiliou) GA refers to an IMCA triggered by the ingestion of gluten in gluten-sensitive individuals [83, 84]. By definition, such patients will have CA in the presence of serological markers of gluten sensitivity (one or more of antigliadin, TG2, and TG6 antibodies) [85]. The presence of enteropathy defines coeliac disease (CD) but it is not a prerequisite for the diagnosis of GA. It has been shown that up to $47\%$ of patients with newly diagnosed CD, presenting to the gastroenterologists have abnormal MR spectroscopy of the cerebellum [86]. Clinical evaluation showed that $29\%$ of the patients had evidence of mild gait ataxia and $11\%$ had nystagmus. None of these patients, however, had been referred to or seen by a neurologist even if on direct questioning $24\%$ reported some gait instability. It could be argued that patients with LACA for whom the diagnosis of CD is based primarily on gastrointestinal symptoms, early treatment and good outcome can be expected given the retained cerebellar reserve. In our experience at the Sheffield Ataxia Centre, we often get referrals of patients who have had brain imaging for various reasons (e.g., headache) who are then noted to have evidence of cerebellar atrophy. We have a cohort of such patients with positive serology for gluten sensitivity who on clinical examination have no evidence of any detectable ataxia. Often these patients have abnormal spectroscopy of the cerebellum that improves with the introduction of gluten-free diet. These examples represent LACA where the cerebellar reserve is sufficient to compensate for the cerebellar atrophy and the clinical intervention with gluten-free diet can result in complete recovery. As such, the use of gluten sensitivity-related antibodies and in particular antigliadin and TG6 Abs may be a useful biomarker of some cases of LACA. Equally the use of MR spectroscopy of the cerebellum may identify patients with reduced NAA/Cr in the cerebellar vermis and/or hemisphere implying cerebellar dysfunction without overt ataxia. A prospective evaluation of healthy volunteers who have serological evidence of gluten sensitivity may be helpful in better understanding LACA in the context of gluten sensitivity. ## Prodromal Stage in Post-infectious Cerebellar Syndrome (PiCS) (José Fidel Baizabal-Carvallo) Post-infectious cerebellar syndrome (PiCS) is defined as acute cerebellar inflammation induced by immune-mediated mechanisms triggered by a bacterial or viral pathogen, such as mycoplasma pneumoniae, Epstein-Barr virus, varicella zoster virus, cytomegalovirus, Coxsackie B3, or following vaccination [6, 87, 88]. More recently, SARS-CoV2, the cause of COVID19, has been identified as a potential trigger [89, 90]. However, in a substantial proportion of these patients, there is no clear underlying or previous infection. Although this condition is more commonly observed in children, there are reports of adult cases as well [91]. PiCS should be readily differentiated from acute infectious cerebellitis, the latter being caused by direct cerebellar tissue invasion by a specific pathogen. Antibodies directed against the glutamate receptor delta 2 (anti-GluRδ2) which is highly expressed in Purkinje cells have been identified in the serum and CSF of some patients with cerebellitis following vaccination and diverse infections [92–94]. Patients have a variable clinical course ranging from a self-limiting to a fulminant course leading to severe cerebellar damage or death in some instances [95]. MRI may show unilateral or bilateral T2-weighted hyperintensities in the cerebellum [96]. Molecular mimicry is presumed as the pathogenic mechanism, similar to Guillain-Barré syndrome or Sydenham chorea in rheumatic fever [97, 98]. It is unclear how the LACA hypothesis applies to PiCS, owing to the following considerations: [1] the disorder has a self-limited course, [2] the latency between infection and onset of neurological manifestations may be unclear, although it is considered to be short, usually lasting less than 10 days, and [3] prodromal symptoms may be related to the immunological response to the infection. However, the presence of fever, malaise, drowsiness, headache, nausea, vomiting, photophobia, or vertigo, once the infection has seemingly resolved [87, 99], suggests the presence of a smoldering inflammatory process that will eventually reach a threshold for CA. ## Biomarkers During Progression of LACA The signs related to the prodromal stage in IMCAs need to be identified for early interventions. First, we examine how eye movements can be objective markers of prodromal phase prior to onset of immune-mediated CA (section “Eye Movement Abnormalities as Physiological Biomarker (Aasef G. Shaikh)”). In this section, we will further identify nature of movement deficits that can suggest prodromal phase of CA induced by diverse etiologies. Paroxysmal deficits that are at the epicenter of the prodromal phase in CA are discussed. Second, we also discuss possible autoimmune biomarkers for LACA (section “Dynamic Changes of Autoimmune Biomarkers During Progression of LACA (Christiane S Hampe)”). Dynamic changes in some cytokines might reflect the early immune-mediated insults to the cerebellum. ## Clinical Features of Subtle and Transient Ocular Motor Deficits Ocular motor deficits are common in IMCA. They can be seen in its early prodromal phase, prior to onset of CA or gait impairment. The most common form of ocular motor deficit in the neurotology clinic is gaze-evoked nystagmus followed by downbeat nystagmus. Relatively rare deficits such as upbeat nystagmus, slow saccades, and opsoclonus are also reported. Traditional diagnostic algorithm of these deficits includes screening for autoantibodies and neuroimaging. The diagnostic workup frequently reveals subtle cerebellar atrophy affecting vermis or para-vermis cerebellar region. It is not uncommon for such deficits to have negative family history, genetic disorders, or any toxic exposure. In select cases, we may find low titers of anti-GAD Abs, or voltage-gated calcium channel Abs. Hence, it is likely that early, mild cases of dizzy patients, who were found to have downbeat nystagmus and/or gaze-evoked nystagmus with mild cerebellar atrophy have prodromal phase and LACA. In such cases, when the aforementioned Abs are detected, treatment with plasma exchange or IVIg has resulted in mixed responses; i.e., one patient had complete resolution of downbeat nystagmus, while in another case, the response was incomplete. Paroxysmal ocular motor and vestibular deficits are not uncommon in LACA [31]. These patients have normal inter-ictal neurological examination. Their typical presentation is acute episodes of vertigo with background constant unsteadiness. Clinical examination may reveal provoked nystagmus, typically after hyperventilation. Occasionally, nystagmus is mild and only present in peculiar gaze orientation. Inter-ictal examination of ocular motor system reveals subtle deficits including curved trajectory of saccades, or mild dysmetria. Low-titer anti-GAD Abs are also associated with gravity independent upbeat nystagmus [67]. It is common to find heterogeneous gaze-holding deficit in the syndrome of anti-GAD Ab. These patients have waveform that has mixture of squarewaves, downbeat nystagmus, and opsoclonus. The opsoclonus superimposes upon the downbeat nystagmus, while robust squarewave jerks are present in the axis orthogonal to the downbeat nystagmus [100]. ## Mechanistic Underpinning of Paroxysmal or Transient Worsening of Ocular Motor Deficits in LACA Transient ocular motor deficits in LACA can be due to reversible loss of motor control. Such deficits are typical of transient ischemia to the brainstem but can also present with early forms of neurodegenerative or immune disease when loss of cerebellar and brainstem function is not complete and there are residual tissues compensating for the damage that has already happened. Acute cerebellar inflammatory disorders present with robust cerebellar dysfunction in the acute phase, but treatment with immunomodulation or steroids results in a complete reversal of the abnormality. Ocular motor deficits can be sensitive markers of acute cerebellar dysfunction due to cerebellitis, acute immune reaction, or decompensated neurodegenerative disorders. One of the fundamental ocular motor behaviors included maintenance of the gaze requiring accurate utilization of the gaze-holding network and the brainstem neural integrator (Fig. 4). This network utilizes eye velocity signal from the saccade burst neurons and transforms it into gaze position signal by virtue of mathematical integration of the velocity signal. A fundamental limitation of the neural integrator is that it is always inaccurate. The inaccuracy is seen in the form of drift in eye position during eccentric gaze holding; the latter is typically compensated by the feedback from the visual system or cerebellar Purkinje neurons [101]. This task requiring accurately calibrated cerebellar signal is vulnerable to any form of cerebellar dysfunction. Any abnormality in the cerebellar outflow, either due to transiently inflamed Purkinje neurons sending aberrant output, or dead Purkinje neurons sending no output, leads to lack of calibration and dysfunction of neural integrator. The consequence is drifting eye position toward the central null position and eye-in-orbit position dependence of slow-phase eye velocity, the deficits that characterize the gaze-evoked nystagmus [101]. The gaze-evoked nystagmus is seen in patients with acute cerebellitis, acute autoimmune cerebellar deficit, and it can be treated with prompt medical management. In cases where damage to Purkinje cells is not complete, or it is mild, as seen in LACA, the deficits such as gaze-evoked nystagmus can be triggered by metabolically challenging the brain — hyperventilation-induced nystagmus is one of such examples. The patient with normal ocular motor examination at baseline may produce gaze-evoked or spontaneous nystagmus after 30-s-long hyperventilation. Fig. 4Schematic presentation of gaze holding network. The pulse of eye velocity signal is integrated by the brainstem neural integrators. This mechanism relies on normal function of the cerebellar Purkinje neurons, visual system, and orbital proprioception. As depicted in panel (A), the three sources of feedback, project to the input of brainstem neural integrators. As depicted in panel (B), the integration fails when one of the sources of feedback is impaired, either by damage of cerebellar Purkinje neurons, visual dysfunction, or disrupted orbital proprioception. The consequence of such abnormality is impaired neural integration. As a consequence, the eyes drift to the central null position, and drifts are followed by correction leading to phenomenology called “nystagmus.” The same sources of feedback, the cerebellum in particular, are also critical for assuring normal amplitude and directional matrix of saccade The matrix of saccades, such as velocity and amplitude, is tightly controlled by the cerebellar Purkinje neurons. These cells provide critical error feedback and facilitate the enhanced accuracy of ongoing movements [101, 102]. Any impairment in the Purkinje neuronal function due to autoimmune or inflammatory disorders can impair the error feedback. Latter manifests in saccadic dysmetria. Reversal of saccade dysmetria is not uncommon after prompt treatment of autoimmune or inflammatory cerebellar conditions; however, permanent cerebellar damage results in irreversible saccadic dysmetria. Subtle saccade dysmetria is not unusual in compensated degenerative cerebellar disorders. Metabolic challenge due to acute medical illness can transiently affect cerebellar error control mechanism causing transient saccade dysmetria. ## Mimics of Ocular Motor Deficits in Prodromal Phase of CA in LACA Autoimmune or inflammatory deficits or prodromal phase of CA such as LACA are one way of transiently affecting the Purkinje neuron function. The other deficits include transient ischemia in the brain region supplied by the posterior circulation. These transient deficits do not present with diffusion abnormalities in the MRI, suggestive of acute stroke. Frequently, these deficits may resolve spontaneously without further intervention, and in some instances, aggressive hemodynamic management is warranted. The transient nature of brainstem attack due to vascular etiology has clear differences from that due to prodromal phase of CA due to immune or inflammatory etiologies. Immune or inflammatory etiologies can be seen in pediatric population, young adults, adults, and the elderly; in contrast, brainstem attack due to vascular etiology is typically accompanied by vascular risk factors and typically affects older adults. The duration of vascular brainstem attacks may span from several minutes or can last for days and may depend on the blood pressure. On the contrary, the brainstem attack due to inflammatory or immune etiology lasts from days to weeks; blood pressure has no correlation with symptoms. Nevertheless, vascular etiology should be seriously considered in differential to prodromal symptoms of LACA, because if untreated with appropriate anti-lipid and anti-platelet management transient ischemia to the posterior circulation can lead to permanent damage in form of cerebellar stroke. Dysfunction of the ion channels determining the cell membrane properties can frequently lead to dysfunction of the cerebellar Purkinje neurons. These deficits can be seen in those with genetic channelopathy, leading to episodic ataxias, or intoxication from pharmacological substances, such as antiepileptics. The episodic ataxias (types EA2, EA3, and EA4) present with transient ocular motor dysfunction such as gaze-evoked nystagmus, positional nystagmus, saccade dysmetria, or acute vestibular dysfunction [103, 104]. The deficits can last from hours to days and are self-limiting. There is a familial trend, and there are minimal to no ocular motor dysfunction in the inter-ictal phase. Toxic increased levels of antiepileptics such as phenobarbital, fosphenytoin, lamotrigine, and carbamazepine are known to cause acute ocular motor cerebellar dysfunction and ataxia [105–112]. Typical features are downbeat nystagmus, gaze-evoked nystagmus, axial and appendicular ataxia, and gait instability. The deficits are transient and resolve with normalizing the antiepileptic levels. The mechanism of ocular motor dysfunction in those with toxic antiepileptic levels could be attributed to ion channel dysregulation. Channelopathy closely resembles immune etiologies and prodromal phase of LACA. They can present at any age; however, unlike immune etiologies, there is a family history. On the contrary, drug-induced vestibular and ocular motor symptoms correlate with increased serum concentration of the offending pharmacotherapeutic agent. ## Dynamic Changes of Autoimmune Biomarkers During Progression of LACA (Christiane S Hampe) Latent autoimmune diseases such as LADA are characterized by a slowly progressing pathogenesis, which eventually results in irreversible tissue damage. The disease progression may be linear, or show a remission/relapse pattern, where periods of disease progression are followed by upregulation of anti-inflammatory immune responses, allowing partial recovery. Eventually, the tissue damage is too severe and restoration to normal function can no longer be achieved. The length of the prodromal period varies and may be impacted by genetic susceptibility, environmental factors, or diet. A clear understanding of the molecular events and timing involved in the final breakdown of the immune response is needed to develop intervention therapies. The dynamic nature of the pathogenesis is reflected in changing levels of immune factors, including cytokines and chemokines. In a recent publication [113], disease progression in LADA patients was found to correlate with a decline in levels of cytokines Interleukin-1 receptor agonist (IL-1ra) and interleukin-1 beta (IL-1b). IL-1ra is a receptor antagonist of IL-1 and its direct correlation with C-peptide levels in type 1 DM patients suggests an involvement of the anti-inflammatory cytokine in disease remission [114]. The concomitant decrease in pro-inflammatory cytokine IL-1 beta and anti-inflammatory cytokine IL-1ra in LADA patients demonstrates the complicated, interconnected system of cytokine regulation, where multiple factors are involved in the dynamic changes of cytokine release. While it remains to be determined whether the dynamic changes in cytokine pattern observed in LADA patients are cause or effect of the progressive decline in beta cell function, they may serve as novel biomarkers for disease progression. Further studies are necessary to establish whether changes in cytokine levels or other immune factors can be observed in LACA and can be used as biomarkers for disease progression. ## Conclusion (Manto M) The concept of LACA has implications in terms of prevention and administration of early therapies. Like for LADA, a personalized approach is recommended. The ultimate goal is the preservation of the cerebellar reserve, both functional and structural. In ataxic patients with no manifestly evident autoimmunity, the significance of associated autoantibodies should be carefully assessed for diagnosis of LACA. Patients suspected to present LACA require a close clinical/biological/radiological follow-up with longitudinal observations. The annual progression rate is currently unknown and might be different at a very early stage and later during follow-up, as observed in genetic ataxias where a non-linear progression is found [115]. In preataxic SCA3 patients, elevated levels of neurofilament light (Nfl) are detected already 7.5 years before onset. There might be a similar window in IMCA [116]. The signs related to the prodromal stage need to be identified. IMCAs patients often report fatigue in the months before the ataxia onset. There is a need to obtain data related to cerebellar involvement and extra-cerebellar involvement. The inventory of non-ataxia signs (INAS total count) might be useful in IMCAs also. Regarding the cerebellar aspect, subtle ocular disturbances, motor deficits, and cognitive/affective symptoms need to be scrutinized and correlated with biomarkers. ## References 1. Hadjivassiliou M. Immune-mediated acquired ataxias. Handb Clin Neurol (2012.0) 103 189-199. DOI: 10.1016/B978-0-444-51892-7.00011-5 2. Mitoma H, Adhikari K, Aeschlimann D, Chattopadhyay P, Hadjivassiliou M, Hampe CS. Consensus paper: neuroimmune mechanisms of cerebellar ataxias. Cerebellum. (2016.0) 15 213-232. DOI: 10.1007/s12311-015-0664-x 3. Mitoma H, Hadjivassiliou M, Honnorat J. Guidelines for treatment of immune-mediated cerebellar ataxias. Cerebellum Ataxias (2015.0) 2 14. DOI: 10.1186/s40673-015-0034-y 4. Joubert B, Rotásky J, Honnorat J. Nonparaneoplastic autoimmune cerebellar ataxia. Handb Clin Neurol (2018.0) 155 313-332. DOI: 10.1016/B978-0-444-64189-2.00021-4 5. Hadjivassiliou M, Mitoma H, Manto M, Mitoma H, Manto M. Autoimmune ataxia. Neuroimmune diseases; from cells to the living brain (2019.0) 599-620 6. Mitoma H, Manto M, Hadjivassiliou M. Immune-mediated cerebellar ataxias: clinical diagnosis and treatment based on immunological and physiological mechanisms. J Mov Disord (2021.0) 14 10-28. DOI: 10.14802/jmd.20040 7. Hadjivassiliou M, Graus F, Honnorat J, Jarius S, Titulaer M, Manto M, Hoggard N, Sarrigiannis P, Mitoma H. Diagnostic criteria for primary autoimmune cerbellar ataxia (PACA)-guidlines from an International Task Force on Immune Mediated Cerbellar Ataxia. Cerebellum. (2020.0) 19 605-610. DOI: 10.1007/s12311-020-01132-8 8. Nanri K, Niwa H, Mitoma H, Takei A, Ikeda J, Harada T. Low-titer anti-GAD-antibody-positive cerebellar ataxia. Cerebellum. (2013.0) 12 171-175. DOI: 10.1007/s12311-012-0411-5 9. Baizabal-Carvallo JF, Alonso-Juarez M. Cerebellar disease associated with anti-glutamic acid decarboxylase antibodies: review. A J Neural Transm (Vienna) (2017.0) 124 1171-1182. DOI: 10.1007/s00702-017-1754-3 10. Petrijan T, Menih M. Low-Titre GAD Antibody-associated late-onset cerebellar ataxia with a significant clinical response to intravenous immunoglobulin treatment. Cerebellum. (2017.0) 16 868-871. DOI: 10.1007/s12311-017-0851-z 11. Muñiz-Castrillo S, Vogrig A, Joubert B, Pinto AL, Gonçalves D, Chaumont H. Transient neurological symptoms preceding cerebellar ataxia with glutamic acid decarboxylase antibodies. Cerebellum. (2020.0) 19 715-721. DOI: 10.1007/s12311-020-01159-x 12. Tuomi T, Groop LC, Zimmet PZ, Rowley MJ, Knowles W, Mackay IR. Antibodies to glutamic acid decarboxylase reveal latent autoimmune diabetes mellitus in adults with a non-insulin-dependent onset of disease. Diabetes. (1993.0) 42 359-362. DOI: 10.2337/diab.42.2.359 13. Stenström G, Gottsäter A, Bakhtadze E, Berger B, Sundkvist G. Latent autoimmune diabetes in adults: definition, prevalence, beta-cell function, and treatment. Diabetes (2005.0) 54 S68-S72. DOI: 10.2337/diabetes.54.suppl_2.S68 14. Yasui J, Kawasaki E, Tanaka S, Awata T, Ikegami H, Imagawa A. Clinical and genetic characteristics of non-insulin-requiring glutamic acid decarboxylase (GAD) autoantibody-positive diabetes: a nationwide survey in Japan. PLoS One (2016.0) 11 e0155643. DOI: 10.1371/journal.pone.0155643 15. Huang G, Yin M, Xiang Y, Li X, Shen W, Luo S. Persistence of glutamic acid decarboxylase antibody (GADA) is associated with clinical characteristics of latent autoimmune diabetes in adults: a prospective study with 3-year follow-up. Diabetes Metab Res Rev (2016.0) 32 615-622. DOI: 10.1002/dmrr.2779 16. Carlsson S. Etiology and pathogenesis of latent autoimmune diabetes in adults (LADA) compared to type 2 diabetes. Front Physiol (2019.0) 10 320. DOI: 10.3389/fphys.2019.00320 17. Mitoma H, Buffo A, Gelfo F, Guell X, Fucà E, Kakei S. Consensus paper. Cerebellar reserve: from cerebellar physiology to cerebellar disorders. Cerebellum (2019.0) 19 131-153. DOI: 10.1007/s12311-019-01091-9 18. Graus F, Saiz A, Dalmau J. GAD antibodies in neurological disorders-insights and challenges. Nat Rev Neurol (2020.0) 16 353-365. DOI: 10.1038/s41582-020-0359-x 19. 19.Kikuchi A, Yoneda M, Hasegawa T, Matsunaga A, Ikawa M, Nakamura T, et al. High prevalence of serum anti-NH2-terminal α-enolase antibodies un patients with multiple system atrophy and corticobasal syndrome. J Neurol. 268(11):4291–5. 20. Siddiqui TG, Whitfield T, Praharaju SJ, Sadiq D, Kazmi H, Ben-Joseph A. Magnetic resonance imaging in stable mild cognitive impairment, prodromal Alzheimer’s disease, and prodromal dementia with Lewy bodies. Dement Geriatr Cogn Disord (2020.0) 49 583-588. DOI: 10.1159/000510951 21. Lavan O, Apter A, Benaroya-Milshtein N, Fennig S. The prodrome of psychotic disorders. Harefuah. (2021.0) 160 104-109. PMID: 33760412 22. Chen CC, Yao NW, Lin CW, Su WS, Wu CT, Chang C. Neuroimaging spectrum at pre-, early, and late symptomatic stages of SCA 17 mice. Cerebellum. (2020.0) 19 487-500. DOI: 10.1007/s12311-020-01127-5 23. Velázquez-Pérez L, Rodriguez-Labrada R, González-Garcés Y, Arrufat-Pie E, Torres-Vega R, Medrano-Montero J. Prodromal spinocerebellar ataxia type 2 subjects have quantifiable gait and postural sway deficits. Mov Disord (2021.0) 36 471-480. DOI: 10.1002/mds.28343 24. de Oliveira CM, Leotti VB, Bolzan G, Cappelli AH, Rocha AG, Ecco G. Pre-ataxic changes of clinical scales and eye movement in Machado-Joseph disease: BIGPRO study. Mov Disord (2021.0) 36 985-994. DOI: 10.1002/mds.28466 25. Jacobi H, du Montcel ST, Romanzetti S, Harmuth F, Mariotti C, Nanetti L. Conversion of individuals at risk for spinocerebellar ataxia type 1,2,3,and 6 to manifest ataxia (RISCA): longitudinal cohort study. Lancet Neurol (2020.0) 19 738-747. DOI: 10.1016/S1474-4422(20)30235-0 26. Kobayashi T, Tamemoto K, Nakanishi K, Kato N, Okubo M, Kajio H. Immunogenetic and clinical characterization of slowly progressive IDDM. Diabetes Care (1993.0) 16 780-788. DOI: 10.2337/diacare.16.5.780 27. Zimmet PZ, Tuomi T, Mackay IR, Rowley MJ, Knowles W, Cohen M. Latent autoimmune diabetes mellitus in adults (LADA): the role of antibodies to glutamic acid decarboxylase in diagnosis and prediction of insulin dependency. Diabet Med (1994.0) 118 299-303. DOI: 10.1111/j.1464-5491.1994.tb00275.x 28. Nociti V, Frisullo G, Tartaglione T, Patanella AK, Iorio R, Tonali PA. Refractory generalized seizures and cerebellar ataxia associated with anti-GAD antibodies responsive to immunosuppressive treatment. Eur J Neurol (2010.0) 17 e5. DOI: 10.1111/j.1468-1331.2009.02839.x 29. Virgilio R, Corti S, Agazzi P, Santoro D, Lanfranconi S, Candelise L. Effect of steroid treatment in cerebellar ataxia associated with anti-glutamic acid decarboxylase antibodies. J Neurol Neurosurg Psychiatry (2009.0) 80 95-96. DOI: 10.1136/jnnp.2007.142745 30. Sunwoo JS, Chu K, Byun JI, Moon J, Lim JA, Kim TJ, Lee ST, Jung KH, Park KI, Jeon D, Jung KY, Kim M, Lee SK. Intrathecal-specific glutamic acid decarboxylase antibodies at low titers in autoimmune neurological disorders. J Neuroimmunol (2016.0) 290 15-21. DOI: 10.1016/j.jneuroim.2015.11.012 31. Ariño H, Gresa-Arribas N, Blanco Y, Martínez-Hernández E, Sabater L, Petit-Pedrol M, Rouco I, Bataller L, Dalmau JO, Saiz A, Graus F. Cerebellar ataxia and glutamic acid decarboxylase antibodies: immunologic profile and long-term effect of immunotherapy. JAMA Neurol (2014.0) 71 1009-1016. DOI: 10.1001/jamaneurol.2014.1011 32. Mitoma H, Manto M, Hampe CS. Pathogenic roles of glutamic acid decarboxylase 65 autoantibodies in cerebellar ataxias. J Immunol Res (2017.0) 2017 2913297. DOI: 10.1155/2017/2913297 33. Baizabal-Carvallo JF. The neurological syndromes associated with glutamic acid decarboxylase antibodies. J Autoimmun (2019.0) 101 35-47. DOI: 10.1016/j.jaut.2019.04.007 34. Matsumoto S, Kusuhara T, Nakajima M, Ouma S, Takahashi M, Yamada T. Acute attacks and brain stem signs in a patient with glutamic acid decarboxylase autoantibodies. J Neurol Neurosurg Psychiatry (2002.0) 73 345-346. DOI: 10.1136/jnnp.73.3.345 35. 35.Muñiz-Castrillo S, Vogrig A, Montagnac C, Joubert B, Benaiteau M, Casez O, et al. Familial autoimmunity in neurological patients with GAD65 antibodies: an interview-based study. J Neurol. 2021; 10.1007/s00415-021-10424-w. 36. Baekkeskov S, Aanstoot HJ, Christgau S, Reetz A, Solimena M, Cascalho M, Folli F, Richter-Olesen H, De Camilli P. Identification of the 64K autoantigen in insulin-dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase. Nature. (1990.0) 347 151-156. DOI: 10.1038/347151a0 37. Daw K, Powers AC. Two distinct glutamic acid decarboxylase auto-antibody specificities in IDDM target different epitopes. Diabetes. (1995.0) 44 216-220. DOI: 10.2337/diab.44.2.216 38. Powers AC, Bavik K, Tremble J, Daw K, Scherbaum WA, Banga JP. Comparative analysis of epitope recognition of glutamic acid decarboxylase (GAD) by autoantibodies from different autoimmune disorders. Clin Exp Immunol (1999.0) 118 349-356. DOI: 10.1046/j.1365-2249.1999.01030.x 39. Hagopian WA, Michelsen B, Karlsen AE, Larsen F, Moody A, Grubin CE, Rowe R, Petersen J, McEvoy R, Lernmark A. Autoantibodies in IDDM primarily recognize the 65,000-Mr rather than the 67,000-Mr isoform of glutamic acid decarboxylase. Diabetes. (1993.0) 42 631-636. DOI: 10.2337/diab.42.4.631 40. Manto M, Honnorat J, Hampe CS, Guerra-Narbona R, López-Ramos JC, Delgado-García JM, Saitow F, Suzuki H, Yanagawa Y, Mizusawa H, Mitoma H. Disease-specific monoclonal antibodies targeting glutamate decarboxylase impair GABAergic neurotransmission. Front Behav Neurosci (2015.0) 9 78. DOI: 10.3389/fnbeh.2015.00078 41. Liimatainen S, Honnorat J, Pittock SJ, McKeon A, Manto M, Radtke JR. GAD65 autoantibody characteristics in patients with co-occurring type 1 diabetes and epilepsy may help identify underlying epilepsy etiologies. Orphanet J Rare Dis (2018.0) 13 55. DOI: 10.1186/s13023-018-0787-5 42. Fouka P, Alexopoulos H, Akrivou S, Trohatou O, Politis PK, Dalakas MC. GAD65 epitope mapping and search for novel autoantibodies in GAD-associated neurological disorders. J Neuroimmunol (2015.0) 281 73-77. DOI: 10.1016/j.jneuroim.2015.03.009 43. Gresa-Arribas N, Ariño H, Martínez-Hernández E, Petit-Pedrol M, Sabater L, Saiz A, Dalmau J, Graus F. Antibodies to inhibitory synaptic proteins in neurological syndromes associated with glutamic acid decarboxylase autoimmunity. PLoS One (2015.0) 10 e0121364. DOI: 10.1371/journal.pone.0121364 44. Graus F. Recommended diagnostic criteria for paraneoplastic neurological syndromes. J Neurol Neurosurg Psychiatry (2004.0) 75 1135-1140. DOI: 10.1136/jnnp.2003.034447 45. Graus F, Vogrig A, Muñiz-Castrillo S, Antoine JG, Desestret V, Dubey D. Updated diagnostic criteria for paraneoplastic neurological syndromes. Neurol Neuroimmunol Neuroinflamm (2021.0) 8 e1014. DOI: 10.1212/NXI.0000000000001014 46. Giometto B. Paraneoplastic neurologic syndrome in the PNS Euronetwork database: a European study from 20 centers. Arch Neurol (2010.0) 67 330. DOI: 10.1001/archneurol.2009.341 47. Kanikannan MA, Sirisha Y, Uppin MS, Jabeen SA, M Kandadai R, Sundaram C. Incidence and spectrum of paraneoplastic neurological syndromes: single center study. J Neuro-Oncol (2015.0) 125 197-206. DOI: 10.1007/s11060-015-1898-7 48. Vogrig A, Gigli GL, Segatti S, Corazza E, Marini A, Bernardini A. Epidemiology of paraneoplastic neurological syndromes: a population-based study. J Neurol (2020.0) 267 26-35. DOI: 10.1007/s00415-019-09544-1 49. Hébert J, Riche B, Vogrig A, Muñiz-Castrillo S, Joubert B, Picard G. Epidemiology of paraneoplastic neurologic syndromes and autoimmune encephalitides in France. Neurol Neuroimmunol Neuroinflamm (2020.0) 7 e883. DOI: 10.1212/NXI.0000000000000883 50. Yshii L, Bost C, Liblau R. Immunological bases of paraneoplastic cerebellar degeneration and therapeutic implications. Front Immunol (2020.0) 11 991. DOI: 10.3389/fimmu.2020.00991 51. Shams’ili S, Grefkens J, de Leeuw B, van den Bent M, Hooijkaas H, van der Holt B. Paraneoplastic cerebellar degeneration associated with antineuronal antibodies: analysis of 50 patients. Brain. (2003.0) 126 1409-1418. DOI: 10.1093/brain/awg133 52. Ducray F, Demarquay G, Graus F, Decullier E, Antoine J-C, Giometto B. Seronegative paraneoplastic cerebellar degeneration: the PNS Euronetwork experience. Eur J Neurol (2014.0) 21 731-735. DOI: 10.1111/ene.12368 53. Bernal F, Shams’ili S, Rojas I, Sanchez-Valle R, Saiz A, Dalmau J. Anti-Tr antibodies as markers of paraneoplastic cerebellar degeneration and Hodgkin’s disease. Neurology. (2003.0) 60 230-234. DOI: 10.1212/01.WNL.0000041495.87539.98 54. de Graaff E, Maat P, Hulsenboom E, van den Berg R, van den Bent M, Demmers J. Identification of delta/notch-like epidermal growth factor-related receptor as the Tr antigen in paraneoplastic cerebellar degeneration. Ann Neurol (2012.0) 71 815-824. DOI: 10.1002/ana.23550 55. Graus F. Anti-Hu-associated paraneoplastic encephalomyelitis: analysis of 200 patients. Brain. (2001.0) 124 1138-1148. DOI: 10.1093/brain/124.6.1138 56. Pittock SJ, Lucchinetti CF, Lennon VA. Anti-neuronal nuclear autoantibody type 2: paraneoplastic accompaniments. Ann Neurol (2003.0) 53 580-587. DOI: 10.1002/ana.10518 57. Simard C, Vogrig A, Joubert B, Muñiz-Castrillo S, Picard G, Rogemond V. Clinical spectrum and diagnostic pitfalls of neurologic syndromes with Ri antibodies. Neurol Neuroimmunol Neuroinflamma (2020.0) 7 e699. DOI: 10.1212/NXI.0000000000000699 58. Dalmau J. Clinical analysis of anti-Ma2-associated encephalitis. Brain. (2004.0) 127 1831-1844. DOI: 10.1093/brain/awh203 59. Dubey D, Wilson MR, Clarkson B, Giannini C, Gandhi M, Cheville J. Expanded clinical phenotype, oncological associations, and immunopathologic insights of paraneoplastic Kelch-like protein-11 encephalitis. JAMA Neurol (2020.0) 77 1420-1429. DOI: 10.1001/jamaneurol.2020.2231 60. Do LD, Gupton SL, Tanji K, Bastien J, Brugière S, Couté Y. TRIM9 and TRIM67 are new targets in paraneoplastic cerebellar degeneration. Cerebellum (2019.0) 18 245-254. DOI: 10.1007/s12311-018-0987-5 61. Ruiz-García R, Martínez-Hernández E, Joubert B, Petit-Pedrol M, Pajarón-Boix E, Fernández V. Paraneoplastic cerebellar ataxia and antibodies to metabotropic glutamate receptor 2. Neurol Neuroimmunol Neuroinflamm (2019.0) 7 e658. DOI: 10.1212/NXI.0000000000000658 62. Peterson K, Rosenblum MK, Kotanides H, Posner JB. Paraneoplastic cerebellar degeneration. I. A clinical analysis of 55 anti-Yo antibody-positive patients. Neurology. (1992.0) 42 1931-1937. DOI: 10.1212/WNL.42.10.1931 63. Vogrig A, Bernardini A, Gigli GL, Corazza E, Marini A, Segatti S. Stroke-like presentation of paraneoplastic cerebellar degeneration: a single-center experience and review of the literature. Cerebellum. (2019.0) 18 976-982. DOI: 10.1007/s12311-019-01075-9 64. Lucchinetti CF, Kimmel DW, Lennon VA. Paraneoplastic and oncologic profiles of patients seropositive for type 1 antineuronal nuclear autoantibodies. Neurology. (1998.0) 50 652-657. DOI: 10.1212/WNL.50.3.652 65. Vogrig A, Fouret M, Joubert B, Picard G, Rogemond V, Pinto A-L. Increased frequency of anti-Ma2 encephalitis associated with immune checkpoint inhibitors. Neurol Neuroimmunol NeuroInflamm (2019.0) 6 e604. DOI: 10.1212/NXI.0000000000000604 66. Baizabal-Carvallo JF, Alonso-Juarez M. Vertical nystagmus associated with glutamic acid decarboxylase antibodies responding to cyclophosphamide. J Neuroimmunol (2018.0) 317 5-7. DOI: 10.1016/j.jneuroim.2018.01.013 67. Feldman D, Otero-Millan J, Shaikh AG. Gravity-independent upbeat nystagmus in syndrome of anti-GAD antibodies. Cerebellum. (2019.0) 18 287-290. DOI: 10.1007/s12311-018-0972-z 68. Aguiar TS, Fragoso A, Albuquerque CR, Teixeira PF, Souza MV, Zajdenverg L. Clinical characteristics of patients with cerebellar ataxia associated with anti-GAD antibodies. Arq Neuropsiquiatr (2017.0) 75 142-146. DOI: 10.1590/0004-282x20170011 69. Yilmaz FM, Little D, Gallagher M, Colcher A. Anti-glutamate dehydrogenase antibody positive cerebellar ataxia and stiff person syndrome responding to dual treatment with steroids and intravenous immunoglobulin: a case presentation and literature review. Cureus. (2019.0) 11 e4851. PMID: 31410334 70. Giometto B, Miotto D, Faresin F, Argentiero V, Scaravilli T, Tavolato B. Anti-gabaergic neuron autoantibodies in a patient with stiff-man syndrome and ataxia. J Neurol Sci (1996.0) 143 57-59. DOI: 10.1016/S0022-510X(96)00065-2 71. Hadjivassiliou M, Aeschlimann D, Grünewald RA, Sanders DS, Sharrack B, Woodroofe N. GAD antibody-associated neurological illness and its relationship to gluten sensitivity. Acta Neurol Scand (2011.0) 123 175-180. DOI: 10.1111/j.1600-0404.2010.01356.x 72. Hadjivassiliou M, Sarrigiannis PG, Shanmugarajah PD, Sanders DS, Grünewald RA, Zis P, Hoggard N. Clinical characteristics and management of 50 patients with anti-GAD ataxia: gluten-free diet has a major impact. Cerebellum (2021.0) 20 179-185. DOI: 10.1007/s12311-020-01203-w 73. Honnorat J, Saiz A, Giometto B, Vincent A, Brieva L, de Andres C. Cerebellar ataxia with anti-glutamic acid decarboxylase antibodies: study of 14 patients. Arch Neurol (2001.0) 58 225-230. DOI: 10.1001/archneur.58.2.225 74. Dagklis IE, Papagiannopoulos S, Theodoridou V, Kazis D, Argyropoulou O, Bostantjopoulou S. Miller-Fisher syndrome: are anti-GAD antibodies implicated in its pathophysiology?. Case Rep Neurol Med (2016.0) 2016 3431849. PMID: 27239355 75. Pedroso JL, Braga-Neto P, Dutra LA, Barsottini OG. Cerebellar ataxia associated to anti-glutamic acid decarboxylase autoantibody (anti-GAD): partial improvement with intravenous immunoglobulin therapy. Arq Neuropsiquiatr (2011.0) 69 993. DOI: 10.1590/S0004-282X2011000700030 76. Nanri K, Okita M, Takeguchi M, Taguchi T, Ishiko T, Saito H. Intravenous immunoglobulin therapy for autoantibody-positive cerebellar ataxia. Intern Med (2009.0) 48 783-790. DOI: 10.2169/internalmedicine.48.1802 77. McKeon A. Purkinje cell cytoplasmic autoantibody type 1 accompaniments: the cerebellum and beyond. Arch Neurol (2011.0) 68 1282-1289. DOI: 10.1001/archneurol.2011.128 78. Hoche F, Guell X, Vangel MG, Sherman JC, Schmahmann JD. The cerebellar cognitive affective/Schmahmann syndrome scale. Brain J Neurol (2018.0) 141 248-270. DOI: 10.1093/brain/awx317 79. Dalmau J, Gonzalez RG, Lerwill MF. A 56-year-old woman with rapidly progressive vertigo and ataxia. N Engl J Med (2007.0) 356 612-620. DOI: 10.1056/NEJMcpc069035 80. Ogawa E, Sakakibara R, Kawashima K, Yoshida T, Kishi M, Tateno F. VGCC antibody-positive paraneoplastic cerebellar degeneration presenting with positioning vertigo. Neurol Sci (2011.0) 32 1209-1212. DOI: 10.1007/s10072-011-0648-7 81. Vogrig A, Péricart S, Pinto AL, Rogemond V, Muñiz-Castrillo S, Picard G. Immunopathogenesis and proposed clinical score for identifying Kelch-like protein-11 encephalitis. Brain Commun (2021.0) 3 fcab185. DOI: 10.1093/braincomms/fcab185 82. Small M, Treilleux I, Couillault C, Pissaloux D, Picard G, Paindavoine S. Genetic alterations and tumor immune attack in Yo paraneoplasctic cerebellar degeneration. Acta Neuropathol (2018.0) 135 569-579. DOI: 10.1007/s00401-017-1802-y 83. Hadjivassiliou M, Grünewald RA, Chattopadhyay AK, Davies-Jones GA, Gibson A, Jarratt JA. Clinical, radiological, neurophysiological and neuropathological characteristics of gluten ataxia. Lancet. (1998.0) 352 1582-1585. DOI: 10.1016/S0140-6736(98)05342-2 84. Hadjivassiliou M, Sanders DS, Grunewald RA, Woodroofe N, Boscolo S, Aeschlimann D. Gluten sensitivity: from gut to brain. Lancet Neurol (2010.0) 9 318-330. DOI: 10.1016/S1474-4422(09)70290-X 85. Hadjivassiliou M, Aeschlimann P, Sanders DS, Mäki M, Kaukinen K, Grünewald RA. Transglutaminase 6 antibodies in the diagnosis of gluten ataxia. Neurology. (2013.0) 80 1-6. DOI: 10.1212/WNL.0b013e3182919070 86. Hadjivassiliou M, Grünewald RA, Sanders DS, Shanmugarajah P, Hoggard N. Effect of gluten-free diet on cerebellar MR spectroscopy in gluten ataxia. Neurolgy (2017.0) 89 705-706. DOI: 10.1212/WNL.0000000000004237 87. Connolly AM, Dodson WE, Prensky AL, Rust RS. Course and outcome of acute cerebellar ataxia. Ann Neurol (1994.0) 35 673-679. DOI: 10.1002/ana.410350607 88. Kornreich L, Shkalim-Zemer V, Levinsky Y, Abdallah W, Ganelin-Cohen E, Straussberg R. Acute cerebellitis in children: a many-faceted disease. J Child Neurol (2016.0) 31 991-997. DOI: 10.1177/0883073816634860 89. Fadakar N, Ghaemmaghami S, Masoompour SM, Shirazi Yeganeh B, Akbari A, Hooshmandi S, Ostovan VR. A first case of acute cerebellitis associated with coronavirus disease (COVID-19): a case report and literature review. Cerebellum. (2020.0) 19 911-914. DOI: 10.1007/s12311-020-01177-9 90. Povlow A, Auerbach AJ. Acute cerebellar ataxia in COVID-19 infection: a case report. J Emerg Med (2021.0) 60 73-76. DOI: 10.1016/j.jemermed.2020.10.010 91. van Samkar A, Poulsen MNF, Bienfait HP, Van Leeuwen RB. Acute cerebellitis in adults: a case report and review of the literature. BMC Res Notes (2017.0) 10 610. DOI: 10.1186/s13104-017-2935-8 92. Kubota M, Takahashi Y. Steroid-responsive chronic cerebellitis with positive glutamate receptor delta 2 antibody. J Child Neurol (2008.0) 23 228-230. DOI: 10.1177/0883073807307973 93. Murakami H, Iijima S, Kawamura M, Takahashi Y, Ichikawa H. A case of acute cerebellar ataxia following infectious mononucleosis accompanied by intrathecal anti-glutamate receptor δ2 antibody. Rinsho Shinkeigaku (2013.0) 53 555-558. DOI: 10.5692/clinicalneurol.53.555 94. Ichikawa K, Kikuchi M, Takeshita S, Nezu A. A case of chronic recurrent cerebellar ataxia responding to steroid therapy. Brain and Development (2009.0) 31 83-85. DOI: 10.1016/j.braindev.2008.04.010 95. Papavasiliou AS, Kotsalis C, Trakadas S. Transient cerebellar mutism in the course of acute cerebellitis. Pediatr Neurol (2004.0) 30 71-74. DOI: 10.1016/S0887-8994(03)00420-X 96. Sawaishi Y, Takada G. Acute cerebellitis. Cerebellum (2002.0) 1 223-228. DOI: 10.1080/14734220260418457 97. Baizabal-Carvallo JF, Jankovic J. Autoimmune and paraneoplastic movement disorders: an update. J Neurol Sci (2018.0) 385 175-184. DOI: 10.1016/j.jns.2017.12.035 98. Baizabal-Carvallo JF, Cardoso F. Chorea in children: etiology, diagnostic approach and management. J Neural Transm (Vienna) (2020.0) 127 1323-1342. DOI: 10.1007/s00702-020-02238-3 99. Al-Shokri SD, Karumannil SA, Mohammed SS, Sadek MS. Post-Epstein-Barr virus acute cerebellitis in an adult. Am J Case Rep (2020.0) 21 e918567. DOI: 10.12659/AJCR.918567 100. 100.Theeranaew W, Wang F, Ghasia FF, Wilmot G, Shaikh AG. Gaze-holding and anti-GAD antibody: prototypic heterogeneous motor dysfunction in immune disease. Cerebellum. 2021; 10.1007/s12311-021-01272-5. 101. Zee DS, Yee RD, Cogan DG, Robinson DA, Engel WK. Ocular motor abnormalities in hereditary cerebellar ataxia. Brain. (1976.0) 99 207-234. DOI: 10.1093/brain/99.2.207 102. Ritchie L. Effects of cerebellar lesions on saccadic eye movements. J Neurophysiol (1976.0) 39 1246-1256. DOI: 10.1152/jn.1976.39.6.1246 103. Strupp M, Thurtell MJ, Shaikh AG, Brandt T, Zee DS, Leigh RJ. Pharmacotherapy of vestibular and ocular motor disorders, including nystagmus. J Neurol (2011.0) 258 1207-1222. DOI: 10.1007/s00415-011-5999-8 104. Leigh RJ, Zee DS. The neurology of eye movements (2016.0) 105. Kim HA, Oh EH, Choi SY, Choi JH, Park JY, Lee H, Choi KD. Transient vestibular symptoms preceding posterior circulation stroke: a prospective nulticenter study. Stroke. (2021.0) 52 e224-e228. DOI: 10.1161/STROKEAHA.120.032488 106. Wirfs L, Whitworth K, Kellar J. Nystagmus associated with carbamazepine toxicity. Clin Pract Cases Emerg Med (2017.0) 1 441-442. DOI: 10.5811/cpcem.2017.6.34772 107. Adamec I, Nanković S, Zadro I, Hajnšek S, Habek M. Oxcarbazepine-induced jerky see-saw nystagmus. Neurol Sci (2013.0) 34 1839-1840. DOI: 10.1007/s10072-013-1315-y 108. Alkawi A, Kattah JC, Wyman K. Downbeat nystagmus as a result of lamotrigine toxicity. Epilepsy Res (2005.0) 63 85-88. DOI: 10.1016/j.eplepsyres.2004.11.004 109. Lifshitz M, Gavrilov V, Sofer S. Signs and symptoms of carbamazepine overdose in young children. Pediatr Emerg Care (2000.0) 16 26-27. DOI: 10.1097/00006565-200002000-00008 110. Chrousos GA, Cowdry R, Schuelein M, Abdul-Rahim AS, Matsuo V, Currie JN. Two cases of downbeat nystagmus and oscillopsia associated with carbamazepine. Am J Ophthalmol (1987.0) 103 221-224. DOI: 10.1016/S0002-9394(14)74231-X 111. Alpert JN. Downbeat nystagmus due to anticonvulsant toxicity. Ann Neurol (1978.0) 4 471-473. DOI: 10.1002/ana.410040516 112. Landy PJ. “Dilatin” overdosage. Med J Aust (1968.0) 2 639. DOI: 10.5694/j.1326-5377.1968.tb83055.x 113. Hals IK, Björklund A, Fiskvik Fleiner H, Grill V. Time-dependent effects on circulating cytokines in patients with LADA: a decrease in IL1-ra and IL-1 beta is associated with progressive disease. Cytokine. (2022.0) 151 155792. DOI: 10.1016/j.cyto.2022.155792 114. Pfleger C, Mortensen HB, Hansen L, Herder C, Roep BO, Hoey H, Aanstoot HJ, Kocova M, Schloot NC. Association of IL-1ra and adiponectin with C-peptide and remission in patients with type 1 diabetes. Diabetes. (2008.0) 57 929-937. DOI: 10.2337/db07-1697 115. Nigri A, Sarro L, Mongelli A, Pinardi C, Porcu L, Castaldo A. Progression of cerebellar atrophy in spinocerebellar ataxia type 2 gene carriers: a longitudinal MRI study in preclinical and early disease stages. Front Neurol (2020.0) 11 616419. DOI: 10.3389/fneur.2020.616419 116. Wilke C, Haas E, Reetz K, Faber J, Garcia-Moreno H, Santana MM. Neurofilaments in spinocerebellar ataxia type 3: blood biomarkers at the preataxic and ataxic stage in human and mice. EMBO Mol Med (2020.0) 12 e11803. DOI: 10.15252/emmm.201911803 117. Gadoth A, Kryzer TJ, Fryer J, McKeon A, Lennon VA, Pittock SJ. Microtubule-associated protein 1B: novel paraneoplastic biomarker: MAP 1B IgG. Ann Neurol (2017.0) 81 266-277. DOI: 10.1002/ana.24872 118. Yu Z, Kryzer TJ, Griesmann GE, Kim K, Benarroch EE, Lennon VA. CRMP-5 neuronal autoantibody: marker of lung cancer and thymoma-related autoimmunity. Ann Neurol (2001.0) 49 146-154. DOI: 10.1002/1531-8249(20010201)49:2<146::AID-ANA34>3.0.CO;2-E 119. Honnorat J, Cartalat-Carel S, Ricard D, Camdessanche JP, Carpentier AF, Rogemond V. Onco-neural antibodies and tumour type determine survival and neurological symptoms in paraneoplastic neurological syndromes with Hu or CV2/CRMP5 antibodies. J Neurol Neurosurg Psychiatry (2008.0) 80 412-416. DOI: 10.1136/jnnp.2007.138016 120. Graus F, Vincent A, Pozo-Rosich P, Sabater L, Saiz A, Lang B. Anti-glial nuclear antibody: marker of lung cancer-related paraneoplastic neurological syndromes. J Neuroimmunol (2005.0) 165 166-171. DOI: 10.1016/j.jneuroim.2005.03.020 121. Pittock SJ, Lucchinetti CF, Parisi JE, Benarroch EE, Mokri B, Stephan CL. Amphiphysin autoimmunity: paraneoplastic accompaniments. Ann Neurol (2005.0) 58 96-107. DOI: 10.1002/ana.20529 122. Graus F, Lang B, Pozo-Rosich P, Saiz A, Casamitjana R, Vincent A. P/Q type calcium-channel antibodies in paraneoplastic cerebellar degeneration with lung cancer. Neurology. (2002.0) 59 764-766. DOI: 10.1212/WNL.59.5.764 123. Zalewski NL, Lennon VA, Lachance DH, Klein CJ, Pittock SJ, Mckeon A. P/Q- and N-type calcium-channel antibodies: oncological, neurological, and serological accompaniments: neuronal Calcium-Channel Abs. Muscle Nerve (2016.0) 54 220-227. DOI: 10.1002/mus.25027 124. Sabater L, Bataller L, Carpentier AF, Aguirre-Cruz ML, Saiz A, Benyahia B. Protein kinase Cgamma autoimmunity in paraneoplastic cerebellar degeneration and non-small-cell lung cancer. J Neurol Neurosurg Psychiatry (2006.0) 77 1359-1362. DOI: 10.1136/jnnp.2006.097188 125. Höftberger R, Kovacs GG, Sabater L, Nagy P, Racz G, Miquel R. Protein kinase Cγ antibodies and paraneoplastic cerebellar degeneration. J Neuroimmunol (2013.0) 256 91-93. DOI: 10.1016/j.jneuroim.2012.12.002 126. Bataller L, Sabater L, Saiz A, Serra C, Claramonte B, Graus F. Carbonic anhydrase-related protein VIII: autoantigen in paraneoplastic cerebellar degeneration. Ann Neurol (2004.0) 56 575-579. DOI: 10.1002/ana.20238 127. Höftberger R, Sabater L, Velasco F, Ciordia R, Dalmau J, Graus F. Carbonic anhydrase-related protein VIII antibodies and paraneoplastic cerebellar degeneration. Neuropathol Appl Neurobiol (2014.0) 40 650-653. DOI: 10.1111/nan.12118 128. Prevezianou A, Tzartos JS, Dagklis IE, Bentenidi E, Angelopoulos P, Bostantjopoulou S. Paraneoplastic cerebellar degeneration in a patient with breast cancer associated with carbonic anhydrase-related protein VIII autoantibodies. J Neuroimmunol (2020.0) 344 577242. DOI: 10.1016/j.jneuroim.2020.577242 129. Bartels F, Prüss H, Finke C. Anti-ARHGAP26 autoantibodies are associated with isolated cognitive impairment. Front Neurol (2018.0) 9 656. DOI: 10.3389/fneur.2018.00656 130. Basal E, Zalewski N, Kryzer TJ, Hinson SR, Guo Y, Dubey D. Paraneoplastic neuronal intermediate filament autoimmunity. Neurology. (2018.0) 91 e1677-e1689. DOI: 10.1212/WNL.0000000000006435 131. Berzero G, Hacohen Y, Komorowski L, Scharf M, Dehais C, Leclercq D. Paraneoplastic cerebellar degeneration associated with anti-ITPR1 antibodies. Neurol Neuroimmunol Neuroinflamm (2017.0) 4 e326. DOI: 10.1212/NXI.0000000000000326 132. Alfugham N, Gadoth A, Lennon VA, Komorowski L, Scharf M, Hinson S. ITPR1 autoimmunity: frequency, neurologic phenotype, and cancer association. Neurol Neuroimmunol Neuroinflamm (2018.0) 5 e418. DOI: 10.1212/NXI.0000000000000418 133. Chan KH, Vernino S, Lennon VA. ANNA-3 anti-neuronal nuclear antibody: Marker of lung cancer-related autoimmunity. Ann Neurol (2001.0) 50 301-311. DOI: 10.1002/ana.1127
--- title: 'Risk Factors of Incident Kidney Stones in Indian Adults: A Hospital-Based Cross-Sectional Study' journal: Cureus year: 2023 pmcid: PMC10060047 doi: 10.7759/cureus.35558 license: CC BY 3.0 --- # Risk Factors of Incident Kidney Stones in Indian Adults: A Hospital-Based Cross-Sectional Study ## Abstract Background The diverse manifestations of urolithiasis provide very interesting epidemiological data. This has prompted various studies to look into the etiopathogenesis of renal stones, which is believed to be multifactorial, both exogenous and endogenous. VDR Fok1 is a risk factor for renal stone formation and could cause the formation of renal stones through the mechanism of crystal induction and crystallization in the urine. While a few recent studies have shown the role of heavy metals like cadmium and lead in the formation of renal stones, the current knowledge is still insufficient. Methods This case-control prospective study was conducted in Guru Teg Bahadur (GTB) Hospital, a tertiary care facility in Delhi with 30 cases and 30 controls. Patients visiting the department of surgery between November 2011 and April 2013 were enrolled in the study. Cases were defined as patients with renal stones diagnosed on the basis of history and radiological investigations. Controls were selected from the patients admitted to the department of surgery for reasons other than renal stones. The study protocol was approved by the Institutional Ethical Committee of the University College of Medical Sciences, GTB Hospital, Delhi. Written informed consent was obtained from all patients. A structured questionnaire was used to collect data. Metal levels were analyzed by an atomic absorption spectrophotometer (Shimadzu Flame AA-680, Shimadzu Corp., Kyoto, Japan) at Delhi University. The vitamin D receptor gene was measured using genomic DNA. Horizontal agarose gel electrophoresis was used for the quantification of the genomic DNA. Results There were 30 cases and 30 controls in the study. Stress was more prevalent among cases ($63\%$) compared to controls ($36\%$). Nearly $83\%$ of cases had the ff allele of the Vitamin D receptor gene compared to $46\%$ of controls. The median arsenic and lead levels were higher among cases compared to controls. In the unadjusted model of logistic regression, we found stressed patients had three times higher odds of developing renal stones compared to non-stressed patients (OR ($95\%$ CI): 2.98 (1.04-8.52); $$p \leq 0.04$$). Similarly, patients with higher blood concentrations of arsenic and lead had higher odds of developing renal stones compared to those with lower concentrations. Conclusions There was a definitive role of heavy metals, including lead, cadmium, and arsenic, seen with renal stones. A significant association was seen between the ff allele of VDR polymorphism (Fok1 enzymes) and patients with renal stones. Other parameters, including male and stress factors, seem to have an important role in renal stone formation. ## Introduction Around $1\%$ of emergency admissions are caused by renal colic and complications of renal stones [1]. Kidney stones are common across the world, with a prevalence of about $12\%$ worldwide [2]. Their prevalence in India also reflects worldwide prevalence and stands at approximately $12\%$ [3] and is relatively more common in the northern part of India, where it is $15\%$ [1,3]. The origin of kidney stones is considered multifactorial, being affected by age, gender, family history, diet, comorbidities, environment, genetic inheritance, and other factors [1]. There are high chances of recurrence of kidney stones irrespective of treatment. It is seen that approximately $98\%$ of patients will develop another stone within 25 years of the first episode [1]. The calcium oxalate variety of renal stones is the most common, constituting $60\%$ of all these stones [4]. It has been seen that genetic polymorphism of vitamin D receptor (VDR), Klotho, and chloride voltage-gated channels (CLCN) genes have a role in the formation of kidney stones [4]. VDR is a polymorphic gene having a role in mineral metabolism. It increases the absorption of calcium and excretion of citrate. Studies reveal that multiple allelic variations in VDR, like ApaI, BsmI, TaqI, and Fok1, are associated with nephrolithiasis. VDR Fok1 is a thymine/cytosine polymorphism located at the start (ATG) codon on the 5’ end of VDR, which also showed a role in the formation of renal stones [5]. Another risk factor of renal stone genesis is heavy metals. Heavy metals are a mixed group of elements with metallic properties. Exposure to these heavy metals is mostly via the respiratory or gastrointestinal tracts in the form of cigarette smoke and contaminated food and water. They may cause the formation of renal stones through the mechanism of crystal induction and crystallization in the urine. The toxic manifestations of these metals are primarily due to an imbalance between pro-oxidant and antioxidant homeostasis, which is termed oxidative stress [6]. Cadmium has been a well-known environmental hazard since ancient times [7]. It is used in electroplating, plastics, batteries, mobile phones, and computer circuit boards. In the case of chronic cadmium poisoning, approximately $50\%$ of the accumulated dose is stored in the kidney [7]. B2Micro globulin (b2M) and retinol-binding protein (RBP) in urine are two indicators of early renal toxicity. RBP is said to be the more definitive marker as compared to beta-2 microglobulin, which is also seen in cancer, amyloidosis, and autoimmune diseases like rheumatoid arthritis [7]. As the tubular injury progresses, more generalized tubular dysfunction occurs with wasting and impaired vitamin D metabolism, as well as the reduced conversion of 25-OH vitamin D to 1,25 OH vitamin D and urinary losses of glucose and amino acids, bicarbonate, and phosphate. Cadmium may directly affect bone mineralization, leading to loss of calcium from bone and increased renal excretion. Continued cadmium exposure causes glomerular damage, leading to albuminuria and a progressive decline in glomerular filtration rate (GFR), eventually causing end-stage renal failure [7]. A few recent studies have shown the role of heavy metals like cadmium and lead in the formation of renal stones, although complete knowledge is still deficient [4,7]. In India, few studies are available regarding the risk factors of kidney stones. The present study aims to evaluate various exogenous and endogenous risk factors in patients suffering from renal stones disease. ## Materials and methods Study population This case-control prospective study was done in Guru Teg Bahadur (GTB) Hospital, a tertiary care facility in Delhi, India with 30 cases and 30 controls. GTB hospital is a 1700-bed government hospital situated in East Delhi and receives 600 patients in the outpatient department and 250 admissions per day [5]. Patients visiting the department of surgery between November 2011 and April 2013 were enrolled in the study. Cases were defined as patients with renal stones diagnosed on the basis of history and radiological investigations (abdominal X-ray or ultrasound abdomen or non-contrast computed tomography of kidney, ureter, and bladder). Controls were selected from the patients admitted to the Department of Surgery for reasons other than renal stones. The study protocol was approved by the Institutional Ethical Committee of the University College of Medical Sciences, GTB Hospital, Delhi. Written informed consent was obtained from all patients. Data collection and measurements A structured questionnaire was used to collect data that had questions on socio-demographic characteristics, such as age, gender, marital status, education, occupation, pregnancy status, present history of illness (symptoms suggestive of renal stones), history of past illnesses (stones, congenital anomaly, systemic diseases, drug intake), family history of stones, and personal history of smoking, alcohol, stress, sleeping, dietary habits (vegetarian or non-vegetarian), and drinking water source, etc. Stress was assessed subjectively by asking a patient if he/she felt it or not, and data was collected as yes/no responses. The questionnaire also captured data on anthropometric measurements (height, weight), clinical parameters (pulse, blood pressure), and biochemical parameters (hemoglobin, serum urea, serum creatinine, blood sugar, serum calcium, lead, cadmium, arsenic, and chromium, urine routine microscopy, and genetic markers (Vitamin D receptor gene with FF, Ff, or ff allele). Patients’ radiological investigations such as abdominal X-ray, abdominal/KUB ultrasound, or non-contrast computed tomography of the kidney, ureter, and bladder were also done in the hospital. Blood sugar levels at fasting, post-prandial, and random state were obtained. Body mass index was calculated from height and weight using the formula. Height was measured using the stadiometer to the nearest 0.5 cm after removing shoes/slippers and placing heels together, and weight was measured using a standard digital weighing scale to the nearest 0.1 kg while barefoot and in light clothes. Blood pressure was measured using a digital sphygmomanometer available in the hospital. The blood sample was withdrawn and sent to the laboratory outside the hospital (National Accredited Laboratory affiliated with Delhi University) for the measurements of all the clinical parameters. Heavy metal was estimated by withdrawing 1.5 ml peripheral blood sample in an EDTA vial, 0.5 ml of blood sample was taken in triplicate in a 100 ml digestion flask fitted with a 30 cm long air condenser, and 5.0 ml distilled HNO3 was added to each sample. The contents were heated at 80°C for 30 minutes. After cooling, 1.5 ml of concentrated perchloric acid ($70\%$) was added, and the sample was heated again at 25°C with occasional shaking till white fumes evolved. The clear solution was cooled and transferred into a 10 ml measuring flask. The volume was made up of 10 ml of deionized water [8]. Thus, the obtained sample was filtered by using a syringe filter of 0.45-micron pore size (RFCL Ltd, New Delhi, India), and the metal levels were analyzed by an atomic absorption spectrophotometer (Shimadzu Flame AA-6800, Shimadzu Corp., Kyoto, Japan) at Delhi University. The vitamin D receptor gene was measured using genomic DNA. Genomic DNA for genotyping was isolated from whole blood by using a commercially available Himedia Hipura blood genomic DNA isolation kit (HiMedia Laboratories Pvt. Ltd., Mumbai, India) as per the manufacturer’s protocol. The DNA was stored at -22ºC till polymorphic analysis was done. The extracted DNA was quantified by taking the optical density (OD) of the sample at 260 nm wavelength by the Shimadzu UV-2450 spectrophotometer. The purity of DNA was ascertained by taking the ratio of OD at 260 and 280nm. The overall purity of the samples (i.e., OD260/ OD280) ranged between 1.3 and 1.7. Horizontal agarose gel electrophoresis was used for the quantification of the genomic DNA. For analysis of VDR polymorphism, PCR-restriction fragment length polymorphism methods were used [9]. A total of 50 ml reaction mixture consisted of 50 ng of genomic DNA, 10 mM of each primer, 0.2 mM of the dNTPs mixture (Bangalore Genei, Bengaluru, India), 1.5 mM of MgCl2, and 1.5 unit of Taq polymerase with 1× PCR reaction buffer (Bangalore Genei). Primer Sequence for Genotyping of VDR gene F5’AGCTGGCCCTGGCACTGACTCTGCTCT-3’ and R5’-ATGGAAACACCTTGCTTCTTCTCCCTC-3’. The restriction enzyme FokI (Fastdigest, Fermentas, Waltham, MA) was used to distinguish the Fok1 polymorphism. The wild-type allele (FF) produced a double band representing the entire 196, 69 base pair (bp) fragment, and the variant allele (ff) resulted in one fragment of 265 bp, whereas the heterozygous allele (Ff) produced all three bands [9]. Statistical analysis Descriptive data were presented as frequencies and percentages for the categorical variables and means/medians with standard deviation or interquartile range for numerical variables. The age variable was categorized into three categories (less than 27 years, 27-45 years, and above 45 years). Chi-square was used to compare the difference in the distribution of independent variables (categorical) between cases and controls. We used the Mann-Whitney test to find a statistical difference between cases and controls in the mean levels of serum arsenic, chromium, lead, cadmium, urinary calcium, uric acid, citrate, blood urea, serum calcium, phosphate, creatinine, serum uric acid, and mean body mass index, and diastolic blood pressure. All the independent variables that showed a statistically significant difference between cases and controls were put in the regression model. Unadjusted and adjusted models of logistic regression were used to explore associations between independent variables and renal stone history. An odds ratio (OR) with a $95\%$ confidence interval (CI) was used to express the strength of the association. Data were analyzed using Statistical Package for Social Sciences (SPSS) for Windows version 27.0 (IBM Corp., Armonk, NY) with a two‑sided p-value of <0.05, which was considered statistically significant. ## Results There were 30 cases and 30 controls in the study. Table 1 shows 60 cases and controls; 42 were males, and 18 were females. There was no statistical difference in the prevalence of history of diseases, smoking, alcohol intake, and drug intake between cases and controls. Stress was more prevalent among cases ($63\%$) compared to controls ($36\%$). In Table 2, nearly $83\%$ of cases had the ff allele of the Vitamin D receptor gene compared to $46\%$ of controls. Table 3 showed no difference in BMI, blood pressure, serum uric acid, serum chromium, serum cadmium, creatinine, serum calcium, and serum phosphate between cases and controls. The median arsenic and lead levels were higher among cases compared to controls. In Table 4, there was no statistically significant difference in the median levels of urinary calcium, urinary citric acid, and urinary uric acid, between cases and controls. Table 5, In the unadjusted model of logistic regression, we found stressed patients had three times higher odds of developing renal stones compared to non-stressed patients (OR ($95\%$ CI): 2.98 (1.04-8.52); $$p \leq 0.04$$). Similarly, patients with higher blood concentrations of arsenic and lead had higher odds of developing renal stones compared to those with lower concentrations. In the adjusted model, however, most of the associations were insignificant except for stress and the presence of the FF or Ff allele of the Vitamin D receptor gene. Patients with FF or Ff allele had lower odds of developing renal stones than their counterparts (OR ($95\%$ CI): 0.15 (0.03-0.80); $$p \leq 0.02$$). ## Discussion Kidney stones are common and potentially preventable causes of morbidity in the general population. The aim of this study was to study the risk factors in patients with renal stones who visited our institution. A total of 30 cases and 30 age- and gender-matched controls were studied. Patients with radiologically diagnosed renal stones were included in the study. In the present study, there was a significant difference in the number of males as compared to females with renal stone disease (22 males and eight females, $$p \leq 0.016$$). The higher incidence of renal stones in males could be contributed due to the presence of testosterone [1, 6]. A study done in Ballabhgarh Health and Demographic Surveillance System (HDSS) from November 2012 to December 2013 on 433 subjects found that the most common age group affected was 20-40 years; many from this age group are a part of the workforce, which increases the burden on the family and society [10]. Another study published in 2021 also showed that the mean age of renal stone occurrence was 21-40 years [1]. A retrospective study on 435 patients who visited the urology outpatient clinic in Dehradun, India, between 2005 and 2018 showed a high prevalence of renal stones in males, which was almost three times higher than in females. It was also suggested that this prevalence might be due to high levels of testosterone in males [6]. Another study done by Lohiya et al. in northern India, which included 435 renal stones patients, demonstrated a high prevalence of stones in males, which was 1.5 times more than in females [10]. A study done by Semins et al. in approximately 3.4 million insured individuals during a five-year period (2002 to 2006) concluded that the occurrence of renal stones is directly proportional to age, and the disease was twice more common in males as compared to females [11]. Another study done in three cadmium-contaminated villages revealed that there were significant differences between age, gender, alcohol consumption, body mass index, urinary cadmium, and prevalence of diabetes and urinary stones [12]. We were unable to find any association between the risk of urolithiasis with the use of medications and systemic diseases. It has been said that long-term medications, which are possible risk factors for kidney stone formation, include indinavir, thiazide, loop diuretics, thyroid hormones, and antacids. Indinavir stones are seen in 4-$12\%$ of treated patients. The possible mechanism of indinavir stone formation is poor solubility and high urinary excretion of this drug. Thiazide causes hypocitraturia whereas loop diuretics cause hypercalcemia [13]. Hyperparathyroidism increases the risk of kidney stone disease by increasing the excretion of calcium, secondary to excessive resorption of bone (known as resorptive hypercalciuria) [14]. Diseases including diabetes mellitus, hypertension, urinary tract infection, gout, inflammatory bowel disease, chronic diarrhea, and cancers like leukemia and lymphomas are associated with the formation of renal stones. In diabetes, the incidence of uric acid stone formation is high due to insulin resistance, a decrease in urinary pH, and obesity which favors uric acid and mixed urate-calcium oxalate stone formation [15]. In hypertension, there is low excretion of citrate and high uric acid excretion along with high BMI, which predisposes to the formation of renal stones [16]. No significance was found between alcohol intake and diet changes with the risk of getting stones. The incidence of renal stone formation increases with a higher intake of alcohol due to an increase in serum uric acid levels [17]. Dietary habits play an important role in the formation of stones. In countries like India, changing lifestyle has increased the tendency for obesity. A fatty diet is thought to create a predisposition to renal stone formation [18]. In the present study, different allele patterns of the VDR (Fok1) gene were found. The incidence of the “ff” allele was significantly higher ($$p \leq 0.009$$) in renal stone patients as compared to controls. However, no definitive consensus can be drawn by analyzing previous studies on the higher occurrence of renal stones related to the ff genotype of the VDR gene. Another case-control study on 60 adults aged between 18-90 years showed that VDR and CLDN genes are associated with recurrent urolithiasis. This study also emphasizes the role of methylation of genes in the genesis of renal stones [19]. Another study done in 2015 that included 105 individuals with renal stones showed a significant association of urolithiasis with the FOK1 f allele, which could be directly contributed to the presence of Randall plaques ($$p \leq 0.047$$) [20]. In a study published in 2005 by Bid et al. from Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, the association of vitamin D receptor-gene (Fok1) polymorphism with calcium oxalate nephrolithiasis was studied in 138 patients of calcium oxalates stones and 166 healthy patients. The study found a significant association between renal stones and VDR Fok1 polymorphism ($p \leq 0.001$) [9]. A meta-analysis done by Lin et al. included 17 studies to explore the association between VDR polymorphic sites ApaI, BsmI, TaqI, and Fok1 and urolithiasis risk. This meta-analysis suggested that in the Asian subgroup, there is an increased risk of urolithiasis with the ff + Ff genotype, and VDR polymorphisms could be potential biomarkers for urolithiasis susceptibility [21]. Metabolic factors including lithogenic conditions such as hypercalciuria, hyperuricosuria, hypercalcemia, hyperoxaluria, hyperphosphaturia, hypocitraturia, the excess load of lithogenic substances like vitamin D, and urinary phytate levels have been implicated as risk factors in kidney stone formation. Hypercalciuria causes supersaturation of urine, which leads to the development of renal stones [22]. Hyperuricosuria is an important metabolic risk factor in the formation of uric acid stones. The etiologic mechanisms for uric acid stone formation are diverse and include congenital, acquired, and idiopathic causes. Major factors for the development of uric acid stones are low urine volume, acidic urine pH, and hyperuricosuria. However, abnormally acidic urine is the principal determinant in uric acid crystallization [22]. Hypocitraturia, with a urinary citrate level of less than 320 mg/day is another important and correctable metabolic cause of renal stones. It is seen in $10\%$ of patients having calcium stones [14]. Our study didn’t find a strong correlation between metabolic disease and the incidence of renal stones. Exposure to heavy metals from cigarette smoke and food from contaminated soil and water. The kidney is the main target organ for these heavy metals. Surprisingly, not too many studies have been done on this topic. A recent study published in 2021 done in China on non-occupational exposure found 3.16 times the odds of renal stones with a high level of lead in blood (>100μg/L) in males; however, the odd ratio became 3.43 in the presence of high cadmium in urine and high-level leads in blood. In females, there is a significant correlation between nephrolithiasis and a combination of both high blood lead and urinary cadmium (OR 2.58) [23]. A study was done on 6,748 individuals with exposure to environmental cadmium. The study population was screened for urinary cadmium and calcium levels and the presence of urolithiasis. A strong association was found between urinary cadmium levels and stone prevalence after adjusting other co-variables. The stone prevalence increased 1.093-fold for every 1 µg increase in urinary cadmium when adjusted for urinary creatinine. It was also found that urinary calcium levels increased parallel to increasing urinary cadmium, and this was possibly the mechanism by which cadmium resulted in urinary stone formation [12]. Another study published in 2007 by Bazin et al. suggested the role of heavy metals in stone formation through crystal induction, and the highest proportion of the heavy metals in calcium stones was observed for Zn (mean ± SD= 525 ± 768ppm), followed by Sr (239 ± 300 ppm), Fe (35 ± 43 ppm) and Pb (19 ± 27 ppm) while the other metals accounted for less than 10 ppm on average [6]. A study done on factory workers suggested that an increase in cadmium dose was associated with multiple renal tubular functional abnormalities, including a decrease in resorption of beta-2 microglobulin, retinol-binding protein (RBP), calcium, and phosphate and a rise in mean systolic and diastolic blood pressures. The study also suggested that serum creatinine concentration also increased with an increase in cadmium dosage due to impaired glomerular function. Serum cadmium concentration was significantly higher in the exposed workers than in the unexposed (7.9 v 1.2 µg/l, $p \leq 0.0001$). The average blood lead concentration was higher in cadmium workers than in the unexposed (11.9 v 8.3 pg/l, $$p \leq 0.0013$$) [24]. A prospective study by Järup and Elinder on cadmium-exposed battery workers showed similar results [25]. In our study, we estimated the serum levels of heavy metals by atomic absorption spectrometry. The serum levels in the patients (cadmium 0.73±0.65; lead 10.29±4.54; arsenic 0.923±0.71ng/ml) were significantly higher than the corresponding levels in the controls (cadmium 0.324±0.27; lead 0.574±0.51; arsenic 0.371±0.29 ng/ml). However, no significant difference was seen in serum levels of chromium in cases and controls. Even in 2022, we don’t have much information regarding the correlation between psychological stress in the formation of renal stones. A study done by Miyaoka et al. on 200 patients with nephrolithiasis concluded that high stress is related to the stones ($$p \leq 0.012$$) and recurrence of excretion of urinary stones($$p \leq 0.022$$). The author used the PSS-10 score for stress level analysis, and females and unemployment were the confounding factors [26]. Another case-controlled study done at the Center of New Jersey in Newark with 200 individuals matched with age, sex, and race found that three important factors, such as low total annual family income, mortgage issues, and emotional life events that last for more than a week were associated with a higher risk of formation of renal stones ($p \leq 0.05$) [27]. Strengths and limitations of the study This study opens a newer perspective to studying the relationship between the FOK1 VDR gene and heavy metals with renal stones. However, this study was conducted in a selected hospital in Delhi, and the sample size was also small; therefore, its generalizability is limited. There was no stress scale used in this study and individual perceptions of stress might be different for different individuals. ## Conclusions In this case-control study, we studied various risk factors in the formation of renal stones in 30 patients with renal stones (with 30 controls) who visited GTB Hospital, New Delhi. There was a definitive role of heavy metals, including lead, cadmium, and arsenic, seen with renal stones (more with lead). A significant association was seen between the ff allele of VDR polymorphism (Fok1 enzymes) and patients with renal stones. Other parameters, including male gender and stress factors, seem to play an important role in renal stone formation. We conclude that people visiting the outpatient department or admitted to wards should be counseled on these risk factors to prevent the incidence or recurrence of renal stones. ## References 1. Kakkar M, Kakkar R. A 13 year hospital based study on the trend of urinary stone disease in Uttarakhand, India. Nepal J Epidemiol (2021) 11 949-958. PMID: 33868741 2. Nojaba L, Guzman N. Nephrolithiasis. StatPearls (2022) 3. Guha M, Banerjee H, Mitra P, Das M. The demographic diversity of food intake and prevalence of kidney stone diseases in the Indian continent. Foods (2019) 8 37. PMID: 30669549 4. Shin S, Srivastava A, Alli NA, Bandyopadhyay BC. Confounding risk factors and preventative measures driving nephrolithiasis global makeup. World J Nephrol (2018) 7 129-142. PMID: 30510912 5. Government of NCT, Delhi. Guru Teg Bahadur Hospital, Delhi. Available from: http://health.delhigovt.nic.in/wps/wcm/connect/doit_gtbh/GTBH/Home/General+Information. Accessed 22 July. (2022) 6. Bazin D, Chevallier P, Matzen G, Jungers P, Daudon M. Heavy elements in urinary stones. Urol Res (2007) 35 179-184. PMID: 17492279 7. Johri N, Jacquillet G, Unwin R. Heavy metal poisoning: the effects of cadmium on the kidney. Biometals (2010) 23 783-792. PMID: 20354761 8. NIOSH Manual of Analytical Methods (NMAM). Chromium and compounds, as Cr: METHOD 7024. NIOSH Manual of Analytical Methods (NMAM) (2023) 4 1-4 9. Bid HK, Kumar A, Kapoor R, Mittal RD. Association of vitamin D receptor-gene (FokI) polymorphism with calcium oxalate nephrolithiasis. J Endourol (2005) 19 111-115. PMID: 15735395 10. Lohiya A, Kant S, Kapil A, Gupta SK, Misra P, Rai SK. Population-based estimate of urinary stones from Ballabgarh, northern India. Natl Med J India (2017) 30 198-200. PMID: 29162751 11. Semins MJ, Shore AD, Makary MA, Magnuson T, Johns R, Matlaga BR. The association of increasing body mass index and kidney stone disease. J Urol (2010) 183 571-575. PMID: 20018330 12. Swaddiwudhipong W, Mahasakpan P, Limpatanachote P, Krintratun S. An association between urinary cadmium and urinary stone disease in persons living in cadmium-contaminated villages in northwestern Thailand: a population study. Environ Res (2011) 111 579-583. PMID: 21272865 13. Pearle MS, Lotan Y. Urinary lithiasis: etiology, epidemiology, and pathogenesis. Campbell-Walsh Urology, 10th ed (2012) 1257-1286 14. Ganesan C, Weia B, Thomas IC. Analysis of primary hyperparathyroidism screening among US veterans with kidney stones. JAMA Surg (2020) 155 861-868. PMID: 32725208 15. Weikert C, Weikert S, Schulze MB. Presence of gallstones or kidney stones and risk of type 2 diabetes. Am J Epidemiol (2010) 171 447-454. PMID: 20089496 16. Losito A, Nunzi EG, Covarelli C, Nunzi E, Ferrara G. Increased acid excretion in kidney stone formers with essential hypertension. Nephrol Dial Transplant (2009) 24 137-141. PMID: 18715964 17. Anderson RA. A complementary approach to urolithiasis prevention. World J Urol (2002) 20 294-301. PMID: 12522585 18. Taylor EN, Stampfer MJ, Curhan GC. Obesity, weight gain, and the risk of kidney stones. JAMA (2005) 293 455-462. PMID: 15671430 19. Khatami F, Gorji A, Khoshchehreh M. The correlation between promoter hypermethylation of VDR, CLDN, and CasR genes and recurrent stone formation. BMC Med Genomics (2022) 15 109. PMID: 35546405 20. Letavernier E, Vandermeersch S, Traxer O. Demographics and characterization of 10,282 Randall plaque-related kidney stones: a new epidemic?. Medicine (Baltimore) (2015) 94 0 21. Lin Y, Mao Q, Zheng X, Chen H, Yang K, Xie L. Vitamin D receptor genetic polymorphisms and the risk of urolithiasis: a meta-analysis. Urol Int (2011) 86 249-255. PMID: 21325790 22. Pak CY. Should patients with single renal stone occurrence undergo diagnostic evaluation?. J Urol (1982) 127 855-858. PMID: 7086982 23. Huang JL, Mo ZY, Li ZY. Association of lead and cadmium exposure with kidney stone incidence: A study on the non-occupational population in Nandan of China. J Trace Elem Med Biol (2021) 68 126852. PMID: 34508950 24. Thun MJ, Osorio AM, Schober S, Hannon WH, Lewis B, Halperin W. Nephropathy in cadmium workers: assessment of risk from airborne occupational exposure to cadmium. Br J Ind Med (1989) 46 689-697. PMID: 2818957 25. Järup L, Elinder CG. Incidence of renal stones among cadmium exposed battery workers. Br J Ind Med (1993) 50 598-602. PMID: 8343420 26. Miyaoka R, Ortiz-Alvarado O, Kriedberg C, Alanee S, Chotikawanich E, Monga M. Correlation between stress and kidney stone disease. J Endourol (2012) 26 551-555. PMID: 21883011 27. Najem GR, Seebode JJ, Samady AJ, Feuerman M, Friedman L. Stressful life events and risk of symptomatic kidney stones. Int J Epidemiol (1997) 26 1017-1023. PMID: 9363523
--- title: Cucurbita maxima Seeds Reduce Anxiety and Depression and Improve Memory authors: - Shahana Wahid - Ali Alqahtani - Rafeeq Alam Khan journal: Behavioural Neurology year: 2023 pmcid: PMC10060065 doi: 10.1155/2023/7509937 license: CC BY 4.0 --- # Cucurbita maxima Seeds Reduce Anxiety and Depression and Improve Memory ## Abstract The current study was planned to assess the neuropharmacological benefits of the Cucurbita maxima seed. These seeds have been conventionally used for the nutritional as well as amelioration of various diseases. However, there was a need to provide a pharmacological basis for such use. Four central nervous system-related functions, that is, anxiety, depression, memory, and motor coordination, were evaluated, and the levels of brain biogenic amines were also assessed. Anxiety was evaluated through selected experimental models, such as light and dark apparatus, elevated plus maze, head dip, and open field test. The head dip test was mainly used to assess exploratory behavior. Depression was assessed by two animal models, that is, the forced swim test and tail suspension test. Memory and learning ability were assessed by the passive avoidance test, stationary rod apparatus, and Morris's water maze test. Motor skilled learning was assessed by stationary rod and rotarod apparatus. Reversed phase high-pressure liquid chromatography was used to determine biogenic amine levels. Results reveal that C. maxima exhibited anxiolytic and antidepressant effects with memory improvement. There was a reduction in the weight of the animal following chronic administration. Furthermore, no remarkable effects were observed on motor coordination. Norepinephrine was found elevated, which may be linked to its antidepressant effects. These biological effects of C. maxima may be due to the presence of secondary metabolites, such as cucurbitacin, beta-sitosterol, polyphenolic compounds, citrulline, kaempferol, arginine, β-carotene, quercetin, and other antioxidants. The outcomes of the present study authenticate that the chronic use of C. maxima seeds reduces the intensity of neurological problems like anxiety and depression. ## 1. Introduction Various neurological conditions, such as multiple sclerosis, traumatic brain injuries, and dementia, might be one of the causes of depression and anxiety. Dementia is a condition that affects the person's ability to execute routine life activities interfering with decisions of persons. To rule out the degree of dementia, the familiar paradigm is to evaluate one's ability to new learning and memory by certain models. Learning is an initial course of forming novel memories in the brain from an initial experience of previously learned events. It involves encrypting, storing, retrieving, and retaining the event in memory or forgetting [1]. Short-term memory is the storage of information for a short period without any repetition, whereas long-term memory is the preservation of data for an extended period due to recurrence. The usual learning procedure is based on the availability of neurotransmitters, such as dopamine, acetylcholine, and 5-hydroxytryptamine (5HT), which stimulate the hippocampus, amygdala, and other brain areas of the cerebral cortex like the sensory, visual, and auditory cortices [2]. Furthermore, several anxiolytics and antidepressant drugs alter learning memory or amnesia. Cholinesterase inhibitors and dopamine agonists are considered the first line of treatment for the management of dementia [3]. Furthermore, some neurodegenerative disorders, such as Alzheimer's disease (AD), have progressive dementia either due to cholinergic neuronal loss or decreased levels of acetylcholine. The first management strategy for AD is to use acetylcholinesterase (AChE) inhibitors, which largely yield an effect by enhancing acetylcholine levels in the brain. Herbal remedies are traditionally popular for the treatment of various ailments because of the bioactive compounds in them. Anxiety is a repeated episode of intense uncontrolled feelings of fear, confusion, tachycardia, and various panic symptoms. The short period of the depressive phase reaction is a normal reaction to everyday stress. However, if episodic symptoms of uneasiness continue that hinder the person's daily activity, then it is considered an anxiety disorder [4]. The major type of anxiety is generalized anxiety disorder. Furthermore, it is also associated with various depressive or behavioral disorders making management difficult [5]. There is a need to discover drug molecules having selectivity for 5-HT1A receptor antagonists and benzodiazepine type suppressant characteristics [6]. Depression is another psychological issue having persistent opposite depressive symptoms for more than 2 weeks. Depression is manifested by a feeling of sadness, guilt, tiredness, diminish concentration, loss of interest, low confidence, and disturbance in appetite or sleep. According to the Diagnostic and Statistical Manual, there are various types of depression, such as persistent depressive disorder, major depression, situational or seasonal affective disorder, postpartum depression, bipolar depression, psychotic depression, premenstrual dysphoric depression, and atypical depression [7]. Depression is a complex disorder that refers to the experience of feeling sadness and some heterogeneous associated symptoms that vary broadly characterized by loss of positive thoughts. Behavioral attributes of depression must incorporate the controlling variables at both interoceptive and environmental levels. These effects themselves are not problematic but rather adaptive, but if it becomes chronic, dysregulated, or maladaptive through several etiologies, then it needs to be treated. Moreover, depressed people commonly exhibit depressive-related behaviours including overthinking and social avoidance. This event may be negatively strengthened and create a cycle of increasing negative thoughts and avoidance, maliciously leading to depression [8]. According to the United States National Research Council and Institute of Medicine, various biological and environmental factors pose the person to depression including low age, genetic tendency, neurological or neuro-endocrinological, hormonal, and immunological. All these factors are cumulatively triggered by environmental factors such as stressful experiences of vulnerable persons due to psychosocial and biological circumstances. The presence of other medical and psychological disorders also worsens depression making it difficult to treat [9]. The initial antidepressant drugs were initially discovered based on hypotheses related to central nervous system (CNS) impairment of norepinephrine levels in the 1950s. After the revolution in drug discovery in neuroscience and genetics, new antidepressant drugs have been discovered, which cleared the basic mechanisms of depression and mechanism of the drug showing that the monoaminergic system is one of the bases of these mechanisms along with multiple interactions with other brain regulatory systems [10]. Cucurbita maxima was selected to confirm its memory-enhancing effect as several neuroprotective and antioxidant substances have been reported in the literature. C. maxima is a plant of Cucurbitaceae important for growth and immunity [11]. Besides its nutritional value, it is also a good source of important metabolites, such as phenolic glycosides [12], carotenoids, γ-aminobutyric acid [13, 14], flavonoids, alkaloids, tannins, saponins, and terpenoids [15]. It has been reported that it has hypocholesteremic, anthelmintic, hypotensive, hypoglycaemics, and antiperoxidative properties [16, 17]. Its use can also relieve symptoms of benign prostatic hyperplasia and some anxiety disorders [18]. Pectin isolated from C. maxima has the highest cytoprotective and antioxidant effect against reactive oxygen species generation in different cell lines [19, 20]. C. maxima seeds are supposed to have a high content of magnesium, which may behave as a N-methyl-D-aspartate (NMDA) receptor blocker, a very good pain killer specifically effective in nerve pain and have a euglycemic effect on diabetes decreasing type 2 diabetes generation in a healthy individual. Furthermore, it is also reported that a daily intake of 400 mg of magnesium oxide acts as a muscle relaxant. Therefore, it may relieve anxiety by relaxing the patients. Thus, due to these reported effects, C. maxima seeds were selected to evaluate their effects on the reduction of neurodegenerative disorders. ## 2.1. Collection of Seeds and Extraction The seeds of C. maxima were bought from the local market and identified by the Plant Conservation center at the University of Karachi, Karachi, Pakistan, as GH #9501. The research was performed after approval of the advanced studies board, University of Karachi. The seeds of C. maxima were soaked in the proportion of 1 kg in 1.5 L using ethanol for 21 days. Seeds were kept in airtight ambered bottles during maceration followed by filtration and evaporation of the solvent. The extract was stored at 4°C till use after freeze-drying. ## 2.2. Experimental Design The study was performed on healthy mice of either sex bred in the animal house, Department of Pharmacology, the University of Karachi, after acclimatization for 1 week. Mice were distributed into six groups having 10 mice per group kept in polycarbonate cages at 25 ± 2°C with access to food and water ad libitum. Animal of the control group received $5\%$ Dimethyl sulfoxide (DMSO), two groups were given standard drugs diazepam 3 mg/kg [21] and imipramine 30 mg/kg [22], whereas mice of the test groups received ethanol extracts of C. maxima at doses of 50, 100, and 200 mg/kg for 30 days. The oral route of administration was selected to give DMSO, standard drugs, and seed extracts daily between the fixed time of 12 am to 1 pm. Behavioral experimentations were primarily conducted on days 1, 8, and 21 using different models for anxiety, depression, and memory. The behavioral sessions were recorded using an android mobile camera. At the end of the study, five mice from each group were sacrificed to determine the level of biogenic amines. ## 2.3. Chemicals Chemicals purchased during the study were DMSO, imipramine hydrochloric acid (HCl; Sigma‑Aldrich, Headquarter Munich, Germany), Diazepam (Roche, Pakistan), some high-pressure liquid chromatography (HPLC) standards, such as homovanillic acid (HVA), noradrenaline (NA), adrenaline hydrochloride, 5HT, 5-hydroxy indole acetic acid, ethylene-diamine-tetra-acetic acid disodium salt, 3,4 dihydroxy-phenyl acetic acid (DOPAC), dopamine hydrochloride (DA), and HCl. Furthermore, some more chemicals were HPLC grade chemicals, such as methanol, acetonitrile, and deionized water. For disinfecting the apparatus after each reading, ethanol was used. ## 2.4.1. Light and Dark Test This model was employed to determine the anxiolytic ability of seed extracts by determining the preference of animals to be in the lighter compartment. This model has two equal compartments of 20 cm × 20 cm. The lighter compartment was brightened with a 100 V bulb, whereas the dark compartment had dark glasses that led to a darker region. The mice were introduced to a darker zone, and 5 minutes test sessions were performed during mice were freely moved between two compartments through an intermediate small gate. The anxiolytic activity was determined after measuring the time spent by mice in the light compartment, their percentages, and number of transitions. An increment in the percentage of time spent in the light area was suggestive of anxiolytic activity [23]. ## 2.4.2. Elevated Plus Maze (EPM) Subsequently, mice were employed in EPM on the eighth and twenty-first days of dosing. The EPM apparatus comprises two open arms (50 cm × 10 cm) and two closed arms (50 cm × 10 cm × 38 cm) linked to the central area (10 cm × 10 cm) at a height above 50 cm from the ground. Mice were placed in the central area towards the open arm and behavior was recorded for a 5-minute test session. Behavior measured were several entries into the open arm as well as the closed arm and time spent by the animal in the open arm and closed arm. An increment in time spent in the open area and the number of entries in the open arm was considered an anxiolytic activity. The anxiety index was calculated using the following formula. Anxiety in mice would be present if the result of the anxiety index was found from 0.6 to 1.0, whereas a reduction in the index exhibited an anxiolytic effect [24]. [ 1]Anxiety index=1−time in open arm/total time+entries of open arm/total entries. ## 2.4.3. Head Dip Test This test includes a white square wooden box of 35 cm × 45 cm × 45 cm containing equally spaced and sized three holes located at each side. Mice were introduced for 5 minutes session and several head dips through such holes were noted on the 8th and 21 days [25]. The increment in head dipping in this model reveals a rise in anxiety, whereas a decline in head dipping shows the anxiolytic effect of extracts [26]. ## 2.4.4. Open Field Test Apparatus It is a gold standard apparatus assessing different behaviors of rodents, such as anxiety, memory, exploration, and even depression. It consists of a plexiglass cube of 75 cm × 75 cm × 40 cm with marked flooring divided into 25 boxes, each of 15 cm × 15 cm with a central area demarcation of 30 cm × 30 cm. Mice were introduced in the center to explore the open field test (OFT) apparatus for 10 minutes session [27, 28]. During the test session, the parameters measured were the total distance covered by mice, the number of entries in the center, and their duration, rearing frequency, and duration. A reduction in the total distance shows a reduction in motor activity that may be due to sedative effects. An increment in time spent in center or center entries was revealing the anxiolytic activity. An increment in the frequency or duration of rearing shows high expressive and thoughtful behavior and an anxiety reduction. Recently, researchers related increment in rearing behavior with antidepressant effects [29]. ## 2.5.1. Passive Avoidance Test Passive avoidance test (PAT) test is used for rapid assessment of the fear disinclination memory of mice. The escaping phenomenon was assessed using similar light and black apparatus having a grid floor and guillotine gate. The dark section's grid floor is attached to an electric source for giving tolerable foot electric shocks. The test was accomplished in three phases, i.e., habituation, education, and test periods. The mice were introduced in the illuminated section for 300 seconds on the first and second days of habituation facing the dark area with the guillotine door open. When the mice entered the dark booth, the door was shut down, and a foot jolt of 0.6 mA for 0.5 seconds was given through the grid floor [31]. During the test session, the mice were re-introduced into the apparatus after three hours, twenty-four hours, the eighth day, and the twenty-first day to assess short-term and long-term memories. Mice were placed in the illuminated section keeping the gate open to provide free access to the dark disinclination section. The latency to re-enter the black section was observed escaping electric shock with a maximum cut-off time of 300 seconds. ## 2.5.2. Morris Water Maze Test This test was designed to evaluate the hippocampus-dependent acquisition of short- and long-term spatial learning and memory. This maize comprises rectangular water pool of dimension 60 cm × 30 cm. The test used water immersed fixed central stage of dimension 15 cm × 13 cm hidden by starchy at 25°C. In the education session, the stage was kept visible by maintaining the water below the stage. Mice were introduced in the pool and allowed to locate the stage. Four training sessions were performed for four consecutive days so that mice learned to discover the stage within a short period. The mice were tested on the first day, the eighth day, and the twenty-first day. A reduction in time was considered an improvement in special learning [33]. ## 2.6.1. Effects on Motor Coordination through Rotarod At present, anxiolytic drugs have effects on grip strength and muscle; therefore, in the current study, grip strength and muscular activity were examined via rotarod apparatus. The rotarod apparatus comprises an enclosed plastic rod of 8 cm × 3 cm. First, mice were given the training to walk on the stationary rods through four successive trails. The test session was performed at two speeds, i.e., 10 and 30 rpm on days 8th and 21st of administration of ethanol extracts. Latency time to keep on the movable rod until mice fall was noted with the cut-off time 180 seconds. An increment in time to keep on the rotating rod was considered an improvement in muscular activity. Drugs recognized to change neuromuscular coordination, such as diazepam, decrease the latency time of mice to keep on the rotating rod [30]. ## 2.6.2. Stationary Rod Test The education capability was assessed by stationary rod test (SRT) comprised raised steel rods with netted base. Mice were first taught by making them walk on the elevated rod with a networking stage. Animals were trained daily through four training sessions for four consecutive days with each trial of 120 seconds. After completing training, DMSO, standard drugs, and C. maxima seed extracts were administered orally for 30 days. The time required by animals to reach the stage was observed on the day first for short-term motor skilled learning, whereas on the eighth day and the twenty-first day for long-term motor skilled learning [32]. ## 2.7.1. Forced Swim Test The test was employed to evaluate the anti-depressant effects of the extracts of the seeds on the 22nd day. The apparatus consists of rectangular plexiglass container of dimension 46 cm × 20 cm. Depression was induced in mice through the initial 2 minutes session by the learned helplessness phenomenon, whereas the last 4 minutes of mice's immobility time was noted as a sign of antidepressant activity [34]. Immobility is considered by the absence of the movement of mice except for those that required mice to keep their head out of water. Percent reduction in immobility time was used to assess the antidepressant effect of extracts [35, 36]. ## 2.7.2. Tail Suspension Test To verify the antidepressant effect, tail suspension method was used on the 22nd day. Animals were suspended with tape with their tails upside down for 6 minutes [37] inducing depression in the initial 2 minutes by the learned helplessness phenomenon. Immobility time was noted in the last 4 minutes, which was associated with antidepressant activity. The immobility time was measured as the absence of the movement of mice to upright themselves. Percent reduction in immobility was considered a degree of antidepressant effect of the extracts [35, 36]. Following the behavioral test, the mice were subsequently sacrificed via cervical dislocation. Their brains were taken out, stored at or below 80°C, and then processed for neurochemical analysis. ## 2.7.3. Neurochemical Analysis Biogenic amines were determined by the reversed phase high-performance liquid chromatography or HPLC electrochemical detection (HPLC-EC) method using octadecylsilyl C18 column and methanol as a mobile phase [38]. These stored brains were initially defrosted, homogenized, and followed by extraction of biogenic amines using an extraction medium comprises of perchloric acid ($70\%$). Consequently, two times centrifugations were performed that separated the homogenate. Homogenate was further separated by a reversed-phase column at a constant flow rate (1 mL/minutes) with the help of an HPLC pump. Electrochemical detection of separated biogenic amines was done using their correspondence standards that run simultaneously along with samples (Shimadzu, Kyoto, Japan) at 0.8 V of operating potential. ## 2.8. Statistical Analysis All result values were calculated as Mean ± SEM by the SPSS statistical software package 26. One way analysis of variance was used followed by a post hoc Dunnet test at $P \leq 0.5$ and $P \leq 0.1.$ ## 3.1. Effect on Weight Table 1 shows the effect of C. maxima on weight variation in mice. C. maxima seed extracts at 100 and 200 mg/kg exhibited an extremely substantial reduction in weight as compared with their initial weights on the 30th day with a percent reduction in weight by $11\%$ at both doses. ## 3.2.1. Passive Avoidance Test Table 2 and Figure 1 reveal the effects of C. maxima seed extract on memory and learning by PAT. C. maxima 50 mg/kg exhibited a greatly substantial increment in latency time at 3 hours and 8th day. C. maxima 100 mg/kg revealed a greatly substantial increment in reaction time at 3 hours, 24 hours, 8th day, and 21st day. C. maxima 200 mg/kg revealed a substantial increment in reaction time at 3 hours and 21st day, whereas an extremely substantial increment in reaction time at 24 hours and 8th day. Diazepam 3 mg/kg exhibited a significant reduction in latency time on the 8th and 21st days, whereas imipramine 30 mg/kg exhibited a significant increment in latency time at 3 hours, 24 hours, 8th day, and 21st day. ## 3.2.2. Water Maze Test Table 3 and Figure 2 reveal the effects of C. maxima seed extract on memory and learning by water maze test. C. maxima 50 and 100 mg/kg revealed a greatly substantial reduction in time to arrive at the central hidden stage on the eighth day and a substantial reduction in time to arrive at the central stage on the twenty-first day as compared with control. C. maxima 200 mg/kg showed a greatly substantial reduction in time to arrive at the stage at twenty-four hours and the eighth day, whereas the substantial reduction in time to arrive at the stage on the twenty-first day. Diazepam 3 mg/kg group revealed a greatly substantial increment in time to arrive at the central hidden stage on the eighth and twenty-first days, whereas imipramine 30 mg/kg revealed a greatly substantial reduction in time to arrive at the central hidden stage on the 8th and 21st days in comparison with control. ## 3.3.1. Rotarod Test C. maxima 100 and 200 mg/kg showed a substantial increment in fall time on the twenty-first day at low speed in comparison with control (Table 4). These C. maxima groups showed a greatly substantial increment in fall time on the 8th and 21st days at high speed in comparison with control. Diazepam 3 mg/kg exhibited a greatly substantial reduction in fall time on the 8th day at both rpm, whereas a highly significant reduction in fall on the 21st day at both rpm. Imipramine 20 mg/kg showed a greatly substantial increment in fall time on the 8th and 21st days at high speed in comparison with control. ## 3.4. Stationary Rod Test Table 5 and Figure 3 reveal the effects of C. maxima seed extract on memory and learning by SRT. C. maxima 50 mg/kg showed an extremely substantial reduction in time to reach the elevated stage at twenty-four hours and the eighth day, whereas a substantial reduction in time to reach the elevated stage on the 21st day in comparison with control. C. maxima 100 mg/kg showed a greatly substantial reduction in time to attain the elevated stage on the twenty-four hours, 8th day, and 21st day. C. maxima 200 mg/kg revealed a greatly substantial reduction in time to reach the stage on the eighth day and a substantial increment in time to reach the stage on the twenty-first day in comparison with the control. Animals who received diazepam 3 mg/kg exhibited a substantial increment in time to reach the elevated stage on the eighth and twenty-first days, whereas imipramine 30 mg/kg exhibited a substantial reduction in time to reach the elevated stage on the eighth and twenty-first days in comparison with control. ## 3.5.1. Light and Dark Model C. maxima 50 mg/kg showed a greatly substantial increment in transitions on the 8th and 22nd day, whereas a greatly substantial increment in the percentage of time spent in the light area on the 21st day in comparison with control. C. maxima 100 200 mg/kg showed an extremely substantial increment in the percentage of time spent in the light area on the 8th and 21st days, whereas C. maxima 100 mg/kg showed a greatly substantial increment in transitions on the 8th day in comparison with the control. Animals who received diazepam 3 mg/kg showed a greatly substantial increment in the percentage of time spent in light area and transitions on the 8th and 21st days in comparison with control. Animals given imipramine 30 mg/kg showed a substantial increment in transitions on the 8th day. Table 6 showed the effects of C. maxima seed extracts on anxiety by light and dark models. ## 3.5.2. Elevated plus Maze Table 7 and Figure 4 show the effects of C. maxima seed extracts on anxiety by eEPM. C. maxima 100 and 200 mg/kg showed an extremely substantial and substantial increment in the percentage of time spent in an open area on the 8th and 22nd days, whereas exhibiting a significant increment in transitions on the 8th day as compared with control. These two groups also showed a greatly substantial and substantial reduction in time spent and transitions in closed arms on the eighth and twenty-first days, respectively. C. maxima 100 and 200 mg/kg also exhibited a reduction in anxiety index on the eighth and twenty-first days, respectively. Animals who received diazepam 3 mg/kg exhibited a greatly significant increment in time spent in the open arm on the eighth and twenty-first days as compared with control, whereas exhibiting a significant increment in transitions on the 21st day. Imipramine 30 mg/kg exhibited an increment in time spent in the open arm as compared with the control. The seed extract of C. maxima 100 and 200 mg/kg on the 8th day exhibited a significant reduction in the anxiety index as per standard. Table 6 showed the effects of C. maxima seed extracts on anxiety by EPM. ## 3.5.3. Head Dip Test C. maxima 100 mg/kg showed a greatly substantial reduction in the number of head dips on the 21st day. C. maxima 200 mg/kg showed an extremely substantial reduction in the number of head dip on the eighth and twenty-first days in comparison with control animals. Animals given the diazepam 3 mg/kg exhibited a greatly substantial reduction in the number of head dip on the eighth day while displaying a greatly substantial reduction in the number of head dips on the twenty-first day in comparison with the control. Animals that received imipramine 30 mg/kg revealed a greatly substantial reduction in head dip in comparison with the control group. Table 8 reveals the effect of C. maxima seed extract on several head dips. ## 3.5.4. Open Field Test Table 9 and Figure 5 reveal the effects of C. maxima seed extract on anxiety by OFT. C. maxima 50 mg/kg showed a greatly substantial rise in the total distance on the eighth day; significant and greatly substantial rise in center entries on the 8th and 21st days, respectively; significant and greatly significant increment in the duration of rearing's at the 8th and 21st days in comparison with control, respectively (Table 8). C. maxima 100 and 200 mg/kg showed an extremely substantial increment in the total distance on the 8th day; greatly significant increment in center entries, center time; number, and duration of rearing 8th and 21st days in comparison with control. Diazepam 3 mg/kg exhibited a significant reduction in the total distance on the 21st day; a greatly significant increment in center entries along with center time on the 21st day; a significant reduction in the number and duration of rearing's on the 21st day as compared with control. Imipramine 30 mg/kg exhibited an extremely significant reduction in center entries on the 21st day and an extremely significant increment in numbers of rearing's at the 8th and 21st days in comparison with the control. ## 3.5.5. Effect on Depression When evaluating the antidepressive research, two models were used. During this examination, every mouse survived. None of the mice drowned when tested using the forced swim test or the tail flick method. ## 3.5.6. Forced Swim Test C. maxima 50, 100, and 200 mg/kg showed an extremely substantial reduction in immobility time with a percent reduction of $36\%$, $35.4\%$, and $49.3\%$, respectively, on the 22nd day in comparison with the control. Imipramine 30 mg/kg shows maximum antidepressant activity, that us, $54.1\%$ as compared with control in FST. ## 3.5.7. Tail Suspension Test C. maxima 50, 100, and 200 mg/kg showed an extremely substantial reduction in immobility time with a percent reduction of $34.3\%$, $35.4\%$, and $42\%$, respectively, on the 22nd day in comparison with control. While imipramine 30 mg/kg showed an extremely substantial reduction in immobility time with a percent reduction of $52.5\%$ on the 22nd day as compared with the control (Table 10 and Figure 6). ## 3.5.8. Brain Biogenic Amines Evaluation Table 11 and Figure 7 show the effects of C. maxima seeds on brain biogenic amines at selected doses. C. maxima 50 mg/kg revealed an extremely substantial increment in concentration of NA and an extremely significant reduction in the concentration of HVA in the brain on the 30th day in comparison with control. C. maxima 100 mg/kg revealed an extremely substantial increment in concentration of NA, DOPAC, and DA, whereas an extremely significant reduction in the concentration of HVA in the brain on the 30th day in comparison with the control. C. maxima 200 mg/kg revealed a greatly substantial increment in concentration of NA, DOPAC, DA, and 5HT in the brain on the 30th day in comparison with the control. ## 4. Discussion Herbal medicines are playing a crucial role in the prevention and treatment of various disorders as these are the main sources of secondary metabolites. The current study was designed to evaluate the pharmacological activity of C. maxima seeds extract to rule out their role in weight reduction, anxiety, depression, and memory amelioration. Diazepam 3 mg/kg exhibited weight gain, whereas weight gained was more pronounced in the case of imipramine 30 mg/kg ($16\%$). A reduction in weight by more than $10\%$ from initial body weight is a remarkable change in weight which was observed by C. maxima at 100 and 200 mg/kg. The weight reduction by C. maxima may be due to the presence of high content of steroidal anti-inflammatory substances like cucurbitacin A, B, C, and D and the absence of cholesterol and saturated fatty acids. Furthermore, C. maxima seed extracts also revealed a marked reduction in spontaneous activity, pain response, touch response, corneal and light responses, grip strength, at 50, 100, and 200 mg/kg doses, whereas there was marked increment in the balance beam light in the light and dark model. C. maxima at 100 mg/kg showed maximum anxiolytic activity, that is, $58.9\%$ and $55\%$ on the 8th and 21st days, respectively, equivalent to diazepam ($54\%$). In the EPM model, the maximum reduction in anxiety index was observed at 200 mg/kg dose on the 8th day, that is, 0.26, whereas diazepam shows a significant reduction in anxiety index, that is, 0.37 and 0.39 at the 8th and the 21st day. Imipramine exhibited no remarkable reduction in anxiety in any model of anxiety. In the OFT model, the effects of C. maxima were almost comparable with the control in terms of total distance covered. Diazepam exhibited a significant reduction in total distance covered by animals on the 21st day, whereas C maxima extract and imipramine have no effect on the reduction of total distance covered by the animal on both the 8th and 21st days. Despite the reduction of total distance, diazepam significantly increased the center time and center entries verifying its anxiolytic effect. Imipramine only increases center entries as well as rearing showing improved learning and exploration. However, C. maxima exhibited more rearing duration as compared with the imipramine 3 mg/kg. An increase in rearing behaviors shows improved learning, whereas an increase in center activity shows an anxiolytic effect. In the PAT, C. maxima at 50 mg/kg displayed an extremely substantial rise in latency time to enter the punished area at 3 hours and 8th day. C. maxima at 100 mg/kg displayed an extremely substantial rise in reaction time at 3 hours, 24 hours, 8th day, and 21st day. C. maxima at 200 mg/kg revealed a substantial rise in reaction time at 3 hours and 21st day, whereas a greatly substantial increase in reaction time at 24 hours and 8th day. Diazepam exhibited a significant reduction in latency time to enter a punished dark box due to its amnesic effect. C. maxima extracts revealed no such effects. Imipramine 30 mg/kg showed a significant increment in latency time at 8 hours, 24 hours, 8th day and 21st day. In the water maze test, C. maxima 50 and 100 mg/kg showed an extremely substantial fall in time to arrive at the stage on the eighth day and a substantial fall in time to attain the stage on the twenty-first day in comparison with the control. C. maxima 200 mg/kg showed an extremely significant decrease in time to reach the stage at a time at 24 hours, and 8th day, whereas a significant decrease in time to reach the stage on the 21st day in comparison with control that is comparable with imipramine. Diazepam significantly increases the time to reach the platform highlighting its amnesic effect. As far as motor coordination was evaluated, C. maxima 100 and 200 mg/kg showed improvement in muscle activity by an increase in the fall time in rotarod. Diazepam 3 mg/kg exhibited a greatly substantial reduction in fall time on the 8th day at both rpm, whereas a highly substantial reduction in fall on the 21st day at both rpm. Imipramine 20 mg/kg showed a greatly substantial increment in fall time on the 8th and 21st days at high speed in comparison with control. In the SRT, the animals given C. maxima seed extract revealed an extremely substantial decline in the time to attain the stage at 24 hours and eighth day, whereas a substantial reduction in the time to achieve the stage on the twenty-first day in comparison with the control. C. maxima at 100 mg/kg showed a greatly substantial fall in time to attain the stage at 24 hours, 8th day, and 21st day in comparison with control. C. maxima at 200 mg/kg showed a greatly substantial increment in time to attain the stage on the eighth day and a substantial fall in time to attain the stage on the twenty-first day in comparison with the control. Diazepam showed a greatly substantial increase in time to attain the stage highlighting its effect on reduction in motor learning due to muscle relaxant activity. The antidepressant effects of seeds were determined and evaluated by the forced swimming and tail suspension tests in mice. C. maxima extracts showed maximum antidepressant effects at 200 mg/kg comparable with imipramine. This may be due to increased concentration of NA and dopamine and a decrease in the metabolism of NA indicated by decreasing concentration of HVA in the brain, whereas at 200 mg/kg C. maxima also revealed an elevation in 5HT level. Dopamine metabolism was also found to increase after C. maxima dosing. The reason may be due to CNS active metabolites, such as polyphenols and β-carotene [39]. Sinha et al. [ 40] reported that ethanol extract of C. maxima is a cholinesterase inhibitor, hence producing a neuroprotective effect since the hydrolysis of the acetylcholine by cholinesterase has been linked with cognition impairment. Increased activity of the brain AChE causes fast hydrolysis of acetylcholine in turn increases the risk for the progression of dementia. Therefore, there is a growing interest in novel cholinesterase inhibitors for the management of cognition impairment [41]. In addition, higher inhibition of butyrylcholinesterase (BChE) activity is often desirable in humans. Any mutilation of the monoaminergic neurotransmission by monoamine oxidase (MAO) inhibitor has been implicated in the pathogenesis and progression of several neurodegenerative diseases, especially Parkinson's and Alzheimer's [42]. Hence, inhibition of MAO activities by suitable agents, particularly plant-derived molecules/extracts, may provide a useful therapeutic strategy in the management of cognitive impairment. Many previous investigations have reported that plant extracts are potent inhibitors of MAO activity. In this study, the ability of the tested extracts of C. maxima, to inhibit MAO activity could be a probable mechanism, contributing to their neuroprotective properties [40]. Hence, inhibition of AChE and BChE activities and stimulation of Na+/K+-ATPase activity by tested extracts can provide an avenue for the development of effective drugs of plant origin for the management of cognitive disorders. Arora et al. [ 43] revealed prominent anxiolytic activity of Cucurbita moschata seed extracts at 200 mg/kg which was comparable with the standard drug alprazolam in both models. Moreover, an alteration was also observed in motor coordination by the ethanol extract at the same dose. The probable mechanism suggested was an increase in chloride ion influx suggesting a γ-Aminobutyric acid type A (GABAA) receptors-mediated mechanism of action. Hence, it may be assumed that C. maxima extract may have produced the effect in a similar pattern. Antidepressant and anxiolytic drugs have a memory suppressing effect [44]. They emphasize the discovery of newer therapies without adverse effects. Hence in the current study, nootropic effects of C. maxima were measured using three different models stationary rod, passive avoidance, and water maze test. C. maxima seed extract exhibited an increasing effect on the memory-recalling process both at short-term and long-term levels in comparison with control in all three selected models. C. maxima is popular for the amelioration of various diseases since contain different secondary polyphenolic molecules, such as quercetin and p-coumaric acid [40]. Several biological activities of C. maxima have been reported due to the presence of these metabolites. It behaves like a potent antioxidant because of a high percentage of β-carotene that enhances immunity and decreases the incidence of other medical problems, such as cancer and the progression of heart disease. Hence in the present study, it is justified to conclude that the reduction in anxiety and depression may be linked with changes in levels of biogenic amines. Recently, a study was conducted on the neuroprotective effect of C. maxima ether seed extracts on ethidium bromide-induced demyelination in Wistar rats. The results of this study are concurrent to our study, which shows that C. maxima seeds have a potential neuroprotective effect in rat-induced demyelination with an improvement in muscle strength and coordination [45]. Sinha et al. [ 40] reveal that modulation of MAOs, cholinesterase, and sodium–potassium ATPase activities in the brain through phyto-molecules has been effective in the management of cognitive disorders. Sinha et al. [ 40] also showed that ethanol and hexane extracts of C. maxima at 50 μg/ml concentration inhibited the AChE and BChE activities compared with the standard drug, donepezil which was linked to the presence of quercetin in Cucurbita species. Quercetin is a plant-derived polyphenol having anti-carcinogenic, anti-inflammatory, and antiviral properties, as well as the capacity to reduce lipid peroxidation, platelet aggregation, and capillary permeability [46]. ## 5. Conclusion Neurological problems are an increasing trend these days. Therefore, it is a need for time to identify natural products with neuropharmacological benefits. C. maxima has now been regarded as an important neuroprotective since several investigators have reported that it contains many neuroprotective and anti-inflammatory metabolites. Thus, it should be included in our daily diet not only to enhance memory, but also to reduce the symptoms of anxiety and depression. Present work authenticates that the chronic use of C. maxima seed in neurological problems has been very efficacious in ameliorating such problems. However, before being used widely, adequate clinical trials are crucial. Moreover, without appropriate coverage by reputable worldwide venues, the advantages of these studies cannot be widely distributed. ## Data Availability Data supporting this research article are available from the corresponding author or first author on reasonable request. ## Ethical Approval This study was supported by approval from the Board of Advance Studies and Research, University of Karachi. Reference no. 03297/Pharm was granted by the Board on April 20, 2017, to conduct the study. This approval was followed by the permission of the Ethical Committee, Department of Pharmacology for the use of animals as per the National Institute of Health guidelines for the care and usage of laboratory animals. ## Conflicts of Interest The author(s) declare(s) that they have no conflicts of interest. ## Authors' Contributions All authors have contributed to the writing of the manuscript. Dr. Shahana Wahid contributed to the material preparation, data collection, experimental work, and analysis. Dr. Ali Alqahtani contributed to the review and editing of the manuscript. Dr. Rafeeq Alam Khan contributed to the conception and design of the study and finalized the manuscript. All authors read and approved the final manuscript. ## References 1. Okano H., Hirano T., Balaban E.. Learning and memory. (2000) 97 12403-12404. DOI: 10.1073/pnas.210381897 2. Wiltgen B., Sanders M., Anagnostaras S., Sage J., Fanselow M.. Context fear learning in the absence of the hippocampus. (2006) 26 5484-5491. DOI: 10.1523/JNEUROSCI.2685-05.2006 3. Luanaigh C. O., Lawlor B., Maeck L., Stoppe G.. Drugs that affect competence. (2008) 209-226. DOI: 10.1007/978-3-211-72369-2 4. Raikhy S., Gautam S., Kanodia S.. Pattern and prevalence of psychiatric disorders among patients attending dermatology OPD. (2017) 29 85-88. DOI: 10.1016/j.ajp.2017.04.018 5. Ballenger J. C., Davidson J. R., Lecrubier Y.. Consensus statement on posttraumatic stress disorder from the international consensus group on depression and anxiety. (2000) 61 60-66. PMID: 10761680 6. Pytka K., Podkowa K., Rapacz A.. The role of serotonergic, adrenergic and dopaminergic receptors in antidepressant-like effect. (2015) 68 263-274. DOI: 10.1016/j.pharep.2015.08.007 7. Rihmer Z., Xenia G.. Antidepressant-resistant depression and antidepressant-associated suicidal behaviour: the role of underlying bipolarity. (2011) 2011 p. 5. DOI: 10.1155/2011/906462 8. Kanter J. W., Busch A. M., Weeks C. E., Landes S. J.. The nature of clinical depression: symptoms, syndromes, and behavior analysis. (2008) 31 1-21. DOI: 10.1007/BF03392158 9. England M. J., Sim L. J.. Committee on depression, parenting practices, and the healthy development of children. (2009) 10. Brigitta B.. Pathophysiology of depression and mechanisms of treatment. (2002) 4 7-20. DOI: 10.31887/DCNS.2002.4.1/bbondy 11. Mansour E. H., Dworschak E., Pollhamer Z., Gergely A., Hovari J.. C. maxima and canola seed proteins and bread quality. (1999) 28 59-70 12. Glew R. H., Glew R. S., Chuang L. T.. Amino acid, mineral and fatty acid content of pumpkin seeds (Cucurbita spp.) and Cyperus esculentus nuts in the Republic of Niger. (2006) 61 51-56. DOI: 10.1007/s11130-006-0010-z 13. Matus Z., Molnar P., Szabo L. G.. Main carotenoids in pressed seeds (Cucurbitae semen) of oil pumpkin (Cucurbita pepo convar. pepo var. styriaca). (1993) 63 247-256. PMID: 8249609 14. Murkovic M., Mulleder U., Neunteufl H.. Carotenoid content in different varieties of pumpkins. (2002) 15 633-638. DOI: 10.1006/jfca.2002.1052 15. Muchirah P. N., Waihenya R., Muya S., Abubakar L., Ozwara H., Makokha A.. Characterization and antioxidant activity of. (2018) 7 134-140. DOI: 10.31254/phyto.2018.7206 16. Nkosi C. Z., Opoku A. R., Terblanche S. E.. Antioxidative effects of pumpkin seed (Cucurbita pepo) protein isolate in CCl. (2006) 20 935-940. DOI: 10.1002/ptr.1977 17. Aguilar Y. M., Yero O. M., López J. C., Navarro M. V., Espinosa E. M.. Phytosterols and squalene as hypocholesterolemic substances in five varieties of Cucurbita maxima and Cucurbita moschata. (2011) 16 72-81 18. Gossell-Williams G., Davis A., O‘Connor N.. Inhibition of testosterone-induced hyperplasia of the prostate of Sprague-Dawley rats by pumpkin seed oil. (2006) 9 284-286. DOI: 10.1089/jmf.2006.9.284 19. Caili F., Huan S., Quanhong L.. A review on pharmacological activities and utilization technologies of. (2006) 61 70-77. DOI: 10.1007/s11130-006-0016-6 20. Torkova A. A., Lisitskaya K. V., Filimonov I. S.. Physicochemical and functional properties of Cucurbita maxima pumpkin pectin and commercial citrus and apple pectins: a comparative evaluation. (2018) 13 p. e0204261. DOI: 10.1371/journal.pone.0204261 21. Cheng L., Pan G. F., Xiao-bo S., Yun-xiang H., You-shun P., Lin-yan Z.. Evaluation of anxiolytic-like effect of aqueous extract of Asparagus Stem in mice. (2013) 2013. DOI: 10.1155/2013/587260 22. Podolan M., Santos J. D., Walber T., Possamai F., Viola G. G., Linode O. C.. A single injection of imipramine affected proliferation in the hippocampus of adult Swiss mice depending on the route of administration, doses, survival time and lodging conditions. (2019) 100 p. 101655. DOI: 10.1016/j.jchemneu.2019.101655 23. Michel B., Martine H.. The mouse light/dark box test. (2003) 463 55-65. PMID: 12600702 24. Contreras C. M., Rodriguez-Landa J. F., Garcia-Rios R. I., Cueto-Escobedo J., Guillen-Ruiz G., Bernal-Morales B.. Myristic acid produces anxiolytic-like effects in Wistar rats in the elevated plus-maze. (2014) 2014. DOI: 10.1155/2014/492141 25. Kliethermes C. L., Crabbe J. C.. Pharmacological and genetic influences on hole-board behaviors in mice. (2006) 85 57-65. DOI: 10.1016/j.pbb.2006.07.007 26. Aslam M., Najam R.. A review of pharmacognostical, phytochemical and pharmacological properties of Lagenaria siceraria: a miracle herb. (2013) 4 266-274. DOI: 10.7439/ijbar.v4i5.329 27. Crupi R., Mazzon E., Marino A.. Melatonin treatment mimics the antidepressant action in chronic corticosterone-treated mice. (2010) 49 123-129. DOI: 10.1111/j.1600-079X.2010.00775.x 28. Russo E., Chimirri S., Aiello R.. Lamotrigine positively affects the development of psychiatric comorbidity in epileptic animals, while psychiatric comorbidity aggravates seizures. (2013) 28 232-240. DOI: 10.1016/j.yebeh.2013.05.002 29. Rizwana B., Maheshwari K. K.. Evaluation of the antidepressant activity of Acyranthes aspera by using open field test in rats. (2019) 8 1183-1195. DOI: 10.20959/wjpr20196-14912 30. Asgharzade S., Rabiei Z., Rafieian-Kopaei M.. Effects of. (2015) 5 829-833. DOI: 10.1016/j.apjtb.2015.06.006 31. Cho J., Kang J. S., Long P. H., Jing J., Back Y., Chung K. S.. Antioxidant and memory-enhancing effects of purple sweet potato anthocyanin and cordyceps mushroom extract. (2003) 26 821-825. DOI: 10.1007/BF02980027 32. Lundquist A. J., Llewellyn G. N., Kishi S. H.. Knockdown of astrocytic monocarboxylate transporter 4 in the motor cortex leads to loss of dendritic spines and a deficit in motor learning. (2022) 59 1002-1017. DOI: 10.1007/s12035-021-02651-z 33. Gallivan L. M., Schmitzer-Torbert N.. A low-cost Morris water maze for undergraduate research: construction and demonstration in a rat model of obesity-induced diabetes. (2018) 16 A143-A151. PMID: 30057496 34. Aiello M., Salvatore E., Cachia A.. Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: a PET/MR hybrid scanner study. (2015) 113 111-121. DOI: 10.1016/j.neuroimage.2015.03.017 35. Planchez B., Surget A., Belzung C.. Animal models of major depression: drawbacks and challenges. (2019) 126 1383-1408. DOI: 10.1007/s00702-019-02084-y 36. Adachi N., Sakhri F. Z., Ikemoto H.. Kamikihito rescued depressive-like behaviors and hippocampus neurogenesis in chronic restraint stress rats. (2022) 12 172-179. DOI: 10.1016/j.jtcme.2021.08.001 37. Steru L., Chermat R., Thierry B., Simon P.. The tail suspension test: a new method for screening antidepressants in mice. (1985) 85 367-370. DOI: 10.1007/BF00428203 38. Tahira P., Roomana R., Sadia H., Darakhsahan J. H.. Increased serotonergic functions following administration of 1-(1-naphthyl) piperazine in propranolol injected rats. (2006) 19 190-194 39. Kistler L., Newsom L. A., Ryan T. M., Clarke A. C., Smith B. D., Perry G. H.. Gourds and squashes (Cucurbita spp.) adapted to megafaunal extinction and ecological anachronism through domestication. (2015) 112 15107-15112. DOI: 10.1073/pnas.1516109112 40. Sinha S., Kumar B., Luqman S., Singh D. K.. Neuroprotective potential of. (2019) 120, article S0254629918310184. DOI: 10.1016/j.sajb.2018.09.027 41. Mehta M., Adem A., Sabbagh M.. New acetylcholinesterase inhibitors for Alzheimer’s disease. (2012) 2012. DOI: 10.1155/2012/728983 42. Behl T., Makkar R., Sehgal A.. Current trends in neurodegeneration: cross talks between oxidative stress, cell death, and inflammation. (2021) 22 p. 7432. DOI: 10.3390/ijms22147432 43. Arora I., Behl T., Groverc M., Sachdeva M., Pal G., Khan N.. Study of anxiolytic and motor co-ordination activity of. (2020) 18 p. 100204. DOI: 10.1016/j.obmed.2020.100204 44. Nicolas A., Ruby P. M.. Dreams, sleep, and psychotropic drugs. (2020) 11. DOI: 10.3389/fneur.2020.507495 45. Neelamma G., Duraiswamy B., Suresh N. S., Vadivelan C., Sandhya R., Justin C.. Evaluation of protective neuro pharmacological activity of seeds of. (2018) 48 83-91 46. Li Y., Yao J., Han C.. Quercetin, inflammation, and immunity. (2016) 8 p. 167. DOI: 10.3390/nu8030167
--- title: 'The Association between Unhealthy Food Consumption and Impaired Glucose Metabolism among Adults with Overweight or Obesity: A Cross-Sectional Analysis of the Indonesian Population' authors: - Adriyan Pramono - Deny Y. Fitranti - K. Heri Nugroho - M. Ali Sobirin - Ahmad Syauqy journal: Journal of Obesity year: 2023 pmcid: PMC10060072 doi: 10.1155/2023/2885769 license: CC BY 4.0 --- # The Association between Unhealthy Food Consumption and Impaired Glucose Metabolism among Adults with Overweight or Obesity: A Cross-Sectional Analysis of the Indonesian Population ## Abstract ### Background It has been shown that dietary patterns are associated with glucose control. However, the association between the types of food consumed and blood glucose in overweight or obese individuals is still unclear. The present study aimed to determine the association between unhealthy food consumption and impaired glucose metabolism in adults with overweight or obesity. ### Methods The analysis presented in this study was based on the data from a population-based, cross-sectional, nationally representative survey (Indonesian Basic Health Research 2018/RISKESDAS 2018). The body mass index (BMI) was calculated as weight (kg)/height squared (m2) and was determined based on the World Health Organization (WHO) criteria for the Asian population. A validated questionnaire and food card were used to assess the diet. Fasting plasma glucose and 2-hpost-prandial glucose were employed to determine blood glucose markers. ### Results In total, 8752 adults with overweight or obesity were included in this analysis. We found that consumption of sweet, grilled, and processed foods was associated with impaired fasting plasma glucose (IFG) before and after adjustment ($p \leq 0.05$). Consumption of high-fat foods was also associated with impaired glucose tolerance (IGT) for all models tested ($p \leq 0.05$). Furthermore, all models showed a link between processed food consumption and combined glucose intolerance (CGI) (p ≤ 0.001). ### Conclusions Differential food group consumption was associated with IFG, IGT, and CGI in Indonesian adults who were overweight or obese. ## 1. Introduction Various epidemiological studies indicate a trend toward an increase in the prevalence of type 2 diabetes (T2D) worldwide. Furthermore, the World Health Organization (WHO) predicts that 350 million people will have T2D in the forthcoming years [1]. Type 2 diabetes prevalence has increased sharply in developed and developing countries [2, 3]. Interestingly, the prevalence of T2D in Southeast Asia is considerably higher than in other developed countries. More specifically, a recent study indicates that Indonesia is among the countries with a high prevalence of T2D [4]. A prediabetes condition often initiates type 2 diabetes. Prediabetes is defined as a condition with impaired glucose tolerance (IGT), defined by a 2-h glucose concentration between 140 and 199 mg/dl and/or an impaired fasting plasma glucose (IFG) level (i. e., between 100 and 125 mg/dl) [5]. A recent review describes that individuals with IFG mainly show hepatic insulin resistance and normal or slightly lower whole-body insulin sensitivity. In contrast, individuals with IGT have normal to slightly reduced hepatic insulin sensitivity and show moderate to severe reduced skeletal muscle insulin sensitivity [6]. Insulin resistance contributes to hyperglycemia and hyperlipidemia, all risk factors for developing T2D and cardiovascular diseases (CVDs) [7]. Dietary pattern management is an essential determinant of blood glucose control, even in people without T2D [8]. According to a recent study on the Iranian population, long-term healthy diet quality is associated with a lower risk of CVDs [9]. In addition, a study on China's population also showed that high salt intake was associated with T2D [10]. Among US adults, increased consumption of added sugar worsens the risk of CVD death in a dose-dependent manner [11]. Moreover, among men and women in the US, fried food consumption was associated with T2D and CVDs. More interestingly, the association was mediated by obesity status in that study [12]. In addition, the findings regarding the relationship between fruit consumption and metabolic disease are also inconsistent. Meta-analyses of randomized controlled trials showed that fruit juice consumption was not associated with T2D risk [13]. However, fruit consumption (whole fruit) was associated with a lower incidence of T2D in a prospective study and meta-analysis [14, 15]. Consumption of fruits and vegetables was linked to better glucose control [16]. A meta-analysis has recently shown that ultraprocessed food increases the risk of T2D [17]. However, the extent to which dietary characteristics affect glucose metabolism has yet to be entirely understood. Indeed, higher caloric intake is associated with increased blood glucose and T2D [18], but the association between the types of food consumed and blood glucose levels still needs to be determined. Furthermore, obesity status may influence the interpretation, since obesity is strongly linked to the impairment of blood glucose and the development of insulin resistance. Therefore, we used nationally representative data from the 2018 Indonesia Basic Health Research (Riskesdas 2018/Riset Kesehatan Dasar 2018) to investigate the association between unhealthy foods and glucose metabolism. This study aimed to determine the link between several components of unhealthy foods and glucose metabolism markers in Indonesian adults with overweight or obesity. ## 2.1. Data Sources This study used a population-based, cross-sectional, nationally representative survey (Indonesia Basic Health Research 2018/Riskesdas 2018/Riset Kesehatan Dasar 2018) conducted by the National Institute of Health Research Development (NIHRD), Ministry of Health, Indonesia. In this analysis, up to 2500 censuses from 26 provinces, including 1446 urban and 1054 rural sites, were subsampled to represent the national level of biomedical data collection. The sampling and survey methods have been described in detail [19]. Inclusion criteria of this study were as follows: individuals aged 18–50 years with a BMI of ˃23 kg/m2 were eligible for this study, as were those who had completed data on food frequency consumption, those who had completed data on physical activity questionnaire, and those who had completed data on fasting glucose and 2-hours postprandial glucose. Exclusion criteria of this study were as follows: respondents had a BMI ≤ 23 kg/m2, and the respondent was sick or did not complete all measurements during the study. ## 2.2. Measurements Basic characteristics and anthropometric measurements (height and weight) were collected using a standardized protocol by well-trained interviewers. A multifunction brand stadiometer with a capacity of 2 m and a precision of 0.1 cm was used to measure the standing height. The body weight was measured on a Camry digital weight scale with a capacity of 150 kg. The weight scale was calibrated daily before use. The body mass index (BMI) was calculated as weight (kg)/height squared (m2) and was determined based on WHO criteria for the Asian population: healthy weight (18.5 to <23 kg/m2), overweight (23.0 to <27.5 kg/m2), and obese (≥27.5 kg/m2) [20]. Several self-reported covariates were collected through interviews: age, gender (men and women), and rural-urban living area. The respondents were asked about the frequency of sweet, salty, high-fat, grilled, processed, and fruit and vegetable intake in the last week using a validated questionnaire and food card. Food frequency was recorded as >one time (1x)/day, 1x/day, 3–6x/week, 1-2x/week, ≤3x/month, and never. It was categorized in a binary form: frequently (≥1x/day) and rarely (<1x/day) [21]. In food questionnaires, refined carbohydrates included flour-processed foods with added sugar, such as flavored bread. Sweet foods include high-sugar foods with additional natural sugar, e. g., cakes and canned fruit. High-fat and fried foods include high-fat foods, e. g., fatty meats, oxtail soup, fried foods, foods containing coconut milk and margarine, and high-cholesterol foods, such as innards (intestines, tripe), eggs, and shrimp. To determine blood glucose markers, fasting plasma glucose and 2-hpost-prandial glucose were employed. According to the American Diabetic Association (ADA) [22], IFG is defined as fasting blood glucose levels of 100–125 mg/dl with normal oral glucose tolerance test (OGTT) results of <140 mg/dl; IGT is defined as OGTT results of 140–199 mg/dl with normal fasting blood glucose levels of <100 mg/dl; or both IFG and IGT. ## 2.3. Statistical Analysis Pearson's Chi-square test was used to describe the IFG/IGT status based on age groups, gender, sedentary activities, unhealthy food intake, and living area (rural or urban) as categorical variables. First, a simple regression analysis (unadjusted model) was performed with unhealthy foods (refined carbohydrates, salty food, high-fat and fried foods, grilled food, fruit, and vegetables as well as ultraprocessed foods) as independent variables, and IFG/IGT/combination between IFG and IGT as dependent variables (model 1). Subsequently, multiple regression analysis was performed with body mass index (BMI) added as an independent variable (model 2). Next, multiple regression analysis was performed with physical activity level added as independent variables (model 3) and BMI and physical activity level added together (model 4). Finally, multiple regression analysis was performed to relate unhealthy foods (refined carbohydrates, salty food, high-fat and fried foods, grilled food, fruit, and vegetables, as well as ultraprocessed foods) and IFG/IGT or combinations of IFG and IGT adjusted by BMI, physical activity level, age, and sex (model 5). All data were analyzed using SPSS for Mac, version 22.0 (IBM Inc.), and statistical significance was set at $p \leq 0.05.$ ## 2.4. Ethical Approval All procedures performed in this study were in accordance with the ethical standards of the institutional research committee, the 1964 Helsinki Declaration and its later amendments, or comparable ethical standards. ## 3.1. Participant Characteristics A total of 8752 subjects were included in this study's analysis. Of these, $16.6\%$ had IFG, $25.6\%$ had IGT, $13.9\%$ had combined glucose intolerance (CGI), and the rest had normal glucose regulations. The majority of individuals in the study were overweight ($49.6\%$). Impaired glucose tolerance was the most common glucose metabolism impairment in the obese, overweight, middle-aged, young, female, urban, rural, frequent consumption of sweet foods, rare consumption of sweet foods, frequent consumption of salty foods, rare consumption of salty foods, frequent consumption of fat foods, rare consumption of fat foods, frequent consumption of grilled foods, rare consumption of grilled foods, rare consumption of processed foods, less consumption of fruit and vegetables, sufficient consumption of fruit and vegetables, lack of physical activity, and sufficient physical activity groups. Interestingly, in the male and frequently consumed processed foods subgroup, IFG was the dominant glucose metabolism disorder (Table 1). Obesity affects $22.6\%$ of the 8752 individuals in the study. In the obese group, the highest percentage of people had impaired glucose metabolism, namely IGT, CGI, and IFG ($28.4\%$, $16.5\%$, and $16.3\%$, respectively). While in the overweight group, the highest proportions were IGT, IFG, and CGI ($26.2\%$, $16.6\%$, and $14.3\%$, respectively) (Table 1). IFG and CGI are the most prevalent in the 46–50 year-old age group, while IGT is most common in the 41–45 year-old age group (Figure 1). ## 3.2. Association between the Dietary Patterns of Specific Food Groups and IFG Consumption of sweet, grilled, and processed foods was significantly associated with IFG for all models tested. After adjustment for physical activity (model 2) and BMI and physical activity (model 4), frequent consumption of sweet foods had a risk of IFG of approximately $15\%$ (OR = 1.153, $95\%$ CI = 1.047–1.268). Frequent consumption of grilled food had higher odds of developing IFG than rare consumption of grilled food (model 2, OR = 1.350, $95\%$ CI = 1.056–1.725). The eating pattern with the highest probability of IFG is the consumption of processed foods. In model 5, after adjusting for BMI, physical activity, age, and gender, consumption of processed food >1x/day had a risk of IFG of approximately $72\%$ (OR = 1.729, $95\%$ CI = 1.311–2.280) (Table 2). ## 3.3. Association between the Dietary Patterns of Specific Food Groups and IGT The association test between sweet foods and IGT was found to have significant results in model 1 (unadjusted, $$p \leq 0.031$$); however, frequent consumption of sweet foods was not a risk factor for IGT (OR = 0.924, $95\%$ CI = 0.859–0.993). Different results were shown in the high-fat food group. Interestingly, in unadjusted model 1, consumption of high-fat foods >1x/day had the highest odds of being IGT, which were approximately $12\%$ (OR = 1.124, $95\%$ CI = 1.032–1.225) (Table 3). ## 3.4. Association between the Dietary Patterns of Specific Food Groups and CGI Consumption of sweet foods was associated with CGI (model 2, $$p \leq 0.013$$; model 3, $$p \leq 0.013$$; model 4, $$p \leq 0.013$$; model 5, $$p \leq 0.016$$). In models 2, 3, and 4, frequent consumption of sweet foods had a greater probability of developing CGI (OR = 1.173, $95\%$ CI = 1.034–1.331). All models indicate an association between processed food consumption and CGI. In model 5, after adjusting for BMI, physical activity, age, and gender, frequent consumption of processed foods had the highest odds of CGI, approximately $62\%$ (OR = 1,620, $95\%$ CI = 1,150–2.283) (Table 4). ## 4. Discussion This study aimed to determine the association between unhealthy eating habits, fruit and vegetable consumption, and impaired glucose status in adults. This study's analysis revealed that eating processed foods >once per day was the strongest risk factor for IFG and CGI, whereas eating high-fat foods frequently was the highest risk factor for IGT. Consistent with the results of the present study, other investigators have reported that consuming foods rich in saturated fat and cholesterol may increase the risk of impaired glucose and insulin regulation [23, 24]. In contrast, a diet high in fruits, vegetables, and whole grains can prevent or control conditions related to insulin resistance, including IFG and IGT [24, 25]. Processed foods and high-fat foods, including fried foods, are high in salt, saturated fat, and cholesterol. The World Health Organization and most nutritional professionals today recognize that a diet rich in salt, saturated fat, and excess sugar is disease-causing. An association between a Western diet characterized by high consumption of red meat, processed meat, fast food, alcoholic beverages, and sugar-sweetened beverages and a higher risk of prediabetes has also been reported [26, 27]. A study also found that poor dietary quality, excessive consumption of cereals and salt, and insufficient consumption of vegetables, fish, and diet variety were all associated with an increased risk of prediabetes [28]. Furthermore, several studies have shown a correlation between eating green leafy vegetables (rich in vitamins, trace elements, and soluble dietary fiber) and a reduced risk of T2D [29, 30]. A healthier diet can lower the risk of the development of prediabetes into diabetes by $40\%$ to $70\%$ [31]. The Mediterranean and DASH (Dietary Approaches to Stop Hypertension) diets protect against the development of insulin resistance and T2D [32]. This lends credence to the theory that a plant-based diet with a balanced glycemic index and load, high in soluble fiber and phytochemicals, might be useful in lowering the risk of dysglycemia and prediabetes. The Mediterranean and DASH diets are relatively high in fat from vegetable sources (extravirgin olive oil, tree nuts). They include an abundance of minimally processed plant foods (vegetables, fruits, whole grains, and legumes), moderate fish consumption, low consumption of meat and meat products, and wine in moderation, usually with meals. It has been hypothesized that their positive impact is related to their components [33]. A biological explanation is possible. The antioxidant profile of the diet may prevent the accumulation of oxidative stress, which has been linked to the development of insulin resistance and β-cell dysfunction [34]. In this study, $72.17\%$ of participants were overweight or obese; $16.53\%$ developed IFG, $26.87\%$ had IGT, and $14.99\%$ developed CGI. IFG occurs due to inadequate glucose control, resulting in higher blood glucose even after an overnight fast. In contrast, IGT develops due to an individual's inability to respond to glucose taken as part of a meal, resulting in increased post-prandial blood glucose. While both IFG and IGT contribute to insulin resistance, the former is caused by hepatic insulin resistance, while the latter is caused predominantly by insulin resistance in skeletal muscle. Notably, pancreatic β-cell dysfunction is shared by both IFG and IGT [35, 36]. It is well established that IFG can be reverted to normal blood glucose homeostasis with effective intervention. Compared to earlier lifestyle intervention research, intensive lifestyle intervention plays a significant role in educating individuals and assisting them in achieving glycemic control [37]. Without lifestyle modifications and adequate assistance, roughly $9\%$ of patients with IFG will acquire type 2 diabetes within three years [38]. Intensive lifestyle programs, which include diet and physical activity interventions, significantly improve fasting plasma glucose, weight, BMI, triglycerides, high-density lipoprotein cholesterol, and total cholesterol in individuals with IFG [39]. IGT is prediabetic hyperglycemia characterized by peripheral insulin resistance, and it has been demonstrated that weight loss and increases in daily energy expenditure reduce the incidence of insulin resistance [40, 41]. In IGT patients, lifestyle changes focused on physical or nutritional therapies, or both, are related to improvements in 2-hour plasma glucose and FPG levels. Furthermore, all individuals with IGT, whether they have normal or low FPG levels, may benefit from lifestyle changes to delay the development and reduce the incidence of T2DM [42, 43]. The strength of this study is that it uses a large and representative sample in Indonesia and data on dietary intake obtained through interviews using questionnaires and dietary intake cards to minimize bias. This study has limitations, including the fact that it was conducted in a cross-sectional design prospective cohort studies, or RCTs, of diet type modification and its effect on glucose control in overweight/obese people should be conducted. In addition, the data on unhealthy food intake was obtained only from frequency data, so the exact weight of the food consumed (in grams) and energy intake (in kcal) could not be determined. Finally, we do not have data on additional confounding biomarkers, such as IGF-1 levels. ## 5. Conclusions This population-based study found that eating unhealthy diets increased the risk of impaired glucose metabolism among adults who were overweight or obese in Indonesia. Longitudinal studies should be considered to investigate the various impacts of food patterns on glucose metabolism in overweight and obese people. More importantly, health promotions on nutrition and physical activities should be encouraged among overweight and obese individuals, as they can play an essential role in developing healthy eating habits and increasing healthy living behaviors. ## Data Availability The data used to support this study are available from the Data Management Laboratory of NIHRD, the Ministry of Health, and the Republic of Indonesia on reasonable request with prior officially written permission. ## Ethical Approval Ethics and permissions for conducting this study followed the Ethical Approval for RISKESDAS 2018 from the Ethical Committee of Health Research, NIHRD, Ministry of Health, Republic of Indonesia No. LB$\frac{.02.01}{2}$/KE$\frac{.267}{2017.}$ As RISKESDAS 2018 allowed the authors to analyze the dataset through the data management laboratory in the NIHRD, the ethics referred to the ethical clearance of RISKESDAS 2018. ## Consent All respondents have provided written approval for their involvement during data collection. ## Conflicts of Interest The authors declare that they have no conflicts of interest. ## Authors' Contributions AP, DYF, KHN, MAS, and AS contributed to the conceptualization and methodology. AP analyzed the data and drafted the manuscript. AP and AS reviewed and edited the drafted manuscript. All the authors have read and approved the submission of the present version of the manuscript. ## References 1. Cosentino F., Grant P. J., Aboyans V.. 2019 esc guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the easd. (2020) 41 255-323. DOI: 10.1093/eurheartj/ehz486 2. Yang J. J., Yu D., Wen W.. Association of diabetes with all-cause and cause-specific mortality in Asia: a pooled analysis of more than 1 million participants. (2019) 2. DOI: 10.1001/jamanetworkopen.2019.2696 3. Forouzanfar M. H., Afshin A., Alexander L. T.. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. (2016) 388 1659-1724. DOI: 10.1016/s0140-6736(16)31679-8 4. Lin X., Xu Y., Pan X.. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. (2020) 10. DOI: 10.1038/s41598-020-71908-9 5. Abdul-Ghani M. A., DeFronzo R. A.. Plasma glucose concentration and prediction of future risk of type 2 diabetes. (2009) 32 S194-S198. DOI: 10.2337/dc09-S309 6. Faerch K., Hulmán A., Pj Solomon T.. Heterogeneity of pre-diabetes and type 2 diabetes: implications for prediction, prevention and treatment responsiveness. (2015) 12 30-41. DOI: 10.2174/1573399811666150416122903 7. Bornfeldt K. E., Tabas I.. Insulin resistance, hyperglycemia, and atherosclerosis. (2011) 14 575-585. DOI: 10.1016/j.cmet.2011.07.015 8. Ali I. A.. Association of glycosylated hemoglobin with physical activity and diet in Sudanese NonDiabetics population. (2020) 1 14-18. DOI: 10.36478/jdme.2020.14.18 9. Nouri F., Sadeghi M., Mohammadifard N., Roohafza H., Feizi A., Sarrafzadegan N.. Longitudinal association between an overall diet quality index and latent profiles of cardiovascular risk factors: results from a population based 13-year follow up cohort study. (2021) 18 p. 28. DOI: 10.1186/s12986-021-00560-5 10. Abdulai T., Li Y., Zhang H.. Prevalence of impaired fasting glucose, type 2 diabetes and associated risk factors in undiagnosed Chinese rural population: the henan rural cohort study. (2019) 9. DOI: 10.1136/bmjopen-2019-029628 11. Yang Q., Zhang Z., Gregg E. W., Flanders W. D., Merritt R., Hu F. B.. Added sugar intake and cardiovascular diseases mortality among us adults. (2014) 174 516-524. DOI: 10.1001/jamainternmed.2013.13563 12. Cahill L. E., Pan A., Chiuve S. E.. Fried-food consumption and risk of type 2 diabetes and coronary artery disease: a prospective study in 2 cohorts of us women and men. (2014) 100 667-675. DOI: 10.3945/ajcn.114.084129 13. Murphy M. M., Barrett E. C., Bresnahan K. A., Barraj L. M.. 100% fruit juice and measures of glucose control and insulin sensitivity: a systematic review and meta-analysis of randomised controlled trials. (2017) 6 p. 59. DOI: 10.1017/jns.2017.63 14. Du H., Li L., Bennett D.. Fresh fruit consumption in relation to incident diabetes and diabetic vascular complications: a 7-Y prospective study of 0.5 million Chinese adults. (2017) 14. DOI: 10.1371/journal.pmed.1002279 15. Muraki I., Imamura F., Manson J. E.. Fruit consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. (2013) 347 p. 5001. DOI: 10.1136/bmj.f5001 16. Carter P., Gray L. J., Talbot D., Morris D. H., Khunti K., Davies M. J.. Fruit and vegetable intake and the association with glucose parameters: a cross-sectional analysis of the let’s prevent diabetes study. (2013) 67 12-17. DOI: 10.1038/ejcn.2012.174 17. Delpino F. M., Figueiredo L. M., Bielemann R. M.. Ultra-processed food and risk of type 2 diabetes: a systematic review and meta-analysis of longitudinal studies. (2021) 51 1120-1141. DOI: 10.1093/ije/dyab247 18. Samocha-Bonet D., Campbell L. V., Mori T. A.. Overfeeding reduces insulin sensitivity and increases oxidative stress, without altering markers of mitochondrial content and function in humans. (2012) 7. DOI: 10.1371/journal.pone.0036320 19. Dany F., Dewi R. M., Tjandrarini D. H.. Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 Years: a cross-sectional analysis of Indonesian basic health research 2018 among normoglycemic and prediabetic individuals. (2020) 20 p. 1509. DOI: 10.1186/s12889-020-09592-7 20. Appropriate body-mass index for asian populations and its implications for policy and intervention strategies. (2004) 363 157-163. DOI: 10.1016/s0140-6736(03)15268-3 21. Nurwanti E., Uddin M., Chang J. S.. Roles of sedentary behaviors and unhealthy foods in increasing the obesity risk in adult men and women: a cross-sectional national study. (2018) 10 p. 704. DOI: 10.3390/nu10060704 22. 2. Classification and diagnosis of diabetes:. (2018) 41 S13-s27. DOI: 10.2337/dc18-S002 23. Doostvandi T., Bahadoran Z., Mozaffari-Khosravi H., Mirmiran P., Azizi F.. Food intake patterns are associated with the risk of impaired glucose and insulin homeostasis: a prospective approach in the tehran lipid and glucose study. (2016) 19 2467-2474. DOI: 10.1017/s1368980016000616 24. Mazidi M., Kengne A. P., Mikhailidis D. P., Toth P. P., Ray K. K., Banach M.. Dietary food patterns and glucose/insulin homeostasis: a cross-sectional study involving 24,182 adult Americans. (2017) 16 p. 192. DOI: 10.1186/s12944-017-0571-x 25. Okada E., Takahashi K., Nakamura K.. Dietary patterns and abnormal glucose tolerance among Japanese: findings from the National Health and Nutrition Survey, 2012. (2019) 22 2460-2468. DOI: 10.1017/S1368980019000120 26. Shen X.-M., Huang Y.-Q., Zhang X.-Y., Tong X.-Q., Zheng P.-F., Shu L.. Association between dietary patterns and prediabetes risk in a middle-aged Chinese population. (2020) 19 p. 77. DOI: 10.1186/s12937-020-00593-1 27. Pestoni G., Riedl A., Breuninger T. A.. Association between dietary patterns and prediabetes, undetected diabetes or clinically diagnosed diabetes: results from the kora Ff4 study. (2021) 60 2331-2341. DOI: 10.1007/s00394-020-02416-9 28. He D., Qiao Y., Xiong S., Liu S., Ke C., Shen Y.. Association between dietary quality and prediabetes based on the diet balance index. (2020) 10 p. 3190. DOI: 10.1038/s41598-020-60153-9 29. Jannasch F., Kröger J., Schulze M. B.. Dietary patterns and type 2 diabetes: a systematic literature review and MetaAnalysis of prospective studies. (2017) 147 1174-1182. DOI: 10.3945/jn.116.242552 30. Li M., Fan Y., Zhang X., Hou W., Tang Z.. Fruit and vegetable intake and risk of type 2 diabetes mellitus: meta-analysis of prospective cohort studies. (2014) 4 p. 5497. DOI: 10.1136/bmjopen-2014-005497 31. Tabák A. G., Herder C., Rathmann W., Brunner E. J., Kivimäki M.. Prediabetes: a high-risk state for diabetes development. (2012) 379 2279-2290. DOI: 10.1016/s0140-6736(12)60283-9 32. Batis C., Mendez M. A., Sotres-Alvarez D., Gordon-Larsen P., Popkin B.. Dietary pattern trajectories during 15 Years of follow-up and Hba1c, insulin resistance and diabetes prevalence among Chinese adults. (2014) 68 773-779. DOI: 10.1136/jech-2013-203560 33. Guasch‐Ferré M., Willett W. C.. The Mediterranean diet and health: a comprehensiveoverview. (2021) 290 549-566. DOI: 10.1111/joim.13333 34. Newsholme P., Keane K. N., Carlessi R., Cruzat V.. Oxidative stress pathways in pancreatic -cells and insulin-sensitive cells and tissues: importance to cell metabolism, function, and dysfunction. (2019) 317 C420-C433. DOI: 10.1152/ajpcell.00141.2019 35. Armato J., DeFronzo R. A., Abdul-Ghani M., Ruby R.. Successful treatment of prediabetes in clinical practice: targeting insulin resistance and. (2012) 18 342-350. DOI: 10.4158/EP11194.OR 36. Yip W., Sequeira I., Plank L., Poppitt S.. Prevalence of pre-diabetes across ethnicities: a review of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) for classification of dysglycaemia. (2017) 9. DOI: 10.3390/nu9111273 37. Kang J. Y., Cho S. W., Sung S. H., Park Y. K., Paek Y. M., Choi T. I.. Effect of a continuous diabetes lifestyle intervention program on male workers in korea. (2010) 90 26-33. DOI: 10.1016/j.diabres.2010.06.006 38. Nichols G. A., Hillier T. A., Brown J. B.. Progression from newly acquired impaired fasting glusose to type 2 diabetes. (2007) 30 228-233. DOI: 10.2337/dc06-1392 39. Yang X. J., Zou S. F., Xu Y., Li Y., Yang S. S.. The influence of intensive lifestyle intervention on patients with isolated impaired fasting glucose: a meta-analysis. (2016) 72 2587-2597. DOI: 10.1111/jan.13015 40. Magkos F., Hjorth M. F., Astrup A.. Diet and exercise in the prevention and treatment of type 2 diabetes mellitus. (2020) 16 545-555. DOI: 10.1038/s41574-020-0381-5 41. Shih K. C., Kwok C. F., Ching F. K.. Exercise reduces body fat and improves insulin sensitivity and pancreatic. (2018) 18 p. 80. DOI: 10.1186/s12887-018-1025-y 42. Gong Q. H., Kang J. F., Ying Y. Y.. Lifestyle interventions for adults with impaired glucose tolerance: a systematic review and meta-analysis of the effects on glycemic control. (2015) 54 303-310. DOI: 10.2169/internalmedicine.54.2745 43. Gong Q., Zhang P., Wang J.. Efficacy of lifestyle intervention in adults with impaired glucose tolerance with and without impaired fasting plasma glucose: a post hoc analysis of da qing diabetes prevention outcome study. (2021) 23 2385-2394. DOI: 10.1111/dom.14481
--- title: 'Control of diabetes, hypertension, and dyslipidemia in Jordan: a cross-sectional study' authors: - Dana Hyassat - Nancy Abu Noor - Qais AlAjlouni - Yazan Jarrar - Raed Qarajeh - Awn Mahasneh - Zaid Elzoubi - Yousef Khader - Oraib Farahid - Mohammed El-Khateeb - Kamel Ajlouni journal: Annals of Medicine and Surgery year: 2023 pmcid: PMC10060078 doi: 10.1097/MS9.0000000000000272 license: CC BY 4.0 --- # Control of diabetes, hypertension, and dyslipidemia in Jordan: a cross-sectional study ## Aims: To determine the level of glycemic, blood pressure (BP), and lipids control among patients with type 2 diabetes mellitus (DM) attending the National Center for Diabetes, Endocrinology and Genetics and to determine factors associated with poor control. ### Methods: A cross-sectional study of 1200 Jordanian type 2 DM patients was included in this study during the period of December 2017–December 2018. We reviewed the charts of these patients until January 2020. Data obtained from medical records included information about sociodemographic variables, anthropometric measurements, glycated hemoglobin (HbA1c), BP, low-density lipoprotein (LDL), the presence of DM complications, and treatment. ### Results: The percentage of subjects who had HbA1c values of less than $7\%$ was $41.7\%$. BP targets (<$\frac{140}{90}$ and $\frac{130}{80}$ mmHg) were achieved in 61.9 and $22\%$ of our patients, respectively. LDL targets less than 100 and 70 mg/dl or less were achieved in 52.2 and $15.9\%$ of our studied population. Only $15.4\%$ of our patients could have simultaneous control of HbA1c less than $7\%$, BP less than $\frac{140}{90}$ mmHg, and LDL less than 100 mg/dl. Factors associated with poor glycemic control were obesity [odds ratio (OR)=1.9], DM duration between 5 and 10 years or more than 10 years (OR=1.8 and 2.5, respectively), and the use of a combination of oral hypoglycemic agent plus insulin or insulin alone (OR=2.4 and 6.2, respectively). Moreover, factors associated with uncontrolled BP (≥$\frac{140}{90}$) were male gender (OR=1.4), age 50–59 years or at least 60 years (OR=3.3 and 6.6, respectively), overweight and obesity (OR=1.6 and 1.4, respectively), insulin use (OR=1.6), and LDL at least 100 mg/dl (OR=1.4). ### Conclusion: The overall prevalence of poor glycemic control was high and alarming. Future research should focus on capturing all variables that may impact glycemic, BP, and dyslipidemia control, with special emphasis on a healthy lifestyle that would be of great benefit in this control. ## Introduction Diabetes mellitus (DM) is a major, rapidly growing health concern that has reached alarming figures. In 2019, nearly half a billion people worldwide were living with diabetes. The estimated number of people (20–79 years) living with diabetes has increased by $62\%$ during the past 10 years, from 285 million in 2009 to 463 million nowadays1. The highest world-age-standardized diabetes prevalence is in the Middle East and North Africa region, where $12.2\%$ of their population is estimated to have diabetes1. In Jordan, the overall age-standardized prevalence of diabetes increased from $13\%$ in 1994 to $17.1\%$ in 2004, $22.2\%$ in 2009, and $23.7\%$ in 2017, foreboding future growth in premature morbidity as well as mortality and mounting huge socioeconomic and healthcare costs2. DM is a leading cause of various complications such as blindness, cerebrovascular disease, and end-stage kidney disease, which are 2–5 times more common in patients with diabetes3. The United Kingdom Prospective Diabetes Study and the Diabetes Control and Complications Trial found that the risk of diabetes complications can be reduced by intensive glycemic, blood pressure (BP), and lipid control. Therefore, successful management of risk factors such as diabetes, hypertension, and dyslipidemia is the benchmark of good clinical care4,5. The American Diabetes Association (ADA) recommends that most adults with diabetes achieve glycated hemoglobin (HbA1c) less than $7\%$, low-density lipoprotein cholesterol (LDL-C) less than 100 mg/dl, and BP less than $\frac{140}{90}$ mmHg6, while the American Association of Clinical Endocrinologists (AACE) had further recommended HbA1c of less than $6.5\%$ as a target glycemic goal7. There is strong evidence of the beneficial effect of simultaneous control of (the ‘ABCs’) HbA1c, BP, and LDL-C in reducing diabetes complications and death; multiple studies have shown that achievement of all three goals together were low (10–$30\%$)8–12. In a previous study in Jordan conducted in 2009, 56, 47, and $10.4\%$ of type 2 DM patients could achieve HbA1c less than $7\%$, BP less than $\frac{130}{80}$, and LDL-C less than 2.6 mmol/l13. In Jordan, there are limited data on achieving targets for diabetic patients with respect to HbA1c, BP, and lipid profile. This study was conducted to determine the level of glycemic, BP, and lipids control among patients attending the National Center for Diabetes, Endocrinology, and Genetics (NCDEG) and to determine factors associated with poor control. ## Study design and study population A cross-sectional study of 1200 patients with type 2 DM was conducted at the NCDEG in Amman–Jordan, during the period from December 2017 to December 2018. We reviewed the charts of these patients until January 2020. All patients with type 2 diabetes (aged>25 years) and had at least three readings of HbA1c, BP, and lipid profile documented in the medical records were included in the study. Patients with type 1 DM and women with gestational DM were excluded. We have used each of the three consecutive measured HbA1c to determine the glycemic control of the patients. Data were collected by trained nurses and general practitioners. Patients’ sociodemographic data including gender, age, marital status as well as educational level; anthropometric and clinical characteristics including weight, height, BMI, waist circumference, presence of other comorbidities such as hypertension, dyslipidemia, and hyperuricemia; as well as diabetes complications (retinopathy, nephropathy, and neuropathy). Additional data, including smoking history, type, and duration of diabetes treatment, were all abstracted from medical records. The data collectors were trained in the abstraction of data from medical records and ensuring data confidentiality. ## Variables definition In the current practice of the NCDEG, anthropometric measurements, including weight, height, and waist circumference, were measured while the subjects were wearing light clothing and no shoes. Waist circumference was estimated at the end of a normal expiration using a nonstretchable tape held in a horizontal plane around the abdomen at the level of the iliac crest. It was considered normal if the waist was between 88.5 and 91.8 cm in men and from 84.5 to 88.5 cm in women according to anthropometric cutoff values for detecting metabolic abnormalities in Jordanian adults14. Waist-to-height ratio was considered elevated if it was greater than 0.5. BMI was calculated by dividing weight in kilograms by height in meters squared. Patients were classified according to BMI following the recommendation of the WHO as adopted by the ADA15. Readings of systolic and diastolic BPs were taken while the subjects were seated, and the arm was kept at the heart level after at least 5 min of rest, using a standardized mercury sphygmomanometer, high BP was defined as systolic BP at least 130 mmHg or diastolic BP at least 80 mmHg or if the patient was already on antihypertensive drugs6. Controlled BP was divided into two categories: either BP less than $\frac{130}{80}$ mmHg or BP less than $\frac{140}{90}$ mmHg. Metabolic abnormalities were defined according to the American Diabetes Association 20176 as follows: total serum cholesterol at least 200 mg/dl, serum LDL at least 100 mg/dl, serum triglyceride at least 150 mg/dl, serum high-density lipoprotein (HDL) 40 mg/dl or less in men, and 50 mg/dl or less in women, or if the patient was already on antidyslipidemic agents. Smoking was classified into three categories according to WHO guidelines 199816. DM was diagnosed if the patient had a fasting plasma glucose 126 mg/dl or less (7.0 mmol/l) on two occasions or if the patient had random plasma glucose 200 mg/dl or less (11.1 mmol/l) in the presence of classic symptoms of hyperglycemia, or if he or she had HbA1c $6.5\%$ or less. Moreover, diabetes was considered to be controlled if the patient had HbA1c less than $7.0\%$ according to the ADA [2017] guidelines6, or HbA1c less than $6.5\%$ according to the American Association of Clinical Endocrinologists (AACE 2015)7. Retinopathy was diagnosed if it was documented by either the ophthalmologist or the treating physician in the medical records or if the patient had received laser treatment. Neuropathy was diagnosed if there were any of the following symptoms (numbness, tingling, or pain in toes, feet, legs, hands, arms, and fingers) in the patient’s medical records or if the patient had done a Nerve Conduction Study which proves the presence of diabetic neuropathy or if the patient was receiving treatment for the above condition. The present study was approved by the National Centre for Diabetes, Endocrinology and Genetics (NCDG) Ethics’ Committee. Identifying information was kept strictly confidential and the data were used only for scientific purposes by the researchers. ## Statistical analysis Data were entered and analyzed using the Statistical Package for Social Science (SPSS version 20). Data were examined initially for data entry errors and outlying values. Any detected errors were corrected as appropriate. In univariate analysis, continuous variables were analyzed as mean±SD and categorical variables as frequencies and percentages. Percentages were compared using χ 2 test. Multivariate analysis using binary logistic regression analysis was conducted to determine factors associated with poor glycemic control, high LDL-C, and uncontrolled BP. A P value of less than 0.05 was considered statistically significant. ## Participants’ characteristics This study included 1200 type 2 diabetic patients, aged between 19 and 87 years, with a mean age (SD) of 55.2 (9.9) years. Fifty-one percent of patients were males, $58\%$ of them were obese, and $33\%$ were overweight. Seventy-five percent of patients had hypertension, while $81\%$ of them had dyslipidemia. Nine percent of diabetic patients had neuropathy and $20\%$ had retinopathy, $39\%$ of the patients were on the combined oral hypoglycemic agent (OHA) and basal insulin. The sociodemographic and clinical characteristics of the study population are presented in Table 1. Table 1 \| Variables \| Total, N (%) \| \| --- \| --- \| \| Age (years) \| Age (years) \| \| <40 \| 90 (7.5) \| \| 40–49 \| 232 (19.3) \| \| 50–59 \| 480 (40.0) \| \| ≥60 \| 398 (33.2) \| \| Gender \| Gender \| \| Females \| 586 (48.8) \| \| Males \| 614 (51.2) \| \| Educational level \| Educational level \| \| Less than high school \| 638 (53.6) \| \| More than high school (diploma+university) \| 552 (46.4) \| \| Smoking \| Smoking \| \| Smoker \| 291 (24.3) \| \| Nonsmoker \| 909 (75.7) \| \| Marital status \| Marital status \| \| Married \| 1077 (89.7) \| \| Never-married \| 123 (10.3) \| \| Duration of diabetes (years) \| Duration of diabetes (years) \| \| <5 \| 438 (36.5) \| \| 5–10 \| 359 (29.9) \| \| >10 \| 403 (33.6) \| \| BMI (kg/m2) \| BMI (kg/m2) \| \| Normal \| 111 (9.3) \| \| Overweight \| 393 (32.7) \| \| Obese \| 696 (58.0) \| \| Increased waist circumference (cm) a \| 1066 (88.8) \| \| Increased waist-to-height ratio b \| 1152 (96.0) \| \| Hypertension c \| 905 (75.4) \| \| Dyslipidemia d \| 969 (80.8) \| \| Neuropathy \| 109 (9.1) \| \| Retinopathy \| 239 (19.9) \| \| Renal impairment \| 204 (17.0) \| \| Hyperuricemia \| 208(17.3) \| \| Treatment \| Treatment \| \| OHA \| 697 (58.1) \| \| OHA+insulin \| 472 (39.3) \| \| Insulin \| 31 (2.6) \| ## Glycemic control Table 2 shows the percentage of subjects who had HbA1c values of less than $6.5\%$, less than $7\%$, $7.5\%$ or less, less than $8\%$ and at least $9\%$. The percentages were $24.4\%$ for HbA1c less than $6.5\%$, $41.7\%$ for HbA1c less than $7\%$, $58.8\%$ for HbA1c $7.5\%$ or less, $72.4\%$ for HbA1c less than $8\%$ and $89.4\%$ for HbA1c $9\%$ or less. Table 2 \| Variables \| Female, N (%) \| Male, N (%) \| P * \| Total, N (%) \| \| --- \| --- \| --- \| --- \| --- \| \| HbA1C (%) \| HbA1C (%) \| HbA1C (%) \| HbA1C (%) \| HbA1C (%) \| \| <6.5 \| 125 (21.5) \| 166 (27.2) \| 0.022 \| 291 (24.4) \| \| <7.0 \| 223 (38.3) \| 275 (45.0) \| 0.056 \| 498 (41.7) \| \| ≤7.5 \| 322 (55.3) \| 379 (62.0) \| 0.028 \| 701 (58.8) \| \| <8.0 \| 415 (71.3) \| 449 (73.5) \| 0.400 \| 864 (72.4) \| \| ≤9 \| 529 (90.7) \| 544 (88.9) \| 0.291 \| 1073 (89.4) \| \| Blood pressure (mmHg) \| \| \| 0.325 \| \| \| Controlled (<140/90) \| 370 (63.4) \| 369 (60.6) \| \| 739 (61.9) \| \| Uncontrolled (≥140/90) \| 214 (36.6) \| 240 (39.4) \| \| 454 (38.1) \| \| Blood pressure (mmHg) \| \| \| 0.006 \| \| \| Controlled (<130/80) \| 149 (25.5) \| 115 (18.9) \| \| 264 (22.1) \| \| Uncontrolled (≥130/80) \| 435(74.5) \| 494 (81.1) \| \| 929 (77.9) \| \| Total cholesterol (mg/dl) \| \| \| 0.191 \| \| \| <200 \| 237 (83.2) \| 255 (87.0) \| \| 492 (85.1) \| \| ≥200 \| 48 (16.8) \| 38 (13.0) \| \| 86 (14.9) \| \| LDL-C (mg/dl) \| \| \| 0.086 \| \| \| <100 \| 196 (49.1) \| 224 (55.2) \| \| 420 (52.2) \| \| ≥100 \| 203 (50.9) \| 182 (44.8) \| \| 385 (47.8) \| \| LDL-C (mg/dl) \| \| \| 0.005 \| \| \| ≤70 \| 49 (12.3) \| 79 (19.5) \| \| 128 (15.9) \| \| >70 \| 350 (87.7) \| 327 (80.5) \| \| 677 (84.1) \| \| LDL-C (mg/dl) \| \| \| 0.261 \| \| \| <130 \| 321 (80.5) \| 339 (83.5) \| \| 660 (82.0) \| \| ≥130 \| 78 (19.5) \| 67 (16.5) \| \| 145 (18.0) \| \| HDL-C (mg/dl) \| \| \| 0.019 \| \| \| F>50, M>40 \| 102 (30.7) \| 130 (39.4) \| \| 232 (35.0) \| \| F≤50, M≤40 \| 230 (69.3) \| 200 (60.6) \| \| 430 (65.0) \| \| Triglycerides (mg/dl) \| \| \| 0.699 \| \| \| <150 \| 217 (53.2) \| 212 (51.8) \| \| 429 (52.5) \| \| ≥150 \| 191 (46.9) \| 197 (48.2) \| \| 388 (47.5) \| ## BP control The percentage of subjects who had controlled BP, defined as BP less than $\frac{140}{90}$, was $61.9\%$, with no statistically significant difference between males and females. ## Dyslipidemia control Concerning lipid profile, $85.1\%$, $52.5\%$, and $35\%$ of patients, respectively, had total cholesterol less than 200 mg/dl, triglycerides level less than 150 mg/dl, and HDL-cholesterol (C) level higher than 50 mg/dl in females or higher than 40 mg/dl in males. When LDL-C level less than 100 mg/dl was taken as a target level, the percentage of patients who achieved this level was $52.2\%$, but upon considering LDL-C level 70 mg/dl or less as the optimal LDL-C level needed to be reached, only $15.9\%$ of patients achieved this recommended target. ## Simultaneous control of HbA1c, BP, and LDL The percentage of patients who met the three ADA targets (HbA1c level <$7\%$, BP <$\frac{140}{90}$, and LDL-C<100 mg/dl) was $15.4\%$, with no difference between males and females ($$P \leq 0.916$$). ## Factors associated with poor glycemic control Using multiple logistic analysis, patients between 50 and 59 years old or at least 60 years old were less likely to have poor glycemic control than those who were less than 50 years old. Males were less likely to have poor glycemic control than females ($$P \leq 0.027$$). Obese patients were 1.9 times more likely to have uncontrolled DM ($$P \leq 0.006$$), patients with DM duration between 5 and 10 years or more than 10 years were 1.8 times and 2.5 times more likely to have poor glycemic control compared to those with DM duration less than 5 years ($$P \leq 0.001$$ and 0.002, respectively). Patients who were on either combination of OHAs and insulin or insulin alone were 2.4 and 6.2 times more likely to have poor glycemic control than patients who were taking only OHAs ($$P \leq 0.039$$ and 0.001, respectively) as shown in Table 3. Table 3 \| Variable \| OR \| 95% CI \| P \| \| --- \| --- \| --- \| --- \| \| Gender \| Gender \| Gender \| Gender \| \| Female* \| 1 \| \| \| \| Male \| 0.7 \| 0.6–0.9 \| 0.027 \| \| BMI \| BMI \| BMI \| BMI \| \| Normal* \| 1 \| \| \| \| Overweight \| 1.2 \| 0.7–1.9 \| 0.497 \| \| Obese \| 1.9 \| 1.2–3.1 \| 0.006 \| \| Duration of diabetes (years) \| Duration of diabetes (years) \| Duration of diabetes (years) \| Duration of diabetes (years) \| \| <5* \| 1 \| \| \| \| 5–10 \| 1.8 \| 1.3–2.5 \| 0.001 \| \| >10 \| 2.5 \| 1.7–3.6 \| 0.002 \| \| Age (years) \| Age (years) \| Age (years) \| Age (years) \| \| <40* \| 1 \| \| \| \| 40–49 \| 0.9 \| 0.5–1.6 \| 0.763 \| \| 50–59 \| 0.5 \| 0.3–0.9 \| 0.020 \| \| ≥60 \| 0.4 \| 0.2–0.8 \| 0.004 \| \| Treatment \| Treatment \| Treatment \| Treatment \| \| OHA \| 1 \| \| \| \| OHA+insulin \| 2.4 \| 1.1–5.5 \| 0.039 \| \| Insulin \| 6.2 \| 4.5–8.4 \| 0.001 \| ## Factors associated with LDL level Table 4 shows the multivariate analysis of factors associated with high LDL. Males were more likely to have LDL-C level at least 100 mg/dl (OR=1.3; $95\%$ CI: 1.1–1.5), whereas people with age at least 50 years or older were less likely to have LDL-C level at least 100 mg/dl. Table 4 \| Variable \| OR \| 95% CI \| P \| \| --- \| --- \| --- \| --- \| \| Gender \| Gender \| Gender \| Gender \| \| Female* \| 1 \| 1.1–1.5 \| 0.035 \| \| Male \| 1.3 \| \| \| \| Age (years) \| Age (years) \| Age (years) \| Age (years) \| \| <40* \| 1 \| 0.5–1.6 \| 0.277 \| \| 40–49 \| 0.7 \| 0.3–0.9 \| 0.024 \| \| 50–59 \| 0.5 \| 0.2–0.8 \| 0.004 \| \| ≥60 \| 0.4 \| \| \| ## Factors associated with uncontrolled BP As shown in Table 5, males were 1.4 times more likely to have uncontrolled BP than females ($$P \leq 0.034$$). Ages between 50 and 59 years or at least 60 years were 3.3 and 6.6 times, respectively, to have uncontrolled BP than those less than 50 years old ($$P \leq 0.001$$ and 0.001, respectively). Patients who were overweight or obese were 1.6 and 1.4 times more likely to have uncontrolled BP ($$P \leq 0.005$$ and 0.045, respectively). Insulin users were 1.6 times more likely to have uncontrolled BP than other treatment groups ($$P \leq 0.001$$). Patients with LDL-C level higher than 100 mg/dl were 1.4 times more likely to have uncontrolled BP compared to those with LDL-C less than 100 mg/dl ($$P \leq 0.045$$) (Table 5). Table 5 \| Variable \| OR \| 95% CI \| P \| \| --- \| --- \| --- \| --- \| \| Gender \| Gender \| Gender \| Gender \| \| Female* \| 1 \| \| \| \| Male \| 1.4 \| 1.02–1.91 \| 0.034 \| \| Age (years) \| Age (years) \| Age (years) \| Age (years) \| \| <40* \| 1 \| \| \| \| 40–49 \| 1.7 \| 0.8–3.7 \| 0.148 \| \| 50–59 \| 3.3 \| 1.7–6.7 \| 0.001 \| \| ≥60 \| 6.6 \| 3.3–13.4 \| 0.001 \| \| BMI \| BMI \| BMI \| BMI \| \| Normal* \| 1 \| \| \| \| Overweight \| 1.6 \| 1.1–3.8 \| 0.005 \| \| Obese \| 1.4 \| 1.2–4.1 \| 0.045 \| \| Treatment \| Treatment \| Treatment \| Treatment \| \| OHA \| 1 \| \| \| \| OHA+insulin \| 0.7 \| 0.3–0.9 \| 0.039 \| \| Insulin \| 1.6 \| 1.1–2.1 \| 0.001 \| \| LDL-C \| LDL-C \| LDL-C \| LDL-C \| \| <100* \| 1 \| \| \| \| ≥100 \| 1.4 \| 1.0–3.8 \| 0.045 \| ## Discussion Good glycemic control (HbA1c<$7\%$) was achieved in $41.7\%$ of patients enrolled in our study. In Jordan, another study conducted by Khattab et al.17 found that $35\%$ of type 2 diabetic patients had HbA1c less than $7\%$. She also found that increased duration of diabetes, not following an eating plan, negative attitude toward diabetes and increased barriers to adherence scale score were all significantly associated with increased odds of poor glycemic control. Many other studies have also found an alarming number of type 2 diabetic patients with uncontrolled DM and have reported a closer and similar percentage of uncontrolled HbA1c18–20. Despite the fact that only $41.7\%$ of our studied population achieved HbA1c less than $7\%$, this percentage is higher than the ones reported by other studies. For example, Noureddine et al.21 reported that only $31.8\%$ of type 2 diabetic patients attained HbA1c less than $7\%$. Xu et al.22 in a cross-sectional survey in North-Western China found that only $25.9\%$ of type 2 DM patients included in this study achieved good glycemic control. Moreover, Yusufali et al.23 in Dubai, also reported that only $33\%$ of type 2 DM patients who were screened had HbA1c less than $7\%$. On the other hand, Yokoyama et al.24 in a large scale survey in Japan reported that HbA1c less than $7\%$ was achieved in $52.9\%$ of type 2 DM patients. In the present study, $15.4\%$ of patients could achieve simultaneous control of HbA1c, BP and LDL-C. Schroeder et al.25, in a retrospective cohort study from 2000 to 2008, reported a higher rate of simultaneous control of 16–$30\%$. At the same time, Xu et al.22 reported a much lower rate, with only $4.5\%$ of patients attaining simultaneous control of the ABCs of diabetes. In the present study, patients with poor glycemic control were more likely to be prescribed a combination of OHAs and insulin, indicating the need for higher doses or additional treatment as an attempt to provide better glycemic control for the deteriorations of diabetes over time. Consistent with this finding, Goudsward et al.26, Al-Nuaim et al.27, and Valle et al.28 also found the association between poor glycemic control with a combination of OHAs and insulin as a treatment for type 2 DM, reflecting the fact that more progressive disease will require more aggressive treatment combination to provide better glycemic control. We found that poor glycemic control was more common among obese patients. In agreement with our findings, Hu et al.19 also reported that obesity is one of the factors associated with poor glycemic control. In addition, Quah et al.29 found that obesity was related to poor glycemic control. This study showed that longer duration of diabetes (5–10 years or >10 years) was associated significantly with poor glycemic control and this is possibly explained by the progressive impairment in insulin secretion that will eventually end by pancreatic failure with increasing DM duration. This finding is consistent with that reported by other studies17,20,30–32. In the present study, uncontrolled HbA1c level was significantly higher among diabetic patients with retinopathy. Consistent with our finding, Ahmed et al.33 also reported that retinopathy was significantly associated with poorly controlled diabetes. Many other studies have documented the close association between high HbA1c and diabetic retinopathy34–40. The current study showed that the overall prevalence of hypertension was $75.4\%$. Females had a more controlled level of BP, less than $\frac{140}{90}$, compared to males. Consistent with our results, Peng et al.41 also reported an overall prevalence of hypertension of $74.8\%$ among the Chinese population in Shanghai. Mubarak et al.42 also found a prevalence rate of hypertension of $72.4\%$ among 1000 patients with type 2 DM attending a national diabetes center in Jordan. Male sex has been associated with an increased prevalence of uncontrolled BP in our study and some other studies43,44. Hypertension among type 2 DM appeared to be age-related. This age-related trend of hypertension is consistent with that reported in the research literature41,42,45–47. Our study also showed that overweight and obese patients have a higher risk of uncontrolled BP than patients with normal BMI. In agreement with our findings, Mubarak et al.42, Dyer et al.43, Wilson et al.44, Lauer et al.48, Sonne-Holm et al.49 and Bertoni et al.50 also found a significant association between greater BMI and uncontrolled hypertension. Our study showed that the use of insulin was significantly associated with uncontrolled BP. Consistent with our finding, Persson 51 also reported that after 2 months of insulin use, a surprisingly uniform increase in BP values was observed. Singh et al.52 also found that noninsulin-dependent DM patients have a tendency to retain sodium under the influence of insulin. Despite the fact that insulin has a vasodilatory effect, it is also known to stimulate the sympathetic neurovascular system and promote renal sodium reabsorption. Many studies have shown that an increase in plasma insulin markedly reduces sodium excretion and increases levels of serum sodium, contributing to the elevation in BP. Increased body weight with the use of insulin could also be a major contributor to the increase in BP. The present study has some limitations. The anthropometric measures and laboratory measures were not measured by the researcher. The main limitation is that it was based on the abstraction of data from medical records. Thus, many important variables, such as medication adherence or patient behavior, such as diet or physical exercise, were not assessed. Another limitation is that the odds ratio might be exaggerated for events with high frequencies, such hypertension, poor glycemic control, and dyslipidemia. ## Conclusion The overall prevalence of poor glycemic control was high and alarming. Factors associated with poor glycemic control were obesity, DM duration between 5 and 10 years or more than 10 years, retinopathy, and the use of a combination of OHA plus insulin or insulin alone. Furthermore, the simultaneous control of HbA1c (<$7\%$), BP (<$\frac{140}{90}$), and LDL (<100 mg/dl), collectively known as the ABCs of diabetes, was achieved in only $15.4\%$ of our patients. Future research should focus on capturing all variables that may impact glycemic, BP, and dyslipidemia control, with special emphasis on a healthy lifestyle that would be of great benefit in this control ## Ethical approval The study was approved by the Ethical Committee at the National Center for Diabetes Endocrinology and Genetics (NCDEG), which is accredited by the National Ethics Committee. The study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Institutional Review Board at NCDEG. Confidentiality has been assured to patients. ## Patient consent An informed written consent was obtained from each participant. The confidentiality of the information was assured and only used for scientific purposes. ## Source of funding The authors have no financial relationship relevant to this article to disclose. ## Author contribution D.H.: wrote the manuscript, was the originator of the manuscript subject, and supervised the research; N.A.N.: helped in developing the idea, setting the protocol, supervised, and edited the manuscript; Q.A.: collected the data; Y.J: helped in developing the idea; A.M: contributed in writing the manuscript, acquisition of data, analysis, and interpretation of data; Y.K.: performed the statistical analysis, approved the protocol from a statistical point of view, analyzed the data, and approved the results; O.F.: helped in the statistical analysis; M.E-K.: contributed to the conception and design of the study and was responsible for lab analyses; K.A.: originator of the manuscript subject, supervised the research, and helped in developing the idea. ## Conflicts of interest disclosure The authors declare that they have no conflicts of interest. ## Research registration unique identifying number (UIN) Name of the registry: a study of the control of diabetes, hypertension, and dyslipidemia in a developing country (Jordan).Unique identifying number or registration ID: researchregistry7406.Hyperlink to your specific registration (must be publicly accessible and will be checked): not applicable. ## Guarantor Prof Kamel Ajlouni. ## Provenance and peer review Not commissioned, externally peer-reviewed. ## References 1. Saeedi P, Petersohn I, Salpea P. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9. Diabetes Res Clin Pract (2019) 157 107843. PMID: 31518657 2. Ajlouni K, Batieha A, Jaddou H. Time trends in diabetes mellitus in Jordan between 1994 and 2017. Diabet Med (2019) 36 1176-1182. PMID: 30614070 3. Roman SH, Harris MI. Management of diabetes mellitus from a public health perspective. Endocrinol Metab Clin North Am (1997) 26 443-474. PMID: 9314008 4. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group. BMJ (1998) 317 703-713. PMID: 9732337 5. Effect of intensive diabetes management on macrovascular events and risk factors in the Diabetes Control and Complications Trial. Am J Cardiol (1995) 75 894-903. PMID: 7732997 6. Marathe PH, Gao HX, Close KL. American Diabetes Association Standards of medical care in diabetes 2017. J Diabetes (2017) 9 320-324. PMID: 28070960 7. Handelsman Y, Bloomgarden ZT, Grunberger G. American Association of Clinical Endocrinologists and American College of Endocrinology – clinical practice guidelines for developing a diabetes mellitus comprehensive care plan – 2015. Endocr Pract (2015) 21 413-37. PMID: 27408942 8. Gaede P, Vedel P, Larsen N. Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. N Engl J Med (2003) 348 383-393. PMID: 12556541 9. Gaede P, Lund-Andersen H, Parving HH. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med (2008) 358 580-591. PMID: 18256393 10. Stark Casagrande S, Fradkin JE, Saydah SH. The prevalence of meeting A1C, blood pressure, and LDL goals among people with diabetes, 1988–2010. Diabetes Care (2013) 36 2271-2279. PMID: 23418368 11. Ali MK, Bullard KM, Gregg EW. A cascade of care for diabetes in the United States: visualizing the gaps. Ann Intern Med (2014) 161 681-689. PMID: 25402511 12. Kemp TM, Barr ELM, Zimmet PZ. Glucose, lipid, and blood pressure control in Australian adults with type 2 diabetes: the 1999–2000 AusDiab. Diabetes Care (2005) 28 1490-1492. PMID: 15920075 13. Al Omari M, Khader Y, Dauod AS. Glycaemic control among patients with type 2 diabetes mellitus treated in primary care setting in Jordan. Prim Care Diabetes (2009) 3 173. PMID: 19733521 14. Khader YS, Batieha A, Jaddou H. Anthropometric cutoff values for detecting metabolic abnormalities in Jordanian adults. Diabetes Metab Syndr Obes (2010) 3 395-402. PMID: 21437109 15. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser (2000) 894 i-xii. PMID: 11234459 16. 16 World Health Organization. Guidelines for Controlling and Monitoring the Tobacco Epidemic. World Health Organization; 1998.. Guidelines for Controlling and Monitoring the Tobacco Epidemic (1998) 17. Khattab M, Khader YS, Al-Khawaldeh A. Factors associated with poor glycemic control among patients with type 2 diabetes. J Diabetes Complications (2010) 24 84-89. PMID: 19282203 18. Alsulaiman TA, Al-Ajmi HA, Al-Qahtani SM. Control of type 2 diabetes in King Abdulaziz Housing City (Iskan) population, Saudi Arabia. J Family Community Med (2016) 23 1-5. PMID: 26929722 19. Hu H, Hori A, Nishiura C. Hba1c, blood pressure, and lipid control in people with diabetes: Japan Epidemiology Collaboration on Occupational Health Study. PLoS One (2016) 11 e0159071. PMID: 27437997 20. Yigazu DM, Desse TA. Glycemic control and associated factors among type 2 diabetic patients at Shanan Gibe Hospital, Southwest Ethiopia. BMC Res Notes (2017) 10 597. PMID: 29141693 21. Noureddine H, Nakhoul N, Galal A. Level of A1C control and its predictors among Lebanese type 2 diabetic patients. Ther Adv Endocrinol Metab (2014) 5 43-52. PMID: 25126407 22. Xu S, Sun F, Xu W. Simultaneous control of blood glucose, blood pressure, and lipid among drug-treated type 2 diabetes patients from Shaanxi province, North-Western China: a multicenter study. Niger J Clin Pract (2016) 19 784-792. PMID: 27811452 23. Yusufali A, Bazargani N, Muhammed K. Opportunistic screening for CVD risk factors: the Dubai Shopping for Cardiovascular Risk Study (DISCOVERY). Glob Heart (2015) 10 265-272. PMID: 26271552 24. Yokoyama H, Oishi M, Takamura H. Large-scale survey of rates of achieving targets for blood glucose, blood pressure, and lipids and prevalence of complications in type 2 diabetes (JDDM 40). BMJ Open Diabetes Res Care (2016) 4 e000294 25. Schroeder EB, Hanratty R, Beaty BL. Simultaneous control of diabetes mellitus, hypertension, and hyperlipidemia in 2 health systems. Circ Cardiovasc Qual Outcomes (2012) 5 645-653. PMID: 22851534 26. Goudswaard AN, Stolk RP, Zuithoff P. Patient characteristics do not predict poor glycaemic control in type 2 diabetes patients treated in primary care. Eur J Epidemiol (2004) 19 541-545. PMID: 15330126 27. Al-Nuaim AR, Mirdad S, Al-Rubeaan K. Pattern and factors associated with glycemic control of Saudi diabetic patients. Ann Saudi Med (1998) 18 109-112. PMID: 17341938 28. Valle T, Koivisto VA, Reunanen A. Glycemic control in patients with diabetes in Finland. Diabetes Care (1999) 22 575-579. PMID: 10189534 29. Quah JH, Liu YP, Luo N. Younger adult type 2 diabetic patients have poorer glycaemic control: a cross-sectional study in a primary care setting in Singapore. BMC Endocr Disord (2013) 13 18. PMID: 23725198 30. Vázquez LA, Rodríguez Á, Salvador J. Relationships between obesity, glycemic control, and cardiovascular risk factors: a pooled analysis of cross-sectional data from Spanish patients with type 2 diabetes in the preinsulin stage. BMC Cardiovasc Disord (2014) 14 153. PMID: 25361574 31. Otiniano ME, Al Snih S, Goodwin JS. Factors associated with poor glycemic control in older Mexican American diabetics aged 75 years and older. J Diabetes Complications (2012) 26 181-6. PMID: 22520403 32. Bayisa B, Bekele M. Glycemic control and associated factors among type II diabetic patients on chronic follow up at Southwest Ethiopia. J Med Health Sci (2017) 6 13-20 33. Ahmed RA, Khalil SN, Al-Qahtani MA. Diabetic retinopathy and the associated risk factors in diabetes type 2 patients in Abha, Saudi Arabia. J Family Community Med (2016) 23 18-24. PMID: 26929725 34. Al-Shammari FK, Al-Meraghi O, Nasif A. The prevalence of diabetic retinopathy and associated risk factors in type 2 diabetes mellitus in Al-Naeem area (Kuwait). Middle East J Fam Med (2005) 3 45-48 35. Al-Bdour MD, Al-Till MI, Abu Samra KM. Risk factors for diabetic retinopathy among Jordanian diabetics. Middle East Afr J Ophthalmol (2008) 15 77-80. PMID: 21346842 36. Khan AR, Wiseberg JA, Lateef ZA. Prevalence and determinants of diabetic retinopathy in Al Hasa region of Saudi Arabia: primary health care centre based cross-sectional survey, 2007–2009. Middle East Afr J Ophthalmol (2010) 17 257-263. PMID: 20844683 37. Al-Khaldi YM, Khan MY, Khairallah SH. Audit of referral of diabetic patients. Saudi Med J (2002) 23 177-181. PMID: 11938394 38. El-Bab MF, Shawky N, Al-Sisi A. Retinopathy and risk factors in diabetic patients from Al-Madinah Al-Munawarah in the Kingdom of Saudi Arabia. Clin Ophthalmol (2012) 6 269-276. PMID: 22368446 39. Al Ghamdi AH, Rabiu M, Hajar S. Rapid assessment of avoidable blindness and diabetic retinopathy in Taif, Saudi Arabia. Br J Ophthalmol (2012) 96 1168-1172. PMID: 22790436 40. El-Asrar AM, Al-Rubeaan KA, Al-Amro SA. Risk factors for diabetic retinopathy among Saudi diabetics. Int Ophthalmol (1998-1999) 22 155-161 41. Peng S, Shen T, Liu J. Uncontrolled hypertension increases with age in an older community-dwelling Chinese population in Shanghai. Aging Dis (2017) 8 558-569. PMID: 28966801 42. Mubarak FM, Froelicher ES, Jaddou HY. Hypertension among 1000 patients with type 2 diabetes attending a national diabetes center in Jordan. Ann Saudi Med (2008) 28 346-351. PMID: 18779643 43. Dyer AR, Liu K, Walsh M. Ten-year incidence of elevated blood pressure and its predictors: the CARDIA study. Coronary Artery Risk Development in (Young) Adults. J Hum Hypertens (1999) 13 13-21. PMID: 9928747 44. Wilson PW, D’Agostino RB, Sullivan L. Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch Intern Med (2002) 162 1867-1872. PMID: 12196085 45. Davis TM, Stratton IM, Fox CJ. U.K. Prospective Diabetes Study 22. Effect of age at diagnosis on diabetic tissue damage during the first 6 years of NIDDM. Diabetes Care (1997) 20 1435-1441. PMID: 9283793 46. Shukuri A, Tewelde T, Shaweno T. Prevalence of old age hypertension and associated factors among older adults in rural Ethiopia. Integr Blood Press Control (2019) 12 23-31. PMID: 31564965 47. Sprafka JM, Bender AP, Jagger HG. Prevalence of hypertension and associated risk factors among diabetic individuals. The Three-City Study. Diabetes Care (1988) 11 17-22. PMID: 3257438 48. Lauer RM, Burns TL, Clarke WR. Childhood predictors of future blood pressure. Hypertension (1991) 18 I74-I81. PMID: 1889860 49. Sonne-Holm S, Sørensen TI, Jensen G. Independent effects of weight change and attained body weight on prevalence of arterial hypertension in obese and non-obese men. BMJ (1989) 299 767-770. PMID: 2508915 50. Bertoni AG, Clark JM, Feeney P. Suboptimal control of glycemia, blood pressure, and LDL cholesterol in overweight adults with diabetes: the Look AHEAD Study. J Diabetes Complications (2008) 22 1-9. PMID: 18191071 51. Persson SU. Blood pressure reactions to insulin treatment in patients with type 2 diabetes. Int J Angiol (2007) 16 135-138. PMID: 22477329 52. Singh SK, Sarkar D, Agrawal JK. Insulin resistance and urinary excretion of sodium in hypertensive patients with non-insulin dependent diabetes mellitus. J Assoc Physicians India (1999) 47 709-711. PMID: 10778592
--- title: Effectiveness and Safety of Rivaroxaban Versus Warfarin among Nonvalvular Atrial Fibrillation Patients with Concomitant Obstructive Sleep Apnea authors: - Nitesh Sood - Veronica Ashton - Youssef Bessada - Katelyn Galli - Brahim K. Bookhart - Craig I. Coleman journal: 'TH Open: Companion Journal to Thrombosis and Haemostasis' year: 2023 pmcid: PMC10060096 doi: 10.1055/a-2013-3346 license: CC BY 4.0 --- # Effectiveness and Safety of Rivaroxaban Versus Warfarin among Nonvalvular Atrial Fibrillation Patients with Concomitant Obstructive Sleep Apnea ## Abstract Background Obstructive sleep apnea (OSA) is associated with an increased incidence of atrial fibrillation (AF), hypertension, diabetes, heart failure, coronary heart disease, stroke, and death. We sought to evaluate the effectiveness and safety of rivaroxaban versus warfarin in nonvalvular AF (NVAF) patients with concomitant OSA. Methods This was an analysis of electronic health record (EHR) data from November 2010 to December 2021. We included adults with NVAF and OSA at baseline, newly initiated on rivaroxaban or warfarin, and with ≥12 months of prior EHR activity. Patients with valvular disease, alternative indications for oral anticoagulation, or who were pregnant were excluded. The incidence rates of developing stroke or systemic embolism (SSE) and bleeding-related hospitalization were evaluated. Hazard ratios (HRs) and $95\%$ confidence intervals (CIs) were calculated using propensity score-overlap weighted proportional hazards regression. Multiple sensitivity and subgroup analyses were performed. Results We included 21,940 rivaroxaban ($20.1\%$ at the 15 mg dose) and 38,213 warfarin (time-in-therapeutic range = 47.3 ± $28.3\%$) patients. Rivaroxaban was found to have similar hazard of SSE compared to warfarin (HR = 0.92, $95\%$ CI = 0.82–1.03). Rivaroxaban was associated with a reduced rate of bleeding-related hospitalizations (HR = 0.85, $95\%$ CI = 0.78–0.92) versus warfarin, as well as reductions in intracranial (HR = 0.76, $95\%$ CI = 0.62–0.94) and extracranial (HR = 0.89, $95\%$CI = 0.81–0.97) bleeding. Upon sensitivity analysis restricting the population to men with a CHA 2 DS 2 VASc score ≥2 or women with a score ≥3, rivaroxaban was associated with a significant $33\%$ risk reduction in SSE and $43\%$ reduction in the risk of bleeding-related hospitalization. No significant interaction for the SSE or bleeding-related hospitalization outcomes was observed upon subgroup analyses. Conclusion Among patients with NVAF and OSA, rivaroxaban had similar SSE risk versus warfarin but was associated with reductions in any intracranial and extracranial bleeding-related hospitalizations. Rivaroxaban was associated with significant reductions in SSE and bleeding-related hospitalizations when the study population was restricted to patients with a moderate-to-high risk of SSE. These data should provide prescribers with additional confidence in selecting rivaroxaban in NVAF patients who have OSA at the time of anticoagulation initiation. ## Introduction Atrial fibrillation (AF) is the most common sustained supraventricular arrhythmia encountered in clinical practice in both the United States and worldwide. 1 Compared to the general population, AF increases patients' risk of stroke by approximately fivefold, as well as their risk of morbidity and mortality. 1 Obstructive sleep apnea (OSA) is characterized by recurrent episodes of partial (obstructive hypopnea) or complete obstruction (obstructive apnea) of the upper airway leading to reduced or absent breathing during sleep. 2 Continued nightly intermittent airway obstruction has been demonstrated to result in large swings in negative intrathoracic airway pressure, intermittent hypoxia, repeated arousals from sleep and neurohumoral activation, each of which contributes to an increased risk of adverse cardiovascular events. 3 OSA has been shown to be associated with an increased incidence of AF, hypertension, diabetes, heart failure, coronary heart disease, stroke, and death. 2 4 OSA and AF frequently coexist, and the relationship between them is likely bidirectional. 4 5 Data suggest that 21 to $74\%$ of patients with AF are thought to have concomitant OSA. 4 OSA has been shown to reduce the efficacy of catheter-based 6 and pharmacological antiarrhythmic management. 7 In some, but not all studies, OSA has been shown to be an independent predictor of stroke in patients with AF. 8 9 10 11 Oral anticoagulation (OAC) with either a direct-acting oral anticoagulant (DOAC) or vitamin K antagonist (VKA) significantly decreases the risk of cardioembolic stroke in AF patients. 5 DOACs, including rivaroxaban, are recommended as first-line oral anticoagulants in the management nonvalvular AF (NVAF). 5 Factor Xa inhibition by rivaroxaban has been shown to prevent oxidative stress and fibrosis due to OSA-induced intermittent hypoxia, which could lead to reduced cardiovascular events. 12 To date, no study has assessed the effectiveness and safety of rivaroxaban compared to VKA therapy in patients with NVAF and OSA. In the present study, we sought to evaluate the effectiveness (stroke or systemic embolism) and safety (bleeding-related hospitalization) of rivaroxaban versus warfarin in NVAF patients with OSA in routine clinical practice. ## Methods We performed a cohort analysis within the US Optum De-Identified EHR data set. 13 EHR data from November 1, 2010 through December 31, 2021 were utilized for this study. Rivaroxaban was approved for NVAF in the United States in November 2011, and therefore, utilization of data back to November 2010 was required to provide a full 12-month preindex period for all patients. The EHR data set includes longitudinal patient-level medical record data for 91+ million patients seen at 700+ hospitals and 7,000+ clinics across the United States. This database contains data on insured and uninsured patients of all ages to provide a representative sample of United States patients with NVAF. It includes records of prescriptions and over-the-counter medications (as prescribed or self-reported by patients), laboratory results, vital signs, anthropometrics, other clinical observations, diagnoses (International Classification of Diseases [ICD-9] and ICD-10), and procedures codes (ICD-9, ICD-10, Current Procedural Terminology-4, Healthcare Common Procedure Coding System, Revenue codes). The use of the provided data set was determined by the New England Institutional Review Board to not constitute research involving human subjects and was therefore exempt from board oversight. ## Cohort Selection Adult patients (≥18 years of age) with NVAF and comorbid OSA diagnosed during the baseline period including the index date, who were OAC naive, newly initiated on rivaroxaban or warfarin after November 1, 2011 (defined as the index date), active in the data set for ≥12 months prior to the index date and with documented care in the EHR from ≥1 provider in the 12 months prior to the index date were eligible for study inclusion. Patients with valvular heart disease (defined as any rheumatic heart disease, mitral stenosis, mitral valve repair, or replacement), any prior OAC use per written/electronic prescription or patient self-report during the 12-month preindex period, known to have received rivaroxaban doses other than 15 mg or 20 mg once daily, having venous thromboembolism as an alternative indication for OAC use, having undergone recent orthopedic knee or hip replacement within the prior 35 days, or who were pregnant were excluded. The identification of OSA was based upon the presence of an ICD-9 and/or -10 billing code 14 of 327.20 (organic sleep apnea, unspecified), 327.23 (OSA [adult, pediatric]), 327.29 (other organic sleep apnea), 780.51 (insomnia with sleep apnea), 780.53 (hypersomnia with sleep apnea), 780.57 (sleep apnea [NOS]), G47.30 (sleep apnea, unspecified), G47.33 (OSA [adult, pediatric]), and G47.39 (other sleep apnea) during the 12-month baseline period. While select codes may have included central sleep apnea, we felt it likely that in most cases, such codes were associated with predominantly OSA and that it is important not to overly restrict case selection by avoiding codes that do not differentiate between central and obstructive. This coding schema has been shown to have a positive predictive value of >$90\%$ for the identification of OSA, 14 with additional data suggesting that PPV improves in the presence of comorbidities frequently present in AF patients (e.g., hypertension and diabetes). 15 ## Confounder Adjustment and Handling of Missing Data To adjust for potential confounding between the rivaroxaban and warfarin cohorts, we calculated propensity scores using multivariable logistic regression. 16 The multivariable logistic regression model included all covariates included in the baseline characteristics table. The presence of comorbid disease diagnoses was determined based upon billing codes and/or supporting laboratory and observation data. The absence of data suggesting a comorbidity exists was assumed to represent the absence of the disease. Consequently, all categorical covariates had complete data for all patients. When dependence on billing codes was required to identify covariates, we utilized validated or endorsed coding algorithms, whenever possible. 17 18 19 20 For continuous laboratory and observation variables with <$25\%$ values missing, data were imputed using multiple imputations based upon a fully conditional specification linear regression model, with all other available covariates and the outcomes included in the model. 21 Generated propensity scores were then used to weight patients for analysis using overlap weighting as described by Thomas and colleagues. 22 This approach assigns weights to patients that are proportional to their probability of belonging to the alternative treatment cohort. Rivaroxaban patients were weighted by the probability of receiving warfarin (i.e., 1—the propensity score), and warfarin patients were weighted by the probability of receiving rivaroxaban (i.e., propensity score). Overlap weighting was utilized for confounder adjustment because it allows for all eligible patients to be included in the analysis, it assigns the greatest weight to patients in which treatment cannot be predicted (and the least weight to patients with extreme propensity scores), and because overlap weighting has the favorable property of exactly balancing all variables included in the multivariable logistic regression model used to derive the propensity score, resulting in absolute standardized differences (ASD) = 0 for each covariate. ## Outcomes Our primary effectiveness outcome was stroke or systemic embolism (SSE) which included ischemic stroke, systemic embolism, or intracranial hemorrhage (ICH) identified using ICD-10 codes I60-I62 and I74 (and corresponding ICD-9 codes per Centers for Medicare and Medicaid Services General Equivalence Mapping files). 23 The primary safety outcome was bleeding-related hospitalization based on the validated Cunningham algorithm. 23 24 Secondary outcomes included ischemic stroke, ICH, and extracranial bleeding as separate outcomes. ## Statistical Analysis Baseline characteristics were analyzed using descriptive statistics. Categorical variables were reported as proportions and continuous variables as means ± standard deviations. Propensity score-overlap weighted Cox proportional hazards regression models including index anticoagulation cohort (rivaroxaban or warfarin) as the only covariate and implementing a robust sandwich estimator were utilized to calculate hazard ratios (HRs) and $95\%$ confidence intervals (CIs). The proportional hazard assumption was tested based on Schoenfeld residuals (and was found valid in all cases). Patients were followed until outcome occurrence, end-of-EHR activity, or end-of-data availability (intent-to-treat approach). A sensitivity analysis in which stabilized inverse probability of treatment weighting (sIPTW) was utilized instead of OLW was performed. We also performed a sensitivity analysis in which we restricted the study population to patients at moderate-to-high risk of SSE (CHA 2 DS 2 VASc ≥2 for men, ≥3 for women). Subgroup analyses stratifying patients by age (≥75 years or <75 years), sex, obesity (body mass index [BMI] ≥30 or <30 kg/m 2), diabetes, heart failure, prior SSE, and CHA 2 DS 2 VASc score (0–1, ≥2, 2–3, ≥4) were performed. Propensity score weighting was rerun for each sensitivity and subgroup analysis using the same variables as the main analysis. Only the primary effectiveness (SSE) and safety (bleeding-related hospitalizations) outcomes were assessed. All database management and statistical analysis were performed using SAS version 9.4 (SAS Institute, Cary, NC, United States) and IBM SPSS version 28.0 (IBM Corp., Armonk, NY, United States). A p -value <0.05 was considered statistically significant unless otherwise noted. p Values for interaction were calculated to test for the presence of statistical interactions. To reduce the chances of obtaining false-positive results (Type I error) because of multiple hypothesis testing, we utilized a Bonferroni corrected p -value <0.007 to indicate a statistically significant interaction for subgroup analyses. ## Research Reporting This report was written in accordance with the reporting of studies conducted using observational routinely collected health data statements for pharmacoepidemiology guidance. 25 ## Results We identified 357,928 NVAF patients treated with rivaroxaban or warfarin (Fig. 1). Of these, 60,153 patients ($16.8\%$) were found to have concomitant OSA. This included 21,940 rivaroxaban and 38,213 warfarin-treated patients. Unweighted and weighted baseline characteristics of included patients are depicted in Table 1. After propensity score overlap weighting, the rivaroxaban and warfarin cohorts were identical (ASD = 0 for all). The mean age of patients included in the study was approximately 67 years. Only $0.3\%$ of patients were receiving treatment with continuous or bilevel positive airway pressure, and $0.3\%$ underwent a surgical procedure to treat OSA during the prior 12 months. Aspirin was used concomitantly with anticoagulation in $27.5\%$ of patients, while $6.5\%$ of patients utilized a P2Y12 inhibitor or another antiplatelet agent. The mean CHA 2 DS 2 VASc score was 3.33, and mean modified HASBLED score was 2.09 for rivaroxaban and 2.08 for warfarin patients. The 15 mg dose was used in $20.1\%$ of rivaroxaban patients. Warfarin patients spent an average of 47.3 ± $28.3\%$ of their time in the target therapeutic international normalized ratio range (using linear interpolation and assuming a target range of 2.0–3.0). Mean follow-up was 1,273 ± 837 days for the entire study cohort and similar between the rivaroxaban (1,290 ± 814 days) and warfarin (1,255 ± 859) groups (ASD = 0.04). Propensity score-overlap weighted proportional hazards regression did not show a significant difference in the primary effectiveness outcome of SSE between rivaroxaban and warfarin (0.74 vs. $0.81\%$/y, HR = 0.92, $95\%$CI = 0.82–1.03; Table 2 and Fig. 2). The similar rate of ischemic stroke alone was observed between groups (HR = 1.01 $95\%$ CI = 0.88–1.16). For the primary safety outcome of bleeding-related hospitalization, rivaroxaban was associated with a decreased rate (1.52 vs. $1.81\%$/y; HR = 0.85, $95\%$ CI = 0.78–0.92; Fig. 3). Both intracranial (HR = 0.76, $95\%$ CI = 0.62-0.94) and extracranial bleeding (HR = 0.89, $95\%$ CI = 0.62–0.84) were reduced with rivaroxaban versus warfarin use. Fig. 2: Kaplan–Meier curve for stroke or systemic embolism. Red/solid line represents rivaroxaban; blue/dashed line represents warfarin. Fig. 3: Kaplan–Meier curve for bleeding-related hospitalization. red/solid line represents rivaroxaban; blue/dashed line represents warfarin. TABLE_PLACEHOLDER:Table 2 Upon sensitivity analysis, utilization of sIPTW instead of OLW did not impact the results for the SSE or bleeding-related hospitalization outcomes (Table 3). When restricting the population to men with a CHA 2 DS 2 VASc score ≥2 or women with a score ≥3, based on treatment recommendations from the 2019 AHA guidelines, 1 rivaroxaban was associated with a significant $33\%$ reduction in the risk of SSE, as well as a more profound reduction in bleeding-related hospitalization (HR = 0.57, $95\%$ CI = 0.53–0.62) compared to the full population analysis. Similar findings were observed upon subgroup analysis comparing CHA 2 DS 2 VASc score of 0 to 1 versus ≥2 (regardless of sex). Patients with a CHA 2 DS 2 VASc score ≥2 receiving rivaroxaban compared to warfarin were less likely to develop SSE (HR = 0.65, $95\%$ CI = 0.59–0.73; p-interaction = 0.10 vs. CHA 2 DS 2 VASc score of 0–1). Rivaroxaban users with a CHA 2 DS 2 VASc score ≥2 were also significantly less likely to experience a bleeding-related hospitalization (HR = 0.56, $95\%$ CI = 0.52–0.60), and the p-interaction value of 0.002, suggests there was a presence of a statistical interaction suggesting rivaroxaban is associated with less bleeding-related hospitalizations in patients with CHA 2 DS 2 VASc scores ≥2 versus 0 to 1. Other subgroup analyses did not find a statistically significant interaction across any subgroup for either the SSE or bleeding-related hospitalization outcome. Table 3 \| Unnamed: 0 \| Stroke or systemic embolism \| Stroke or systemic embolism.1 \| Stroke or systemic embolism.2 \| Stroke or systemic embolism.3 \| Bleeding-related hospitalization \| Bleeding-related hospitalization.1 \| Bleeding-related hospitalization.2 \| Bleeding-related hospitalization.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Sensitivity or subgroup analysis \| Rivaroxaban%/y \| Warfarin%/y \| HR (95%CI) \| Heterogeneityp -value a \| Rivaroxaban%/y \| Warfarin%/y \| HR (95%CI) \| Heterogeneityp -value a \| \| Overall, N = 60,153 \| 0.74 \| 0.81 \| 0.92 (0.82–1.03) \| – \| 1.52 \| 1.81 \| 0.85 (0.78–0.92) \| – \| \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| Sensitivity analyses \| \| sIPTW, N = 60,153 \| 0.84 \| 0.93 \| 0.90 (0.78–1.03) \| – \| 1.79 \| 2.13 \| 0.84 (0.76–0.94) \| – \| \| Moderate-to-High CHA 2 DS 2 VASc, N = 51,346 \| 0.79 \| 1.15 \| 0.67 (0.60–0.74) \| – \| 1.55 \| 2.63 \| 0.57 (0.53–0.62) \| – \| \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| Subgroup analyses \| \| Age \| \| \| \| 0.23 \| \| \| \| 0.05 \| \| ≥75 y, N = 18,198 \| 1.21 \| 1.25 \| 0.98 (0.81–1.18) \| 0.23 \| 2.51 \| 2.64 \| 0.96 (0.84–1.01) \| 0.05 \| \| < 75 y, N = 41,995 \| 0.58 \| 0.69 \| 0.85 (0.73–0.98) \| 0.23 \| 1.20 \| 1.59 \| 0.76 (0.68–0.84) \| 0.05 \| \| Sex \| \| \| \| 0.77 \| \| \| \| >0.99 \| \| Female, N = 19,402 \| 0.81 \| 0.91 \| 0.90 (0.74–1.10) \| 0.77 \| 1.63 \| 1.93 \| 0.85 (0.74–0.98) \| >0.99 \| \| Male, N = 40,751 \| 0.71 \| 0.78 \| 0.93 (0.81–1.08) \| 0.77 \| 1.47 \| 1.74 \| 0.85 (0.77–0.94) \| >0.99 \| \| Body mass index \| \| \| \| 0.66 \| \| \| \| 0.46 \| \| < 30 kg/m 2 , N = 14,370 \| 0.96 \| 1.01 \| 0.96 (0.77–1.20) \| 0.66 \| 1.75 \| 1.98 \| 0.89 (0.76–1.05) \| 0.46 \| \| ≥30 kg/m 2 , N = 45,783 \| 0.68 \| 0.75 \| 0.91 (0.79–1.04) \| 0.66 \| 1.46 \| 1.76 \| 0.83 (0.76–0.91) \| 0.46 \| \| Diabetes \| \| \| \| 0.23 \| \| \| \| 0.05 \| \| Yes, N = 28,599 \| 0.83 \| 0.97 \| 0.86 (0.73–1.01) \| 0.23 \| 1.73 \| 2.23 \| 0.79 (0.70–0.88) \| 0.05 \| \| No, N = 31,554 \| 0.69 \| 0.69 \| 0.99 (0.84–1.16) \| 0.23 \| 1.38 \| 1.49 \| 0.93 (0.83–1.04) \| 0.05 \| \| Heart failure \| \| \| \| 0.21 \| \| \| \| 0.09 \| \| Yes, N = 26, 415 \| 1.01 \| 102 \| 0.99 (0.84–1.17) \| 0.21 \| 2.30 \| 2.57 \| 0.91 (0.81–1.01) \| 0.09 \| \| No, N = 33,738 \| 0.60 \| 0.70 \| 0.86 (0.73–1.00) \| 0.21 \| 1.10 \| 1.41 \| 0.79 (0.70–0.89) \| 0.09 \| \| Prior stroke or systemic embolism \| \| \| \| 0.06 \| \| \| \| 0.23 \| \| Yes, N = 5,154 \| 3.66 \| 3.40 \| 1.10 (0.89–1.35) \| 0.06 \| 2.65 \| 2.73 \| 0.97 (0.76–1.24) \| 0.23 \| \| No, N = 54,999 \| 0.54 \| 0.63 \| 0.86 (0.75–0.99) \| 0.06 \| 1.44 \| 1.74 \| 0.83 (0.76–0.91) \| 0.23 \| \| CHA 2 DS 2 VASc b (low vs. moderate-high) \| \| \| \| 0.10 \| \| \| \| 0.002 \| \| 0–1, N = 6,948 \| 0.23 \| 0.22 \| 1.06 (0.60–1.87) \| 0.10 \| 0.60 \| 0.59 \| 1.03 (0.71–1.49) \| 0.002 \| \| ≥2, N = 53,205 \| 0.75 \| 1.11 \| 0.65 (0.59–0.73) \| 0.10 \| 1.48 \| 2.57 \| 0.56 (0.52–0.60) \| 0.002 \| \| CHA 2 DS 2 VASc b (low vs. moderate vs. high) \| \| \| \| \| \| \| \| \| \| 0–1, N = 6,948 \| 0.23 \| 0.22 \| 1.06 (0.60–1.87) \| 0.24 \| 0.60 \| 0.59 \| 1.03 (0.71–1.49) \| 0.01 \| \| 2–3, N = 23, 674 \| 0.44 \| 0.56 \| 0.79 (0.64–0.98) \| 0.24 \| 1.06 \| 1.48 \| 0.72 (0.62–0.83) \| 0.01 \| \| ≥4, N = 29,531 \| 1.29 \| 1.33 \| 0.98 (0.85–1.13) \| 0.24 \| 2.40 \| 2.65 \| 0.92 (0.83–1.01) \| 0.01 \| ## Discussion Our study utilized detailed EHR data to evaluate more than 60,000 patients with NVAF and comorbid OSA newly started on OAC with either rivaroxaban or warfarin with a mean follow-up period of approximately 3.5 years. Rivaroxaban was found to have a similar SSE risk compared to warfarin in the analysis of all NVAF patients with OSA; however, sensitivity analysis did show rivaroxaban to be associated with a $33\%$ significant reduction in SSE versus warfarin when the population was restricted to patients with a moderate-to-high risk of SSE based on CHA 2 DS 2 VASc (men with a CHA 2 DS 2 VASc score ≥2 or women with a score ≥3). Similar findings were seen in the subgroup analysis of patients stratified by CHA 2 DS 2 VASc score ≥2 irrespective of sex. Rivaroxaban was associated with a significant $15\%$ relative hazard reduction in any bleeding-related hospitalizations compared to warfarin. This outcome was particularly driven by a $24\%$ and $11\%$ hazard reduction in intracranial and extracranial events, respectively. Outcomes did not differ when sIPTW was utilized instead of OLW or across non-CHA 2 DS 2 VASc score stratified subgroups evaluated. To our knowledge, this is the first study to evaluate the comparative effectiveness and safety of a DOAC versus a VKA in NVAF patients with concomitant OSA. The results of our present real-world analysis are generally consistent with the overall results of the 14,000+ patient Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET-AF). 26 In both ROCKET-AF and the present real-world study, the rates of SSE were shown to be similar between rivaroxaban and warfarin-treated patients, with reductions in the risk of ICH in patients receiving rivaroxaban ($33\%$ risk hazard reduction in ROCKET-AF and a $24\%$ relative hazard reduction in the present study). Our finding of a significant $15\%$ reduction in any bleeding-related hospitalization was not anticipated based on the results of ROCKET-AF, which showed no difference in major bleeding. This difference may be explained, at least in part, by the decreased amount of time in therapeutic range (TTR) in warfarin users in our study (47.3 ± $28.3\%$), which was approximately $8\%$ lower than the mean TTR seen in ROCKET-AF (approximately $55\%$). 26 Of note, prior data suggest that OSA patients are more difficult to maintain in therapeutic range, while on a VKA. 27 Whether a diagnosis of concomitant OSA independently increases the risk of ischemic stroke in patients with NVAF remains unclear, with conflicting evidence being published. 8 10 11 Yaranov and colleagues assessed over 300 patients with AF as part of a chart review and identified a significant 3.65-fold increased risk of stroke in patients with OSA compared to those without. 8 The overall impact a diagnosis of OSA on outcomes in NVAF patients was assessed in an analysis of the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). 10 Patients with OSA and AF in ORBIT-AF were younger (69 vs. 76 years; $p \leq 0.0001$) and had a more extensive past medical history, including an increased frequency of history of diabetes and obesity than those without OSA. This finding was consistent with the OSA population in our analysis who had a similar age (mean approximately 67 years) with elevated percentages of patients with obesity or BMI ≥ 30 ($77.1\%$), and diabetes ($44.7\%$), all of which are independent risk factors for increased rates of cardiovascular outcomes in NVAF. 28 29 Despite these differences in baseline characteristics, the adjusted risk of major adverse cardiovascular events (composite of cardiovascular death, MI, stroke/transient ischemic attack) in ORBIT-AF were found to be similar in those with and without OSA (HR = 1.07, $95\%$ CI = 0.85–1.34). 10 An additional retrospective analysis of the Danish national registry by Koch et al 11 assessed the risk of ischemic stroke over a 5-year period in patients with first-time AF (1,766 had prior sleep apnea and these were matched with 7064 without sleep apnea) and found that a history of sleep apnea was not associated with an increased risk of ischemic stroke (HR = 1.06, $95\%$ CI = 0.86–1.30). Interestingly, our study population of NVAF patients, all with OSA, was associated with low rates of ischemic stroke in both groups ($0.52\%$/y in the rivaroxaban and $0.51\%$/y in the warfarin cohort). This may be attributed to patients' relatively younger age and low CHADS 2 /CHA 2 DS 2 VASc scores, 1 as well as the fact that Optum's EHR repository does not encompass all institutions and therefore may have missed relevant follow-up events. 13 Rates of ischemic stroke did increase (as high as $1.15\%$/y in the warfarin cohort) when the population was restricted to men with a CHA 2 DS 2 VASc score ≥2 or women with a score ≥3. There are no formal recommendations on the choice of anticoagulant in AF patients with concomitant OSA, though both U.S. and European guidelines recommend that patients who are eligible for OAC receive a DOAC in preference to a VKA, except in patients with mechanical heart valves or moderate-to-severe mitral stenosis (class 1A recommendations). 1 30 Both U.S. and European guidelines 1 30 recommend OSA be screened for in AF patients and properly managed when found to reduce symptoms of AF. Continuous PAP is considered the therapy of choice for OSA, with observational studies and meta-analyses suggesting appropriate PAP treatment of OSA may improve rhythm control in AF patients. 30 Interestingly, our study identified very low rates of PAP use across our OSA population. This finding may be explained through several mechanisms. First, studies have suggested that PAP therapy is underprescribed in OSA. 31 Moreover, inconvenience and other device factors, lack of perceived benefit, poor disease perception, embarrassment, and cost/insurance coverage have been shown to be major barriers to the availability and adherence/persistence to PAP treatment. 32 Only $8.5\%$ of patients in our study underwent a sleep study in the prior 12 months. This may suggest that many patients included in our study had OSA diagnosed one or more years ago. Consequently, patients trying PAP therapy at some point after their OSA diagnosis but who were non-persistent, would not have been classified as being on PAP therapy due to our limited look-back period to identify baseline characteristics and treatments. Finally, it is also possible that oral appliances were used as an alternative to PAP therapy. Though not effective for severe OSA (apnea-hypopnea index ≥ 30 events/h), oral appliances are indicated for patients with mild-to-moderate OSA who either prefer them over PAP therapy or who have failed or rejected PAP therapy. 33 Unfortunately, we were not able to assess OSA severity in the present study. There are several limitations of this study worth discussing. The study's nonrandomized and retrospective study design may result in misclassification and confounding bias. Strategies for minimizing the probability of misclassification implemented in our study included using validated coding schema and leveraging EHR laboratory and clinical observation data. Billing codes for OSA are accurate at identifying patients with OSA, but less helpful at ruling out those without disease. 14 15 Consequently, our analysis likely underestimated the proportion of NVAF patients with OSA (and excluded some cases from the analysis). This may be further exacerbated by the fact that OSA has been historically underdiagnosed. 31 32 The data set used in our study also did not allow for the determination of duration or severity of the OSA. Propensity score-overlap weighting was utilized to reduce the risk of confounding bias, 21 and while the ASD was zero for all covariates after weighting, residual confounding on covariates not collected and entered in the model cannot be ruled out. Next, the observational nature of this study prohibited any control over warfarin dosing. As previously noted, the TTR for warfarin patients in our study was lower (approximately $47\%$) than typically seen in RCTs and in dedicated anticoagulation clinics, 26 34 though this is a common finding of studies assessing TTR in routine clinical practice and may be further exacerbated by the difficulty of maintain goal TTR in OSA patients. 27 The results of this study should be viewed as being most generalizable to a U.S. population, as the EHR data was limited to the United States and practice patterns for the treatment of AF and OSA may vary from country to country. 13 Furthermore, the extent to which these results are applicable to those patients receiving successful PAP therapy could not be determined with this dataset. The data set used also lacked information on prescription medication claims and instead only provide data on medications prescribed or self-reported. 13 While the latter is beneficial in identifying important medications that may otherwise be over the counter and not identified in claims data (e.g., aspirin), the lack of prescription claims makes an accurate assessment of anticoagulant adherence challenging. Therefore, analyses in the present study utilize an intent-to-treat methodology only. Lastly, Optum's EHR repository solicits data from both insured and uninsured patients, but it does not encompass all institutions; therefore, relevant follow-up events could potentially be missed. 13 ## Conclusion In patients with NVAF and concomitant OSA, rivaroxaban-treated patients had similar SSE compared to warfarin but was associated with reductions in any, intracranial, and extracranial bleeding-related hospitalizations. When the population was restricted to only patients with a moderate-to-high risk of SSE, rivaroxaban was associated with significant reductions in both SSE and bleeding-related hospitalization. These data should provide prescribers with additional confidence in selecting rivaroxaban in NVAF patients who have OSA at the time of anticoagulation initiation. ## References 1. January C T, Wann L S, Calkins H. 2019 AHA/ACC/HRS focused update of the 2014 AHA/ACC/HRS Guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol (2019.0) 74 104-132. PMID: 30703431 2. Gottlieb D J, Punjabi N M. Diagnosis and management of obstructive sleep apnea: a review. JAMA (2020.0) 323 1389-1400. PMID: 32286648 3. Goes C M, Falcochio P PNF, Drager L F. Strategies to manage obstructive sleep apnea to decrease the burden of atrial fibrillation. Expert Rev Cardiovasc Ther (2018.0) 16 707-713. PMID: 30136604 4. Linz D, McEvoy R D, Cowie M R. Associations of obstructive sleep apnea with atrial fibrillation and continuous positive airway pressure treatment: a review. JAMA Cardiol (2018.0) 3 532-540. PMID: 29541763 5. May A M, Van Wagoner D R, Mehra R. OSA and cardiac arrhythmogenesis: mechanistic insights. Chest (2017.0) 151 225-241. PMID: 27693594 6. Ng C Y, Liu T, Shehata M, Stevens S, Chugh S S, Wang X. Meta-analysis of obstructive sleep apnea as predictor of atrial fibrillation recurrence after catheter ablation. Am J Cardiol (2011.0) 108 47-51. PMID: 21529734 7. Monahan K, Brewster J, Wang L. Relation of the severity of obstructive sleep apnea in response to anti-arrhythmic drugs in patients with atrial fibrillation or atrial flutter. Am J Cardiol (2012.0) 110 369-372. PMID: 22516529 8. Yaranov D M, Smyrlis A, Usatii N. Effect of obstructive sleep apnea on frequency of stroke in patients with atrial fibrillation. Am J Cardiol (2015.0) 115 461-465. PMID: 25529543 9. Pengo M F, Faini A, Grote L. Impact of sleep apnea on cardioembolic risk in patients with atrial fibrillation: data from the ESADA Cohort. Stroke (2021.0) 52 712-715. PMID: 33272126 10. Holmqvist F, Guan N, Zhu Z. Impact of obstructive sleep apnea and continuous positive airway pressure therapy on outcomes in patients with atrial fibrillation—results from the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). Am Heart J (2015.0) 169 647-65400. PMID: 25965712 11. Koch H, Carlson N, Jakobsen C. Sleep apnea and risk of ischemic stroke in patients with atrial fibrillation. Eur Heart J (2016.0) 37 302-303 12. Imano H, Kato R, Tanikawa S. Factor Xa inhibition by rivaroxaban attenuates cardiac remodeling due to intermittent hypoxia. J Pharmacol Sci (2018.0) 137 274-282. PMID: 30055890 13. 13Optum® de-identified Electronic Health Record dataset (2007–2021). 14. Keenan B T, Kirchner H L, Veatch O J. Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea. J Clin Sleep Med (2020.0) 16 175-183. PMID: 31992429 15. Laratta C R, Tsai W H, Wick J, Pendharkar S R, Johannson K A, Ronksley P E. Validity of administrative data for identification of obstructive sleep apnea. J Sleep Res (2017.0) 26 132-138. PMID: 27761958 16. Austin P C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res (2011.0) 46 399-424. PMID: 21818162 17. Chronic conditions data warehouse 18. Elixhauser A, Steiner C, Harris D R, Coffey R M. Comorbidity measures for use with administrative data. Med Care (1998.0) 36 8-27. PMID: 9431328 19. Quan H, Li B, Couris C M. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol (2011.0) 173 676-682. PMID: 21330339 20. Gilbert T, Neuburger J, Kraindler J. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet (2018.0) 391 1775-1782. PMID: 29706364 21. Sterne J A, White I R, Carlin J B. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ (2009.0) 338 b2393. PMID: 19564179 22. Thomas L E, Li F, Pencina M J. Overlap weighting: a propensity score method that mimics attributes of a randomized clinical trial. JAMA (2020.0) 323 2417-2418. PMID: 32369102 23. General Equivalence Mappings 24. Cunningham A, Stein C M, Chung C P, Daugherty J R, Smalley W E, Ray W A. An automated database case definition for serious bleeding related to oral anticoagulant use. Pharmacoepidemiol Drug Saf (2011.0) 20 560-566. PMID: 21387461 25. Langan S M, Schmidt S A, Wing K. The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ (2018.0) 363 k3532. PMID: 30429167 26. Patel M R, Mahaffey K W, Garg J. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med (2011.0) 365 883-891. PMID: 21830957 27. Szymanski F M, Lip G Y, Filipiak K J, Platek A E, Karpinski G. Usefulness of the SAME-TT2R2 score to predict anticoagulation control on VKA in patients with atrial fibrillation and obstructive sleep apnea. Int J Cardiol (2016.0) 204 200-205. PMID: 26670173 28. Coleman C I, Costa O S, Brescia C W, Vardar B, Abdelgawwad K, Sood N. Thromboembolism, bleeding and vascular death in nonvalvular atrial fibrillation patients with type 2 diabetes receiving rivaroxaban or warfarin. Cardiovasc Diabetol (2021.0) 20 52. PMID: 33637082 29. Deitelzweig S, Keshishian A, Kang A. Effectiveness and safety of oral anticoagulants among NVAF patients with obesity: insights from the ARISTOPHANES Study. J Clin Med (2020.0) 9 1633. PMID: 32481607 30. Hindricks G, Potpara T, Dagres N. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J (2021.0) 42 373-498. PMID: 32860505 31. Mandereau-Bruno L, Léger D, Delmas M C. Obstructive sleep apnea: a sharp increase in the prevalence of patients treated with nasal CPAP over the last decade in France. PLoS One (2021.0) 16 e0245392. PMID: 33434230 32. Lyons M M, Bhatt N Y, Pack A I, Magalang U J. Global burden of sleep-disordered breathing and its implications. Respirology (2020.0) 25 690-702. PMID: 32436658 33. Ramar K, Dort L C, Katz S G. Clinical practice guideline for the treatment of obstructive sleep apnea and snoring with oral appliance therapy: an update for 2015. J Clin Sleep Med (2015.0) 11 773-827. PMID: 26094920 34. Baker W L, Cios D A, Sander S D, Coleman C I. Meta-analysis to assess the quality of warfarin control in atrial fibrillation patients in the United States. J Manag Care Pharm (2009.0) 15 244-252. PMID: 19326955
--- title: 'Holobiont Urbanism: sampling urban beehives reveals cities’ metagenomes' authors: - Elizabeth Hénaff - Devora Najjar - Miguel Perez - Regina Flores - Christopher Woebken - Christopher E. Mason - Kevin Slavin journal: Environmental Microbiome year: 2023 pmcid: PMC10060141 doi: 10.1186/s40793-023-00467-z license: CC BY 4.0 --- # Holobiont Urbanism: sampling urban beehives reveals cities’ metagenomes ## Abstract ### Background Over half of the world’s population lives in urban areas with, according to the United Nations, nearly $70\%$ expected to live in cities by 2050. Our cities are built by and for humans, but are also complex, adaptive biological systems involving a diversity of other living species. The majority of these species are invisible and constitute the city’s microbiome. Our design decisions for the built environment shape these invisible populations, and as inhabitants we interact with them on a constant basis. A growing body of evidence shows us that human health and well-being are dependent on these interactions. Indeed, multicellular organisms owe meaningful aspects of their development and phenotype to interactions with the microorganisms—bacteria or fungi—with which they live in continual exchange and symbiosis. Therefore, it is meaningful to establish microbial maps of the cities we inhabit. While the processing and sequencing of environmental microbiome samples can be high-throughput, gathering samples is still labor and time intensive, and can require mobilizing large numbers of volunteers to get a snapshot of the microbial landscape of a city. ### Results Here we postulate that honeybees may be effective collaborators in gathering samples of urban microbiota, as they forage daily within a 2-mile radius of their hive. We describe the results of a pilot study conducted with three rooftop beehives in Brooklyn, NY, where we evaluated the potential of various hive materials (honey, debris, hive swabs, bee bodies) to reveal information as to the surrounding metagenomic landscape, and where we conclude that the bee debris are the richest substrate. Based on these results, we profiled 4 additional cities through collected hive debris: Sydney, Melbourne, Venice and Tokyo. We show that each city displays a unique metagenomic profile as seen by honeybees. These profiles yield information relevant to hive health such as known bee symbionts and pathogens. Additionally, we show that this method can be used for human pathogen surveillance, with a proof-of-concept example in which we recover the majority of virulence factor genes for Rickettsia felis, a pathogen known to be responsible for “cat scratch fever”. ### Conclusions We show that this method yields information relevant to hive health and human health, providing a strategy to monitor environmental microbiomes on a city scale. Here we present the results of this study, and discuss them in terms of architectural implications, as well as the potential of this method for epidemic surveillance. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40793-023-00467-z. ## Introduction Over half of the world’s human population lives in urban areas and, according to the United Nations (UN), nearly $70\%$ of us will live in cities by 2050 [1]. Our cities are built by and for humans, but are also complex, adaptive biological systems involving a diversity of living species [2]. The majority of these species are invisible and constitute the city’s microbiome. Our design decisions for the built environment shape these invisible populations, and we interact with them on a constant basis [3, 4]. A growing body of evidence shows us that our health and well-being are dependent on these interactions [5]. Indeed, multicellular organisms owe meaningful aspects of their development and phenotype to interactions with the microorganisms—bacteria or fungi—with which they live in symbiosis [6, 7]. Accumulated evidence confirms that mammalian phenotypes are related to a combination of an individual’s genotype as well as that of its microbiota, including disease states such as obesity [8] and influence on neuro-psychiatric disorders as well [9]. Beyond human consequences, plants’ flowering time has been found to depend on the soil microbiome [10] and the useful metabolic compounds in medicinal plants are possibly synthesized in conjunction with their symbiont bacteria [11], both traits formerly thought to depend only on the plant’s genotype. Metagenomic studies such as these are facilitated by the rapidly decreasing cost of high-throughput DNA sequencing, and support a growing understanding that the phenotype of a multicellular organism depends on both its own genotype and that of its associated microbes. As capacity for gathering and analyzing genomic and metagenomic data grows, our capacity to understand interspecies relationships is growing alongside it, with the potential of elucidating fundamental biological questions of host-symbiont selection and evolution mechanisms such as testing hologenome [12, 13] theories of evolution. Metagenomics is a rapidly growing field that is well-situated to survey across all domains and kingdoms of life, including city-scale efforts of urban metagenomics. Microbial classification using high-throughput DNA sequencing is faster and more comprehensive than culture-based methods, and has enabled city-wide mapping of microbial populations [14–16]. Mapping indoor environments [3, 17] also provides insights into the relationship between humans and the indoor microbiome, which holds promise for designing buildings that optimize this metric. Thus, we are moving away from the germ-centric paradigm of microbes to the quantification of a ubiquitous, continuous and commensal map of the environmental microbiome within which we live, work, and sleep. While the processing and sequencing of samples can be high-throughput (with automation, hundreds at a time), gathering samples is still very expensive, labor intensive, and can require mobilizing large numbers of volunteers to get a snapshot of the microbial landscape of a city, such as global City Sampling Day (metasub.org). Moreover, samples collected manually with swabs represent a limited area: 0.1–0.5m2. While this scale of resolution is important for applications such as tracking contamination through a hospital, it is not always easily implemented for city-scale studies and leads researchers to look for pinch points where samples might be most meaningful. Examples of this have been MetaSub sampling subways [16], air sampling in indoor environments [18], or sewers [19, 20]. Setting out to collect a more distributed and comprehensive sample of the urban landscape, following conversations with artists Timo Arnall and Jack Schulze, we investigated the potential of using honeybees as proxy sampling mechanisms for the urban microbiome. On average, honeybees forage within a 1–2 mile radius around their hive in rural environments [21] and 0.3–1 miles in urban environments [22], and we hypothesized that their travel would permit them to interact with various microbial environments including air, water, and mammalian sources in addition to their known plant targets. We designed a pilot study to test for geo-specific microbial residues corresponding to all of these environments within material found in a hive. Here we describe the results of a pilot study conducted with three rooftop beehives in Brooklyn, NY, where we evaluated the potential of various hive materials (honey, debris, hive swabs, bee bodies) to reveal information as to the surrounding metagenomic landscape, and where we conclude that the hive debris are the richest substrate. Based on these results, we profiled four additional cities by collecting hive debris: Sydney, Melbourne, Venice and Tokyo. Here we present the results of this study, and discuss them in terms of architectural implications, as well as the potential of this method for epidemic surveillance. ## U.S.A.—Brooklyn The hives of three independent beekeepers were sampled in New York City. The first location (AS) were Langstroth hives located in Astoria, Queens, NY. The second location (CH) were Langstroth and Top Bar hives located in Crown Heights, Brooklyn, NY. The third location (FG) were Langstroth hives located in Fort Greene, Brooklyn, NY. Samples of honey, bees, hive debris, and swabs of the inside of the hive were collected using sterile one-time-use scrapers and transferred into sterile 50 ml Falcon tubes. Bee bodies were submerged in isopropyl alcohol for storage. ## Australia—Sydney and Melbourne Hive debris from two Langstroth hives in Sydney (SYD1, SYD2) and two in Melbourne (MEL, SH) were sampled. Custom collection trays with self-sealing apertures, designed to be placed under the hives to collect hive debris, were developed and fabricated at MIT, and shipped to Sydney and Melbourne for deployment. Trays were installed for 1 week collections, then removed and hive debris samples were transferred to sterile 50ml Falcon tubes. ## Italy—Venice Hive debris from one Langstroth hive at the Palazzo Mora, Venice, Italy was sampled. Debris were collected from the hive using a sterile one-time-use scraper and transferred to 50ml Falcon tube. ## Japan—Tokyo Hive debris amples were collected from 12 hives distributed over 4 neighborhoods. Samples were collected with sterile one-time-use scrapers and stored in sterile 50ml Falcon tubes. The locations were Marunouchi (MA), 丸の内千代田區東京 100-0005, Mita (MI), 港區東京 108-0073日本, Marronnier Gate (MR), マロニエゲート銀座1, and Ginza (GK), 銀座中央區東京 104-0061. ## Sample preparation The general approach to DNA extraction involved a combination of lysis methods including mechanical, thermal, and enzymatic disruption to try and ensure that DNA from plant, microbe, and human sources would be extracted for sequencing. ## Honey The honey samples were diluted in a 1:1 ratio of grams of honey to mL of ultrapure water and then vortexed vigorously. The mixture was then spun down in the centrifuge at 3900 RCF for 20 minutes, the supernatant was discarded and the pellet along with ~ 200 µL residual liquid was moved to an Eppendorf, and placed in the − 20 °C freezer until the DNA extraction step. ## Bee debris The bee debris was diluted in a 1:5 ratio of grams of bee debris to mL of ultrapure water. The mixture was then heated in a water bath at 70 °C for 5 minutes in order to soften the debris and have it disperse in the liquid and then spun on the vortex vigorously. The liquid and solids were then separated, and both were placed into Eppendorfs and placed in the − 20 °C freezer so that a freeze-thaw cycle would help disrupt the cell membranes. The bee debris material was then ground with a mortar and pestle to break down any large pieces of bee debris, and resuspended in 1X PBS to bring all of the tubes to a final volume of 20 mL. Then material was then allowed to settle, spun down at 3900 RCF for 20 minutes along with 1–2 grams of 100µm glass beads to further mechanically disrupt the samples. The pellet and a small amount of the supernatant was then used for DNA extraction. ## Bees The isopropyl alcohol was drained from the tubes, then bees were placed in a mortar and pestle that was pre-chilled to − 80 °C before use. The bees were crushed vigorously into a paste. The paste was then placed in Eppendorf tubes and placed in the − 20 °C freezer until the DNA extraction step. ## Swabs The swabs, Copan Liquid Amies Elution Swab 481C, were stored in the − 20 °C freezer until the DNA extraction step. ## DNA extraction The protocol for 3-5 mL of starting material of the Promega Wizard® Genomic DNA Purification Kit (A1120) was used, with the following alterations to the standard protocol: one hour incubation at 37 °C in a shaker after the neutralization step; the samples were vortexed vigorously for about 1–2 minutes after the lysis and neutralization buffer were added to mechanically disturb the material; following this a phenol/chloroform step was done to remove any remaining organic matter before being placed in the spin column; the DNA was eluted with 20 uL of TE buffer warmed to 65 °C; there was a 2 minute incubation time at room temperature before spinning down. ## Library preparation The Library preparation protocol was performed at the Mason Lab at Weill Cornell Medicine, using the following kits according to manufacturer’s instructions. It was used to prepare libraries for all samples. Illumina/Qiagen 500bp Prep:Size selection with Agencourt AMPure XP Beads (A63881)End repair and A-tailing: Qiagen GeneRead DNA Library I Core Kit [180,432]Amplification: Qiagen GeneRead DNA Library I Amp Kit [180,455]Illumina TruSeq DNA LT adapter kits A and B for up to 24-plex per sequencing pool. ## Sequencing Brooklyn Pilot Study: The samples were sequenced at the BioMicro Center at MIT. The sequencing requested was a 150bp paired end sequence on one lane of the Illumina MiSeq. Venice Study: The sample was sequenced at the CNAG supercomputing center in Barcelona, Spain, with 150bp paired end reads on a Illumina MiSeq lane. Australia and Tokyo samples: Sequencing was performed on the Illumina HiSeq platform at Weill Cornell Medicine, with 125bp paired-end reads. See Additional file 6: Table S1 for read counts for all samples. ## Metagenomic classification Read quality was assessed with FastQC [23] and read quality was sufficient to not require trimming (see Additional file 7 for sample metadata, and Additional file 8 for MetaQC [24] reports). DIAMOND [25] – MEGAN [26] against the NCBI-nr database was used for read classification, as described in [27]. run diamond: for file in .fastq.gz; do name=${file/.fastq.gz/}; diamond blastx -d /path/to/NCBI_nr/nr -q $file -a $name -p 16 convert binary DIAMOND format to BLAST tabular format: for file in .daa; do diamond view --daa $file --out ${file/.daa/}.tab --outfmt tab; echo $file; done perform read-by-read taxonomy classification with MEGAN: for file in .tab; do /path/to/programs/megan/tools/blast2lca -- input $file --format BlastTAB --topPercent 10 --gi2taxa /path/to/programs/megan/GI_Tax_mapping/gi_taxid-March2015X.bin-- output $file.read_assignments.txt; done Heatmaps were generated with the script metaphlan_hclust_heatmap.py from the MetaPhlan package, displaying the abundances for species only (default –tax_lev s), in logarithmic scale (-s log). The clustering is performed with "average" linkage (default -m average), using "Bray–Curtis" distance for clades (default -d braycurtis) and "correlation" for samples (default -f correlation). metaphlan_hclust_heatmap.py –in $file –out $file.Blues.minv0.maxv1.Blues.log.pdf -c Blues -s log −minv 0.0 –maxv 1. ## Diversity quantification Beta-diversity was calculated according to the Bray-Curtis dissimilarity metric (Bray and Curtis 1957) as implemented by the Qiime2 package [28]. $ metaphlan2biom.py merged.samples.metaphlan.out merged.samples.biom $ beta_diversity.py -i merged.samples.biom -m bray_curtis -o merged.samples.beta_div.bray_curtis P-value was calculated based on 100 bootstrapped subsamples of the Brooklyn debris* sample, each subsample being of 1 million reads. Bootstrapped samples were classified using the same methods as described above, and pairwise beta-diversity calculated as above. P-value was calculated as the number of bootstrap samples with lesser dissimilarity value than the test value. ## Assembly and contig annotation Co-assembly of Tokyo samples (assembly of all sequences pooled together) was performed with MegaHit [29] and reads for each individual sample were mapped to contigs with Bowtie2 [30]. Assembly yielded 3207501 contigs with a total of 2802811167 base pairs. Contig length ranged from 200 to 488034 base pairs, with an average of 874bp and an N50 of 1515bp. Contigs were annotated with Anvio [23]. ## Virulence factor identification Virulence factors for Rickettsia felis were downloaded from the Virulence Factors of Pathogenic Bacteria database http://www.mgc.ac.cn/cgi-bin/VFs/compvfs.cgi). BLAST [31] was used to align the virulence factor genes to the assembled contigs, reporting the query coverage and percent identity. ## Brooklyn pilot study In order to assess the potential of using honeybees as metagenomic “sample collectors”, we designed a pilot study with three Langstroth hives in Brooklyn, wherein we sampled the interior of the hive, the debris at the bottom, bee bodies, and honey. We sequenced the DNA of each sample using a high-throughput shotgun approach, and classified the reads using DIAMOND-MEGAN against the NCBI NR nucleotide database, which includes all kingdoms and domains of life (see Methods for more details) (Fig. 1). The honey of each hive is largely dominated by the species Lactobacillus kunkeei (Fig. 1A), an obligate fructophilic lactic acid bacteria found in flowers, wine, and honey [32]. Also of note are Acinetobacter nectaris, found in flowers [33], and Zygosaccharomyces rouxii, known to thrive under salt or sugar osmotic stress and thus cause food spoilage [34]. Bee gut commensals were found in low abundance in honey, and include the species identified in the bee body samples, described below. Traces of plant DNA were also identified, including Medicago truncatula and Vitis vinifera. The bee body samples (Fig. 1B) contain sequences representative of both Apis mellifera (European honeybee) and Apis dorsata (Giant honeybee), indicating the hives are likely hybrids of these two species. The most abundant microbes in the bee body samples include species described as bee commensals such as Snodgrassella alvi and Gilliamella apicola [35], as well as Lactobacillus wkB8 and wkB10 [34]. The bees from AS and FG hives display almost identical species distribution, however the bees from the CH hive show lower abundances of the aforementioned commensals, and present species absent from the other two. These include Nosema ceranae, a fungal parasite of the honeybee affecting both larvae and adults [37], as well as various human-related bacteria such as Sporosarcina newyorkensis, isolated from clinical samples in New York State [38] and Enterobacter species. We hypothesize the colonization of atypical bacteria in this bee is correlated to the dysbiosis caused by Nosema infection. Fig. 1Species classification by type of sample in Brooklyn pilot study: A Honey, B Bee body, C Hive interior, D Debris. Hives are abbreviated as: AS Astoria, CH Crown Heights, FG Fort Greene. Color map scale corresponds to the log of relative abundance in each sample The inside of the hives (Fig. 1C) was quite uniform across locations, and dominated by environmental bacterial species usually described as found in polluted environments. These include Acidovorax sp. KKS102, known to degrade biphenyl/polychlorinated biphenyls (PCBs) [39], Sphingomonas sp. S17 [40], found in high-altitude Andean lakes and tolerant to high pH and desiccation. The interior of beehives is coated with propolis, a resinous substance including polyphenols from essential oils and with a pH of 8.5 [41]. It is a strong antimicrobial, antifungal and antiviral agent [42] and therefore we hypothesize the presence of extremophile bacteria, and their similar distribution across hives, is a result of selection by the chemical properties of propolis. The species identified in the debris samples (Fig. 1 C) were the most diverse (Table 1), and include several species of plants as well as plant-associated microbes such at the fungus Aureobasium pullulans, also an opportunistic human pathogen [43], aquatic microbes such as the alkane-degrading Aquabacterium sp. NJ1 [44] and honeybee associated such as Stenotrophomonas maltophilia [45] (also known as an opportunistic mammalian pathogen [46]). Taken together, the samples cluster according to sample type, versus sample location (Additional file 1: Fig S1). As a control, we also sampled a beekeeper’s hands and hive scraper tool (in one instance) as well as the hive exterior, and these samples were notably different than the debris as well (Additional file 1: Fig S1). The former control indicates that the signatures in the debris collected are not just from manipulation, and the latter indicates that the debris composition is not just from settling of material from the environment immediately exterior to the hive. Table 1Beta-diversity according to sample type (Bray–Curtis dissimilarity)P-value calculated against 100 random subsamples of a debris sample. Hives are abbreviated as: AS Astoria, CH Crown Heights, FG Fort Greene While samples from different hives within a sample type are significantly different from each other ($$P \leq 0.0$$) according to Bray–Curtis dissimilarity (Table 1), we found the debris samples to be the most diverse, as well as have the highest proportion of environmental bacteria. As our interest was to collect metagenomic information of the environment the bees traverse, rather than that of their hive, we concluded that bee debris is the best material for that purpose. ## Urban metagenomes as seen by bees We next sampled bee hive debris from four cities across the world: Venice, Italy; Sydney and Melbourne in Australia; several neighborhoods in Tokyo, Japan. Over all of these locations, we recovered DNA from plants, mammals, insects, arachnids, bacteria and fungi. Taken together, $53\%$ of the classified reads were from multicellular organisms, and $47\%$ from microorganisms. ( Fig 2).Fig. 2Distribution among kingdoms of classified reads across all samples, including most abundant species in each category All metagenomes characterized show different signatures according to cities (Additional file 2: Fig S2), and have particularities that can be related to the identity of the city. The metagenome of the debris collected from the hive in Venice was largely dominated by fungi related to wood rot (Additional file 3: Fig S3), which is a common feature of the buildings, built on submerged wooden pilings, and date palm DNA. Melbourne’s sample was dominated by Eucalyptus DNA, while Sydney’s showed little plant DNA, but bacteria such as Gordonia polyisoprenivorans, which degrades rubber[47] (Additional file 4: Fig S4). Tokyo’s metagenome includes plant DNA from Lotus and wild soybean, as well as the soy sauce fermenting yeast Zygosaccharomyces rouxii [34] (Additional file 5: Fig S5). Overall, each city has a unique metagenomic signature as viewed by bees, with microbes coming from a variety of sources: environmental, insect-related, mammalian and aquatic (see Table 2 for relative abundances of bacteria associated with different hosts or environments).Table 2Major classes of bacteria across samplesThis table summarizes the most abundant bacterial species (accounting for $90\%$ of bacterial contribution to a sample’s profile) according to their associated host or environment. Numbers are relative abundance (normalized to 1 for each sample). Colored cells indicate values over 0 ## Debris as indicator of hive health As the debris include parts of bees, we looked to the data to see if we could find microbes related to bee health. We found three honey and bee crop related species such as Lactobacillus kunkeii, Saccharibacter sp. AM169 and Frishella perrara and five bee gut species, with Gilliamella apicola being found in the most samples (Table 3)[48]. We also identified known bee pathogens, namely Paenibacillus larvae and Melissococcus plutonius, as well as the parasite Varroa destructor. These results indicate that debris may be used to assess overall hive health, or to assess the interaction of bee related species with environmental microbial species. Table 3Bee related species: known bee gut species, honey and bee crop species, pathogens, and parasites ## Debris as indicator of human health As the bees are traversing densely populated urban areas, we tested the hypothesis that they may be able to recover human pathogens and assess their pathogenic capacity by identifying virulence factor genes. Virulence factors are the molecules that enable the specific pathogenicity of the micro-organism [49]. Given the high level of genomic variation within species, asserting the presence of a pathogen through taxonomic classification is not sufficient to assert its pathogenicity. For this, we proceeded by performing de-novo co-assembly of the sequences from a given city, then using a metagenomic-specific classifier targeted to identify bacterial species from the contigs. We identified various opportunistic pathogens as well as some known disease-causing pathogens, including Shigella dysenteriae Sd197 (causing bacillary dysentry [50]) and Rickettsia felis (causing “cat scratch fever” [51]). We selected the Tokyo dataset for assembly as this location presented the highest number of samples, samples collected at two timepoints, as well as highest sequencing coverage per sample. We chose Rickettsia felis as an example to demonstrate the ability to identify a pathogen and its virulence factors with this sample collection method as it was the most represented in the assembled contigs. To go beyond species classification and assess pathogenic potential, we queried the assembled metagenome for Rickettsia felis virulence factor genes, as their presence is required for pathogenic capacity. We used R. felis as a proof-of-principle example that it is possible to verify pathogenic capacity of classified species with this type of data. In the Tokyo dataset, we recovered 28 of the 31 Rickettsia felis virulence genes with high coverage and at high similarity on the nucleotide level (Table 4). While co-assembly of these complex metagenomes led to less than optimal N50 values (N50=1515bp), this assembly quality was sufficient for virulence factor gene identification, as the genes tested for Rickettsia felis were covered over $97\%$ of their length on average (Table 5) when aligned to the assembled contigs. Table 4Alignment statistics of Rickettsia felis virulence factor genes mapped to assembled contigs of Tokyo metagenomeTable 5Abundance of virulence factors in samples collected at 1-week interval in TokyoNumbers reported are Reads per Million. Colored bars represent numerical values of each cell We assessed the persistence of virulence factors in the debris by analyzing samples taken at a 1- week interval in the Tokyo hives. After the first sampling, the bottom trays were cleaned and debris was collected after a week. In some cases, no markers were observed in the second samples, indicating that the cleaning was effective. In the Marunouchi hive H2, markers were found again, and more abundantly (Table 5). This indicates virulence markers that are either very abundant in the bee’s range or that they can change rapidly in abundance. ## Discussion Here we show that honeybees are relevant sensors for the urban microbiome, and that the debris collected contain a trace of the microbial clouds the bees are traversing as well as carry indicators of hive health. While these methods are cost prohibitive for amateur or even professional beekeepers as pathogen detection, and existing targeted methods already exist, these results present a methodology to assess additional dimensions of hive health. Indeed, we show that bees interact with a wide range of microbial species and thus future apiculture research could consider individual hive health in relation to the bees’ microbial environment, exploiting for example existing databases and scripts describing bee-associated bacteria [52]. Indeed, these bees recover microbes associated with plants, with which they have physical interactions, but also of mammals and aquatic environments, with which they presumably do not have direct contact. This implies that these microbes were constituents of the respective “microbial clouds” [53] of these entities and that the bees collect a trace of these clouds. Biological content in the atmosphere—the biosphere—was first described in 1978 [54] and has since been characterized as an integral part of ecosystem function [55]. The biosphere is an indicator of climate change, for example, increasing frequency of dust storms from the African continent are carrying plant and aquatic pathogens to the Americas, affecting coral populations [56]. Urban aerosols contain a diverse microbial component including species of potential health and bioterrorism concern. This study demonstrates a novel sampling methodology, with consistent results with a recent study using shotgun sequencing of honey to assess bee core gut microbiomes as well as plant species interaction while foraging [57], while also providing additional environmental microbiome data than the honey substrate. This reveals that different neighborhoods have different clouds just as different humans do, and that the collected microbiome can reveal information about the built environment and its inhabitants. For example, the Venetian bees carried a signature of wood rot and aquatic species, similar to previous work showing how flooded areas of a city can carry a “molecular echo” of the aquatic events of its past [14]. Indeed, it has been shown that microbial communities can serve as quantitative geochemical indicators [58] and the metabolic properties of the recovered communities can yield information about the environment. Furthermore, metagenomic data can be mined for human-health related information [59]. Future uses of data collected in this manner could be assessment of antibiotic resistance gene profiles, and while the molecular and computational methods used here were based on DNA analysis, it is possible they could be used to monitor RNA-based viruses such as Sars-Cov-2 or other future airborne pathogens, as demonstrated by targeted analyses using swab-based collection at hive doors during the COVID19 global pandemic [60]. ## Conclusions Our ability to recover virulence factors associated with human disease indicates that this method can serve for early detection of human-associated pathogens, in a complimentary modality to existing biosurveillance methods such as indoor air or sewage monitoring. However, this multi-species methodological approach may hold even more hope for a diversified understanding of urban microbiomes, their relationship to the built environment, and their relationship to human and other non-human species. Indeed, insect-based, city-wide microbial monitoring is likely more spatially comprehensive, even if lower resolution, compared to discrete, human-based sampling techniques, such as swabbing or air-sampling. This method offers the capacity to further catalog the urban environmental microbiome, contributing information to our understanding of its impact on humans. Additionally, this methodology offers a framework to understand multispecies interactions in the built environment, namely understanding hive health in the context of the microbiome of the bees’ foraging range. We have the unique possibility to understand our built environment and therefore design it, not just for ourselves but for all its inhabitants, from environments as common and public as subways [61] to those as specialized and hermetic as space stations [62, 63]. As Jane Jacobs says, “Cities are an immense laboratory of trial and error, failure and success, in city planning and city design” [64]. Through studies such as the one presented here, and using interdisciplinary approaches including art practice [65], we aim to further understand this accidentally engineered multispecies experiment of our built, shared, environment. ## Supplementary Information Additional file 1. Clustered heatmap of Brooklyn pilot samples including hive debris, bee bodies, honey, propolis, swabs of the hive structure as well as the beekeepers’ hands. Additional file 2. Clustered heatmap of hive debris samples from USA, Italy, Australia and Japan. Additional file 3. Heatmap of Venice (Italy) hive debris samples. Additional file 4. Heatmap of Sydney and Melbourne (Australia) hive debris samples. Additional file 5. Heatmap of Tokyo (Japan) hive debris samples. Additional file 6. Read counts for all samples. Additional file 7. Metadata for all samples in MIXS format. Additional file 8. MultiQC reports for samples by country. ## References 1. 1.United Nations, Department of economic and social affairs, and population division, World urbanization prospects: the 2018 revision. 2019. 2. 2.J A Puppim de Oliveira et al. Cities, biodiversity and governance: perspectives and challanges of the implementation of the convention on biological diversity at the city level. 2010. 3. Kembel SW. Architectural design drives the biogeography of indoor bacterial communities. PLoS ONE (2014.0) 9 1-10. DOI: 10.1371/journal.pone.0087093 4. Ruiz-Calderon JF, Cavallin H, Song SJ, Novoselac A, Pericchi LR, Hernandez JN, Rios R, Branch OH, Pereira H, Paulino LC, Blaser MJ. Walls talk: microbial biogeography of homes spanning urbanization. Sci Adv (2016.0) 2 e1501061. DOI: 10.1126/sciadv.1501061 5. Sharma A, Gilbert JA. Microbial exposure and human health. Curr Opin Microbiol (2018.0) 44 79-87. DOI: 10.1016/j.mib.2018.08.003 6. McFall-Ngai MJ. Unseen forces: the influence of bacteria on animal development. Dev Biol (2002.0) 242 1-14. DOI: 10.1006/dbio.2001.0522 7. McFall-Ngai M. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA (2013.0) 110 3229-3236. DOI: 10.1073/pnas.1218525110 8. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature (2006.0) 444 1027-1031. DOI: 10.1038/nature05414 9. Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci (2012.0) 13 701-712. DOI: 10.1038/nrn3346 10. Wagner MR, Lundberg DS, Coleman-Derr D, Tringe SG, Dangl JL, Mitchell-Olds T. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol Lett (2014.0) 17 717-726. DOI: 10.1111/ele.12276 11. Köberl M, Schmidt R, Ramadan EM, Bauer R, Berg G. The microbiome of medicinal plants: diversity and importance for plant growth, quality and health. Front Microbiol (2013.0) 4 400. DOI: 10.3389/fmicb.2013.00400 12. Zilber-Rosenberg I, Rosenberg E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol Rev (2008.0) 32 723-735. DOI: 10.1111/j.1574-6976.2008.00123.x 13. Bordenstein SR, Theis KR. Host biology in light of the microbiome: ten principles of holobionts and hologenomes. PLoS Biol (2015.0) 13 e1002226. DOI: 10.1371/journal.pbio.1002226 14. Afshinnekoo E. Geospatial resolution of human and bacterial diversity with city-scale metagenomics. Cell Syst (2015.0). DOI: 10.1016/j.cels.2015.01.001 15. Hsu T, Joice R, Vallarino J, Abu-Ali G, Hartmann EM, Shafquat A, DuLong C, Baranowski C, Gevers D, Green JL, Morgan XC. Urban Transit system microbial communities differ by surface type and interaction with humans and the environment. Msystems. (2016.0) 1 e00018-e116. DOI: 10.1128/mSystems.00018-16 16. Mason C. The metagenomics and metadesign of the subways and urban biomes (MetaSUB) international consortium inaugural meeting report. Microbiome (2016.0) 4 24. DOI: 10.1186/s40168-016-0168-z 17. Lax S. Bacterial colonization and succession in a newly opened hospital. Sci Transl Med (2017.0) 9 1-12. DOI: 10.1126/scitranslmed.aah6500 18. Meadow JF. Indoor airborne bacterial communities are influenced by ventilation, occupancy, and outdoor air source. Indoor Air (2014.0) 24 41-48. DOI: 10.1111/ina.12047 19. Maritz JM. An 18S rRNA workflow for characterizing protists in sewage, with a focus on zoonotic trichomonads. Microb Ecol (2017.0). DOI: 10.1007/s00248-017-0996-9 20. Newton RJ, McLellan SL, Dila DK, Vineis JH, Morrison HG, Eren AM, Sogin ML. Sewage reflects the microbiomes of human populations. MBio (2015.0) 6 e02574-e2614. DOI: 10.1128/mBio.02574-14 21. Eckert JE. The flight range of the honeybee. J Agric Res (1931.0) 47 257-285. DOI: 10.2307/1541 22. Garbuzov M, Schürch R, Ratnieks FLW. Eating locally: dance decoding demonstrates that urban honey bees in Brighton, UK, forage mainly in the surrounding urban area. Urban Ecosyst (2015.0) 18 411-418. DOI: 10.1007/s11252-014-0403-y 23. 23.“Babraham bioinformatics - FastQC a quality control tool for high throughput sequence data.” https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed Jan. 20, 2023). 24. Kang DD, Sibille E, Kaminski N, Tseng GC. MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis. Nucleic Acids Res (2012.0) 40 e15. DOI: 10.1093/nar/gkr1071 25. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using diamond. Nat Methods (2014.0) 12 59-60. DOI: 10.1038/nmeth.3176 26. Huson DH, Auch AF, Qi J, Schuster SC. Megan analysis of metagenomic data. Genome Res (2007.0) 17 377-386. DOI: 10.1101/gr.5969107 27. McIntyre ABR. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol (2017.0) 18 182. DOI: 10.1186/s13059-017-1299-7 28. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnol (2019.0) 37 852-857. DOI: 10.1038/s41587-019-0209-9 29. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics (2015.0) 31 1674-1676. DOI: 10.1093/bioinformatics/btv033 30. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods (2012.0) 9 357-359. DOI: 10.1038/nmeth.1923 31. Camacho C. BLAST+: architecture and applications. BMC Bioinformatics (2009.0) 10 421. DOI: 10.1186/1471-2105-10-421 32. Endo A, Irisawa T, Futagawa-Endo Y, Takano K, du Toit M, Okada S, Dicks LM. Characterization and emended description of lactobacillus kunkeei as a fructophilic lactic acid bacterium. Int J Syst Evolution Microbiol (2012.0) 62 500-504. DOI: 10.1099/ijs.0.031054-0 33. Álvarez-Pérez S, Lievens B, Jacquemyn H, Herrera CM. Acinetobacter. Int J Syst Evol Microbiol (2013.0) 63 1532-1539. DOI: 10.1099/ijs.0.043489-0 34. Pribylova L, de Montigny J, Sychrova H. Osmoresistant yeast Zygosaccharomyces rouxii: the two most studied wild-type strains (ATCC 2623 and ATCC 42981) differ in osmotolerance and glycerol metabolism. Yeast Chichester Engl (2007.0) 24 171-180. DOI: 10.1002/yea.1470 35. Kwong WK, Moran NA. Cultivation and characterization of the gut symbionts of honey bees and bumble bees: description of. Int J Syst Evol Microbiol (2013.0) 63 2008-2018. DOI: 10.1099/ijs.0.044875-0 36. 36.W K Kwong, A L Mancenido, and A Moran “Members of the firm-5 clade , from honey bee guts.” 2(6), 5–6, 2014, 10.1128/genomeA.01176-14.Copyright. 37. Eiri DM, Suwannapong G, Endler M, Nieh JC. Nosema ceranae can infect honey bee larvae and reduces subsequent adult longevity. PLoS ONE (2015.0) 10 1-17. DOI: 10.1371/journal.pone.0126330 38. Wolfgang WJ. Int J Syst Evol Microbiol (2012.0) 62 322-329. DOI: 10.1099/ijs.0.030080-0 39. Ohtsubo Y, Maruyama F, Mitsui H, Nagata Y, Tsuda M. Complete genome sequence of. J Bacteriol (2012.0) 194 6970-6971. DOI: 10.1128/JB.01848-12 40. Farias ME. Genome sequence of. J Bacteriol (2011.0) 193 3686-3687. DOI: 10.1128/JB.05225-11 41. Marcucci MC. Propolis : chemical composition, biological properties and therapeutic activity. Apidologie (1994.0) 26 83-99. DOI: 10.1051/apido:19950202 42. Kujumgieva A, Tsvetkovaa I, Serkedjievaa Y, Bankovab V, Christovb R, Popovb S. Antibacterial, antifungal and antiviral activity of propolis of different geographic origin. J Ethnopharmacol (1999.0) 64 235-240. DOI: 10.1016/S0378-8741(98)00131-7 43. Bolignano G, Criseo G. Disseminated nosocomial fungal infection by. J Clin Microbiol (2003.0) 41 4483-4485. DOI: 10.1128/JCM.41.9.4483-4485.2003 44. Masuda H, Shiwa Y, Yoshikawa H, Zylstra GJ. Draft genome sequence of the versatile alkane-degrading bacterium. Genome Announc (2014.0) 2 7-8. DOI: 10.1128/genomeA.01271-14 45. Evans JD, Armstrong T-N. Antagonistic interactions between honey bee bacterial symbionts and implications for disease. BMC Ecol (2006.0) 6 4. DOI: 10.1186/1472-6785-6-4 46. Brooke JS. Clin Microbiol Rev (2012.0) 25 2-41. DOI: 10.1128/CMR.00019-11 47. Linos A, Steinbüchel A, Spröer C, Kroppenstedt RM. Int J Syst Evolution Microbiol (1999.0) 49 1785-1791. DOI: 10.1099/00207713-49-4-1785 48. Engel P, Martinson VG, Moran NA. Functional diversity within the simple gut microbiota of the honey bee. Proc Nat Acad Sci (2012.0) 109 11002-11007. DOI: 10.1073/pnas.1202970109 49. Sharma AK. Bacterial virulence factors: secreted for survival. Indian J Microbiol (2017.0) 57 1-10. DOI: 10.1007/s12088-016-0625-1 50. Yang F. Genome dynamics and diversity of. Nucleic Acids Res (2005.0) 33 6445-6458. DOI: 10.1093/nar/gki954 51. Yazid Abdad M, Stenos J, Graves S. Rickettsia felis, an emerging flea-transmitted human pathogen. Emerg Health Threats J (2011.0) 4 7168. DOI: 10.3402/ehtj.v4i0.7168 52. Ellegaard KM, Suenami S, Miyazaki R, Engel P. Vast differences in strain-level diversity in the Gut microbiota of two closely related honey bee species. Curr Biol CB (2020.0) 30 2520-2531.e7. DOI: 10.1016/j.cub.2020.04.070 53. Meadow JF, Altrichter AE, Bateman AC, Stenson J, Brown GZ, Green JL, Bohannan BJ. Humans differ in their personal microbial cloud. PeerJ (2015.0) 22 e1258. DOI: 10.7717/peerj.1258 54. Imshenetsky AA, Lysenko SV, Kazakov GA. Upper boundary of the biosphere. Appl Environ Microbiol (1978.0) 35 1-5. DOI: 10.1128/aem.35.1.1-5.1978 55. Burrows SM, Elbert W, Lawrence MG, Pöschl U. Bacteria in the global atmosphere – Part 1: review and synthesis of literature data for different ecosystems. Atmospheric Chem Phys (2009.0) 9 9263-9280. DOI: 10.5194/acp-9-9263-2009 56. Behzad H, Mineta K, Gojobori T. Global ramifications of dust and sandstorm microbiota. Genome Biol Evol (2018.0) 10 1970-1987. DOI: 10.1093/gbe/evy134 57. Galanis A. Bee foraging preferences, microbiota and pathogens revealed by direct shotgun metagenomics of honey. Mol Ecol Resour (2022.0) 22 2506-2523. DOI: 10.1111/1755-0998.13626 58. Smith MB, Rocha AM, Smillie CS, Olesen SW, Paradis C, Wu L, Campbell JH, Fortney JL, Mehlhorn TL, Lowe KA, Earles JE. Natural Bacterial communities serve as quantitative geochemical biosensors. MBio (2015.0) 6 e00326-e415. DOI: 10.1128/mBio.00326-15.Editor 59. Rosenfeld JA, Reeves D, Brugler MR, Narechania A, Simon S, Durrett R, Foox J, Shianna K, Schatz MC, Gandara J, Afshinnekoo E. Genome assembly and geospatial phylogenomics of the bed bug Cimex lectularius. Nat Commun (2016.0) 7 10164. DOI: 10.1038/ncomms10164 60. Cilia G, Bortolotti L, Albertazzi S, Ghini S, Nanetti A. Honey bee (. Sci Total Environ (2022.0) 20 150327. DOI: 10.1016/j.scitotenv.2021.150327 61. Danko D. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell (2021.0) 184 3376-3393.e17. DOI: 10.1016/j.cell.2021.05.002 62. 62.N K Singh, D Bezdan, A Checinska Sielaff, K Wheeler, C E Mason, and K Venkateswaran “Multi-drug resistant enterobacter bugandensis species isolated from the international space station and comparative genomic analyses with human pathogenic strains.” BMC Microbiol. 10.1186/s12866-018-1325-2. 63. Garrett-Bakelman FE, Darshi M, Green SJ, Gur RC, Lin L, Macias BR, McKenna MJ, Meydan C, Mishra T, Nasrini J, Piening BD. The NASA twins study: a multidimensional analysis of a year-long human spaceflight. Science (2019.0) 364 8650. DOI: 10.1126/science.aau8650 64. Jacobs J. The death and life of great American cities (1992.0) 65. 65.“Time space existence - biennale architettura 2016, VENEZIA, Italy,” google arts & culture. https://artsandculture.google.com/partner/time-space-existence-biennale-architettura-2016 (accessed Jan. 20, 2023).
--- title: 3-Mercaptopyruvate sulfur transferase is a protein persulfidase authors: - Brandán Pedre - Deepti Talwar - Uladzimir Barayeu - Danny Schilling - Marcin Luzarowski - Mikolaj Sokolowski - Sebastian Glatt - Tobias P. Dick journal: Nature Chemical Biology year: 2023 pmcid: PMC10060159 doi: 10.1038/s41589-022-01244-8 license: CC BY 4.0 --- # 3-Mercaptopyruvate sulfur transferase is a protein persulfidase ## Abstract Protein S-persulfidation (P-SSH) is recognized as a common posttranslational modification. It occurs under basal conditions and is often observed to be elevated under stress conditions. However, the mechanism(s) by which proteins are persulfidated inside cells have remained unclear. Here we report that 3-mercaptopyruvate sulfur transferase (MPST) engages in direct protein-to-protein transpersulfidation reactions beyond its previously known protein substrates thioredoxin and MOCS3/Uba4, associated with H2S generation and transfer RNA thiolation, respectively. We observe that depletion of MPST in human cells lowers overall intracellular protein persulfidation levels and identify a subset of proteins whose persulfidation depends on MPST. The predicted involvement of these proteins in the adaptation to stress responses supports the notion that MPST-dependent protein persulfidation promotes cytoprotective functions. The observation of MPST-independent protein persulfidation suggests that other protein persulfidases remain to be identified. Mercaptopyruvate sulfur transferase (MPST) is revealed as a protein persulfidase that acts directly on numerous and diverse target proteins, revealing potential origins of persulfidation as a common posttranslational modification. ## Main Protein persulfidation (P-SSH) is now recognized as a posttranslational modification that naturally occurs inside cells across all domains of life1. Proteomic analyses have shown that persulfidation affects a large number of functionally diverse proteins1–3. However, the physiological role and importance of protein persulfidation remains to be fully understood. Protein persulfidation exists under homeostatic conditions and is often observed to increase under conditions of oxidative stress1,2,4. Similar to other oxidative protein thiol modifications, persulfidation may activate or inactivate individual proteins, thus potentially adapting protein function to changing conditions5–7. In addition, protein persulfidation may protect protein thiols against irreversible oxidation8,9. However, it is not known how proteins are actually persulfidated inside cells. Potential nonenzymatic mechanisms of protein persulfidation have been discussed previously10. First, some protein thiols may react with H2O2 to become sulfenylated, and then with H2S to become persulfidated. Second, some protein disulfide bonds may react with H2S to generate a persulfide. However, both reactions are slow11 and unlikely to explain the fact that so many proteins can be detected in the persulfidated state, even under apparent nonstress conditions. We recently noted that conditions that oxidize protein thiols to sulfenic acids also oxidize any resulting hydropersulfides to perthiosulfonic acids, at least in vitro12. It is thus difficult to see how H2O2 can trigger the formation of hydropersulfides without immediately oxidizing these. Third, low molecular-weight (LMW) persulfides, such as GSSH and Cys-SSH, known to be generated inside cells13, have been proposed to transfer single sulfur atoms to thiols14, but in fact are not observed to engage in transpersulfidation10. Given these and other considerations, it has long been speculated that posttranslational protein persulfidation is facilitated enzymatically by one or more sulfur transferases15. In this study we investigated 3-mercaptopyruvate sulfur transferase (MPST), a sulfur transferase that so far has been mainly associated with H2S generation and transfer RNA thiolation. MPST desulfurates 3-mercaptopyruvate (3MP), a product of cysteine catabolism, to generate pyruvate and an enzyme-bound persulfide. In contrast to small molecule persulfides, the MPST-bound persulfide is capable of transferring its outer sulfur atom to thiol acceptors, facilitated by a specialized steric and electronic environment16. Until now, two protein substrates of MPST have been known. MPST persulfidates thioredoxin in the context of H2S generation17, and MOCS3/Uba4 for subsequent tRNA thiolation and protein urmylation18,19. However, it has been observed previously that overexpression of MPST increases intracellular ‘bound’ sulfane sulfur (S0) content, potentially indicating a direct role for MPST in general protein persulfidation20–23. Nonetheless, this possibility has not been tested so far. In this study, we present experimental evidence supporting the notion that MPST acts as a protein persulfidase and makes a major contribution to overall protein persulfidation. Starting out with purified proteins in vitro, we observed that redox-sensitive green fluorescent protein (roGFP2), a protein with two thiol groups on its surface, acts as a highly efficient sulfur acceptor for MPST. We also observed bovine serum albumin (BSA) accepting sulfur from MPST. These observations indicated to us that MPST may have a broad protein persulfidating activity. We confirmed that transfer of sulfur from MPST to roGFP2 also takes place inside living yeast cells and that roGFP2 oxidation is due to a direct protein-to-protein transpersulfidation reaction. Further in vitro experiments indicated that MPST is not a major producer of inorganic polysulfides, again supporting the notion that direct transpersulfidation is the predominant mode of MPST-mediated protein persulfidation. We then showed that depletion of MPST in human cells significantly lowers overall cellular protein persulfidation levels. We identified a set of 64 target proteins whose persulfidation largely depends on MPST. Taken together, we conclude that MPST has the intrinsic ability to persulfidate a broad range of target proteins under physiologically relevant conditions. The predicted involvement of these proteins in the adaptation to stress responses likely explains previously reported phenotypes of MPST deletion in model organisms. ## MPST-roGFP2 couples 3MP desulfuration to roGFP2 oxidation Following the design principle of previous roGFP2-based biosensors24, we engineered an MPST-roGFP2 fusion protein, Tum1-roGFP2, based on the yeast MPST homolog thiouridine modifying protein 1 (Tum1). We hypothesized that 3MP desulfuration by MPST should lead to roGFP2 disulfide formation through a mechanism that involves three consecutive steps, namely [1] formation of an MPST-bound persulfide (Pyr-SH (≡ 3MP) + MPST-SH → Pyr-H + MPST-SSH), [2] transpersulfidation of roGFP2 (MPST-SSH + roGFP2(-SH)2 → MPST-SH + roGFP2(-SH)(-SSH)) and [3] roGFP2 intramolecular disulfide bond formation coupled to H2S release (roGFP2(-SH)(-SSH) → roGFP2(S-S) + H2S). To investigate the recombinant fusion protein, we compared it to roGFP2 and to a mutant fusion protein, Tum1(C259S)-roGFP2, in which the active site cysteine of Tum1 is replaced by serine. First, we tested its response to 3MP in vitro. The intact fusion protein, but neither roGFP2 nor Tum1(C259S)-roGFP2, was oxidized in response to 3MP (Fig. 1a), thus demonstrating chemical communication between the active site of the MPST domain and the dithiol-disulfide site of roGFP2. Of note, Tum1-roGFP2 did not show any response to either l-cysteine (l-Cys) or 3-mercaptolactate (Fig. 1b, left and middle panels). These compounds are chemically related molecules upstream and downstream of 3MP in metabolism. Likewise, Tum1-roGFP2 did not respond to thiosulfate (Fig. 1b, right panel), the preferred substrate of thiosulfate sulfur transferases, which are the members of the rhodanese family most closely related to the MPSTs. Tum1-roGFP2 was nonresponsive toward GSSG (oxidized glutathione) and showed only a marginal response to H2O2 (Extended Data Fig. 1a). Considering the mechanism proposed above, 3MP-mediated roGFP2 oxidation is expected to be accompanied by the generation of H2S. Indeed, the reaction of 3MP with Tum1-roGFP2, but not with Tum1(C259S)-roGFP2, led to the release of H2S (Fig. 1c and Extended Data Fig. 1b).Fig. 1Tum1-roGFP2 couples 3MP desulfuration to roGFP2 oxidation, releasing H2S in the process.a, Degree of oxidation (OxD) of 100 nM Tum1-roGFP2 (left panel), roGFP2 (center panel) and Tum1(C259S)-roGFP2 (right panel), in response to increasing 3MP concentrations. b, Degree of oxidation of 100 nM Tum1-roGFP2, in response to increasing concentrations of l-cysteine (left panel), 3-mercaptolactate (center panel) and thiosulfate (right panel). c, H2S release from 2.5 μM Tum1-roGFP2 on addition of 50 μM 3MP, as measured by an H2S-selective electrode. d, The half-maximum inhibitory concentration (IC50) values for LMW compounds acting as Tum1 sulfur acceptors. e, IC50 values for proteins acting as Tum1 sulfur acceptors. All data are based on $$n = 3$$ independent experiments. Source data ## Sulfite and proteins are preferred sulfur acceptors for MPST Having established that Tum1-fused roGFP2 acts as a sulfur acceptor for Tum1, we then investigated the ability of other potential sulfur acceptors to compete with roGFP2 for taking over the 3MP-derived sulfur from the Tum1 domain. Therefore, we added increasing concentrations of acceptor candidates and monitored their impact on 3MP-dependent roGFP2 oxidation. As a proof of concept, we first used cyanide, a well-established sulfur acceptor for MPST25. As expected, increasing amounts of cyanide suppressed 3MP-dependent roGFP2 oxidation in a concentration-dependent fashion (Extended Data Figs. 1c and 2a), forming thiocyanate as the product (Extended Data Fig. 1d). Comparing several small molecule sulfur acceptors (Fig. 1d), we identified sulfite as an outstanding competitor (Extended Data Fig. 1e, left panel and Extended Data Fig. 2b), while l-Cys and glutathione (GSH) were less efficient (Extended Data Fig. 1e, middle and right panels and Extended Data Fig. 2c,d). GSH acted as a bona fide competitor of sulfur transfer, as it was not able to reduce the already oxidized fusion protein (Extended Data Fig. 2e). Comparing protein acceptors (Fig. 1e), we found that the known MPST/Tum1 substrates yeast Uba4 and human Trx1 are very good competitors (Extended Data Fig. 1f, left and middle panels and Extended Data Fig. 2f,g). However, BSA, which is not a natural substrate of MPST/Tum1, was almost as effective (Extended Data Fig. 1f, right panel and Extended Data Fig. 2h), suggesting that MPST has a general ability to persulfidate accessible thiol groups on other proteins. In summary, we found that the Tum1-roGFP2 fusion protein couples 3MP desulfuration to roGFP2 thiol oxidation through the mediacy of a transferable sulfur atom, with high specificity and efficiency. Competition experiments further revealed that protein clients are the most efficient sulfur acceptors, with the exception of sulfite. Obviously, MPST does not only sulfurate previously characterized protein substrates (Trx, Uba4), but also other proteins not normally encountered by MPST in its natural context (roGFP2, BSA). ## Response of the MPST-roGFP2 fusion protein in yeast Having characterized MPST-dependent roGFP2 oxidation in vitro, we expressed the fusion protein in the cytosol and mitochondrial matrix of yeast cells, along with the two controls, roGFP2 and Tum1(C259S)-roGFP2. First, we tested the response of the probes to exogenously supplied 3MP. The mitochondrial probe responded markedly (Fig. 2a, middle panel), while the cytosolic one responded only weakly (Extended Data Fig. 3a, middle panels). Next, we tested the response to exogenously supplied l-Cys, the metabolic precursor of 3MP. l-Cys gave rise to a stronger probe response than 3MP, presumably due to more efficient cellular uptake of l-Cys and its subsequent intracellular conversion to 3MP. Again, we observed a prominent response in the mitochondria (Fig. 2b, middle panel), but only a weak one in the cytosol (Extended Data Fig. 3b, middle panels). Based on these findings, we further investigated the response of the mitochondrial probe to exogenous l-Cys. To confirm that the observed response reflects the endogenous generation of 3MP, we deleted aat1, the gene encoding the mitochondrially located transaminase that is known to convert l-Cys to 3MP. Indeed, the response of the mitochondrial probe to l-Cys was blunted in the absence of aat1 (Fig. 3a). We then directly generated 3MP inside mitochondria, by expressing mitochondrially targeted d-amino acid oxidase (DAAO), which converts d-Cys into 3MP. Since DAAO also produces H2O2, which may cause roGFP2 oxidation, we also tested the probe response to d-alanine (d-Ala), which also produces H2O2, but not 3MP. Indeed, we observed that mitochondrial Tum1-roGFP2 responded more strongly to d-Cys than to d-Ala, unlike roGFP2 or Tum1(C259S)-roGFP2 (Fig. 3b). The observed response was DAAO-dependent, as d-Cys did not trigger a response in the absence of DAAO (Extended Data Fig. 4a). In conclusion, the Tum1-roGFP2 fusion protein, expressed in yeast mitochondria, responds to 3MP, which is endogenously produced from l-Cys. Fig. 2Mitochondrial Tum1-roGFP2 responds to 3MP and l-Cys.a, Response of roGFP2 (left), Tum1-roGFP2 (center) and Tum1(C259S)-roGFP2 (right), expressed in the mitochondrial matrix (mt), to exogenously added 3MP. b, Response of roGFP2 (left), Tum1-roGFP2 (center) and Tum1(C259S)-roGFP2 (right), expressed in the mitochondrial matrix (mt), to exogenously added l-Cys. Data are based on $$n = 3$$ independent experiments, except for Tum1-roGFP2 + 3MP ($$n = 2$$).Source dataFig. 3Mitochondrial Tum1-roGFP2 responds to endogenously produced 3MP.a, Response of mitochondrial Tum1-roGFP2 to l-Cys in the parental strain (WT, left panel) and in a strain lacking mitochondrial cysteine transaminase (Δaat1, right panel). b, Response of mitochondrial probes roGFP2 (left), Tum1-roGFP2 (center) and Tum1(C259S)-roGFP2 (right) to exogenously added d-Cys (purple) or d-Ala (green), in a strain expressing mitochondrial DAAO. All data are based on $$n = 3$$ independent experiments. Source data ## MPST sulfurates roGFP2 independently of forced proximity While mitochondrial Tum1-roGFP2 responded much better than mitochondrial roGFP2 to 3MP (or the 3MP precursor l-Cys), this was not the case in the cytosol, where we could not detect differences in responsiveness. Notably, roGFP2 responded almost as strongly to l-Cys as did Tum1-roGFP2 (Extended Data Fig. 3b, left and middle panels). We hypothesized that this may be due to the fact that endogenous Tum1 is predominantly located in the cytosol26. Hence, the response of unfused roGFP2 in the cytosol may be facilitated by interactions with endogenous Tum1, thus making the fusion of Tum1 dispensable. To investigate the influence of endogenous Tum1 we compared the reactivity of the probe in wild-type and Δtum1 cells. We found that the response of unfused roGFP2 in the cytosol largely depends on the presence of endogenous Tum1 (Fig. 4a). This experiment shows that Tum1 efficiently transfers sulfur to roGFP2 when both proteins are expressed separately in the same cellular compartment. Using purified proteins we confirmed that free Tum1 is efficient in oxidizing free roGFP2 on addition of 3MP (Fig. 4b). A direct side-by-side comparison revealed that an equimolar mixture of free proteins is as efficient in facilitating roGFP2 oxidation as the corresponding Tum1-roGFP2 fusion protein (Fig. 4c). The rate of roGFP2 oxidation was further enhanced by increasing Tum1 concentration (Fig. 4d). These results indicate that Tum1 efficiently oxidizes roGFP2 irrespectively of an artificially enforced proximity. Fig. 4Tum1 oxidizes roGFP2 independently of forced proximity.a, Response of cytosolic (ct) Tum1-roGFP2 to l-Cys in the parental (WT, left) and Δtum1 strain (right). b, In vitro response of roGFP2 (100 nM) to increasing 3MP concentrations in the presence of an equimolar amount of 100 nM wild-type (left panel) or mutant Tum1 (right panel). c, In vitro response of the Tum1-roGFP2 fusion protein (100 nM) to 3MP (solid lines) in comparison to the response of an equimolar mixture of 100 nM roGFP2 and 100 nM Tum1 (dashed lines). d, In vitro response of roGFP2 (100 nM) to 3MP in the presence of increasing concentrations of Tum1. All data are based on $$n = 3$$ independent experiments. Source data The above proposed mechanism predicts that Tum1 transpersulfidates roGFP2 on one of its two surface thiols. To directly detect protein persulfidation we performed whole protein mass spectrometry, using monobromobimane to trap protein persulfides. As a positive control, we incubated Tum1 with a mono-thiol Trx1 mutant (TrxC35S) and added 3MP. In the presence of active Tum1, but not in the presence of mutated Tum1(C259S), we observed the addition of 1 to 3 sulfur masses to Trx(C35S) (corresponding to Trx-SSH, Trx-SSSH and Trx-SSSSH) (Fig. 5a). This result confirmed that Tum1 transfers sulfur to Trx1. We then asked whether Tum1 would also transfer sulfur to roGFP2. First, we incubated Tum1 with normal (dithiol) roGFP2. On addition of 3MP, roGFP2 was oxidized to the disulfide form, and we also observed the appearance of a trisulfide (Extended Data Fig. 5a). To capture the persulfide intermediate of the reaction, we then repeated the experiment with the two mono-thiol variants of roGFP2 (C148S and C205S). In both cases, we detected the formation of the corresponding persulfide (Fig. 5b and Extended Data Fig. 5b). We did not detect a mixed disulfide between Tum1 and roGFP2 in any of these experiments (Extended Data Fig. 5c,d). These observations directly confirmed that MPST can transfer sulfur not only to its dedicated protein substrates, but also to an arbitrary nonnatural substrate (roGFP2), suggesting that MPST acts as a general protein persulfidase, potentially contributing to overall intracellular protein persulfidation. In addition, the outcome of our experiments provides unequivocal proof that roGFP2 is indeed oxidized through transpersulfidation and not by a thiol-disulfide exchange reaction with MPST.Fig. 5Tum1 directly persulfidates other proteins.a,b, Mass spectra of H. sapiens Trx1(C35S) (a) and roGFP2(C205S) (b) exposed to Tum1 and 3MP (red curve), or to an inactive Tum1 system (black curve). $$n = 1$.$ c, Oxidation of roGFP2 (420 nM) by immobilized Tum1-SSH, in the absence of LMW compounds (beads; red line), or by the corresponding supernatant (SN; black line) of the reaction between immobilized Tum1 (2 µM) and 3MP (100 µM), that is, in the absence of Tum1-SSH. Control (ctrl) experiments were performed in absence of 3MP. $$n = 2$$ independent experiments. d, The same experiment as in c, but with the initial reaction between immobilized Tum1 (2 µM) and 3MP (100 µM) conducted in the presence of GSH (100 µM), thus diminishing formation of MPST-SSH. $$n = 2$$ independent experiments. e, Oxidation of roGFP2 (420 nM) by immobilized Tum1-SSH in the presence of GSH (beads + GSH; purple line), or by the corresponding supernatant in the presence of GSH (SN + GSH; blue line). Left panel: 420 nM GSH. Right panel: 2,100 nM GSH. The curves obtained in c (beads and SN in the absence of GSH; red and black lines, respectively) are included for direct comparison. $$n = 2$$ independent experiments. Source data ## Sulfur transfer does not involve small molecule intermediates While the above-described experiments indicated direct protein-to-protein transpersulfidation, they did not rule out an alternative possibility. Previous studies have suggested that MPST can generate inorganic polysulfides (H2S2, H2S3)22,23, which in principle could facilitate target protein persulfidation (for example, P-SH + H2S2 → P-SSH + H2S). To find out whether MPST-mediated protein persulfidation is direct (that is, protein-to-protein) or indirect (that is, through soluble LMW polysulfides), we devised an experiment that allowed us to distinguish between the two possible mechanisms. To this end, we immobilized recombinantly expressed and purified streptavidin-binding-peptide (SBP)-tagged Tum1 on streptavidin agarose (SA) beads and incubated it with 3MP to generate bead-bound Tum1-SSH. The supernatant was separated from the beads and the beads were washed to ensure the absence of small molecules. Beads and supernatants were then tested separately and side-by-side. The reaction with the S0 probe SSP4 revealed that >$95\%$ of S0 is associated with the beads (Extended Data Fig. 6a), suggesting that generation of inorganic polysulfides by MPST is a minor process. The additional presence of H2S did not make a difference (Extended Data Fig. 6b), suggesting that even at supraphysiological concentration (10 µM) H2S is not an efficient sulfur acceptor for MPST and hence does not promote H2S2 generation. As expected, the presence of a large (100-fold) molar excess of GSH (during the incubation of bead-bound MPST with 3MP) largely abolished bead-associated SSP4 reactivity (Extended Data Fig. 6c,d), as S0 is transferred to and reduced by GSH. Next, we tested the reaction of roGFP2 with beads and supernatants. About $90\%$ of roGFP2 oxidizing activity was associated with the beads (Fig. 5c), again showing that small molecule products make a minor contribution to the observed roGFP2 oxidation. Moreover, bead-associated oxidation was much faster, indicating a kinetic advantage of direct protein-to-protein transpersulfidation. Again, the presence of additional H2S did not make a difference (Extended Data Fig. 6e). Similar to the SSP4 experiment, the presence of a large excess of GSH (during the incubation of bead-bound MPST with 3MP) diminished the yield of Tum1-SSH, and therefore of roGFP2 oxidation (Fig. 5d). In sum, these experiments confirmed that the observed oxidation of roGFP2 is predominantly the result of direct transpersulfidation. LMW species potentially generated by MPST (polysulfides) appear to play a minor role, if any. As shown above, a roughly 200-fold excess of GSH over Tum1-roGFP2 inhibited roGFP2 oxidation by $50\%$ (Extended Data Fig. 1e, right panel), and a 50-fold excess of GSH over Tum1 diminished Tum1-SSH availability for roGFP2 by roughly $90\%$ (Fig. 5d). This raised the question whether GSH is likely to outcompete MPST-mediated protein persulfidation under intracellular conditions. GSH is often considered to outnumber all other thiols inside cells. However, the pool of accessible protein thiols is at least as large as the GSH pool27,28. To directly test whether GSH limits the protein persulfidase activity of MPST when protein thiols are as abundant as GSH, we first prepared and washed Tum1-SSH, and then monitored the direct competition of GSH and roGFP2 at molar ratios of 1:1 and 5:1. Neither ratio had a notable influence on roGFP2 oxidation (Fig. 5e). This suggests that under intracellular conditions, when MPST is as likely to meet a protein thiol as a LMW thiol, protein persulfidation is not substantially limited by the presence of GSH. ## MPST contributes to overall protein persulfidation Following our observation that Tum1 was capable of persulfidating several thiol-containing proteins and facilitated intracellular roGFP2 persulfidation when coexpressed in the same cellular compartment, we then asked whether Tum1 contributes to overall intracellular protein persulfidation. To this end, we monitored protein persulfidation using the dimedone switch assay1. Initially we tried to apply this technique to yeast cells either expressing or lacking Tum1. However, despite intense effort, we were unable to obtain reliable and reproducible results. We were also not able to obtain reproducible results for yeast cells expressing or lacking cystathionine-γ-lyase, reported previously1, suggesting technical limitations in applying the technique to yeast. We therefore decided to test the validity of our previous findings by monitoring overall protein persulfidation in human cells subjected to MPST depletion. In contrast to yeast cells, the results obtained for human cells were highly reproducible. We found baseline protein persulfidation levels to be lower in MPST-depleted cells (Fig. 6a and Extended Data Figs. 7a,b). Moreover, provisioning of extra l-Cys to the medium increased persulfidation in MPST-proficient, but not in MPST-depleted cells (Fig. 6a and Extended Data Figs. 7a,b). Conversely, ectopic overexpression of MPST, but not of the catalytically inactive MPST mutant, increased intracellular persulfidation levels (Fig. 6b and Extended Data Figs. 7c,d). To identify individual target proteins of MPST, we again performed the dimedone switch assay, this time coupling persulfides to biotin instead of a fluorescent dye. We compared the abundance of biotinylated proteins in mock-depleted and MPST-depleted cells using mass spectrometry-based label free quantitation and identified 64 proteins that were substantially depleted on MPST depletion (Fig. 6c). Many of these proteins are directly or indirectly involved in stress responses. Interaction analysis further suggests selectivity toward particular processes and protein families (Fig. 6d). In conclusion, we find that the MPST expression level (in connection with cysteine availability) is a major contributing factor to overall protein persulfidation in human cells. Fig. 6MPST contributes to global protein persulfidation.a, Overall persulfidation levels in HEK293 MSR cells before and after depletion of MPST (left panel). Cells were treated with 5 mM l-Cys for 30 min or were left untreated (UT). Relative persulfidation levels are indicated by Coomassie-normalized fluorescence intensity (right panel). Data are presented as mean and individual values ($$n = 3$$ biologically independent experiments) ± s.e.m. Statistical analysis based on a two-tailed unpaired t-test. b, Overall persulfidation levels in HEK293 MSR cells ectopically overexpressing roGFP2, MPST-roGFP2 or MPST(C248S)-roGFP2 (MPSTmut-roGFP2) (left panel). Relative persulfidation levels are indicated by Coomassie-normalized fluorescence intensity (right panel). Data are presented as mean and individual values ($$n = 3$$ biologically independent experiments) ± s.e.m. Statistical analysis based on a two-tailed unpaired t-test. c, Influence of MPST depletion on the persulfidation of individual proteins. Proteins depleted by at least twofold in MPST-depleted cells are marked in red. d, Interaction analysis of candidate MPST target proteins. Edges represent experimentally supported protein–protein interactions (confidence score >0.4) acquired from the STRING database50. The graph was generated with Cytoscape51. e–g, Summary of MPST-driven transpersulfidation. The MPST-bound persulfide (MPST-SSH) sulfurates thiol-containing molecules, the outcome depending on the type of acceptor. e, Sulfur transfer to proteins (P) with vicinal dithiols (roGFP2, Trx1) generates a protein disulfide and releases H2S. f, Sulfur transfer to protein monothiols leads to longer-lived protein persulfides. g, Sulfur transfer to GSH generates GSSH, which releases H2S to generate GSSG or (dotted lines) to glutathionylate proteins. Source data ## Discussion Protein persulfidation has been recognized as a common posttranslational modification of physiological relevance, but the actual mechanism of protein persulfidation has remained elusive. Previously, several nonenzymatic mechanisms of protein persulfidation have been proposed. One idea is that LMW persulfides (GSSH or Cys-SSH) react with protein thiols to generate protein persulfides (for example, P-SH + GSSH → P-SSH + GSH)14. However, this is not observed and also seems chemically implausible, as thiols can be expected to attack the inner sulfur atom of the persulfide to release H2S as the leaving group10. Thus, the reaction with LMW persulfides should rather lead to protein S-glutathionylation and S-cysteinylation, respectively (for example, P-SH + GSSH → P-SSG + H2S). A second idea is that protein persulfidation occurs through a two-step reaction with H2O2 and H2S: first, oxidation of the thiol to the sulfenic acid (P-SH + H2O2 → P-SOH + H2O), followed by condensation with H2S to form the persulfide (P-SOH + H2S → P-SSH + H2O)1,11. This mechanism is chemically feasible11 and may occur under conditions of substantial oxidative stress (for example, when cells are exposed to high amounts of oxidants)1. However, it is unlikely to explain basal protein persulfidation or to be of general relevance, because H2O2 and H2S are naturally produced at nanomolar concentrations, and most protein thiols are not very reactive toward H2O2 (k ≅ 1–10 M−1 s−1)29. A third idea is the cleavage of protein disulfide bonds by H2S. However, the intrinsic reactivity of HS− toward disulfides is even one order of magnitude lower than that of thiolates11. A fourth idea is that inorganic LMW polysulfides, in particular H2S2, could be responsible for driving protein persulfidation (P-SH + H2S2 → P-SSH + H2S). This reaction is known to be efficient and has been exploited to persulfidate proteins in vitro12. However, it remains unclear to which extent H2S2 is formed inside cells and whether it contributes to protein persulfidation. In this paper, we identified an enzymatic pathway that contributes to overall intracellular protein persulfidation. We found that the sulfur transferase MPST is highly efficient in persulfidating diverse proteins, both in vitro and inside cells. To investigate the mechanism by which MPST mediates protein persulfidation, we primarily used the model target protein roGFP2. Like the physiological MPST target protein Trx1, roGFP2 has two vicinal thiols, which means that persulfidation triggers subsequent disulfide bond formation (roGFP2(-SH)(-SSH) → roGFP2(S-S) + H2S). Using single Cys mutants of roGFP2 we directly detected MPST-mediated roGFP2 persulfidation by mass spectrometry (Fig. 5b and Extended Data Fig. 5b), confirming that MPST-mediated roGFP2 oxidation is indeed due to sulfur transfer (MPST-SSH + roGFP2(-SH)2 → MPST-SH + roGFP2(-SH)(-SSH) → MPST-SH + roGFP2(S-S) + H2S) (Fig. 6e) and not due to disulfide bond exchange (MPST-SSH + roGFP2(-SH)2 → MPST-S-S-roGFP2(-SH) + H2S → MPST-SH + roGFP2(S-S) + H2S). Previously, it has been suggested that MPST is capable of generating inorganic polysulfides, including H2S2 (ref. 22). We therefore asked whether the observed sulfur transfer from MPST to roGFP2 is indeed a direct one or whether it may be mediated through a diffusible LMW inorganic polysulfide. We found that MPST-SSH, in the absence of LMW products, is highly efficient in persulfidating roGFP2, while LMW products, in the absence of MPST-SSH, were barely able to oxidize roGFP2. This outcome was not influenced by the presence of H2S, suggesting that H2S is not an efficient sulfur acceptor for MPST (MPST-SSH + H2S → MPST-SH + H2S2). Together, these results confirmed that the MPST-bound persulfide is capable of direct sulfur transfer to protein thiols (Fig. 6f). This finding is in line with previous structural and mechanistic insights: The MPST active site allows an attack on the outer sulfur atom while shielding the inner sulfur atom of the enzyme-bound persulfide against nucleophilic attack16. This explains why MPST-SSH transpersulfidates other thiols while LMW persulfides (GSSH, Cys-SSH) do not. Despite the preference of MPST for protein substrates, a large excess of GSH can outcompete roGFP2 oxidation (MPST-SSH + GSH + GSH → MPST-SH + GSSH + GSH → MPST-SH + GSSG + H2S) (Fig. 6g). Inhibition of roGFP2 oxidation by a large excess of GSH has also been observed for MPSTs from Arabidopsis thaliana30. This raises the question whether GSH can be expected to inhibit MPST-mediated protein persulfidation in the cellular context. Although GSH is the most abundant LMW thiol in eukaryotic cells (roughly 2–10 mM), the pool of accessible protein thiols seems to be at least as large as the GSH pool. The protein thiol pool has been estimated to constitute up to $70\%$ of cellular thiol content27 and to be roughly 25-fold more concentrated than GSH in mitochondria28. A direct competition experiment showed that an equimolar concentration of GSH does not affect MPST-mediated protein persulfidation and that a moderate (fivefold) molar excess of GSH affected protein persulfidation only slightly (Fig. 5e). Thus, it seems that MPST-mediated sulfur transfer to other proteins can take place under typical intracellular conditions, that is, even in the presence of millimolar GSH concentrations. We started out by investigating an MPST-roGFP2 fusion protein because we initially assumed that enforced proximity is needed to allow for efficient sulfur transfer between the two proteins. This expectation was based on previous experience with other roGFP2 fusion proteins. In particular, close proximity enables the function of H2O2 probes in which roGFP2 is fused to and oxidized by a thiol peroxidase. For example, the fusion between roGFP2 and the thiol peroxidase Orp1 responds much faster to H2O2 than the corresponding equimolar mixture of the individual protein domains31. Moreover, inside cells the sulfenic acid and disulfide intermediates formed on the thiol peroxidase are likely to react with other thiols (for example, GSH) if the target protein (roGFP2) is not kept in close proximity. In contrast to thiol peroxidases, we found MPST to be efficient in oxidizing roGFP2 regardless of being fused, even when both proteins were coexpressed as independent proteins in the yeast cytosol. This suggests that inside the cell the MPST-bound persulfide is not rapidly intercepted by LMW thiols, but long-lived enough to be transferred to other proteins. This is also in line with our observation that proteins are generally better MPST substrates than LMW thiols. In the MPST crystal structure32, the active site persulfide appears to be relatively inaccessible. This may explain why it is only moderately reactive toward GSH and other LMW substrates, with the exception of sulfite. Notably, the active site is located at the interface between two rhodanese domains, potentially suggesting that this cleft can be opened up by protein–protein interactions, thus allowing for protein-to-protein transpersulfidation. When we depleted MPST in human cells overall intracellular protein persulfidation was clearly diminished, although not totally abolished. This indicates that MPST facilitates a substantial part of overall protein persulfidation, and also suggests that there are proteins persulfidated by other sulfur transferases or other mechanisms. It is conceivable that other rhodanese family sulfur transferases (such as thiosulfate sulfur transferase) can also act as protein persulfidases. Our findings do not exclude the possibility that a fraction of protein persulfidation is caused by nonenzymatic mechanisms, as discussed above. It may be speculated that nonselective nonenzymatic protein persulfidation serves to protect thiols against hyperoxidation, while enzymatic protein persulfidation is more selective and serves to adapt protein functions. Using a proteomics approach, we identified 64 proteins whose persulfidation levels were clearly decreased on MPST depletion. All of them are predicted to be located in the (nucleo)cytoplasm or in the mitochondria, conforming to the known intracellular distribution of MPST. Notably, more than half of the identified proteins have a known nuclear localization. The only seeming exceptions are calnexin and ERp57 (PDIA3), which are best known as ER-resident proteins. However, the transmembrane protein calnexin has a cysteine-containing cytoplasmic tail33 and ERp57 has previously been detected outside the ER34, thus potentially explaining their identification as MPST target proteins. Notably, three quarters of the proteins identified here as MPST targets were previously identified as persulfidated in a mouse ‘persulfidome’ study35. For example, the three proteins whose persulfidation was most strongly affected by MPST depletion, alpha hemoglobin (HBA2/HBA1), triosephosphate isomerase (TPI1) and heterogeneous nuclear ribonucleoprotein Q (hnRNP Q, SYNCRIP), were previously found to be persulfidated in mouse kidney, liver, skeletal muscle and heart35. In addition, TPI1 was found to be persulfidated in HEK293 cells36, erythrocytes1 and A549 cells37. Across a broad range of organisms, MPST knockouts have been found to be more sensitive to oxidative stress and/or to exhibit higher levels of endogenous oxidant levels16. We therefore wondered whether the MPST target proteins identified here may have roles in stress adaptation. Indeed, for many of them a connection to oxidant or electrophile stress adaptation has been reported or suggested. For example, alpha hemoglobin has been found to be upregulated in nonerythrocytes under oxidative stress conditions and to have a cytoprotective function38,39. TPI has been observed to undergo thiol redox modifications during stress responses40–42, potentially indicating a role in stress adaptation. hnRNP Q has been reported to regulate NADPH oxidase 2 expression in macrophages43. Glutathione S-transferase π1 (GSTP1), also previously observed to be persulfidated35,36, may depend on persulfidation for some of its detoxifying functions44. It is also interesting to note that four proteins identified here (ERp57, tubulin beta-3, nucleolin and hnRNP K), were reported to form a complex whose upregulation was associated with increased chemoresistance45. Interactome analysis further suggests that MPST has a preference to persulfidate hnRNPs and proteins involved in proteostasis, including various heat shock proteins. Cysteine residues of hnRNPs are often found oxidized46 and are known to be susceptible to electrophile adduction, especially within RNA recognition motifs47. Specifically, the thiol redox state of hnRNP K has been reported to modulate the heat shock response48, supporting the idea that hnRNP thiol modifications can play adaptive regulatory roles. Another cluster of MPST target proteins is organized around vimentin. Vimentin is sensitive to electrophiles and oxidants and its redox state reorganizes the vimentin network in response to stresses49. In conclusion, many proteins we identified as MPST target proteins appear to be regulated by thiol modifications and in turn regulate processes that adapt cells to stressful conditions. Thus, it is plausible that persulfidation of these proteins by MPST contributes to cytoprotection. Therefore, the recognition of MPST as a protein persulfidase may help to explain the observed phenotypes of MPST deficiency, namely diminished cytoprotection. ## Reagents All reagents used in this study are listed in Supplementary Table 1. ## Expression constructs Yeast expression constructs are based on a sequence encoding codon-optimized roGFP2 and a 5xGGSGG linker repeat52. The sequence encoding of Saccharomyces cerevisiae Tum1 was obtained by PCR-amplification from yeast genomic DNA. The coding sequence for the fusion protein was assembled in the p415TEF vector, using the NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs). For targeting to the mitochondrial matrix, constructs additionally include the N-terminal mitochondrial targeting sequence from F0-ATPase subunit 9 (Su9) from Neurospora crassa (p415TEF Su9roGFP2). Cysteines of Tum1 (C259) and roGFP2 (C148 and C205) were changed to serines using the Quikchange Site-Directed Mutagenesis Kit (Agilent). The DAAO coding sequence was PCR-amplified from pC1-CMV-DAAO-NES (ref. 53), kindly provided by V. Belousov. All plasmids and primers used in this study are listed in Supplementary Tables 2 and 3, respectively. The Tum1-linker-roGFP2 construct was recloned into the pET-His-SUMO vector (Thermo Fisher Scientific) using the NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs). ## Recombinant protein expression and purification Expression and purification of recombinant roGFP2-His and H. sapiens Trx1 was performed as described previously54,55. His-SUMO-Tum1-roGFP2 and His-SUMO-Tum1 were expressed in Escherichia coli BL21(DE3). Luria-Bertani (LB) medium was inoculated with a single colony, incubated overnight and then diluted 1:100 in terrific broth. The culture was grown at 37 °C with shaking, and on reaching an optical density (OD) absorbance of A600nm = 0.6, expression was induced by adding 1 mM isopropyl-β-d-thiogalactopyranoside (IPTG). Following overnight incubation at room temperature, cells were collected by centrifugation at 4,000g for 15 min at 4 °C. After one freeze–thaw cycle, the cells were resuspended in B-PER Bacterial Protein Extraction Reagent (Thermo Fisher Scientific) supplemented with 0.5 mM dithiothreitol (DTT), 5 mM imidazole, EDTA-free Protease Inhibitor Cocktail (cOmplete, Roche) and Benzonase Nuclease (Merck Millipore). The lysate was clarified by centrifugation at 18,000g for 45 min at 4 °C, filtered through a 0.45 mm filter and added to Ni2+-Sepharose beads equilibrated with 50 mM Tris, pH 8, 500 mM NaCl, 5 mM imidazole and 0.5 mM DTT. The lysate-bead suspension was rotated for 30 min at 4 °C and subsequently packed into a 5-ml Pierce centrifuge column (Thermo Fisher Scientific). After washing with 50 mM Tris, pH 8, 500 mM NaCl, 5 mM imidazole and 0.5 mM DTT, the protein was eluted by increasing the imidazole concentration in 50 mM steps. Imidazole was removed by overnight dialysis in 50 mM Tris, pH 8, 200 mM NaCl, 0.5 mM DTT at 4 °C and the His-SUMO tag was cleaved by digestion with 2 U per 100 µg of PreScission Protease, overnight at 4 °C. The His-SUMO tag and PreScission Protease were removed by passing the reaction mixture through a Ni2+-Sepharose column and a GST-sepharose column, preequilibrated with the same buffer. The protein was finally purified by size-exclusion chromatography using a Superdex 75 Increase $\frac{10}{300}$ GL column equilibrated with 20 mM Tris, pH 8, 150 mM NaCl, 1 mM EDTA and 0.5 mM DTT, using the ÄKTA Pure fast protein liquid chromatography (LC) system (Cytiva). Finally, the purified protein was flash-frozen in liquid nitrogen and stored at −80 °C. SBP-His-SUMO-Tum1 was expressed in E. coli BL21(DE3). LB medium was inoculated with a single colony, incubated overnight and then diluted 1:100 in terrific broth. The culture was grown at 37 °C with shaking, and on reaching an OD absorbance of A600nm = 0.7 expression was induced by adding 0.5 mM IPTG. Following overnight incubation at 37 °C, cells were gathered by centrifugation at 4,000g for 15 min at 4 °C. After one freeze–thaw cycle, the cells were resuspended in B-PER Bacterial Protein Extraction Reagent (Thermo Fisher Scientific) supplemented with 0.5 mM DTT, EDTA-free Protease Inhibitor Cocktail (cOmplete, Roche) and Benzonase Nuclease (Merck Millipore). The lysate was clarified by centrifugation at 18,000g for 45 min at 4 °C, filtered through a 0.45 mm filter and streptavidin sepharose high performance beads (SA beads; GE Healthcare) were added for affinity purification of protein. After 1 h incubation of the lysate-bead suspension at 4 °C, SA beads were washed three times with the wash buffer (50 mM Tris, pH 8, 300 mM NaCl, 1 mM EDTA and 0.5 mM DTT). The protein was eluted after incubation of SA beads in wash buffer containing 4 mM biotin for 20 min at 4 °C. Biotin was removed by overnight dialysis in 50 mM Tris, pH 8, 250 mM NaCl and 0.5 mM DTT at 4 °C. The purified protein was flash-frozen in liquid nitrogen and stored at −80 °C. S. cerevisiae Uba4 was expressed from the pRARE plasmid in E. coli BL21 (DE3) grown in LB media at 18 °C and induced overnight with 0.5 M IPTG. The bacterial pellet was resuspended in lysis buffer (30 mM HEPES pH 8.0; 300 mM NaCl; 20 mM imidazole; $0.15\%$ TX-100; 10 mM MgSO4; 1 mM 2-mercaptoethanol; 10 mg ml−1 DNase; 1 mg ml−1 lysozyme; $10\%$ glycerol and a cocktail of protease inhibitors) and lysed to homogeneity using a high-pressure homogenizer (Emulsiflex C3, Avestin). The protein was purified with Ni-NTA agarose (Qiagen) under standard conditions. The tag was cleaved with tobacco etch virus protease and removed with a second Ni-NTA purification step. Subsequently, the protein was purified by size-exclusion chromatography on a HiLoad $\frac{26}{600}$ Superdex 200 prep grade column (Cytiva) using ÄKTA start (Cytiva). Purified proteins were stored at −80 °C in a storage buffer (20 mM HEPES pH 8.0; 150 mM NaCl and 1 mM DTT). ## Reduction and desalting of purified proteins Unless specified otherwise, purified proteins were reduced with freshly prepared DTT (10 mM) for 30 min at 4 °C. Excess DTT was removed by dual desalting with 0.5 ml Zeba Spin Desalting Columns (Thermo Fisher Scientific), preequilibrated with N2-purged assay buffer. ## Measurement of the roGFP2 redox state Measurements were carried out in N2-purged 100 mM sodium phosphate buffer, pH 7.4, containing 100 μM diethylenetriaminepentaacetic acid, at 30 °C and $0.5\%$ O2 in a CLARIOstar plate reader (BMG Labtech), using the top optics function (excitation at 405 and 485 nm, emission at 520 nm). Unless specified otherwise, prereduced roGFP2-containing proteins were dispensed at 100 nM final concentration and in 200 μl of final volume per well, in a black body, clear bottom Greiner F-bottom 96-well plate. The $\frac{405}{520}$ and $\frac{488}{520}$ fluorescence intensities were measured for 4 min before the addition of the test compound or corresponding vehicle. Fully oxidizing and fully reducing control conditions were applied at the end of each experiment, by adding 200 μM diamide and then 10 mM DTT. Competition assays were performed by adding freshly prepared compounds at least 15 min before the addition of 3-mercaptopyruvate. In the case of competing proteins, S. cerevisiae Uba4 and H. sapiens Trx1 were prereduced with DTT and desalted, as described above. Fatty acid free BSA was reduced as described previously56. In brief, 500 μM of freshly dissolved BSA was prereduced overnight with 50 mM beta-mercaptoethanol, at 4 °C. Excess beta-mercaptoethanol was removed by desalting three times with Zeba Spin Desalting Columns. ## Measurement of Tum1-dependent sulfur transfer to roGFP2 or SSP4 Here, 250 μl of streptavidin (SA) sepharose beads ($50\%$ slurry in storage buffer, 50 mM Tris-HCl, pH 8) were washed three times with 100 mM sodium phosphate buffer, pH 7.4, containing 100 μM diethylenetriaminepentaacetic acid. In all further steps the same phosphate buffer was used. All centrifugation steps were performed at 20g and 4 °C for 3 min. After the final washing step, SA beads were resuspended in 250 μl of phosphate buffer to obtain a $50\%$ slurry. SA beads were then incubated with 125 μl of protein solution (13 μM SBP-His-SUMO-Tum1) for 1 h at 4 °C with gentle rotation. Beads were then incubated with DTT (10 mM final concentration) for 30 min at 4 °C. Then, beads were washed five times with 10 ml of phosphate buffer to remove DTT. After the final washing step, protein-saturated beads were resuspended in 250 μl of N2-purged phosphate buffer. For all further steps N2-purged phosphate buffer was used. For each reaction 25 μl of SA beads suspension was mixed with the reagents (as indicated in the corresponding figure legends) in a total volume of 100 μl. The samples were incubated for 5 min with gentle rotation at room temperature. Then the beads were spun down and 60 μl of supernatant was collected and further diluted with another 60 μl of buffer. The remaining beads were washed with 1 ml of phosphate buffer and resuspended again in 120 μl of buffer. Then, 10 μl of the resulting beads suspension or 10 μl of supernatant were added to the respective well in a 96-well plate (black body, clear bottom Greiner F-bottom 96) containing 420 nM roGFP2 or 100 μM SSP4 in 90 μl of buffer. Before use, roGFP2 was reduced and desalted as described above. For the competition experiment additional GSH was present in the well as indicated in the corresponding figure legend. roGFP2 and SSP4 fluorescence was recorded at $0.1\%$ O2 and 30 °C with a CLARIOstar plate reader as described above. Following the reaction, diamide (200 μM) and DTT (10 mM) were added to determine the fully oxidized and reduced state. ## Electrode-based H2S measurements Electrode measurements were carried out in a 100 mM sodium phosphate buffer, pH 7.4, at room temperature. The DTT-reduced and desalted Tum1-roGFP2 and Tum1(C259S)-roGFP2 fusions protein (2.5 μM) were mixed with 50 μM 3MP in a final volume of 1 ml (12-well Falcon plate, Corning). Detection of H2S was performed using the WPI Four-Channel Free Radical Analyzer with Lab-Trax $\frac{4}{16}$ and an ISO-H2S-2 electrode. The concentration of released H2S was calculated from a previously determined Na2S standard curve. ## Thiocyanate measurement Thiocyanate quantitation was based on the cyanolysis method of Wood57. The assay was carried out in 100 mM sodium phosphate, pH 7.4. Then 5 μM of DTT-reduced and doubly desalted Tum1-roGFP2 and Tum1(C259S)-roGFP2 proteins were mixed with 3,500 μM potassium cyanate. The mixture was preincubated at 30 °C for 5 min, and then incubated with 50 μM 3MP (final volume: 150 μl) at 30 °C for 10 min. The reaction was stopped by the addition of 150 μl of Goldstein’s reagent (61.88 mM ferric nitrate nonahydrate, $18.375\%$ HNO3). The mixture was then centrifuged at 16,100g for 2 min and the supernatant transferred to clear 96-well plates. The product [Fe(SCN)(H2O)5]2+ was detected by measuring absorbance at 460 nm using a microplate reader (FLUOstar Omega, BMG Labtech). Thiocyanate levels were determined by use of a potassium thiocyanate standard curve. ## Yeast knockout library and transformation procedure The BY4742 strain (MATα his3Δ1 leu2Δ0 lys2Δ0 MET15 ura3Δ0) was used in all experiments. Single-gene deletion strains were from the Euroscarf knockout library, generated using the KANMX marker58. Yeast strains BY4742 and Δtum1 were transformed with p415TEF or p416TEF plasmids with standard yeast transformation methods59. Transformants were selected at 30 °C on synthetically defined -Leu (for p415TEF) or -Leu-Ura (for p415TEF and p416TEF) (Formedium) agar plates, containing 1× yeast nitrogen base (BD Difco) and $2\%$ glucose (Sigma-Aldrich). ## Measurement of the roGFP2 redox state in yeast cells Yeast strains were grown in synthetically defined medium -Leu (for selection of p415TEF-based plasmids) or in synthetically defined medium -Leu -Ura (for selection of p415TEF and p416TEF-based plasmids) (Formedium), containing 1× yeast nitrogen base and $2\%$ glucose. A single colony was used for inoculation and grown for roughly 20 h in 5 ml of growth medium at 30 °C under constant shaking. The following day, the culture was diluted to an OD of 0.25 and grown to OD 1.5 at 30 °C under constant shaking. Cells were collected by centrifugation at 4,000g for 10 min at 25 °C, washed with assay buffer (100 mM MES/Tris buffer, pH 6, $2\%$ glucose) and then aliquoted in assay buffer at OD=4 (0.2 ml per well) in a black body, clear bottom Greiner F-bottom 96-well plate. Cells were then sedimented by centrifugation at 25g for 3 min at 25 °C. RoGFP2 fluorescence (excitation at 405 and 485 nm, emission at 520 nm) was measured with either a CLARIOstar or PHERAstar plate reader (BMG Labtech), at 30 °C, using the bottom optics option. The $\frac{405}{520}$ and $\frac{488}{520}$ fluorescence intensities were measured for 15 min before the addition of the testing compound or its corresponding vehicle. Yeast cells expressing an empty p415TEF plasmid were used as a background control to subtract autofluorescence. Fully reduced and fully oxidized controls were generated by adding 25 mM DTT and 20 mM diamide, respectively. ## Detection of persulfides by whole protein mass spectrometry 10 µM of reduced Tum1 was mixed with 10 µM of reduced Trx1(C35S), roGFP2, roGFP2(C148S) or roGFP2(C205S) in 100 mM sodium phosphate buffer, pH 7.4. The mixture was incubated at 30 °C for 5 min, before the addition of 60 µM 3MP and further incubation at 30 °C for 5 min. Resulting protein persulfides were alkylated by adding 1 mM monobromobimane (mBBr) (stock solution: 10 mM mBBr in $10\%$ DMSO and 50 mM ammonium carbonate, pH 7.4) and incubating at room temperature and in the dark for 30 min. Excess mBBr was removed by twofold desalting with Zeba Spin 0.5 ml columns preequilibrated with 50 mM ammonium carbonate, pH 7.4. Samples were injected into a liquid chromatograph equipped with a POROS 10R1 column (Applied Biosystems) using $0.3\%$ formic acid as the mobile phase. After 3 min, the mobile phase was switched to $50\%$ of $0.3\%$ formic acid and $50\%$ of a $80\%$ isopropanol/$10\%$ acetonitrile/$0.3\%$ formic acid mixture and held for 15 min. Mass spectra were obtained with a maXis electrospray ionization–time of flight mass spectrometer (Bruker Daltonik). Data were analyzed with Data Analysis v.4.2 (Bruker) and ESI Compass v.1.3. ## Depletion and overexpression of MPST MPST was depleted in HEK293 MSR cells (GripTite, Thermo Fisher Scientific) using ON-TARGET plus small-interfering RNA SMARTpool (Dharmacon). The ON-TARGET plus nontargeting pool (Dharmacon D-001810-10-05) was used as a control. The siRNAs were transfected using DharmaFECT1 reagent (Dharmacon) as per the manufacturer’s instructions. For overexpression, HEK293 MSR cells were transfected with plasmids encoding roGFP2, MPST-roGFP2 or MPST(C248S)-roGFP2 using Lipofectamine 2000 (Thermo Fisher Scientific) following the manufacturer’s instructions. Protein expression levels were evaluated by immunoblotting, using anti-MPST (sc-374326) and anti-GFP (sc-9996) antibodies, both at 1:1,000 dilution. ## Fluorescent labeling of protein persulfides in mammalian cells The dimedone switch method1 was used for relative quantitation of protein persulfides in mammalian cells with slight modifications. HEK293 MSR cells were grown to 80–$90\%$ confluency. Cells were lysed using cold HEN lysis buffer (50 mM HEPES, 1 mM EDTA, 0.1 mM neocuproine, $1\%$ IGEPAL and $2\%$ SDS; adjusted to pH 7.4) supplemented with protease inhibitor and 5 mM 4-chloro-7-nitrobenzofurazan (NBF-Cl). The lysate was then incubated at 37 °C for 30 min with occasional vortexing. The alkylated protein sample was precipitated with methanol/chloroform, as previously described1, and the resulting protein pellet was redissolved in 50 mM HEPES (pH 7.4) with SDS ($2\%$ final concentration). After adjusting the protein concentration to 3 mg ml−1, protein lysates were incubated with 50 μM Cy5-conjugated 4-(3-azidopropyl)cyclohexane-1,3-dione (DAz-2/Cy5) and subjected to copper(I)-catalyzed alkyne-azide cycloaddition at 37 °C for 30 min in the dark. The protein sample was again precipitated with methanol/chloroform, and the resulting pellet redissolved in HEPES with SDS ($2\%$ final concentration). The sample was then mixed with SDS loading buffer, boiled at 95 °C for 5 min, and loaded on a $12\%$ acrylamide/bis-acrylamide gel (50 μg protein per lane). In-gel Cy5 fluorescence was recorded at 700 nm (LI-COR Odyssey Fc), followed by Coomassie staining to account for total protein content. Cy5 fluorescence and Coomasie staining intensity was quantified with ImageJ v.1.53p. ## Affinity purification of persulfidated proteins from mammalian cells Affinity enrichment of persulfidated proteins from mammalian cells was based on the dimedone switch method1, with slight modifications. HEK293 MSR cells (MPST or mock depleted) were grown to 80–$90\%$ confluency in a 10 cm dish. Cells were lysed using cold HEN lysis buffer containing 5 mM NBF-Cl and the lysate was incubated for 30 min at 37 °C. Following methanol/chloroform precipitation, the protein pellet was dissolved in 50 mM HEPES buffer (pH 7.4) supplemented with $0.1\%$ SDS. Preclearing of the protein solution (to remove endogenous biotinylated proteins) was performed by incubation with SA beads for 1 h. Following methanol/chloroform precipitation, the resulting protein pellet was redissolved in 50 mM HEPES buffer containing $2\%$ SDS. The protein solution was then incubated with or without 50 μM DCP-Bio1 at 37 °C for 1 h, followed by methanol/chloroform precipitation. The protein pellet was redissolved in 50 mM HEPES buffer containing $0.1\%$ SDS and the solution was incubated with SA beads at 4 °C overnight with agitation. The beads were washed three times with 50 mM HEPES buffer containing $0.001\%$ Tween-20 and three times with plain 50 mM HEPES buffer. After washing, persulfidated proteins were eluted by incubation with 4 mM biotin for 30 min. The eluates were run on a SDS–PAGE gel and stained with Coomassie. Samples were then subjected to LC–tandem mass spectrometry (LC–MS/MS) analysis (below). ## LC–MS/MS analysis of affinity enriched persulfidated proteins Following SDS–PAGE, four gel pieces per lane were manually excised. The gel pieces were washed once with 60 µl of 1:1 (v/v) 50 mM triethylammonium bicarbonate buffer (TEAB) and acetonitrile (ACN), pH 8.5 for 10 min and shrunk three times for 10 min each in 60 µl of ACN and washed in 60 µl of 50 mM TEAB, pH 8.5. Following reduction of proteins with 10 mM DTT in 100 mM TEAB at 57 °C for 30 min and dehydration of gel pieces, proteins were alkylated with 10 mM iodoacetamide in 100 mM TEAB at 25 °C for 20 min in the dark. Before protein digestion, gel pieces were washed with 60 µl of 100 mM TEAB and shrunk twice for 10 min in 60 µl of ACN. A total of 30 µl of 8 ng µl−1 in 50 mM TEAB trypsin solution (sequencing grade, Thermo Fisher Scientific) was added to the dry gel pieces and incubated 4 h at 37 °C. The reaction was quenched by addition of 20 µl of $0.1\%$ trifluoroacetic acid (TFA). The resulting peptides were extracted once for 30 min with 30 µl 1:1 (v/v) $0.1\%$ TFA and ACN, followed by gel dehydration with 20 µl ACN for 20 min, and washed with 30 µl of 100 mM TEAB for another 20 min. Finally, the gel was shrunk twice with 20 µl of ACN for 20 min. The supernatant from each extraction step was collected, concentrated in a vacuum centrifuge and dissolved in 15 µl $0.1\%$ TFA. Nanoflow LC-MS2 analysis was carried out using an Ultimate 3000 LC system coupled to an Orbitrap QE HF (Thermo Fisher). An in-house packed analytical column (75 µm × 200 mm, 1.9 µm ReprosilPur-AQ 120 C18 material; Dr. Maisch) was used. Mobile phase solutions were prepared as follows, solvent A: $0.1\%$ formic acid/$1\%$ acetonitrile, solvent B: $0.1\%$ formic acid, $89.9\%$ acetonitrile. Peptides were separated in a 25 min linear gradient from 3 to $23\%$ B over 21 min and then to $38\%$ B over 4 min, followed by washout with $95\%$ B. The mass spectrometer was operated in data-dependent acquisition mode, automatically switching between MS and MS2. MS spectra (m/z 400–1,600) were acquired in the Orbitrap at 60,000 (m/z 400) resolution and MS2 spectra were generated for up to 15 precursors with normalized collision energy of 27 and isolation width of 1.4 m/z. The MS/MS spectra were searched against the Swiss-Prot H. sapiens protein database modified June 2020 and containing 20,531 sequences (UP000005640), and a customized contaminant database using Proteome Discoverer v.2.5 with Sequest HT (Thermo Fisher Scientific). A fragment ion mass tolerance was set to 0.2 Da and a parent ion mass tolerance to 10 ppm. Trypsin was specified as the enzyme. Following modifications of peptides were allowed: carbamidomethylation (cysteine), oxidation (methionine), deamidation (asparagine and glutamine), acetylation (protein N terminus), methionine loss (protein N terminus), NBF (163.0012, lysine and cysteine), DCP-Bio1 (394.1557, cysteine) and hydrolyzed DCP-Bio1 (168.0786, cysteine). Peptide quantification was done using a precursor ion quantifier node with summed abundances method set for protein abundance calculation. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD038309. ## Identification of MPST target proteins by LC–MS/MS data analysis Common contaminants, proteins identified with less than two peptides, proteins not being a master protein within a protein group and proteins not containing cysteine residues were filtered out. This procedure generated a list of 322 quantified proteins. We only considered proteins that were at least twofold more abundant in the DCP-Bio1 treated sample compared to the nontreated control or were quantified in the DCP-Bio1 treated sample but did not reach the quantification threshold in the nontreated control. For the obtained 300 proteins, the ratio of protein abundance between MPST-depleted and wild-type samples was calculated and log2 transformed. Proteins depleted more than twofold in MPST-depleted cells were considered candidate MPST target proteins. The 64 most depleted candidate target proteins were queried against the STRING database50 considering only protein interactions for which there is experimental evidence with at least medium confidence. The network was edited in Cytoscape51. Protein subcellular localization was downloaded from ProteinAtlas60. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41589-022-01244-8. ## Supplementary information Supplementary InformationSupplementary Tables 1–3. Reporting Summary The online version contains supplementary material available at 10.1038/s41589-022-01244-8. ## Extended data Extended Data Fig. 1Comparison of sulfur acceptors for MPST.(a) Degree of oxidation (OxD) of Tum1-roGFP2 in response to increasing glutathione disulfide (left panel) or hydrogen peroxide concentrations (right panel). ( b) Concentrations of H2S as measured 5 min after addition of 3MP. Data are presented as mean and individual values ($$n = 3$$ independent experiments) +/- SEM. ( c) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of potassium cyanide (KCN). ( d) Catalytically active Tum1-roGFP2 converts cyanide to thiocyanate (SCN), in a 3MP- dependent manner. Data are presented as mean and individual values ($$n = 3$$ independent experiments) +/- SEM. ( e) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of sulfite (left panel), l-cysteine (center panel), and glutathione (right panel). ( f) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of S. cerevisiae Uba4 (left panel), H. sapiens thioredoxin 1 (Trx1) (center panel), and bovine serum albumin (right panel). All data are based on $$n = 3$$ independent experiments. Source data Extended Data Fig. 2Competition between roGFP2 and other sulfur acceptors.(a) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of potassium cyanide (KCN). ( b) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of sulfite. ( c) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of L-cysteine. ( d) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of glutathione. ( e) Lack of reduction of oxidized Tum1-roGFP2 upon the addition of 150 μM glutathione. ( f) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of S. cerevisiae Uba4. ( g) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of H. sapiens thioredoxin 1 (Trx1). ( h) Competition of 3MP-dependent Tum1-roGFP2 oxidation (OxD) by increasing concentrations of bovine serum albumin. All data are based on $$n = 3$$ independent experiments, except (e) ($$n = 1$$). Source data Extended Data Fig. 3Response of cytosolic probes to 3MP and l-Cys.(a) Response of roGFP2 (left panels), Tum1-roGFP2 (center panels) and Tum1(C259S)-roGFP2 (right panels), expressed in the cytosol (ct), to exogenously added 3MP. The lower panels show the same curves on a smaller y axis scale. ( b) Response of roGFP2 (left panels), Tum1-roGFP2 (center panels) and Tum1(C259S)-roGFP2 (right panels), expressed in the cytosol (ct), to exogenously added l-Cys. The lower panels show the same curves on a smaller y axis scale. All data are based on $$n = 3$$ independent experiments. Source data Extended Data Fig. 4Response of mitochondrial Tum1-roGFP2 to d-Cys.(a) Mitochondrial Tum1-roGFP2 does not respond to exogenously added d-cysteine (d-Cys) when expressed in cells lacking D-amino acid oxidase. Data are based on $$n = 3$$ independent experiments. Source data Extended Data Fig. 5Tum1 oxidizes roGFP2 by transpersulfidation, not disulfide exchange.(a-b) Mass spectra of roGFP2 (a) and roGFP2(C148S) (b) exposed to Tum1 in the presence (red curves) or absence (black curves) of 3MP. $$n = 1$$ experiment. ( c-d) Mass spectra of roGFP2(C148S) (c) and roGFP2(C205S) (d) in the m/$z = 60500$-62000 region. The lack of signals indicates the absence of mixed disulfide conjugates between Tum1 and roGFP2. $$n = 1$$ experiment. Source data Extended Data Fig. 6MPST-bound sulfane sulfur is not released into the supernatant.(a-d) Reactivity of SSP4 towards immobilized Tum1-SSH (beads, red lines), or towards the corresponding supernatant (SN, black lines) of the reaction between immobilized Tum1 and 3MP, in the absence (a), or presence of Na2S (10 µM) (b), in the presence of GSH (100 µM) (c), and in the presence of both GSH (100 µM) and Na2S (10 µM) (d). Control (ctrl) experiments were performed in absence of 3MP. Data are based on $$n = 2$$ independent experiments. ( e) Oxidation of roGFP2 by immobilized Tum1-SSH (beads, red line), or by the corresponding supernatant (SN, black line) of the reaction of immobilized Tum1 and 3MP, performed in the presence of Na2S (10 µM). Data are based on $$n = 2$$ independent experiments. Source data Extended Data Fig. 7Depletion and overexpression of MPST.(a) Loading control for the experiment shown in main Fig. 6a. ( b) Depletion of MPST in HEK293 MSR cells, as demonstrated by anti-MPST immunoblotting. ( c) Loading control for the experiment shown in main Fig. 6b. ( d) Overexpression of roGFP2 (left), MPST-roGFP2 (center), and MPST(C259S)-roGFP2 (MPSTmut-roGFP2, right) in HEK293 MSR cells, as demonstrated by anti-GFP immunoblotting. All data are based on n ≥ 3 replicates. Source data is available for this paper at 10.1038/s41589-022-01244-8. ## Source data Source Data Fig. 1roGFP2 degree of oxidation (OxD) values (Fig. 1a,b), Δintensity values (Fig. 1c), IC50 values (Fig. 1d,e). Source Data Fig. 2roGFP2 OxD values (Fig. 2a,b). Source Data Fig. 3roGFP2 OxD values (Fig. 3a,b). Source Data Fig. 4roGFP2 OxD values (Fig. 4a–d). Source Data Fig. 5Mass Spectrometry normalized peak intensity (Fig. 5a,b), roGFP2 OxD values (Fig. 5c–e). Source Data Fig. 6Relative Cy5 fluorescence (Fig. 6a,b), list of persulfidated proteins identified on mass spectrometry, along with their abundance in wild-type or MPST-depleted cells (Fig. 6c). Source Data Extended Data Fig. 1roGFP2 OxD values (Extended Data Fig. 1a), calculated H2S concentration values (Extended Data Fig. 1b), roGFP2 endpoint OxD values (Extended Data Fig. 1c,e,f) and calculated KCN concentration values (Extended Data Fig. 1d). Source Data Extended Data Fig. 2roGFP2 OxD values (Extended Data Fig. 2a–h). Source Data Extended Data Fig. 3roGFP2 OxD values (Extended Data Fig. 3a,b). Source Data Extended Data Fig. 4roGFP2 OxD values (Extended Data Fig. 4a). Source Data Extended Data Fig. 5Mass spectrometry normalized peak intensity (Extended Data Fig. 5a–d). Source Data Extended Data Fig. 6roGFP2 OxD values (Extended Data Fig. 6a–e). Source Data Extended Data Fig. 7Uncropped western blots. ## Peer review information Nature Chemical Biology thanks Moran Benhar, Michael Pluth and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ## References 1. Zivanovic J. Selective persulfide detection reveals evolutionarily conserved antiaging effects of s-sulfhydration. Cell Metab. (2019.0) 30 1152-1170.e13. DOI: 10.1016/j.cmet.2019.10.007 2. Gao X-H. Quantitative H2S-mediated protein sulfhydration reveals metabolic reprogramming during the integrated stress response. eLife (2015.0) 4 e10067. DOI: 10.7554/eLife.10067 3. Dóka É. A novel persulfide detection method reveals protein persulfide- and polysulfide-reducing functions of thioredoxin and glutathione systems. Sci. Adv. (2016.0) 2 e1500968. DOI: 10.1126/sciadv.1500968 4. Wedmann R. Improved tag-switch method reveals that thioredoxin acts as depersulfidase and controls the intracellular levels of protein persulfidation. Chem. Sci. (2016.0) 7 3414-3426. DOI: 10.1039/C5SC04818D 5. Xie L. Hydrogen sulfide induces Keap1 S-sulfhydration and suppresses diabetes-accelerated atherosclerosis via Nrf2 activation. Diabetes (2016.0) 65 3171-3184. DOI: 10.2337/db16-0020 6. Bestetti S. A persulfidation-based mechanism controls aquaporin-8 conductance. Sci. Adv. (2018.0) 4 eaar5770. DOI: 10.1126/sciadv.aar5770 7. 7.Cuevasanta, E. et al. Kinetics of formation and reactivity of the persulfide in the one-cysteine peroxiredoxin from Mycobacterium tuberculosis. J. Biol. Chem. 10.1074/jbc.RA119.008883 (2019). 8. Millikin R. The chemical biology of protein hydropersulfides: studies of a possible protective function of biological hydropersulfide generation. Free Radic. Biol. Med. (2016.0) 97 136-147. DOI: 10.1016/j.freeradbiomed.2016.05.013 9. Nagahara N, Nirasawa T, Yoshii T, Niimura Y. Is novel signal transducer sulfur oxide involved in the redox cycle of persulfide at the catalytic site cysteine in a stable reaction intermediate of mercaptopyruvate sulfurtransferase?. Antioxid. Redox Signal (2012.0) 16 747-753. DOI: 10.1089/ars.2011.4468 10. Filipovic MR, Zivanovic J, Alvarez B, Banerjee R. Chemical biology of H. Chem. Rev. (2018.0) 118 1253-1337. DOI: 10.1021/acs.chemrev.7b00205 11. Cuevasanta E. Reaction of hydrogen sulfide with disulfide and sulfenic acid to form the strongly nucleophilic persulfide. J. Biol. Chem. (2015.0) 290 26866-26880. DOI: 10.1074/jbc.M115.672816 12. Schilling D. Commonly used alkylating agents limit persulfide detection by converting protein persulfides into thioethers. Angew. Chem. Int. Ed. Engl. (2022.0) 61 e202203684. PMID: 35506673 13. Ida T. Reactive cysteine persulfides and S-polythiolation regulate oxidative stress and redox signaling. Proc. Natl Acad. Sci. USA (2014.0) 111 7606-7611. DOI: 10.1073/pnas.1321232111 14. Toohey JI, Cooper AJL. Thiosulfoxide (sulfane) sulfur: new chemistry and new regulatory roles in biology. Molecules (2014.0) 19 12789-12813. DOI: 10.3390/molecules190812789 15. Kumar R, Banerjee R. Regulation of the redox metabolome and thiol proteome by hydrogen sulfide. Crit. Rev. Biochem. Mol. Biol. (2021.0) 56 221-235. DOI: 10.1080/10409238.2021.1893641 16. Pedre B, Dick TP. 3-Mercaptopyruvate sulfurtransferase: an enzyme at the crossroads of sulfane sulfur trafficking. Biol. Chem. (2021.0) 402 223-237. DOI: 10.1515/hsz-2020-0249 17. Yadav PK, Yamada K, Chiku T, Koutmos M, Banerjee R. Structure and kinetic analysis of H2S production by human mercaptopyruvate sulfurtransferase. J. Biol. Chem. (2013.0) 288 20002-20013. DOI: 10.1074/jbc.M113.466177 18. Noma A, Sakaguchi Y, Suzuki T. Mechanistic characterization of the sulfur-relay system for eukaryotic 2-thiouridine biogenesis at tRNA wobble positions. Nucleic Acids Res. (2009.0) 37 1335-1352. DOI: 10.1093/nar/gkn1023 19. Jüdes A, Bruch A, Klassen R, Helm M, Schaffrath R. Sulfur transfer and activation by ubiquitin-like modifier system Uba4•Urm1 link protein urmylation and tRNA thiolation in yeast. Micro. Cell (2016.0) 3 554-564. DOI: 10.15698/mic2016.11.539 20. Shibuya N. 3-Mercaptopyruvate sulfurtransferase produces hydrogen sulfide and bound sulfane sulfur in the brain. Antioxid. Redox Signal. (2009.0) 11 703-714. DOI: 10.1089/ars.2008.2253 21. Shibuya N. A novel pathway for the production of hydrogen sulfide from D- cysteine in mammalian cells. Nat. Commun. (2013.0) 4 1-7. DOI: 10.1038/ncomms2371 22. Kimura Y. Identification of H2S3 and H2S produced by 3-mercaptopyruvate sulfurtransferase in the brain. Sci. Rep. (2015.0) 5 14774. DOI: 10.1038/srep14774 23. Kimura Y. 3-Mercaptopyruvate sulfurtransferase produces potential redox regulators cysteine- and glutathione-persulfide (Cys-SSH and GSSH) together with signaling molecules H. Sci. Rep. (2017.0) 7 10459. DOI: 10.1038/s41598-017-11004-7 24. Meyer AJ, Dick TP. Fluorescent protein-based redox probes. Antioxid. Redox Signal (2010.0) 13 621-650. DOI: 10.1089/ars.2009.2948 25. Vachek H, Wood JL. Purification and properties of mercaptopyruvate sulfur transferase of. Biochim. Biophys. Acta (1972.0) 258 133-146. DOI: 10.1016/0005-2744(72)90973-4 26. Masinas MPD, Usaj MM, Usaj M, Boone C, Andrews BJ. TheCellVision.org: a database for visualizing and mining high-content cell imaging projects. G3 Genes. Genome. Genet. (2020.0) 10 3969-3976 27. Hansen RE, Roth D, Winther JR. Quantifying the global cellular thiol-disulfide status. Proc. Natl Acad. Sci. USA (2009.0) 106 422-427. DOI: 10.1073/pnas.0812149106 28. Requejo R, Hurd TR, Costa NJ, Murphy MP. Cysteine residues exposed on protein surfaces are the dominant intramitochondrial thiol and may protect against oxidative damage. FEBS J. (2010.0) 277 1465-1480. DOI: 10.1111/j.1742-4658.2010.07576.x 29. Winterbourn CC. The biological chemistry of hydrogen peroxide. Meth. Enzymol. (2013.0) 528 3-25. DOI: 10.1016/B978-0-12-405881-1.00001-X 30. Moseler A, Dhalleine T, Rouhier N, Couturier J. J. Biol. Chem. (2021.0) 296 100429. DOI: 10.1016/j.jbc.2021.100429 31. Gutscher M. Proximity-based protein thiol oxidation by H. J. Biol. Chem. (2009.0) 284 31532-31540. DOI: 10.1074/jbc.M109.059246 32. Qiu R, Wang F, Liu M, Lou T, Ji C. Crystal structure of the Tum1 protein from the yeast. Protein Pept. Lett. (2012.0) 19 1139-1143. DOI: 10.2174/092986612803217060 33. Ou WJ, Bergeron JJ, Li Y, Kang CY, Thomas DY. Conformational changes induced in the endoplasmic reticulum luminal domain of calnexin by Mg-ATP and Ca2+. J. Biol. Chem. (1995.0) 270 18051-18059. DOI: 10.1074/jbc.270.30.18051 34. Chichiarelli S. ERp57/PDIA3: new insight. Cell. Mol. Biol. Lett. (2022.0) 27 12. DOI: 10.1186/s11658-022-00315-x 35. Bithi N. Dietary restriction transforms the mammalian protein persulfidome in a tissue-specific and cystathionine γ-lyase-dependent manner. Nat. Commun. (2021.0) 12 1745. DOI: 10.1038/s41467-021-22001-w 36. Longen S. Quantitative persulfide site identification (qPerS-SID) reveals protein targets of H. Sci. Rep. (2016.0) 6 29808. DOI: 10.1038/srep29808 37. Wu Q. Site-specific quantification of persulfidome by combining an isotope-coded affinity tag with strong cation-exchange-based fractionation. Anal. Chem. (2019.0) 91 14860-14864. DOI: 10.1021/acs.analchem.9b04112 38. Liu W, Baker SS, Baker RD, Nowak NJ, Zhu L. Upregulation of hemoglobin expression by oxidative stress in hepatocytes and its implication in nonalcoholic steatohepatitis. PLoS ONE (2011.0) 6 e24363. DOI: 10.1371/journal.pone.0024363 39. Li X. Characterization of adult α- and β-globin elevated by hydrogen peroxide in cervical cancer cells that play a cytoprotective role against oxidative insults. PLoS ONE (2013.0) 8 e54342. DOI: 10.1371/journal.pone.0054342 40. Rookyard AW. A Global profile of reversible and irreversible cysteine redox post-translational modifications during myocardial ischemia/reperfusion injury and antioxidant intervention. Antioxid. Redox Signal (2021.0) 34 11-31. DOI: 10.1089/ars.2019.7765 41. Ito H, Iwabuchi M, Ogawa K. The sugar-metabolic enzymes aldolase and triose-phosphate isomerase are targets of glutathionylation in. Plant Cell Physiol. (2003.0) 44 655-660. DOI: 10.1093/pcp/pcg098 42. Shenton D, Grant CM. Protein S-thiolation targets glycolysis and protein synthesis in response to oxidative stress in the yeast. Biochem. J. (2003.0) 374 513-519. DOI: 10.1042/bj20030414 43. Kuchler L. SYNCRIP-dependent Nox2 mRNA destabilization impairs ROS formation in M2-polarized macrophages. Antioxid. Redox Signal (2014.0) 21 2483-2497. DOI: 10.1089/ars.2013.5760 44. Abiko Y. Involvement of reactive persulfides in biological bismethylmercury sulfide formation. Chem. Res. Toxicol. (2015.0) 28 1301-1306. DOI: 10.1021/acs.chemrestox.5b00101 45. Cicchillitti L. Comparative proteomic analysis of paclitaxel sensitive A2780 epithelial ovarian cancer cell line and its resistant counterpart A2780TC1 by 2D-DIGE: the role of ERp57. J. Proteome Res (2009.0) 8 1902-1912. DOI: 10.1021/pr800856b 46. Go Y-M, Duong DM, Peng J, Jones DP. Protein cysteines map to functional networks according to steady-state level of oxidation. J. Proteom. Bioinform. (2011.0) 4 196-209 47. Dennehy MK, Richards KAM, Wernke GR, Shyr Y, Liebler DC. Cytosolic and nuclear protein targets of thiol-reactive electrophiles. Chem. Res. Toxicol. (2006.0) 19 20-29. DOI: 10.1021/tx050312l 48. Kim H-J. Heterogeneous nuclear ribonucleoprotein K inhibits heat shock-induced transcriptional activity of heat shock factor 1. J. Biol. Chem. (2017.0) 292 12801-12812. DOI: 10.1074/jbc.M117.774992 49. Pérez-Sala D. Vimentin filament organization and stress sensing depend on its single cysteine residue and zinc binding. Nat. Commun. (2015.0) 6 7287. DOI: 10.1038/ncomms8287 50. Szklarczyk D. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. (2021.0) 49 D605-D612. DOI: 10.1093/nar/gkaa1074 51. Shannon P. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. (2003.0) 13 2498-2504. DOI: 10.1101/gr.1239303 52. Morgan B. Multiple glutathione disulfide removal pathways mediate cytosolic redox homeostasis. Nat. Chem. Biol. (2013.0) 9 119-125. DOI: 10.1038/nchembio.1142 53. Bogdanova YA, Schultz C, Belousov VV. Local generation and imaging of hydrogen peroxide in living cells. Curr. Protoc. Chem. Biol. (2017.0) 9 117-127. DOI: 10.1002/cpch.20 54. Gutscher M. Real-time imaging of the intracellular glutathione redox potential. Nat. Methods (2008.0) 5 553-559. DOI: 10.1038/nmeth.1212 55. Schwertassek U. Selective redox regulation of cytokine receptor signaling by extracellular thioredoxin-1. EMBO J. (2007.0) 26 3086-3097. DOI: 10.1038/sj.emboj.7601746 56. Turell L. Reactivity of sulfenic acid in human serum albumin. Biochemistry (2008.0) 47 358-367. DOI: 10.1021/bi701520y 57. Wood JL. Sulfane sulfur. Methods Enzymol. (1987.0) 143 25-29. DOI: 10.1016/0076-6879(87)43009-7 58. Entian KD. Functional analysis of 150 deletion mutants in. Mol. Gen. Genet (1999.0) 262 683-702. DOI: 10.1007/PL00013817 59. 59.Clontech Laboratories, Inc. Yeast Protocols Handbook (Clontech, 2009). 60. Uhlén M. Proteomics. Tissue-based map of the human proteome. Science (2015.0) 347 1260419. DOI: 10.1126/science.1260419
--- title: Diminishing benefits of urban living for children and adolescents’ growth and development authors: - Anu Mishra - Anu Mishra - Bin Zhou - Andrea Rodriguez-Martinez - Honor Bixby - Rosie K. Singleton - Rodrigo M. Carrillo-Larco - Kate E. Sheffer - Christopher J. Paciorek - James E. Bennett - Victor Lhoste - Maria L. C. Iurilli - Mariachiara Di Cesare - James Bentham - Nowell H. Phelps - Marisa K. Sophiea - Gretchen A. Stevens - Goodarz Danaei - Melanie J. Cowan - Stefan Savin - Leanne M. Riley - Edward W. Gregg - Wichai Aekplakorn - Noor Ani Ahmad - Jennifer L. Baker - Adela Chirita-Emandi - Farshad Farzadfar - Günther Fink - Mirjam Heinen - Nayu Ikeda - Andre P. Kengne - Young-Ho Khang - Tiina Laatikainen - Avula Laxmaiah - Jun Ma - Michele Monroy-Valle - Malay K. Mridha - Cristina P. Padez - Andrew Reynolds - Maroje Sorić - Gregor Starc - James P. Wirth - Leandra Abarca-Gómez - Ziad A. Abdeen - Shynar Abdrakhmanova - Suhaila Abdul Ghaffar - Hanan F. Abdul Rahim - Zulfiya Abdurrahmonova - Niveen M. Abu-Rmeileh - Jamila Abubakar Garba - Benjamin Acosta-Cazares - Ishag Adam - Marzena Adamczyk - Robert J. Adams - Seth Adu-Afarwuah - Kaosar Afsana - Shoaib Afzal - Valirie N. Agbor - Imelda A. Agdeppa - Javad Aghazadeh-Attari - Hassan Aguenaou - Carlos A. Aguilar-Salinas - Charles Agyemang - Mohamad Hasnan Ahmad - Ali Ahmadi - Naser Ahmadi - Nastaran Ahmadi - Imran Ahmed - Soheir H. Ahmed - Wolfgang Ahrens - Gulmira Aitmurzaeva - Kamel Ajlouni - Hazzaa M. Al-Hazzaa - Badreya Al-Lahou - Rajaa Al-Raddadi - Huda M. Al Hourani - Nawal M. Al Qaoud - Monira Alarouj - Fadia AlBuhairan - Shahla AlDhukair - Maryam A. Aldwairji - Sylvia Alexius - Mohamed M. Ali - Abdullah Alkandari - Ala’a Alkerwi - Buthaina M. Alkhatib - Kristine Allin - Mar Alvarez-Pedrerol - Eman Aly - Deepak N. Amarapurkar - Pilar Amiano Etxezarreta - John Amoah - Norbert Amougou - Philippe Amouyel - Lars Bo Andersen - Sigmund A. Anderssen - Odysseas Androutsos - Lars Ängquist - Ranjit Mohan Anjana - Alireza Ansari-Moghaddam - Elena Anufrieva - Hajer Aounallah-Skhiri - Joana Araújo - Inger Ariansen - Tahir Aris - Raphael E. Arku - Nimmathota Arlappa - Krishna K. Aryal - Nega Aseffa - Thor Aspelund - Felix K. Assah - Batyrbek Assembekov - Maria Cecília F. Assunção - May Soe Aung - Juha Auvinen - Mária Avdičová - Shina Avi - Ana Azevedo - Mohsen Azimi-Nezhad - Fereidoun Azizi - Mehrdad Azmin - Bontha V. Babu - Maja Bæksgaard Jørgensen - Azli Baharudin - Suhad Bahijri - Marta Bakacs - Nagalla Balakrishna - Yulia Balanova - Mohamed Bamoshmoosh - Maciej Banach - José R. Banegas - Joanna Baran - Rafał Baran - Carlo M. Barbagallo - Valter Barbosa Filho - Alberto Barceló - Maja Baretić - Amina Barkat - Joaquin Barnoya - Lena Barrera - Marta Barreto - Aluisio J. D. Barros - Mauro Virgílio Gomes Barros - Anna Bartosiewicz - Abdul Basit - Joao Luiz D. Bastos - Iqbal Bata - Anwar M. Batieha - Aline P. Batista - Rosangela L. Batista - Zhamilya Battakova - Louise A. Baur - Pascal M. Bayauli - Robert Beaglehole - Silvia Bel-Serrat - Antonisamy Belavendra - Habiba Ben Romdhane - Judith Benedics - Mikhail Benet - Gilda Estela Benitez Rolandi - Elling Bere - Ingunn Holden Bergh - Yemane Berhane - Salim Berkinbayev - Antonio Bernabe-Ortiz - Gailute Bernotiene - Ximena Berrios Carrasola - Heloísa Bettiol - Manfred E. Beutel - Augustin F. Beybey - Jorge Bezerra - Aroor Bhagyalaxmi - Sumit Bharadwaj - Santosh K. Bhargava - Hongsheng Bi - Yufang Bi - Daniel Bia - Katia Biasch - Elysée Claude Bika Lele - Mukharram M. Bikbov - Bihungum Bista - Dusko J. Bjelica - Anne A. Bjerregaard - Peter Bjerregaard - Espen Bjertness - Marius B. Bjertness - Cecilia Björkelund - Katia V. Bloch - Anneke Blokstra - Moran Blychfeld Magnazu - Simona Bo - Martin Bobak - Lynne M. Boddy - Bernhard O. Boehm - Jolanda M. A. Boer - Jose G. Boggia - Elena Bogova - Carlos P. Boissonnet - Stig E. Bojesen - Marialaura Bonaccio - Vanina Bongard - Alice Bonilla-Vargas - Matthias Bopp - Herman Borghs - Pascal Bovet - Khadichamo Boymatova - Lien Braeckevelt - Lutgart Braeckman - Marjolijn C. E. Bragt - Imperia Brajkovich - Francesco Branca - Juergen Breckenkamp - João Breda - Hermann Brenner - Lizzy M. Brewster - Garry R. Brian - Yajaira Briceño - Lacramioara Brinduse - Miguel Brito - Sinead Brophy - Johannes Brug - Graziella Bruno - Anna Bugge - Frank Buntinx - Marta Buoncristiano - Genc Burazeri - Con Burns - Antonio Cabrera de León - Joseph Cacciottolo - Hui Cai - Roberta B. Caixeta - Tilema Cama - Christine Cameron - José Camolas - Günay Can - Ana Paula C. Cândido - Felicia Cañete - Mario V. Capanzana - Naděžda Čapková - Eduardo Capuano - Rocco Capuano - Vincenzo Capuano - Marloes Cardol - Viviane C. Cardoso - Axel C. Carlsson - Esteban Carmuega - Joana Carvalho - José A. Casajús - Felipe F. Casanueva - Maribel Casas - Ertugrul Celikcan - Laura Censi - Marvin Cervantes‐Loaiza - Juraci A. Cesar - Snehalatha Chamukuttan - Angelique Chan - Queenie Chan - Himanshu K. Chaturvedi - Nish Chaturvedi - Norsyamlina Che Abdul Rahim - Miao Li Chee - Chien-Jen Chen - Fangfang Chen - Huashuai Chen - Shuohua Chen - Zhengming Chen - Ching-Yu Cheng - Yiling J. Cheng - Bahman Cheraghian - Angela Chetrit - Ekaterina Chikova-Iscener - Mai J. M. Chinapaw - Anne Chinnock - Arnaud Chiolero - Shu-Ti Chiou - María-Dolores Chirlaque - Belong Cho - Kaare Christensen - Diego G. Christofaro - Jerzy Chudek - Renata Cifkova - Michelle Cilia - Eliza Cinteza - Massimo Cirillo - Frank Claessens - Janine Clarke - Els Clays - Emmanuel Cohen - Laura-María Compañ-Gabucio - Hans Concin - Susana C. Confortin - Cyrus Cooper - Tara C. Coppinger - Eva Corpeleijn - Lilia Yadira Cortés - Simona Costanzo - Dominique Cottel - Chris Cowell - Cora L. Craig - Amelia C. Crampin - Amanda J. Cross - Ana B. Crujeiras - Juan J. Cruz - Tamás Csányi - Semánová Csilla - Alexandra M. Cucu - Liufu Cui - Felipe V. Cureau - Sarah Cuschieri - Ewelina Czenczek-Lewandowska - Graziella D’Arrigo - Eleonora d’Orsi - Liliana Dacica - Jean Dallongeville - Albertino Damasceno - Camilla T. Damsgaard - Rachel Dankner - Thomas M. Dantoft - Parasmani Dasgupta - Saeed Dastgiri - Luc Dauchet - Kairat Davletov - Maria Alice Altenburg de Assis - Guy De Backer - Dirk De Bacquer - Amalia De Curtis - Patrícia de Fragas Hinnig - Giovanni de Gaetano - Stefaan De Henauw - Pilar De Miguel-Etayo - Paula Duarte de Oliveira - David De Ridder - Karin De Ridder - Susanne R. de Rooij - Delphine De Smedt - Mohan Deepa - Alexander D. Deev - Vincent DeGennaro - Hélène Delisle - Francis Delpeuch - Stefaan Demarest - Elaine Dennison - Katarzyna Dereń - Valérie Deschamps - Meghnath Dhimal - Augusto Di Castelnuovo - Juvenal Soares Dias-da-Costa - María Elena Díaz-Sánchez - Alejandro Diaz - Pedro Díaz Fernández - María Pilar Díez Ripollés - Zivka Dika - Shirin Djalalinia - Visnja Djordjic - Ha T. P. Do - Annette J. Dobson - Liria Dominguez - Maria Benedetta Donati - Chiara Donfrancesco - Guanghui Dong - Yanhui Dong - Silvana P. Donoso - Angela Döring - Maria Dorobantu - Ahmad Reza Dorosty - Kouamelan Doua - Nico Dragano - Wojciech Drygas - Jia Li Duan - Charmaine A. Duante - Priscilla Duboz - Vesselka L. Duleva - Virginija Dulskiene - Samuel C. Dumith - Anar Dushpanova - Azhar Dyussupova - Vilnis Dzerve - Elzbieta Dziankowska-Zaborszczyk - Guadalupe Echeverría - Ricky Eddie - Ebrahim Eftekhar - Eruke E. Egbagbe - Robert Eggertsen - Sareh Eghtesad - Gabriele Eiben - Ulf Ekelund - Mohammad El-Khateeb - Laila El Ammari - Jalila El Ati - Denise Eldemire-Shearer - Marie Eliasen - Paul Elliott - Ronit Endevelt - Reina Engle-Stone - Rajiv T. Erasmus - Raimund Erbel - Cihangir Erem - Gul Ergor - Louise Eriksen - Johan G. Eriksson - Jorge Escobedo-de la Peña - Saeid Eslami - Ali Esmaeili - Alun Evans - David Faeh - Ildar Fakhradiyev - Albina A. Fakhretdinova - Caroline H. Fall - Elnaz Faramarzi - Mojtaba Farjam - Victoria Farrugia Sant’Angelo - Mohammad Reza Fattahi - Asher Fawwad - Wafaie W. Fawzi - Edit Feigl - Francisco J. Felix-Redondo - Trevor S. Ferguson - Romulo A. Fernandes - Daniel Fernández-Bergés - Daniel Ferrante - Thomas Ferrao - Gerson Ferrari - Marika Ferrari - Marco M. Ferrario - Catterina Ferreccio - Haroldo S. Ferreira - Eldridge Ferrer - Jean Ferrieres - Thamara Hubler Figueiró - Anna Fijalkowska - Mauro Fisberg - Krista Fischer - Leng Huat Foo - Maria Forsner - Heba M. Fouad - Damian K. Francis - Maria do Carmo Franco - Zlatko Fras - Guillermo Frontera - Flavio D. Fuchs - Sandra C. Fuchs - Isti I. Fujiati - Yuki Fujita - Matsuda Fumihiko - Viktoriya Furdela - Takuro Furusawa - Zbigniew Gaciong - Mihai Gafencu - Manuel Galán Cuesta - Andrzej Galbarczyk - Henrike Galenkamp - Daniela Galeone - Myriam Galfo - Fabio Galvano - Jingli Gao - Pei Gao - Manoli Garcia-de-la-Hera - María José García Mérida - Marta García Solano - Dickman Gareta - Sarah P. Garnett - Jean-Michel Gaspoz - Magda Gasull - Adroaldo Cesar Araujo Gaya - Anelise Reis Gaya - Andrea Gazzinelli - Ulrike Gehring - Harald Geiger - Johanna M. Geleijnse - Ronnie George - Ebrahim Ghaderi - Ali Ghanbari - Erfan Ghasemi - Oana-Florentina Gheorghe-Fronea - Alessandro Gialluisi - Simona Giampaoli - Francesco Gianfagna - Christian Gieger - Tiffany K. Gill - Jonathan Giovannelli - Glen Gironella - Aleksander Giwercman - Konstantinos Gkiouras - Natalya Glushkova - Natalja Gluškova - Ramesh Godara - Justyna Godos - Sibel Gogen - Marcel Goldberg - David Goltzman - Georgina Gómez - Jesús Humberto Gómez Gómez - Luis F. Gomez - Santiago F. Gómez - Aleksandra Gomula - Bruna Gonçalves Cordeiro da Silva - Helen Gonçalves - Mauer Gonçalves - Ana D. González-Alvarez - David A. Gonzalez-Chica - Esther M. González-Gil - Marcela Gonzalez-Gross - Margot González-Leon - Juan P. González-Rivas - Clicerio González-Villalpando - María-Elena González-Villalpando - Angel R. Gonzalez - Frederic Gottrand - Antonio Pedro Graça - Sidsel Graff-Iversen - Dušan Grafnetter - Aneta Grajda - Maria G. Grammatikopoulou - Ronald D. Gregor - Maria João Gregório - Else Karin Grøholt - Anders Grøntved - Giuseppe Grosso - Gabriella Gruden - Dongfeng Gu - Viviana Guajardo - Emanuela Gualdi-Russo - Pilar Guallar-Castillón - Andrea Gualtieri - Elias F. Gudmundsson - Vilmundur Gudnason - Ramiro Guerrero - Idris Guessous - Andre L. Guimaraes - Martin C. Gulliford - Johanna Gunnlaugsdottir - Marc J. Gunter - Xiu-Hua Guo - Yin Guo - Prakash C. Gupta - Rajeev Gupta - Oye Gureje - Enrique Gutiérrez González - Laura Gutierrez - Felix Gutzwiller - Xinyi Gwee - Seongjun Ha - Farzad Hadaegh - Charalambos A. Hadjigeorgiou - Rosa Haghshenas - Hamid Hakimi - Jytte Halkjær - Ian R. Hambleton - Behrooz Hamzeh - Willem A. Hanekom - Dominique Hange - Abu A. M. Hanif - Sari Hantunen - Jie Hao - Carla Menêses Hardman - Rachakulla Hari Kumar - Tina Harmer Lassen - Javad Harooni - Seyed Mohammad Hashemi-Shahri - Maria Hassapidou - Jun Hata - Teresa Haugsgjerd - Alison J. Hayes - Jiang He - Yuan He - Yuna He - Regina Heidinger-Felső - Margit Heier - Tatjana Hejgaard - Marleen Elisabeth Hendriks - Rafael dos Santos Henrique - Ana Henriques - Leticia Hernandez Cadena - Sauli Herrala - Marianella Herrera-Cuenca - Victor M. Herrera - Isabelle Herter-Aeberli - Karl-Heinz Herzig - Ramin Heshmat - Allan G. Hill - Sai Yin Ho - Suzanne C. Ho - Michael Hobbs - Doroteia A. Höfelmann - Michelle Holdsworth - Reza Homayounfar - Clara Homs - Wilma M. Hopman - Andrea R. V. R. Horimoto - Claudia M. Hormiga - Bernardo L. Horta - Leila Houti - Christina Howitt - Thein Thein Htay - Aung Soe Htet - Maung Maung Than Htike - Yonghua Hu - José María Huerta - Ilpo Tapani Huhtaniemi - Laetitia Huiart - Constanta Huidumac Petrescu - Martijn Huisman - Abdullatif Husseini - Chinh Nguyen Huu - Inge Huybrechts - Nahla Hwalla - Jolanda Hyska - Licia Iacoviello - Ellina M. Iakupova - Jesús M. Ibarluzea - Mohsen M. Ibrahim - Norazizah Ibrahim Wong - M. Arfan Ikram - Carmen Iñiguez - Violeta Iotova - Vilma E. Irazola - Takafumi Ishida - Godsent C. Isiguzo - Muhammad Islam - Sheikh Mohammed Shariful Islam - Duygu Islek - Ivaila Y. Ivanova-Pandourska - Masanori Iwasaki - Tuija Jääskeläinen - Rod T. Jackson - Jeremy M. Jacobs - Michel Jadoul - Tazeen Jafar - Bakary Jallow - Kenneth James - Kazi M. Jamil - Konrad Jamrozik - Anna Jansson - Imre Janszky - Edward Janus - Juel Jarani - Marjo-Riitta Jarvelin - Grazyna Jasienska - Ana Jelaković - Bojan Jelaković - Garry Jennings - Chao Qiang Jiang - Ramon O. Jimenez - Karl-Heinz Jöckel - Michel Joffres - Jari J. Jokelainen - Jost B. Jonas - Jitendra Jonnagaddala - Torben Jørgensen - Pradeep Joshi - Josipa Josipović - Farahnaz Joukar - Jacek J. Jóźwiak - Debra S. Judge - Anne Juolevi - Gregor Jurak - Iulia Jurca Simina - Vesna Juresa - Rudolf Kaaks - Felix O. Kaducu - Anthony Kafatos - Mónika Kaj - Eero O. Kajantie - Natia Kakutia - Daniela Kállayová - Zhanna Kalmatayeva - Ofra Kalter-Leibovici - Yves Kameli - Freja B. Kampmann - Kodanda R. Kanala - Srinivasan Kannan - Efthymios Kapantais - Eva Karaglani - Argyro Karakosta - Line L. Kårhus - Khem B. Karki - Philippe B. Katchunga - Marzieh Katibeh - Joanne Katz - Peter T. Katzmarzyk - Jussi Kauhanen - Prabhdeep Kaur - Maryam Kavousi - Gyulli M. Kazakbaeva - François F. Kaze - Calvin Ke - Ulrich Keil - Lital Keinan Boker - Sirkka Keinänen-Kiukaanniemi - Roya Kelishadi - Cecily Kelleher - Han C. G. Kemper - Maryam Keramati - Alina Kerimkulova - Mathilde Kersting - Timothy Key - Yousef Saleh Khader - Arsalan Khaledifar - Davood Khalili - Kay-Tee Khaw - Bahareh Kheiri - Motahareh Kheradmand - Alireza Khosravi - Ilse M. S. L. Khouw - Ursula Kiechl-Kohlendorfer - Sophia J. Kiechl - Stefan Kiechl - Japhet Killewo - Hyeon Chang Kim - Jeongseon Kim - Jenny M. Kindblom - Andrew Kingston - Heidi Klakk - Magdalena Klimek - Jeannette Klimont - Jurate Klumbiene - Michael Knoflach - Bhawesh Koirala - Elin Kolle - Patrick Kolsteren - Jürgen König - Raija Korpelainen - Paul Korrovits - Magdalena Korzycka - Jelena Kos - Seppo Koskinen - Katsuyasu Kouda - Éva Kovács - Viktoria Anna Kovacs - Irina Kovalskys - Sudhir Kowlessur - Slawomir Koziel - Jana Kratenova - Wolfgang Kratzer - Vilma Kriaucioniene - Susi Kriemler - Peter Lund Kristensen - Helena Krizan - Maria F. Kroker-Lobos - Steinar Krokstad - Daan Kromhout - Herculina S. Kruger - Ruan Kruger - Łukasz Kryst - Ruzena Kubinova - Renata Kuciene - Urho M. Kujala - Enisa Kujundzic - Zbigniew Kulaga - Mukhtar Kulimbet - R. Krishna Kumar - Marie Kunešová - Pawel Kurjata - Yadlapalli S. Kusuma - Vladimir Kutsenko - Kari Kuulasmaa - Catherine Kyobutungi - Quang Ngoc La - Fatima Zahra Laamiri - Carl Lachat - Karl J. Lackner - Youcef Laid - Lachmie Lall - Tai Hing Lam - Maritza Landaeta Jimenez - Edwige Landais - Vera Lanska - Georg Lappas - Bagher Larijani - Simo Pone Larissa - Tint Swe Latt - Martino Laurenzi - Laura Lauria - Maria Lazo-Porras - Gwenaëlle Le Coroller - Khanh Le Nguyen Bao - Agnès Le Port - Tuyen D. Le - Jeannette Lee - Jeonghee Lee - Paul H. Lee - Nils Lehmann - Terho Lehtimäki - Daniel Lemogoum - Branimir Leskošek - Justyna Leszczak - Katja B. Leth-Møller - Gabriel M. Leung - Naomi S. Levitt - Yanping Li - Merike Liivak - Christa L. Lilly - Charlie Lim - Wei-Yen Lim - M. Fernanda Lima-Costa - Hsien-Ho Lin - Xu Lin - Yi-Ting Lin - Lars Lind - Vijaya Lingam - Birgit Linkohr - Allan Linneberg - Lauren Lissner - Mieczyslaw Litwin - Jing Liu - Lijuan Liu - Wei-Cheng Lo - Helle-Mai Loit - Khuong Quynh Long - Guadalupe Longo Abril - Luis Lopes - Marcus V. V. Lopes - Oscar Lopes - Esther Lopez-Garcia - Tania Lopez - Paulo A. Lotufo - José Eugenio Lozano - Janice L. Lukrafka - Dalia Luksiene - Annamari Lundqvist - Nuno Lunet - Charles Lunogelo - Michala Lustigová - Edyta Łuszczki - Jean-René M’Buyamba-Kabangu - Guansheng Ma - Xu Ma - George L. L. Machado-Coelho - Aristides M. Machado-Rodrigues - Enguerran Macia - Luisa M. Macieira - Ahmed A. Madar - Anja L. Madsen - Gladys E. Maestre - Stefania Maggi - Dianna J. Magliano - Sara Magnacca - Emmanuella Magriplis - Gowri Mahasampath - Bernard Maire - Marjeta Majer - Marcia Makdisse - Päivi Mäki - Fatemeh Malekzadeh - Reza Malekzadeh - Rahul Malhotra - Kodavanti Mallikharjuna Rao - Sofia K. Malyutina - Lynell V. Maniego - Yannis Manios - Masimango Imani Manix - Jim I. Mann - Fariborz Mansour-Ghanaei - Taru Manyanga - Enzo Manzato - Anie Marcil - Paula Margozzini - Joany Mariño - Anastasia Markaki - Oonagh Markey - Eliza Markidou Ioannidou - Pedro Marques-Vidal - Larissa Pruner Marques - Jaume Marrugat - Yves Martin-Prevel - Rosemarie Martin - Reynaldo Martorell - Eva Martos - Katharina Maruszczak - Stefano Marventano - Giovanna Masala - Luis P. Mascarenhas - Shariq R. Masoodi - Ellisiv B. Mathiesen - Prashant Mathur - Alicia Matijasevich - Piotr Matłosz - Tandi E. Matsha - Victor Matsudo - Christina Mavrogianni - Artur Mazur - Jean Claude N. Mbanya - Shelly R. McFarlane - Stephen T. McGarvey - Martin McKee - Stela McLachlan - Rachael M. McLean - Scott B. McLean - Margaret L. McNairy - Breige A. McNulty - Sounnia Mediene Benchekor - Jurate Medzioniene - Parinaz Mehdipour - Kirsten Mehlig - Amir Houshang Mehrparvar - Aline Meirhaeghe - Jørgen Meisfjord - Christa Meisinger - Jesus D. Melgarejo - Marina Melkumova - João Mello - Fabián Méndez - Carlos O. Mendivil - Ana Maria B. Menezes - Geetha R. Menon - Gert B. M. Mensink - Maria Teresa Menzano - Indrapal I. Meshram - Diane T. Meto - Jie Mi - Kim F. Michaelsen - Nathalie Michels - Kairit Mikkel - Karolina Miłkowska - Jody C. Miller - Olga Milushkina - Cláudia S. Minderico - G. K. Mini - Juan Francisco Miquel - J. Jaime Miranda - Mohammad Reza Mirjalili - Daphne Mirkopoulou - Erkin Mirrakhimov - Marjeta Mišigoj-Duraković - Antonio Mistretta - Veronica Mocanu - Pietro A. Modesti - Sahar Saeedi Moghaddam - Bahram Mohajer - Mostafa K. Mohamed - Shukri F. Mohamed - Kazem Mohammad - Mohammad Reza Mohammadi - Zahra Mohammadi - Noushin Mohammadifard - Reza Mohammadpourhodki - Viswanathan Mohan - Salim Mohanna - Muhammad Fadhli Mohd Yusoff - Iraj Mohebbi - Farnam Mohebi - Marie Moitry - Line T. Møllehave - Niels C. Møller - Dénes Molnár - Amirabbas Momenan - Charles K. Mondo - Roger A. Montenegro Mendoza - Eric Monterrubio-Flores - Kotsedi Daniel K. Monyeki - Jin Soo Moon - Mahmood Moosazadeh - Hermine T. Mopa - Farhad Moradpour - Leila B. Moreira - Alain Morejon - Luis A. Moreno - Francis Morey - Karen Morgan - Suzanne N. Morin - Erik Lykke Mortensen - George Moschonis - Alireza Moslem - Malgorzata Mossakowska - Aya Mostafa - Seyed-Ali Mostafavi - Anabela Mota-Pinto - Jorge Mota - Mohammad Esmaeel Motlagh - Jorge Motta - Marcos André Moura-dos-Santos - Yeva Movsesyan - Kelias P. Msyamboza - Thet Thet Mu - Magdalena Muc - Florian Muca - Boban Mugoša - Maria L. Muiesan - Martina Müller-Nurasyid - Thomas Münzel - Jaakko Mursu - Elaine M. Murtagh - Kamarul Imran Musa - Sanja Musić Milanović - Vera Musil - Geofrey Musinguzi - Muel Telo M. C. Muyer - Iraj Nabipour - Shohreh Naderimagham - Gabriele Nagel - Farid Najafi - Harunobu Nakamura - Hanna Nalecz - Jana Námešná - Ei Ei K. Nang - Vinay B. Nangia - Martin Nankap - Sameer Narake - Paola Nardone - Take Naseri - Matthias Nauck - William A. Neal - Azim Nejatizadeh - Chandini Nekkantti - Keiu Nelis - Ilona Nenko - Martin Neovius - Flavio Nervi - Tze Pin Ng - Chung T. Nguyen - Nguyen D. Nguyen - Quang Ngoc Nguyen - Michael Y. Ni - Rodica Nicolescu - Peng Nie - Ramfis E. Nieto-Martínez - Yury P. Nikitin - Guang Ning - Toshiharu Ninomiya - Nobuo Nishi - Sania Nishtar - Marianna Noale - Oscar A. Noboa - Helena Nogueira - Maria Nordendahl - Børge G. Nordestgaard - Davide Noto - Natalia Nowak-Szczepanska - Mohannad Al Nsour - Irfan Nuhoğlu - Baltazar Nunes - Eha Nurk - Fred Nuwaha - Moffat Nyirenda - Terence W. O’Neill - Dermot O’Reilly - Galina Obreja - Caleb Ochimana - Angélica M. Ochoa-Avilés - Eiji Oda - Augustine N. Odili - Kyungwon Oh - Kumiko Ohara - Claes Ohlsson - Ryutaro Ohtsuka - Örn Olafsson - Maria Teresa A. Olinto - Isabel O. Oliveira - Mohd Azahadi Omar - Saeed M. Omar - Altan Onat - Sok King Ong - N. Charlotte Onland-Moret - Lariane M. Ono - Pedro Ordunez - Rui Ornelas - Ana P. Ortiz - Pedro J. Ortiz - Merete Osler - Clive Osmond - Sergej M. Ostojic - Afshin Ostovar - Johanna A. Otero - Kim Overvad - Ellis Owusu-Dabo - Fred Michel Paccaud - Ioannis Pagkalos - Elena Pahomova - Karina Mary de Paiva - Andrzej Pająk - Alberto Palloni - Luigi Palmieri - Wen-Harn Pan - Songhomitra Panda-Jonas - Arvind Pandey - Francesco Panza - Mariela Paoli - Sousana K. Papadopoulou - Dimitrios Papandreou - Rossina G. Pareja - Soon-Woo Park - Suyeon Park - Winsome R. Parnell - Mahboubeh Parsaeian - Ionela M. Pascanu - Patrick Pasquet - Nikhil D. Patel - Marcos Pattussi - Halyna Pavlyshyn - Raimund Pechlaner - Ivan Pećin - Mangesh S. Pednekar - João M. Pedro - Nasheeta Peer - Sergio Viana Peixoto - Markku Peltonen - Alexandre C. Pereira - Marco A. Peres - Cynthia M. Pérez - Valentina Peterkova - Annette Peters - Astrid Petersmann - Janina Petkeviciene - Ausra Petrauskiene - Olga Petrovna Kovtun - Emanuela Pettenuzzo - Niloofar Peykari - Norbert Pfeiffer - Modou Cheyassin Phall - Son Thai Pham - Rafael N. Pichardo - Daniela Pierannunzio - Iris Pigeot - Hynek Pikhart - Aida Pilav - Lorenza Pilotto - Francesco Pistelli - Freda Pitakaka - Aleksandra Piwonska - Andreia N. Pizarro - Pedro Plans-Rubió - Alina G. Platonova - Bee Koon Poh - Hermann Pohlabeln - Nadija S. Polka - Raluca M. Pop - Stevo R. Popovic - Miquel Porta - Georg Posch - Anil Poudyal - Dimitrios Poulimeneas - Hamed Pouraram - Farhad Pourfarzi - Akram Pourshams - Hossein Poustchi - Rajendra Pradeepa - Alison J. Price - Jacqueline F. Price - Antonio Prista - Rui Providencia - Jardena J. Puder - Iveta Pudule - Maria Puiu - Margus Punab - Muhammed S. Qadir - Radwan F. Qasrawi - Mostafa Qorbani - Hedley K. Quintana - Pedro J. Quiroga-Padilla - Tran Quoc Bao - Stefan Rach - Ivana Radic - Ricardas Radisauskas - Salar Rahimikazerooni - Mahfuzar Rahman - Mahmudur Rahman - Olli Raitakari - Manu Raj - Tamerlan Rajabov - Sherali Rakhmatulloev - Ivo Rakovac - Sudha Ramachandra Rao - Ambady Ramachandran - Otim P. C. Ramadan - Virgílio V. Ramires - Jacqueline Ramke - Elisabete Ramos - Rafel Ramos - Lekhraj Rampal - Sanjay Rampal - Lalka S. Rangelova - Vayia Rarra - Ramon A. Rascon-Pacheco - Cassiano Ricardo Rech - Josep Redon - Paul Ferdinand M. Reganit - Valéria Regecová - Jane D. P. Renner - Judit A. Repasy - Cézane P. Reuter - Luis Revilla - Abbas Rezaianzadeh - Yeunsook Rho - Lourdes Ribas-Barba - Robespierre Ribeiro - Elio Riboli - Adrian Richter - Fernando Rigo - Attilio Rigotti - Natascia Rinaldo - Tobias F. Rinke de Wit - Ana I. Rito - Raphael M. Ritti-Dias - Juan A. Rivera - Reina G. Roa - Louise Robinson - Cynthia Robitaille - Romana Roccaldo - Daniela Rodrigues - Fernando Rodríguez-Artalejo - María del Cristo Rodriguez-Perez - Laura A. Rodríguez-Villamizar - Andrea Y. Rodríguez - Ulla Roggenbuck - Peter Rohloff - Fabian Rohner - Rosalba Rojas-Martinez - Nipa Rojroongwasinkul - Dora Romaguera - Elisabetta L. Romeo - Rafaela V. Rosario - Annika Rosengren - Ian Rouse - Vanessa Rouzier - Joel G. R. Roy - Maira H. Ruano - Adolfo Rubinstein - Frank J. Rühli - Jean-Bernard Ruidavets - Blanca Sandra Ruiz-Betancourt - Maria Ruiz-Castell - Emma Ruiz Moreno - Iuliia A. Rusakova - Kenisha Russell Jonsson - Paola Russo - Petra Rust - Marcin Rutkowski - Marge Saamel - Charumathi Sabanayagam - Hamideh Sabbaghi - Elena Sacchini - Harshpal S. Sachdev - Alireza Sadjadi - Ali Reza Safarpour - Sare Safi - Saeid Safiri - Mohammad Hossien Saghi - Olfa Saidi - Nader Saki - Sanja Šalaj - Benoit Salanave - Eduardo Salazar Martinez - Calogero Saleva - Diego Salmerón - Veikko Salomaa - Jukka T. Salonen - Massimo Salvetti - Margarita Samoutian - Jose Sánchez-Abanto - Inés Sánchez Rodríguez - Susana Sans - Loreto Santa Marina - Ethel Santacruz - Diana A. Santos - Ina S. Santos - Lèlita C. Santos - Maria Paula Santos - Osvaldo Santos - Rute Santos - Tamara R. Santos - Jouko L. Saramies - Luis B. Sardinha - Nizal Sarrafzadegan - Thirunavukkarasu Sathish - Kai-Uwe Saum - Savvas Savva - Mathilde Savy - Norie Sawada - Mariana Sbaraini - Marcia Scazufca - Beatriz D. Schaan - Angelika Schaffrath Rosario - Herman Schargrodsky - Anja Schienkiewitz - Karin Schindler - Sabine Schipf - Carsten O. Schmidt - Ida Maria Schmidt - Andrea Schneider - Peter Schnohr - Ben Schöttker - Sara Schramm - Stine Schramm - Helmut Schröder - Constance Schultsz - Matthias B. Schulze - Aletta E. Schutte - Sylvain Sebert - Moslem Sedaghattalab - Rusidah Selamat - Vedrana Sember - Abhijit Sen - Idowu O. Senbanjo - Sadaf G. Sepanlou - Guillermo Sequera - Luis Serra-Majem - Jennifer Servais - Ľudmila Ševčíková - Svetlana Shalnova - Teresa Shamah-Levy - Seyed Morteza Shamshirgaran - Coimbatore Subramaniam Shanthirani - Maryam Sharafkhah - Sanjib K. Sharma - Jonathan E. Shaw - Amaneh Shayanrad - Ali Akbar Shayesteh - Lela Shengelia - Zumin Shi - Kenji Shibuya - Hana Shimizu-Furusawa - Tal Shimony - Rahman Shiri - Namuna Shrestha - Khairil Si-Ramlee - Alfonso Siani - Rosalynn Siantar - Abla M. Sibai - Labros S. Sidossis - Natalia Silitrari - Antonio M. Silva - Caroline Ramos de Moura Silva - Diego Augusto Santos Silva - Kelly S. Silva - Xueling Sim - Mary Simon - Judith Simons - Leon A. Simons - Agneta Sjöberg - Michael Sjöström - Natalia A. Skoblina - Gry Skodje - Tatyana Slazhnyova - Jolanta Slowikowska-Hilczer - Przemysław Slusarczyk - Liam Smeeth - Hung-Kwan So - Fernanda Cunha Soares - Grzegorz Sobek - Eugène Sobngwi - Morten Sodemann - Stefan Söderberg - Moesijanti Y. E. Soekatri - Agustinus Soemantri - Reecha Sofat - Vincenzo Solfrizzi - Mohammad Hossein Somi - Emily Sonestedt - Yi Song - Sajid Soofi - Thorkild I. A. Sørensen - Elin P. Sørgjerd - Charles Sossa Jérome - Victoria E. Soto-Rojas - Aïcha Soumaré - Alfonso Sousa-Poza - Slavica Sovic - Bente Sparboe-Nilsen - Karen Sparrenberger - Phoebe R. Spencer - Angela Spinelli - Igor Spiroski - Jan A. Staessen - Hanspeter Stamm - Kaspar Staub - Bill Stavreski - Jostein Steene-Johannessen - Peter Stehle - Aryeh D. Stein - George S. Stergiou - Jochanan Stessman - Ranko Stevanović - Jutta Stieber - Doris Stöckl - Jakub Stokwiszewski - Ekaterina Stoyanova - Gareth Stratton - Karien Stronks - Maria Wany Strufaldi - Lela Sturua - Ramón Suárez-Medina - Machi Suka - Chien-An Sun - Liang Sun - Johan Sundström - Yn-Tz Sung - Jordi Sunyer - Paibul Suriyawongpaisal - Nabil William G. Sweis - Boyd A. Swinburn - Rody G. Sy - René Charles Sylva - Moyses Szklo - Lucjan Szponar - Lorraine Tabone - E. Shyong Tai - Konstantinos D. Tambalis - Mari-Liis Tammesoo - Abdonas Tamosiunas - Eng Joo Tan - Xun Tang - Maya Tanrygulyyeva - Frank Tanser - Yong Tao - Mohammed Rasoul Tarawneh - Jakob Tarp - Carolina B. Tarqui-Mamani - Radka Taxová Braunerová - Anne Taylor - Julie Taylor - Félicité Tchibindat - Saskia Te Velde - William R. Tebar - Grethe S. Tell - Tania Tello - Yih Chung Tham - K. R. Thankappan - Holger Theobald - Xenophon Theodoridis - Nihal Thomas - Barbara Thorand - Betina H. Thuesen - Ľubica Tichá - Erik J. Timmermans - Dwi H. Tjandrarini - Anne Tjonneland - Hanna K. Tolonen - Janne S. Tolstrup - Murat Topbas - Roman Topór-Mądry - Liv Elin Torheim - María José Tormo - Michael J. Tornaritis - Maties Torrent - Laura Torres-Collado - Stefania Toselli - Giota Touloumi - Pierre Traissac - Thi Tuyet-Hanh Tran - Mark S. Tremblay - Areti Triantafyllou - Dimitrios Trichopoulos - Antonia Trichopoulou - Oanh T. H. Trinh - Atul Trivedi - Yu-Hsiang Tsao - Lechaba Tshepo - Maria Tsigga - Panagiotis Tsintavis - Shoichiro Tsugane - John Tuitele - Azaliia M. Tuliakova - Marshall K. Tulloch-Reid - Fikru Tullu - Tomi-Pekka Tuomainen - Jaakko Tuomilehto - Maria L. Turley - Gilad Twig - Per Tynelius - Evangelia Tzala - Themistoklis Tzotzas - Christophe Tzourio - Peter Ueda - Eunice Ugel - Flora A. M. Ukoli - Hanno Ulmer - Belgin Unal - Zhamyila Usupova - Hannu M. T. Uusitalo - Nalan Uysal - Justina Vaitkeviciute - Gonzalo Valdivia - Susana Vale - Damaskini Valvi - Rob M. van Dam - Bert-Jan van den Born - Johan Van der Heyden - Yvonne T. van der Schouw - Koen Van Herck - Wendy Van Lippevelde - Hoang Van Minh - Natasja M. Van Schoor - Irene G. M. van Valkengoed - Dirk Vanderschueren - Diego Vanuzzo - Anette Varbo - Gregorio Varela-Moreiras - Luz Nayibe Vargas - Patricia Varona-Pérez - Senthil K. Vasan - Daniel G. Vasques - Tomas Vega - Toomas Veidebaum - Gustavo Velasquez-Melendez - Biruta Velika - Maïté Verloigne - Giovanni Veronesi - W. M. Monique Verschuren - Cesar G. Victora - Giovanni Viegi - Lucie Viet - Frøydis N. Vik - Monica Vilar - Salvador Villalpando - Jesus Vioque - Jyrki K. Virtanen - Sophie Visvikis-Siest - Bharathi Viswanathan - Mihaela Vladulescu - Tiina Vlasoff - Dorja Vocanec - Peter Vollenweider - Henry Völzke - Ari Voutilainen - Martine Vrijheid - Tanja G. M. Vrijkotte - Alisha N. Wade - Thomas Waldhör - Janette Walton - Elvis O. A. Wambiya - Wan Mohamad Wan Bebakar - Wan Nazaimoon Wan Mohamud - Rildo de Souza Wanderley Júnior - Ming-Dong Wang - Ningli Wang - Qian Wang - Xiangjun Wang - Ya Xing Wang - Ying-Wei Wang - S. Goya Wannamethee - Nicholas Wareham - Adelheid Weber - Karen Webster-Kerr - Niels Wedderkopp - Daniel Weghuber - Wenbin Wei - Aneta Weres - Bo Werner - Leo D. Westbury - Peter H. Whincup - Kremlin Wickramasinghe - Kurt Widhalm - Indah S. Widyahening - Andrzej Więcek - Philipp S. Wild - Rainford J. Wilks - Johann Willeit - Peter Willeit - Julianne Williams - Tom Wilsgaard - Rusek Wojciech - Bogdan Wojtyniak - Kathrin Wolf - Roy A. Wong-McClure - Andrew Wong - Emily B. Wong - Jyh Eiin Wong - Tien Yin Wong - Jean Woo - Mark Woodward - Frederick C. Wu - Hon-Yen Wu - Jianfeng Wu - Li Juan Wu - Shouling Wu - Justyna Wyszyńska - Haiquan Xu - Liang Xu - Nor Azwany Yaacob - Uruwan Yamborisut - Weili Yan - Ling Yang - Xiaoguang Yang - Yang Yang - Nazan Yardim - Tabara Yasuharu - Martha Yépez García - Panayiotis K. Yiallouros - Agneta Yngve - Moein Yoosefi - Akihiro Yoshihara - Qi Sheng You - San-Lin You - Novie O. Younger-Coleman - Yu-Ling Yu - Yunjiang Yu - Safiah Md Yusof - Ahmad Faudzi Yusoff - Luciana Zaccagni - Vassilis Zafiropulos - Ahmad A. Zainuddin - Seyed Rasoul Zakavi - Farhad Zamani - Sabina Zambon - Antonis Zampelas - Hana Zamrazilová - Maria Elisa Zapata - Abdul Hamid Zargar - Ko Ko Zaw - Ayman A. Zayed - Tomasz Zdrojewski - Magdalena Żegleń - Kristyna Zejglicova - Tajana Zeljkovic Vrkic - Yi Zeng - Luxia Zhang - Zhen-Yu Zhang - Dong Zhao - Ming-Hui Zhao - Wenhua Zhao - Yanitsa V. Zhecheva - Shiqi Zhen - Wei Zheng - Yingfeng Zheng - Bekbolat Zholdin - Maigeng Zhou - Dan Zhu - Marie Zins - Emanuel Zitt - Yanina Zocalo - Nada Zoghlami - Julio Zuñiga Cisneros - Monika Zuziak - Zulfiqar A. Bhutta - Robert E. Black - Majid Ezzati journal: Nature year: 2023 pmcid: PMC10060164 doi: 10.1038/s41586-023-05772-8 license: CC BY 4.0 --- # Diminishing benefits of urban living for children and adolescents’ growth and development ## Abstract Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified. The advantage of living in cities compared with rural areas with respect to height and BMI in children and adolescents has generally become smaller globally from 1990 to 2020, except in sub-Saharan Africa. ## Main The growth and development of school-aged children and adolescents (ages 5–19 years) are influenced by their nutrition and environment at home, in the community and at school. Healthy growth and development at these ages help consolidate gains and mitigate inadequacies from early childhood and vice versa1, with lifelong implications for health and well-being2–6. Until recently, the growth and development of older children and adolescents received substantially less attention than in early childhood and adulthood7. Increasing attention on the importance of health and nutrition during school years has been accompanied by a presumption that differences in nutrition and the environment lead to distinct, and generally less healthy, patterns of growth and development at these ages in cities compared to rural areas8–17. This presumption is despite some empirical studies showing that food quality and nutrition are better in cities18,19. Data on growth and developmental outcomes during school ages are needed, alongside data on the efficacy of specific interventions and policies, to select and prioritize policies and programmes that promote health and health equity, both for the increasing urban population and for children who continue to grow up in rural areas. Consistent and comparable global data also help benchmark across countries and territories and draw lessons on good practice. Yet, globally, there are fewer data on growth trajectories in rural and urban areas in these formative ages than for children under 5 years of age20 or for adults21. The available studies have been in one country, at one point in time and/or in one sex and narrow age groups. The few studies that covered more than one country22–24 mostly focused on older girls and used at most a few dozen data sources and hence could not systematically measure long-term trends. Consequently, many policies and programmes that aim to enhance healthy growth and development in school ages focus narrowly and generically on specific features of nutrition or the environment in either cities or rural areas10,13,25–28. Little attention has been paid to the similarities and differences between relevant outcomes in these settings or to the heterogeneity of the urban–rural differences across countries. Here we report on the mean height and BMI of school-aged children and adolescents residing in rural and urban areas of 200 countries and territories (referred to as countries hereafter) from 1990 to 2020. Height and BMI are anthropometric measures of growth and development that are influenced by the quality of nutrition and healthiness of the living environment and are highly predictive of health and well-being throughout life in observational and Mendelian randomization studies2–6. These studies have shown that having low height and excessively low BMI increases the risk of morbidity and mortality, and low height impairs cognitive development and reduces educational performance and work productivity in later life2–4. A high BMI in these ages increases the lifelong risk of overweight and obesity and several non-communicable diseases, and might contribute to poor educational outcomes5,6. We used 2,325 population-based studies that measured height and weight in 71 million participants in 194 countries (Extended Data Fig. 1 and Supplementary Table 2). We used these data in a Bayesian hierarchical meta-regression model to estimate mean height and BMI of children and adolescents aged 5–19 years by rural and urban place of residence, year and age for 200 countries. Details of data sources and statistical methods are provided in the Methods. Our results represent the height and BMI for children and adolescents of the same age over time (that is, successive cohorts) in rural and urban areas of each country, and the difference between the two. For presentation, we summarize the 15 age-specific estimates, for single years of age from 5 to 19, through age standardization, which puts each country-year’s child and adolescent population on the same age distribution and enables comparisons to be made over time and across countries. We also show results, graphically and numerically, for index ages of 5, 10, 15 and 19 years in the Supplementary Information. In 1990, school-aged boys and girls who lived in cities had a height advantage (that is, were taller) compared with their rural counterparts. The exception was in high-income countries, where the urban height advantage was either negligible (<1.2 cm for age-standardized mean height; posterior probability (PP) for children living in urban areas being taller ranging from 0.51 to >0.99) or there was a small rural advantage (for example, Belgium, the Netherlands and the United Kingdom) (PP for children in rural areas being taller ranging from 0.53 to >0.95 where there was a rural height advantage) (Fig. 1 and Extended Data Fig. 2). The largest height differences between children and adolescents in cities and rural areas in 1990 occurred in some countries in Latin America (for example, Mexico, Guatemala, Panama and Peru), east and southeast Asia (China, Indonesia and Vietnam), central and eastern Europe (Bulgaria, Hungary and Romania) and sub-Saharan Africa (Democratic Republic of Congo (DR Congo) and Rwanda). The urban height advantage in boys and girls in the named countries ranged from 2.4 to 5.0 cm, and the PP of children living in urban areas being taller than children living in rural areas was >0.99 (see Supplementary Table 3 for country-specific numerical values of height in children living in rural versus urban areas, their difference and the corresponding credible intervals (CrIs)).Fig. 1Change in the urban–rural height difference from 1990 to 2020.a,b, Change in the urban–rural difference in age-standardized mean height in relation to the change in age-standardized mean rural height in girls (a) and boys (b). Each solid arrow in lighter shade shows one country beginning in 1990 and ending in 2020. The dashed arrows in darker shade show the regional averages, calculated as the unweighted arithmetic mean of the values for all countries in each region along the horizontal and vertical axes. For the urban–rural difference, a positive number shows a higher urban mean height and a negative number shows higher rural mean height. See Extended Data Fig. 2 for urban–rural differences in age-standardized mean height and their change over time shown as maps, together with uncertainties in the estimates. See Supplementary Fig. 4a for results at ages 5, 10, 15 and 19 years. We did not estimate the difference between rural and urban height for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau). The urban–rural height gap in the late twentieth century among low-income and middle-income countries was determined by how much children and adolescents in cities and rural areas had approached as opposed to fallen behind their peers in high-income countries, where there was little difference between urban and rural height. In countries such as Bulgaria, Hungary and Romania, the height of children and adolescents living in urban areas approached that of high-income countries, whereas children and adolescents in rural areas lagged behind, leading to a relatively large gap. In much of sub-Saharan Africa and south Asia, the height of children and adolescents lagged behind their peers in high-income countries regardless of where they lived, such that the urban–rural gap was relatively small. In a third group of low-income or middle-income countries that included Indonesia, Vietnam, Panama, Peru, DR Congo and Rwanda, children living in urban areas remained shorter than in high-income countries, but children from rural areas lagged even further behind, such that the urban–rural gap became large. By 2020, the urban height advantage in school ages became smaller in much of the world. In many high-income western countries and some central European countries, it disappeared or reversed into a small (typically <1 cm) urban disadvantage (Fig. 1 and Extended Data Figs. 2 and 8). Countries with substantial convergence over these three decades were in central and eastern Europe (for example, Croatia), Latin America and the Caribbean (for example, Argentina, Brazil, Chile and Paraguay), east and southeast Asia (for example, Taiwan) and for girls in central Asia (for example, Kazakhstan and Uzbekistan). The urban height advantage in the named countries declined by around 1–2.5 cm from 1990 to 2020 (the PP of urban–rural height difference having declined ≥0.90 for the named countries). In many other middle-income countries (for example, China, Romania and Vietnam), the urban–rural height gaps declined, but children and adolescents living in cities remained taller than their rural counterparts (by 1.7–2.5 cm in the named countries for boys and girls; the PP of children in cities being taller than children in rural areas in 2020 >0.99). The exception to this convergence was for boys in most countries in sub-Saharan Africa and some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa, where the urban height advantage slightly increased over these three decades. The largest increase in the urban height advantage for boys occurred in countries in east Africa such as Ethiopia (0.9 cm larger height gap in 2020 than 1990; $95\%$ CrI −0.9 to 2.9, and PP of an increase of 0.86), Rwanda (1.0 cm larger gap, $95\%$ CrI −0.7 to 3.0, and PP 0.88) and Uganda (1.1 cm larger gap, $95\%$ CrI of −0.6 to 3.1, and PP 0.89). For girls, the urban–rural gap remained largely unchanged in many countries in sub-Saharan Africa and south Asia. In middle-income countries and emerging economies (newly high-income and industrialized countries) where the height of children and adolescents residing in rural areas converged to those in cities, successive cohorts of children and adolescents living in rural areas outpaced their urban counterparts in becoming taller and attained heights that urban children in the same countries had done decades earlier: growing to heights closer to those seen in high-income countries (Figs. 2 and 3). Successive cohorts of children and adolescents residing in rural areas in sub-Saharan Africa did not experience the accelerated height gain seen in cohorts in rural areas of middle-income countries. Notably, in the case of boys living in sub-Saharan Africa, there was no gain, or possibly a decrease, in height, which in turn led to a persistence or even widening of the urban–rural gap. As a result of these global trends, by 2020, the largest urban–rural gaps in height were seen in Andean and central Latin America (for example, Bolivia, Panama and Peru, by up to 4.7 cm ($95\%$ CrI 4.0–5.5 cm) for boys and 3.8 cm ($95\%$ CrI 3.3–4.3 cm) for girls) and, especially for boys, in sub-Saharan Africa (for example, DR Congo, Ethiopia, Mozambique and Rwanda, by up to 4.2 cm ($95\%$ CrI 2.7–5.7 cm)).Fig. 2Urban and rural height in 2020 and the change from 1990 to 2020 for girls.a, Age-standardized mean height in 2020 by urban and rural place of residence for girls. The density plots show the distribution of estimates across countries. b, Age-standardized change in mean height from 1990 to 2020 by urban and rural place of residence for girls. The density plots show the distribution of estimates across countries. c, Change in mean height from 1990 to 2020 in relation to the uncertainty of the change measured by posterior standard deviation. Each point in the scatter plots shows one country. Shaded areas approximately show the PP of an estimated change being a true increase or decrease. The PP of a decrease is one minus that of an increase. If an increase in mean height is statistically indistinguishable from a decrease, the PP of an increase and a decrease is 0.50. PPs closer to 0.50 indicate more uncertainty, whereas those towards 1 indicate more certainty of change. d, Age-standardized mean height in 2020 for all countries. The height of each column is the posterior mean estimate shown together with its $95\%$ CrI. Countries are ordered by region and super-region. See Extended Data Fig. 4 for a map of PP of the estimated change. See Supplementary Fig. 5 for results at ages 5, 10, 15 and 19 years. See Supplementary Table 3 for numerical results, including Crls, as age-standardized and at ages 5, 10, 15 and 19 years. We did not estimate mean rural height in countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore), mean urban height in countries classified as entirely rural (Tokelau) or their change over time in these countries, as indicated in grey. Countries are labelled using their International Organization for Standardization (ISO) 3166-1 alpha-3 codes. Afghanistan, AFG; Albania, ALB; Algeria, DZA; American Samoa, ASM; Andorra, AND; Angola, AGO; Antigua and Barbuda, ATG; Argentina, ARG; Armenia, ARM; Australia, AUS; Austria, AUT; Azerbaijan, AZE; Bahamas, BHS; Bahrain, BHR; Bangladesh, BGD; Barbados, BRB; Belarus, BLR; Belgium, BEL; Belize, BLZ; Benin, BEN; Bermuda, BMU; Bhutan, BTN; Bolivia, BOL; Bosnia and Herzegovina, BIH; Botswana, BWA; Brazil, BRA; Brunei Darussalam, BRN; Bulgaria, BGR; Burkina Faso, BFA; Burundi, BDI; Cabo Verde, CPV; Cambodia, KHM; Cameroon, CMR; Canada, CAN; Central African Republic, CAF; Chad, TCD; Chile, CHL; China, CHN; Colombia, COL; Comoros, COM; Congo, COG; Cook Islands, COK; Costa Rica, CRI; Cote d'Ivoire, CIV; Croatia, HRV; Cuba, CUB; Cyprus, CYP; Czechia, CZE; Denmark, DNK; Djibouti, DJI; Dominica, DMA; Dominican Republic, DOM; DR Congo, COD; Ecuador, ECU; Egypt, EGY; El Salvador, SLV; Equatorial Guinea, GNQ; Eritrea, ERI; Estonia, EST; Eswatini, SWZ; Ethiopia, ETH; Fiji, FJI; Finland, FIN; France, FRA; French Polynesia, PYF; Gabon, GAB; Gambia, GMB; Georgia, GEO; Germany, DEU; Ghana, GHA; Greece, GRC; Greenland, GRL; Grenada, GRD; Guatemala, GTM; Guinea Bissau, GNB; Guinea, GIN; Guyana, GUY; Haiti, HTI; Honduras, HND; Hungary, HUN; Iceland, ISL; India, IND; Indonesia, IDN; Iran, IRN; Iraq, IRQ; Ireland, IRL; Israel, ISR; Italy, ITA; Jamaica, JAM; Japan, JPN; Jordan, JOR; Kazakhstan, KAZ; Kenya, KEN; Kiribati, KIR; Kuwait, KWT; Kyrgyzstan, KGZ; Lao PDR, LAO; Latvia, LVA; Lebanon, LBN; Lesotho, LSO; Liberia, LBR; Libya, LBY; Lithuania, LTU; Luxembourg, LUX; Madagascar, MDG; Malawi, MWI; Malaysia, MYS; Maldives, MDV; Mali, MLI; Malta, MLT; Marshall Islands, MHL; Mauritania, MRT; Mauritius, MUS; Mexico, MEX; Micronesia (Federated States of), FSM; Moldova, MDA; Mongolia, MNG; Montenegro, MNE; Morocco, MAR; Mozambique, MOZ; Myanmar, MMR; Namibia, NAM; Nauru, NRU; Nepal, NPL; Netherlands, NLD; New Zealand, NZL; Nicaragua, NIC; Niger, NER; Nigeria, NGA; Niue, NIU; North Korea, PRK; North Macedonia, MKD; Norway, NOR; Occupied Palestinian Territory, PSE; Oman, OMN; Pakistan, PAK; Palau, PLW; Panama, PAN; Papua New Guinea, PNG; Paraguay, PRY; Peru, PER; Philippines, PHL; Poland, POL; Portugal, PRT; Puerto Rico, PRI; Qatar, QAT; Romania, ROU; Russian Federation, RUS; Rwanda, RWA; Saint Kitts and Nevis, KNA; Saint Lucia, LCA; Samoa, WSM; Sao Tome and Principe, STP; Saudi Arabia, SAU; Senegal, SEN; Serbia, SRB; Seychelles, SYC; Sierra Leone, SLE; Singapore, SGP; Slovakia, SVK; Slovenia, SVN; Solomon Islands, SLB; Somalia, SOM; South Africa, ZAF; South Korea, KOR; South Sudan, SSD; Spain, ESP; Sri Lanka, LKA; Saint Vincent and the Grenadines, VCT; Sudan, SDN; Suriname, SUR; Sweden, SWE; Switzerland, CHE; Syrian Arab Republic, SYR; Taiwan, TWN; Tajikistan, TJK; Tanzania, TZA; Thailand, THA; Timor-Leste, TLS; Togo, TGO; Tokelau, TKL; Tonga, TON; Trinidad and Tobago, TTO; Tunisia, TUN; Turkey, TUR; Turkmenistan, TKM; Tuvalu, TUV; Uganda, UGA; Ukraine, UKR; United Arab Emirates, ARE; United Kingdom, GBR; United States of America, USA; Uruguay, URY; Uzbekistan, UZB; Vanuatu, VUT; Venezuela, VEN; Vietnam, VNM; Yemen, YEM; Zambia, ZMB.Fig. 3Urban and rural height in 2020 and change from 1990 to 2020 for boys.a–d, See the caption for Fig. 2 for descriptions of the contents of the figure and for definitions. We did not estimate mean rural height in countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore), mean urban height in countries classified as entirely rural (Tokelau) or their change over time, as indicated in grey. The urban–rural BMI difference was relatively small throughout these three decades, <1.4 kg m–2 in all countries and years and <1.1 kg m–2 in all but nine countries, for age-standardized mean BMI (Fig. 4 and Extended Data Figs. 3 and 9). In 1990, the urban–rural BMI gap was largest in sub-Saharan Africa (for example, Ethiopia, Kenya, Malawi, South Africa and Zimbabwe) and south Asia (for example, Bangladesh and India), followed by parts of Latin America (for example, Mexico and Peru). The urban–rural BMI gap in the two sexes in the named countries ranged from 0.4 to 1.2 kg m–2, and the PP of children and adolescents living in urban areas having a higher BMI than those in rural areas was ≥0.89. At that time, girls and/or boys in rural areas of some of these countries had mean BMI levels that were close to, and in some ages below, the thresholds of being underweight (>1 s.d. below the median of the World Health Organization (WHO) reference population).Fig. 4Change in the urban–rural BMI difference from 1990 to 2020.a,b, Change in urban–rural difference in age-standardized mean BMI for girls (a) and boys (b) in relation to change in age-standardized mean rural BMI. See the caption for Fig. 1 for a description of the contents of this figure. See Extended Data Fig. 3 for urban–rural differences in age-standardized mean BMI and their change over time shown as maps, together with uncertainties in the estimates. See Supplementary Fig. 4b for results at ages 5, 10, 15 and 19 years. We did not estimate the difference between rural and urban BMI for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau). From 1990 to 2020, the BMI of successive cohorts of children and adolescents in both urban and rural areas increased in all but a few mostly high-income countries (for example, Denmark, Italy and Spain) (Figs. 5 and 6). There was heterogeneity in low-income and middle-income countries in how much the BMI increased in cities compared with rural areas. In the majority of countries in sub-Saharan Africa and south Asia, the BMI of successive cohorts of children and adolescents increased more in rural areas than in cities, leading to a closing of the urban–rural difference. The urban–rural BMI gap declined by up to 0.65 kg m–2 for both girls and boys, and the PP that the urban–rural BMI difference declined from 1990 to 2020 ranged from 0.52 to 0.95. In both sub-Saharan Africa and south Asia, these changes shifted the mean BMI of boys and girls in rural areas out of the range for being underweight. Moreover, in many countries in sub-Saharan Africa, this shift continued beyond the median of the WHO reference population and in some cases approached the threshold for being overweight (>1 s.d. above the median of the WHO reference population). The opposite, a larger increase in urban BMI, happened in most other low-income and middle-income countries, leading to a slightly larger urban BMI excess in 2020 than in 1990. High-income countries and those in central and eastern Europe experienced a mix of increasing and decreasing urban BMI excess, but remained within a small range (−0.3 to 0.6 kg m–2 for almost all countries) over the entire period of analysis. At the regional level, the urban–rural BMI difference changed by <0.25 kg m–2 in these regions. Fig. 5Urban and rural BMI in 2020 and change from 1990 to 2020 for girls.a–d, See the caption for Fig. 2 for descriptions of the contents of the figure and for definitions. See Extended Data Fig. 5 for a map of PP of the estimated change. See Supplementary Fig. 6 for results at ages 5, 10, 15 and 19 years. See Supplementary Table 4 for numerical results, including CrIs, as age-standardized and at ages 5, 10, 15 and 19 years. We did not estimate mean rural BMI in countries classified as entirely urban (Singapore, Bermuda and Nauru), mean urban BMI in areas classified as entirely countries (Tokelau) or their change over time, as indicated in grey. Fig. 6Urban and rural BMI in 2020 and change from 1990 to 2020 for boys.a–d, See the caption for Fig. 2 for descriptions of the contents of the figure and for definitions. We did not estimate mean rural BMI in countries classified as entirely urban (Singapore, Bermuda and Nauru), mean urban BMI in countries classified as entirely rural (Tokelau) or their change over time, as indicated in grey. The urban height advantage was larger in boys than girls in most countries (Supplementary Fig. 3). Urban excess BMI was larger in boys than girls in only about one-half of the countries. For the other half, mostly in high-income western countries and those in sub-Saharan Africa, urban excess BMI was larger in girls than boys. The urban height advantage was slightly larger at 5 years of age than at 19 years of age in most low-income and middle-income countries, especially for girls, but there was little difference across ages in high-income regions and in central and eastern Europe (Supplementary Fig. 4). Since the introduction of modern sanitation in the nineteenth century, cities provided substantial nutritional and health advantages in high-income and subsequently low-income and middle-income countries19. Our results show that in the twenty-first century, during school ages, these advantages have disappeared in high-income countries and diminished in middle-income countries and emerging economies in Asia, Latin America and the Caribbean, and parts of Middle East and north Africa. Specifically, in these settings, successive cohorts of school-aged children and adolescents living in cities were outpaced by those in rural areas in terms of height gain but gained slightly more weight by 2020, typically in the unhealthy range (Fig. 7). This contrasted with the poorest region in the world: sub-Saharan Africa. In this region, the urban height advantage persisted or even expanded, whereas rural mean BMI went beyond remedying underweight and surpassed the median of the WHO reference population in 2020, hence consolidating the urban advantage. South Asia had a mixed pattern of urban versus rural trends from 1990 to 2020, with children and adolescents in rural areas gaining both more height and more weight for their height than those in cities. Notably, our results also show that differences in height and BMI between urban and rural populations within most countries are smaller than the differences across countries, even those in the same region. Fig. 7Change in the urban–rural height and BMI difference from 1990 to 2020.a,b, Change in the urban–rural difference in age-standardized mean height and the urban–rural difference in age-standardized mean BMI in girls (a) and boys (b). See the caption for Fig. 1 for a description of the contents of this figure. See Supplementary Fig. 4c for results at ages 5, 10, 15 and 19 years. We did not estimate the difference between rural and urban height and BMI for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau). We also found that the urban–rural BMI gap, although dynamic, changed much less than the BMI of either subgroup of the population and less than commonly assumed when discussing the role of cities in the obesity epidemic8,10,12,13,15,16. Urban–rural BMI differences were especially small in high-income countries, which is consistent with evidence from a few countries that show diets and behaviours are affected more by household socioeconomic status than whether children and adolescents live in cities or rural areas29,30. Urban BMI excess increased slightly more in middle-income countries in east and southeast Asia, Latin America and the Caribbean, and Middle East and north Africa, a trend that was the opposite of the convergence in BMI of adults in these same regions21. Additional analyses of data collected by the NCD Risk Factor Collaboration (NCD-RisC) for young adults (20–29 and 30–39 years) showed that the shift from a small divergent trend to convergence of BMI between urban and rural areas happens in young adulthood (Extended Data Figs. 6 and 7), a period during which there is substantial, but variable, weight gain among population subgroups31. These shifts in trends from adolescence to young adulthood might be a result of changes in diet and energy expenditure that accompany changes in household structure, social and economic roles and the living environment32–34. Long-term follow-up studies have shown that children and adolescents do not achieve their height potential if they do not consume sufficient and diverse nutritious foods or if they are exposed to repeated or persistent infections, which result in loss of nutrients2. Studies that use data on household socioeconomic and environmental factors have indicated that these physiological determinants of height are themselves affected by income, education, quality of the living environment and access to healthcare in rural as well as urban areas35. This evidence indicates that the relatively small urban–rural height differentials in high-income countries may be because of a greater abundance of nutritious foods, including some fortified foods, better education and healthcare and greater ability to finance programmes that promote healthy growth in countries with greater per-capita income and better infrastructure. Variations across these countries in the urban–rural height gap within this small range may be due to the extent of socioeconomic inequalities and poverty, differences in the availability and cost of nutritious foods between cities and rural areas and whether there are specific programmes (for example, food assistance or school food programmes) that improve nutrition in disadvantaged groups30,36,37. The more marked changes in height in urban versus rural areas took place in middle-income countries and emerging economies. Case studies in some countries where the heights of children and adolescents living in rural and urban areas converged show that the convergence was partly due to using the growth in national income towards programmes and services that helped close gaps in nutrition, sanitation and healthcare between different areas and social groups38–40. In countries in central and eastern Europe, transition to a market economy and increases in trade may have reduced the disparity in access to, and seasonality of, healthy foods between urban and rural areas41, and partly underlie the convergence of height seen in our results. By contrast, case studies in some countries have shown that where economic growth was accompanied by large inequalities in income, nutrition and/or services, the urban advantage persisted42–44. The notable exception in the global trends was sub-Saharan Africa, where a stagnation or reversal of height gain in rural areas led to the persistence or widening of urban–rural height differences, whereas the opposite happened for BMI (Fig. 7). Case studies of specific countries have indicated that unfavourable trends in nutrition in rural Africa, where the majority of the poorest people in the world live, started from macroeconomic shocks in the late twentieth century45 and subsequent agriculture, trade and development policies that limited improvements in income and services, and emphasized agricultural exports over local food security and diversity45. These macroeconomic factors in turn led to less diverse diets, with higher caloric intake rather than a shift to protein-rich and nutrient-rich foods (for example, animal products, seafood, fruits and vegetables)46–48. Moreover, the slow expansion of infrastructure and services in rural areas restricted improvements in other determinants of healthy growth, such as clean water, sanitation and health care49. Several other factors may have had a secondary role in the observed trends in height and BMI and their difference in rural and urban areas. First, weight gain during childhood may reduce the age of puberty onset, which in turn may limit height gain during adolescence50,51. No comparable global data currently exist on age at menarche and timing of pubertal growth, even at the national level. Second, rural-to-urban migration and reclassification of previously rural areas to urban as they grow and industrialize may account for some of the observed population-level trends. However, migration tends to be less common in childhood and adolescence than in adulthood in most countries. Finally, improvements in survival among children aged under 5 years in rural areas, particularly low-birthweight children, may have influenced the height and weight of those who survive beyond 5 years of age. However, current data on changes in child survival in rural and urban areas in sub-Saharan Africa are limited and inconclusive in terms of whether mortality declined faster in rural or urban areas52,53. As attention in global health turns to children and adolescents, there is a need to consider and evaluate how growth and development in these formative ages may be affected both by social and economic policies that influence household income and poverty and by programmes that affect nutrition, health services, infrastructure and living environments in rural and urban areas. The need to identify, implement and evaluate policies and programmes that improve growth and development outcomes is particularly relevant as the increase in poverty and the cost of food, especially of nutrient-rich foods, as a result of the macroeconomic changes resulting from the COVID-19 pandemic and the war in Ukraine, may hinder further gains or even set back healthy growth and development in children and adolescents. ## Methods We estimated trends in mean height and BMI for children and adolescents aged 5–19 years from 1990 to 2020 by rural and urban place of residence for the 200 countries and territories listed in Supplementary Table 1. We pooled, in a Bayesian meta-regression, repeated cross-sectional population-based data on height and BMI. Our results represent estimates of height and BMI for children and adolescents of the same age over time (that is, for successive cohorts) in rural and urban settings for each country. ## Data sources We used a database on cardiometabolic risk factors collated by NCD-RisC. Data were obtained from publicly available multi-country and national measurement surveys, for example, Demographic and Health Surveys (DHS), WHO-STEPwise approach to Surveillance (STEPS) surveys, and those identified through the Inter-University Consortium for Political and Social Research, UK Data Service and European Health Interview & Health Examination Surveys Database. With the help of the WHO and its regional and country offices as well as the World Heart Federation, we identified and accessed population-based survey data from national health and statistical agencies. We searched and reviewed published studies as previously detailed54 and invited eligible studies to join NCD-RisC, as we did with data holders from earlier pooled analyses of cardiometabolic risk factors55–58. The NCD-RisC database is continuously updated through all the above routes and through periodic requests to NCD-RisC members to ask them to suggest additional sources in their countries. We carefully checked that each data source met our inclusion criteria, as listed below. Potential duplicate data sources were first identified by comparing studies from the same country and year, followed by checking with NCD-RisC members that had provided data about whether the sources from the same country and year, with similar samples, were the same or distinct. If two sources were confirmed as duplicates, one was discarded. All NCD-RisC members were also periodically asked to review the list of sources from their country to verify that the included data met the inclusion criteria and were not duplicates. For each data source, we recorded the study population, the sampling approach, the years of measurement and the measurement methods. Only data that were representative of the population were included. All data sources were assessed in terms of whether they covered the entire country, one or more subnational regions (that is one or more provinces or states, more than three cities, or more than five rural communities), or one or a small number of communities (limited geographical scope not meeting above national or subnational criteria), and whether participants in rural, urban or both areas were included. As stated in the sections on the statistical model, these study-level attributes were used in the Bayesian hierarchical model to estimate mean height and BMI by country, year, sex, age and place of residence using all available data while taking into account differences in the populations from which different studies had sampled. All submitted data were checked by at least two independent individuals. Questions and clarifications were discussed with NCD-RisC members and resolved before data were incorporated into the database. Anonymized individual data from the studies in the NCD-RisC database were re-analysed according to a common protocol. We calculated the mean height and the mean BMI, and the associated standard errors, by sex, single year of age from 5 to 19 years and rural or urban place of residence. Additionally, for analysis of height, participants aged 20–30 years were included, assigned to their corresponding birth cohort, because mean height in these ages would be at least that when they were aged 19 years given that the decline in height with age begins in the third and fourth decades of life59. All analyses incorporated sample weights and complex survey design, when applicable, in calculating summary statistics. For studies that had used simple random sampling, we calculated the mean as the average of all individuals within the group and the associated standard error (s.d. divided by the square root of sample size); for studies that had used multistage (stratified) sampling, we accounted for survey design features, including clusters, strata and sample weights, to weight each observation by the inverse sampling probability and estimated standard error through Taylor series linearization, as implemented in the R ‘survey’ package60. Computer code was provided to NCD-RisC members who requested assistance. For surveys without information on the place of residence, we calculated summary statistics stratified by age and sex for the entire sample, which represented the population-weighted sum of rural and urban means; data on the share of population in urban and rural areas were from the United Nations Population Division61. Additionally, summary statistics for nationally representative data from sources that were identified but not accessed using the above routes were extracted from published reports. Data were also extracted for two STEPS surveys that were not publicly available. We also included data from a previous global-data pooling study58, when not accessed through the above routes. ## Data inclusion and exclusion Data sources were included in the NCD-RisC height and weight database if the following criteria were met: measured data on height and weight were available; study participants were 5 years of age or older; data were collected using a probabilistic sampling method with a defined sampling frame; data were from population samples at the national, subnational or community level as defined above; and data were from the countries and territories listed in Supplementary Table 1. We excluded all data sources that were solely based on self-reported weight and height without a measurement component because these data are subject to biases that vary by geography, time, age, sex and socioeconomic characteristics62–64. Owing to these variations, approaches to correcting self-reported data may leave residual bias. We also excluded data sources on population subgroups for which anthropometric status may differ systematically from the general population, including the following: studies that had included or excluded people based on their health status or cardiovascular risk; studies in which participants were only ethnic minorities; specific educational, occupational or socioeconomic subgroups (with the exception noted below); those recruited through health facilities (with the exception noted below); and females aged 15–19 years in surveys that sampled only ever-married women or measured height and weight only among mothers. We used school-based data in countries and age–sex groups with school enrolment of $70\%$ or higher. We used data for which the sampling frame was health insurance schemes in countries where at least $80\%$ of the population were insured. Finally, we used data collected through general practice and primary care systems in high-income and central European countries with universal insurance because contact with the primary care systems tends to be as good as or better than response rates for population-based surveys. We excluded participants whose age was <18 years and whose data were not reported by single year of age (<$0.01\%$ of all participants) because height and weight may have nonlinear age associations in these ages, especially during growth spurts. We excluded BMI data for females who were pregnant at the time of measurement (<$0.01\%$ of all participants). We excluded <$0.2\%$ of all participants who had recorded height: <60 cm or >180 cm for ages <10 years; <80 cm or >200 cm for ages 10–14 years; <100 cm or >250 cm for ages ≥15 years, or who had recorded weight: <5 kg or >90 kg for age <10 years; <8 kg or >150 kg for ages 10–14 years; <12 kg or >300 kg for ages ≥15 years, or who had recorded BMI: <6 kg m–2 or >40 kg m–2 for ages <10 years; <8 kg m–2 or >60 kg m–2 for ages 10–14 years; <10 kg m–2 or >80 kg m–2 for ages ≥15 years. ## Conversion of BMI prevalence metrics to mean BMI In $0.5\%$ of our data points, mostly extracted from published reports or from a previous pooling analysis58, the mean BMI was not reported but data were available for the prevalence of one or more BMI categories, for example BMI ≥30 kg m–2. To use these data, we used previously validated conversion regressions65 to estimate the missing primary outcome from the available BMI prevalence metric or metrics. Additional details on regression model specifications along with the regression coefficients are reported at https://github.com/NCD-RisC/ncdrisc-methods/. ## Statistical model overview We used a Bayesian hierarchical meta-regression model to estimate the mean height and BMI by country, year, sex, age and place of residence using the aforementioned data. For presentation, we summarized the 15 age-specific estimates, for single years of age from 5 to 19 years, through age standardization, which puts the child and adolescent population for each country-year on the same age distribution, and hence enables comparisons to be made over time and across countries. We generated age-standardized estimates by taking weighted means of age-specific estimates using age weights from the WHO standard population66. We also show results, graphically and numerically, for index ages of 5, 10, 15 and 19 years in the Supplementary Information. The statistical model is described in detail in statistical papers67,68, related substantive papers7,20,21,55–58,65,69 and in the section below on model specification. In summary, the model had a hierarchical structure in which estimates for each country and year were informed by its own data, if available, and by data from other years in the same country and from other countries, especially those in the same region and super-region, with data for similar time periods. The extent to which estimates for each country-year were influenced by data from other years and other countries depended on whether the country had data, the sample size of the data, whether they were national, and the within-country and within-region variability of the available data. For the purpose of hierarchical analysis, countries and territories were organized into 21 regions, mostly based on geography and national income (Supplementary Table 1). Regions were in turn organized into nine super-regions. We used observation year, that is, the year in which data were collected, as the timescale for the analysis of BMI and birth year as the timescale for the analysis of height, consistent with previous analyses7,65,70. Time trends were modelled through a combination of a linear term, to capture gradual long-term change, and a second-order random walk, which allows for nonlinear trends71, both modelled hierarchically. The age associations of height and BMI were modelled, using cubic splines, to allow for nonlinear changes over age, including periods of rapid and slow rise. Periods of rapid rise representing adolescent growth spurts, which occur earlier in girls than boys72–74, were reflected in the placement of spline knots for boys and girls, respectively, as detailed in the section on model specification. Spline coefficients were allowed to vary across countries, informed by their own data as well as data from other countries as specified by a hierarchical structure, as previously described69. The model also accounted for the possibility that height or BMI in subnational and community samples might differ systematically from nationally representative samples and have larger variation than in national studies. These features were accounted for through the inclusion of fixed-effect and random-effect terms for subnational and community data as detailed in the model specification section below. The fixed effects accounted for systematic differences between subnational or community studies and national studies. The inclusion of random effects allowed national data to have greater influence on the estimates than subnational or community data with similar sample sizes because the subnational and community data have additional variance from the random-effect terms. Both were estimated empirically as a part of model fitting. Following the approach of previous papers20,21,67, the model included parameters representing the urban–rural height or BMI difference, which is empirically estimated and allowed to vary by country and year. We further expanded the model to allow urban–rural difference in height or BMI to vary by age, as height or weight with age may vary between children residing in rural versus urban areas. If data for a country-year-age group contained a mix of children living in urban and rural areas but were not stratified by place of residence ($21\%$ of all data sources), the estimated height or BMI difference was informed by stratified data from other age groups, years and countries, especially those in the same region with data from similar time periods and/or ages. ## Statistical model specification As stated earlier, for each data source, we calculated mean height and BMI, together with corresponding standard errors, stratified by sex, age and rural or urban place of residence. For sources that did not stratify the sample on the place of residence, we obtained age-and-sex-stratified data. Each study contributed up to 30 mean BMI data points or 32 mean height data points for each sex, with the exact number depending on how many age groups were represented in the study and whether the study provided data stratified on urban and rural place of residence. The likelihood for an observation at urbanicity level s (urban-only, rural-only or mixed; referred to as stratum hereafter) and age group h, with age zh, from study i, carried out in country j at time t is as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{c}{y}_{s,h,i}\sim N({a}_{j[i]}+{b}_{j[i]}{t}_{i}+{u}_{j[i],{t}_{i}}+{\gamma }_{i}({z}_{h})+{{\boldsymbol{X}}}_{{\boldsymbol{i}}}\,{\boldsymbol{\beta }}+{e}_{i}\\ \,\,+{I}_{s,i}[{p}_{j[i]}+{q}_{j[i]}{t}_{i}+{r}_{j[i]}{z}_{h}+{d}_{i}],{{\rm{S}}{\rm{D}}}_{s,h,i}^{2}/{n}_{s,h,i}+\,{\tau }^{2}),\end{array}$$\end{document}ys,h,i∼N(aj[i]+bj[i]ti+uj[i],ti+γi(zh)+Xiβ+ei+Is,i[pj[i]+qj[i]ti+rj[i]zh+di],SDs,h,i2/ns,h,i+τ2),where the country-specific intercept and linear time slope from the jth country ($j = 1$ … J, where $J = 200$ which is the total number of countries in our analysis) are denoted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}_{j}$$\end{document}aj and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{j}$$\end{document}bj, respectively. We describe the hierarchical model used for the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}^{{\prime} }$$\end{document}a′s and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}^{{\prime} }s$$\end{document}b′s in the section ‘Linear components of country time trends’. Letting $T = 31$ be the total number of years from 1990 to 2020, the T-length vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}$$\end{document}uj captures smooth nonlinear change over time in country j, as described in the section ‘Nonlinear change’. The age effects of the hth age group (with age \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z}_{h}$$\end{document}zh) in study \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i are denoted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\gamma }_{i}$$\end{document}γi; we describe the age model in the section ‘Age model’. The matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\boldsymbol{X}}$$\end{document}X contains terms describing whether studies were representative at the national, subnational or community level. In addition, a random effect, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e}_{i}$$\end{document}ei, is estimated for each study, described in the section ‘Study-level term and study-specific random effects’. ## Linear components of country time trends The model had a hierarchical structure, whereby studies were nested in countries, which were nested in regions (indexed by k), which were nested in super-regions (indexed by l), which were all nested in the globe (see Supplementary Table 1 for a list of countries and territories in each region, and regions in each super-region). This structure allowed the model to share information across units to a greater degree when data were non-existent or weakly informative (for example, had a small sample size or were not nationally representative) and, to a lesser extent, in data-rich countries and regions75. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a$$\end{document}a and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b$$\end{document}b terms are country-specific linear intercepts and time slopes with terms at each level of the hierarchy, denoted by the superscripts c, r, s and g, respectively:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}\,{a}_{j}={a}_{j}^{c}+{a}_{k[\,j]}^{r}+{a}_{l[k]}^{s}+{a}^{{\rm{g}}},\\ \,{b}_{j}={b}_{j}^{c}+{b}_{k[\,j]}^{r}+{b}_{l[k]}^{s}+{b}^{{\rm{g}}},\\ {a}_{j}^{x}\sim N(0,{\kappa }_{a}^{x}),\\ {b}_{j}^{x}\sim N(0,{\kappa }_{b}^{x}),\end{array}$$\end{document}aj=ajc+ak[j]r+al[k]s+ag,bj=bjc+bk[j]r+bl[k]s+bg,ajx∼N(0,κax),bjx∼N(0,κbx),where x = {c, r, s}. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ terms were each assigned a flat prior on the s.d. scale76. We also assigned flat priors to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}^{{\rm{g}}}$$\end{document}ag and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}^{{\rm{g}}}$$\end{document}bg. ## Nonlinear change Mean BMI or height may change nonlinearly over time7,54,58,65,70. We captured smooth nonlinear change in time in urban and rural strata of country j using the vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}$$\end{document}uj. Just as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}_{j}$$\end{document}aj and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{j}$$\end{document}bj are each defined as the sum of country, region, super-region and global components, we defined\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}={u}_{j}^{c}+{u}_{k[\,j]}^{r}+{u}_{l[k]}^{s}+{u}^{{\rm{g}}}.$$\end{document}uj=ujc+uk[j]r+ul[k]s+ug. To allow the model to differentiate between the degrees of nonlinearity that exist at the country, region, super-region and global levels, we assigned the four components of each \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u a Gaussian autoregressive prior71,77. In particular, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T$$\end{document}T vectors \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{c}$$\end{document}ujc ($j = 1$ … J), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{k}^{r}$$\end{document}ukr ($k = 1$ … K), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{l}^{s}$$\end{document}uls ($l = 1$ … L) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{g}}}$$\end{document}ug each have a normal prior with mean zero and precision \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{c}}}P$$\end{document}λcP, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{r}}}P$$\end{document}λrP, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{s}}}P$$\end{document}λsP and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{g}}}P$$\end{document}λgP, respectively, where the scaled precision matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P$$\end{document}P in the Gaussian autoregressive prior penalizes first and second differences as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{c}P\,=\,[\begin{array}{ccccc}\,1 & \,0 & \,0 & \,\cdots & \,0\\ -2 & \,1 & \,0 & \,\cdots & \,0\\ \,1 & -2 & \,1 & \,\cdots & \,0\\ \,0 & \,1 & -2 & \,\cdots & \,0\\ \,0 & \,0 & \,1 & \,\cdots & \,0\\ \,\vdots & \,\vdots & \,\vdots & \,\ddots & \,\vdots \\ \,0 & \,0 & \,0 & \,\cdots & \,1\end{array}]\,[\begin{array}{ccccccc}\,1 & -2 & \,1 & \,0 & \,0 & \,\cdots & \,0\\ \,0 & \,1 & -2 & \,1 & \,0 & \,\cdots & \,0\\ \,0 & \,0 & \,1 & -2 & \,1 & \,\cdots & \,0\\ \,\vdots & \,\vdots & \,\vdots & \,\vdots & \,\vdots & \,\ddots & \,\vdots \\ \,0 & \,0 & \,0 & \,0 & \,0 & \,\cdots & \,1\end{array}]\\ \,=\,[\begin{array}{ccccccc}\,1 & -2 & \,1 & \,0 & \,0 & \,\cdots & \,0\\ -2 & \,5 & -4 & \,1 & \,0 & \,\cdots & \,0\\ \,1 & -4 & \,6 & -4 & \,1 & \,\cdots & \,0\\ \,0 & \,1 & -4 & \,6 & -4 & \,\cdots & \,0\\ \,0 & \,0 & \,1 & -4 & \,6 & \,\cdots & \,0\\ \,\vdots & \,\vdots & \,\vdots & \,\vdots & \,\vdots & \,\ddots & \,\vdots \\ \,0 & \,0 & \,0 & \,0 & \,0 & \,\cdots & \,1\end{array}].\end{array}$$\end{document}P=[100⋯0−210⋯01−21⋯001−2⋯0001⋯0⋮⋮⋮⋱⋮000⋯1][1−2100⋯001−210⋯0001−21⋯0⋮⋮⋮⋮⋮⋱⋮00000⋯1]=[1−2100⋯0−25−410⋯01−46−41⋯001−46−4⋯0001−46⋯0⋮⋮⋮⋮⋮⋱⋮00000⋯1]. P is multiplied by the estimated precision parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{c}}}$$\end{document}λc, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{r}}}$$\end{document}λr, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{s}}}$$\end{document}λs and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{g}}}$$\end{document}λg, thus upweighting or downweighting the strength of its penalties and ultimately determining the degree of smoothing at each level. For each of the four precision parameters, we used a truncated flat prior on the s.d. scale (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/\surd \lambda $$\end{document}1/√λ)76. We truncated these priors such that log\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}λ ≤ 20 for each of the four \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }^{{\prime} }s$$\end{document}λ′s. This upper bound is enforced as a computational convenience, whereby models with log\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}λ > 20 are treated as equivalent to a model with log\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}λ = 20 as they essentially have no extralinear variability in time. In practice, this upper bound had little effect on the parameter estimates. Furthermore, we ordered the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }^{{\prime} }s$$\end{document}λ′s a priori as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{c}}}$$\end{document}λc < \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{r}}}$$\end{document}λr < \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{s}}}$$\end{document}λs < \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{{\rm{g}}}$$\end{document}λg. This prior constraint conveys the natural expectation that, for example, the global height or BMI trend has less extralinear variability than the trend of any given region, which in turn has less variability than those of constituent countries. The matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P$$\end{document}P has rank \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T$$\end{document}T − 2, corresponding to a flat, improper prior on the mean and the slope of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{{\rm{c}}}$$\end{document}ujc’s, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{k}^{{\rm{r}}}$$\end{document}ukr’s and the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{l}^{{\rm{s}}}$$\end{document}uls’s and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{g}}}$$\end{document}ug, and is not invertible78. Thus, we had a proper prior in a reduced-dimension space71, with the prior expressed as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P({u}_{j}^{{\rm{c}}}\| {\lambda }_{{\rm{c}}})\propto {\lambda }_{{\rm{c}}}^{\frac{T-2}{2}}\exp \left\{-\frac{{\lambda }_{{\rm{c}}}}{2}{u}_{j}^{{\rm{c}}{\prime} }P{u}_{j}^{{\rm{c}}}\right\}.$$\end{document}P(ujc∣λc)∝λcT−22exp−λc2ujc′Pujc. Note that if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{{\rm{c}}}$$\end{document}ujc had a non-zero mean, this would introduce non-identifiability with respect to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}_{j}^{{\rm{c}}}$$\end{document}ajc. By the same token, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{j}^{{\rm{c}}}$$\end{document}bjc would not be identifiable if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}$$\end{document}uj had a non-zero time slope, and similarly for the other means and slopes. Thus, to achieve identifiability of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}^{{\prime} }s$$\end{document}a′s, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}^{{\prime} }s$$\end{document}b′s, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\prime} }{\rm{s}}$$\end{document}u′s, we constrained the mean and slope of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{g}}}$$\end{document}ug and of each \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{s}}}$$\end{document}us, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{r}}}$$\end{document}ur and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{c}}}$$\end{document}uc to be zero. Enforcing orthogonality between the linear and nonlinear portions of the time trends meant that each can be interpreted independently. For the cases in which we have observations for at least two different time points, this improper prior will not lead to an improper posterior because the data will provide information about the mean and slope. In order to enforce the desired orthogonality between the linear and nonlinear portions of the model, we constrained the mean and slope of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{{\rm{c}}}$$\end{document}ujc’s, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{k}^{{\rm{r}}}$$\end{document}ukr’s and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{l}^{{\rm{s}}}$$\end{document}uls’s and of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}^{{\rm{g}}}$$\end{document}ug to be zero71. For the six countries with no height data, and seven countries with no BMI data, we took the Moore–Penrose pseudoinverse of P 79, setting to infinity those eigenvalues that correspond to the non-identifiability. This effectively constrained the non-identified portions of the model to zero, as the corresponding variances are set to zero77; in this case the Rue and Held correction71 is not needed. An intermediate case occurs when data are observed for only one time point in a country. In this case, the full conditional precision has rank \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T-1$$\end{document}T−1 because the mean but not the linear trend of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{{\rm{c}}}$$\end{document}ujc is identified by the data. We therefore constrained the linear trend of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{{\rm{c}}}$$\end{document}ujc to zero by taking the generalized inverse of the full conditional precision. We then constrained the mean of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{j}^{{\rm{c}}}$$\end{document}ujc to zero using the one-dimensional version of the Rue and Held correction71. ## Age model To capture sex-specific patterns of growth, especially adolescent growth spurts, we modelled age using cubic splines. The number and position of the knots of the splines were selected on the basis of a combination of physiological and statistical considerations, as described in a national level analysis7. For age group h with age zh, in study i, the age effect for height and BMI is given, respectively, as follows:height\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}{\gamma }_{i}({z}_{h})={\gamma }_{1i}\,{z}_{h}+{\gamma }_{2i}{z}_{h}^{2}+{\gamma }_{3i}{z}_{h}^{3}+{\gamma }_{4i}{({z}_{h}-{k}_{1})}_{+}^{3}+{\gamma }_{5i}{({z}_{h}-{k}_{2})}_{+}^{3}+{\gamma }_{6i}{({z}_{h}-{k}_{3})}_{+}^{3}+{\gamma }_{7i}{({z}_{h}-{k}_{4})}_{+}^{3},\end{array}$$\end{document}γi(zh)=γ1izh+γ2izh2+γ3izh3+γ4i(zh−k1)+3+γ5i(zh−k2)+3+γ6i(zh−k3)+3+γ7i(zh−k4)+3,BMI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}{\gamma }_{i}({z}_{h})={\gamma }_{1i}\,{z}_{h}+{\gamma }_{2i}{z}_{h}^{2}+{\gamma }_{3i}{z}_{h}^{3}+{\gamma }_{4i}{({z}_{h}-{k}_{1})}_{+}^{3}+{\gamma }_{5i}{({z}_{h}-{k}_{2})}_{+}^{3}.\end{array}$$\end{document}γi(zh)=γ1izh+γ2izh2+γ3izh3+γ4i(zh−k1)+3+γ5i(zh−k2)+3. For height, four spline knots were placed at ages {\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{1},{k}_{2},{k}_{3},{k}_{4}\}\,=$$\end{document}k1,k2,k3,k4}=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{\,8,\,10,\,12,14\}$$\end{document}{8,10,12,14} for girls and at ages {\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{1}$$\end{document}k1,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{2},{k}_{3},{k}_{4}\}=\{10,12,14,16\}$$\end{document}k2,k3,k4}={10,12,14,16} for boys. For BMI, we used two spline knots (at ages 10 and 15 years) because, at the population level, changes in BMI with age are smoother than those in height7,72,73. Each of the spline coefficients was allowed to vary across countries, with a hierarchical structure as described in a previous paper69, using the equation below, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\psi $$\end{document}ψ is the global intercept, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c,r\,{\rm{and}}\,s$$\end{document}c,rands are the country, region and super-region random intercepts, respectively. The kth age effect coefficients for study i (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\gamma }_{k,i}$$\end{document}γk,i) for each age group h, with age zh, are given as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}{\gamma }_{k,i}={\psi }_{k}+{c}_{k,j[i]}+{r}_{k,l[i]}+{s}_{k,m[i]},\\ {c}_{k,j} \sim N(0,{\sigma }_{k,c}^{2}),\\ {r}_{k,l} \sim N(0,{\sigma }_{k,r}^{2}),\\ {s}_{k,l} \sim N(0,{\sigma }_{k,s}^{2}).\end{array}$$\end{document}γk,i=ψk+ck,j[i]+rk,l[i]+sk,m[i],ck,j~N(0,σk,c2),rk,l~N(0,σk,r2),sk,l~N(0,σk,s2). A flat improper prior was placed on each of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\sigma }}_{k}$$\end{document}σk’s. ## Study-level term and study-specific random effects Mean height or BMI from individual studies may deviate from the true country-year mean owing to factors associated with sampling, response or measurement. We used a study-level term to help account for potential systematic differences associated with data sources that are representative of subnational and community populations. Our model therefore included time-varying offsets (referred to as fixed effects above) for subnational and community data in the term \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\boldsymbol{X}}}_{{\boldsymbol{i}}}{\boldsymbol{\beta }}$$\end{document}Xiβ:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}{{\boldsymbol{X}}}_{{\boldsymbol{i}}}{\boldsymbol{\beta }}={\beta }_{1}I\{{X}_{j[i],t[i]}^{{\rm{cvrg}}}={\rm{subnational}}\}+{\beta }_{2}I\{{X}_{j[i],t[i]}^{{\rm{cvrg}}}={\rm{subnational}}\}{t}_{i}\\ \,+\,{\beta }_{3}I\{{X}_{j[i],t[i]}^{{\rm{cvrg}}}={\rm{community}}\}+{\beta }_{4}I\{{X}_{j[i],t[i]}^{{\rm{cvrg}}}={\rm{community}}\}{t}_{i},\end{array}$$\end{document}Xiβ=β1I{Xj[i],t[i]cvrg=subnational}+β2I{Xj[i],t[i]cvrg=subnational}ti+β3I{Xj[i],t[i]cvrg=community}+β4I{Xj[i],t[i]cvrg=community}ti,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{j\left[i\right],t\left[i\right]}^{{\rm{cvrg}}}$$\end{document}Xji,ticvrg is the indicator for whether the coverage of study i, in country j and year t, is subnational or community. Even after accounting for sampling variability, national studies may still not reflect the true mean height or BMI level of a country with perfect accuracy, and subnational and community studies have even larger variability. In study i, the study-specific random effect \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e}_{i}$$\end{document}ei allows all age groups from the same study to have an unusually high or an unusually low mean after conditioning on the other terms in the model. Each \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e}_{i}$$\end{document}ei is assigned a normal prior with variance depending on whether study i is representative at the national, subnational or community level. Random effects from national studies were constrained to have smaller variance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{{\rm{n}}}$$\end{document}vn) than random effects of subnational studies (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{{\rm{s}}}$$\end{document}vs), which were in turn constrained to have smaller variance than community studies (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{{\rm{c}}}$$\end{document}vc). To make country-level predictions, we set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e}_{i}=0$$\end{document}ei=0, thus not including random effects arising from imperfections and variations in study design and implementation and from within-country variability of height or BMI means. ## Urban and rural strata To model mean height and BMI by urban and rural places of residence, the model included offsets for the two strata. The offsets were captured by country-specific intercept, linear time and age effects, using a centred indicator term (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{s,i}$$\end{document}Is,i):\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{s,i}[{p}_{j[i]}+{q}_{j[i]}{t}_{i}+{r}_{j[i]}{z}_{h}+{d}_{i}],$$\end{document}Is,i[pj[i]+qj[i]ti+rj[i]zh+di], where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{s,i}=-1+2{X}_{s,i}^{{\rm{u}}{\rm{r}}{\rm{b}}}$$\end{document}Is,i=−1+2Xs,iurb, with\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{s,i}^{{\rm{urb}}}=\left\{\begin{array}{ll}1, & {\rm{if}}\,{\rm{stratum}}\,s\,{\rm{contains}}\,{\rm{only}}\,{\rm{urban}}\,{\rm{individuals}},\\ 0, & {\rm{if}}\,{\rm{stratum}}\,s\,{\rm{contains}}\,{\rm{only}}\,{\rm{rural}}\,{\rm{individuals}},\\ {X}_{j[i],t[i]}^{{\rm{urb}}}, & {\rm{if\; stratum}}\,s\,{\rm{contains\; a\; mixture\; of\; urban}}\,{\rm{and}}\,{\rm{rural}}\,{\rm{individuals}}.\end{array}\right.$$\end{document}Xs,iurb=1,ifstratumscontainsonlyurbanindividuals,0,ifstratumscontainsonlyruralindividuals,Xj[i],t[i]urb,if stratumscontains a mixture of urbanandruralindividuals. In other words, for data not stratified by place of residence, the model treated the unstratified mean height or BMI as equivalent to the weighted sum of the (unobserved) urban sample mean height or BMI and rural sample mean height or BMI, with the weights based on the proportion of the population of that country living in urban areas in the year of the survey (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{j\left[i\right],t[i]}^{{\rm{urb}}}$$\end{document}Xji,t[i]urb). The intercept (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p) and slope (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q$$\end{document}q) terms capture the country-to-country variation in the magnitude of the height or BMI difference between urban and rural populations and how the difference changes over time. The slope (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r$$\end{document}r) captures the country-to-country variation in the BMI or height difference between urban and rural populations across age groups. These were specified with the same geographical hierarchy as the country-specific intercepts (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a$$\end{document}a) and slopes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b$$\end{document}b) as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}\,{p}_{j}={p}_{j}^{c}+{p}_{k[\,j]}^{r}+{p}_{l[k]}^{s}+{p}^{{\rm{g}}},\\ \,{q}_{j}={q}_{j}^{c}+{q}_{k[\,j]}^{r}+{q}_{l[k]}^{s}+{q}^{{\rm{g}}},\\ \,{{\rm{r}}}_{j}={r}_{j}^{c}+{r}_{k[\,j]}^{r}+{r}_{l[k]}^{s}+{r}^{{\rm{g}}},\\ {p}_{j}^{x}\sim N(0,{\kappa }_{p}^{x}),\\ {q}_{j}^{x}\sim N(0,{\kappa }_{q}^{x}),\\ {r}_{j}^{x}\sim N(0,{\kappa }_{r}^{x}),\end{array}$$\end{document}pj=pjc+pk[j]r+pl[k]s+pg,qj=qjc+qk[j]r+ql[k]s+qg,rj=rjc+rk[j]r+rl[k]s+rg,pjx∼N(0,κpx),qjx∼N(0,κqx),rjx∼N(0,κrx), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x=\{c,r,s\}$$\end{document}x={c,r,s}. The study random effect term \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d}_{i}$$\end{document}di incorporates deviations from the country-level urban–rural difference in each study and is analogous to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e}_{i}$$\end{document}ei. ## Residual age-by-study variability The age patterns across communities within a given country may differ from the overall age pattern of that country. This within-study variability cannot be captured by the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e$$\end{document}e terms, which are equal across age-specific observations in each study, so we included an additional variance component for each study, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }^{2}$$\end{document}τ2. ## Model implementation All analyses were done separately by sex because age, geographical and temporal patterns of height and BMI differ between girls and boys7,65. We fitted the statistical model using Markov chain Monte Carlo (MCMC). We started 35 parallel MCMC runs from randomly generated overdispersed starting values. For computational efficiency, each chain was run for a total of 75,000 iterations. All chains converged to the same target distribution within this number, but due to the overdispersed initial values, the length of burn-in required to converge to the target distribution varied. After the runs were completed, we used trace plots to monitor convergence and to select chains that had completed burn-in within 35,000 iterations. This resulted in 16 chains for boys and 17 for girls for BMI, and 14 chains for boys and 16 for girls for height. Within each of these chains, post-burn-in iterations were thinned by keeping every 10th iteration, which were then combined for all chains and further thinned to a final set of 5,000 draws of the model parameter estimates. We used the posterior distribution of the model parameters to obtain the posterior distributions of our outcomes: mean urban and rural height and BMI, and the urban–rural difference in mean height and BMI. Posterior estimates were made for one-year age groups from 5 to 19 years, as well as for age-standardized outcomes, by year. The reported Crls represent the 2.5th and the 97.5th percentiles of the posterior distributions. We also report the posterior s.d. of estimates, and PP that the estimated change in height or BMI in rural or urban areas, and in the urban–rural height or BMI difference over time, represents a true increase or decrease. Convergence was confirmed for the country-sex specific posterior outcomes—namely mean urban height and BMI, mean rural height and BMI and the urban–rural difference in mean height and BMI—for reporting ages (5, 10, 15, 19 years and age-standardized) and years (1990 and 2020) using the R-hat diagnostic80,81. For height, the 2.5th to 97.5th percentiles of the R-hats for the reporting ages and years were 0.999–1.010 for girls and 0.999–1.004 for boys. For BMI, the 2.5th to 97.5th percentiles of the R-hats were 0.999–1.004 for girls and 0.999–1.005 for boys. We applied the pool-adjacent-violators algorithm, a monotonic regression that uses an iterative algorithm based on least squares to fit a free-form line to a sequence of observations such that the fitted line is non-decreasing82,83, on the posterior height estimates to ensure that the height for each birth cohort increased monotonically with age. In practice, this had little effect on the results, with height at age 19 years adjusted by an average of 0.26 cm or less for both boys and girls. All analyses were conducting using the statistical software R (v.4.1.2)84. ## Strengths and limitations An important strength of our study is its novel scope of presenting consistent and comparable estimates of urban and rural height and BMI among school-aged children and adolescents, which is essential to formulate and evaluate policies that aim to improve health in these formative ages. We used a large number of population-based studies from 194 countries and territories covering around $99\%$ of the population of the world. We maintained a high level of data quality and representativeness through repeated checks of study characteristics against our inclusion and exclusion criteria, and did not use any self-reported data to avoid bias in height and weight. Data were analysed according to a consistent protocol, and the characteristics and quality of data from each country were rigorously verified through repeated checks by NCD-RisC members. We used a statistical model that used all available data and took into account the epidemiological features of height and BMI during childhood and adolescence by using nonlinear time trends and age associations. The model used the available information on the urban–rural difference in height and BMI and estimated the age-varying and time-varying urban–rural difference for all countries and territories hierarchically. Despite our extensive efforts to identify and access data, some countries had fewer data, especially those in the Caribbean, Polynesia, Micronesia and sub-Saharan Africa. Of the studies used, fewer than half had data for children aged 5–9 years compared to nearly $90\%$ with data for children and adolescents aged 10–19 years. The scarcity of data is reflected in the larger uncertainty of our estimates for these countries and regions, and younger age groups. This reflects the need to systematically include school-aged children in both health and nutrition surveys, and, especially in countries where school enrolment is high, to use schools as a platform for monitoring growth and developmental outcomes for entire national populations and key subgroups such as those in rural and urban areas. Although urban and rural classifications are commonly based on definitions by national statistical offices, classification of cities and rural areas may, appropriately, vary by country according to their demographic characteristics (for example, population size or density), economic activities, administrative structures, infrastructure and environment. Similarly, urbanization takes place through a variety of mechanisms such as changes in fertility in rural and urban areas, migration and reclassification of previously rural areas to urban as they grow and industrialize. Each of these mechanisms may have different implications for nutrition and physical activity, and hence height and/or BMI, and should be a subject of studies that follow individual participants and changes in their place of residence. Finally, there is variation in growth and development of children within rural or urban areas based on household socioeconomic status and community characteristics that affect access to and the quality of nutrition, the living environment and healthcare35,85,86. Among these, in some cities, a large number of families live in slums19,87. School-aged children and adolescents living in slums have nutrition, environment and healthcare access that is typically worse than other residents of the city, although often better than those in rural areas19,87–90. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Online content Any methods, additional references, Nature Portfolio reporting summaries, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-05772-8. ## Supplementary information Supplementary InformationSupplementary Tables 1–4, Supplementary Figs. 1–8 and Supplementary References; see contents page for details. Reporting Summary Peer Review File The online version contains supplementary material available at 10.1038/s41586-023-05772-8. ## Extended data figures and tables Extended Data Fig. 1Number of data sources used in the analysis, by country. Extended Data Fig. 2Urban-rural height difference in 2020 and change from 1990 to 2020.The top two maps show the urban-rural difference in age-standardised mean height in 2020 for girls and boys resepectively. A positive number shows higher urban mean height and a negative number shows higher rural mean height. The bottom two maps show the change from 1990 to 2020. The density plot below each map shows the distribution of estimates across countries. The top two scatter plots show the urban-rural difference in age-standardised mean height in relation to the uncertainty of the difference measured by posterior s.d. The bottom two scatter plots show the change from 1990 to 2020 in urban-rural difference in mean height in relation to the uncertainty of the change measured by posterior s.d. Each point in the scatter plots shows one country. Shaded areas approximately show the posterior probability (PP) of a true difference (top two scatter plots) and of a true increase or decrease in difference (bottom two scatter plots). See Extended Data Fig. 8 for PPs of the urban-rural difference in age-standardised mean height and its change. See Supplementary Fig. 7 for results at ages 5, 10, 15 and 19 years. We did not estimate the difference between rural and urban height for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau), as indicated in grey. Extended Data Fig. 3Urban-rural body-mass index (BMI) difference in 2020 and change from 1990 to 2020.See Extended Data Fig. 2 caption for descriptions of the contents of the figure and for definitions. See Extended Data Fig. 9 for PP of the urban-rural difference in age-standardised mean BMI and its change. See Supplementary Fig. 8 for results at ages 5, 10, 15 and 19 years. We did not estimate the difference between rural and urban BMI for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau), as indicated in grey. Extended Data Fig. 4Posterior probability of increase in mean height in urban and rural areas from 1990 to 2020.The maps show the PP that the age-standardised mean height increased from 1990 to 2020. The PP of a decrease is one minus that of an increase. If an increase in mean height is statistically indistinguishable from a decrease, the PP is 0.50. PPs closer to 0.50 indicate more uncertainty, those towards 1 indicate more certainty of an increase, and those towards 0 indicate more certainty of a decrease. We did not estimate PP for change in mean rural height for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or change in mean urban height for countries classified as entirely rural (Tokelau), as indicated in grey. Extended Data Fig. 5Posterior probability of increase in mean body-mass index (BMI) in urban and rural areas from 1990 to 2020.The maps show the posterior probability (PP) that the age-standardised mean BMI increased from 1990 to 2020. The PP of a decrease is one minus that of an increase. We did not estimate PP for change in mean rural BMI in countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or change in mean urban BMI in countries classified as entirely rural (Tokelau), as indicated in grey. Extended Data Fig. 6Trends in body-mass index (BMI) by place of residence for children, adolescents and young adults for females. The figure shows trends in mean BMI at ages five and 19 years, and in age-standardised mean BMI for young adults (20–29 years and 30–39 years) for females. Shaded areas show the $95\%$ CrIs. Trend for young adults were estimated using a model similar to the one described in Methods, where BMI-age patterns were allowed to vary flexibly via a cubic spline function without knots. Extended Data Fig. 7Trends in body-mass index (BMI) by place of residence for children, adolescents and young adults for males. The figure shows trends in mean BMI at ages five and 19 years, and in age-standardised mean BMI for young adults (20–29 years and 30–39 years) for males. See Extended Data Fig. 6 caption for description of figure contents. Extended Data Fig. 8Posterior probability of urban-rural height difference in 2020 and its increase from 1990 to 2020.The maps show the posterior probability (PP) that age-standardised mean height in 2020 in urban areas was higher than in rural areas (left-hand panels), and the PP that the urban-rural difference in age-standardised mean height increased from 1990 to 2020 (right-hand panels). For 2020, if estimated age-standardised mean urban height is statistically indistinguishable from rural height, the PP is 0.50. PPs closer to 0.50 indicate more uncertainty, those towards 1 indicate more certainty of urban children being taller, and those towards 0 indicate more certainty of rural being taller. For change, if an increase in urban-rural difference in mean height is statistically indistinguishable from a decrease, the PP is 0.50. PPs closer to 0.50 indicate more uncertainty, those towards 1 indicate more certainty of an increase in the urban-rural height difference, and those towards 0 indicate more certainty of a decrease. We did not estimate the PP for differences between rural and urban height for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau), as indicated in grey. Extended Data Fig. 9Posterior probability of urban-rural body-mass index (BMI) difference in 2020 and its increase from 1990 to 2020.The maps show the posterior probability (PP) that age-standardised mean BMI in 2020 in urban areas was higher than in rural areas (left-hand panels), and the PP that the urban-rural difference in mean BMI increased from 1990 to 2020 (right-hand panels). We did not estimate the PP for differences between rural and urban BMI for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau), as indicated in grey. ## Extended data is available for this paper at 10.1038/s41586-023-05772-8. ## Peer review information Nature thanks Sam Harper, Jessica Jones-Smith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. ## References 1. Prentice AM. Critical windows for nutritional interventions against stunting. Am. J. Clin. Nutr. (2013.0) 97 911-918. DOI: 10.3945/ajcn.112.052332 2. Tanner JM. Growth as a mirror of the condition of society: secular trends and class distinctions. Acta Paediatr. Jpn. (1987.0) 29 96-103. DOI: 10.1111/j.1442-200X.1987.tb00015.x 3. Strauss J, Thomas D. Health, nutrition, and economic development. J. Econ. Lit. (1998.0) 36 766-817 4. Nüesch E. Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis. Int. J. Epidemiol. (2016.0) 45 1927-1937. DOI: 10.1093/ije/dyv074 5. Park MH, Falconer C, Viner RM, Kinra S. The impact of childhood obesity on morbidity and mortality in adulthood: a systematic review. Obes. Rev. (2012.0) 13 985-1000. DOI: 10.1111/j.1467-789X.2012.01015.x 6. Caird J. Does being overweight impede academic attainment? A systematic review. Health Educ. J. (2014.0) 73 497-521. DOI: 10.1177/0017896913489289 7. Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants. Lancet (2020.0) 396 1511-1524. DOI: 10.1016/S0140-6736(20)31859-6 8. 8.Global Report on Urban Health: Equitable Healthier Cities for Sustainable Development (World Health Organization & UN-Habitat, 2016). 9. 9.Healthy Environments for Healthy Children: Key Messages for Action. Report No. 924159988X (World Health Organization, 2010). 10. 10.Report of the Commission on Ending Childhood Obesity (World Health Organization, 2016). 11. Smith DM, Cummins S. Obese cities: how our environment shapes overweight. Geogr. Compass (2009.0) 3 518-535. DOI: 10.1111/j.1749-8198.2008.00198.x 12. Fraser B. Latin America’s urbanisation is boosting obesity. Lancet (2005.0) 365 1995-1996. DOI: 10.1016/S0140-6736(05)66679-2 13. Pirgon Ö, Aslan N. The role of urbanization in childhood obesity. J. Clin. Res. Pediatr. Endocrinol. (2015.0) 7 163-167. DOI: 10.4274/jcrpe.1984 14. 14.The State of the World’s Children 2012: Children in an Urban World (UNICEF, 2012). 15. 15.Kirchengast, S. & Hagmann, D. “Obesity in the city”—urbanization, health risks and rising obesity rates from the viewpoint of human biology and public health. Hum. Biol. Public Health.2, 1–21 (2021). 16. Congdon P. Obesity and urban environments. Int. J. Environ. Res. (2019.0) 16 464 17. Gong P. Urbanisation and health in China. Lancet (2012.0) 379 843-852. DOI: 10.1016/S0140-6736(11)61878-3 18. Liese AD, Weis KE, Pluto D, Smith E, Lawson A. Food store types, availability, and cost of foods in a rural environment. J. Am. Diet. Assoc. (2007.0) 107 1916-1923. DOI: 10.1016/j.jada.2007.08.012 19. Krumdiek CL. The rural-to-urban malnutrition gradient: a key factor in the pathogenesis of urban slums. JAMA (1971.0) 215 1652-1654. DOI: 10.1001/jama.1971.03180230062012 20. Paciorek CJ, Stevens GA, Finucane MM, Ezzati M. Children’s height and weight in rural and urban populations in low-income and middle-income countries: a systematic analysis of population-representative data. Lancet Glob. Health (2013.0) 1 e300-e309. DOI: 10.1016/S2214-109X(13)70109-8 21. Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature (2019.0) 569 260-264. DOI: 10.1038/s41586-019-1171-x 22. Jaacks LM, Slining MM, Popkin BM. Recent trends in the prevalence of under- and overweight among adolescent girls in low- and middle-income countries. Pediatr. Obes. (2015.0) 10 428-435. DOI: 10.1111/ijpo.12000 23. Wang Y, Monteiro C, Popkin BM. Trends of obesity and underweight in older children and adolescents in the United States, Brazil, China, and Russia. Am. J. Clin. Nutr. (2002.0) 75 971-977. DOI: 10.1093/ajcn/75.6.971 24. Neuman M, Kawachi I, Gortmaker S, Subramanian SV. Urban–rural differences in BMI in low- and middle-income countries: the role of socioeconomic status. Am. J. Clin. Nutr. (2013.0) 97 428-436. DOI: 10.3945/ajcn.112.045997 25. 25.Hidden Cities: Unmasking and Overcoming Health Inequities in Urban Settings. Report No. 9241548037 (World Health Organization & Centre for Health Development, 2010). 26. 26.Population-based Approaches to Childhood Obesity Prevention. Report No. 9789241504782 (World Health Organization, 2012). 27. 27.Prevention of Overweight and Obesity in Children and Adolescents: UNICEF Programming Guidance (UNICEF, 2019). 28. de Sa TH. Urban design is key to healthy environments for all. Lancet Glob. Health (2022.0) 10 e786-e787. DOI: 10.1016/S2214-109X(22)00202-9 29. Kelly C, Callaghan M, Molcho M, Nic Gabhainn S, Alforque Thomas A. Food environments in and around post-primary schools in Ireland: associations with youth dietary habits. Appetite (2019.0) 132 182-189. DOI: 10.1016/j.appet.2018.08.021 30. Conrad D, Capewell S. Associations between deprivation and rates of childhood overweight and obesity in England, 2007–2010: an ecological study. BMJ Open (2012.0) 2 e000463. DOI: 10.1136/bmjopen-2011-000463 31. Katsoulis M. Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records. Lancet Diabetes Endocrinol. (2021.0) 9 681-694. DOI: 10.1016/S2213-8587(21)00207-2 32. Arnett JJ. Emerging adulthood: a theory of development from the late teens through the twenties. Am. Psychol. (2000.0) 55 469-480. DOI: 10.1037/0003-066X.55.5.469 33. Poobalan AS, Aucott LS, Clarke A, Smith WCS. Diet behaviour among young people in transition to adulthood (18–25 year olds): a mixed method study. Health Psychol. Behav. Med. (2014.0) 2 909-928. DOI: 10.1080/21642850.2014.931232 34. Winpenny EM. Changes in diet through adolescence and early adulthood: longitudinal trajectories and association with key life transitions. Int. J. Behav. Nutr. Phys. Act. (2018.0) 15 86. DOI: 10.1186/s12966-018-0719-8 35. Smith LC, Ruel MT, Ndiaye A. Why is child malnutrition lower in urban than in rural areas? Evidence from 36 developing countries. World Develop. (2005.0) 33 1285-1305. DOI: 10.1016/j.worlddev.2005.03.002 36. Lundborg P, Rooth D-O, Alex-Petersen J. Long-term effects of childhood nutrition: evidence from a school lunch reform. Rev. Econ. Stud. (2022.0) 89 876-908. DOI: 10.1093/restud/rdab028 37. Graça P, Gregório MJ, Freitas MG. A decade of food and nutrition policy in Portugal (2010–2020). Port. J. Public Health (2020.0) 38 94-118. DOI: 10.1159/000510566 38. Winichagoon P. Thailand nutrition in transition: situation and challenges of maternal and child nutrition. Asia Pac. J. Clin. Nutr. (2013.0) 22 6-15. PMID: 23353605 39. Paes-Sousa R, Vaitsman J. The Zero Hunger and Brazil without Extreme Poverty programs: a step forward in Brazilian social protection policy. Cien Saude Colet (2014.0) 19 4351-4360. DOI: 10.1590/1413-812320141911.08812014 40. Núñez J, Pérez G. The escape from malnutrition of Chilean boys and girls: height-for-age. Int. J. Environ. Res. Public Health (2021.0) 18 10436. DOI: 10.3390/ijerph181910436 41. 41.Herrmann, R., Möser, A. & Weber, S. A. Grocery retailing in Germany: Situation, development and pricing strategies PDF Logo (Justus-Liebig-Universität Gießen, 2009). 42. Thang NM, Popkin B. Child malnutrition in Vietnam and its transition in an era of economic growth. J. Hum. Nutr. Diet. (2003.0) 16 233-244. DOI: 10.1046/j.1365-277X.2003.00449.x 43. Subramanyam MA, Kawachi I, Berkman LF, Subramanian SV. Socioeconomic inequalities in childhood undernutrition in India: analyzing trends between 1992 and 2005. PLoS ONE (2010.0) 5 e11392. DOI: 10.1371/journal.pone.0011392 44. Jingzhong Y, Lu P. Differentiated childhoods: impacts of rural labor migration on left-behind children in China. J. Peasant Stud. (2011.0) 38 355-377. DOI: 10.1080/03066150.2011.559012 45. 45.Sundberg, S. Agriculture, poverty and growth in Africa: linkages and policy challenges. CABI Rev. (2009). 46. Bentham J. Multidimensional characterization of global food supply from 1961 to 2013. Nat. Food (2020.0) 1 70-75. DOI: 10.1038/s43016-019-0012-2 47. Micha R. Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide. BMJ Open (2015.0) 5 e008705. DOI: 10.1136/bmjopen-2015-008705 48. 48.Fraval, S. et al. Food access deficiencies in sub-Saharan Africa: prevalence and implications for agricultural interventions. Front. Sustain. Food Syst.3, 104 (2019). 49. Thomson M, Kentikelenis A, Stubbs T. Structural adjustment programmes adversely affect vulnerable populations: a systematic-narrative review of their effect on child and maternal health. Public Health Rev. (2017.0) 38 13. DOI: 10.1186/s40985-017-0059-2 50. Frisch RE, Revelle R. Height and weight at menarche and a hypothesis of critical body weights and adolescent events. Science (1970.0) 169 397-399. DOI: 10.1126/science.169.3943.397 51. Holmgren A. Pubertal height gain is inversely related to peak BMI in childhood. Pediatr. Res. (2017.0) 81 448-454. DOI: 10.1038/pr.2016.253 52. Alhassan JAK, Adeyinka DA, Olakunde BO. Equity dimensions of the decline in under-five mortality in Ghana: a joinpoint regression analysis. Tropi. Med. Int. Health (2020.0) 25 732-739. DOI: 10.1111/tmi.13391 53. Wolde KS, Bacha RH. Trend and correlates of under-5 mortality in Ethiopia: a multilevel model comparison of 2000–2016 EDHS data. SAGE Open Med. (2022.0) 10 20503121221100608. DOI: 10.1177/20503121221100608 54. Trends in adult body–mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet (2016.0) 387 1377-1396. DOI: 10.1016/S0140-6736(16)30054-X 55. Danaei G. National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5·4 million participants. Lancet (2011.0) 377 568-577. DOI: 10.1016/S0140-6736(10)62036-3 56. Danaei G. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet (2011.0) 378 31-40. DOI: 10.1016/S0140-6736(11)60679-X 57. Farzadfar F. National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3·0 million participants. Lancet (2011.0) 377 578-586. DOI: 10.1016/S0140-6736(10)62038-7 58. Finucane MM. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet (2011.0) 377 557-567. DOI: 10.1016/S0140-6736(10)62037-5 59. Cline MG, Meredith KE, Boyer JT, Burrows B. Decline of height with age in adults in a general population sample: estimating maximum height and distinguishing birth cohort effects from actual loss of stature with aging. Hum. Biol. (1989.0) 61 415-425. PMID: 2807265 60. Lumley T. Analysis of complex survey samples. J. Stat. Softw. (2004.0) 9 1-19. DOI: 10.18637/jss.v009.i08 61. 61.United Nations Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision (United Nations, 2019). 62. Hayes AJ, Clarke PM, Lung TW. Change in bias in self-reported body mass index in Australia between 1995 and 2008 and the evaluation of correction equations. Popul. Health Metr. (2011.0) 9 53. DOI: 10.1186/1478-7954-9-53 63. Connor Gorber S, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes. Rev. (2007.0) 8 307-326. DOI: 10.1111/j.1467-789X.2007.00347.x 64. Ezzati M, Martin H, Skjold S, Vander Hoorn S, Murray CJL. Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys. J. R. Soc. Med. (2006.0) 99 250-257. DOI: 10.1177/014107680609900517 65. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet (2017.0) 390 2627-2642. DOI: 10.1016/S0140-6736(17)32129-3 66. 66.Ahmad, O. B. et al. Age Standardization of Rates: A New WHO Standard (World Health Organization, 2001). 67. Finucane MM, Paciorek CJ, Stevens GA, Ezzati M. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J. Am. Stat. Assoc. (2015.0) 110 889-901. DOI: 10.1080/01621459.2014.937487 68. Finucane MM, Paciorek CJ, Danaei G, Ezzati M. Bayesian estimation of population-level trends in measures of health status. Stat. Sci. (2014.0) 29 18-25. DOI: 10.1214/13-STS427 69. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet (2021.0) 398 957-980. DOI: 10.1016/S0140-6736(21)01330-1 70. A century of trends in adult human height. eLife (2016.0) 5 e13410. DOI: 10.7554/eLife.13410 71. 71.Rue, H. V. & Held, L. Gaussian Markov Random Fields: Theory and Applications (Chapman & Hall/CRC, 2005). 72. 72.Cameron, N. L. Human Growth and Development (Academic Press, 2002). 73. de Onis M. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ. (2007.0) 85 660-667. DOI: 10.2471/BLT.07.043497 74. Tanner JM, Whitehouse RH, Marubini E, Resele LF. The adolescent growth spurt of boys and girls of the Harpenden growth study. Ann. Hum. Biol. (1976.0) 3 109-126. DOI: 10.1080/03014467600001231 75. 75.Gelman, A. & Jill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007). 76. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Anal. (2006.0) 1 515-533. DOI: 10.1214/06-BA117A 77. Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. (1993.0) 88 9-25 78. 78.Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC, 2006). 79. 79.Harville, D. A. Matrix Algebra from a Statistician’s Perspective (Springer-Verlag, 2008). 80. Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. Rank-normalization, folding, and localization: an improved R for assessing convergence of MCMC (with discussion). Bayesian Anal. (2021.0) 16 667-718. DOI: 10.1214/20-BA1221 81. 81.Stan Development Team. RStan: the R interface to Stanhttps://mc-stan.org/ (2022). 82. Best MJ, Chakravarti N. Active set algorithms for isotonic regression; a unifying framework. Math. Program. (1990.0) 47 425-439. DOI: 10.1007/BF01580873 83. De Leeuw J, Hornik K, Mair P. Isotone optimization in R: pool-adjacent-violators algorithm (PAVA) and active set methods. J. Stat. Softw. (2010.0) 32 1-24 84. 84.The R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). 85. Huang R, Moudon AV, Cook AJ, Drewnowski A. The spatial clustering of obesity: does the built environment matter?. J. Hum. Nutr. Diet. (2015.0) 28 604-612. DOI: 10.1111/jhn.12279 86. Menon P, Ruel MT, Morris SS. Socio-economic differentials in child stunting are consistently larger in urban than in rural areas. Food Nutr. Bull. (2000.0) 21 282-289. DOI: 10.1177/156482650002100306 87. Ezeh A. The history, geography, and sociology of slums and the health problems of people who live in slums. Lancet (2017.0) 389 547-558. DOI: 10.1016/S0140-6736(16)31650-6 88. Fink G, Günther I, Hill K. Slum residence and child health in developing countries. Demography (2014.0) 51 1175-1197. DOI: 10.1007/s13524-014-0302-0 89. Jonah CMP, May JD. The nexus between urbanization and food insecurity in South Africa: does the type of dwelling matter?. Int. J. Urban Sustain. Dev. (2020.0) 12 1-13. DOI: 10.1080/19463138.2019.1666852 90. 90.Progress Report: Sustainable Development Goal 11 (Target 11.1) (Habitat for Humanity International, 2019).
--- title: Recent Advances in Understanding of Pathogenesis of Alcohol-Associated Liver Disease authors: - Xiaoqin Wu - Xiude Fan - Tatsunori Miyata - Adam Kim - Christina K. Cajigas-Du Ross - Semanti Ray - Emily Huang - Moyinoluwa Taiwo - Rakesh Arya - Jianguo Wu - Laura E. Nagy journal: Annual review of pathology year: 2022 pmcid: PMC10060166 doi: 10.1146/annurev-pathmechdis-031521-030435 license: CC BY 4.0 --- # Recent Advances in Understanding of Pathogenesis of Alcohol-Associated Liver Disease ## Abstract Alcohol-associated liver disease (ALD) is one of the major diseases arising from chronic alcohol consumption and is one of the most common causes of liver-related morbidity and mortality. ALD includes asymptomatic liver steatosis, fibrosis, cirrhosis, and alcohol-associated hepatitis and its complications. The progression of ALD involves complex cell-cell and organ-organ interactions. We focus on the impact of alcohol on dysregulation of homeostatic mechanisms and regulation of injury and repair in the liver. In particular, we discuss recent advances in understanding the disruption of balance between programmed cell death and prosurvival pathways, such as autophagy and membrane trafficking, in the pathogenesis of ALD. We also summarize current understanding of innate immune responses, liver sinusoidal endothelial cell dysfunction and hepatic stellate cell activation, and gut-liver and adipose-liver cross talk in response to ethanol. In addition, we describe the current potential therapeutic targets and clinical trials aimed at alleviating hepatocyte injury, reducing inflammatory responses, and targeting gut microbiota, for the treatment of ALD. ## INTRODUCTION Lifestyle, including diet, has an important impact on the development of multiple diseases. Excessive alcohol consumption is one controllable lifestyle factor known to contribute to tissue injury and to a number of diseases. Worldwide, 3 million deaths every year result from harmful use of alcohol, representing $5.3\%$ of all deaths [1]. Alcohol-associated liver disease (ALD) is one of the major diseases arising from chronic, heavy alcohol consumption and is one of the most common causes of liver-related morbidity and mortality [2, 3]. In 2010, the worldwide rate of alcohol-attributable cirrhosis death was 7.2 deaths per 100,000 people (4.6 in females and 9.7 in males) [3]. In 2019 in the United States, 53,486 deaths from liver disease occurred in males, with $45.6\%$ involving alcohol, and 32,202 deaths from liver disease occurred in females, $39.0\%$ of which were alcohol related. Importantly, ALD is currently a major indication for liver transplantation in the United States and Europe, as the incidence of hepatitis C is declining [3, 4]. ALD encompasses a spectrum of diseases including asymptomatic liver steatosis, fibrosis, cirrhosis, and alcohol-associated hepatitis (AH) and its complications. Approximately 8–$20\%$ of chronic heavy drinkers will develop alcohol-related cirrhosis and, of these patients, approximately $2\%$ will develop hepatocellular carcinoma [2]. ALD, which progresses from fatty liver, as the initial stage, to fibrosis and AH, accounts for approximately half of the causes of nonviral cirrhosis, particularly as viral hepatitis is waning, and is also major cause of nonviral liver cancer. Genetic polymorphisms of alcohol-metabolizing enzymes are associated with heavy drinking habits and dependence, while genetic polymorphisms such as patatin-like phospholipase encoding 3 (PNPLA3) are risk factors for fatty liver formation and progression of liver pathology [5]. However, the specific genes involved in the progression of ALD or its complications are not well studied. In addition to cellular damage caused by reactive oxygen species (ROS) generated during alcohol metabolism, changes in the gut microbiota (dysbiosis) and the immune response to these changes play major roles in the development and progression of ALD [6]. In this review, we focus on the impact of chronic alcohol on dysregulation of homeostatic mechanisms regulating injury and repair in the liver. In particular, we discuss recent advances in understanding disruption of pathways regulating cell death and survival, innate immune responses, and organ-organ interactions in ALD. We also summarize the current potential therapeutic targets and clinical trials for the treatment of ALD. ## ALCOHOL METABOLISM Alcohol metabolism occurs via both oxidative pathways, involving alcohol dehydrogenases (ADHs), microsomal cytochrome P450 enzymes (CYPs), or peroxisomal catalase, and nonoxidative pathways. ADHs, a class of zinc dehydrogenases residing in the cytosol, catalyze the oxidation of primary and secondary alcohols to their corresponding aldehydes or ketones with the reduction of NAD+ to NADH and can also catalyze the reverse reaction. Human ADHs are encoded by at least seven genes on chromosome 4, divided into five classes, and differentially distributed in tissues. Most classes of ADHs exhibit the highest activity in the liver. The class I ADHs (ADH1A, ADH1B, and ADH1C) carry out oxidation of most of the ingested alcohol (ethanol) in the liver, generating acetaldehyde [7]. Acetaldehyde is highly reactive and forms adducts with cellular proteins, nucleic acids, and lipids, compromising normal cellular functions and underlying alcohol’s pathogenicity. Acetaldehyde also competes with other endogenous aldehydes originating from the metabolism of physiological chemicals such as dopamine, norepinephrine, and serotonin. Once generated, the toxic acetaldehyde is quickly converted to less toxic acetate by aldehyde dehydrogenases (ALDHs), and the acetate is broken down into water and CO2 for easy elimination. Chronic alcohol consumption increases the expression and activity of some CYPs, particularly CYP2E1, an isoform that is conserved across mammalian species and expressed in multiple cell types and tissues. CYP2E1 assumes a significant role in the oxidation of ethanol to acetaldehyde, especially in the presence of high concentrations of ethanol (Km = 8 to 10 mM, compared with 0.2 to 2.0 mM of hepatic ADH). It is also considered a leaky cytochrome, generating excess ROS even in the absence of substrate [8]. Catalase is a heme-containing enzyme found in all living organisms and particularly expressed in the peroxisomes. Catalase normally decomposes H2O2 to H2O and O2 in the presence of electron donors. Ethanol, as an electron donor, is thus oxidized to acetaldehyde by catalase [8]. Although catalase and CYP2E1 play a much smaller role in alcohol oxidation compared with ADHs, they significantly impact the rate of oxidation of ethanol to acetaldehyde in the brain, where ADH activity is low [9]. Several nonoxidative pathways of alcohol metabolism result in the enzymatic conjugation of ethanol to endogenous metabolites to produce nonoxidative ethanol end products such as ethyl glucuronide, ethyl sulfate, phosphatidylethanol, and fatty acid ethyl ester. Despite accounting for an overall low fraction of total ethanol metabolism, the resulting ethanol metabolites can be useful as retrospective biomarkers in assessing ethanol intake due to their relatively slower elimination rates [10]. In summary, the generation of highly reactive acetaldehyde and excess ROS during oxidative ethanol metabolism leads to cellular injury, particularly in hepatocytes. The hepatic response to the accumulation of damaged cellular proteins, nucleic acids, and lipids impairs cellular functions and stimulates the induction of pathways, such as the unfolded protein response or the endoplasmic reticulum (ER) stress response. If the hepatocyte response to stress is insufficient, multiple pathways of cell death are activated. ## CELL DEATH AND PROSURVIVAL PATHWAYS The intricate balance between prosurvival and death pathways of parenchymal and nonparenchymal cells is critical for regulating liver injury and inflammation during the progression of ALD [6]. Programmed cell death (PCD) is a crucial and active process, serving to maintain tissue homeostasis in multicellular organisms. There are four major modes of PCD (Figure 1): apoptosis, necroptosis, pyroptosis, and ferroptosis [11]. Recently, a newly recognized pathway for proinflammatory PCD called PANoptosis that is activated by bacterial and viral triggers was determined to be controlled by a multimeric protein complex—the PANoptosome. The PANoptosome can in parallel engage pyroptosis, apoptosis, and necroptosis [12]. Additionally, autophagy and membrane trafficking, which share common components and affect each other, play an important role in repair of cell damage and cell survival [13]. Dysregulation or hyperactivation of autophagy and membrane trafficking is associated with cell injury and death and contributes to the development of ALD (14–16). Hepatocellular fate in response to extracellular signaling depends on the cellular environment. Alcohol induces cellular oxidative and/or ER stress through ethanol metabolism, resulting in increased exposure to damage-associated molecular patterns (DAMPs). The liver is also exposed to gastrointestinal-derived pathogen-associated molecular patterns (PAMPs) due to the impact of ethanol on gut integrity. Cumulatively, these ethanol-induced insults result in activation of different cell death pathways. Here, we summarize the current understanding of the regulation of cell death and prosurvival pathways in the pathogenesis of ALD in multiple scenarios. ## Apoptosis The extrinsic apoptotic pathway is typically activated by members of the tumor necrosis factor (TNF) family of death receptor (DR) ligands, comprising TNF, Fas ligand, and TNF-related apoptosis-inducing ligand (TRAIL) [11]. The intrinsic pathway is commonly triggered via members of the B cell lymphoma 2 (Bcl-2) family, which control mitochondrial outer membrane permeabilization, cytochrome c release, and, subsequently, caspase activation [11]. Francis et al. [ 17] previously reported that ethanol exposure induced Fas ligand and DR5-mediated extrinsic apoptotic pathways through microRNA-21 (miRNA-21). Recent studies have been focused on the retinoic acid-inducible gene I (RIG-I)-like receptor (RLR)-induced interferon regulatory factor 3 (IRF3)-mediated pathway of apoptosis (RIPA). In the RIPA branch, IRF3 is activated by LUBAC-mediated linear ubiquitination, which triggers its interaction with BAX to cause mitochondrial activation and apoptotic cell death [18]. Szabo and colleagues [19] found that activation of IRF3 initiates alcohol-induced hepatocyte apoptosis, which fuels a robust secondary inflammatory response that drives ALD. Importantly, cGAS-driven IRF3 signaling spreads through hepatic gap junction communication between hepatocytes via connexin 32 (Cx32), thereby amplifying inflammation and accelerating hepatocyte apoptosis and, subsequently, damage [20]. Additionally, hepatic monocyte/macrophage apoptosis also contributes to liver homeostasis and response to ethanol-induced damage. Lotersztajn and colleagues [21] revealed that interleukin 10 (IL-10) released from M2 Kupffer cells (KCs) promoted M1 KC death via apoptosis, to protect against alcohol-induced inflammation and injury. Sanz-Garcia et al. [ 22] demonstrated that Irf3−/− mice were protected from Gao-binge ethanol-induced liver injury, associated with the suppression of ethanol-induced apoptosis in the Ly6Clow population (restorative). On the basis of these findings (Figure 1a), it is plausible that intervention in the apoptotic death pathway is a potential strategy to prevent alcohol-induced injury. However, genetic [Bid−/− [23] or Caspase-8−/− [24]] or pharmacological [23] (VX166, a pan-caspase inhibitor) inhibition of apoptosis is not completely protective in murine models of early ALD. These data suggest that cell-specific and/or additional forms of PCD are critical during progression of ALD. ## Necroptosis Necroptosis classically depends on the phosphorylation of receptor interacting protein kinase 3 (RIP3) by RIP1. RIP3 then phosphorylates the critical effector MLKL, leading to its translocation to the plasma membrane, where it oligomerizes and forms pores that trigger necroptotic cell death [25]. In addition to death receptors, other receptors, for example, Toll-like receptors (TLRs) and interferon (IFN) receptors, also induce MLKL-mediated necroptosis. Importantly, multiple studies have identified differential contributions of the RIP1-RIP3-MLKL axis to disease progression in murine models of ALD (mALD) (Figure 1b). Chronic ethanol feeding induces RIP3 expression in mouse livers and primary hepatocytes, while the mRNA and protein levels of RIP1 are markedly decreased by Gao-binge (acute-on-chronic) alcohol exposure. Inhibition of RIP1 by necrostatin-1 attenuates alcohol-mediated inflammation but not hepatocyte injury [26], while Rip3−/− mice are protected from chronic ethanol-induced liver injury [26, 27]. These data suggest that RIP3 likely contributes to the progression of ALD in a RIP1-independent mechanism. Indeed, casein kinase family members directly phosphorylate RIPK3 to activate necroptosis, likely interacting with RIP3 through its RHIM domain [28]. Mlkl−/− mice are only partially protected from Gao-binge and chronic ethanol-induced liver injury [29], suggesting that RIP3 and MLKL also likely function via independent, noncanonical mechanisms in mALD. Further study is needed to determine the underlying mechanism for regulating both the canonical and noncanonical functions, as well as domain-specific functions, of the RIP1-RIP3-MLKL axis in the context of alcohol exposure. Of translational significance, circulating concentrations of RIP1 and RIP3 distinguish patients with AH from healthy controls (HCs), as well as from patients with nonalcoholic steatohepatitis (NASH). RIP3, but not RIP1, is likely a promising biomarker to predict prognosis in AH after diagnosis [29]. These data are consistent with reports that circulating concentrations of cytokeratin-18 (M65) versus its caspase-3-mediated cleavage product (M30), released by necrotic/necroptotic versus apoptotic cell death, respectively, are also diagnostic and prognostic indicators in patients with AH [30, 31]. ## Pyroptosis The canonical pathway of pyroptotic cell death requires the priming and assembly of multiple signals, triggered by multiple DAMPs and PAMPs through TLRs. Signal integration is accomplished by the assembly of cytosolic pattern recognition receptors (PRRs), including nucleotide oligomerization domain (NOD)-like receptor (NLR) family pyrin domain containing 1 (NLRP1), NLRP3, NLRC4, absent in melanoma 2 (AIM2), and pyrin, and activation of caspase-1. Noncanonical pyroptosis is driven by caspase-4 and caspase-5 (human) or caspase-11 (mouse) [32]. Upon activation, these caspases cleave gasdermin D (GSDMD), which then binds to lipids at the plasma membrane and forms oligomeric pores, thereby leading to pyroptosis. Meanwhile, activated caspase-1 controls the maturation of IL-1β and IL-18. The activated caspases (caspase-4, -5, and -11) also cleave pannexin-1, inducing ATP release and P2X7R-related pyroptotic cell death. In addition to GSDMD, pyroptosis can also be triggered by other members of the GSDM family. Active caspase-3 and caspase-8 can cleave GSDME and GSDMD, respectively. In addition, under hypoxic conditions, programmed death-ligand 1 (PDL1) translocates to the nucleus and regulates the transcription of GSDMC together with phosphorylated signal transducer and activator of transcription 3 (p-STAT3), resulting in the conversion of apoptosis to pyroptosis after TNF-α activates caspase-8. In the granzyme (Gzm)-mediated pathway, GzmA and GzmB in cytotoxic lymphocytes enter target cells through perforin and induce pyroptosis. GzmA hydrolyzes GSDMB, and GzmB, in turn, directly activates GSDME [32]. There is a growing body of evidence demonstrating that GSDM family–mediated pyroptosis is a key driver of ALD in both patients and animal models (Figure 1c). Previously, Tlr4−/− mice were shown to be protected from early alcohol-induced injury [33]. Heo and colleagues [34] found that ethanol induces caspase-1-mediated pyroptosis via miRNA-148a-targeted overexpression of TXNIP in hepatocytes. The NLRP3 inflammasome pathway is activated in hepatocytes in response to lipopolysaccharide (LPS)-induced ER stress. Metabolically derived DAMPs, including ATP and soluble uric acid, released from damaged hepatocytes in response to alcohol binge, trigger the production of inflammasome-dependent IL-1β from immune cells. Nlrp3-deficient mice were resistant to alcohol-induced inflammation and injury [35]. Consistent with these observations, inhibition of ATP or uric acid prevents inflammasome activation and IL-1β production, thereby protecting mice from ethanol-induced damage [36]. Pharmacological inhibition of IL-1β/IL-1R1 signaling by recombinant human IL-1R antagonist attenuated alcohol-induced liver inflammation, steatosis, and damage [37]. Khanova et al. [ 38] reported that, after the transition from chronic alcohol-associated steatosis to AH, activation of noncanonical caspase-11-GSDMD signaling, but not canonical caspase-1-IL-1β signaling, was evident in livers from both murine models of ALD and patients with AH. Caspase-11 deficiency prevented ethanol-mediated GSDMD activation, hepatocellular death, and bacterial burden. ## Ferroptosis Ethanol feeding results in iron-dependent ferroptotic cell death, which is characterized by excessive accumulation of intracellular lipid ROS and consequent lipid peroxidation resulting from depletion of iron-dependent glutathione and inactivation of glutathione peroxidase 4 (GPX4) [39]. As Gpx4−/− mice exhibit early embryonic lethality [40], multiple chemical compounds, targeting either GPX4 or other regulators of ferroptosis, are broadly used to elucidate the role of ferroptosis in multiple disorders [41]. For instance, ferrostatin-1, which serves as a lipid ROS scavenger, notably ameliorates ethanol-induced hepatocellular injury, both in vitro and in vivo [42]. Ferroptosis is also implicated in the progression of ALD through the liver-gut axis and the liver-adipose axis (Figure 1d). Zhou et al. [ 43] first found that adipose-specific overexpression of lipin-1 exacerbates steatosis and hepatobiliary damage and leads to mild fibrotic injury by a GPX4-independent induction of hepatic iron overload lipid peroxidation. In this scenario, they speculated that alternative mechanisms might be at play in parallel with the regulation of ferroptosis by GPX4. Further, they reported that intestinal SIRT1 was also required for ethanol-induced dysfunction of hepatic iron metabolism and ferroptosis. This pathway involved changes in the circulating LCN2-SAA1 axis in a GPX4-independent mechanism, ultimately contributing to ethanol-induced liver injury [44]. In summary, dampening hepatic ferroptosis signaling may have a therapeutic potential for preventing mALD. The current data suggest that targeting ferroptosis will likely involve a better understanding of the cross talk between liver and other organs, such as adipose and gut. Further, while these initial studies characterizing ferroptosis have focused on describing characteristic hallmarks of this pathway of cell death, very little is known regarding the precise mechanisms that drive the ferroptotic cascade downstream of lipid peroxidation. For example, it is not clear whether intestinal microbiota or metabolites migrating into the liver through the portal vein can trigger hepatic ferroptosis in ALD. ## Autophagy and Membrane Trafficking The involvement of hepatocyte autophagy in ALD is important and complex. Acute alcohol consumption activates autophagy [45], whereas chronic ethanol exposure decreases lysosome-mediated lipid droplet turnover in hepatocytes through inactivation of Rab7 [46] or reduced dynamin 2 activity [47]. In both acute and acute-on-chronic models of ethanol exposure, ethanol impairs transcription factor EB (TFEB)-mediated lysosome biogenesis through activation of mTORC1, resulting in impaired/insufficient autophagy [16]. Furthermore, dysregulated autophagy and lysosome function have been linked to exosome production in ALD [48]. The total number of circulating extracellular vesicles (EVs) was increased in patients with AH and in mice after chronic ethanol feeding [49, 50]. Studies have shown that the EV cargo, miR-192 and miR-30a, as well as other proteins including heat shock protein 90 and CD40 ligand, are potential biomarkers and mediators of ethanol-induced injury (49–51). There are considerable interactions between autophagy and membrane trafficking and different forms of PCD. Autophagy is considered as an early adaptive response to injury that occurs prior to apoptosis; however, hyperactivation of autophagy also results in apoptotic cell death through common regulators, such as beclin1 and Bcl-2 [52, 53]. Proteins in the autophagy pathway can control the switching of cell death between apoptosis and necroptosis [54]. Recent advances have illuminated the important involvement of MLKL in diverse cellular processes (Figure 1b) pertaining to membrane trafficking, including autophagy [55, 56], endosomal trafficking, and EV generation [57]. Moreover, autophagy can negatively regulate and/or promote pyroptosis and the release of inflammatory cytokines, depending on the cellular context [58]. For example, macrophage-derived EVs shuttle HMGB1 via endocytosis and promote hepatocyte pyroptosis by activating the NLRP3 inflammasome [59]. In summary, the regulatory mechanisms for maintaining the intricate balance between cell prosurvival and death pathways in the liver are complex. It is assumed that apoptosis may occur in the early stages of ALD but not in the more advanced inflammatory stages, when necrotic/necroptotic cell death likely fuels inflammation in AH. After the transition to AH, coincident with endotoxemia/bacteremia, pyroptosis may dominate as a form of hepatocellular death, contributing to the recruitment of polymorphonuclear neutrophils and further promoting inflammation. It is also likely that different types of PCD may coexist at each stage of ALD, depending on the specific microenvironment. The question of whether there is an optimal time point for intervening in a specific pathway of cell death in the progression of ALD is of translational importance. Given the evidence for the overlapping and cell-specific pathways of PCD in murine models of ALD, it will be critical for future studies to comprehensively explore the contribution of PCD, in specific cell types and at different stages of disease, to expand our understanding of the precise role of PCD in the pathogenesis of ALD. ## Innate and Adaptive Immune Cells In ALD, the immune system plays two critical roles throughout the body: removing foreign, gut-derived microbial byproducts via PAMPs and responding to tissue damage and cell death via DAMPs. The immune system responds with a combination of pro- and anti-inflammatory signals within the tissue that lead to clearance of pathogens and dead cells, infiltration of peripheral cells, and resolution of inflammation. Most research efforts over the last few decades have focused on the molecular and cellular responses that are perturbed directly by alcohol and/or are altered in ALD. For example, binge alcohol consumption causes leakage of gut-derived LPS, leading to TLR4 signaling in liver macrophages, including both resident KCs and monocytes [6]. In ALD, macrophages are hypersensitive to LPS, leading to increased inflammatory responses. In recent years, a greater appreciation has developed for a more diverse set of molecular signals and cell types involved in disease progression. Moreover, single-cell RNA-sequencing (scRNA-seq) studies are allowing us to better understand the greater diversity of immune cells in the liver and how they are individually regulated in progression of ALD. Detection of PAMPs and DAMPs is an essential step in the pathogenesis of ALD in both rodent models and human studies. PAMPs and DAMPs signal through PRRs such as TLR4 to activate the immune system. Many PRRs play essential roles in ALD. PRRs can be classified by the cellular localization, where PRRs at the cell surface detect bacterial and fungal byproducts and DAMPs while intracellular PRRs sense microbial and viral DNAs and RNAs and some host-derived nucleic acids. Often, though, multiple pathways are activated sequentially. As a result, almost every PRR has been implicated in progression of ALD [6, 60] (Figure 2), including intracellular receptors such as TLR$\frac{3}{7}$/$\frac{8}{9}$, cGAS, AIM2, NLRs, and extracellular receptors such as TLR$\frac{2}{4}$ and the C-type lectin receptors (CLRs). The CLRs have become particularly interesting because they sense a much broader repertoire of PAMPs, compared with the TLR family, including many diverse fungi, viruses, commensal bacteria, eukaryotic pathogens, and DAMPs that originate from distinct cells and tissues. Many of these CLRs are upregulated in ALD, in peripheral blood mononuclear cells from AH patients [61], in livers from patients with ALD (A. Kim & L.E. Nagy, unpublished observations), and in rodent models of ALD [62, 63]. Dectin-1, a CLR that detects the pathogenic fungus Candida albicans, is upregulated in patients with ALD and increases liver inflammation in response to gut-derived C. albicans β-glucans [63]. Other CLRs, including mincle, dectin-2, and dectin-3, are upregulated in response to LPS [61]. By upregulating CLRs in response to TLR4 signaling, this secondary immune surveillance pathway makes monocytes more sensitive to a broader range of PAMPs and DAMPs, which contribute to inflammation and monocyte infiltration into tissues where damaged cells and foreign particles are found. Both Mincle and Dectin-1 knockout mice are protected from ethanol-induced liver injury in murine models of ALD (62–64). Activation of PRR signaling leads to cytokine and chemokine expression. Expression of cytokines and chemokines is often exacerbated in ALD and contributes to significant additional tissue damage. Neutrophils, which are recruited by specific inflammatory cytokines and chemokines, play a controversial role in disease progression, as they can help remove dead and dying cells as well as contribute to tissue damage. Other immune cells also respond to these inflammatory signals, including natural killer (NK) cells and T cells. Interestingly, in ALD, NK cells and T cells are thought to have reduced functions and cellular activity, despite increased numbers in the liver, though peripheral cell numbers are decreased and patients manifest lymphopenia [65]. Much of the work discussed so far is rooted in studies from rodent models of ALD, but in patients, ALD is a diverse spectrum of diseases where immune cells have different roles in different stages. Unfortunately, rodent models are unable to replicate certain aspects of AH and alcohol-associated cirrhosis. In addition to species differences, rodent models are unable to account for differences in human diversity, including factors such as sex, genetics, environment, and diet. Thus, much of the field is looking to harness the power of omics technologies to better understand multifactorial aspects of ALD by leveraging access to patient samples. For example, scRNA-seq and bulk RNA-seq technologies have already proven to be useful for understanding the role of immune cells in ALD and AH [61, 66, 67]. A recent scRNA-seq study found peripheral monocytes from AH patients to be less functionally diverse than those from HCs. For example, in response to LPS challenge, different monocyte subpopulations in HCs responded with diverse pro- and anti-inflammatory pathways, but in patients with AH, all monocytes were proinflammatory [61]. Because ALD and especially AH have increased peripheral and liver inflammation, many therapies currently in development focus on decreasing inflammation. Future studies should consider the diversity of innate immune cell responses to specific challenges. ## Cytokines and Chemokines Multiple factors and processes that contribute to the progression of ALD are mediated by low-molecular-weight polypeptides known as cytokines and chemokines, produced and released by various cells including liver cells [6, 60]. ## Cytokines. In patients with ALD and animals exposed to chronic ethanol feeding, a variety of cytokines are reported to be elevated including TNF-α, various ILs (such as IL-1, IL-4, IL-6, IL-10, IL-12, IL-17, and IL-22), and IFN-γ, as well as high-sensitivity C-reactive protein, transforming growth factor beta (TGF-β), and adiponectin. Most of them play dual functions in pathogenesis of ALD [6, 60]. TNF-α is a critical proinflammatory cytokine in ALD [6, 60]. Chronic ethanol exposure results in the translocation of LPS from the gut to activate KCs via TLR4. Enhanced production of proinflammatory cytokines, including IL-1 and TNF-α, thereby contributes to hepatocyte dysfunction and PCD, as well as activation of hepatic stellate cells to generate extracellular matrix (ECM) proteins leading to fibrosis/cirrhosis [68]. For example, Tnf-α knockout mice, mice deficient in different components of the IL-1 pathway, and mice treated with IL-1 receptor antagonist to neutralize the activity of IL-1 are all protected from ethanol-induced liver injury [69]. Other cytokines, in particular TGF-β, are also associated with the activation of hepatic stellate cells and collagen production, contributing to the development of fibrosis in patients with ALD [70]. IL-6 is another pleiotropic cytokine that exerts a dual role in liver homeostasis. Elevated IL-6 can reduce liver injury and inflammation through activation of STAT3 and participate in mitochondrial DNA repair in chronic alcohol-fed animals and in patients with alcohol-use disorder with or without liver disease [6, 60]. On the other hand, IL-6 promotes human Th17 differentiation and IL-17 production, therefore contributing to ethanol-induced liver inflammation via enhanced recruitment of neutrophils [6, 60]. Interestingly, IL-10, an anti-inflammatory cytokine, known for its hepatoprotective effects, is secreted simultaneously with proinflammatory cytokines. When Il-10 knockout mice are exposed to chronic ethanol, they exhibit increased inflammatory responses in the liver, associated with increased IL-6/STAT3 activation, but less steatosis and lower serum aspartate aminotransferase and alanine aminotransferase enzyme activity [71]. IL-22, another member of the IL-10 family of cytokines, is also associated with protecting the liver from ethanol-induced injury. In the Gao-binge murine model of ALD, treatment with IL-22 recombinant protein activates hepatic STAT3 and protects mice from hepatic oxidative stress and hepatocyte injury. Importantly, in a human phase II study using a recombinant IL-22 fusion protein (F-652), clinicians observed improved clinical scores as well as decreased liver injury markers when patients with ALD were treated with recombinant IL-22 [72, 73]. ## Chemokines. A plethora of chemokines, including Gro-α/CXCL1, PF-4/CXCL4, CXCL5, CXCL6, IL-8/CXCL8, CXCL10, CCL2, and CCL20, are positively correlated with higher mortality in patients with AH [74]. Among them, hepatic CXCL8/IL-8, a critical and highly upregulated chemokine, is specifically associated with recruiting neutrophils to the liver. IL-8 is induced by TNF-α and by TLR-dependent activation of nuclear factor kappa B (NF-κB). In murine models of ALD, blockade of IL-8 receptors (CXCR$\frac{1}{2}$) with pepducin antagonist protects mice from liver injury [75, 76]. Among the CC chemokines, CCL20, ligand for CCR6, is one of the most upregulated chemokines in liver samples from patients with AH. Expression of CCL20 is induced by many inflammatory mediators, such as LPS, TNF-α, and IL-1β, and regulates liver inflammation and fibrosis by acting as a chemoattractant for lymphocytes and neutrophils [77]. Higher concentrations of macrophage migration inhibitory factor (MIF), another pleiotropic cytokine/chemokine, in the suprahepatic circulation are associated with higher mortality in AH patients. Interestingly, hepatocyte-derived MIF contributes to the upregulation of a series of chemokines, including Cxcl1, Cxcl5, Cxcl6, Cxcl8, Ccl2, and Ccl20, in livers of mice exposed to Gao-binge acute-on-chronic ethanol feeding [78]. The coordinate expression of chemokines suggests that therapeutic targets for upstream signals in the chemokine expression pathway may be useful therapeutic targets for treatment and/or prevention of ALD. ## Complement Complement comprises a system of more than 40 plasma and membrane-associated proteins [79] consisting of activation components, regulatory factors, and receptors. Complement is part of the innate system and provides links to adaptive immunity. It plays a vital role in host response to microbial infection and in the response to tissue injury, thus playing a vital role in rapid immune responses, as well as maintaining multiple metabolic responses, including lipid metabolism and wound healing. Hepatocytes are the primary source for circulating complement factors, although growing evidence points to the importance of local complement production by a number of cell types [79, 80]. The system is activated via the classical pathway (CP), the lectin pathway (LP), or the alternative pathway (AP). The CP, involving the C1 complex (C1q, C1r, and C1s), C2, C3, and C4 components, is activated by antigen-antibody immune complexes binding to C1. The LP, though functionally similar to the CP, differs in its mode of activation. It is activated by the binding of lectins or ficolins to carbohydrate ligands on pathogens. The AP, on the other hand, is not activated by exogenous materials but is constantly activated at low levels through spontaneous plasma C3 hydrolysis, and it involves complement factors B, D, H, I, and P. Activation of these three pathways converges at the terminal pathway with the formation of C3 and C5 convertases, subsequently generating the main effector molecules with the eventual formation of the membrane attack complex, C5b-9 [79]. Mounting evidence in murine models has implicated complement in the initiation and development of ALD. Mice deficient in C3 or C5 are protected from chronic ethanol-induced liver injury, while mice deficient in CD55, a complement regulator, have exacerbated injury [81]. Specific inhibition of C3 activation with complement receptor 2 (CR2)-Crry significantly decreased inflammatory responses and hepatic steatosis in mice exposed to ethanol [82]. One mechanism for complement activation in response to ethanol was found to be via binding of C1q to apoptotic hepatocytes, causing an initial rise in inflammatory cytokine expression [83]. Mice with a C1qa deficiency or treated with Cinryze (C1-INH), a purified C1 inhibitor, are protected from chronic ethanol [84]. Taken together, these studies suggest that activation of complement via the CP contributes to ethanol-induced liver injury. However, the role of complement in disease progression is more complex, with recent data demonstrating cell-specific roles for C5aR1 in myeloid and non-myeloid cells in liver and adipose tissue [85, 86]. Similarly, the specific pathways of complement activation may also be important. For example, in contrast to the injurious role of CP activation, activity of the AP via factor D (FD) protected mice from chronic ethanol-induced injury [87]. A few studies have also implicated complement in patients with ALD. For example, plasma C3a concentrations are associated with fatty liver and hepatocellular damage in heavy drinkers [88]. In another study, C1q, C3, C5, and C5aR immunoreactivities were increased in the liver biopsies of patients with AH compared with HCs; expression of C1q and C5, but not C3, mRNA was also increased in livers of patients with AH [80]. In a recent study quantifying complement components in plasma from patients with moderate and severe AH, factors C4b, C4d, CFD, CFI, C5, and sC5b9 could distinguish healthy subjects from patients with AH. Importantly, both CFI and sC5b9 were negatively associated with 90-day mortality in patients with AH [89]. Collectively, the data from murine models of ALD and from patients indicate a complex contribution of complement in ALD and suggest that complement may be useful as a prognostic and diagnostic indicator in patients with AH. ## HEPATIC REGENERATION: ROLE OF CELL-CELL CROSS TALK The liver is considered the only fully regenerative organ in the human body, but during chronic liver diseases, such as ALD, hepatocyte regeneration is compromised. In a healthy liver, hepatocytes have different functions depending on their position between the portal triad and the central vein, termed hepatic zonation. Hippo/YAP signaling is required for periportal hepatocyte gene expression. In severe AH, upregulation of YAP, downregulation of ESRP2, and differential splicing of the nuclear receptor HNF4α results in aberrant upregulation of periportal hepatocyte genes, hepatocyte fetal reprogramming, and activation of the ductular reaction, where periportal hepatocytes and liver progenitor cells activate and become either mature hepatocytes or cholangiocytes [67, 90, 91]. Hepatic endothelial cells (ECs), including periportal ECs, liver sinusoidal ECs (LSECs) and periportal ECs, also play a critical role in liver regeneration by direct interaction with macrophages and hepatocytes. While periportal hepatocytes are expanded and central hepatocytes are depleted in liver regeneration, periportal ECs are diminished and central vein ECs are expanded in severe AH [66]. ECs can promote inductive angiogenesis through release of angiocrine factors, including hepatocyte growth factor, Wnt2 [92], and Wnt9 [93]. Wnt2 is expressed in both central vein and sinusoidal ECs and macrophages, while Wnt9b is specifically expressed in central vein ECs and secondarily in macrophages [93, 94]. Pericentral hepatocytes are regulated by WNT signaling, and, in ALD, WNT signaling is also dysregulated. In AH, expression of WNTs and their receptors, the FZD family, varies with disease severity [66]. In healthy livers, the predominant genes are WNT2 and FZD4, while in moderate AH, WNT5a and FZD5 are upregulated, which is notable because WNT5a is thought to play a role in active liver regeneration. In severe AH, WNT5a is downregulated, while many other WNTs and FZDs are expressed. Many of these FZD genes have been previously implicated in human hepatocellular carcinoma (HCC) [95]. Considering WNT signaling alone, we hypothesize that there is a progression from healthy human liver to moderate AH, where liver regeneration can occur, to severe AH, where regeneration is dysregulated and signs of HCC may start to develop. ## Liver Sinusoidal Endothelial Cell Dysfunction The liver filters toxins through sinusoidal channels lined with KCs. This is mediated by LSECs that, when exposed to blood from the gut and systemic circulation, remove and recycle blood-borne proteins and lipids through the presence of highly permeable fenestrae [96]. The lack of a basement membrane contributes to the permissive nature of sinusoidal endothelium [96], allowing the LSECs and KCs to endocytically take up and eliminate invading pathogens [97]. Dynamic changes in hepatic fenestration number and size are highly regulated, with vascular endothelial growth factor (VEGF) implicated as essential in this regulation [97]. Alcohol and dietary constituents can also modulate fenestration function by changing the access of macro-molecules to parenchymal cells and by allowing circulating viruses to infect hepatocytes [98]. LSECs maintain a balance between tolerance and effector immune responsiveness, facilitated by their innate and adaptive immunological functions [96]. Defenestration and LSEC activation occur early in animal models of fatty liver disease [99], contributing to the activation of hepatic stellate cells (HSCs) and fibrogenesis. Capillarization also increases hedgehog signaling [100] and impairs VEGF-dependent endothelial nitric oxide (NO) synthase activity [97]. Interestingly, LSECs also promote reversion from activated HSCs to a quiescent phenotype via NO production [101]. LSECs undergo many changes during injury that promote the recruitment of proinflammatory immune cells including increased expression of adhesion molecules such as intercellular adhesion molecule 1, vascular adhesion protein 1, and stabilin 1, which promote T and B cell adhesion; chemokines such as CXCL16, CXCL9, and CX3C-chemokine ligand 1, which contribute to adhesion of transmigrating T cells and monocytes; and hyaluronan, which promotes neutrophil adhesion [96]. ## Hepatic Stellate Cell Activation Long-term liver fibrosis leads to accumulation of ECM proteins and results in the replacement of parenchyma with nonfunctional scar tissue. HSCs are the cells predominately responsible for the production of ECM in the fibrogenic process. HSCs, located in the space of Disse, are surrounded by hepatocytes and LSECs [102]. HSCs secrete laminin, proteoglycans, and collagen IV to form basement membrane structures and remain in a quiescent state until activated. Activated and pro-liferating HSCs express α-smooth actin and upregulate the synthesis of type I and III collagens and ECM proteins such as fibronectin [103]. HSC activation is promoted by a number of factors present within the hepatic microenvironment during chronic ethanol exposure. For example, HSCs are activated by apoptotic hepatocytes as well as multiple paracrine signals from neighboring cells including KCs, LSECs, platelets, and infiltrating immune cells. KCs produce cytokines such as TGF-β, TNF-α, and IL-1 that stimulate proliferation of HSCs [103]. HSCs also respond to ROS released from neutrophils, and inflammatory lipid peroxides from damaged hepatocytes during progression of liver disease [102, 104] and complement C5a stimulate HSC chemotaxis and migration [105]. ## microRNAs: Function at All Stages of Liver Injury miRNAs play a critical role in mediating steatosis, inflammation, injury, and gut permeability in the pathogenesis of ALD. Ethanol and its metabolites induce miRNA dysregulation in a variety of organ systems and circulation. Using RNA sequencing, global changes in miRNA regulation have been identified that are associated with polarization phenotypes in KCs from rats after chronic ethanol and in peripheral blood mononuclear cells from patients with AH. These polarization-associated miRNAs are localized to coordinately regulated clusters. For example, miR-125a-5p, miR-125a-3p, and miR-99b-5p and the host gene sperm acrosome associated protein 6 (SPACA6) were upregulated in AH patients [106]. In the liver, miR-122 in hepatocytes regulates steatosis [107, 108], while miR-155 in KCs yields a proinflammatory state, perpetuating liver injury [107]. miR-212 has also been implicated in the alcohol-induced impairment of gut barrier function [109]. Additionally, ALD is associated with an increase in hepatocellular death through death receptor ligands regulated in part by ubiquitination enzymes (E1–E3) that control protein degradation and localization. Recent work implicated miR-150–5p as affecting Fas-associated death domain (FADD) degradation by inhibiting cytokine-inducible SH2-containing protein (CISH) expression. In addition, miR-150–5p is elevated in livers of patients with AH and mice after Gao-binge ethanol exposure, suggesting a role in the pathogenesis of ALD. miRNA profiles of human HSCs change during activation in cell culture [110], and modulatory roles of miRNAs in the expression of liver fibrosis-associated genes have been reported [111]. Several miRNAs are classified as antifibrotic such as miR-19b, miR-34a-5p, miR-146a, miR-133, miR-23b-27, and miR-134 [112, 113]. Overexpression of miR-133 in HSCs inhibits collagen expression and downregulation during fibrogenesis and TGF-β treatment [112]. Profibrogenic miRNAs include miR-942 and miR-125b [111, 114]. Codelivery of miR-29b1 with a hedgehog inhibitor can decrease collagen and α-SMA [113]. Upregulated in fibrosis, miR-542–3p was found to control the activation of HSCs and promote liver fibrosis by downregulating BMP-7 expression. Overexpression of miR-199 and miR-200 families was correlated with the TGF-β/SMAD signaling pathway during liver fibrogenesis in both a fibrotic mouse model and human clinical samples of patients with fibrosis [115]. Transfection of miR-129–5p mimic reduced the expression of type I collagen in activated HSCs and was associated with a reduction in fibrotic injury [116]. ## Gut-Liver Axis The liver and intestine communicate via the portal vein, biliary system, and other circulating soluble mediators [117]. Therefore, the liver is the first organ exposed to gut-derived microbial components and metabolites. Alcohol consumption influences multiple aspects of gut physiology, specifically increasing gut permeability as well as affecting microbial composition and metabolism [117]. Most long-term heavy drinkers develop steatosis, but only 10–$20\%$ develop progressive liver disease [6]. Current opinion indicates that, in addition to the direct effects of alcohol on the liver, altered gut physiology contributes to the progression of ALD [118] (Figure 3). ## Dysbiosis. Alcohol intake causes a striking reduction in fungal and bacterial diversity. Dysbiosis is not only a consequence of alcohol intake but also regulates the individual susceptibility and severity of ALD [118]. Commensal fungi are reduced while there is an overgrowth of Candida species [63]. Similarly, there is decreased abundance of beneficial bacteria such as Ruminococcaceae, Faecalibacterium, and Prevotella, with a concomitant increase in gram-negative bacteria, for example, Proteobacteria, Enterobacteriaceae, and Escherichia. Most of the beneficial bacteria produce short-chain fatty acids, reported to maintain and improve gut health [117]. The dysbiotic pressure leads to production of virulence factors by certain bacteria. For example, cytolysin, produced by Enterococcus faecalis, directly affects hepatocyte survival. While there is no overall increase in the number of E. faecalis, the presence of cytolysin is highly correlated with disease severity in patients with AH [119]. ## Loss of gut integrity. Gut permeability increases with ethanol consumption [120] and is a prerequisite for development of ALD in murine models [121]. Alcohol consumption reduces mRNA of several junctional proteins such as occludin, zonula occludens-1, and claudins 3 and 4 [120]. A loss of permeability contributes to translocation of microbes and microbial components into the systemic circulation. Importantly, the epithelial barrier has additional defenses, including a thick mucilaginous layer composed mainly of mucin (Muc) 2 produced by goblet cells, and secretes various antimicrobial peptides (AMPs). Chronic ethanol has complex effects on these defenses. For example, ethanol increases Muc-2 levels in patients with ALD, and Muc2 deficiency protects mice from chronic ethanol-induced injury. In contrast, chronic ethanol reduces expression of C-type lectin AMPs Reg3β and Reg3γ, and overexpression of Reg3γ protects from injury [122]. ## Translocation of PAMPs, viable microbes, and microbial metabolites. As discussed above, loss of gut integrity and bacterial dysbiosis contribute to increased exposure of the liver to PAMPs. In addition, viable bacteria can also cross the damaged gut epithelial barrier, contributing to liver injury [119, 123]. There is also a growing appreciation that many microbial metabolites are detectable in the portal and systemic circulation in response to ethanol; some of these metabolites likely contribute to ethanol-induced injury. For example, ethanol-mediated gut dysbiosis reduces the short-chain fatty acid (SCFA) composition of the gut [124], thereby affecting colonocyte and enterocyte survival and maintenance of gut barrier. This causes loss of the anti-inflammatory effect of SCFAs in the gut [125]. In patients with advanced cirrhosis, low circulating levels of butyrate correlate with increased proinflammatory markers and serum endotoxin [126]. The gut microbial metabolite trimethylamine is also elevated in patients with AH and contributes to chronic ethanol-induced liver injury in murine models [127]. ## Bile acids. Ethanol intake alters qualitative and quantitative bile acid composition in both the liver and gut. Gut bacteria deconjugate primary bile acids via bile salt hydrolase. Deconjugation prevents their reabsorption, thereby maintaining bile acid homeostasis. Chronic ethanol administration increases hepatic bile acid synthesis as well as plasma and fecal concentrations of unconjugated bile acids [128]. In patients with AH, bile acid homeostasis and its associated signaling is dysregulated [129]. For example, farnesoid X receptor (FXR) signaling, an essential part of the negative feedback mechanism regulating hepatic bile acid synthesis, as well as glucose and lipid metabolism, is reduced in patients with ALD [130]. ## Adipose-Liver Axis While changes in adipose tissue function are traditionally associated with nonalcoholic-associated liver disease, considerable evidence indicates that chronic alcohol also impairs the function of adipose tissue. Chronic ethanol is associated is associated with impaired metabolic, endocrine, and immune functions of adipose tissue, changes that likely contribute to the progression of ALD [131, 132] (Figure 3). ## Metabolic regulation. Chronic alcohol exposure increases lipolytic activity in adipose tissue, thereby increasing circulating nonesterified fatty acids (NEFAs) and increasing the exposure of the liver NEFAs, where they are esterified and contribute to hepatic steatosis [133]. Saturated fatty acids have a greater hepatotoxic effect and trigger hepatocyte apoptosis through activation of the c-Jun N-terminal kinase pathway [134]. Saturated NEFAs also exert proinflammatory effects through the NF-κB pathway and activation of KCs [135], thereby furthering ALD. ## Endocrine regulation. Alcohol abuse increases circulating levels of leptin, visfatin, and chemerin, causing exacerbated fibrotic response, proinflammatory cytokine production from myeloid cells, and immune cell infiltration [132], respectively. Provision of exogenous adiponectin to mice protects from ethanol-induced liver injury [136]; however, the relevance of these data to human ALD is not known. ## Immune regulation. Chronic ethanol exposure increases inflammatory responses in adipose tissue. Interestingly, adipocytes express CYP2E1 in response to chronic ethanol, likely contributing to oxidative stress and adipocyte cell death, characterized by crown structures [131]. Dying adipocytes recruit complement C1q to facilitate clearance, but in the context of ethanol, this response leads to activation of complement and generation of anaphylatoxins [86, 131, 137]. Complement activation in turn leads to increased expression of inflammatory cytokines and contributes to impaired regulation of lipid metabolism [131, 132]. Of translational interest, the interaction between ethanol and obesity is particularly important, as epidemiological data suggest that obesity and metabolic syndrome exacerbate progression of ALD (138–140). Importantly, for treatment of heavy drinkers, cessation of drinking rapidly normalizes adipose tissue function [141]. ## Targeting Hepatocyte Injury Accumulating evidence suggests that hepatocyte injury resulting, at least in part, from ethanol-induced oxidative stress and innate immune responses plays a crucial role in progression of ALD [60]. Thus, protecting hepatocytes from injury is viewed as a potential therapeutic strategy (Figure 4). Chronic exposure to ethanol induces glutathione depletion, which makes hepatocytes more vulnerable to oxidative stress [142]. Oxidative stress is one of the key mechanisms leading to hepatocyte injury in ALD; however, classical antioxidant molecules alone (N-acetylcysteine or metadoxine) are not effective in severe forms of AH [143, 144]. One of the reasons for the failure of these antioxidant therapies in AH may be the lack of specific mitochondrial antioxidant effect. Colell and colleagues [145] reported that S-adenosylmethionine could be a potential therapeutic option for ALD, because this molecule restores glutathione in the mitochondria and improves steatosis in rodents. More clinical studies are required to clarify the benefits of mitochondrial-targeted antioxidants for the treatment of AH. Hepatocyte death is another promising therapeutic target for ALD. As reviewed above, multiple pathways are associated with ALD [146]. However, currently there is a lack of therapeutic agents that target these modes of hepatocyte death. In addition, since multiple forms of cell death are associated with ALD, inhibiting individual cell death pathways may be insufficient to improve AH. In a phase II clinical trial, selonsertib (GS-4997), an oral inhibitor of apoptosis signal regulating kinase-1 (ASK-1) enzyme, combined with prednisolone showed no advantage over prednisolone alone in the treatment of severe AH (NCT02854631). Due to the difficulty in inhibiting hepatocyte death, promoting liver regeneration is considered a complementary therapeutic strategy. The granulocyte colony stimulating factor (G-CSF), a potent growth factor, has been proposed to promote hepatocyte regeneration in severe AH. Results of a meta-analysis suggest that G-CSF is associated with a reduction in mortality by more than $70\%$ at 90 days in patients with AH [147]. However, due to the heterogeneity of clinical studies in Asia and Europe, the therapeutic effects of G-CSF need to be interpreted with caution. IL-22, one of the major cytokines of the anti-inflammatory family, provides liver protection and promotes regeneration. Currently, a phase II open-label clinical trial is investigating the impact of an IL-22 agonist (F-652) on AH patients [MELD (model for end-stage liver disease) scores of 11–28]. F-652, which has the same mechanism of action as the native IL-22, is a recombinant fusion protein of human IL-22 and human IgG2 fragments. F-652 associated with a high rate of improvement, as determined by MELD and Lille scores, increases in markers of hepatic regeneration, and reductions in markers of inflammation [73]. ## Reducing Inflammatory Responses Chronic inflammation is a critical factor in the development of ALD [148], suggesting that modulating the inflammatory response is a promising therapeutic strategy for the improvement of ALD (Figure 3). Glucocorticoids (e.g., prednisolone) are currently commonly used as first-line anti-inflammatory agents in patients with severe AH; however, prednisolone is ineffective in most patients and increases the risk for bacterial and fungal infections. Since many immune cells (KCs, neutrophils, NK cells, etc.) and inflammatory mediators (TNF-α, TLR4, IL-1β, etc.) play a dual role in liver injury and liver regeneration, it is necessary to consider comprehensive treatment strategy, rather than simply suppressing or promoting inflammatory responses. Two randomized controlled studies with anti-TNF agents (infliximab and etanercept) were negative, with more deaths occurring in the anti-TNF arm [149, 150]. Two ongoing randomized clinical trials are investigating the impact of anti-IL-1 on AH patients. The first trial, being conducted in the United Kingdom, is testing the efficacy and safety of IL-1β antibody (canakinumab) in patients with severe AH [modified Maddrey’s discriminant function (mDF) ≥ 32]. The primary end point is histological improvement of AH on liver biopsy after 28 days of treatment (NCT03775109). The other study, based in the United States, is mainly evaluating the effects of IL-1 receptor antagonist (anakinra) on 90-day mortality in patients with severe AH (NCT04072822). On the basis of the role of TLR4 in the pathophysiology of ALD, TLR4 antagonists seem to be promising candidates for the treatment of AH. Hyaluronic acid of 35 kD (HA35), a small and specific-sized molecule of hyaluronic acid, inhibits the ethanol-induced TLR4 signaling pathway in KCs in mouse models [151]. A randomized controlled trial (RCT) on the effects of HA35 on the change of skeletal muscle mass in patients with AH is registered, but patient recruitment has not started (NCT05018481). FXR agonists provide hepatoprotective effects by exerting anti-inflammatory and antioxidant effects and by regulating lipid and bile acid metabolism. A phase II randomized clinical trial using obeticholic acid, an FXR agonist, in patients with AH (MELD scores of 12–19) was conducted; however, the clinical trial was terminated because of hepatotoxicity associated with obeticholic acid (NCT02039219). Inhibition of the activation of inflammatory cells and the release of inflammatory mediators via the reduction of LPS had positive effects for the improvement of ALD in animal models. The ability of antioxidant agents, HA35, and the microbiome-based therapies to reduce liver damage by preventing LPS flux from the intestinal tract has been demonstrated in animal models [152], but further clinical studies are needed to validate these effects. ## Microbiome-Based Therapies In recent years, as the awareness of the impact of ethanol on the gut pathophysiology has increased, the gut microbiota have become one of the major targets in the development of therapeutics for ALD (Figure 3). Accumulating evidence from early-stage clinical studies shows interesting results. For example, Han and colleagues [153] reported the importance of probiotics in a multicenter RCT, with the probiotic group having significantly reduced TNF-α ($$p \leq 0.042$$) and LPS ($$p \leq 0.028$$) compared with the placebo group. Two ongoing randomized clinical trials are investigating the impact of probiotics on AH patients. The first trial, being conducted in the United States, is testing the efficacy and safety of Lactobacillus rhamnosus GG in patients with moderate AH (MELD score <21). The primary end point is the change in MELD score after 30 days (NCT01922895). The other study, based in Korea, is evaluating the effects of Lactobacillus rhamnosus R0011 and acidophilus R0052 on liver enzyme, endotoxin, and cytokine levels after 7 days in 140 AH patients (NCT02335632). Antibiotics can also alter the gut microbiota. However, hepatitis and systemic inflammation were not improved after 7 days of antibiotic cocktail using vancomycin, gentamicin, and meropenem [9]. A multicenter, double-blind RCT evaluating the combination effect of corticosteroids and the antibiotic amoxicillin in severe AH has been completed in France (NCT02281929), and the results need to be confirmed. For now, the role of routine antibiotics in the management of AH has yet to be established. Fecal microbiota transplantation (FMT) may be a solution for restoring healthy gut flora. Philips and colleagues [154, 155] reported in a pilot study and subsequent open-label trial that FMT in patients with severe AH from healthy donors improved survival and liver function due to reduced gut bacteria that contribute to the development of AH. The first report showed that daily 7-day administration of FMT through a nasoduodenal tube significantly improved 1-year survival ($87.5\%$ versus $33.3\%$, $$p \leq 0.018$$) in patients with steroid-resistant AH ($$n = 8$$) compared with a control group using a standard surgical seat ($$n = 18$$) [154]. Furthermore, they analyzed the microbiota in stool and reported the coexistence of donor and recipient species at 6–12 months after FMT, suggesting that FMT alters the recipient’s gut microbiota network and that these changes are sustained over time. In their second study, the prognostic impact of FMT was tested in a cohort of 51 male patients with severe AH. Patients were divided into four treatment groups: FMT group ($$n = 16$$), steroid treatment group ($$n = 8$$), nutritional support treatment group ($$n = 17$$), and pentoxifylline treatment group ($$n = 10$$), and the effects on prognosis were investigated retrospectively. Survival rate after 3 months was significantly better in the FMT treatment group than in the other treatment groups, at $75\%$, $38\%$, $30\%$, and $29\%$, respectively ($$p \leq 0.036$$). Further more, favorable changes in the composition of intestinal microflora were observed [155]. These studies suggest that the donor microbiota may modify the recipient microbiota and improve ALD without complications, even in patients with severe AH. Duan and colleagues [119] recently found that cytolysin-secreting E. faecalis strains are an important factor in exacerbating liver cell damage and death in patients with severe alcoholism. The authors found that patients with alcoholism had much higher numbers of E. faecalis in their stool than nonalcoholics or patients with alcohol use disorders. Interestingly, the total number of E. faecalis, not just the presence of cytolysin-positive strains, may be important in the severity of liver disease and subsequent mortality. Using humanized mice colonized with bacteria collected from the feces of AH patients, the researchers showed that certain bacteriophages specifically target cytolytic E. faecalis, reduce cytolysin in the liver, and eliminate ethanol-induced liver disease. Importantly, this approach provides a way to precisely edit the gut microbiota. Clinical trials with a larger number of people are needed to validate these results. ## References 1. 1.World Health Organ. (WHO). 2018. Global status report on alcohol and health 2018. Rep., WHO, Geneva. https://www.who.int/publications/i/item/9789241565639. Global status report on alcohol and health 2018 (2018) 2. Seitz HK, Bataller R, Cortez-Pinto H, Gao B, Gual A. Alcoholic liver disease. Nat. Rev. Dis. Primers (2018) 4 16. PMID: 30115921 3. Thursz M, Kamath PS, Mathurin P, Szabo G, Shah VH. Alcohol-related liver disease: areas of consensus, unmet needs and opportunities for further study. J. Hepatol (2019) 70 521-30. PMID: 30658117 4. Lee BP, Vittinghoff E, Dodge JL, Cullaro G, Terrault NA. National trends and long-term outcomes of liver transplant for alcohol-associated liver disease in the United States. JAMA Intern. Med (2019) 179 340-48. PMID: 30667468 5. Tian C, Stokowski RP, Kershenobich D, Ballinger DG, Hinds DA. Variant in. Nat. Genet (2010) 42 21-23. PMID: 19946271 6. Nagy LE, Ding WX, Cresci G, Saikia P, Shah VH. Linking pathogenic mechanisms of alcoholic liver disease with clinical phenotypes. Gastroenterology (2016) 150 1756-68. PMID: 26919968 7. Edenberg HJ, McClintick JN. Alcohol dehydrogenases, aldehyde dehydrogenases, and alcohol use disorders: a critical review. Alcohol. Clin. Exp. Res (2018) 42 2281-97. PMID: 30320893 8. Zakhari S. Overview: How is alcohol metabolized by the body?. Alcohol. Res. Health (2006) 29 245-54. PMID: 17718403 9. Wilson DF, Matschinsky FM. Ethanol metabolism: the good, the bad, and the ugly. Med. Hypotheses (2020) 140 109638. PMID: 32113062 10. Heier C, Xie H, Zimmermann R. Nonoxidative ethanol metabolism in humans—from biomarkers to bioactive lipids. IUBMB Life (2016) 68 916-23. PMID: 27714979 11. Tang D, Kang R, Berghe TV, Vandenabeele P, Kroemer G. The molecular machinery of regulated cell death. Cell Res (2019) 29 347-64. PMID: 30948788 12. Zheng M, Kanneganti TD. The regulation of the ZBP1-NLRP3 inflammasome and its implications in pyroptosis, apoptosis, and necroptosis (PANoptosis). Immunol. Rev (2020) 297 26-38. PMID: 32729116 13. Stolz A, Ernst A, Dikic I. Cargo recognition and trafficking in selective autophagy. Nat. Cell Biol (2014) 16 495-501. PMID: 24875736 14. Shen Y, Malik SA, Amir M, Kumar P, Cingolani F. Decreased hepatocyte autophagy leads to synergistic IL-1β and TNF mouse liver injury and inflammation. Hepatology (2020) 72 595-608. PMID: 32108953 15. Wan J, Weiss E, Ben Mkaddem S, Mabire M, Choinier PM. LC3-associated phagocytosis protects against inflammation and liver fibrosis via immunoreceptor inhibitory signaling. Sci. Transl. Med (2020) 12 eaaw8523. PMID: 32295902 16. Chao X, Wang S, Zhao K, Li Y, Williams JA. Impaired TFEB-mediated lysosome biogenesis and autophagy promote chronic ethanol-induced liver injury and steatosis in mice. Gastroenterology (2018) 155 865-79.e12. PMID: 29782848 17. Francis H, McDaniel K, Han Y, Liu X, Kennedy L. Regulation of the extrinsic apoptotic pathway by microRNA-21 in alcoholic liver injury. J. Biol. Chem (2014) 289 27526-39. PMID: 25118289 18. Chattopadhyay S, Kuzmanovic T, Zhang Y, Wetzel JL, Sen GC. Ubiquitination of the transcription factor IRF-3 activates RIPA, the apoptotic pathway that protects mice from viral pathogenesis. Immunity (2016) 44 1151-61. PMID: 27178468 19. Petrasek J, Iracheta-Vellve A, Csak T, Satishchandran A, Kodys K. STING-IRF3 pathway links endoplasmic reticulum stress with hepatocyte apoptosis in early alcoholic liver disease. PNAS (2013) 110 16544-49. PMID: 24052526 20. Luther J, Khan S, Gala MK, Kedrin D, Sridharan G. Hepatic gap junctions amplify alcohol liver injury by propagating cGAS-mediated IRF3 activation. PNAS (2020) 117 11667-73. PMID: 32393626 21. Wan J, Benkdane M, Teixeira-Clerc F, Bonnafous S, Louvet A. M2 Kupffer cells promote M1 Kupffer cell apoptosis: a protective mechanism against alcoholic and nonalcoholic fatty liver disease. Hepatology (2014) 59 130-42. PMID: 23832548 22. Sanz-Garcia C, Poulsen KL, Bellos D, Wang H, McMullen MR. The non-transcriptional activity of IRF3 modulates hepatic immune cell populations in acute-on-chronic ethanol administration in mice. J. Hepatol (2019) 70 974-84. PMID: 30710579 23. Roychowdhury S, Chiang DJ, Mandal P, McMullen MR, Liu X. Inhibition of apoptosis protects mice from ethanol-mediated acceleration of early markers of CCl. Alcohol. Clin. Exp. Res (2012) 36 1139-47. PMID: 22273278 24. Hao F, Cubero FJ, Ramadori P, Liao L, Haas U. Inhibition of Caspase-8 does not protect from alcohol-induced liver apoptosis but alleviates alcoholic hepatic steatosis in mice. Cell Death Dis (2017) 8 e3152. PMID: 29072704 25. Linkermann A, Green DR. Necroptosis. N. Engl. J. Med (2014) 370 455-65. PMID: 24476434 26. Wang S, Ni HM, Dorko K, Kumer SC, Schmitt TM. Increased hepatic receptor interacting protein kinase 3 expression due to impaired proteasomal functions contributes to alcohol-induced steatosis and liver injury. Oncotarget (2016) 7 17681-98. PMID: 26769846 27. Roychowdhury S, McMullen MR, Pisano SG, Liu X, Nagy LE. Absence of receptor interacting protein kinase 3 prevents ethanol-induced liver injury. Hepatology (2013) 57 1773-83. PMID: 23319235 28. Hanna-Addams S, Liu S, Liu H, Chen S, Wang Z. CK1α, CK1δ, and CK1ε are necro-some components which phosphorylate serine 227 of human RIPK3 to activate necroptosis. PNAS (2020) 117 1962-70. PMID: 31932442 29. Miyata T, Wu X, Fan X, Huang E, Sanz-Garcia C. Differential role of MLKL in alcohol-associated and non-alcohol-associated fatty liver diseases in mice and humans. JCI Insight (2021) 6 e140180. PMID: 33616081 30. Vatsalya V, Cave MC, Kong M, Gobejishvili L, Falkner KC. Keratin 18 is a diagnostic and prognostic factor for acute alcoholic hepatitis. Clin. Gastroenterol. Hepatol (2020) 18 2046-54. PMID: 31811953 31. Atkinson SR, Grove JI, Liebig S, Astbury S, Vergis N. In severe alcoholic hepatitis, serum keratin-18 fragments are diagnostic, prognostic, and theragnostic biomarkers. Am. J. Gastroenterol (2020) 115 1857-68. PMID: 33156105 32. Yu P, Zhang X, Liu N, Tang L, Peng C, Chen X. Pyroptosis: mechanisms and diseases. Signal Transduct. Target. Ther (2021) 6 128. PMID: 33776057 33. Uesugi T, Froh M, Arteel GE, Bradford BU, Thurman RG. Toll-like receptor 4 is involved in the mechanism of early alcohol-induced liver injury in mice. Hepatology (2001) 34 101-8. PMID: 11431739 34. Heo MJ, Kim TH, You JS, Blaya D, Sancho-Bru P, Kim SG. Alcohol dysregulates miR-148a in hepatocytes through FoxO1, facilitating pyroptosis via TXNIP overexpression. Gut (2019) 68 708-20. PMID: 29475852 35. Petrasek J, Iracheta-Vellve A, Saha B, Satishchandran A, Kodys K. Metabolic danger signals, uric acid and ATP, mediate inflammatory cross-talk between hepatocytes and immune cells in alcoholic liver disease. J. Leukoc. Biol (2015) 98 249-56. PMID: 25934928 36. Iracheta-Vellve A, Petrasek J, Satishchandran A, Gyongyosi B, Saha B. Inhibition of sterile danger signals, uric acid and ATP, prevents inflammasome activation and protects from alcoholic steatohepatitis in mice. J. Hepatol (2015) 63 1147-55. PMID: 26100496 37. Petrasek J, Bala S, Csak T, Lippai D, Kodys K. IL-1 receptor antagonist ameliorates inflammasome-dependent alcoholic steatohepatitis in mice. J. Clin. Investig (2012) 122 3476-89. PMID: 22945633 38. Khanova E, Wu R, Wang W, Yan R, Chen Y. Pyroptosis by caspase11/4-gasdermin-D pathway in alcoholic hepatitis in mice and patients. Hepatology (2018) 67 1737-53. PMID: 29108122 39. Stockwell BR, Friedmann Angeli JP, Bayir H, Bush AI, Conrad M. Ferroptosis: a regulated cell death nexus linking metabolism, redox biology, and disease. Cell (2017) 171 273-85. PMID: 28985560 40. Carlson BA, Tobe R, Yefremova E, Tsuji PA, Hoffmann VJ. Glutathione peroxidase 4 and vitamin E cooperatively prevent hepatocellular degeneration. Redox Biol (2016) 9 22-31. PMID: 27262435 41. Wu J, Wang Y, Jiang R, Xue R, Yin X. Ferroptosis in liver disease: new insights into disease mechanisms. Cell Death Discov (2021) 7 276. PMID: 34611144 42. Liu CY, Wang M, Yu HM, Han FX, Wu QS. Ferroptosis is involved in alcohol-induced cell death in vivo and in vitro. Biosci. Biotechnol. Biochem (2020) 84 1621-28. PMID: 32419644 43. Zhou Z, Ye TJ, Bonavita G, Daniels M, Kainrad N. Adipose-specific lipin-1 overexpression renders hepatic ferroptosis and exacerbates alcoholic steatohepatitis in mice. Hepatol. Commun (2019) 3 656-69. PMID: 31061954 44. Zhou Z, Ye TJ, DeCaro E, Buehler B, Stahl Z. Intestinal SIRT1 deficiency protects mice from ethanol-induced liver injury by mitigating ferroptosis. Am. J. Pathol (2020) 190 82-92. PMID: 31610175 45. Ding WX, Li M, Chen X, Ni HM, Lin CW. Autophagy reduces acute ethanol-induced hepatotoxicity and steatosis in mice. Gastroenterology (2010) 139 1740-52. PMID: 20659474 46. Schulze RJ, Rasineni K, Weller SG, Schott MB, Schroeder B. Ethanol exposure inhibits hepatocyte lipophagy by inactivating the small guanosine triphosphatase Rab7. Hepatol Commun (2017) 1 140-52. PMID: 29404450 47. Rasineni K, Donohue TM, Thomes PG, Yang L, Tuma DJ. Ethanol-induced steatosis involves impairment of lipophagy, associated with reduced Dynamin2 activity. Hepatol. Commun (2017) 1 501-12. PMID: 29152606 48. Babuta M, Furi I, Bala S, Bukong TN, Lowe P. Dysregulated autophagy and lysosome function are linked to exosome production by micro-RNA 155 in alcoholic liver disease. Hepatology (2019) 70 2123-41. PMID: 31090940 49. Momen-Heravi F, Saha B, Kodys K, Catalano D, Satishchandran A, Szabo G. Increased number of circulating exosomes and their microRNA cargos are potential novel biomarkers in alcoholic hepatitis. J. Transl. Med (2015) 13 261. PMID: 26264599 50. Sehrawat TS, Arab JP, Liu M, Amrollahi P, Wan M. Circulating extracellular vesicles carrying sphingolipid cargo for the diagnosis and dynamic risk profiling of alcoholic hepatitis. Hepatology (2021) 73 571-85. PMID: 32246544 51. Saha B, Momen-Heravi F, Furi I, Kodys K, Catalano D. Extracellular vesicles from mice with alcoholic liver disease carry a distinct protein cargo and induce macrophage activation through heat shock protein 90. Hepatology (2018) 67 1986-2000. PMID: 29251792 52. Allaire M, Rautou PE, Codogno P, Lotersztajn S. Autophagy in liver diseases: time for translation?. J. Hepatol (2019) 70 985-98. PMID: 30711404 53. Fernandez AF, Sebti S, Wei Y, Zou Z, Shi M. Disruption of the beclin 1-BCL2 autophagy regulatory complex promotes longevity in mice. Nature (2018) 558 136-40. PMID: 29849149 54. Goodall ML, Fitzwalter BE, Zahedi S, Wu M, Rodriguez D. The autophagy machinery controls cell death switching between apoptosis and necroptosis. Dev. Cell (2016) 37 337-49. PMID: 27219062 55. Frank D, Vaux DL, Murphy JM, Vince JE, Lindqvist LM. Activated MLKL attenuates autophagy following its translocation to intracellular membranes. J. Cell Sci (2019) 132 jcs220996. PMID: 30709919 56. Wu X, Poulsen KL, Sanz-Garcia C, Huang E, McMullen MR. MLKL-dependent signaling regulates autophagic flux in a murine model of non-alcohol-associated fatty liver and steatohepatitis. J. Hepatol (2020) 73 616-27. PMID: 32220583 57. Yoon S, Kovalenko A, Bogdanov K, Wallach D. MLKL, the protein that mediates necroptosis, also regulates endosomal trafficking and extracellular vesicle generation. Immunity (2017) 47 51-65.e7. PMID: 28666573 58. Guo R, Wang H, Cui N. Autophagy regulation on pyroptosis: mechanism and medical implication in sepsis. Mediators Inflamm (2021) 2021 9925059. PMID: 34257519 59. Wang G, Jin S, Huang W, Li Y, Wang J. LPS-induced macrophage HMGB1-loaded extracellular vesicles trigger hepatocyte pyroptosis by activating the NLRP3 inflammasome. Cell Death Discov (2021) 7 337. PMID: 34743181 60. Wang H, Mehal W, Nagy LE, Rotman Y. Immunological mechanisms and therapeutic targets of fatty liver diseases. Cell. Mol. Immunol (2021) 18 73-91. PMID: 33268887 61. Kim A, Bellar A, McMullen MR, Li X, Nagy LE. Functionally diverse inflammatory responses in peripheral and liver monocytes in alcohol-associated hepatitis. Hepatol. Commun (2020) 4 1459-76. PMID: 33024916 62. Zhou H, Yu M, Zhao J, Martin BN, Roychowdhury S. IRAKM-Mincle axis links cell death to inflammation: pathophysiological implications for chronic alcoholic liver disease. Hepatology (2016) 64 1978-93. PMID: 27628766 63. Yang AM, Inamine T, Hochrath K, Chen P, Wang L. Intestinal fungi contribute to development of alcoholic liver disease. J. Clin. Investig (2017) 127 2829-41. PMID: 28530644 64. Kim JW, Roh YS, Jeong H, Yi HK, Lee MH. Spliceosome-associated protein 130 exacerbates alcohol-induced liver injury by inducing NLRP3 inflammasome-mediated IL-1β in mice. Am. J. Pathol (2018) 188 967-80. PMID: 29355515 65. Laso FJ, Madruga JI, Giron JA, Lopez A, Ciudad J. Decreased natural killer cytotoxic activity in chronic alcoholism is associated with alcohol liver disease but not active ethanol consumption. Hepatology (1997) 25 1096-100. PMID: 9141423 66. Kim A, Wu X, Allende DS, Nagy LE. Gene deconvolution reveals aberrant liver regeneration and immune cell infiltration in alcohol-associated hepatitis. Hepatology (2021) 74 987-1002. PMID: 33619773 67. Argemi J, Latasa MU, Atkinson SR, Blokhin IO, Massey V. Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis. Nat. Commun (2019) 10 3126. PMID: 31311938 68. Tilg H, Moschen AR, Szabo G. Interleukin-1 and inflammasomes in alcoholic liver disease/acute alcoholic hepatitis and nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. Hepatology (2016) 64 955-65. PMID: 26773297 69. Mathews S, Gao B. Therapeutic potential of interleukin 1 inhibitors in the treatment of alcoholic liver disease. Hepatology (2013) 57 2078. PMID: 23609413 70. Lavallard VJ, Bonnafous S, Patouraux S, Saint-Paul M-C, Rousseau D. Serum markers of hepatocyte death and apoptosis are non invasive biomarkers of severe fibrosis in patients with alcoholic liver disease. PLOS ONE (2011) 6 e17599. PMID: 21445263 71. Hill DB, D’Souza NB, Lee EY, Burikhanov R, Deaciuc IV, de Villiers WJ. A role for interleukin-10 in alcohol-induced liver sensitization to bacterial lipopolysaccharide. Alcohol. Clin. Exp. Res (2002) 26 74-82. PMID: 11821657 72. Ki SH, Park O, Zheng M, Morales-Ibanez O, Kolls JK. Interleukin-22 treatment ameliorates alcoholic liver injury in a murine model of chronic-binge ethanol feeding: role of signal transducer and activator of transcription 3. Hepatology (2010) 52 1291-300. PMID: 20842630 73. Arab JP, Sehrawat TS, Simonetto DA, Verma VK, Feng D. An open-label, dose-escalation study to assess the safety and efficacy of IL-22 agonist F-652 in patients with alcohol-associated hepatitis. Hepatology (2020) 72 441-53. PMID: 31774566 74. Gao B, Xu M. Chemokines and alcoholic hepatitis: Are chemokines good therapeutic targets?. Gut (2014) 63 1683-84. PMID: 24515805 75. Wieser V, Adolph TE, Enrich B, Kuliopulos A, Kaser A. Reversal of murine alcoholic steatohepatitis by pepducin-based functional blockade of interleukin-8 receptors. Gut (2017) 66 930-38. PMID: 26858343 76. Sheron N, Bird G, Koskinas J, Portmann B, Ceska M. Circulating and tissue levels of the neutrophil chemotaxin interleukin-8 are elevated in severe acute alcoholic hepatitis, and tissue levels correlate with neutrophil infiltration. Hepatology (1993) 18 41-46. PMID: 8325620 77. Affò S, Morales-Ibanez O, Rodrigo-Torres D, Altamirano J, Blaya D. CCL20 mediates lipopolysaccharide induced liver injury and is a potential driver of inflammation and fibrosis in alcoholic hepatitis. Gut (2014) 63 1782-92. PMID: 24415562 78. Poulsen KL, Fan X, Kibler CD, Huang E, Wu X. Role of MIF in coordinated expression of hepatic chemokines in patients with alcohol-associated hepatitis. JCI Insight (2021) 6 e141420. PMID: 33945507 79. Santiesteban-Lores LE, Carneiro MC, Isaac L, Bavia L. Complement system in alcohol-associated liver disease. Immunol. Lett (2021) 236 37-50. PMID: 34111475 80. Shen H, French BA, Liu H, Tillman BC, French SW. Increased activity of the complement system in the liver of patients with alcoholic hepatitis. Exp. Mol. Pathol (2014) 97 338-44. PMID: 25217811 81. Pritchard MT, McMullen MR, Stavitsky AB, Cohen JI, Lin F. Differential contributions of C3, C5, and decay-accelerating factor to ethanol-induced fatty liver in mice. Gastroenterology (2007) 132 1117-26. PMID: 17383432 82. Zhong F, Hu Z, Jiang K, Lei B, Wu Z. Complement C3 activation regulates the production of tRNA-derived fragments Gly-tRFs and promotes alcohol-induced liver injury and steatosis. Cell Res (2019) 29 548-61. PMID: 31076642 83. Cohen JI, Roychowdhury S, McMullen MR, Stavitsky AB, Nagy LE. Complement and alcoholic liver disease: role of C1q in the pathogenesis of ethanol-induced liver injury in mice. Gastroenterology (2010) 139 664-74.e1. PMID: 20416309 84. Smathers RL, Chiang DJ, McMullen MR, Feldstein AE, Roychowdhury S, Nagy LE. Soluble IgM links apoptosis to complement activation in early alcoholic liver disease in mice. Mol. Immunol (2016) 72 9-18. PMID: 26922040 85. McCullough RL, McMullen MR, Das D, Roychowdhury S, Strainic MG. Differential contribution of complement receptor C5aR in myeloid and non-myeloid cells in chronic ethanol-induced liver injury in mice. Mol. Immunol (2016) 75 122-32. PMID: 27280845 86. McCullough RL, McMullen MR, Poulsen KL, Kim A, Medof ME, Nagy LE. Anaphylatoxin receptors C3aR and C5aR1 are important factors that influence the impact of ethanol on the adipose secretome. Front. Immunol (2018) 9 2133. PMID: 30294325 87. McCullough RL, McMullen MR, Sheehan MM, Poulsen KL, Roychowdhury S. Complement Factor D protects mice from ethanol-induced inflammation and liver injury. Am. J. Physiol. Gastrointest. Liver Physiol (2018) 315 G66-79. PMID: 29597356 88. Wlazlo N, van Greevenbroek MM, Ferreira I, Jansen EH, Feskens EJ. Activated complement factor 3 is associated with liver fat and liver enzymes: the CODAM study. Eur. J. Clin. Investig (2013) 43 679-88. PMID: 23586841 89. Fan X, McCullough RL, Huang E, Bellar A, Kim A. Diagnostic and prognostic significance of complement in patients with alcohol-associated hepatitis. Hepatology (2021) 73 983-97. PMID: 32557728 90. Bou Saleh M, Louvet A, Ntandja-Wandji LC, Boleslawski E, Gnemmi V. Loss of hepatocyte identity following aberrant YAP activation: a key mechanism in alcoholic hepatitis. J. Hepatol (2021) 75 912-23. PMID: 34129887 91. Hyun J, Sun Z, Ahmadi AR, Bangru S, Chembazhi UV. Epithelial splicing regulatory protein 2-mediated alternative splicing reprograms hepatocytes in severe alcoholic hepatitis. J. Clin. Investig (2020) 130 2129-45. PMID: 31945016 92. Ding BS, Nolan DJ, Butler JM, James D, Babazadeh AO. Inductive angiocrine signals from sinusoidal endothelium are required for liver regeneration. Nature (2010) 468 310-15. PMID: 21068842 93. Preziosi M, Okabe H, Poddar M, Singh S, Monga SP. Endothelial Wnts regulate β-catenin signaling in murine liver zonation and regeneration: a sequel to the Wnt-Wnt situation. Hepatol. Commun (2018) 2 845-60. PMID: 30027142 94. Wang B, Zhao L, Fish M, Logan CY, Nusse R. Self-renewing diploid Axin2. Nature (2015) 524 180-85. PMID: 26245375 95. Bengochea A, de Souza MM, Lefrancois L, Le Roux E, Galy O. Common dysregulation of Wnt/Frizzled receptor elements in human hepatocellular carcinoma. Br. J. Cancer (2008) 99 143-50. PMID: 18577996 96. Shetty S, Lalor PF, Adams DH. Liver sinusoidal endothelial cells—gatekeepers of hepatic immunity. Nat. Rev. Gastroenterol. Hepatol (2018) 15 555-67. PMID: 29844586 97. DeLeve LD. Liver sinusoidal endothelial cells in hepatic fibrosis. Hepatology (2015) 61 1740-46. PMID: 25131509 98. Protzer U, Maini MK, Knolle PA. Living in the liver: hepatic infections. Nat. Rev. Immunol (2012) 12 201-13. PMID: 22362353 99. Miyao M, Kotani H, Ishida T, Kawai C, Manabe S. Pivotal role of liver sinusoidal endothelial cells in NAFLD/NASH progression. Lab. Investig (2015) 95 1130-44. PMID: 26214582 100. Xie G, Choi SS, Syn WK, Michelotti GA, Swiderska M. Hedgehog signalling regulates liver sinusoidal endothelial cell capillarisation. Gut (2013) 62 299-309. PMID: 22362915 101. Deleve LD, Wang X, Guo Y. Sinusoidal endothelial cells prevent rat stellate cell activation and promote reversion to quiescence. Hepatology (2008) 48 920-30. PMID: 18613151 102. Friedman SL. Hepatic stellate cells: protean, multifunctional, and enigmatic cells of the liver. Physiol. Rev (2008) 88 125-72. PMID: 18195085 103. Khomich O, Ivanov AV, Bartosch B. Metabolic hallmarks of hepatic stellate cells in liver fibrosis. Cells (2019) 9 24. PMID: 31861818 104. Puche JE, Saiman Y, Friedman SL. Hepatic stellate cells and liver fibrosis. Compr. Physiol (2013) 3 1473-92. PMID: 24265236 105. Das D, Barnes MA, Nagy LE. Anaphylatoxin C5a modulates hepatic stellate cell migration. Fibrogenesis Tissue Repair (2014) 7 9. PMID: 24917887 106. Kim A, Saikia P, Nagy LE. miRNAs involved in M1/M2 hyperpolarization are clustered and coordinately expressed in alcoholic hepatitis. Front. Immunol (2019) 10 1295. PMID: 31231396 107. Bala S, Marcos M, Kodys K, Csak T, Catalano D. Up-regulation of microRNA-155 in macrophages contributes to increased tumor necrosis factor α (TNFα) production via increased mRNA half-life in alcoholic liver disease. J. Biol. Chem (2011) 286 1436-44. PMID: 21062749 108. Szabo G, Satishchandran A. MicroRNAs in alcoholic liver disease. Semin. Liver Dis (2015) 35 36-42. PMID: 25632933 109. Tang Y, Banan A, Forsyth CB, Fields JZ, Lau CK. Effect of alcohol on miR-212 expression in intestinal epithelial cells and its potential role in alcoholic liver disease. Alcohol. Clin. Exp. Res (2008) 32 355-64. PMID: 18162065 110. Roderburg C, Luedde M, Vargas Cardenas D, Vucur M, Mollnow T. miR-133a mediates TGF-β-dependent derepression of collagen synthesis in hepatic stellate cells during liver fibrosis. J. Hepatol (2013) 58 736-42. PMID: 23183523 111. Dewidar B, Meyer C, Dooley S, Meindl-Beinker AN. TGF-β in hepatic stellate cell activation and liver fibrogenesis—updated 2019. Cells (2019) 8 1419. PMID: 31718044 112. Coll M, El Taghdouini A, Perea L, Mannaerts I, Vila-Casadesús M. Integrative miRNA and gene expression profiling analysis of human quiescent hepatic stellate cells. Sci. Rep (2015) 5 11549. PMID: 26096707 113. Zhao Z, Lin CY, Cheng K. siRNA- and miRNA-based therapeutics for liver fibrosis. Transl. Res (2019) 214 17-29. PMID: 31476281 114. Brandon-Warner E, Benbow JH, Swet JH, Feilen NA, Culberson CR. Adeno-associated virus serotype 2 vector-mediated reintroduction of microRNA-19b attenuates hepatic fibrosis. Hum. Gene Ther (2018) 29 674-86. PMID: 29281894 115. Murakami Y, Toyoda H, Tanaka M, Kuroda M, Harada Y. The progression of liver fibrosis is related with overexpression of the miR-199 and 200 families. PLOS ONE (2011) 6 e16081. PMID: 21283674 116. Chen Y, Ou Y, Dong J, Yang G, Zeng Z. Osteopontin promotes collagen I synthesis in hepatic stellate cells by miRNA-129–5p inhibition. Exp. Cell Res (2018) 362 343-48. PMID: 29196165 117. Lang S, Schnabl B. Microbiota and fatty liver disease—the known, the unknown, and the future. Cell Host Microbe (2020) 28 233-44. PMID: 32791115 118. Llopis M, Cassard AM, Wrzosek L, Boschat L, Bruneau A. Intestinal microbiota contributes to individual susceptibility to alcoholic liver disease. Gut (2016) 65 830-39. PMID: 26642859 119. Duan Y, Llorente C, Lang S, Brandl K, Chu H. Bacteriophage targeting of gut bacterium attenuates alcoholic liver disease. Nature (2019) 575 505-11. PMID: 31723265 120. Rao R. Endotoxemia and gut barrier dysfunction in alcoholic liver disease. Hepatology (2009) 50 638-44. PMID: 19575462 121. Chen P, Starkel P, Turner JR, Ho SB, Schnabl B. Dysbiosis-induced intestinal inflammation activates tumor necrosis factor receptor I and mediates alcoholic liver disease in mice. Hepatology (2015) 61 883-94. PMID: 25251280 122. Wang L, Fouts DE, Starkel P, Hartmann P, Chen P. Intestinal REG3 lectins protect against alcoholic steatohepatitis by reducing mucosa-associated microbiota and preventing bacterial translocation. Cell Host Microbe (2016) 19 227-39. PMID: 26867181 123. Llorente C, Jepsen P, Inamine T, Wang L, Bluemel S. Gastric acid suppression promotes alcoholic liver disease by inducing overgrowth of intestinal. Nat. Commun (2017) 8 837. PMID: 29038503 124. Couch RD, Dailey A, Zaidi F, Navarro K, Forsyth CB. Alcohol induced alterations to the human fecal VOC metabolome. PLOS ONE (2015) 10 e0119362. PMID: 25751150 125. Schwenger KJ, Clermont-Dejean N, Allard JP. The role of the gut microbiome in chronic liver disease: the clinical evidence revised. JHEP Rep (2019) 1 214-26. PMID: 32039372 126. Juanola O, Ferrusquia-Acosta J, Garcia-Villalba R, Zapater P, Magaz M. Circulating levels of butyrate are inversely related to portal hypertension, endotoxemia, and systemic inflammation in patients with cirrhosis. FASEB J (2019) 33 11595-605. PMID: 31345057 127. Helsley RN, Miyata T, Kadam A, Varadharajan V, Sangwan N. Gut microbial trimethylamine is elevated in alcohol-associated hepatitis and contributes to ethanol-induced liver injury in mice. Elife (2022) 11 e76554. PMID: 35084335 128. Kakiyama G, Hylemon PB, Zhou H, Pandak WM, Heuman DM. Colonic inflammation and secondary bile acids in alcoholic cirrhosis. Am. J. Physiol. Gastrointest. Liver Physiol (2014) 306 G929-37. PMID: 24699327 129. Brandl K, Hartmann P, Jih LJ, Pizzo DP, Argemi J. Dysregulation of serum bile acids and FGF19 in alcoholic hepatitis. J. Hepatol (2018) 69 396-405. PMID: 29654817 130. Manley S, Ding W. Role of farnesoid X receptor and bile acids in alcoholic liver disease. Acta Pharm. Sin. B (2015) 5 158-67. PMID: 26579442 131. Kema VH, Mojerla NR, Khan I, Mandal P. Effect of alcohol on adipose tissue: a review on ethanol mediated adipose tissue injury. Adipocyte (2015) 4 225-31. PMID: 26451277 132. Parker R, Kim SJ, Gao B. Alcohol, adipose tissue and liver disease: mechanistic links and clinical considerations. Nat. Rev. Gastroenterol. Hepatol (2018) 15 50-59. PMID: 28930290 133. Wei X, Shi X, Zhong W, Zhao Y, Tang Y. Chronic alcohol exposure disturbs lipid homeostasis at the adipose tissue-liver axis in mice: analysis of triacylglycerols using high-resolution mass spectrometry in combination with in vivo metabolite deuterium labeling. PLOS ONE (2013) 8 e55382. PMID: 23405143 134. Malhi H, Bronk SF, Werneburg NW, Gores GJ. Free fatty acids induce JNK-dependent hepatocyte lipoapoptosis. J. Biol. Chem (2006) 281 12093-101. PMID: 16505490 135. Tang T, Sui Y, Lian M, Li Z, Hua J. Pro-inflammatory activated Kupffer cells by lipids induce hepatic NKT cells deficiency through activation-induced cell death. PLOS ONE (2013) 8 e81949. PMID: 24312613 136. Xu A, Wang Y, Keshaw H, Xu LY, Lam KS, Cooper GJ. The fat-derived hormone adiponectin alleviates alcoholic and nonalcoholic fatty liver diseases in mice. J. Clin. Investig (2003) 112 91-100. PMID: 12840063 137. Sebastian BM, Roychowdhury S, Tang H, Hillian AD, Feldstein AE. Identification of a cytochrome P4502E1/Bid/C1q-dependent axis mediating inflammation in adipose tissue after chronic ethanol feeding to mice. J. Biol. Chem (2011) 286 35989-97. PMID: 21856753 138. Chiang DJ, McCullough AJ. The impact of obesity and metabolic syndrome on alcoholic liver disease. Clin. Liver Dis (2014) 18 157-63. PMID: 24274871 139. Ikejima K, Kon K, Yamashina S. Nonalcoholic fatty liver disease and alcohol-related liver disease: from clinical aspects to pathophysiological insights. Clin. Mol. Hepatol (2020) 26 728-35. PMID: 33053942 140. Ntandja Wandji LC, Gnemmi V, Mathurin P, Louvet A. Combined alcoholic and non-alcoholic steatohepatitis. JHEP Rep (2020) 2 100101. PMID: 32514497 141. Voican CS, Njiké-Nakseu M, Boujedidi H, Barri-Ova N, Bouchet-Delbos L. Alcohol withdrawal alleviates adipose tissue inflammation in patients with alcoholic liver disease. Liver Int (2015) 35 967-78. PMID: 24766056 142. Lauterburg BH, Velez ME. Glutathione deficiency in alcoholics: risk factor for paracetamol hepatotoxicity. Gut (1988) 29 1153-57. PMID: 3197987 143. Moreno C, Langlet P, Hittelet A, Lasser L, Degré D. Enteral nutrition with or without. J. Hepatol (2010) 53 1117-22. PMID: 20801542 144. Higuera-de la Tijera F, Servín-Caamaño AI, Cruz-Herrera J, Serralde-Zúñiga AE, Abdo-Francis JM. Treatment with metadoxine and its impact on early mortality in patients with severe alcoholic hepatitis. Ann. Hepatol (2014) 13 343-52. PMID: 24756009 145. Colell A, García-Ruiz C, Morales A, Ballesta A, Ookhtens M. Transport of reduced glutathione in hepatic mitochondria and mitoplasts from ethanol-treated rats: effect of membrane physical properties and. Hepatology (1997) 26 699-708. PMID: 9303501 146. Miyata T, Nagy LE. Programmed cell death in alcohol-associated liver disease. Clin. Mol. Hepatol (2020) 26 618-25. PMID: 32951412 147. Marot A, Singal AK, Moreno C, Deltenre P. Granulocyte colony-stimulating factor for alcoholic hepatitis: a systematic review and meta-analysis of randomised controlled trials. JHEP Rep (2020) 2 100139. PMID: 32775975 148. Ventura-Cots M, Argemi J, Jones PD, Lackner C, El Hag M. Clinical, histological and molecular profiling of different stages of alcohol-related liver disease. Gut (2022) 71 1856-66. PMID: 34992134 149. Naveau S, Chollet-Martin S, Dharancy S, Mathurin P, Jouet P. A double-blind randomized controlled trial of infliximab associated with prednisolone in acute alcoholic hepatitis. Hepatology (2004) 39 1390-97. PMID: 15122768 150. Boetticher NC, Peine CJ, Kwo P, Abrams GA, Patel T. A randomized, double-blinded, placebo-controlled multicenter trial of etanercept in the treatment of alcoholic hepatitis. Gastroenterology (2008) 135 1953-60. PMID: 18848937 151. Saikia P, Bellos D, McMullen MR, Pollard KA, de la Motte C, Nagy LE. MicroRNA 181b-3p and its target importin α5 regulate toll-like receptor 4 signaling in Kupffer cells and liver injury in mice in response to ethanol. Hepatology (2017) 66 602-15. PMID: 28257601 152. Singal AK, Shah VH. Current trials and novel therapeutic targets for alcoholic hepatitis. J. Hepatol (2019) 70 305-13. PMID: 30658731 153. Han SH, Suk KT, Kim DJ, Kim MY, Baik SK. Effects of probiotics (cultured. Eur. J. Gastroenterol. Hepatol (2015) 27 1300-6. PMID: 26302024 154. Philips CA, Pande A, Shasthry SM, Jamwal KD, Khillan V. Healthy donor fecal microbiota transplantation in steroid-ineligible severe alcoholic hepatitis: a pilot study. Clin. Gastroenterol. Hepatol (2017) 15 600-2. PMID: 27816755 155. Philips CA, Phadke N, Ganesan K, Ranade S, Augustine P. Corticosteroids, nutrition, pentoxifylline, or fecal microbiota transplantation for severe alcoholic hepatitis. Indian J. Gastroenterol (2018) 37 215-25. PMID: 29931479
--- title: Seasonal variations in renal biopsy numbers and primary glomerular disease features based on the Japan renal biopsy registry authors: - Go Kanzaki - Nobuo Tsuboi - Takashi Yokoo - Noriko Uesugi - Kengo Furuichi - Akira Shimizu - Hitoshi Sugiyama - Hiroshi Sato - Hitoshi Yokoyama - Hiroshi Sato - Hiroshi Sato - Akira Shimizu - Hitoshi Sugiyama - Hiroshi Kitamura - Ritsuko Katafuchi - Shinichi Nishi - Motoshi Hattori - Ryohei Yamamoto - Toshiharu Ninomiya - Yoshihiko Ueda - Michio Nagata - Hirofumi Makino - Hitoshi Yokoyama - Shoji Kagami journal: Scientific Reports year: 2023 pmcid: PMC10060207 doi: 10.1038/s41598-023-32182-7 license: CC BY 4.0 --- # Seasonal variations in renal biopsy numbers and primary glomerular disease features based on the Japan renal biopsy registry ## Abstract We analyzed the seasonal variations in the number of renal biopsies and clinical characteristics of primary glomerular disease in Japan using the Japan Renal Biopsy Registry (J-RBR). We retrospectively collected clinical and pathological data of patients with primary glomerular disease who were registered in the J-RBR between 2007 and 2018. Immunoglobulin A nephropathy (IgAN), minimal change nephrotic syndrome (MCNS), membranous nephropathy (MN), and postinfectious acute glomerulonephritis (PIAGN) constituted the four major glomerular disorders included in this study (total, 13,989; IgAN, 9121; MCNS, 2298; MN, 2447; and PIAGN, 123). The number of patients with IgAN or MCNS was higher during summer. However, no overt seasonal variations were observed in patients with MN or PIAGN. Subgroup analyses suggested that in the patients with IgAN, more renal biopsies of severe cases were performed during winter, probably owing to age and blood pressure. Furthermore, more renal biopsies of severe cases were performed during spring and winter in patients with MCNS even after adjusting for the abovementioned host factors. This study suggests that seasonal factors influence the decision to perform renal biopsy as well as the pathogenesis of primary glomerular disease. Thus, our findings may provide important insights regarding the pathophysiology of primary glomerular disease. ## Introduction The association between seasonal factors and the onset and prevalence of various diseases have been previously investigated1. Reportedly, respiratory diseases2, hypertension3, and cardiocerebrovascular diseases4,5 tend to develop and worsen during winter, which is also when the rate of respiratory infection peaks6. Furthermore, cold weather alters physiological hemodynamics and hematological factors, thereby contributing to arterial thrombosis7,8. Consequently, acute kidney injury onset and dialysis introduction are also common during winter9,10. The onset of glomerular disease is believed to be due to the interaction of genetic and environmental factors. Briefly, certain genetic factors predispose individuals toward an immune response that leads to glomerulonephritis, and inflammatory and noninflammatory immune mechanisms are considered to be involved in the pathogenesis of this glomerular injury11. Particularly, infectious and allergic diseases constitute the priamary external factors that influence the pathogenesis of lifetime diseases12. Group A beta-hemolytic streptococcus, a culprit pathogen of epidemic infections during winter, has been causally associated with acute glomerulonephritis13. The persistence of respiratory viruses may contribute to the development and progression of minimal change nephrotic syndrome (MCNS)14. Recent studies suggest that gut microbiota are involved in the pathogenesis of primary glomerular diseases, such as immunoglobulin A nephropathy (IgAN) and membranous nephropathy (MN). Gut microbiota are susceptible to environmental factors, such as personal habits and nutrition15. Additionally, children with idiopathic nephrotic syndrome exhibit a high incidence of allergic diseases, including atopic dermatitis and allergic rhinitis, and the number of cases is reportedly high during spring and autumn16,17. In adults, idiopathic nephrotic syndrome reportedly occurs more frequently during winter. Furthermore, IgAN, which is probably associated with viral infections and tonsillitis, tends to worsen during winter18. Patients with early diabetic nephropathy also exhibit considerably higher proteinuria and albuminuria during autumn and winter than during spring and summer, probably because of increased systolic blood pressure (BP) during winter19. However, there have only been few studies regarding the relationship between primary glomerulonephritis and seasonal factors closely associated with the development of the infections and allergies, and to the best of our knowledge, there have been no such studies involving a large cohort of patients. The Japan Renal Biopsy Registry (J-RBR) is a nationwide, multicenter, web-based, prospective renal biopsy registry established in 2007 to record clinical and pathological data of patients undergoing renal biopsy20. J-RBR data is particularly beneficial for analyzing the association of seasonal variations with disease pathogenesis, as Japan is a country with a homogeneous society that experiences four distinct seasons. This study aimed to analyze the influence of seasonal variations on the number of renal biopsies and the clinical features of primary glomerular disease in Japan using the J-RBR and clarify the relationship between the various types of glomerulonephritis, which are closely related to external factors, and the seasons. We focused on the following four diseases: IgAN, MCNS, MN, and postinfectious acute glomerulonephritis (PIAGN), which are reportedly associated with allergies and infectious diseases and have a high incidence in Japan. ## J-RBR system and patient selection The J-RBR is a nationwide, multicenter registry system that was organized by the Committee for the Standardization of Renal Pathological Diagnosis and the Working Group for the Renal Biopsy Database of the Japanese Society of Nephrology in 200720. Individual patient data, including basic patient information, clinical diagnosis, renal pathological findings, biochemical features, and urinalysis, were uploaded to the J-RBR website using the Internet Data and Information Center for Medical Research system of the University Hospital Medical Information Network (UMIN). The J-RBR is registered under the Clinical Trial Registry of UMIN (registration number, UMIN000000618). This study was approved by the Ethics Committee of the Japanese Society of Nephrology and conducted per the principles of the Declaration of Helsinki (Research of J-RBR in Japanese Society of Nephrology, No.79, J-RBR201904, September 2, 2019). Written informed consent was obtained from all the study participants or the parents if the participant was a child. This retrospective study study included Japanese patients with primary IgAN, MCNS, MN, or PIAGN who were registered in the J-RBR from July 1, 2007 to January 1, 2018. The baseline characteristics of the patients, including clinical and pathological features at the time of the renal biopsies, were obtained from the J-RBR database. During the registration period, 17,281 patients were registered in the J-RBR. Of these, 3292 patients were excluded because of missing data that were critical for the analysis. Overall, 13,989 patients (IgAN: 9121; MCNS: 2298; MN: 2447; and PIAGN: 123) were finally included in the analysis (Fig. 1).Figure 1Patient selection. Extraction of patients with primary glomerulonephritis according to the Japan Renal Biopsy Registry (J-RBR), IgA nephropathy (IgAN), minimal change nephrotic syndrome (MCNS), membranous nephropathy (MN), and postinfectious acute glomerulonephritis (PIAGN). Patients who were diagnosed with other renal or systemic diseases were excluded. ## Clinical measurements and definitions The clinical data of the patienta, including age, sex, body mass index (BMI), systolic and diastolic BP, serum creatinine (sCr) levels, estimated glomerular filtration rate (eGFR), serum albumin levels, serum total cholesterol levels, and urinary protein excretion (UPE) rate, were evaluated. The eGFR was calculated using a three-variable equation modified for Japanese populations as follows: eGFR = 194 × age−0.287 × sCr−1.094 (× 0.739 if female)22. The four seasons relevant to the climate in Japan were defined month-wise as follows: spring (March–May), summer (June–August), autumn (September–November), and winter (December–February)23. To examine the possibility of applying the findings of this study in clinical practice, the ages of the patietns were classified into three categories: children (aged < 18 years), adults (aged 18–64 years), and older individuals (aged ≥ 65 years). Hematuria was defined as more than five red cells per high power field (HPF) in urinary sediments and graded based on the number of red cells per HPF as follows: 0–4, 5–10, 11–30, and ≥ 30. Based on the KDIGO 2012 guidelines modified for the Japanese population, the UPE rates at biopsy were classified as normal (< 0.15 g/day or g/gCr; A1), mild (0.15–0.49 g/day or g/gCr; A2), and severe (≥ 0.5 g/day or g/gCr; A3). Additionally, the eGFR at the time of biopsy was classified into five groups: G1, G2, G3a, G3b, G4, and G5 for ≥ 90, 60–89, 45–59, 30–44, 15–29, and < 15 mL/min/1.73 m2, respectively. According to these UPE rates and eGFR values, the chronic kidney disease (CKD) heat map classified renal prognosis as low, medium, high, and very high24–26. ## Statistical analysis We first described the baseline characteristics of the entire study population and of the patients in the four groups (IgAN, MCNS, MN, and PIAGN). Continuous variables were presented as the mean and standard deviation or frequencies with percentages (in parentheses). Seasonal differences in the number of kidney biopsies per glomerular disease were compared using the four (seasons) × four (diseases) table chi-squared test. The differences in continuous and categorical variables were assessed using independent-samples t-test or Mann–Whitney U-test and chi-square test or Fisher’s exact test where appropriate, respectively. In addition, multiple comparisons were performed using analysis of variance and applying the Bonferroni correction. A multivariable linear regression analysis that defined summer as the reference was constructed to identify the possible influence of seasonal variations on the severity of glomerular disease biopsy. In each analysis, the age, sex, and mean BP of the patients were treated as fixed covariates. Statistical significance was defined as a two-sided P-value of < 0.05. All statistical analyses were performed using SPSS v.25.0 (IBM Corp., Armonk, NY, USA). ## Ethical approval All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee where the studies were conducted (IRB approval number: the Japanese Society of Nephrology, No. 79, September 2, 2019, and the Jikei University School of Medicine, 31–284[9783]) and per the principles of the 1964 Helsinki declaration and its later amendments or comparable ethical standards. ## Informed consent Informed consent was obtained from all study participants or the parents if the participant is a child. ## Clinical characteristics Table 1 summarizes the clinical characteristics of all the 13,989 patients at the time of their renal biopsy. Overall, the mean age of the patients was 43.8 ± 21.0 years ($53.6\%$ men). The mean eGFR was 74.6 ± 30.9 mL/min/1.73 m2. In total, 9,121 ($65.2\%$) patients had IgAN, 2,298 ($16.4\%$) had MCNS, 2,447 ($17.5\%$) had MN, and 123 ($0.88\%$) had PIAGN (Fig. 2).Table 1Background characteristics and laboratory parameters of the patients. Totaln13,989Age, mean (SD), years43.8 ± 21.0BMI, mean (SD), kg/m222.8 ± 4.5Male (%)53.6Systolic blood pressure, mean (SD), mmHg124.9 ± 18.7Diastolic blood pressure, mean (SD), mmHg74.5 ± 13.1Mean blood pressure, mean (SD), mmHg91.3 ± 13.8TP, mean (SD), g/dL6.3 ± 1.1Alb, mean (SD), g/dL3.5 ± 1.1T-chol, mean (SD), mg/dL241.5 ± 101.0Creatinine, mean (SD), mg/dL1.03 ± 1.02eGFR, mean (SD), mL/min/1.73 m274.6 ± 30.9BMI, body mass index; TP, total protein; Alb, albumin; T-cho, total cholesterol; eGFR, estimated glomerular filtration rate. Values are expressed as mean ± standard deviation (SD).Figure 2Frequency of the primary glomerular diseases. The number of renal biopsies was the highest in patients with IgAN ($65.20\%$) and the lowest in patients with PIAGN ($0.88\%$). IgA nephropathy (IgAN), minimal change nephrotic syndrome (MCNS), membranous nephropathy (MN), and postinfectious acute glomerulonephritis (PIAGN). ## Seasonal variations in the number of renal biopsies The number of kidney biopsies per season was as follows: 3,374 ($24.1\%$) during spring, 4,015 ($28.7\%$) during summer, 3,246 ($23.2\%$) during autumn, and 3,354 ($24.0\%$) during winter. The seasonal variation was significant ($P \leq 0.001$), and the number of renal biopsies was the highest during summer (Fig. 3a). Figure 3b displays the distribution of the number of renal biopsies across the seasons for the four glomerular diseases. Figure 3Seasonal variations in the number of renal biopsies. ( A) Number of renal biopsies across the seasons. ( b) Number of renal biopsies of the diseases across the seasons. ## Seasonal variations in IgAN The seasonal variations in the biochemical and clinical parameters of the patients with IgAN are summarized in Table 2. The number of kidney biopsies was significantly ($P \leq 0.001$) higher during summer in patients with IgAN than those with other diseases. Significant seasonal differences were observed in age ($P \leq 0.001$), BMI ($P \leq 0.001$), systolic BP ($P \leq 0.001$), diastolic BP ($P \leq 0.001$), mean BP ($P \leq 0.001$), serum creatinine levels ($P \leq 0.001$), and UPE ($P \leq 0.001$), with the lowest peak values for these parameters occurring during summer. The distribution of age, UPE rates, and the CKD heat map categories significantly varied across the seasons. Cases involving younger patients were more common during summer compared with older patients, whereas adult cases were less common during summer and more common during autumn and winter. Regarding UPE, there were fewer cases involving severe proteinuria and more cases involving mild proteinuria during summer, contrary to the trend observed during winter. In the CKD heat map categories, the low-risk group was more common during summer and less common during autumn and winter. The high-risk and very high-risk groups were less common during summer. Table 2Seasonal changes in biochemical and clinical parameters in the patients with IgAN.SpringSummerAutumnWinterP valueN (%)2,166 (23.7)2,721 (29.8) ▲2,076 (22.8)2158 (23.7)-Age (year)40.4 ± 17.937.1 ± 18.241.4 ± 17.941.3 ± 17.6* < 0.001 < 18, n (%)214 (20.9)480 (47.0)▲150 (14.7) ▽178 (17.4) ▽ < 0.001 18–64, n (%)1,697 (23.9)2,000 (28.2)▽1,672 (23.5)▲1,731 (24.4)▲ 65 ≤, n (%)255 (25.5)241 (24.1)254 (25.4)249 (24.9)Male, n (%)1085 (50.1)1364 (50.1)1065 (51.3)1123 (52.0)0.484BMI (kg/m2)22.8 ± 4.122.2 ± 4.122.9 ± 4.122.8 ± 5.7* < 0.001Systolic BP (mmHg)123.9 ± 17.8122.0 ± 17.7125.3 ± 17.9125.2 ± 17.9* < 0.001Diastolic BP (mmHg)74.3 ± 12.772.9 ± 13.275.7 ± 13.175.4 ± 13.1* < 0.001Mean BP (mmHg)90.8 ± 13.289.2 ± 13.792.3 ± 13.891.0 ± 13.6* < 0.001Serum creatinine (mg/dL)1.06 ± 1.030.98 ± 0.761.08 ± 1.191.09 ± 1.21* < 0.001eGFR (mL/min/1.73m2)73.6 ± 30.377.5 ± 30.671.8 ± 29.671.9 ± 29.3* < 0.001UPE (g/day or g/gCr)1.17 ± 1.691.11 ± 1.861.29 ± 1.811.23 ± 1.59 < 0.001 < 0.15, n (%)327 (24.1)503 (37.1) ▲273 (20.1)252 (18.6) ▽ < 0.001 0.15–0.49, n (%)541 (22.3)752 (31.0)550 (22.7)580 (23.9) 0.50 ≥, n (%)1,298 (24.3)1,466 (27.4) ▽1,253 (23.5)1,326 (24.8) ▲Hematuria grade 2–3n (%)1,656 (23.8)2,064 (29.6)1,595 (22.9)1,655 (23.7)0.857KDIGO prognosis risk of CKD, n (%)Very high risk618 (23.7)704 (27.0) ▽624 (23.9)665 (25.0) < 0.001High risk819 (24.5)922 (27.6) ▽778 (23.3)819 (24.5)Moderately increased risk451 (22.3)649 (32.1)450 (22.3)469 (23.2)Low risk278 (24.1)446 (38.7) ▲224 (19.4) ▽205 (17.8) ▽Data are presented as the mean ± SD or the frequency (percentage). Seasonal variations in the number of kidney biopsies per glomerular disease were examined using the four (seasons) × four (diseases) table chi-squared test. Chi-squared test with Bonferroni’s correction for multiple comparisons was also used for categorical variables. ▲ and ▽ show significantly higher and lower values than expected, respectively, in residual analyses using chi-squared tests. One-way analysis of variance with Tukey’s multiple comparison test was employed for continuous variables. P-values from chi-squared or Kruskal–Wallis test. IgAN, immunoglobulin A nephropathy; BMI, body mass index; BP, blood pressure; eGFR, estimated glomerular filtration rate; UPE; urinary protein excretion.$P \leq 0.05$ versus summer. ## Seasonal variations in MCNS Seasonal variations in the biochemical and clinical parameters of patients with MCNS are summarized in Table 3. Significant seasonal differences were detecetd in age ($P \leq 0.001$), BMI ($P \leq 0.001$), systolic BP ($P \leq 0.001$), diastolic BP ($P \leq 0.001$), mean BP ($P \leq 0.001$), serum creatinine levels ($P \leq 0.01$), and UPE ($P \leq 0.001$), with the lowest peak values for these parameters occurring during summer. The distribution of age, UPE rates, and CKD heat map categories significantly varied across the seasons. Cases involving younger patients were more common and during summer than those involving adult patients. Regarding UPE, there were fewer cases involving severe proteinuria and more cases involving mild proteinuria during summer, contrary to the trends observed during spring. In the CKD heat map categories, the low-risk group was more common during summer and less common during spring and winter, whereas the opposite trend was observed in the high-risk group. Table 3Seasonal differences in biochemical and clinical parameters in the patients with MCNS.SpringSummerAutumnWinterP valueN (%)574 (25.0)655 (28.5)538 (23.4)531 (23.1)–Age (year)40.8 ± 24.234.2 ± 24.538.9 ± 24.638.8 ± 24.6* < 0.001 < 18, n (%)131 (20.4) ▽241 (37.6) ▲128 (20.0)141 (22.0) < 0.001 18–64, n (%)299 (25.9)296 (25.6) ▽285 (24.7)276 (23.8) 65 ≤, n (%)144 (28.7)118 (23.5) ▽125 (24.9)115 (22.9)Male, n (%)338 (58.9)378 (57.7)298 (55.4)314 (57.8)0.583BMI (kg/m2)23.0 ± 4.521.9 ± 4.722.9 ± 4.722.8 ± 4.6* < 0.001Systolic BP (mmHg)122.0 ± 18.9117.5 ± 17.6121.6 ± 18.0122.2 ± 18.3* < 0.001Diastolic BP (mmHg)73.0 ± 12.970.4 ± 12.973.0 ± 12.573.6 ± 12.6* < 0.001Mean BP (mmHg)89.4 ± 13.886.1 ± 13.489.2 ± 13.389.8 ± 13.3* < 0.001Serum creatinine (mg/dL)1.00 ± 1.010.87 ± 0.801.00 ± 0.970.92 ± 0.730.002eGFR (mL/min/1.73m2)81.5 ± 36.688.8 ± 35.683.0 ± 37.084.0 ± 36.9 < 0.001UPE (g/day or g/gCr)6.47 ± 6.605.01 ± 5.815.90 ± 6.335.91 ± 6.20 < 0.001 < 0.15, n (%)94 (19.0) ▽203 (41.1) ▲104 (21.1)93 (18.8) < 0.001 0.15–0.49, n (%)20 (23.8)21 (25.0)19 (22.6)24 (28.6) 0.50 ≥, n (%)460 (26.7) ▲431 (25.1) ▽415 (24.1)414 (24.1)Hematuria grade 2–3n (%)199 (28.1)180 (25.4)167 (23.6)163 (23.0)0.06KDIGO prognosis risk of CKD, n (%)Very high risk159 (27.6)147 (25.5)136 (23.6)135 (23.4) < 0.001High risk302 (26.2)288 (25.0) ▽282 (24.4)282 (24.4)Moderately increased risk19 (23.8)18 (22.5)17 (21.3)26 (32.5)Low risk94 (19.3) ▽202 (41.5) ▲103 (21.1)88 (18.1) ▽Data are presented as the mean ± SD or the frequency (percentage). Seasonal differences in the number of kidney biopsies per glomerular disease were assessed using the four (seasons) × four (diseases) table chi-squared test. For categorical variables, chi-squared test with Bonferroni's correction for multiple comparisons was also utilized. ▲ and ▽ show significantly higher and lower values than expected, respectively, in residual analyses using chi-squared tests. One-way analysis of variance with Tukey’s multiple comparison test was used for continuous variables. P-values from the chi-squared or Kruskal–Wallis test. IgAN, immunoglobulin A nephropathy; BMI, body mass index; BP, blood pressure; eGFR, estimated glomerular filtration rate; UPE; urinary protein excretion.$P \leq 0.05$ versus summer. ## Seasonal variations in MN Seasonal variations in the biochemical and clinical parameters of the patients with MN are summarized in Table 4. The number of kidney biopsies was significantly lower during summer in the patients with MN than those with other diseases. Significant seasonal differences were detected in systolic BP ($P \leq 0.001$), diastolic BP ($P \leq 0.001$), mean BP ($P \leq 0.001$), and UPE ($P \leq 0.001$), with the lowest peak values for these parameters occurring during summer. The distribution of age categories significantly varied across the seasons. Cases involving younger patients were more common during summer than during winter. In UPE and CKD heat map categories, no significant differences were found in the analyses of seasonal patterns. Table 4Seasonal variations in biochemical and clinical parameters in the patients with MN.SpringSummerAutumnWinterP valueN (%)603 (24.6)606 (24.8) ▽611 (25.0)627 (25.6)–Age (year)64.2 ± 14.261.3 ± 17.864.3 ± 14.464.6 ± 13.60.079 <18, n (%)11 (16.9)38 (58.5) ▲12 (18.5)4 (6.2) ▽ < 0.001 18–64, n (%)238 (24.6)240 (24.8)239 (24.7)250 (25.9) 65 ≤ n (%)354 (25.0)328 (23.2)360 (25.4)373 (26.4)Male, n (%)349 (57.9)376 (62.0)359 (58.8)376 (60.0)0.482BMI (kg/m2)23.6 ± 3.723.5 ± 4.323.7 ± 3.923.5 ± 3.80.597Systolic BP (mmHg)132.6 ± 19.9128.7 ± 19.4132.1 ± 19.6133.4 ± 20.6 < 0.001Diastolic BP (mmHg)76.9 ± 13.474.4 ± 12.677.0 ± 12.877.9 ± 13.4* < 0.001Mean BP (mmHg)95.4 ± 13.792.5 ± 13.795.4 ± 13.396.4 ± 14.3* < 0.001Serum creatinine (mg/dL)1.02 ± 1.160.93 ± 1.100.94 ± 0.580.98 ± 0.620.305eGFR (mL/min/1.73m2)66.4 ± 24.371.7 ± 26.968.4 ± 25.568.5 ± 25.30.041UPE (g/day or g/gCr)5.02 ± 4.064.40 ± 3.825.17 ± 4.65.08 ± 4.840.010 < 0.15, n (%)11 (26.8)8 (19.5)10 (24.4)12 (29.3)0.759 0.15–0.49, n (%)29 (21.6)41 (30.6)32 (23.9)32 (23.9) 0.50 ≥, n (%)563 (24.8)557 (24.5)569 (25.0)583 (25.7)Hematuria grade 2–3n (%)249 (24.5)249 (24.5)274 (26.9)245 (24.1)0.223KDIGO prognosis risk of CKD, n (%)0.765Very high risk234 (26.3)207 (23.3)218 (24.5)230 (25.9)High risk38 (23.9)358 (25.3)360 (25.4)361 (25.5)Moderately increased risk23 (21.3)33 (30.6)23 (21.3)29 (26.9)Low risk8 (24.2)8 (24.2)10 (30.3)7 (21.2)Data are presented as the mean ± SD or the frequency (percentage). Seasonal variations in the number of kidney biopsies per glomerular disease were analyzed using the four (seasons) × four (diseases) table chi-squared test. Chi-squared test with Bonferroni’s correction for multiple comparisons was also employed for categorical variables. ▲ and ▽ indicate significantly higher and lower values than expected, respectively, in residual analyses utilizing chi-squared tests. One-way analysis of variance with Tukey’s multiple comparison test was used for continuous variables. P-values from the chi-squared or Kruskal–Wallis test. IgAN, immunoglobulin A nephropathy; BMI, body mass index; BP, blood pressure; eGFR, estimated glomerular filtration rate; UPE; urinary protein excretion.$P \leq 0.05$ versus summer. ## Seasonal variations in PIAGN Seasonal variations in the biochemical and clinical parameters of the patients with PIAGN are summarized in Table 5. Significant seasonal differences were detected in systolic BP ($P \leq 0.028$), diastolic BP ($P \leq 0.036$), mean BP ($P \leq 0.02$), serum creatinine levels ($P \leq 0.03$), and eGFR ($P \leq 0.02$). Serum creatinine levels during autumn were the highest, and the eGFR values during autumn were lower than those during spring. In the distributions of age, UPE rates, and CKD heat map categories, no significant differences were found during the analysis of seasonal patterns. Table 5Seasonal variations in biochemical and clinical parameters in the patients with PIAGN.SpringSummerAutumnWinterP valueN (%)31 (25.2)33 (26.8)21 (17.1)38 (30.9)–Age (year)36.7 ± 23.446.5 ± 23.149.1 ± 25.249.3 ± 21.70.076 < 18, n (%)8 (36.4)4 (18.2)4 (18.2)6 (27.3)0.747 18–64, n (%)17 (25.4)20 (29.9)10 (14.9)20 (29.9) 65 ≤, n (%)6 (17.6)9 (26.5)7 (20.6)12 (35.3)Male, n (%)16 (51.6)22 (66.7)14 (66.7)18 (47.4)0.277BMI (kg/m2)22.7 ± 5.422.1 ± 4.225.4 ± 4.824.8 ± 6.50.078Systolic BP (mmHg)127.4 ± 28.2137.9 ± 19.0142.4 ± 13.6135.0 ± 26.10.028Diastolic BP (mmHg)71.5 ± 15.680.5 ± 11.879.7 ± 13.675.8 ± 14.90.036Mean BP (mmHg)90.1 ± 18.299.6 ± 12.6100.6 ± 11.795.5 ± 16.90.02Serum creatinine (mg/dL)1.47 ± 1.561.50 ± 1.113.54 ± 3.841.75 ± 1.590.03eGFR (mL/min/1.73m2)66.0 ± 34.459.8 ± 33.839.1 ± 30.8*49.0 ± 30.80.02UPE (g/day or g/gCr)1.97 ± 2.182.64 ± 2.873.67 ± 4.023.05 ± 3.590.38 < 0.15, n (%)4 (30.8)3 (23.1)3 (23.1)3 (23.1)0.151 0.15–0.49, n (%)7 (38.9)8 (44.4)1 (5.6)2 (11.1) 0.50 ≥, n (%)20 (21.7)22 (23.9)17 (18.5)33 (35.9)Hematuria grade 2–3n (%)24 (23.1)29 (27.9)19 (18.3)32 (30.8)0.561KDIGO prognosis risk of CKD, n (%)Very high risk13 (18.3)17 (23.9)13 (18.3)28 (39.4)0.32High risk9 (33.3)7 (25.9)5 (18.5)6 (22.2)Moderately increased risk5 (31.3)7 (43.8)2 (12.5)2 (12.5)Low risk4 (44.4)2 (22.2)1 (11.1)2 (22.2)Data are presented as the mean ± SD or frequency (percentage). Seasonal changes in the number of kidney biopsies per glomerular disease were compared using the four (seasons) × four (diseases) table chi-squared test. Chi-squared test with Bonferroni’s correction for multiple comparisons was also used for categorical variables. ▲ and ▽ revealed significantly higher and lower values than expected, respectively, in residual analyses using chi-squared tests. One-way analysis of variance with Tukey’s multiple comparison test was employed for continuous variables. P-values from the chi-squared or Kruskal–Wallis test. IgAN, immunoglobulin A nephropathy; BMI, body mass index; BP, blood pressure; eGFR, estimated glomerular filtration rate; UPE; urinary protein excretion.$P \leq 0.05$ versus summer. ## Association between the seasons and the eGFR and UPE rates of the glomerular diseases Table 6 shows the results of the multivariate linear regression analysis of eGFR and UPE rates without adjusting for the host factors. Both eGFR values and UPE rates were calculated with reference to summer as they tend to be lower during summer than during other seasons27. Multiple linear regression analyses revealed that summer was associated with higher eGFR in patients with IgAN, MCNS, and MN. Alternatively, proteinuria analyses indicated that summer was associated with low levels of proteinuria in patients with MCNS and MN. However, after adjusting for age, sex, BMI, and mean BP, multiple regression analyses revealed that spring ($$P \leq 0.038$$) and winter ($$P \leq 0.014$$) were associated with higher proteinuria in patients with MCNS and that spring ($$P \leq 0.036$$) was associated with lower eGFR compared with summer in patients with MN (Table7).Table 6Multivariable linear regression. IgANMCNSDependent variableseGFRUPEeGFRUPESeasonsΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pSpring − 0.055(− 5.545 − − 2.161) < 0.0010.016(− 0.035 − 0.163)0.204 − 0.087(− 11.414 − − 3.236) < 0.0010.078(0.328 − 1.860)0.005SummerRefRefRefRefAutumn − 0.080(− 7.462 − − 4.038) < 0.0010.044(0.082 − 0.281) < 0.001 − 0.067(− 9.919 − − 1.596)0.0070.052(− 0.035 − 1.541)0.061Winter − 0.079(− 7.311 − − 3.924) < 0.0010.029(0.021 − 0.219)0.017 − 0.055 − 8.972 − − 0.620)0.0240.086(0.461 − 2.033)0.002R20.0060.0010.0050.005MNPIAGNDependent variableseGFRUPEeGFRUPESeasonsΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pSpring − 0.089(− 8.088 − − 2.383) < 0.0010.060(0.121 − 1.103)0.0150.080(− 9.968 − 22.271)0.451 − 0.092(− 2.251 − 0.903)0.399SummerRefRefRefRefAutumn − 0.056(− 6.117 − − 0.431)0.0240.076(0.278 − 1.257)0.002 − 0.233(− 38.668 − − 2.687)0.0250.121(− 0.736 − 2.785)0.251Winter − 0.074(− 7.095 − − 1.446)0.0030.067(0.188 − 1.161)0.007 − 0.150(− 26.185 – 4.486)0.1640.059(− 1.094 − 1.906)0.593R20.0050.0040.0580.008IgAN, immunoglobulin A nephropathy; MCNS, minimal change nephrotic syndrome; MN, membranous nephropathy; PIAGN, post-infectious acute glomerulonephritis; eGFR, estimated glomerular filtration rate; UPE; urinary protein excretion. Table 7Multivariable linear regression (all analyses were adjusted for age, sex, BMI and MBP).IgANMCNSDependent variableseGFRUPEeGFRUPESeasonsΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pSpring0.002(− 1.083 − 1.366)0.821 − 0.006(− 0.118 − 0.072)0.6360.009(− 2.102 − 3.559)0.6140.057(0.046 − 1.562)0.038SummerRefRefRefRefAutumn − 0.004(− 1.5083 − 0.979)0.6760.013(− 0.044 − 0.149)0.2820.005(− 2.447 − 3.298)0.7710.030(− 0.338 − 1.219)0.267Winter − 0.006(− 1.644 − 0.813)0.5070.002(− 0.087 − 0.103)0.8670.014(− 1.664 − 4.109)0.4060.067(0.193—1.747)0.014R20.4860.0700.5330.043MNPIAGNDependent variableseGFRUPEeGFRUPESeasonsΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pΒ ($95\%$ CI)pSpring − 0.044(− 4.972 − − 0.168)0.0360.034(− 0.136 − 0.819)0.161 − 0.006(− 13.990 − 13.126)0.9500.051(− 1.210 − 1.962)0.640SummerRefRefRefRefAutumn − 0.007(− 2.809 − 1.982)0.7350.046(− 0.016 − 0.936)0.058 − 1.881(− 28.492 − 0.736)0.0620.144(− 0.491 − 2.928)0.161Winter − 0.019(− 3.485 − 1.282)0.3650.036(− 0.112 − 0.835)0.1340.876(− 18.500 − 7.151)0.3830.126(− 0.630 − 2.370)0.253R20.3080.0760.4050.122BMI, body mass index; MBP, mean blood pressure IgAN, immunoglobulin A nephropathy; MCNS, minimal change nephrotic syndrome; MN, membranous nephropathy; PIAGN, post-infectious acute glomerulonephritis; eGFR, estimated glomerular filtration rate; UPE; urinary protein excretion. ## Discussion Furthermore, we examined whether seasonal variatoins affected the clinical characteristics of the patients at the time of biopsy. The overall analysis revealed that the number of renal biopsies was significantly higher during summer, particularly for patients with IgAN. This may be because school urinalysis and physical examinations are often conducted during spring in Japan28, and admissions for kidney biopsies are more common during the summer holidays than during the other periods of the year. Thus, it is reasonable to assume that IgAN and MCNS are more common in younger individuals presenting with mild cases during summer, similar to the results of the present study. The analyses of seasonal variations in patients with IgAN in this study revealed a significant increase in the number of renal biopsies in adult patients during autumn and winter. The number of patients with severe proteinuria was particularly high during winter. Previous retrospective studies involving patients with IgAN have reported high proteinuria exacerbation during autumn and winter29. Similarly, patients with diabetic nephropathy and pediatric MCNS are reportedly more prone to proteinuria and albuminuria exacerbations during autumn and winter compared with summer. The mechanism underlying this worsening of proteinuria during autumn and winter is not well understood; however, the lack of seasonal variations in the degree of occult urine suggests that age, BP, and renal function may be responsible for the worsening of proteinuria30. BP rises during winter owing to vasoconstriction and falls during summer because of vasodilation due to temperature changes31. Our results also indicate that BP increases in autumun and winter, which supports the results reported in previous studies3. However, differences in BP values are trivial and may not be clinically important. Furthermore, other unknown factors may be involved. In addition, IgAN may be associated with the exacerbation of proteinuria due to preceding upper respiratory tract infections, which tends to occur more frequently during winter18. The seasonal variation in the patients with MCNS in this study was characterized by a higher number of renal biopsies during summer in young people than in adults. Regarding proteinuria, there were more cases of mild proteinuria during summer and more cases of severe proteinuria during spring than during the other seasons. Several previous studies have reported seasonal variations in patients with pediatric MCNS characterized by a peak during autumn for primary MCNS cases and a peak during spring for recurrent MCNS cases, suggesting an association with the amount of mite allergen in hay fever and house dust17,18. A previous study reported that prior respiratory viral infections are associated with recurrent nephrotic syndrome, irrespective of the type of virus32. Herein, no exacerbation of proteinuria was observed during autumn; however, proteinuria exacerbated during spring, as reported previously, suggesting some type of spring allergic factor. Our analyses did not detect any significant seasonal variations in the patients with MN and PIAGN. To the best of our knowledge, there have been no reports regarding seasonal variations in patients with MN, and although our study suggests that renal dysfunction is more common during spring than during the other seasons, it was difficult to identify a mechanism or hypothesis to support this finding. For PIAGN, a causal relationship with streptococcal infections has been suggested, which increases the number of cases and exacerbates the disease during winter32,33. However, the results of the present study did not appear to be statistically significant owing to the small number of study participants3. This may be due to the low absolute number of PIAGN cases in our study owing to the widespread use of antibiotics in recent years. Notably, the clinical features of the disease are influenced by various factors and not only seasonal variations. Therefore, we assessed seasonal variations in clinical features adjusted for age, sex, body size, and BP. Notably, when adjusted for the abovementioned factors, the seasonal factor was weaker in all the glomerular diseases, and only MCNS exhibited proteinuria exacerbation during spring and winter. These results support previous evidence stating that certain infectious or allergic factors contribute to MCNS exacerbation during spring and winter16,17. Previous studies have reported that ambient temperature, humidity, and air pollution (NO2, SO2, and PMs) with seasonal variations can cause dehydration and abnormal immune responses that have been associated with the risk of developing urolithiasis, acute kidney injury, CKD, and urinary tract infections34,35. Although, to the best of our knowledge, no studies have yet demonstrated an association between primary glomerular diseases and these external factors, it is possible that these factors have influenced the seasonal variation observed in this study. This study has several limitations. First, it was a national study, and thus, its findings cannot be internationally generalized. Second, the institutions providing data to the J-RBR are not evenly distributed throughout Japan. Third, as the analysis was based on renal biopsy data, there are no uniform indications for renal biopsies, which may have caused sampling bias and data collection not reflecting disease incidence and severity. Moreover, location-specific data, such as temperature and regional differences, were not assessed. Further studies are warranted regarding the time of disease onset and treatment initiation to clarify the strong causal role of seasonal factors in the development of glomerular diseases. Although there are various limitations in exploring the mechanisms and causes of seasonal variations in glomerular diseases, the greatest strength of this study is the large sample of patients collected from multiple facilities across Japan. In conclusion, the results of this study demonstrate seasonal variations in the number of renal biopsies in Japan and indicate that seasonal variations in disease severity exist, particularly in patients with IgAN and MCNS. In both cases, the number of kidney biopsies increases during summer. In patients with IgAN, increase in the number of severe cases during winter may be largely owing to age and BP. Conversely, increase in the number of severe cases of patients with MCNS during winter may be due to seasonal variations. Understanding the seasonal variations in the number of renal biopsies will help establish a better presystem for renal biopsy decision and help identify triggers in the patient background, such as BP, lifestyle, infections, and allergies. Furthermore, our findings may provide important insights for the future understanding of the pathophysiology of primary glomerular diseases. ## Supplementary Information Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-32182-7. ## References 1. Stewart S, Keates AK, Redfern A, McMurray JJV. Seasonal variations in cardiovascular disease. Nat. Rev. Cardiol. (2017.0) 14 654-664. DOI: 10.1038/nrcardio.2017.76 2. Jenkins CR, Celli B, Anderson JA, Ferguson GT, Jones PW, Vestbo J, Yates JC, Calverley PM. Seasonality and determinants of moderate and severe COPD exacerbations in the TORCH study. Eur. Respir. J. (2012.0) 39 38-45. DOI: 10.1183/09031936.00194610 3. Iwahori T, Miura K, Obayashi K, Ohkubo T, Nakajima H, Shiga T, Ueshima H. Seasonal variations in home blood pressure: findings from nationwide web-based monitoring in Japan. BMJ Open (2018.0) 8 e017351. DOI: 10.1136/bmjopen-2017-017351 4. Arntz HR, Willich SN, Schreiber C, Brüggemann T, Stern R, Schultheiss HP. Diurnal, weekly and seasonal variations of sudden death. Population-based analysis of 24,061 consecutive cases. Eur. Heart J. (2000.0) 21 315-320. DOI: 10.1053/euhj.1999.1739 5. Rothwell PM, Wroe SJ, Slattery J, Warlow CP. Is stroke incidence related to season or temperature? The Oxfordshire Community Stroke Project. Lancet (1996.0) 347 934-936. DOI: 10.1016/s0140-6736(96)91415-4 6. Woodhouse PR, Khaw KT, Plummer M, Foley A, Meade TW. Seasonal variations of plasma fibrinogen and factor VII activity in the elderly: Winter infections and death from cardiovascular disease. Lancet (1994.0) 343 435-439. DOI: 10.1016/s0140-6736(94)92689-1 7. Kawahara J, Sano H, Fukuzaki H, Saito K, Hirouchi H. Acute effects of exposure to cold on blood pressure, platelet function and sympathetic nervous activity in humans. Am. J. Hypertens. (1989.0) 2 724-726. DOI: 10.1093/ajh/2.9.724 8. Balaforlu B, Eskiyoruk I, Kus B, Tozar M, Bekiroglu N, Koc M. Seasonal variations of C-reactive protein and atherosclerotic cardiovascular events in hemodialysis patients. Ren. Fail. (2010.0) 32 825-831. DOI: 10.3109/0886022X.2010.494800 9. Selby NM. Acute kidney injury changes with the seasons. Nephrol. Dial. Transplant. (2018.0) 33 1281-1283. DOI: 10.1093/ndt/gfy070 10. Maeoka Y, Naito T, Irifuku T, Shimizu Y, Ogawa T, Masaki T. Seasonal variations in hemodialysis initiation: A single-center retrospective analysis. PLoS ONE (2017.0) 12 e0178967. DOI: 10.1371/journal.pone.0178967 11. Couser WG, Johnson RJ. The etiology of glomerulonephritis: Roles of infection and autoimmunity. Kidney Int. (2014.0) 86 905-914. DOI: 10.1038/ki.2014.49 12. Boland MR, Shahn Z, Madigan D, Hripcsak G, Tatonetti NP. Birth month affects lifetime disease risk: A phenome-wide method. J. Am. Med. Inform. Assoc. (2015.0) 22 1042-1053. DOI: 10.1093/jamia/ocv046 13. White CB, Bass JW, Yamada SM. Rapid latex agglutination compared with the throat culture for the detection of group A streptococcal infection. Pediatr. Infect. Dis. (1986.0) 5 208-212. DOI: 10.1097/00006454-198603000-00010 14. Zhang H, Wang Z, Dong L, Guo Y, Wu J, Zhai S. New insight into the pathogenesis of minimal change nephrotic syndrome: role of the persistence of respiratory tract virus in immune disorders. Autoimmun. Rev. (2016.0) 15 632-637. DOI: 10.1016/j.autrev.2016.02.007 15. Ivanova M, Creary LE, Al Hadra B, Lukanov T, Mazzocco M, Sacchi N, Ameen R, Al-Shemmari S, Moise A, Ursu LD, Constantinescu I, Vayntrub T, Fernández-Viňa MA, Shivarov V, Naumova E. 17th IHIW component “Immunogenetics of Ageing”—New NGS data. Hum. Immunol. (2019.0) 80 703-713. DOI: 10.1016/j.humimm.2019.07.287 16. Toyabe S, Nakamizo M, Uchiyama M, Akazawa K. Circannual variation in the onset and relapse of steroid-sensitive nephrotic syndrome. Pediatr. Nephrol. (2005.0) 20 470-473. DOI: 10.1007/s00467-004-1780-x 17. Odaka J, Kanai T, Uehara R, Kusano E, Yamagata T. Seasonal variations in first episode of childhood idiopathic steroid-sensitive nephrotic syndrome and adult minimal change nephrotic syndrome. Clin. Exp. Nephrol. (2015.0) 19 146-147. DOI: 10.1007/s10157-014-0966-1 18. Inagaki K. Seasonal proteinuria changes in IgA nephropathy patients after proteinuria remission. PLoS ONE (2017.0) 12 e0187607. DOI: 10.1371/journal.pone.0187607 19. Wada Y. Seasonal variations of urinary albumin creatinine ratio in Japanese subjects with Type 2 diabetes and early nephropathy. Diabet. Med. (2012.0) 29 506-508. DOI: 10.1111/j.1464-5491.2011.03472.x 20. Sugiyama H, Yokoyama H, Sato H, Saito T, Kohda Y, Nishi S, Tsuruya K, Kiyomoto H, Iida H, Sasaki T, Higuchi M, Hattori M, Oka K, Kagami S, Nagata M, Kawamura T, Honda M, Fukasawa Y, Fukatsu A, Morozumi K, Yoshikawa N, Yuzawa Y, Matsuo S, Kiyohara Y, Joh K, Taguchi T, Makino H. Committee for Standardization of Renal Pathological Diagnosis and Working Group for Renal Biopsy Database, Japanese Society of Nephrology, Tokyo, Japan: the first nationwide, web-based, and prospective registry system of renal biopsies in Japan. Clin. Exp. Nephrol. (2011.0) 15 493-503. DOI: 10.1007/s10157-011-0430-4 21. Sugiyama H, Yokoyama H, Sato H, Saito T, Kohda Y, Nishi S, Tsuruya K, Kiyomoto H, Iida H, Sasaki T, Higuchi M, Hattori M, Oka K, Kagami S, Kawamura T, Takeda T, Hataya H, Fukasawa Y, Fukatsu A, Morozumi K, Yoshikawa N, Shimizu A, Kitamura H, Yuzawa Y, Matsuo S, Kiyohara Y, Joh K, Nagata M, Taguchi T, Makino H. Committee for Standardization of Renal Pathological Diagnosis; Committee for Kidney Disease Registry; Japanese Society of Nephrology. Japan renal biopsy registry and Japan kidney disease registry: committee report for 2009 and 2010. Clin. Exp. Nephrol. (2013.0) 17 155-173. DOI: 10.1007/s10157-012-0746-8 22. Matsuo S. Revised equations for estimated GFR from serum creatinine in Japan. Am. J. Kidney Dis. (2009.0) 53 982-992. DOI: 10.1053/j.ajkd.2008.12.034 23. Kitano N, Suzuki H, Takeuchi T. Patient age and the seasonal pattern of onset of Kawasaki’s disease. N. Engl. J. Med. (2018.0) 378 2048-2049. DOI: 10.1056/NEJMc1804312 24. 24.Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl.3, 1–59 (2013) 25. Tomino Y. Diagnosis and treatment of patients with IgA nephropathy in Japan. Kidney Res. Clin. Pract. (2016.0) 35 197-203. DOI: 10.1016/j.krcp.2016.09.001 26. Okabayashi Y, Tsuboi N, Amano H, Miyazaki Y, Kawamura T, Ogura M, Narita I, Ninomiya T, Yokoyama H, Yokoo T. Distribution of nephrologists and regional variation in the clinical severity of IgA nephropathy at biopsy diagnosis in Japan: a cross-sectional study. BMJ Open (2018.0) 8 e024317. DOI: 10.1136/bmjopen-2018-024317 27. Narita K, Hoshide S, Fujiwara T, Kanegae H, Kario K. Seasonal variations of home blood pressure and its association with target organ damage. Am. J. Hypertens. (2020.0) 33 620-628. DOI: 10.1093/ajh/hpaa027 28. Murakami M, Yamamoto H, Ueda Y, Murakami K, Yamauchi K. Urinary screening of elementary and junior high-school children over a 13-year period in Tokyo. Pediatr. Nephrol. (1991.0) 5 50-53. DOI: 10.1007/BF00852844 29. Masugata H. Seasonal variations in estimated glomerular filtration rate based on serum creatinine levels in hypertensive patients. Tohoku J. Exp. Med. (2011.0) 224 137-142. DOI: 10.1620/tjem.224.137 30. Narita K, Hoshide S, Kario K. Seasonal variations in blood pressure: current evidence and recommendations for hypertension management. Hypertens. Res. (2021.0) 44 1363-1372. DOI: 10.1038/s41440-021-00732-z 31. MacDonald NE, Wolfish N, McLaine P, Phipps P, Rossier E. Role of respiratory viruses in exacerbations of primary nephrotic syndrome. J. Pediatr. (1986.0) 108 378-382. DOI: 10.1016/s0022-3476(86)80876-9 32. Hahn RG, Knox LM, Forman TA. Evaluation of poststreptococcal illness. Am. Fam. Phys. (2005.0) 71 1949-1954 33. Link-Gelles R, Toews KA, Schaffner W, Edwards KM, Wright C, Beall B, Barnes B, Jewell B, Harrison LH, Kirley PD, Lorentzson L, Aragon D, Petit S, Bareta J, Spina NL, Cieslak PR, Van Beneden C. Characteristics of intracranial group A streptococcal infections in US children, 1997–2014. J. Pediatr. Infect. Dis. Soc. (2020.0) 9 30-35. DOI: 10.1093/jpids/piy108 34. Kuźma Ł, Małyszko J, Bachórzewska-Gajewska H, Kralisz P, Dobrzycki S. Exposure to air pollution and renal function. Sci. Rep. (2021.0) 11 11419. DOI: 10.1038/s41598-021-91000-0 35. Borg M, Bi P, Nitschke M, Williams S, McDonald S. The impact of daily temperature on renal disease incidence: An ecological study. Environ. Health (2017.0) 16 114. DOI: 10.1186/s12940-017-0331-4
--- title: Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren’s syndrome authors: - Xiaomei Chen - Xue Wu - Chen Chen - Cainan Luo - Yamei Shi - Zhengfang Li - Xiaoyi Lv - Cheng Chen - Jinmei Su - Lijun Wu journal: Scientific Reports year: 2023 pmcid: PMC10060214 doi: 10.1038/s41598-023-29943-9 license: CC BY 4.0 --- # Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren’s syndrome ## Abstract The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were $88.89\%$, $100\%$ and $94.44\%$, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value. ## Introduction Primary Sjögren syndrome (pSS) is a chronic systemic autoimmune disease that primarily affects the exocrine glands, particularly the lacrimal and salivary glands, resulting in symptoms of dry eyes and dry mouth. It is sometimes accompanied by systemic features affecting extraglandular sites such as the joints, blood, kidneys, lungs, vessels, and nerves1. Due to its systemic involvement, pSS can present a variety of clinical manifestations that lead to confusion and delay in diagnosis. Moreover, definitive diagnosis of pSS mainly depends on the clinical manifestations, specific immunological changes, and other special examinations, such as dry eye examination, labial gland biopsy, and parotid gland tomography2. Early multiple examinations for pSS lead to a cumbersome diagnostic process that is costly and complex, and invasive tests such as labial gland biopsy have limitations in clinical application and are difficult to repeat. Therefore, a rapid, efficient and convenient method based on serum is more suitable. Recently, Raman spectroscopy combined with machine learning algorithms has provided a more rapid and efficient method for the early diagnosis of many diseases3–5. Raman spectroscopy is an optical spectroscopic technique based on the inelastic scattering of light. It can be used to detect biological macromolecules, including proteins, lipids, and DNA, in biological samples and provides abundant molecular information at the microscopic level6,7. Therefore, Raman spectroscopy is commonly used in biomolecular detection. Raman spectroscopy can be used to detect changes in diseases at the biomolecular level and aid in the early diagnosis of diseases. It has been widely used in the early screening of Alzheimer's disease8, meningioma9, dengue virus infection5, cervical cancer10, oral cancers11, and so on. Due to the high dimensionality of spectral data, redundant interference occurs, reducing the accuracy of the model. Therefore, we used the PCA dimensionality reduction method for feature selection to improve the accuracy of the model. This experiment was based on the serum Raman spectrum combined with the support vector machine algorithm, the radial basis kernel function was selected as the kernel function, and the PSO algorithm was the parameter optimization method. Finally, the PSO-SVM classification model was established. Through the classification results of the model, the feasibility of Raman spectroscopy combined with a support vector machine algorithm for the rapid detection of pSS patients and healthy controls (HCs) was verified. ## Patient selection Thirty patients with pSS who met the American–European classification criteria (AECG) and 30 healthy controls were enrolled in this study. Patients with other autoimmune diseases, malignant tumors, or active infections were excluded from this study. A signed consent form was obtained from all patients. The study was approved by the ethics committee of the People's Hospital of Xinjiang Uygur Autonomous Region. ## Sample preparation Three milliliters of whole blood was collected into tubes without any anticoagulant and centrifuged at 1500g for 10 min to isolate the serum. The serum was then collected into EP tubes and frozen at − 80 °C until detection by Raman spectroscopy. For each measurement, approximately 15 µL of the serum sample was prepared in a quartz cuvette. ## Raman spectral data acquisition All Raman spectra were recorded using a Raman spectrometer (LabRAM HR Evolution Raman Spectrometer, HORIBA Scientific Ltd.) in the range of 400–4000 cm−1. An Ar+ laser with a wavelength of 532 nm and power of 50 mW was used for Raman excitation. Spectra were acquired using a 10 × objective within 3 s. Three spectra per location were recorded in the wavenumber interval of 400–4000 cm−1. To exclude experimental interference and artifactual errors, three Raman spectra of each sample were recorded at different positions in the same plane. ## Algorithm description Support vector machine (SVM) is a powerful supervised learning method capable of transforming data into a high-dimensional space for classification problems12. The SVM algorithm can be used to analyze data from small samples and data with high dimensions. It not only has a good nonlinear fitting ability and high generalization but also has the advantages of obtaining a global optimum through the objective function. At present, SVM has been widely used in the detection of diseases such as diabetes, breast cancer, and lung cancer13–15. In the process of SVM modeling, it is more important to choose the appropriate C and g parameters. At the same time, the application of the kernel function can also improve the performance of SVM. Separate collections. In this study, the radial basis kernel function was chosen as the kernel function. PSO is an evolutionary computation technique for solving optimization problems16. The core idea of PSO is to find the optimal solution through collaboration and information sharing among individuals in the group. PSO was originally developed by Kennedy and Eberhart. It was inspired by research on bird and fish flock movement behaviors. First, a population of random particles is initialized, and then the system is updated at each iteration through searches for the optimal solution. PSO has the advantages of simple operation and a small amount of calculation, which can further reduce the time for optimizing parameters17. PSO-SVM has the advantages of a strong learning ability and sensitivity to small sample data and is widely used in machine learning methods. Therefore, in this study, the PSO-SVM algorithm was used to build a diagnostic model to achieve a rapid distinction between patients with pSS and HCs. ## Data analysis Raman spectra were normalized to [0,1] by the "mapminmax" function in MATLAB r2018a. The normalization process can reduce the effect of laser power fluctuation on the sample data18. To improve the diagnostic accuracy and efficiency of SVM, PCA was used to characterize the serum Raman spectra. All algorithms were implemented in MATLAB r2018a. SVM classification analysis was performed using the libsvm toolbox created by Lin and Chang. ## Informed consent This study was approved by the ethics committee of the People's Hospital of Xinjiang Uygur Autonomous Region (in these studies). Informed consent was obtained from all participants before participating in the interview study. All methods were carried out in accordance with relevant guidelines and regulations (e.g., Helsinki guidelines). ## Spectral comparison The means of the raw spectra of patients with pSS and HCs were calculated (Fig. 1). A comparison of the Raman spectra showed that five peaks [proline (959 cm−1), phenylalanine (1003 cm−1), carotenoids (1155 cm−1), tryptophan (1355 cm−1), and beta-carotene (1514 cm−1)] were different between patients with pSS and HCs. The spectral features of these substances were assigned based on the available literature (Table 1)19–21. Compared to those of HCs, the Raman peak intensities of proline, phenylalanine, carotenoids, tryptophan and beta-carotene were lower in patients with pSS.Figure 1The differences in Raman spectroscopy between pSS patients and HCs. ( A) Mean Raman spectra of HCs and pSS patients. ( B) Major Raman peak differences between HCs and pSS patients. Table 1Peak location of the main Raman spectra of human serum. Peak (cm−1)AssignmentRaman peak intensity comparison to healthy controls (Increase/decrease)959ProlineDecrease1003PhenylalanineDecreasePhenylalanine (collagen assignment)1155Carotenoids (absent in normal tissue)Decrease1355TryptophanDecrease1514β-Carotene accumulation (C–C stretch mode)Decrease ## Feature extraction After feature extraction, principal component analysis (PCA) was used for dimensionality reduction. PCs with an overall contribution of $90\%$ will generally be retained. In this study, the total contribution of these 29 PCs was $99.99\%$. In addition, the most significant three PCs were extracted to plot the principal component scatter plots of the training and test sets (Fig. 2). There is a degree of variation between patients with pSS and healthy controls. Figure 23D scatter plot of the principal components of the training and test sets. ( A) 3D scatter plot of the principal components of the training set. ( B) 3D scatter plot of the principal components of the test set. ## Model evaluation Forty-two samples (21 from patients with pSS and 21 from HCs) were randomly selected as the training set, and 18 samples (9 from patients with pSS and 9 from HCs) were selected as the test set. Classification of pSS patients and HCs was performed using an SVM classifier. In the SVM model, PSO-SVM was employed to optimize the penalty parameter C and Gaussian width g. In PSO-SVM, the search range of C was set to [2−8,28], the search range of g was set to [2−8,28], and the size step was set to 0.3. The PSO parameter local search ability was set to 1.5, and the overall search ability was set to 1.7; the maximum evolutionary number (MaxGen) was set to 200, and the maximum population size (sizepop) was 20. The radial basis function (RBF) was chosen as the kernel function for the SVM. The accuracy, sensitivity, and specificity of the PSO-SVM classification model were $88.89\%$, $100\%$, and $94.44\%$, respectively. In addition, to further illustrate the classification capability of the model, as shown in Table 2, we used the confusion matrix to evaluate the performance of the PSO-SVM algorithm. Table 2PSO-SVM model confusion matrix. PredictionpSSHealthy controlsTrue pSS90 Healthy controls18 ## Discussion pSS is a chronic systemic autoimmune disease characterized by lymphocyte proliferation and progressive exocrine gland damage22. Since the onset of dry syndrome is insidious, the clinical manifestations of patients are different, and the severity of the disease also varies greatly, so the early and clear diagnosis of the disease has important clinical significance to improve the prognosis of patients. However, because the pathogenesis of primary Sjögren syndrome is not yet completely clear, there is still no clear diagnostic standard, and the diagnostic standard used in clinical practice is actually a classification standard23. Therefore, the diagnosis of pSS needs to be confirmed by experienced specialists to prevent a large number of missed diagnoses and misdiagnoses. In addition, the main method to diagnose pSS is through a labial gland biopsy, but this method is invasive and less accepted by patients. Moreover, a labial gland biopsy has certain limitations, and the results are often inconsistent with clinical manifestations and laboratory test results in the early stage of the disease. In addition, the prevalence of pSS is as high as 3–$4\%$ in the elderly population, and these patients are often unable to tolerate a labial biopsy. Parotid angiography, parotid ultrasound and MRI are also helpful in the diagnosis of pSS24, but they are not included in the guidelines due to the lack of standardization of these testing techniques. Therefore, the search for new, rapid and noninvasive tests has been a hot research topic in this field. Raman spectroscopy is a vibrational spectroscopy technique based on the Raman scattering principle25. Relevant studies have demonstrated the feasibility of Raman spectroscopy in different disease fields, and achieved high accuracy in many diagnoses26–28. Li M et al. provided a non-invasive and rapid technology for the screening of gastric cancer patients based on serum Raman spectroscopy combined with one-dimensional convolutional neural network, random forest and other machine learning methods29. Hyunku Shin et al. used a variety of deep learning algorithms combined with surface-enhanced Raman spectroscopy (SERS) to achieve early diagnosis of lung cancer and achieved good results30. Similarly, in this exploratory study, we demonstrated that Raman spectroscopy techniques combined with support vector machine algorithms can be used as an effective diagnostic method for pSS. Furthermore, we found that Raman spectroscopy can detect changes in biomolecular composition induced by pathological changes occurring between pSS and HCs, which was consistent with previous study31. In the experiment, due to the weak Raman signal in the detection, it is easily interfered by the fluorescent background, and the signal-to-noise ratio of the spectrum is low, which makes it difficult to distinguish different types of molecular spectral information32,33. Therefore, we need to use advanced pattern recognition algorithms to improve the classification accuracy. Principal component analysis (PCA), an unsupervised feature extraction algorithm that can reduce the dimensionality of Raman spectral data34, has been widely used by many researchers for the extraction of Raman spectral features. Similarly, pattern recognition requires powerful classifiers. In recent years, support vector machine (SVM) have been widely used in the field of pattern recognition with obvious effects. In this study, particle swarm optimization (PSO)-SVM was selected as the method of parameter optimization to rapidly classify patients with pSS and healthy controls. In this study, we found some differences in the Raman spectra of serum from patients with pSS and HCs. Compared to those in HCs, the proline, carotenoids, and tryptophan peaks were of lower intensity in patients with pSS. This may indicate that pSS patients experience metabolic changes that result in less proline, carotenoids, and tryptophan than HCs. Studies have shown that metabolic levels of proline and tryptophan are significantly altered by the effects of pSS35. And carotenoids can be converted into vitamin A36, which in appropriate concentrations can in turn improve the immune function of cells37. The main etiology of pSS is associated with abnormal immune function38, which represents a possible deficiency of vitamin A in patients with pSS. Based on the differences in serum spectra, the accuracy rate of the PSO-SVM classification model reached $94.44\%$. Thus, it can be used to rapidly discriminate patients with pSS and HCs. As there was a limited sample size in this study, we plan to collect more samples to validate the results of this exploratory experiment in the future and evaluate the effect of serum Raman spectroscopy for pSS diagnosis. ## Conclusion In this study, we used Raman spectroscopy combined with the PSO-SVM algorithm to rapidly diagnose pSS based on serum samples obtained from pSS patients and healthy controls. The spectral data were reduced using PCA, and the first 29 PCs were taken as input. Through the evaluation metrics of the model, we found that PSO-SVM performed stably, with model specificity, sensitivity and accuracy results of $88.89\%$, $100\%$ and $94.44\%$, respectively. This study showed that Raman spectroscopy combined with a support vector machine algorithm could be used as an effective pSS diagnosis method. ## References 1. Vitali C, Minniti A, Pignataro F, Maglione W, Del Papa N. Management of Sjögren's syndrome: Present issues and future perspectives. Front. Med. (2021) 8 676885. DOI: 10.3389/fmed.2021.676885 2. Del Papa N, Vitali C. Management of primary Sjögren’s syndrome: Recent developments and new classification criteria. Ther. Adv. Musculoskelet. Dis. (2018) 10 39. DOI: 10.1177/1759720X17746319 3. Parlatan U, Inanc MT, Ozgor BY, Oral E, Bastu E, Unlu MB. Raman spectroscopy as a non-invasive diagnostic technique for endometriosis. Sci. Rep. (2019) 9 1-7. DOI: 10.1038/s41598-019-56308-y 4. Zheng X, Lv G, Du G, Zhai Z, Mo J, Lv X. Rapid and low-cost detection of thyroid dysfunction using Raman spectroscopy and an improved support vector machine. IEEE Photonics J. (2018) 10 1-12. DOI: 10.1109/JPHOT.2018.2876686 5. Khan S, Ullah R, Khan A, Wahab N, Bilal M, Ahmed M. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM). Biomed. Opt. Express (2016) 7 2249-2256. DOI: 10.1364/BOE.7.002249 6. Butler HJ, Ashton L, Bird B, Cinque G, Curtis K, Dorney J. Using Raman spectroscopy to characterize biological materials. Nat. Protoc. (2016) 11 664-687. DOI: 10.1038/nprot.2016.036 7. Paidi SK, Rodriguez Troncoso J, Raj P, Monterroso Diaz P, Ivers JD, Lee DE. Raman spectroscopy and machine learning reveals early tumor microenvironmental changes induced by immunotherapy. Cancer Res. (2021) 81 5745-5755. DOI: 10.1158/0008-5472.CAN-21-1438 8. Ryzhikova E, Kazakov O, Halamkova L, Celmins D, Malone P, Molho E. Raman spectroscopy of blood serum for Alzheimer's disease diagnostics: Specificity relative to other types of dementia. J. Biophotonics (2015) 8 584-596. DOI: 10.1002/jbio.201400060 9. Mehta K, Atak A, Sahu A, Srivastava S. An early investigative serum Raman spectroscopy study of meningioma. Analyst (2018) 143 1916-1923. DOI: 10.1039/C8AN00224J 10. Rubina S, Amita M, Bharat R, Krishna CM. Raman spectroscopic study on classification of cervical cell specimens. Vib. Spectrosc. (2013) 68 115-121. DOI: 10.1016/j.vibspec.2013.06.002 11. Sahu A, Sawant S, Mamgain H, Krishna CM. Raman spectroscopy of serum: An exploratory study for detection of oral cancers. Analyst (2013) 138 4161-4174. DOI: 10.1039/c3an00308f 12. Long Y, Du Z-J, Wang W-D, Zhao G-Y, Xu G-Q, He L. PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons. Sensors (2016) 16 1408. DOI: 10.3390/s16091408 13. Thaiyalnayaki K. Classification of diabetes using deep learning and SVM techniques. Int. J. Curr. Res. Rev. (2021) 13 146. DOI: 10.31782/IJCRR.2021.13127 14. Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement (2019) 146 800-805. DOI: 10.1016/j.measurement.2019.05.083 15. Nanglia P, Kumar S, Mahajan AN, Singh P, Rathee D. A hybrid algorithm for lung cancer classification using SVM and neural networks. ICT Express (2021) 7 335-341. DOI: 10.1016/j.icte.2020.06.007 16. Shen L, Huang X, Fan C. Double-group particle swarm optimization and its application in remote sensing image segmentation. Sensors (2018) 18 1393. DOI: 10.3390/s18051393 17. Zhao S, Zhao Z. A comparative study of landslide susceptibility mapping using SVM and PSO-SVM models based on Grid and Slope Units. Math. Probl. Eng. (2021) 2021 1-15 18. Martyna A, Menżyk A, Damin A, Michalska A, Martra G, Alladio E. Improving discrimination of Raman spectra by optimising preprocessing strategies on the basis of the ability to refine the relationship between variance components. Chemom. Intell. Lab. Syst. (2020) 202 104029. DOI: 10.1016/j.chemolab.2020.104029 19. Li X, Yang T, Li S, Wang D, Song Y, Yu K. Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases. J. Raman Spectrosc. (2016) 47 917-925. DOI: 10.1002/jrs.4924 20. Xiao R, Zhang X, Rong Z, Xiu B, Yang X, Wang C. Non-invasive detection of hepatocellular carcinoma serum metabolic profile through surface-enhanced Raman spectroscopy. Nanomed. Nanotechnol. Biol. Med. (2016) 12 2475-2484. DOI: 10.1016/j.nano.2016.07.014 21. Movasaghi Z, Rehman S, Rehman IU. Raman spectroscopy of biological tissues. Appl. Spectrosc. Rev. (2007) 42 493-541. DOI: 10.1080/05704920701551530 22. Zhao J, An Q, Zhu X, Yang B, Gao X, Niu Y. Research status and future prospects of extracellular vesicles in primary Sjögren’s syndrome. Stem Cell Res. Ther. (2022) 13 1-11. DOI: 10.1186/s13287-022-02912-1 23. Vitali C, Bombardieri S, Jonsson R, Moutsopoulos HM, Alexander EL, Carsons SE. Classification criteria for Sjögren's syndrome: A revised version of the European criteria proposed by the American-European Consensus Group. Ann. Rheum. Dis. (2002) 61 554-558. DOI: 10.1136/ard.61.6.554 24. Knopf A, Mansour N, Chaker A, Bas M, Stock K. Multimodal ultrasonographic characterisation of parotid gland lesions—A pilot study. Eur. J. Radiol. (2012) 81 3300-3305. DOI: 10.1016/j.ejrad.2012.01.004 25. Lussier F, Thibault V, Charron B, Wallace GQ, Masson J-F. Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering. TrAC Trends Anal. Chem. (2020) 124 115796. DOI: 10.1016/j.trac.2019.115796 26. Ma D, Shang L, Tang J, Bao Y, Fu J, Yin J. Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. (2021) 256 119732. DOI: 10.1016/j.saa.2021.119732 27. Hong Y, Li Y, Huang L, He W, Wang S, Wang C. Label-free diagnosis for colorectal cancer through coffee ring-assisted surface-enhanced Raman spectroscopy on blood serum. J. Biophotonics (2020) 13 e201960176. DOI: 10.1002/jbio.201960176 28. Koster HJ, Guillen-Perez A, Gomez-Diaz JS, Navas-Moreno M, Birkeland AC, Carney RP. Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics. Sci. Rep. (2022) 12 18464. DOI: 10.1038/s41598-022-22197-x 29. Li M, He H, Huang G, Lin B, Tian H, Xia K. A novel and rapid serum detection technology for non-invasive screening of gastric cancer based on Raman spectroscopy combined with different machine learning methods. Front. Oncol. (2021) 11 665176. DOI: 10.3389/fonc.2021.665176 30. Shin H, Oh S, Hong S, Kang M, Kang D, Ji Y-G. Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes. ACS Nano (2020) 14 5435-5444. DOI: 10.1021/acsnano.9b09119 31. Xue L, Sun P, Ou D, Chen P, Chen M, Yan B. Diagnosis of pathological minor salivary glands in primary Sjogren’s syndrome by using Raman spectroscopy. Lasers Med. Sci. (2014) 29 723-728. DOI: 10.1007/s10103-013-1398-y 32. Xia L, Lu J, Chen Z, Cui X, Chen S, Pei D. Identifying benign and malignant thyroid nodules based on blood serum surface-enhanced Raman spectroscopy. Nanomed. Nanotechnol. Biol. Med. (2021) 32 102328. DOI: 10.1016/j.nano.2020.102328 33. Meng C, Li H, Chen C, Wu W, Gao J, Lai Y. Serum Raman spectroscopy combined with Gaussian—Convolutional neural network models to quickly detect liver cancer patients. Spectrosc. Lett. (2022) 55 79-90. DOI: 10.1080/00387010.2022.2027988 34. Vrábel J, Pořízka P, Kaiser J. Restricted Boltzmann machine method for dimensionality reduction of large spectroscopic data. Spectrochim. Acta Part B (2020) 167 105849. DOI: 10.1016/j.sab.2020.105849 35. Fernández-Ochoa Á, Borrás-Linares I, Quirantes-Piné R, Alarcón-Riquelme ME, Beretta L, Segura-Carretero A. Discovering new metabolite alterations in primary Sjögren’s syndrome in urinary and plasma samples using an HPLC-ESI-QTOF-MS methodology. J. Pharm. Biomed. Anal. (2020) 179 112999. DOI: 10.1016/j.jpba.2019.112999 36. Meléndez-Martínez AJ. An overview of carotenoids, apocarotenoids, and vitamin A in agro-food, nutrition, health, and disease. Mol. Nutr. Food Res. (2019) 63 1801045. DOI: 10.1002/mnfr.201801045 37. Fernandes G. Beta-carotene supplementation: Friend or foe?. J. Lab. Clin. Med. (1997) 129 285-287. DOI: 10.1016/S0022-2143(97)90175-X 38. Nocturne G, Mariette X. Advances in understanding the pathogenesis of primary Sjögren's syndrome. Nat. Rev. Rheumatol. (2013) 9 544-556. DOI: 10.1038/nrrheum.2013.110
--- title: Histone demethylase KDM2A is a selective vulnerability of cancers relying on alternative telomere maintenance authors: - Fei Li - Yizhe Wang - Inah Hwang - Ja-Young Jang - Libo Xu - Zhong Deng - Eun Young Yu - Yiming Cai - Caizhi Wu - Zhenbo Han - Yu-Han Huang - Xiangao Huang - Ling Zhang - Jun Yao - Neal F. Lue - Paul M. Lieberman - Haoqiang Ying - Jihye Paik - Hongwu Zheng journal: Nature Communications year: 2023 pmcid: PMC10060224 doi: 10.1038/s41467-023-37480-2 license: CC BY 4.0 --- # Histone demethylase KDM2A is a selective vulnerability of cancers relying on alternative telomere maintenance ## Abstract Telomere length maintenance is essential for cellular immortalization and tumorigenesis. $5\%$ − $10\%$ of human cancers rely on a recombination-based mechanism termed alternative lengthening of telomeres (ALT) to sustain their replicative immortality, yet there are currently no targeted therapies. Through CRISPR/Cas9-based genetic screens in an ALT-immortalized isogenic cellular model, here we identify histone lysine demethylase KDM2A as a molecular vulnerability selectively for cells contingent on ALT-dependent telomere maintenance. Mechanistically, we demonstrate that KDM2A is required for dissolution of the ALT-specific telomere clusters following recombination-directed telomere DNA synthesis. We show that KDM2A promotes de-clustering of ALT multitelomeres through facilitating isopeptidase SENP6-mediated SUMO deconjugation at telomeres. Inactivation of KDM2A or SENP6 impairs post-recombination telomere de-SUMOylation and thus dissolution of ALT telomere clusters, leading to gross chromosome missegregation and mitotic cell death. These findings together establish KDM2A as a selective molecular vulnerability and a promising drug target for ALT-dependent cancers. Alternative lengthening of telomeres (ALT) provides cancer cells a mechanism to sustain replicative immortality. Here, the authors identify KDM2A as a molecular vulnerability in ALT-dependent cancer cells and demonstrate its role in the resolution of ALT-specific telomere clusters via recruitment of SENP6. ## Introduction Telomeres are specialized nucleoprotein structures that shield the linear chromosome ends of eukaryotes from promiscuous DNA repair and nucleolytic degradation activities1. Due to the chromosome “end-replication” problem, telomere DNA undergoes progressive attrition with each cell division2. Consequentially, proliferative tumor cells necessitate counteracting activity to maintain adequate telomere length and sustain their replicative immortality. While a majority of human cancers achieve this through telomerase activation, the remaining 5–$10\%$ of them rely on a homologous recombination-based mechanism termed alternative lengthening of telomeres (ALT)3,4. Recent studies further reveal ALT as a conservative DNA damage repair pathway analogous to break-induced replication (BIR) in budding yeast5–8. But the molecular pathway(s) that control ALT activation and termination still remain largely unclear. In human cancers, ALT activation is intimately linked to the mutational status of the chromatin modulator genes ATRX and DAXX9–13. Functionally, ATRX and DAXX form a histone H3.3-specific chaperone complex that facilitates replication-independent nucleosome assembly at heterochromatic regions, including telomeres14–18. A survey of ~7000 patient samples in 31 cancer types found that $5\%$ of them harbor genetic alterations of ATRX or DAXX that also concurrently present ALT features19. This tight association raises the possibility that ALT activation is a consequence of histone management dysfunction. The ALT mechanism relies on homologous recombination-directed telomere DNA synthesis. Cumulative evidences suggest that ALT activation emanates from the telomere replication stress and the stalled replication forks5,20,21. Indeed, depletion of ATRX or DAXX disrupts replication-independent nucleosome incorporation and induces telomere chromatin de-condensation that progressively activates the recombination-directed telomere repair pathway22. As a consequence, the homologous repair-based ALT mechanism becomes the only viable path for the ATRX or DAXX mutant cells to achieve replicative immortality. In this sense, ATRX or DAXX loss, while promoting tumorigenesis by activating the ALT-directed telomere maintenance pathway, also simultaneously creates an intrinsic telomere replication defect that can potentially be exploited for synthetic lethal-like interactions. Through CRISPR/Cas9-based genetic screens of isogenic ALT- and paired TERT-immortalized cell lines, here we identify histone demethylase KDM2A as a selective molecular vulnerability of cells that depend on ALT-directed telomere maintenance. We demonstrate that KDM2A functions to facilitate the dissolution of the ALT-specific multitelomere clusters following recombination-directed telomere synthesis. We further show that KDM2A promotes ALT multitelomere de-clustering by facilitating isopeptidase SENP6-mediated SUMO deconjugation at telomeres. These findings together establish KDM2A as a promising therapeutic target for ALT-dependent cancers. ## A CRISPR-based genetic screen of chromatin regulators required by ALT cells To uncover the molecular vulnerabilities of cells that rely on ALT-directed telomere maintenance, we developed isogenic pairs of ALT cell lines from ATRX-depleted human lung IMR90 fibroblasts using our established immortalization protocol (Fig. 1a)22. Compared to the control IMR90-T lines that were immortalized by telomerase (TERT) expression, the ALT-immortalized IMR90 cells exhibited characteristically high levels of 53BP1-associated telomere dysfunction-induced foci (TIF) (Supplementary Fig. 1a, b), consistent with the notion that ALT telomeres experience chronic replication stress and are intrinsically unstable5,12,23,24.Fig. 1CRISPR-based screens identify KDM2A as selectively essential in ALT-dependent cells.a Western blot analysis of ATRX protein expression in whole-cell lysates prepared from the indicated cells. b Ranking of sgRNAs by log2 fold-change (log2FC) of abundance (ratio of start to endpoint) in ALT#1 cells. the x-axis shows targeting sgRNAs; the y-axis shows the log2FC of each targeting sgRNA after 16 population doublings. c Gene dependency scores (GDS) in IMR90-T (x-axis) versus ALT#1, ALT#2, or ALT#3 cells (y-axis). The GDS was calculated by averaging the log2FC of all sgRNAs targeting that gene. d Heatmap depicts log2FC of average sgRNA abundance of selected genes in indicated cells after 16 population doublings. e, f Competition-based proliferation assay of KDM2A-targeted sgK#1 and sgK#2 in ALT#1 (e) or Cas9-expressing Saos2 cells (f). A GFP reporter is linked to sgRNA expression. Plotted is the %GFP cells (normalized to the d0 measurement) at the indicated time points. The non-targeting sgCtrl was included as a negative and sgRNA targeting PCNA as a positive control. g, h Clonogenic assay (g) and KDM2A western blot analysis (h) of sgCtrl, sgK#1, or sgK#2-transduced ALT#1 cells. Crystal violet staining was conducted on day 20 post-seeding. i, j Crystal violet-based clonogenic survival assay (i) and KDM2A western blot analysis (j) of sgCtrl, sgK#1, or sgK#2-transduced Saos2 cells. Crystal violet staining was conducted on day 24 post-seeding. k Competition-based proliferation assay of GFP-linked sgRNA in Saos2 cells complemented with either empty vector control or the CRISPR-resistant KDM2A synonymous mutants (KDM2A-1r or KDM2A-2r). Note, the KDM2A-1r-transduced cells are resistant to sgK#1 but sensitive to sgK#2 action; the KDM2A-2r-transduced cells are resistant to sgK#2 but sensitive to sgK#1 action. l Tumor growth curves of sgCtrl, sgK#1 or sgK#2-transduced Saos2 cells. Data were expressed as mean ± s.e.m. of six biological replicates; two-tailed unpaired t-test. m Image of tumors collected at week 8 post subcutaneous transplantation. In (e, f, k, data were expressed as mean ± s.e.m. of three independent experiments; two-tailed paired t-test. Source data are provided as a Source Data file. To profile chromatin regulators that are selectively required for ALT-immortalized cells, we constructed a library that contained ~5000 sgRNAs targeting 455 chromatin modifiers, readers, and effectors (~10 sgRNAs/gene) as well as ~100 control sgRNAs. To enhance the targeting efficiency, the sgRNAs were designed using an algorithm linked to protein domain annotation. The genetic screens were conducted by transducing the sgRNA library into three independently established ALT-immortalized IMR90 cells (hereafter referred to as ALT#1, ALT#2, and ALT#3) and two paired TERT-immortalized IMR90-T cells (referred to as IMR90-T#1 and #2). The pools of library-transduced cells were passaged for 16 population doublings before being subjected to next-generation sequencing-based quantification. The relative effect of each sgRNA on cell growth was scored by calculating the log2 fold-change (log2FC) of sgRNA abundances at the beginning and end of the culture periods (Fig. 1b and Supplementary Fig. 2a). The spike-in positive (sgPCNA, sgRPA3, sgCDK1, sgCDK9 sgTIP60, and sgTTF2) and non-targeting negative (sgNeg1 − 100) control sgRNAs served as quality controls to validate the overall accuracy of the screening strategy. For each gene, we calculated the gene dependency score (GDS) by averaging the log2FC of its targeting sgRNAs (4−14 per gene) (Supplementary Data 1). To rank the priority of ALT-selective vulnerabilities, the GDS of individual genes in indicated ALT cell lines (x-axis) were plotted against their scores in control IMR90-T cells (y-axis) (Fig. 1c). As expected, many of the identified gene dependencies were pan-essential and scored comparably in the ALT and control IMR90-T cells. Among those selectively required for ALT cells were several genes with diversified chromatin regulatory functions, including ASM2L, KDM2A, KMT5B, RNF8, and SETDB1 (Fig. 1d). The most prominent hit in this screen was KDM2A, a member of the Jumonji C (Jmjc) domain-containing histone lysine demethylase family that targets lower methylation states of H3K36 (Kme1 and Kme2) but has no known telomere functions25,26. Notably, the majority (7 of 12) of sgRNAs targeting KDM2A included in the library screens caused robust growth inhibition phenotypes in all three ALT lines (log2FC <−5.0) but were ineffective against the paired IMR90-T control cells (Supplementary Fig. 2b), suggesting that KDM2A is selectively essential for ALT cells. To validate the pooled screens, we next analyzed the growth impact of sgRNA targeting through fluorescence-activated cell sorting (FACS)-based competition assays22. Consistent with the screen result, targeting KDM2A using two newly designed sgRNAs (referred to as sgK#1 and sg#2) profoundly suppressed the growth of ALT-positive ALT#1 (Fig. 1e), osteosarcoma Saos2 (Fig. 1f), U2OS (Supplementary Fig. 2c), G292 (Supplementary Fig. 2d), rhabdomyosarcoma Hs729 (Supplementary Fig. 2e), and patient-derived pGBM6 glioblastoma cells (Supplementary Fig. 2f). As a complementary approach, we also conducted crystal violet-based clonogenic growth assays. As expected, sgK#1 or sgK#2-mediated KDM2A ablation in ALT#1 (Fig. 1g, h), Saos2 (Fig. 1i, j), or U2OS cells (Supplementary Fig. 2g, h) greatly inhibited their clonogenic growth. Moreover, the competition- and clonogenic-based proliferation assays demonstrated that complementation of the sgK#1- or sgK#2-resistant KDM2A cDNAs could fully rescue the growth inhibition caused by the respective sgRNAs (Fig. 1k and Supplementary Fig. 2i–l), confirming their on-target effect. Finally, to assess whether KDM2A is essential for in vivo ALT tumor cell growth, Saos2 cells transduced with sgCtrl or KDM2A-specific sgRNAs were subcutaneously grafted into immunocompromised recipient mice. Analysis of tumor growth revealed that inactivation of KDM2A by sgK#1 or sgK#2 significantly inhibited the in vivo tumorigenicity (Fig. 1l, m), indicating that KDM2A is required for ALT-driven tumor propagation. ## KDM2A is not essential for non-ALT cell growth and survival To ascertain whether KDM2A is selectively essential for ALT cells, we next examined the growth effect of KDM2A depletion on the wild-type ATRX cDNA-complemented U2OS cells. In line with the previous study27, re-expression of ATRX in the ATRX-null U2OS cells suppressed ALT and APB formation (Supplementary Fig. 3a–c). Compared to the parental U2OS cells, targeting the ATRX-complemented U2OS with sgK#1 or sgK#2 caused a much-attenuated growth inhibition phenotype (Supplementary Fig. 3d), indicating that KDM2A is selectively essential for ALT cells. Consistently, ectopic expression of wild-type DAXX cDNA in the DAXX-mutant G292 cells suppressed the APB formation and rescued the growth inhibition caused by KDM2A-specific sgRNAs (Supplementary Fig. 3e–h)28,29. To further assess the selectivity of the KDM2A dependency, we next conducted competition-based proliferation assays in a panel of non-ALT cells of diversified tissue origins. Compared to ALT-positive cells in which ablation of KDM2A by sgK#1 or #2 led to 35−100-fold dropout in a period of 4 weeks, targeting KDM2A by the same sgRNAs incurred <2.5-fold growth inhibition in the panel of non-ALT human cell lines, including IMR90-T (Fig. 2a), HeLa of cervical cancer (Fig. 2b), NCI-H1299 of non-small cell lung carcinoma (Fig. 2c), MG63 of osteosarcoma (Supplementary Fig. 4a), MCF7 of breast cancer (Supplementary Fig. 4b), glioma cell lines A172, LN464, and U118 (Supplementary Fig. 4c–e), and primary lung fibroblast IMR90 cells (Supplementary Fig. 4f). As a positive control, sgRNAs targeting PCNA induced growth arrest in all the tested cell lines, ruling out the possibility of inefficient genome editing. These results were further verified by clonogenic assays of sgK#1 or #2-transduced IMR90-T (Fig. 2d, e), HeLa (Fig. 2f, g), or NCI-H1299 cells (Fig. 2h, i).Fig. 2KDM2A is dispensable for non-ALT cells.a–c Competition-based proliferation assays of the indicated sgRNAs in IMR90-T (a), HeLa (b), or NCI-H1299 cells (c). A GFP reporter is linked to sgRNA expression. sgCtrl and sgPCNA were included as a negative or positive control, respectively. Data were expressed as mean ± s.e.m. of three independent experiments; two-tailed paired t-test. d, e Clonogenic assay (d) and KDM2A western blot analysis (e) of sgCtrl, sgK#1, or sgK#2-transduced IMR90-T cells. Crystal violet staining was conducted on day 22 post-seeding. f, g Clonogenic assay (f) and KDM2A western blot analysis (g) of sgCtrl, sgK#1, or sgK#2-transduced HeLa cells. Crystal violet staining was conducted on day 15 post-seeding. h, i Clonogenic assay (h) and KDM2A western blot analysis (i) of sgCtrl, sgK#1, or sgK#2-transduced NCI-H1299 cells. Crystal violet staining was conducted on day 15 post-seeding. j Western blot analysis of KDM2A and ACTB in whole-cell lysates prepared from indicated HeLa cell lines. The KDM2A-depleted lines (clone#1–7) were established from clonally isolated HeLa cells transduced with sgK#1. The sgCtrl-transduced HeLa line was included as a control. SE short exposure, LE long exposure. Source data are provided as a Source Data file. To determine whether KDM2A protein expression could be totally dispensable for non-ALT cell survival, we next applied the CRISPR/Cas9 system to deplete KDM2A in HeLa and LN464 cells. By western blot survey of clonally derived cultures, we identified 5 (out of 17) HeLa and 4 (out of 15) LN464 clones that were completely devoid of KDM2A protein expression (Fig. 2j and Supplementary Fig. 4g). These KDM2A-null cells were viable and proliferated at slightly slower rates than the parental HeLa or LN464 cells (Supplementary Fig. 4h, i), indicating that KDM2A is not a pan-essential gene. By contrast, we were not able to recover any KDM2A-null clones from sgK#1- or sgK#2-transduced ALT#1, Hs729, Saos2, or U2OS cell cultures. These findings support KDM2A as a potential therapeutic target selectively for ALT-dependent human cancers. ## ALT cell growth depends on multiple functional activities of KDM2A As a modular protein, KDM2A consists of a variety of structural motifs that serve different activities (Fig. 3a). To map the critical KDM2A protein domains for its ALT-supporting function, we synthesized a CRISPR exon-tilling library that comprised 492 sgRNAs targeting the entire KDM2A open reading frame (Supplementary Data 2). The tiling library was transduced into Saos2, ALT#1, ALT#2, and control IMR90-T cells. Using massively parallel sequencing, we calculated the depletion fold of each sgRNA over 16 population doublings. Among the sgRNAs that induced the most robust growth inhibition phenotypes in ALT-dependent Saos2, ALT#1, and ALT#2, were enriched for those that target the exons encoding the N-terminal demethylase domain, CXXC-type zinc finger (ZnF), or PHD domain of KDM2A (Fig. 3b, c and Supplementary Fig. 5a), indicating that DNA binding and demethylase activities of KDM2A are required for mediating its ALT-supporting function. By contrast, the sgRNAs that target the C-terminal domain, F-Box, and other linker regions had less pronounced growth inhibition effects, suggesting that the E3 ligase activity of KDM2A is likely not required for sustaining ALT cell growth. Finally, none of the sgRNAs induced robust dropout phenotypes in the control IMR90-T cells (Supplementary Fig. 5b), further supporting that KDM2A is a selective vulnerability of ALT-dependent cells. Fig. 3DNA binding and demethylase activities of KDM2A are both required for its ALT-supporting function.a Schematic of full-length KDM2A protein. The domains are labeled and color-coded. b, c CRISPR-based KDM2A tiling assay in ALT#1 (b) and Saos2 cells (c). Plotted is the fold changes of sgRNA abundance (ratio of start to endpoint) after 16 population doublings in culture. x-axis shows targeting sgRNAs and the domain location of each sgRNA within the KDM2A protein is indicated by colors; the y-axis shows the fold changes of each targeting sgRNA following the culture period. d Western blot analysis of KDM2A, Flag, and ACTB in whole-cell lysates prepared from Saos2 cells transduced with indicated constructs. The Flag-tagged wild-type and KDM2A mutants were generated from the CRISPR-resistant KDM2A-1r construct. e Competition-based proliferation assay of KDM2A-targeted sgK#1 (linked with GFP expression) in Saos2 cells transduced with the indicated constructs. The bar graphs are expressed as mean ± s.e.m. of three independent experiments; two-tailed paired t-test. f Clonogenic assay of sgK#1 in Saos2 cells transduced with the indicated CRISPR-resistant wild-type or KDM2A mutants. Crystal violet staining was conducted on day 24 post-cell seeding. Source data are provided as a Source Data file. To validate the exon-tiling scan results, we next transduced Sao2 cells with sgK#1-resistant KDM2A cDNAs encoding wild-type or mutants defective of DNA binding (S603D)30,31, PHD domain structural integrity (C$\frac{620}{623}$A)30, HP1 protein interaction (V801A/L803A)30, or demethylase activity (D214A or N298A)25. Despite being expressed at comparable levels, western blot analysis found that only cells transduced with wild-type or the V801A/L803A mutant showed visible H3K36me2 reduction as compared to the vector transduced control cells (Fig. 3d). Consistently, competition and clonogenic-based proliferation assays revealed that complementation of wild-type or V801A/L803A, but not other mutants, rescued sgK#1-induced growth inhibition in Saos2 cells (Fig. 3e, f), indicating that HP1 protein interaction activity is dispensable for its ALT-supporting function. The other identified KDM2A essential region in the exon-tilling scan of ALT cells was the C-terminal leucine-rich repeats (LRR). To test its ALT-supporting function, we constructed a KDM2A ΔLRR mutant (aa 1-aa 945) that is deleted of the LRR motif. Consistent with the exon-tiling scan results, a competition-based proliferation assay in Saos2 cells found that complementation of the CRISPR-resistant ΔLRR mutant was not able to rescue the sgK#1-induced growth inhibition phenotype (Supplementary Fig. 6a, b). ## KDM2A physically binds to ALT telomeres In human cancers and immortalized cell lines, ALT activation is closely associated with genetic alterations that affect the histone H3.3 chaperone ATRX-DAXX complex11,12,19,32. Consistently, a western blot survey of cell lines used in this study revealed that ATRX protein was broadly expressed in the non-ALT cells but absent in ALT-dependent cells (Fig. 4a). By contrast, KDM2A protein was expressed across the panel of cell lines regardless of their tissues of the origin or ATRX expression status, suggesting that their selective KDM2A dependency is not due to differential protein expression. Fig. 4KDM2A regulates H3K36me2 at ALT telomeres.a Western blot analysis of ATRX protein expression in whole-cell lysates prepared from the indicated non-ALT and ALT-dependent (ALT+) cells. b Western blot analysis of ATRX expression in whole-cell lysates prepared from control or ATRX-depleted IMR90-T cells. The ATRX-depleted lines (dATRX clone#1–7) were established from clonally isolated IMR90-T cells transduced with ATRX-targeted sgRNA. The lysates from U2OS and ALT#1 cells were included as negative controls of ATRX expression. c Representative immuno-FISH images of 53BP1 and telomeres in ALT#1, IMR90-T (T#1), or ATRX-depleted IMR90-T (dATRX#1 and dATRX#2) cells. Scale bar, 10 μm. d Percentages of cells containing ≥4 53BP1-associated telomere dysfunction-induced foci (TIFs). Data were expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. e Competition-based proliferation assay of KDM2A-targeted sgK#1 (linked with GFP expression) in the indicated cell lines. Data were expressed as mean ± s.e.m. of three independent experiments. f Western blot analysis of Flag and ACTB (loading control) using whole-cell lysates prepared from control or Flag-tagged wild-type KDM2A-transduced HeLa, ALT#1, or Saos2 cells. g, h Telomere dot-blot analysis (g) and quantification (h) of anti-Flag or IgG chromatin immunoprecipitation (ChIP) in the indicated cell lines. The IgG ChIP was included as a control for non-specific signals. The input and ChIP DNAs processed against the indicated antibodies were assayed by dot-blotting and hybridized with a 32P-labeled TelG probe. The relative enrichment was calculated after the normalization of ChIP DNA signals to the respective input DNA signals. Data were expressed as mean ± s.e.m. of three independent experiments; two-tailed paired t-test. i, j Telomere dot-blot analysis (i) and quantification (j) of anti-H3K36me2, anti-H3, or IgG ChIP in sgCtrl or sgK#1-transduced Saos2 cells. The relative enrichment was calculated after normalization to the respective ChIP DNA signals of sgCtrl-transduced samples. Data were expressed as mean ± s.e.m. of three independent experiments; two-tailed paired t-test. k Western blot analysis of KDM2A, H3K36me2, and H3 in cell lysates prepared from sgCtrl or sgK#1-transduced Saos2 cells. Source data are provided as a Source Data file. To explore whether KDM2A is a synthetic vulnerability of ATRX deficiency independently of its ALT status, we generated the ATRX knockout cells from TERT-transduced IMR90-T or LN464-T cells (Fig. 4b and Supplementary Fig. 7a). These clonally derived ATRX-null IMR90-T or LN464-T cells (referred to as dATRX#1 and #2) were viable and proliferated at slightly slower rates than their respective control cells (Supplementary Fig. 7b, c). Consistent with our previous findings22, these TERT-overexpressed ATRX-null cells exhibited low levels of telomere dysfunction and TIF formation (Fig. 4c, d and Supplementary Fig. 7d, e). Competition-based proliferation assays further revealed that compared to ALT#1 cells, where KDM2A inhibition caused robust growth arrest, targeting these ATRX-knockout IMR90-T or LN464-T cells by KDM2A-specific sgRNAs only moderately affected their fitness (Fig. 4e and Supplementary Fig. 7f), suggesting that KDM2A is not a synthetic lethal vulnerability of simple ATRX deficiency. Among the 21 histone lysine demethylases reported in the literature, notably, our screen identified KDM2A as the only one essential for ALT cell growth (Supplementary Fig. 7g). To examine whether KDM2A may act physically at telomeres, we transduced Flag-tagged KDM2A into Saos2, ALT#1 and HeLa cells. Consistent with a previous study33, telomere dot-blot analysis of anti-Flag chromatin immunoprecipitation (ChIP) revealed significant Flag-KDM2A enrichment at telomeres, preferentially in ALT cells (Fig. 4f–h). By comparison, anti-Flag ChIP/Alu dot-blot analysis did not detect the ALT-preferential enrichment (Supplementary Fig. 8a, b). Finally, anti-H3K36me2 ChIP/telomere dot-blot analysis of KDM2A-depleted Saos2 and ALT#1 cells revealed significantly increased levels of telomere H3K36me2 as compared to the sgCtrl-transduced cells (Fig. 4i–k and Supplementary Fig. 8c–e). These results together support direct involvement of KDM2A in ALT-directed telomere maintenance. ## KDM2A facilitates the chromosomal segregation of ALT cells ALT cells are characterized by persistent telomere DNA replication stress and rely on recombination-based DNA repair pathways to elongate their telomeres5,8,23. Cell cycle analysis of KDM2A-depleted ALT#1 or Saos2 cells found a markedly elevated G2/M phase accumulation as compared to their respective control cells (Fig. 5a and Supplementary Fig. 9a, b). Despite its ALT-supporting function, surprisingly, depletion of KDM2A did not seem to significantly affect many ALT-associated activities. For example, quantitation of control and KDM2A-depleted ALT#1 or Saos2 cells showed comparable levels of APB formation (Fig. 5b, c and Supplementary Fig. 9c, d), a hallmark of ALT activation34. Similarly, analysis of telomere length and C-rich extrachromosomal telomere repeats (C-circles) in control and KDM2A-depleted ALT#1 or Saos2 cells also did not reveal significant changes in telomere length heterogeneity and C-circle formation (Fig. 5d, e and Supplementary Fig. 9e, f).Fig. 5KDM2A depletion disrupts ALT chromosomal segregation and mitotic division.a Cell cycle distribution analysis of sgCtrl, sgK#1 or sgK#2-transduced ALT#1 cells. b Representative immuno-FISH images of ALT-associated PML bodies (APBs) in sgCtrl or sgK#1-transduced ALT#1 cells. Scale bar = 10 µm. c Percentages of cells containing ≥4 APBs are expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. d Telomere restriction fragment analysis of telomere length of sgCtrl, sgK#1, or sgK#2-transduced ALT#1 cells. Genomic DNAs prepared from the indicated cells were assayed by a 32P-labeled TelG probe. e C-circle assays of sgCtrl, sgK#1 or sgK#2-transduced ALT#1 cells. Genomic DNAs were prepared from the indicated cells and assayed by a 32P-labeled TelC probe. f Representative immuno-FISH images of EdU colocalized APBs (EdU-APBs) in sgCtrl or sgK#1-transduced ALT#1 cells. Cells were synchronized in G2 with sequential thymidine and CDK1 inhibitor treatment and then labeled with EdU for 2 h. EdU was assayed by Click-It reaction, PML was analyzed by IF, and telomeres were detected by FISH. The arrows denote EdU-APB foci. g Percentages of cells containing ≥3 EdU-APB foci. Data were expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. h Representative frames of time-lapse fluorescence live cell imaging of GFP-H2B-expressing ALT#1 cells transduced with sgCtrl or sgK#1. The cells were synchronized in G2 with sequential thymidine and CDK1 inhibitor treatment before timed release into the M phase. Time in minutes is shown in the upper left corners. i Percentages of aberrant mitosis are expressed as mean ± s.e.m. of four independent experiments; two-tailed unpaired t-test. Source data are provided as a Source Data file. To investigate whether KDM2A inactivation affects ALT-directed telomere DNA synthesis, sgCtrl, and sgK#1-transduced ALT#1 cells were synchronized to the G2 phase with sequential thymidine and CDK1 inhibitor Ro-3306 treatment before 5-ethynyl-2´-deoxyuridine (EdU) labeling. Interestingly, the following assay of ALT telomere DNA synthesis in APBs (ATSA) found no significant difference in their levels of telomere EdU incorporation (Fig. 5f, g). A similar observation was also made in comparing sgCtrl and sgK#1-transduced Saos2 cells (Supplementary Fig. 9g, h), suggesting that KDM2A acts downstream of recombination-directed telomere DNA synthesis. KDM2A depletion in ALT cells induces G2/M phase accumulation. This abnormal cell cycle distribution could be caused by cell cycle arrest or dysfunctional mitosis. To monitor mitotic cell division by time-lapse live cell imaging, the sgCtrl or sgK#1-transduced GFP-H2B-expressing ALT#1 cells were synchronized by sequential thymidine and CDK1 inhibitor treatment before timed release. For the control sgRNA-transduced ALT#1 cells, a majority ($\frac{102}{134}$; $76\%$) that had entered mitosis during the imaging periods underwent normal chromosomal segregation and cytokinesis (Supplementary Movie 1). By comparison, of the 112 KDM2A-depleted cells that were tracked for their mitotic division, we found that $74\%$ (83 of 112) of them displayed aberrant chromosomal segregation and eventually underwent mitotic catastrophe, as indicated by their hyper-condensed chromatin aggregates and/or DNA fragmentation (Fig. 5h, i and Supplementary Movie 2). These gross mitotic failures were also visualized in the live imaging of GFP-H2B-expressing ALT#2 cells following KDM2A inactivation (Supplementary Fig. 10a, b and Supplementary Movies 3, 4). By contrast, depletion of KDM2A in GFP-H2B-expressing IMR90-T cells did not significantly affect their mitotic division (Supplementary Fig. 10c, d and Supplementary Movies 5, 6). Mitotic catastrophe represents a regulated mechanism that responds to aberrant mitoses by removing damaged cells from the cycling population35,36. Indeed, analysis of mitotic outcomes of the KDM2A-depleted ALT#1 cells revealed a significantly elevated mitotic death (Supplementary Fig. 10e, f). Western blot analysis further uncovered an increased level of apoptosis in sgK#1-transduced ALT#1 cells, as evidenced by the cleaved PARP1 production (Supplementary Fig. 10g). These findings suggest that KDM2A functions to facilitate mitotic chromosomal segregation of ALT cells. ## KDM2A is required for ALT multitelomere de-clustering ALT-directed telomere synthesis occurs within APBs where recombinogenic telomeres from different chromosomes cluster together8,37,38. This process is cell cycle-regulated, and the clustered telomeres must be disassembled prior to anaphase to ensure proper chromosome segregation38. To test whether KDM2A is involved in the regulation of ALT telomere de-clustering, we synchronized the sgCtrl and sgK#1-transduced ALT#1 cells to the G2 phase. The immuno-FISH analysis of the G2-synchronized cells revealed comparable levels of APB formation (Fig. 6a–c), suggesting that KDM2A loss did not affect telomere clustering and APB assembly. Following timed release from the CDK1 inhibitor block, we found that a majority of the control ALT#1 cells that entered mitosis and were marked by H3-Ser10 phosphorylation (pH3S10) had cleared their multitelomere clusters (Fig. 6a–c). By comparison, ~$73\%$ of mitotic ALT#1 cells that were depleted of KDM2A retained telomere cluster foci and eventually underwent aberrant segregation and mitotic catastrophe (Fig. 6d), indicating a defect in telomere de-clustering. Similar phenotypes were also observed in KDM2A-depleted Saos2 (Supplementary Fig. 11a–d), ALT#2 (Supplementary Fig. 11e–g), and G292 cells (Supplementary Fig. 11h–j).Fig. 6KDM2A promotes ALT telomere de-clustering after recombination.a Representative immuno-FISH images of multitelomere clusters in G2 or mitotic ALT#1 cells transduced with sgCtrl or sgK#1. The G2 cells were synchronized from sequential thymidine and CDK1 inhibitor treatment. The mitotic (M) cells were from G2-synchronized cells upon 1 h release from the CDK1 inhibitor. PML and mitotic marker phospho-Histone H3-Ser10 (pH3S10) were analyzed by IF and telomeres were detected by FISH. b Percentages of cells containing ≥3 multitelomere cluster foci. c Western blot analysis of KDM2A and ACTB in whole-cell lysates prepared from sgCtrl or sgK#1-transduced ALT#1 cells. d Representative images of abnormal mitotic multitelomere clusters in sgK#1-transduced ALT#1 cells. G2-synchronized ALT#1 cells transduced with sgCtrl or sgK#1 were released into mitosis for 2 h. Telomeres were detected by FISH. e Representative immuno-FISH images of TRF1-telomere association in G2 or M-phase ALT#1 cells transduced with sgCtrl or sgK#1. The M-phase cells were from G2-synchronized cells upon 1 h release from the CDK1 inhibitor. TRF1 was analyzed by IF and telomeres were detected by FISH. The arrows denote TRF1-associated telomere foci. f Percentages of mitotic cells containing ≥3 TRF1-associated telomere foci. g Immuno-FISH analysis of BLM and telomeres colocalization in G2- or M-phase ALT#1 cells transduced with sgCtrl or sgK#1. BLM was analyzed by IF and telomeres were detected by FISH. The arrows denote BLM-associated telomere foci. h Percentages of cells containing ≥3 BLM-associated telomere foci. Note, all bar graphs are expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. Scale bar, 10 μm. Source data are provided as a Source Data file. To further validate KDM2A’s role in the regulation of ALT telomere de-clustering, we conducted a reconstitution experiment. Indeed, the complementation of CRISPR-resistant KDM2A cDNAs into ALT#1 cells fully rescued the sgK#1-induced telomere segregation defects (Supplementary Fig. 11k–m). In addition, re-expression of ATRX in ALT#1 cells suppressed KDM2A depletion-induced multitelomere cluster formation and segregation dysfunction (Supplementary Fig. 12a–c), confirming that KDM2A is required for post-recombination ALT telomere de-clustering. To characterize those aberrantly retained M-phase multitelomere clusters, we next analyzed their association with telomere-binding proteins. In line with the previous reports38,39, mitotic telomeres of the control ALT#1 cells were largely condensed and exhibited reduced interaction with telomere-binding protein TRF1 (Fig. 6e, f). By contrast, the aberrantly clustered mitotic telomeres in the KDM2A-depleted ALT#1 cells were still strongly associated with TRF1, suggesting compromised telomere condensation. Similar findings were also obtained in KDM2A-depleted Saos2 cells (Supplementary Fig. 13a, b). Intermediates of homologous recombination are potential sources of chromosome missegregation if not removed before anaphase40,41. The Bloom’s syndrome protein BLM is a RecQ family helicase that drives ALT-associated telomere synthesis and intermediate telomere structure processing41–44. Consistent with our finding that KDM2A inactivation does not affect recombination-directed telomere DNA synthesis, immuno-FISH analysis of G2-synchronized control and KDM2A-depleted ALT#1 or Saos2 cells found comparable levels of telomere-associated BLM foci formation (Fig. 6g, h and Supplementary Fig. 13c, d). Upon mitotic entry following release from the CDK1 inhibitor block, as expected, telomeres in the sgCtrl-transduced control cells was dissociated from BLM binding. Similarly, BLM was also cleared from the aberrant telomere clusters of mitotic ALT#1 or Saos2 cells that were depleted of KDM2A, indicating that those abnormal telomere clusters are not unresolved DNA recombination intermediates. ## KDM2A promotes SENP6-mediated ALT telomere de-SUMOylation SMC$\frac{5}{6}$ complex-activated protein SUMOylation is critical for ALT-directed telomere clustering and APB formation45. Indeed, we found that SMC5 colocalized with APBs and its knock-down in ALT#1 cells blocked APB and telomere cluster foci formation (Supplementary Fig. 14a, b). Moreover, compared to the scrambled control siRNA treatment, transduction of SMC5 siRNA in sgK#1-transduced ALT#1 cells strongly attenuated the aberrant M-phased telomere clusters following KDM2A depletion (Fig. 7a–c), suggesting that KDM2A functions downstream of SMC$\frac{5}{6}$ action. Fig. 7KDM2A facilitates SENP6-mediated ALT telomere de-SUMOylation.a Representative immuno-FISH images of multitelomere clusters in control or SMC5 siRNA transfected mitotic ALT#1 cells expressing sgCtrl or sgK#1. The mitotic (M) cells were prepared from G2-synchronized cells after 1 h release from the CDK1 inhibitor. The arrows denote abnormal mitotic telomere cluster foci. b Percentages of mitotic cells containing ≥3 telomere cluster foci are expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. c Western blot analysis of SMC5 expression in whole-cell lysates prepared from the siCtrl or siSMC5-transfected ALT#1 cells. d Representative immuno-FISH images of telomere SUMOylation in sgCtrl or sgK#1-transduced ALT#1 cells. The mitotic (M) cells were from G2-synchronized cells upon 1 h release from the CDK1 inhibitor. The arrows denote SUMO$\frac{2}{3}$-associated telomere foci. e Percentages of cells containing ≥2 SUMO-associated telomere foci. Data were expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. f Competition-based proliferation assay of the indicated SENP6-targeted sgRNAs (sgSEN#1 and sgSEN#2) on the fitness of IMR90-T, ALT#1 and Saos2 cells. The data are expressed as mean ± s.e.m. of three independent experiments; two-tailed paired t-test. g Western blot analysis of SENP6 in whole-cell lysates prepared from sgCtrl, sgSEN#1 or sgSEN#2-transduced ALT#1 cells. h Immuno-FISH analysis of telomere SUMOylation in mitotic ALT#1 cells transduced with sgCtrl, sgSEN#1, or sgSEN#2. The mitotic cells were from G2-synchronized cells upon 1 h release from the CDK1 inhibitor. The arrows denote SUMO$\frac{2}{3}$-associated telomere foci. i Percentages of mitotic cells containing ≥2 SUMO-associated telomere foci are expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. j Western blot analysis of Flag and ACTB (loading control) in whole-cell lysates prepared from the indicated ALT#1 cells transduced with Flag-tagged SENP6C1030A mutant. k Immuno-FISH analysis of telomere and SENP6C1030A association in Flag-SENP6C1030A -expressing ALT#1 cells transduced with sgCtrl or sgK#1. The arrows denote SENP6C1030A-associated telomere foci. l Percentages of cells containing ≥3 Flag-SENP6C1030A-associated telomere foci are expressed as mean ± s.e.m. of three independent experiments; two-tailed unpaired t-test. m A model illustrating the key events in the ALT pathway. Source data are provided as a Source Data file. ALT telomere clustering and phase-separated APB assembly rely on SUMOylation of telomere-associated proteins41,44,45. Notably, immuno-FISH analysis of G2-synchronized control and KDM2A-depleted ALT#1 cells revealed comparable levels of SUMO$\frac{2}{3}$-associated telomere foci (SUMO-T) formation (Fig. 7d, e), suggesting that KDM2A is not crucial for telomere protein SUMOylation. Upon release from the CDK1 inhibitor block, the control ALT#1 cells that had entered mitosis largely cleared their SUMO-T foci and multitelomere clusters. By contrast, a large portion of sgK#1-transduced mitotic ALT#1 cells maintained their SUMOylation at the aberrantly retained multitelomere foci (Fig. 7d, e), suggesting a role of KDM2A in the regulation of telomere de-SUMOylation. Similar results were also obtained in KDM2A-depleted Saos2 cells (Supplementary Fig. 14c, d). SUMO deconjugation from substrates is catalyzed by a group of SENP family of isopeptidases46. To interrogate the role of de-SUMOylation in ALT-directed telomere maintenance, we assembled a focused sgRNA sub-library targeting the seven human SENP family members (10 sgRNAs/gene) (Supplementary Table 1). The dropout screens of the isogenic ALT (ALT#1, #2, and #3) and paired IMR90-T control cells (IMR90-T#1 and #2) scored SENP6 as the only member that was differentially required by ALT cells (Supplementary Fig. 14e). Competition-based proliferation assays of two independent SENP6-sgRNAs (sgSEN#1 and #2) confirmed its essentiality to ALT-dependent ALT#1 and Saos2 cells (Fig. 7f). Notably, depletion of SENP6 also significantly affected the growth of IMR90-T#1 cells, although to a lesser extent than ALT#1 and Saos2 cells. To examine whether SENP6 loss interferes with the de-SUMOylation at ALT telomeres, the sgCtrl, sgSEN#1, or sgSEN#2-transduced ALT#1 cells were synchronized to the G2 phase by the sequential thymidine and CDK1 inhibitor treatment. Immuno-FISH analysis of the G2-synchronized control and SENP6-depleted ALT#1 cells found comparable levels of SUMO-T foci formation (Supplementary Fig. 14f, g), suggesting that SENP6 is not crucial for ALT telomere SUMOylation or APB formation. After being released from CDK1 inhibitor treatment, the control ALT#1 cells that had entered mitosis largely cleared their telomere SUMOylation and SUMO-T foci. By contrast, a large percentage of SENP6-depleted mitotic ALT#1 cells retained SUMOylated multitelomere clusters (Fig. 7g–i), reminiscent of KDM2A depletion. Similar phenotypes were also observed in Saos2 cells that were depleted of SENP6 expression (Supplementary Fig. 14h–l). These findings indicate that SENP6-mediated de-SUMOylation is required for ALT telomere de-clustering following recombination-directed telomere synthesis. Since the inactivation of KDM2A and SENP6 in ALT cells induced a similar telomere de-clustering phenotype, we next examined whether KDM2A loss might interfere with ALT telomere protein de-SUMOylation through blocking SENP6 recruitment to its substrates. Notably, while SENP6 binds to its substrates transiently, mutation of the catalytic cysteine to alanine (C1030A) stabilizes the interaction47. To test whether SENP6 physically interacts with ALT telomeres, we expressed the SENP6C1030A mutant transgene in ALT#1 cells (Fig. 7j). Analysis of the G2-synchronized ALT#1 cells found that the Flag-tagged SENP6C1030A was indeed localized to the clustered ALT telomeres (Fig. 7k). Importantly, quantitation of the telomere-associated SENP6C1030A foci revealed that KDM2A depletion significantly diminished the telomere recruitment of SENP6C1030A (Fig. 7l). Similar results were also obtained in Saos2 cells depleted of KDM2A expression (Supplementary Fig. 14m–o), suggesting that KDM2A may function to promote ALT telomere de-clustering by facilitating SENP6-mediated telomere de-SUMOylation. Consistently, complementation of CRISPR-resistant cDNAs encoding wild-type, but not KDM2A mutant defective of demethylase (D214A) or chromatin binding (S603D), restored SENP6C1030A telomere recruitment in sgK#1-transduced ALT#1 cells (Supplementary Fig. 15a–c). Finally, it is worth noting that our co-immunoprecipitation analysis uncovered no evidence of physical interaction between KDM2A and SENP6 (Supplementary Fig. 16a). Depletion of SENP6 in ALT#1 cells also did not affect KDM2A protein SUMOylation as compared to the control ALT#1 cells (Supplementary Fig. 16b). These findings suggest that KDM2A may indirectly regulate SENP6 recruitment to ALT telomeres through modulating telomere H3K36 methylation. ## Discussion The implementation of the genetic concept of synthetic lethal or synthetic lethal-like interaction holds great promise in anticancer target discovery. By undertaking chromatin regulator-focused genetic screens in a well-controlled isogenic ALT cellular model system, we identified histone lysine demethylase KDM2A as a selective molecular vulnerability of ALT-dependent cells. We further demonstrate that KDM2A is required for post-recombination ALT multitelomere de-clustering. We show that depletion of KDM2A in ALT cells impairs isopeptidase SENP6-mediated SUMO deconjugation at ALT telomeres. Inactivation of KDM2A or SENP6 compromises post-recombination ALT multitelomere de-SUMOylation and de-clustering that subsequently lead to mitotic chromosome missegregation and cell death. Results from this study thus support efforts to develop KDM2A inhibitors targeting ALT-dependent cancers. Synthetic lethality provides a framework for targeting the loss of function of tumor suppressor and DNA repair genes, as exemplified by the success of PARP inhibitors in the treatment of BRCA$\frac{1}{2}$-deficient tumors48,49. The advance of CRISPR-based screen methodology has further enabled large-scale studies to profile synthetic lethal or synthetic lethal-like interactions in a diverse collection of human cancer cells across many genetic contexts50–52. But despite the fact that ALT is utilized in a substantial fraction (5–$10\%$) of human cancers, ALT cancer cells were rarely included in those screening initiatives. The poor representation is likely due to the paucity of ALT cancer cell lines suitable for large-scale functional genomic screens. To overcome this hurdle, we employed an in vitro ALT-immortalized isogenic model system. The inclusion of isogenic telomerase-positive and ALT-positive cells derived from the same genetic background greatly reduced potential confounding variables such as cell type differences and co-occurring genetic changes, making it easy to infer the genetic interactions from a small panel of paired cell lines. Our study thus demonstrates the applicability of well-controlled isogenic models in identifying and prioritizing targets from genetic interaction screens. Ideal anticancer therapies should demonstrate a robust therapeutic window toward tumor cells by sparing normal cells and limiting off-tumor toxicity. This study identifies KDM2A as a promising therapeutic target that is selective for ALT-dependent cancers. Inactivation of KDM2A in ALT cells causes robust cell growth inhibition by inducing mitotic chromosome missegregation and cell death. By comparison, our competition-based proliferation and total knock-out assays of a panel of non-ALT cells of diverse tissue origins found that KDM2A is largely dispensable for their growth and survival. Consistently, conditional Kdm2a deletions in the mouse myeloid compartment revealed no discernible impact on the development and cell maturation53. These findings suggest a potentially large therapeutic window for future KDM2A-targeted therapies. Emerging evidence suggests that ALT-directed telomere elongation emanates from telomere replication stress and proceeds through a conservative BIR-like pathway5,6,20,22,41. However, the molecular signals that control ALT pathway initiation and termination still remain largely unclear. For example, although APB formation is known to depend on protein SUMOylation45, how telomere replication stress signals proceed to promote APB formation in ALT cells is poorly understood. Equally unclear are the molecular events that govern the multitelomere cluster dissolution after the ALT-directed telomere DNA synthesis. In this study, we found that post-recombination ALT telomere de-clustering requires SENP6-mediated de-SUMOylation. Our data further indicate that this process is regulated by KDM2A-directed telomere H3K36me2 demethylation, underscoring its importance in ALT-directed telomere maintenance. Histone methylation pathways play important roles in orchestrating DNA damage signaling54–56. Among the total of 21 histone lysine demethylases, our genetic screen identifies KDM2A as the only one that is selectively essential for ALT cell growth. Consistent with a previous proteomic study33, our telomere dot-blot assay of chromatin immunoprecipitation demonstrate the physical interaction of KDM2A with ALT telomeres, supporting a direct KDM2A involvement in ALT-directed telomere maintenance. KDM2A is a lysine-specific demethylase that targets lower methylation states of H3K36 (Kme1 and Kme2)25,26. Indeed, our data indicate that depletion of KDM2A in ALT cells leads to increased telomere H3K36me2. Interestingly, KDM2A is not required for ALT-directed telomere DNA synthesis. Instead, we found that KDM2A-mediated demethylation promotes post-recombination ALT telomere de-clustering, at least partly through facilitating SENP6-mediated telomere de-SUMOylation. Given that H3K36 methylation is a major chromatin change following DNA double-strand break (DSB)57,58, we propose a model in which KDM2A functions as an epigenetic eraser of H3K36me2-dependent signaling that safeguards proper chromosome segregation by facilitating SENP6-mediated ALT telomere de-SUMOylation and de-clustering (Fig. 7m). Loss of KDM2A impairs SENP6 recruitment to ALT telomeres and thus their de-clustering, leading to mitotic chromosome missegregation. But despite that our data support a direct KDM2A involvement in ALT-directed telomere maintenance, we cannot exclude the possibility that KDM2A may contribute indirectly through transcriptional regulation of genes involved in the process. Also, noticeably, our co-immunoprecipitation analysis did not find evidence of physical KDM2A and SENP6 interaction. Future studies are needed to sort out the detailed molecular events that follow KDM2A action. Our study nominates KDM2A as a selective molecular vulnerability of ALT-dependent cells. In human cancers, ALT activation is strongly associated with mutations of the chromatin modulator genes ATRX and DAXX9–13. But notably, our study reveals that KDM2A is not a synthetic lethal vulnerability with simple ATRX or DAXX loss. Instead, we found that KDM2A is critical for ALT-directed telomere maintenance, which is utilized by cells deficient in ATRX or DAXX. These findings suggest that KDM2A-mediated demethylation may play an important role in resolving the telomere replication stress and repair-associated intermediate structures following recombination-directed telomere synthesis. As replication stress-induced DNA damage and repair are common features of human cancers, it will be interesting in the future to evaluate the molecular functions of KDM2A and, more broadly, H3K36 methylation and demethylation in those processes. ## Cell lines and plasmids The human cell lines A172, G292, HEK293T, HeLa, Hs792, IMR90, MCF7, MG63, NCI-H1299, Saos2, U118, and U2OS were obtained from ATCC. The IMR90-T, ALT#1, #2, and #3 cells were derived from large T-transformed IMR90 cells as previously described in ref. 22. The patient-derived ATRX-mutant glioblastoma cell line pGBM6 was established from collected tumor specimens after obtaining written informed consent preoperatively and approved by the Institutional Reviewer Boards of the Southwest Hospital (KY2020147). The human glioblastoma cell line LN464 was kindly provided by F. Furnari (University of California, San Diego). The clonally derived KDM2A-knockout HeLa and LN464 cell lines, or ATRX-depleted IMR90-T and LN464-T cells were generated using lentiviruses produced in HEK293T cells with lentiCRISPR-v2 vectors containing KDM2A or ATRX targeting sgRNA and selected with blasticidin as previously described22. For the cDNA expression experiments, full-length cDNAs was cloned into a lentiviral expression vector pLU-IRES-Puro, -Blast, or -Neo vector containing 3xN terminal Flag or C-terminal GFP tag. CRISPR sgRNA-resistant synonymous or functional domain point mutations were introduced by PCR mutagenesis using NEBuilder HiFi DNA Assembly Master Mix (NEB, E2631). Stable cell lines were generated using the pLU vectors and selected with puromycin or blasticidin. Cell lines obtained were not authenticated and were tested negative for mycoplasma. In this study, all cells were cultured with respective media in a humidified 37 °C, $5\%$ CO2 incubator. All the sgRNAs targeting human genes were cloned into lentiCRISPR-v2 (Addgene, #52961), lentiCas9-Blast (Addgene, #52962), LRG2.1 (U6-sgRNA-GFP, Addgene, #108098), or LRPuro (U6-sgRNA-Puromycin) as indicated. Single sgRNAs were cloned by annealing two DNA oligos and ligating into a BsmB1-digested vector. To improve U6 promoter transcription efficiency, an additional 5′ G nucleotide was added to all sgRNA oligo designs that did not already start with a 5′ G. A list of sgRNA information is provided in Supplementary Table 2. ## Animal experiments NSG (NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ) mice were purchased from Jackson laboratories. Mice were group-housed (up to 5 per cage) in individually ventilated cages with ad libitum access to food and acidified water (pH 2.5 to 2.8) in a temperature (22.2 ± 0.5 °C) and humidity (30–$70\%$) controlled facility with $\frac{12}{12}$-h light/dark cycle. The animal care and use program is accredited by AAALAC. All animal experiments were approved by the Weill Cornell Institutional Animal Care and Use Committee. For all mouse studies, mice of either sex were used, and mice were randomly allocated to experimental groups, but blinding was not performed. Mice (aged 5–8 weeks) were age-matched for tumor inoculation. Group sizes were selected on the basis of prior knowledge. For subcutaneous grafting, sgCtrl, sgK#1 or #2-transduced Saos2 cells were resuspended in $50\%$ Matrigel (BD Bioscience, #356231) in PBS and ~5,000,000 cells were injected into each flank of NSG mice. Tumor growth was monitored and measured every 7 days by caliper, and volume was calculated by the formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V=\frac{4{{{{{\rm{\pi }}}}}}}{3} * \frac{a}{2} * \frac{b}{2} * \frac{c}{2}$$\end{document}$V = 4$π3a2b2c2 (V tumor volume; a tumor length; b tumor width; c tumor height). The endpoints were determined on the basis of the level of animal discomfort and tumor sizes. The maximum allowable tumor size is 20 mm in diameter. ## Construction of pooled sgRNA library A gene list of 455 chromatin modification-associated factors in the human genome was manually curated. 4–14 sgRNA were designed against the functional domains of each gene based on the domain sequence information retrieved from NCBI Conserved Domains Database. The design principle of sgRNA was based on previous reports and the sgRNAs with the predicted high off-target effect were excluded59–61. All of the sgRNAs oligos, including positive and negative control sgRNAs, were synthesized in a pooled format (CustomArray Inc) and PCR amplified. The library was constructed by cloning the PCR-amplified products into the BsmB1-digested LRPuro vector. The identity and relative representation of sgRNAs in the pooled plasmids were verified by a deep-sequencing-based analysis. ## Construction of CRISPR-based KDM2A exon-tiling library A list of total of 492 sgRNAs that covered the entire open reading frame of KDM2A was designed by excluding the ones with predicted high off-target effect59–61. All of the sgRNAs oligos, including positive and negative control sgRNAs were synthesized in a pooled format (CustomArray Inc). The library was generated by cloning the PCR-amplified products of the synthesized sgRNA oligos into the BsmB1-digested LRPuro vector. The identity and relative representation of sgRNAs in the pooled plasmids were verified by a deep-sequencing-based analysis. ## Lentiviral transduction Lentiviruses were produced by co-transfection of indicated plasmids and packaging vectors into HEK293T packaging cells, as previously described in ref. 22. In brief, to generate lentivirus, 8 × 106 293T cells in 100 mm tissue culture dishes were transfected with a mixture of 8.5 μg of plasmid DNA, 4 μg of pMD2.G, and 6 μg of psPAX2 packaging vectors, and 45 μl of 1 mg/mL Polyethylenimine (PEI 25000). The media was replaced 6–8 h post-transfection. The virus-containing supernatant were collected at 48 and 72 h post-transfection and pooled. For infection, virus-containing supernatant was mixed with the indicated cell lines supplied with 4 mg/mL polybrene and then centrifuged at 2000 rpm for 30 min at room temperature. Fresh media was changed 24 h post-infection. Antibiotics (10 μg/mL blasticidin, 2 μg/mL puromycin, 500 μg/mL G418, and/or 200 μg/mL hygromycin) were added 48 h post-infection when the selection was required. ## Pooled CRISPR-Cas9 and KDM2A exon-tilling screen The pooled CRISPR-based negative selection and KDM2A exon-tiling screens were carried out as previously described with some modifications62. In brief, Cas9-expressing cells were infected with the lentiviral chromatin modification-associated factor- or KDM2A exon-tiling sgRNA library at an MOI 0.3–0.4 such that every sgRNA is represented in ~1000 cells. Fresh media was changed 24 h post-infection. At 48 h post-infection, puromycin (2 μg/mL) was added, and the infected cells were selected for 48–72 h. To maintain the representation of sgRNAs, the number of infected cells was kept at least 1000 times the sgRNA number in the library during the screen. Start-point cells were harvested at day 5 post-infection and served as a reference representation of the pooled sgRNA library. Cells were cultured for 16 population doublings and harvested as the end time point. Genomic DNA was extracted from cell pellets using QIAamp DNA Blood Maxi Kit (QIAGEN, #51194). The sgRNA cassette was PCR amplified from genomic DNA using Phusion High-Fidelity PCR Master Mix (New England Biolabs, M0531S). The amplified products were pooled and amplified again via PCR using primers harboring Illumina TruSeq adapters with i5 and i7 barcodes, and the resulting libraries were sequenced on an Illumina Nextseq500. The sequencing data was de-multiplexed. The read count of each sgRNA was calculated with no mismatches by comparing it to the reference sequence. Individual sgRNAs with a read count lower than 50 in the initial time point were discarded. ## Competition-based proliferation assay The flow cytometry analysis (FACS)-based dropout assay was performed as previously described in ref. 22. In brief, the indicated Cas9-expressing cell lines were transduced with a lentiviral LRG2.1 (U6-sgRNA-GFP, Addgene, #108098) sgRNA vector that co-expresses a GFP reporter. The percentage of sgRNA-transduced GFP-positive cell population in culture was measured at indicated time points using an LSRII flow cytometer (BD Biosciences). The change in GFP percentage was used to assess the proliferation of sgRNA-transduced cells relative to the non-transduced cells in the culture. ## Clonogenic-based proliferation assays To evaluate the growth rate difference, indicated control and experimental cells were plated at 10,000–20,000 cells per well of a six-well plate. Cells were cultured with respective media in a humidified 37 °C, $5\%$ CO2 incubator for 15–25 days before being fixed with $10\%$ formalin and stained with $0.1\%$ crystal violet. ## Immuno-FISH assay Indirect immunofluorescence (IF) combined with fluorescence in situ hybridization (FISH) analysis was performed as previously described in ref. 22. Briefly, cells grown on coverslips were fixed for 15 min in $4\%$ paraformaldehyde at RT, followed by permeabilization for 10 min in PBS with $0.3\%$ Triton X-100 at RT. After washing with 1x PBS, cells were incubated for 60 min in blocking solution ($1\%$ BSA, $10\%$ FBS, $0.2\%$ fish gelatin, $0.1\%$ Triton X-100, and 1 mM EDTA in 1x PBS) before immuno-staining. Primary antibodies were prepared in blocking solution as following dilutions: 53BP1 (1:500, IHC-00001; Bethyl Laboratories), 53BP1 (1:300, AF1877; R&D Systems), BLM (1:250, Cat# A300-110A; Bethyl Laboratories), Flag (1:200, F1804; Sigma), γH2AX (1:2,000, A300-081A; Bethyl Laboratories), phospho-Histone H3 (Ser10) (1:200, #9701; Cell Signaling), PML (1:200, sc-966; Santa Cruz Biotechnology), PML (1:500, ab96051; Abcam), SMC5 (1:400, A300-236A; Bethyl Laboratories), SUMO$\frac{2}{3}$ (1:250, ab3742; Abcam), and TRF1 (1:200, ab10579; Abcam). After incubated with indicated primary antibodies at RT for 2 h, cells were washed three times with PBST (1x PBS containing $0.1\%$ Tween-20) before being incubated with indicated secondary antibodies conjugated to fluorophores diluted in the same solution for 45 min at RT. After three washes with PBST, cells were fixed again with PFA for 10 min, then washed in 1x PBS, dehydrated in ethanol series (70, 95, $100\%$), and air-dried. Coverslips were denatured for 10 min at 85 °C in a hybridization mix [$70\%$ formamide, 10 mM Tris-HCl, pH 7.2, and $0.5\%$ blocking solution (Roche)] containing 100 nM telomeric PNA probe TelC-FITC (F1009, PNA Bio), and hybridization was continued at RT for 2 h in the dark moisturized chambers. Coverslips were washed three times with Wash solution ($70\%$ formamide, 2x SSC) for 10 min each and in 2x SSC, $0.1\%$ tween-20 three times for 5 min each. During the second wash, cells were stained with DAPI. Slides were mounted with VectorShield (Vector Laboratories). Images were captured with a 60x lens on an Olympus FLUOVIEW laser scanning confocal microscope (Olympus). ## Protein extraction and western blotting Cells were lysed in RIPA buffer (150 mM NaCl, 50 mM Tris, $0.5\%$ Na-Deoxycholate, $0.1\%$ SDS, and $1\%$ NP-40), and an equal amount of protein was resolved using Nupage Novex 4–$12\%$ Bis-Tris Gel (Thermo Fisher Scientific) as previously described in ref. 22. Primary antibodies used were β-ACTIN (ACTB) (1:5,000, #2228; Sigma), ATRX (1:1,000, sc-15408; Santa Cruz Biotechnology), Flag (1:1,000, F1804; Sigma), H3 (1:2,000, ab1791; Abcam), H3K36me2 (1:1,000, 2901; Cell Signaling), H3K36me3 (1:2,000, 61101; Active Motif), KDM2A (1:1,000, A301-476A; Bethyl Laboratories), SENP6 (1:500, HPA024376; Sigma-Aldrich), SMC5 (1:2000, A300-236A; Bethyl Laboratories), and TUBULIN (1:2,000, ab15246; Abcam). Secondary antibodies used were donkey anti-rabbit HRP (1:1,000, sc-2077; Santa Cruz Biotechnology), donkey anti-mouse HRP (1:1,000; sc-2096, Santa Cruz Biotechnology), and donkey anti-goat HRP (1:1,000, sc-2056, Santa Cruz Biotechnology). Antibody signal was detected using the ECL Western Blotting Substrate (W1015, Promega) and X-ray film (F-BX810, Phenix). ## Co-immunoprecipitation The co-immunoprecipitation was conducted according to a standard protocol as described previously in ref. 63 with minor modifications. In brief, ALT#1 cells were transduced with vector control or Flag-tagged SENP6C1030A. Cell lysates were prepared by lysing cells in lysis buffer (50 mM Tris pH 7.5, 120 mM NaCl, $1\%$ NP-40, 5 mM EDTA supplemented with a protease inhibitor cocktail). After quantification, cell lysates with 500 μg of proteins were incubated with IgG or anti-Flag M2 antibodies overnight at 4 °C. The next day, lysates were incubated with Protein G magnetic beads for 2 h and washed three times using lysis buffer. The captured proteins were eluted by boiling them in a 2x loading buffer containing 200 mM dithiothreitol (DTT). The levels of Flag-SENP6C1030A, KDM2A, and PML in input and pull-down samples were then analyzed by western blot. ## Cell cycle synchronization Cells were synchronized in the G2 phase by sequential treatment of thymidine and CDK1 inhibitor Ro-3306 (S7747, Selleckchem). Briefly, cells were first cultured in a medium containing 2 mM thymidine (Sigma-Aldrich) for 21 h. After washing twice with PBS followed by once with growth media, the cells were released into fresh medium for 3 h before treatment with 10 mM CDK1 inhibitor for 12 h. ## Flow cytometry analysis (FACS) Cells collected by trypsin and resuspended in PBS containing 1 mM EDTA were fixed in ice-cold ethanol overnight. Ethanol-fixed single-cell suspensions were stained for DNA analysis with $1\%$ BSA, $0.1\%$ Tween-20, 0.1 mM EDTA, 0.5 mg/mL RNaseA, and 10 µg/mL propidium iodide (PI) in 0.25 ml PBS. Cells were incubated for 30 min at 37 °C and equilibrated at room temperature in the dark for at least 10 min. Cells were analyzed by an LSRII flow cytometer (BD Biosciences). Cell cycle population analysis was conducted with FlowJo v5 software (FlowJo). ## Terminal restriction fragment (TRF) analysis TRF analysis was conducted as previously described in ref. 22. In brief, genomic DNA was prepared using Wizard genomic DNA purification kit (Promega) as manufacturer’s instruction. For telomere length and Southern blot analysis, genomic DNA (~ 5 μg) was digested with AluI + MboI restriction endonucleases, fractionated in a $0.7\%$ agarose gel, denatured, and transferred onto a GeneScreen Plus hybridization membrane (PerkinElmer). The membrane was cross-linked, hybridized at 42 °C with 5′-end-labeled 32P-(TTAGGG)4 probe in Church buffer, and washed twice for 5 min each with 0.2 M wash buffer (0.2 M Na2HPO4 pH 7.2, 1 mM EDTA, and $2\%$ SDS) at room temperature and once for 10 min with 0.1 M wash buffer at 42 °C. The images were analyzed by Phosphor-imager, visualized by Typhoon 9410 Imager (GE Healthcare), and processed with ImageQuant 5.2 software (Molecular Dynamics). ## C-circle assay C-circle assay was performed as described in refs. 21,64 with minor modifications. Briefly, genomic DNA digested with AluI and MboI was cleaned up by phenol-chloroform extraction and precipitation. An aliquot of purified DNA was diluted in nuclease-free water, and concentrations were measured to the indicated quantity (~15 ng/µl) using a Nanodrop spectrophotometer (Thermal Scientific). Sample DNA (30 ng in a total volume of 10 μl) was combined with 10 μl reaction mix [0.2 mg/ml BSA, $0.1\%$ Tween, 0.2 mM each dATP, dGTP, dTTP, 2 × ɸ29 Buffer, and 7.5 U ɸDNA polymerase (NEB)]. The reactions were mixed well, incubated at 30 °C for 8 h, and then at 65 °C for 20 min. The reaction products were diluted to 400 μl with 2 × SSC, dot-blotted onto a 2 × SSC-soaked GeneScreen Plus membrane, and hybridized with a 32P-labeled (CCCTAA)4 probe at 37 °C overnight to detect C-circle amplification products. The blots were washed four times at 37 °C in 0.5 × SSC/$0.1\%$ SDS buffer, exposed to Phosphor-imager screens, visualized by Typhoon 9410 Imager (GE Healthcare Life Sciences), and quantified with ImageQuant 5.2 software (Molecular Dynamics). ## Chromatin immunoprecipitation (ChIP) assays ChIP assays were performed as described previously in ref. 22. Briefly, cells (~1 × 107) were cross-linked in $1\%$ formaldehyde with shaking for 15 min, quenched by the addition of glycine to a final concentration of 0.125 M, and lysed in 1 ml SDS lysis buffer ($1\%$ SDS, 10 mM EDTA, and 50 mM Tris-HCl, pH 8.0) supplemented with 1 mM PMSF and protease inhibitor cocktails (Sigma-Aldrich). The lysates were sonicated with a Diagenode Bioruptor, cleared by centrifugation to remove insoluble materials, and diluted tenfold into IP Buffer ($0.01\%$ SDS, $1.1\%$ Triton X-100, 1.2 mM EDTA, 16.7 mM Tris pH 8.1, 167 mM NaCl, 1 mM PNSF, and protease inhibitors cocktail) for IP reaction at 4 °C overnight. Each immuno-complex was washed five times (1 ml wash, 10 min each) in ChIP-related wash buffer at 4 °C, eluted by the addition of 150 µl Elution buffer (10 mM Tris, pH 8.0, 5 mM EDTA, and $1\%$ SDS) at 65 °C for 30 min, and the elutes were placed at 65 °C for overnight to reverse cross-linking. The elutes was further treated with Proteinase K in a final concentration of 100 µg/ml at 50 °C for 2 h and ChIP DNA was purified by Quick PCR Purification Kit (Life Technologies) following the manufacturer’s instruction. ChIP DNA was denatured, dot-blotted onto GeneScreen Plus blotting membranes (PerkinElmer) and cross-linked at 125 mJ. The Oligonucleotide probe for telomere or Alu repeats was labeled with [32P]-ATP (3,000 Ci/mmol) and T4 nucleotide kinase (New England Biolabs). The membrane was hybridized in Church hybridization buffer containing a 32P-labeled probe at 42 °C overnight, washed three times in 0.04 N Na-phosphate, $1\%$ SDS, 1 mM EDTA at 42 °C, developed with a Typhoon 9410 Imager (GE Healthcare Life Sciences) and quantified with ImageQuant 5.2 software (Molecular Dynamics). Antibodies used in the ChIP assay were anti-Flag (F1804, Sigma), anti-H3K36me2 (2901, Cell Signaling), and mouse IgG (sc2025, Santa Cruz Biotechnology). ## Detection of ALT-directed telomere DNA synthesis To visualize DNA synthesis at telomeres, synchronized G2 cells were incubated with 20 mM EdU for 2 h. Cells were permeabilized, then fixed with a $4\%$ formaldehyde PBS solution. The Click-iT® Alexa Fluor 555 azide reaction was then performed according to the manufacturer’s instructions (Click Chemistry Tools). After washing with 1x PBS, cells were incubated for 60 min in blocking solution ($1\%$ BSA, $10\%$ FBS, $0.2\%$ fish gelatin, $0.1\%$ Triton X-100, and 1 mM EDTA in 1x PBS) before PML immuno-staining at RT for 2 h. After incubation of secondary antibodies and three washes with PBST, cells were fixed again with PFA for 10 min at RT, washed in 1x PBS, dehydrated in ethanol series (70, 95, $100\%$), and air-dried. Coverslips were denatured for 10 min at 85 °C in a hybridization mix [$70\%$ formamide, 10 mM Tris-HCl, pH 7.2, and $0.5\%$ blocking solution (Roche)] containing 100 nM telomeric PNA probe TelC-FITC (F1009, PNA Bio), and hybridization was continued for 2 h at room temperature in the dark moisturized chambers. Coverslips were washed three times with Wash solution ($70\%$ formamide, 2x SSC) for 10 min each and in 2x SSC, $0.1\%$ tween-20 three times for 5 min each. During the second wash, cells were stained with DAPI. Slides were mounted with VectorShield (Vector Laboratories). Images were captured with a 60x lens on an Olympus FLUOVIEW laser scanning confocal microscope (Olympus). ## Live cell imaging H2B-GFP-expressing control or KDM2A targeting sgRNA-transduced cells were seeded at 8 × 104 cells per well in fibronectin-coated 12-well 1.5 mm glass-bottom wells (Cellvis). Following culture in DMEM medium supplemented with $15\%$ FBS for 24 h, cells were synchronized in the G2 phase by sequential treatment of thymidine and CDK1 inhibitor Ro-3306 (S7747, Selleckchem). Cells were firstly cultured in a medium containing 2 mM thymidine (Sigma-Aldrich) for 24 h. After washing twice with PBS followed by once with growth media, the cells were then released into fresh medium for 2 h before treatment with 10 mM CDK1 inhibitor for 16 h. Finally, cells were washed twice with PBS and once with growth media before being subjected to live cell imaging with Zeiss Cell Observer (ZEISS). Cells were monitored for 8 h at 5 min intervals. Movies are output by Zeiss ZEN software (ZEISS). ## Statistics and reproducibility Details regarding quantitation and statistical analysis are provided in the figures and figure legends. We determined experimental sample sizes on the basis of preliminary data. All results are expressed as mean ± s.e.m. GraphPad Prism software (version 7.0e) was used for all statistical analysis. The normal distribution of the sample sets was determined before applying Student’s two-tailed t-test for two group comparisons. Differences were considered significant when $P \leq 0.05.$ For western blotting micrographs, the experiments were repeated three times independently with similar results, and representative images/blots are shown. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Information Reporting Summary Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Movie 1 Supplementary Movie 2 Supplementary Movie 3 Supplementary Movie 4 Supplementary Movie 5 Supplementary Movie 6 The online version contains supplementary material available at 10.1038/s41467-023-37480-2. ## Source data Source Data ## Peer review information Nature Communications thanks Makoto Tsuneoka and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. ## References 1. de Lange T. How telomeres solve the end-protection problem. Science* (2009) 326 948-952. DOI: 10.1126/science.1170633 2. Olovnikov AM. A theory of marginotomy. The incomplete copying of template margin in enzymic synthesis of polynucleotides and biological significance of the phenomenon. J. Theor. Biol. (1973) 41 181-190. DOI: 10.1016/0022-5193(73)90198-7 3. Bryan TM, Englezou A, Dalla-Pozza L, Dunham MA, Reddel RR. Evidence for an alternative mechanism for maintaining telomere length in human tumors and tumor-derived cell lines. Nat. Med. (1997) 3 1271-1274. DOI: 10.1038/nm1197-1271 4. Cesare AJ, Reddel RR. Alternative lengthening of telomeres: models, mechanisms and implications. Nat. Rev. Genet. (2010) 11 319-330. DOI: 10.1038/nrg2763 5. Dilley RL. Break-induced telomere synthesis underlies alternative telomere maintenance. Nature (2016) 539 54-58. DOI: 10.1038/nature20099 6. Roumelioti FM. Alternative lengthening of human telomeres is a conservative DNA replication process with features of break-induced replication. EMBO Rep. (2016) 17 1731-1737. DOI: 10.15252/embr.201643169 7. Verma P. RAD52 and SLX4 act nonepistatically to ensure telomere stability during alternative telomere lengthening. Genes Dev. (2019) 33 221-235. DOI: 10.1101/gad.319723.118 8. Zhang JM, Yadav T, Ouyang J, Lan L, Zou L. Alternative lengthening of telomeres through two distinct break-induced replication pathways. Cell Rep. (2019) 26 955-968.e953. DOI: 10.1016/j.celrep.2018.12.102 9. Ceccarelli M. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell (2016) 164 550-563. DOI: 10.1016/j.cell.2015.12.028 10. Cheung NK. Association of age at diagnosis and genetic mutations in patients with neuroblastoma. JAMA (2012) 307 1062-1071. DOI: 10.1001/jama.2012.228 11. Heaphy CM. Altered telomeres in tumors with ATRX and DAXX mutations. Science (2011) 333 425. DOI: 10.1126/science.1207313 12. Lovejoy CA. Loss of ATRX, genome instability, and an altered DNA damage response are hallmarks of the alternative lengthening of telomeres pathway. PLoS Genet (2012) 8 e1002772. DOI: 10.1371/journal.pgen.1002772 13. Schwartzentruber J. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature (2012) 482 226-231. DOI: 10.1038/nature10833 14. Drane P, Ouararhni K, Depaux A, Shuaib M, Hamiche A. The death-associated protein DAXX is a novel histone chaperone involved in the replication-independent deposition of H3.3. Genes Dev. (2010) 24 1253-1265. DOI: 10.1101/gad.566910 15. Goldberg AD. Distinct factors control histone variant H3.3 localization at specific genomic regions. Cell (2010) 140 678-691. DOI: 10.1016/j.cell.2010.01.003 16. Law MJ. ATR-X syndrome protein targets tandem repeats and influences allele-specific expression in a size-dependent manner. Cell (2010) 143 367-378. DOI: 10.1016/j.cell.2010.09.023 17. Lewis PW, Elsaesser SJ, Noh KM, Stadler SC, Allis CD. Daxx is an H3.3-specific histone chaperone and cooperates with ATRX in replication-independent chromatin assembly at telomeres. Proc. Natl Acad. Sci. USA (2010) 107 14075-14080. DOI: 10.1073/pnas.1008850107 18. Voon HP. ATRX plays a key role in maintaining silencing at interstitial heterochromatic loci and imprinted genes. Cell Rep (2015) 11 405-418. DOI: 10.1016/j.celrep.2015.03.036 19. Barthel FP. Systematic analysis of telomere length and somatic alterations in 31 cancer types. Nat. Genet. (2017) 49 349-357. DOI: 10.1038/ng.3781 20. Lu R. The FANCM-BLM-TOP3A-RMI complex suppresses alternative lengthening of telomeres (ALT). Nat. Commun. (2019) 10 2252. DOI: 10.1038/s41467-019-10180-6 21. O’Sullivan RJ. Rapid induction of alternative lengthening of telomeres by depletion of the histone chaperone ASF1. Nat. Struct. Mol. Biol. (2014) 21 167-174. DOI: 10.1038/nsmb.2754 22. Li F. ATRX loss induces telomere dysfunction and necessitates induction of alternative lengthening of telomeres during human cell immortalization. EMBO J. (2019) 38 e96659. DOI: 10.15252/embj.201796659 23. Dunham MA, Neumann AA, Fasching CL, Reddel RR. Telomere maintenance by recombination in human cells. Nat. Genet. (2000) 26 447-450. DOI: 10.1038/82586 24. Flynn RL. Alternative lengthening of telomeres renders cancer cells hypersensitive to ATR inhibitors. Science (2015) 347 273-277. DOI: 10.1126/science.1257216 25. Cheng Z. A molecular threading mechanism underlies Jumonji lysine demethylase KDM2A regulation of methylated H3K36. Genes Dev. (2014) 28 1758-1771. DOI: 10.1101/gad.246561.114 26. Tsukada Y. Histone demethylation by a family of JmjC domain-containing proteins. Nature (2006) 439 811-816. DOI: 10.1038/nature04433 27. Clarke J. Multicenter, phase 1, dose escalation study of hypofractionated stereotactic radiation therapy with bevacizumab for recurrent glioblastoma and anaplastic astrocytoma. Int. J. Radiat. Oncol. Biol. Phys. (2017) 99 797-804. DOI: 10.1016/j.ijrobp.2017.06.2466 28. Mason-Osann E. Identification of a novel gene fusion in ALT positive osteosarcoma. Oncotarget (2018) 9 32868-32880. DOI: 10.18632/oncotarget.26029 29. Yost KE. Rapid and reversible suppression of ALT by DAXX in osteosarcoma cells. Sci. Rep. (2019) 9 4544. DOI: 10.1038/s41598-019-41058-8 30. Borgel J. KDM2A integrates DNA and histone modification signals through a CXXC/PHD module and direct interaction with HP1. Nucleic Acids Res. (2017) 45 1114-1129. PMID: 28180290 31. Cierpicki T. Structure of the MLL CXXC domain-DNA complex and its functional role in MLL-AF9 leukemia. Nat. Struct. Mol. Biol. (2010) 17 62-68. DOI: 10.1038/nsmb.1714 32. Sieverling L. Genomic footprints of activated telomere maintenance mechanisms in cancer. Nat. Commun. (2020) 11 733. DOI: 10.1038/s41467-019-13824-9 33. Garcia-Exposito L. Proteomic profiling reveals a specific role for translesion DNA polymerase η in the alternative lengthening of telomeres. Cell Rep. (2016) 17 1858-1871. DOI: 10.1016/j.celrep.2016.10.048 34. Yeager TR. Telomerase-negative immortalized human cells contain a novel type of promyelocytic leukemia (PML) body. Cancer Res. (1999) 59 4175-4179. PMID: 10485449 35. Masamsetti VP. Replication stress induces mitotic death through parallel pathways regulated by WAPL and telomere deprotection. Nat. Commun. (2019) 10 4224. DOI: 10.1038/s41467-019-12255-w 36. Vitale I, Galluzzi L, Castedo M, Kroemer G. Mitotic catastrophe: a mechanism for avoiding genomic instability. Nat. Rev. Mol. Cell Biol. (2011) 12 385-392. DOI: 10.1038/nrm3115 37. Cho NW, Dilley RL, Lampson MA, Greenberg RA. Interchromosomal homology searches drive directional ALT telomere movement and synapsis. Cell (2014) 159 108-121. DOI: 10.1016/j.cell.2014.08.030 38. Draskovic I. Probing PML body function in ALT cells reveals spatiotemporal requirements for telomere recombination. Proc. Natl Acad. Sci. USA (2009) 106 15726-15731. DOI: 10.1073/pnas.0907689106 39. Zhu Q. GNL3L stabilizes the TRF1 complex and promotes mitotic transition. J. Cell Biol. (2009) 185 827-839. DOI: 10.1083/jcb.200812121 40. Chan YW, Fugger K, West SC. Unresolved recombination intermediates lead to ultra-fine anaphase bridges, chromosome breaks and aberrations. Nat. Cell Biol. (2018) 20 92-103. DOI: 10.1038/s41556-017-0011-1 41. Zhang JM, Genois MM, Ouyang J, Lan L, Zou L. Alternative lengthening of telomeres is a self-perpetuating process in ALT-associated PML bodies. Mol. Cell (2021) 81 1027-1042.e1024. DOI: 10.1016/j.molcel.2020.12.030 42. Barefield C, Karlseder J. The BLM helicase contributes to telomere maintenance through processing of late-replicating intermediate structures. Nucleic Acids Res. (2012) 40 7358-7367. DOI: 10.1093/nar/gks407 43. Chan KL, North PS, Hickson ID. BLM is required for faithful chromosome segregation and its localization defines a class of ultrafine anaphase bridges. EMBO J. (2007) 26 3397-3409. DOI: 10.1038/sj.emboj.7601777 44. Min J, Wright WE, Shay JW. Clustered telomeres in phase-separated nuclear condensates engage mitotic DNA synthesis through BLM and RAD52. Genes Dev. (2019) 33 814-827. DOI: 10.1101/gad.324905.119 45. Potts PR, Yu H. The SMC5/6 complex maintains telomere length in ALT cancer cells through SUMOylation of telomere-binding proteins. Nat. Struct. Mol. Biol. (2007) 14 581-590. DOI: 10.1038/nsmb1259 46. Hickey CM, Wilson NR, Hochstrasser M. Function and regulation of SUMO proteases. Nat. Rev. Mol. Cell Biol. (2012) 13 755-766. DOI: 10.1038/nrm3478 47. Hattersley N, Shen L, Jaffray EG, Hay RT. The SUMO protease SENP6 is a direct regulator of PML nuclear bodies. Mol. Biol. Cell (2011) 22 78-90. DOI: 10.1091/mbc.e10-06-0504 48. Huang A, Garraway LA, Ashworth A, Weber B. Synthetic lethality as an engine for cancer drug target discovery. Nat. Rev. Drug Discov. (2020) 19 23-38. DOI: 10.1038/s41573-019-0046-z 49. Setton J. Synthetic lethality in cancer therapeutics: the next generation. Cancer Discov. (2021) 11 1626-1635. DOI: 10.1158/2159-8290.CD-20-1503 50. Behan FM. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature (2019) 568 511-516. DOI: 10.1038/s41586-019-1103-9 51. Ghandi M. Next-generation characterization of the cancer cell line encyclopedia. Nature (2019) 569 503-508. DOI: 10.1038/s41586-019-1186-3 52. Tsherniak A. Defining a cancer dependency map. Cell (2017) 170 564-576.e516. DOI: 10.1016/j.cell.2017.06.010 53. Chen L. Kdm2a deficiency in macrophages enhances thermogenesis to protect mice against HFD-induced obesity by enhancing H3K36me2 at the Pparg locus. Cell Death Differ. (2021) 28 1880-1899. DOI: 10.1038/s41418-020-00714-7 54. Gong F, Miller KM. Histone methylation and the DNA damage response. Mutat. Res. Rev. Mutat. Res. (2019) 780 37-47. DOI: 10.1016/j.mrrev.2017.09.003 55. Millan-Zambrano G, Burton A, Bannister AJ, Schneider R. Histone post-translational modifications - cause and consequence of genome function. Nat. Rev. Genet. (2022) 23 563-580. DOI: 10.1038/s41576-022-00468-7 56. Zhao S, Allis CD, Wang GG. The language of chromatin modification in human cancers. Nat. Rev. Cancer (2021) 21 413-430. DOI: 10.1038/s41568-021-00357-x 57. Fnu S. Methylation of histone H3 lysine 36 enhances DNA repair by nonhomologous end-joining. Proc. Natl Acad. Sci. USA (2011) 108 540-545. DOI: 10.1073/pnas.1013571108 58. Nguyen HV. Histone methyltransferase MMSET promotes AID-mediated DNA breaks at the donor switch region during class switch recombination. Proc. Natl Acad. Sci. USA (2017) 114 E10560-E10567. DOI: 10.1073/pnas.1701366114 59. Doench JG. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. (2016) 34 184-191. DOI: 10.1038/nbt.3437 60. Hsu PD. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. (2013) 31 827-832. DOI: 10.1038/nbt.2647 61. Sanson KR. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. (2018) 9 5416. DOI: 10.1038/s41467-018-07901-8 62. Wang T, Lander ES, Sabatini DM. Viral packaging and cell culture for CRISPR-based screens. Cold Spring Harb. Protoc. (2016) 2016 pdb prot090811. DOI: 10.1101/pdb.prot090811 63. Zheng H. The prolyl isomerase Pin1 is a regulator of p53 in genotoxic response. Nature (2002) 419 849-853. DOI: 10.1038/nature01116 64. Henson JD. DNA C-circles are specific and quantifiable markers of alternative-lengthening-of-telomeres activity. Nat. Biotechnol. (2009) 27 1181-1185. DOI: 10.1038/nbt.1587
--- title: Transgelin promotes lung cancer progression via activation of cancer-associated fibroblasts with enhanced IL-6 release authors: - Chanjun Sun - Kaishang Zhang - Chen Ni - Jiajia Wan - Xixi Duan - Xiaohan Lou - Xiaohan Yao - Xiangnan Li - Ming Wang - Zhuoyu Gu - Pengyuan Yang - Zhenzhen Li - Zhihai Qin journal: Oncogenesis year: 2023 pmcid: PMC10060230 doi: 10.1038/s41389-023-00463-5 license: CC BY 4.0 --- # Transgelin promotes lung cancer progression via activation of cancer-associated fibroblasts with enhanced IL-6 release ## Abstract Cancer-associated fibroblasts (CAFs), the principal constituent of the heterogenous tumor microenvironment, have been shown to promote tumor progression; however, the underlying mechanism is still less clear. Here, we find that transgelin (TAGLN) protein levels increased in primary CAFs isolated from human lung cancer, compared with those in paired normal fibroblasts. Tumor microarrays (TMAs) revealed that increased stromal TAGLN levels correlates with more lymphatic metastasis of tumor cells. In a subcutaneous tumor transplantation model, overexpression of Tagln in fibroblasts also increased tumor cell spread in mice. Further experiments show that Tagln overexpression promoted fibroblast activation and mobility in vitro. And TAGLN facilitates p-p65 entry into the nucleus, thereby activating the NF-κB signaling pathway in fibroblasts. Activated fibroblasts promote lung cancer progression via enhancing the release of pro-inflammatory cytokines, especially interleukine-6 (IL-6). Our study revealed that the high levels of stromal TAGLN is a predictive risk factor for patients with lung cancer. Targeting stromal TAGLN may present an alternative therapeutic strategy against lung cancer progression. ## Introduction Lung cancer is the most common cause of cancer-related death worldwide, accounting for ~$27\%$ of all cancer-related deaths annually [1]. The overall survival rate for patients with lung cancer remains unsatisfactory; less than $7\%$ survive more than 10 years after diagnosis, independent of the cancer stage [2]. Current treatments and therapies are insufficient to reduce this mortality rate. Diagnosis at an advanced stage and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms underlying lung cancer progression. Therefore, it is particularly important to discover new markers for the early diagnosis of lung cancer. Cancers are composed of cancer cells and many types of non-cancerous cells, including cancer-associated fibroblasts (CAFs), cancer-associated macrophages, and lymphocytes. These cells, together with the tumor vasculature and extracellular matrix, constitute the tumor microenvironment (TME). The TME is critical not only for cancer development and progression but also for tumor immunity and chemotherapy resistance [3]. CAFs are often the most abundant stromal cells and play a crucial and complex role in cancer development. Although the pro- or anti-tumor effects of CAFs remain controversial, it is generally accepted that CAFs can promote tumor growth [4], disease progression [5], and chemotherapy resistance [6]. Moreover, CAFs have been shown to increase tumor aggressiveness (survival, invasion, and chemoresistance) by secreting soluble factors, including cytokines, growth factors, and chemokines [7–9]. Transgelin (TAGLN), also called SM22 and first identified in 1987, is an actin-binding protein that belongs to the calponin family [10]. TAGLN promotes the aggregation of G-actin to F-actin by regulating actin cytoskeleton dynamics [11]. Zhong et al. found that activated TAGLN-actin could modulate the cytoskeleton and promote cell contraction [12]. As an actin crosslinking protein, TAGLN participates in cell movement by improving the formation of podosomes and several biological processes related to cancer progression, such as differentiation, proliferation, migration/invasion, and apoptosis. Chen et al. found that in bladder cancer, TAGLN is highly expressed and correlated with prognosis [13]. Recently, TAGLN was reported to be a poor prognostic factor in advanced stage colorectal cancer, promoting tumor growth and metastasis [14]. Increased levels of TAGLN have also been associated to poor prognosis and metastasis in other types of cancer, such as esophageal [15, 16], pancreatic [17, 18], lung [19] and colorectal cancers [20]. Importantly, TAGLN is expressed not only in epithelial cells but also in several different cell types such as fibroblasts, endothelial cells, and immune cells [21]. Recent studies highlighted the TAGLN functions in fibroblasts and their crosstalk with cancer cells. Stromal TAGLN levels are enhanced during gastric cancer progression and related to tumor metastasis through increased matrix metalloproteinase-2 signaling [22]. Rho et al. observed that TAGLN upregulation was strictly localized to the tumor-induced reactive myofibroblastic stromal tissue compartment in human lung adenocarcinoma tissue [23]. TAGLN has also been identified as a fibroblast-specific biomarker of poor prognosis in colorectal cancer (CRC) in a single-cell multiomics sequencing study, with 21 patients with CRC and 6 cancer-free individuals [24]. Elsafadi et al. evaluated 275 tumor and 349 non-tumor tissues for TAGLN expression using the TCGA/GTEx COAD dataset [14]. Though TAGLN was found to be downregulated in CRC, increased TAGLN levels were associated with advanced CRC stages and correlated with a poor overall survival and disease-free survival [14]. Despite these studies, whether TAGLN promotes development of CAF phenotype in normal fibroblasts (NFs) and the mechanism by which TAGLN-positive CAFs modulate tumor, especially lung cancer, progression remain largely unclear. Here, we show that TAGLN overexpression can promote tumor spreading and tumor cell migration/invasion through the release of pro-inflammatory cytokines, namely interleukin-6 (IL-6), via NF-κB signaling pathway activation. Targeting TAGLN in CAFs may be a promising strategy for lung cancer therapy. ## Patient samples Tumors and adjacent normal tissues (at least 5 cm from the tumor), resected surgically from patients with lung cancer, were obtained from the Department of Thoracic Surgery of the First Affiliated Hospital of Zhengzhou University (Zhengzhou, China). Lung cancer samples were processed for CAF isolation after informed consent was obtained, in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University. ## EGFR-driven spontaneous lung cancer model CCSP rtTA/EGFRL858R (C/L858R) mice, a previously described mouse model expressing the mutant EGFRL858R in type II pneumocytes [25], were obtained from Professor Lin Xi of Tsinghua University. For the induction of lung tumor formation, doxycycline (1 mg/ml) was administered in drinking water to 5-week-old mice, for 3 months. ## Subcutaneous transplantation tumor mouse model: lung cancer cells and fibroblasts syngeneic and orthotopic co-grafting For the subcutaneous tumor model, a 1:3 mixture of LLC cells (5 × 105) and fibroblasts (1.5 × 106) contained in 100 μl of PBS was injected subcutaneously into the back of anesthetized ($2\%$ isoflurane, RWD) 8-week-old female C57BL/6N mice (Vital River Laboratories, Beijing, China). For the fibroblasts, mouse cancer-associated fibroblasts (mCAFs) and mouse normal fibroblasts (mNFs) were used in the first animal experiment, and Tagln-overexpressing (TaglnOE) or Tagln-knockdown (Taglnsh) fibroblasts were used in the second experiment. Each group consisted of at least five mice. Tumor length (L) and width (W) were measured every other day, starting on day 6 (after injection). The tumor volume was calculated using the following formula: LW$\frac{2}{2.}$ Twenty-one days after injection, mice were euthanized, and the primary subcutaneous tumors and lungs were removed, analyzed, and paraffin-embedded before slicing and staining. For the animal experimental protocol of IL-6 neutralization, TaglnOE iMEFs (1.5 × 106) mixed with LLCs cells (5 × 105) were subcutaneously injected into the backs of C57BL/6N mice. One group was injected with an isotype control, and the other group was treated with an IL-6 neutralizing antibody (MP5-20F3; BioXcell). For this treatment, mice were intraperitoneally injected with 100 μg/mouse of IL-6 neutralizing antibody, on days 8, 10 and 12. Mice were treated according to the methods described above. All animal experiments were approved by the Review Board of the First Affiliated Hospital of Zhengzhou University. ## Tumor microarrays (TMAs) Commercial tissue microarrays (HLugA180Su06, HLugA020PG02) of human lung cancer were obtained from Shanghai Xinchao Biotechnology Co., Ltd. (Shanghai, China), and immunohistochemistry (IHC) was performed as described later (“IHC staining” section). Slides were stained for TAGLN (1:200, Abcam, #ab14106), α-SMA (Abcam, #ab5694), or PDGFR-β (Abcam, #ab32570) and imaged using the Slide Scanner System SQS-1000 (Teksqray). Two fields per slide per patient were double-blinded and quantified for TAGLN staining intensity and percentage (a total of four quantifications were performed per patient and the mean was calculated). For immunohistochemistry scoring, the intensity of staining (0 = negative, 1 = weak, 2 = moderate, 3 = strong) and the percentage of positively stained tumor cells (1 = 0–$25\%$, 2 = 26–$50\%$, 3 = 51–$75\%$, 4 = 75–$100\%$) were used for the quantification. The total IHC score equals the product of the intensity of staining and the percentage of positively stained tumor cells. The total IHC scores ≤6 was defined as low expression, and >6 was defined as high expression [26]. ## Human cancer-associated fibroblasts CAFs and NFs were isolated from lung cancer tissues and benign tissues at least 5 cm from the tumor, respectively, using the outgrowth method described previously [27, 28]. Briefly, sterile fresh surgical tissue was placed on ice in Dulbecco’s modified Eagle medium (DMEM, Hyclone) supplemented with 10× penicillin–streptomycin (1000 U/ml penicillin and 1000 μg/ml streptomycin). The tissue was washed two to three times with 1× phosphate-buffered saline (PBS, Hyclone) to remove blood contamination. The tissue was then cut into fine pieces using a sterile scalpel and digested with DMEM containing type 1A collagenase (Sigma) for 2 h at 37 °C, with agitation every 20 min. Next, the digest was removed, and the debris was washed with DMEM without fetal bovine serum (FBS; PAN-Biotech). The cell suspension was filtered through a 100 μm nylon mesh (BD Biosciences) and centrifuged at 2000 × g for 5 min at 4 °C. Cell pellets were then resuspended and cultured in 25 cm2 culture flasks (Corning), in DMEM containing $10\%$ FBS supplemented with L-glutamine (2 mmol/l), penicillin, and streptomycin. Cells were cultured at 37 °C in a $5\%$ CO2-air humidified atmosphere; CAFs grew out of the tissue blocks 10–14 days later. Human CAFs and NFs were used between the fourth and eighth generation to ensure the maintenance of the phenotypic and functional properties of CAFs and NFs. ## Mouse cancer-associated fibroblasts The mCAFs and mNFs were isolated from the lungs of a spontaneous lung cancer mouse model, as previously described [29]. Briefly, mouse lungs were minced and dissociated in DMEM containing $10\%$ FBS, supplemented with L-glutamine (2 mmol/l), penicillin, and streptomycin (herein defined as DMEM medium), with $0.5\%$ of collagenase type I for 1 h at 37 °C in a thermo-shaker. Cell suspensions were centrifuged at 1500 rpm for 5 min, and pellets were resuspended in DMEM medium and plated in culture dishes. ## Mouse lung cancer cells and embryonic fibroblasts Mouse LLCs cells were kindly provided by Prof. Yan Li of the Academy of Military Medical Sciences. Immortalized mouse embryonic fibroblasts (iMEFs) were gifted by Prof. Xi Lin of the Tsinghua University. Cells were cultured in high-glucose DMEM supplemented with $10\%$ FBS, 100 U/ml penicillin, and 100 mg/ml streptomycin, incubated at 37 °C in a humidified atmosphere with $5\%$ CO2, and tested monthly for detection of mycoplasma contamination. For NF-κB inhibition, cells were pretreated with pyrrolidine dithiocarbamate (PDTC) (25 μM, S3633, Selleck) or SC75741 (8 μM, HY-10496, MedChem Express) for 24 h and then used for subsequent experiments. ## Lentivirus transfection and selection of stable transfectants Lentivirus/GV492-Tagln (Ubi-MCS-3FLAG-CBh-gcGFP-IRES-puromycin), lentivirus/GV118-shTagln (U6-MCS-Ubi-EGFP), and the corresponding control lentiviruses were purchased from GeneChem (Shanghai, China). Stable cell lines were constructed using lentiviral gene delivery system. iMEF (1 × 105) were seeded in a six‐well plate and transducer the next day with ∼$50\%$ confluency. Cells were transduced with the lentiviruses, following manufacturer’s instructions. To ensure only transduced cells were used, we selected the GFP+ cells through a dual-selection process, using puromycin (presented in the lentiviruses as a resistance cassette) and flow-sorting. Stably infected clones were selected and tested by western blot and qRT-PCR. Multiple stable clones were used to eliminate potential clonal effects. Knockdown clone #1 and clone #2 were selected for subsequent experiments. ## RNA sequencing (RNA-seq) and data analysis The RNA samples were sent to a commercial gene sequencing company BGI (Shenzhen, China), for library construction and transcriptome sequencing. RNA-seq libraries were prepared using an Illumina RNA-Seq Preparation Kit and sequenced on a HiSeq 2500 sequencer. For RNA-seq data analysis, the Wald test was used to calculate p values, with false discovery rate set to a threshold of <0.05. Differentially expressed genes were selected and categorized using Gene Ontology (GO) biological process analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The data mining and graph presentation process, including Venn diagram, KEGG, heat map, and clustering, were all performed by Dr. Tom, a customized data mining system within the BGI. ## Western blot analysis Cells were harvested and lysed with RIPA buffer. Protein lysates were quantified using the protein BCA Assay Kit (#23228; Thermo Fisher), according to the manufacturer’s instructions. Equal amounts of protein lysates were resolved by SDS-PAGE, transferred to nitrocellulose membranes (pore size 0.45 µm, Merck Millipore, Darmstadt, Germany), and detected by immunoblotting. The primary antibodies were incubated overnight at 4 °C, followed by immunoblotting with horseradish peroxidase-coupled secondary antibodies for 1 h at 25 °C. The bands corresponding to the interest proteins were visualized using an ECL western blotting Kit (#CW00495; CWBIO) and detected using a ChemiDoc MP Imaging System (Bio-Rad, Hercules, CA, USA). The following primary antibodies were used: TAGLN (#ab14106; Abcam), α-SMA (#ab5694; Abcam), PDGFR-β (#ab32570; Abcam), p-iKKβ (#AP0546; Abclonal), IKKβ (#A0714, Abclonal), p-p65 (#3033S; CST), p65 (#8242S; CST), E-cadherin (#3195S; CST), vimentin (#GTX100619; Genetex), SOX2 (#11064-1-AP; Proteintech), OCT4 (#11263-1-AP; Proteintech), and GAPDH (1:10,000, #AC001; Abclonal). All primary antibodies were used at a dilution of 1:1000, and secondary antibodies (Abclonal, #AS014, #AS003) at 1:3000, unless otherwise stated. Anti-TAGLN antibody was validated by western blot; 293T cells were used as negative controls and mCAFs and Hela cells as positive controls (Supplementary Fig. 1A). ## RNA isolation and quantitative RT-PCR Total RNA was extracted using TRIzol (#108-95-2; TAKARA) according to standard procedures, and cDNA was synthesized using a reverse transcription kit (#RR036A; TAKARA), following manufacturer’s instructions. Quantitative real-time PCR was performed using SYBR® Green FastMIX® (#RR820A; TAKARA) in a StepOne™ Real-Time PCR System. Primers used in this study are listed in Supplementary Table 1. mRNA expression was calculated using the 2−ΔΔCt method, and GAPDH or 18S RNA was used as a reference for gene expression. The experiments were repeated at least thrice. ## IHC staining Previously described standard procedures were used for IHC [30, 31]. The primary antibodies used were TAGLN (1:200, #ab14106; Abcam), α-SMA (1:200, #ab5694; Abcam), PDGFR-β (1:200, #ab32570; Abcam,), and IL-6 (1:200, #GB11117; Servicebio). The secondary antibody used for all IHC procedures was the horseradish peroxidase‑conjugated goat anti-rabbit (#CW0103S; CWBIO). For the TAGLN IHC expression analysis, normal mouse bladder tissues with and without primary antibody were used as positive and negative controls, respectively (Supplementary Fig. 1B). ## Immunofluorescence (IF) staining IF staining was performed according to standard protocols [31, 32]. The primary antibodies used were Ki-67 (1:200, #ab16667; Abcam) and anti-α-SMA (1:200, #ab240654; Abcam). The secondary antibodies used were donkey anti-rabbit Alexa Fluor 488 (1:200, #A21206; Thermo Fisher) and goat anti-mouse Alexa Fluor 555 (1:200, #A21422; Thermo Fisher). Nuclei were stained with 4′,6‐diamidino‐2‐phenylindole (DAPI, Life Technologies) for 5 min. Images were obtained using an inverted fluorescence microscope (Leica). Ki-67 positive cells and Ki-67/α-SMA double-positive cells were counted and averaged for quantitative analysis. ## Time-lapse live cell microscopy imaging Cell motility was assessed using a confocal microscope (Perkin Elmer Ultra VIEW VOX), according to the manufacturer’s protocol. TaglnOE/Taglnsh fibroblasts and their corresponding control cells were seeded at a density of 20,000 cells/well in confocal glass-bottom dishes, incubated at 37 °C and $5\%$ CO2 for 24 h, to allow for cell attachment. Imaging was then performed for a 2 h period, with images collected every 3 min. Time-lapse images were subsequently analyzed to track and quantify cell motility using ImageJ software (NIH, Bethesda, MD, USA). ## Cell proliferation assay Cells were seeded into 96-well plates at a density of 2000 cells/well with 100 μl DMEM (with $10\%$ FBS), incubated at 37 °C for different times, as indicated in the figures, followed by incubation with the Cell Counting Kit-8 (CCK-8) solution. Cells in 100 μl of medium were treated with 10 μl of the CCK-8 solution and incubated for 2 h at 37 °C. Absorbance was measured at a wavelength of 450 nm. ## Transwell migration and invasion assay The cell migration and invasion assay were performed using a proliferation blocker (mitomycin C), to observe the effect of TAGLN on the migratory or invasive potential of cells, without an effect on cell proliferation. The cell migration assay was performed using transwell chambers (8-μm pores, #3422; Corning), while the cell invasion assay was performed using Matrigel-coated (#356234; Corning) transwell chambers (coating on the upper surface). Cells (that migrated or invaded through the Matrigel to the lower surface of the membrane) were fixed with $4\%$ paraformaldehyde for 10 min and stained with $0.5\%$ crystal violet for 30 min, according to standard protocols. Image fields were randomly chosen, and the number of fixed cells was counted using the ImageJ software. ## 3D gel invasion assay The 3D gel invasion assay was performed as described previously [33, 34]. Briefly, 200 μl of serum-free gel containing Col1a1 (#07001; Stemcell) and Matrigel (#356234; Corning) were used to coat a transwell chamber (3-μm pores, #3415; Corning) in 24-well plates. LLCs were labeled with CellTracker CM-Dil (red) (#40718ES60; Yeasen) following the manufacturer’s instructions, and iMEFs were transfected with green fluorescent protein, as previously described. The cells were mixed (4.5 × 104 cells for each cell type) and placed on the gels in a medium containing $0.2\%$ FBS. DMEM was added to the bottom chamber. After incubation at 37 °C for 7 days, gels were fixed in $4\%$ paraformaldehyde and cut vertically into 50-μm slices using a vibrating microtome (Leica VT-1200S; Leica). Images were obtained using a confocal microscope (LSM880, Zeiss), and the area of invading cells was quantified using the ImageJ software. Invasion index was calculated as follows: invasive index = (invasive cells)/(non-invading cells + invasive cells). ## Conditioned medium stimulation Transfected iMEFs (2 × 106 cells) were plated into 10 cm2 culture dishes, fresh medium (DMEM, $10\%$ FBS, and $1\%$ penicillin–streptomycin) was added the next day, and the cells were grown for the subsequent 3 days. The conditioned medium (CM) was harvested and concentrated at 700 × g for 10 min at 4 °C. ## Colony formation assay For the colony formation assay, 500–1000 cells were seeded in 6-well plates and cultured for 2 weeks. At the indicated time points, cells were fixed with $4\%$ paraformaldehyde, stained with $0.5\%$ crystal violet methanol solution for 30 min and imaged. ## Tumor sphere formation assay The sphere formation assay was performed in 24-well ultralow-attachment plates (#3473; Corning). LLCs (1000 cells/well) were seeded in serum-free DMEM, containing 10 mM HEPES, 10 ng/ml of basic fibroblast growth factor (#450-33-10 μg; Proteintech,), $2\%$ B27 (serum-free supplement, #17504044; Gibco), and 20 ng/ml of epidermal growth factor (#315-09-100 μg; Proteintech). Each well was examined under a light microscope, and the total number of spheroids was counted. ## Enzyme-linked immunosorbent assay (ELISA) The levels of interleukin (IL)-6 were quantified using ELISA kits (#KE10007, FineTest), according to the manufacturer’s protocols. Absorbance was measured at 450 nm using a multifunctional microplate reader (Thermo Fisher Scientific). Protein levels were calculated in pg/ml. ## Statistical analyses Statistical analyses were performed using GraphPad Prism 8 (GraphPad Software Inc.) or IBM SPSS version 23.0. Logistic regression was used for multivariate analysis. Data were tested for Gaussian distribution using the D’Agostino–Pearson omnibus normality test. For Gaussian distributions, a paired or unpaired two-tailed Student’s t test was performed for comparisons between two groups, and one-way ANOVA with Tukey’s post test was applied for multiple comparisons. For non-Gaussian distributions, Mann–Whitney and Kruskal–Wallis tests, with Dunn’s post test (for multiple comparisons), were performed. All values are presented as the mean ± standard error of mean (SEM). Differences were considered statistically significant when $p \leq 0.05$ ($p \leq 0.05$, $p \leq 0.01$, or $p \leq 0.001$). ## TAGLN is highly expressed in lung cancer stroma First, we examined TAGLN expression in human lung cancer and adjacent non-tumor tissues through IHC. As shown in Fig. 1A, the expression in the stroma of human lung cancer cells was significantly higher than that in the stroma of normal lung tissue. Co-localization of α-SMA, PDGFR-β, and TAGLN in three sequential sections of human lung cancer tissue microarrays revealed TAGLN localization in stromal fibroblasts (Fig. 1B). To further investigate fibroblasts features and their interaction with tumor cells, CAFs and paired NFs were isolated from human lung cancer and adjacent non-tumor tissues. Fibroblasts were identified as CAFs mainly based on the analysis of α-SMA and PDGFR-β (Fig. 1C and Supplementary Fig. 2A). Western blot analysis confirmed that TAGLN protein levels were markedly higher in CAFs than in NFs (Fig. 1C). Combining these results, we showed that TAGLN exists predominantly in fibroblasts from the tumor stroma. Further data analysis revealed that increased stromal TAGLN expression was correlated with positive lymph node metastasis ($$p \leq 0.0001$$), higher Tumor Node Metastasis (TNM) stage ($$p \leq 0.0003$$), and higher histopathological grade ($$p \leq 0.0383$$) (Table 1). Multivariate logistic regression analysis revealed that the size of tumor was a significant independent prognostic factor (odds ratio (OR) = 6.2532, $$p \leq 0.0178$$) (Table 2). Moreover, stromal TAGLN expression was higher in metastatic tissues than in nonmetastatic cancer tissues (Supplementary Fig. 2B). Based on these findings, we surmise that stroma-derived TAGLN may be associated with human lung cancer metastasis. Fig. 1Transgelin is highly expressed in human lung cancer stroma. A Immunohistochemical (IHC) staining for transgelin (TAGLN) in lung cancer and adjacent normal tissues from a tissue microarray of 94 tumors and 86 paired adjacent normal tissues. IHC score of transgelin in lung cancer and adjacent tissues. B IHC staining for TAGLN, α-SMA, and PDGFR-β in one patient from the human lung cancer tissue microarray ($$n = 10$$). C Western blot analysis of protein levels of α-SMA, PDGFR-β, and TAGLN in CAFs and NFs (replicates from four patients). Red arrows represent stromal fibroblasts, green arrows represent lung cancer cells. Data are represented as mean ± SEM. * $p \leq 0.001$, *$p \leq 0.01$, $p \leq 0.05.$Table 1Clinicopathological variables of patient samples and expression of transgelin in lung cancer stroma. VariablesPatientsTAGLN low (%)TAGLN high (%)χ2p valueAge (years)≥605118 ($35.29\%$)33 ($64.71\%$)3.1400.0764<604323 ($53.49\%$)20 ($46.51\%$)GenderMale5325 ($47.17\%$)28 ($52.83\%$)0.62370.4297Female4116 ($39.02\%$)25 ($60.98\%$)Size of tumor<5 cm7037 ($52.86\%$)33 ($47.14\%$)9.5180.0020≥5 cm244 ($16.67\%$)20 ($83.33\%$)Lymph node statusNegative4328 ($65.12\%$)15 ($34.88\%$)14.900.0001Positive5113 ($25.49\%$)38 ($74.51\%$)T-primary tumorT1 + T27038 ($54.29\%$)32 ($45.71\%$)12.690.0004T3 + T4243 ($12.5\%$)21 ($87.5\%$)TNM stagesI–II5131 ($60.78\%$)20 ($39.22\%$)13.360.0003III–IV4310 ($23.26\%$)33 ($76.74\%$)Pathological gradeI118 ($72.73\%$)3 ($27.27\%$)4.2930.0383II–III8333 ($39.76\%$)50 ($60.24\%$)Chi-square tests for all analyses. TAGLN transgelin. Table 2Univariate and multivariate analysis of factors associated with transgelin expression. Univariate analysisMultivariate analysisVariablesOR$95\%$ CIp valueOR$95\%$ CIp valueAge (<60 years vs. ≥60 years)0.47430.2055–1.0580.07641.91320.6406–5.71420.2452Gender (male vs. female)1.3950.6335–3.1960.42971.78030.5914–5.35950.3050Size of tumor (<5 cm vs. ≥5 cm)5.6061.784–16.150.00206.25321.3738–28.46320.0178Lymph node status (Negative vs. Positive)5.4562.290–13.260.00010.26280.0653–1.05720.0599T-primary tumor (T1 + T2 vs. T3 + T4)8.3132.300–27.790.00040.24020.0507–1.13750.0722TNM stages (I–II vs. III–IV)5.1152.113–12.180.00031.70550.4182–6.95520.4567Pathological grade (I vs. II–III)4.0401.084–14.690.03830.14390.0072–2.87350.2044OR odds ratio, CI confidence interval. Furthermore, TAGLN was mainly expressed in the stromal region in the EGFR-driven spontaneous lung cancer mice (Fig. 2A). Additionally, TAGLN expression levels were markedly higher in mCAFs than in mNFs (Fig. 2B). In vivo experiments showed that mCAFs significantly promoted tumor growth and metastasis, compared with mNFs (Fig. 2C–F). Taken together, these data show not only that TAGLN is highly expressed in lung CAFs but also that this high expression may be associated with the metastasis process. Fig. 2CAFs facilitate tumor growth and lung metastasis in vivo. A IHC staining for TAGLN, α-SMA, and PDGFR-β in the lung cancer tissue from EGFRL858R transgenic mice ($$n = 6$$). B Western blot analysis of protein levels of α-SMA, PDGFR-β, and TAGLN in mCAFs and mNFs ($$n = 3$$). C Diagram of the mouse model with subcutaneous tumor implantation. D Photographs of tumors from mice. E Tumor volume and tumor weight. F Representative hematoxylin and eosin staining images and quantification data of lung metastasis in mice. Upper panel scale bar: 1 mm; lower panel scale bar: 50 µm. Results are shown as mean ± SEM and compared by unpaired t-test. * $p \leq 0.001$, $p \leq 0.01$, $p \leq 0.05.$ ## TAGLN promotes fibroblasts activation Considering the stability and repeatability of the experimental results, we used an iMEF cell line to construct an in vitro experimental system. We first investigated whether TAGLN affects the phenotypic conversion of fibroblasts. We established TaglnOE and Taglnsh iMEF cell lines by infection with the corresponding lentiviruses or subsequent experiments. The knockdown of Tagln (Taglnsh) was confirmed in two separate clones of iMEFs, Taglnsh1 and Taglnsh2. Successful overexpression and knockdown were confirmed by qRT-PCR and western blot analysis (Supplementary Fig. 3A). We found higher protein and mRNA levels of α-SMA and PDGFR-β, which are CAF markers, in TaglnOE cells, compared to negative control-transfected cells (Fig. 3A and Supplementary Fig. 3B). Contrarily, mRNA and protein levels of α-SMA and PDGFR-β were markedly reduced in Taglnsh fibroblasts (Fig. 3A and Supplementary Fig. 3B). Time-lapse microscopy revealed that Tagln overexpression significantly increased iMEFs mobility (Fig. 3B) and proliferation (Fig. 3C). Using a transwell assay, we observed that Tagln overexpression remarkably promoted iMEFs migration and invasion (Fig. 3D). Taglnsh fibroblasts exhibited decreased motility, proliferation, and migration/invasion (Fig. 3B–D). Taken together, these findings suggest that TAGLN activates fibroblasts and, in turn, induces their pro-tumor phenotype. Fig. 3Transgelin promotes fibroblasts activation. A The effect of transgelin (TAGLN) on the protein levels of cancer-associated fibroblast (CAF) markers, evaluated by western blot ($$n = 3$$). B Representative images of time-lapse microscopy imaging of immortalized mouse embryonic fibroblast (iMEF) overexpressing Tagln or with Tagln knockdown. Each color line represents the tracking of a different cell. Total distance traveled for different fibroblasts. ( TaglnOE iMEF $$n = 240$$, Taglnsh iMEF $$n = 120$$). Scale bar: 50 µm. C Changes in iMEF cell proliferation with Tagln overexpression or knockdown. D Effects of Tagln overexpression or knockdown on cell migration and invasion. $p \leq 0.001$, $p \leq 0.01$, $p \leq 0.05.$ ## Tagln-overexpressing fibroblasts promote cell migration, invasion and cancer cell stemness in lung cancer cells CAFs are known to contribute to tumor development and progression by regulating the malignant phenotype of tumor cells [35–37]. To investigate the function of Tagln-overexpressing fibroblasts in tumor cells, we performed 3D-gel invasion assays. Results showed increased LLCs cell invasion (Fig. 4A) after indirect co-culture with TaglnOE iMEFs. Next, we cultured LLCs with CM derived from TaglnOE iMEFs (and control cells), and observed increased LLCs proliferation (Fig. 4B). Furthermore, a transwell assay was used to detect the migration and invasion abilities of LLCs. As shown in Fig. 4C, TaglnOE iMEFs-derived CM increased LLCs migration and invasion. Additionally, the number of colonies formed as well as LLCs number was also significantly elevated (Fig. 4D). Western blot results showed higher protein levels of cancer stem cell markers, including SOX-2 and OCT-4, in cancer cells cultured in TaglnOE iMEFs-derived CM, compared to those in the negative control groups (Fig. 4E). E-cadherin was downregulated, and vimentin was upregulated in the CM-treated TaglnOE iMEF group, indicating higher activation of the EMT program (Fig. 4E). Additionally, OCT-4, SOX-2, E-cadherin, and vimentin mRNA levels showed similar trends as protein levels (Supplementary Fig. 4).Fig. 4TaglnOE fibroblasts promote cell migration, invasion, and cancer cell stemness in lung cancer cells. A Immortalized mouse embryonic fibroblasts (iMEFs; green) and Lewis lung cancer cells (LLCs; red) were subjected to a 3D gel invasion assay. Scale bar: 100 µm. B Effect of Tagln overexpression or knockdown on LLCs proliferation. C Effects of conditioned medium (CM) derived from TaglnOE iMEFs or Taglnsh iMEFs on LLC migration and invasion. Scale bar: 100 µm. D Colony formation by LLCs with CM from T TaglnOE iMEFs or Taglnsh iMEFs. The sphere numbers of LLCs were counted by ImageJ software. Scale bar: 1 mm. E Western blot analysis of epithelial–mesenchymal transition-related proteins and cancer stem cell markers in LLCs with different CM. Data are represented as mean ± SEM from at least three independent experiments. $p \leq 0.001$; $p \leq 0.01$; $p \leq 0.05.$ We next tested whether silencing Tagln in iMEFs affected the tumor-supportive function. In 3D-gel invasion assays, the mixture of Taglnsh iMEFs and LLCs did not show effective invasive properties. Tagln knockdown in iMEFs inhibited cancer cell invasion (Fig. 4A) and decreased proliferation of LLCs (Fig. 4B). Additionally, Tagln knockdown in iMEFs effectively inhibited the migration and invasion of LLCs (Fig. 4C), as well as colony formation capacity and tumorigenesis (Fig. 4D). Moreover, CM from Taglnsh iMEFs decreased SOX-2 and OCT-4, increased E-cadherin, and downregulated vimentin expression in LLCs (Fig. 4E). Together, these results indicate that Tagln overexpression in fibroblasts might enhance the malignant phenotype of LLCs. ## Tagln-overexpressing fibroblasts promote the growth and spread of lung cancers To further explore the importance of transgelin in tumor progression in vivo, we mixed LLCs and TaglnOE iMEFs or Taglnsh1 iMEFs, at a ratio of 1:3 and inoculated them into C57BL/6 mice to establish a model of subcutaneous tumor transplantation (Fig. 5A). As shown in Fig. 5B, C, the tumor volume and weight in the TaglnOE iMEFs group were significantly increased compared with those in the control group. Moreover, the co-injection of TaglnOE iMEFs and LLCs promoted LLC metastasis, as shown in the HE staining images (Fig. 5D).Fig. 5TaglnOE fibroblasts promote the growth and spread of lung cancers. A Scheme of the mouse experiment design. B Images of the tumors from the different groups. C Tumor growth curves and weights ($$n = 6$$); $p \leq 0.001.$ D Representative hematoxylin and eosin staining images of lungs and areas of lung metastasis for each group. Upper panel scale bar: 1 mm; lower panel scale bar: 50 µm. Results are represented as mean ± SEM and compared by unpaired t-test. $p \leq 0.01.$ E Immunofluorescence staining for Ki-67 (green) and nuclear staining DAPI (blue) of tumors from the different groups (upper panels). Immunofluorescence staining for α-SMA (red), Ki-67 (green), and nuclear staining DAPI (blue) of tumors from the different groups (lower panels). Quantification of co-expression of α-SMA+ and Ki-67+ cells. Data are represented as mean ± SEM from at least three independent experiments. Ki-67 is a well-known cell proliferation marker that correlates with tumor aggressiveness and is considered a prognostic parameter. We found higher Ki-67 levels in tumor tissues of the TaglnOE iMEF group than in the control group. Furthermore, the number of α-SMA+ and α-SMA+/Ki-67+ cells also increased in the TaglnOE iMEF group (Fig. 5E). Results from mice with subcutaneous tumor transplantation with the Taglnsh iMEFs further supported these observations (Fig. 5B–E). Taken together, the in vivo data supports the hypothesis that Tagln-overexpressing fibroblasts may promote tumor growth and spreading. ## Tagln-overexpressing fibroblasts release more IL-6 via the activation of the NF-κB signaling pathway RNA-seq and bioinformatic analyses were performed to analyze differentially expressed genes in TaglnOE iMEFs. In total, 725 gene were upregulated and 273 were downregulated (Supplementary Fig. 5A, $p \leq 0.05$). KEGG pathway analysis was performed on the 998 differentially expressed genes. A bubble map showed that these 998 genes were enriched in the TNF signaling pathway, cytokine-cytokine receptor interaction, and NF-κB signaling pathway (Fig. 6A). The differences in gene expression of in these major pathways between TaglnOE iMEFs and control iMEFs are illustrated as a heatmap (Fig. 6B). Details of the genes enriched in the 10 KEGG pathways are listed in Supplementary Table 2. Through qRT-PCR we verified that Il-6 was upregulated in TaglnOE iMEFs (Fig. 6C). Moreover, we observed increased IL-6 secretion in the culture medium supernatants of TaglnOE iMEFs (Fig. 6D). We found that TaglnOE iMEFs exhibited enhanced p-IKKβ and p-p65 expression, associated with the activation of the NF-κB signaling pathway (Fig. 6E). In contrast, Taglnsh iMEFs exhibited decreased p-IKKβ and p-p65 expressions (Supplementary Fig. 5B). These data were consistent with the RNA-seq results. To further confirm the activation of the NF-κB signaling pathway, TaglnOE iMEFs were treated with PDTC, a potent NF-κB inhibitor that prevents IκB phosphorylation and blocks NF-κB translocation to the nucleus, thereby reducing the expression of downstream cytokines [38, 39]. PDTC prevented the Tagln-induced increase in cytoplasmic p-IKKβ and nuclear p-p65 protein levels (Fig. 6F). IF staining further confirmed that Tagln overexpression facilitated p-p65 translocation to the nucleus (Fig. 6G). Moreover, PDTC inhibited Tagln-induced IL-6 secretion and mRNA expression (Fig. 6H, I). Another NF-κB inhibitor SC75741, had an effect that was close to that of PDTC (Supplementary Fig. 5C–E). These results suggest that Tagln-overexpressing fibroblasts promote the release of inflammatory cytokines via the activation of the NF-κB signaling pathway. Fig. 6TaglnOE fibroblasts release more IL-6 via the activation of the NF-κB signaling pathway. A Bubble map representing significantly enriched pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis conducted for differentially expressed genes. B Heatmap representing the log2 fold changes of differentially expressed genes between TaglnOE iMEFs and TaglnNC iMEFs involved in the major pathways identified through KEGG analysis. C Tagln overexpression upregulated the mRNA levels of cytokines Il-6 in iMEFs. D ELISA analysis confirmed the increased protein levels of secreted IL-6 in the conditioned medium from TaglnOE iMEFs. E Western blot analysis of proteins of the NF-κB signaling pathway, activated by Tagln overexpression. F Increased protein levels of p-p65 in the nucleus of iMEFs, prevented by NF-κB inhibitor pyrrolidine dithiocarbamate (PDTC). G p65 translocation to the nucleus assessed through immunofluorescence assay. H Quantitative RT-PCR revealed that PDTC obviously inhibited the upregulated Il-6 mRNA levels caused by transgelin overexpression in iMEFs. I ELISA analysis confirmed that PDTC markedly prevented the increased levels of secreted IL-6 in the conditioned medium from TaglnOE iMEFs. Data are represented as mean ± SEM from at least three independent experiments. * $p \leq 0.001$; $p \leq 0.01$; $p \leq 0.05.$ ## IL-6 from Tagln-overexpressing fibroblasts may promote the malignant phenotype of lung cancer cells IL-6 is associated with tumor growth, differentiation, apoptosis, drug resistance, and immune regulation. Furthermore, IL-6 levels have been reported as significantly increased in patients with advanced tumors [40, 41]. Moreover, IL-6 is considered important for lung cancer development [42]. To further investigate whether Tagln-overexpressing fibroblasts promoted the malignant phenotypes of LLCs via IL-6, we blocked IL-6 secreted by TaglnOE iMEFs. With a transwell assay we showed that IL-6 neutralizing antibodies could prevent the invasion and migration of LLCs cultured with CM from TaglnOE iMEFs (Fig. 7A), and suppress the number of tumor spheres in LLCs (Fig. 7B). Additionally, IL-6 neutralizing antibodies also reversed the expression profile of cancer stem cells and EMT markers induced by CM from TaglnOE iMEFs (Fig. 7C). These results suggest that IL-6 secreted from TaglnOE iMEFs may promote the malignant phenotype of LLCs. To further support these findings, we randomly selected subcutaneously transplanted tumor-bearing mice for IL-6 neutralization treatment (Fig. 7D). Treatment with anti-IL6 promoted a decreasing trend in tumor volume and weight (Fig. 7E, F). In addition, IL-6 neutralization also improved lung metastasis (Fig. 7G).Fig. 7IL-6 from TaglnOE iMEFs may promote the malignant phenotype of lung cancer cells. A Transwell assay was performed to determine the effects of IL-6 neutralizing antibody on migration and invasion properties of Lewis lung cancer cells (LLCs) fed with different conditioned media (CM). B Tumor sphere formation assay upon IL-6 neutralizing antibody treatment. C Quantitative RT-PCR analysis of mRNA levels of epithelial–mesenchymal transition-related proteins and cancer stem cell markers in LLCs fed with different CM, with or without IL-6 neutralizing antibodies. D Anti-IL-6-neutralizing antibody treatment in mice. E Photographs of tumors from mice. F Tumor volume and tumor weight ($$n = 5$$). G Representative hematoxylin and eosin staining images and quantification data of lung metastasis in mice. Upper panel scale bar: 1 mm; lower panel scale bar: 50 µm. Data are represented as mean ± SEM from at least three independent experiments. $p \leq 0.001$; $p \leq 0.01$; $p \leq 0.05.$ ## Discussion In the current study, we found that high fibroblastic TAGLN expression in human lung cancer is associated with increased cancer cell lymph node metastasis. Fibroblasts overexpressing Tagln promoted the malignant phenotype of LLCs, and the Tagln-induced fibroblast activation facilitated the release of inflammatory cytokines via the activation of the NF-κB signaling pathway. IL-6 secreted by TAGLN-positive fibroblasts may promote lung cancer progression (Fig. 8). Together, these findings suggest that TAGLN expression is essential for fibroblasts to acquire the CAF phenotype, which plays an important role in lung cancer progression. Fig. 8TAGLN promotes lung cancer progression via activation of cancer-associated fibroblasts with enhanced IL-6 release. TAGLN promoted the pro-tumor phenotype of fibroblasts and increased their secretion of pro-inflammatory cytokines, such as IL-6, via the activation of the NF-κB signaling pathway which in turn regulates lung cancer cells migration/invasion. TAGLN, a member of the calmodulin family, acts as an actin-binding protein and regulates cytoskeletal remodeling, through the promotion of actin aggregation [12]. Previous studies have highlighted TAGLN as a tumor metastasis initiator [21]. Increased TAGLN levels have also been associated with prognosis and metastasis in certain tumors, such as esophageal [43], pancreatic [17], and colorectal [14]. There are only few studies on the role of TAGLN in lung cancer, especially since there are no reports on the function of TAGLN in the lung cancer stroma. In this study, we showed a correlation between cancer cell lymph node metastasis and high stromal TAGLN levels using TMAs. Enhanced stromal TAGLN levels are considered an independent risk factor for lymph node metastasis. Our data are in line with those of a previous study in gastric cancer, in which TAGLN overexpression in stromal fibroblasts promoted tumor metastasis [22]. Although we did not directly observe tumor metastasis in vivo, the pathological morphology of subcutaneously transplanted tumors suggests that TaglnOE iMEFs tend to promote metastasis. High TAGLN levels were negatively associated with survival and disease-free survival in colon adenocarcinoma, whereas low levels were positively correlated with survival in patients with stage III colorectal cancer [14, 44]. However, in the present study, we did not observe any correlation between overall survival and stromal TAGLN levels. A larger sample size and more detailed clinical staging information are required to further characterize the effects of CAF-derived TAGLN on lung cancer survival. Recently, Zhou et al. identified five genes (BGN, RCN3, TAGLN, MYL9, and TPM2) as fibroblast-specific markers for prediction of poor prognosis in colorectal cancer [24]. Similarly, TAGLN was reported as a specific marker of CAFs in the mesenchymal stroma of pancreatic ductal adenocarcinoma [45]. In line with these studies, we demonstrated that Tagln overexpression activates normal fibroblasts and promotes the shift to the CAF phenotype in vitro. However, understanding the specific mechanism of fibroblast activation by Tagln overexpression requires further studies. It is well-known that CAFs are a heterogeneous population in TME [46]. As one of the markers of CAFs, Tagln-positive fibroblasts may represent an aspect of CAF heterogeneity. Lately, Zheng’s team identified seven CAF subtypes through high-resolution clustering of the integrated data, termed pan-CAF 1-7 [47]. These pan-CAF subtypes were present in the three cancer types (including lung cancer). The results showed that pan-CAF 1 was classified as pan-myCAFs based on elevated expression of activated fibroblast markers (ACTA2) and smooth muscle cell markers (MYH11, MCAM, Tagln, and MYLK). In a recent paper published by our group, we used single-cell sequencing to group CAF cells from mice that were subcutaneously inoculated with LLC transplant tumors, resulting in the identification of 11 distinct CAF clusters [48]. Among them, cluster 10 exhibited high expression levels of both ACTA2 and Tagln (unpublished data). The above studies suggest that this particular group of CAFs with high expression of Tagln may be associated with the myofibroblast-associated CAFs (myCAFs) subtype. Moreover, in the present study, we observed a significant increase in the expression of α-SMA in iMEF cells following Tagln overexpression, while the opposite was observed after Tagln knockdown. The same trend was seen in subcutaneously transplanted tumors in mice. These results imply that perturbing Tagln expression may have an impact on α-SMA-positive myCAFs. However, the exact mechanism remains to be investigated. In addition, future studies should complement the α-SMA/TAGLN double-staining assay in human lung cancer tissues, and subject the staining results to multivariate analysis in relation to clinical variables to further correct for tumor cellularity. In the current study, we observed that fibroblasts overexpressing Tagln were able to promote the malignant phenotype of lung cancer cells, including invasion and migration abilities, EMT, and cancer cell stemness. Furthermore, we explored the possible molecular biological mechanisms involved in these Tagln-mediated effects. Our RNA-seq data showed that Tagln overexpression can alter several inflammatory pathways, including the NF-κB and TNF signaling pathways, indicating that Tagln may participate in the crosstalk between cancer cells and CAFs by mediating the inflammatory process in the TME. Consistent with this, our in vitro results showed that TaglnOE iMEFs were able to promote p-p65 translocation into the nucleus, which in turn activates the NF-κB signaling pathway and leads to IL-6 production. TAGLN can bind to the Poly (ADP-ribose) polymerase-1 (PARP1) promoter [49], and the PAR-dependent formation of a nuclear PARP1-IKKγ signalosome can promote IKK activation [50]. This may be one of the mechanisms through which TAGLN activates the NF-κB signaling pathway. We will further analyze this mechanism in a follow-up study. CAFs secrete different cytokines that promote tumor progression [51, 52]. IL-6 is one of these, a multifunctional molecule involved in regulating immune and inflammatory responses, and known to promote tumor growth and cancer cells invasion [53, 54]. Blocking IL-6/STAT3 signal transduction can significantly inhibit tumor growth and STAT3 phosphorylation in mice xenografts with non-small cell lung cancer [55]. Our in vivo studies identified an immune suppressive environment upon Tagln overexpression, consistent with those described above (Supplementary Fig. 6). Moreover, pro-inflammatory cytokines enhance CAF glycolysis [56], which may result in local energy-rich metabolites and tumor growth. This may occur due to the CAFs transgelin-induced secretion of pro-inflammatory cytokines, ultimately accelerating tumor progression. However, this hypothesis also requires confirmation in future studies. Noteworthily, recent studies have provided insight into the regulation of TAGLN by transforming growth factor (TGF) -beta. Chen et al. found that TGF-β-mediated migration was abolished by TAGLN suppression in bladder cancer [13]. Yu et al. identified Tagln as a target of the TGF-β/Smad3-dependent gene expression in alveolar epithelial type II (ATII) cells [57]. However, whether TGF-β can also act as an upstream regulator gene of TAGLN in fibroblasts, thus inducing a positive feedback loop, is currently unknown and requires further studies. Nevertheless, we acknowledge the limitations of this study. RNA-seq and subsequent experimental analyses demonstrated that high Tagln expression in iMEFs promoted the pro-tumor phenotype of fibroblasts and increased IL-6 secretion via the activation of the NF-κB signaling pathway, by enhancing the phosphorylation of IKKβ and p65. However, TAGLN has no phosphokinase activity, therefore the associated mechanisms of NF-κB activation require further exploration. Combining Tagln knockdown with TAGLN mutants might help to detect phenotypic changes that could provide some mechanistic insights. Secondly, in the present study, we focused on IL-6 as it is a key inflammatory cytokine involved in different types of cancer [58–62]. However, it remains unexplored whether the same observations would occur for other cytokines. Additionally, considering that one of the key characters of metastatic cells is chemoresistance, future works should focus on the role of TAGLN in chemoresistance. Thirdly, we did not assess the correlation between TAGLN stromal expression and potential mutations of frequent oncogenes in lung cancer. Therefore, we were unable to determine whether tumor cells contribute to high TAGLN levels which in turn promotes proliferation of lung cancer cells and metastasis. ## Conclusion Here we identified stromal TAGLN as a predictive factor for lymph node metastasis in human lung cancer. We showed that Tagln overexpression in fibroblasts promotes lung cancer cell migration and invasion, which may be related to IL-6 secretion resulting from the increased activation of the NF-κB signaling pathway. These findings provide a substructure for further understanding the mechanism by which stromal TAGLN regulates inflammation in the TME, suggesting CAF-derived TAGLN as a potential target for lung cancer therapy. ## Supplementary information Supplementary figure legends supplementary figure 1 supplementary figure 2 supplementary figure 3 supplementary figure 4 supplementary figure 5 supplementary figure 6 Supplementary Table 1 Supplementary Table 2 The online version contains supplementary material available at 10.1038/s41389-023-00463-5. ## References 1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin* (2021) 71 7-33. DOI: 10.3322/caac.21654 2. Crinò L, Weder W, van Meerbeeck J, Felip E. Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO Clinical Practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. (2010) 21 v103-15. DOI: 10.1093/annonc/mdq207 3. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. (2011) 144 646-74. DOI: 10.1016/j.cell.2011.02.013 4. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer (2021) 20 131. DOI: 10.1186/s12943-021-01428-1 5. Wu X, Zhou Z, Xu S, Liao C, Chen X, Li B. Extracellular vesicle packaged LMP1-activated fibroblasts promote tumor progression via autophagy and stroma-tumor metabolism coupling. Cancer Lett (2020) 478 93-106. DOI: 10.1016/j.canlet.2020.03.004 6. Li Y, Chen Y, Miao L, Wang Y, Yu M, Yan X. Stress-induced upregulation of TNFSF4 in cancer-associated fibroblast facilitates chemoresistance of lung adenocarcinoma through inhibiting apoptosis of tumor cells. Cancer Lett (2021) 497 212-20. DOI: 10.1016/j.canlet.2020.10.032 7. Naito Y, Yamamoto Y, Sakamoto N, Shimomura I, Kogure A, Kumazaki M. Cancer extracellular vesicles contribute to stromal heterogeneity by inducing chemokines in cancer-associated fibroblasts. Oncogene. (2019) 38 5566-79. DOI: 10.1038/s41388-019-0832-4 8. Yamanaka T, Harimoto N, Yokobori T, Muranushi R, Hoshino K, Hagiwara K. Nintedanib inhibits intrahepatic cholangiocarcinoma aggressiveness via suppression of cytokines extracted from activated cancer-associated fibroblasts. Br J Cancer (2020) 122 986-94. DOI: 10.1038/s41416-020-0744-7 9. Biffi G, Tuveson DA. Diversity and biology of cancer-associated fibroblasts. Physiol Rev (2021) 101 147-76. DOI: 10.1152/physrev.00048.2019 10. Lees-Miller JP, Heeley DH, Smillie LB, Kay CM. Isolation and characterization of an abundant and novel 22-kDa protein (SM22) from chicken gizzard smooth muscle. J Biol Chem (1987) 262 2988-93. DOI: 10.1016/S0021-9258(18)61457-7 11. Elsafadi M, Manikandan M, Dawud RA, Alajez NM, Hamam R, Alfayez M. Transgelin is a TGFβ-inducible gene that regulates osteoblastic and adipogenic differentiation of human skeletal stem cells through actin cytoskeleston organization. Cell Death Dis (2016) 7 e2321. DOI: 10.1038/cddis.2016.196 12. Zhong W, Sun B, Gao W, Qin Y, Zhang H, Huai L. Salvianolic acid A targeting the transgelin-actin complex to enhance vasoconstriction. EBioMedicine. (2018) 37 246-58. DOI: 10.1016/j.ebiom.2018.10.041 13. Chen Z, He S, Zhan Y, He A, Fang D, Gong Y. TGF-β-induced transgelin promotes bladder cancer metastasis by regulating epithelial-mesenchymal transition and invadopodia formation. EBioMedicine. (2019) 47 208-20. DOI: 10.1016/j.ebiom.2019.08.012 14. Elsafadi M, Manikandan M, Almalki S, Mahmood A, Shinwari T, Vishnubalaji R. Transgelin is a poor prognostic factor associated with advanced colorectal cancer (CRC) stage promoting tumor growth and migration in a TGFβ-dependent manner. Cell Death Dis (2020) 11 341. DOI: 10.1038/s41419-020-2529-6 15. Qi Y, Chiu JF, Wang L, Kwong DL, He QY. Comparative proteomic analysis of esophageal squamous cell carcinoma. Proteomics. (2005) 5 2960-71. DOI: 10.1002/pmic.200401175 16. Zhang J, Wang K, Zhang J, Liu SS, Dai L, Zhang JY. Using proteomic approach to identify tumor-associated proteins as biomarkers in human esophageal squamous cell carcinoma. J Proteome Res (2011) 10 2863-72. DOI: 10.1021/pr200141c 17. Zhou L, Zhang R, Zhang L, Sun Y, Yao W, Zhao A. Upregulation of transgelin is an independent factor predictive of poor prognosis in patients with advanced pancreatic cancer. Cancer Sci (2013) 104 423-30. DOI: 10.1111/cas.12107 18. Sitek B, Lüttges J, Marcus K, Klöppel G, Schmiegel W, Meyer HE. Application of fluorescence difference gel electrophoresis saturation labelling for the analysis of microdissected precursor lesions of pancreatic ductal adenocarcinoma. Proteomics. (2005) 5 2665-79. DOI: 10.1002/pmic.200401298 19. Wu X, Dong L, Zhang R, Ying K, Shen H. Transgelin overexpression in lung adenocarcinoma is associated with tumor progression. Int J Mol Med (2014) 34 585-91. DOI: 10.3892/ijmm.2014.1805 20. Lin Y, Buckhaults PJ, Lee JR, Xiong H, Farrell C, Podolsky RH. Association of the actin-binding protein transgelin with lymph node metastasis in human colorectal cancer. Neoplasia. (2009) 11 864-73. DOI: 10.1593/neo.09542 21. Dvorakova M, Nenutil R, Bouchal P. Transgelins, cytoskeletal proteins implicated in different aspects of cancer development. Expert Rev Proteomics (2014) 11 149-65. DOI: 10.1586/14789450.2014.860358 22. Yu B, Chen X, Li J, Qu Y, Su L, Peng Y. Stromal fibroblasts in the microenvironment of gastric carcinomas promote tumor metastasis via upregulating TAGLN expression. BMC Cell Biol (2013) 14 17. DOI: 10.1186/1471-2121-14-17 23. Rho JH, Roehrl MH, Wang JY. Tissue proteomics reveals differential and compartment-specific expression of the homologs transgelin and transgelin-2 in lung adenocarcinoma and its stroma. J Proteome Res (2009) 8 5610-8. DOI: 10.1021/pr900705r 24. Zhou Y, Bian S, Zhou X, Cui Y, Wang W, Wen L. Single-cell multiomics sequencing reveals prevalent genomic alterations in tumor stromal cells of human colorectal cancer. Cancer Cell (2020) 38 818-28.e5. DOI: 10.1016/j.ccell.2020.09.015 25. Politi K, Zakowski MF, Fan PD, Schonfeld EA, Pao W, Varmus HE. Lung adenocarcinomas induced in mice by mutant EGF receptors found in human lung cancers respond to a tyrosine kinase inhibitor or to down-regulation of the receptors. Genes Dev (2006) 20 1496-510. DOI: 10.1101/gad.1417406 26. Huang Y, Yang X, Lu Y, Zhao Y, Meng R, Zhang S. UBE2O targets Mxi1 for ubiquitination and degradation to promote lung cancer progression and radioresistance. Cell Death Differ (2021) 28 671-84. DOI: 10.1038/s41418-020-00616-8 27. Orimo A, Gupta PB, Sgroi DC, Arenzana-Seisdedos F, Delaunay T, Naeem R. Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell. (2005) 121 335-48. DOI: 10.1016/j.cell.2005.02.034 28. Amornsupak K, Insawang T, Thuwajit P, O-Charoenrat P, Eccles SA, Thuwajit C. Cancer-associated fibroblasts induce high mobility group box 1 and contribute to resistance to doxorubicin in breast cancer cells. BMC Cancer (2014) 14 955. DOI: 10.1186/1471-2407-14-955 29. Zhang Y, Bian Y, Wang Y, Wang Y, Duan X, Han Y. HIF-1α is necessary for activation and tumour-promotion effect of cancer-associated fibroblasts in lung cancer. J Cell Mol Med (2021) 25 5457-69. DOI: 10.1111/jcmm.16556 30. Wang Y, Liu H, Bian Y, An J, Duan X, Wan J. Low SCRIB expression in fibroblasts promotes invasion of lung cancer cells. Life Sci (2020) 256 117955. DOI: 10.1016/j.lfs.2020.117955 31. Wang R, Lou X, Feng G, Chen J, Zhu L, Liu X. IL-17A-stimulated endothelial fatty acid β-oxidation promotes tumor angiogenesis. Life Sci (2019) 229 46-56. DOI: 10.1016/j.lfs.2019.05.030 32. Du C, Duan X, Yao X, Wan J, Cheng Y, Wang Y. Tumour-derived exosomal miR-3473b promotes lung tumour cell intrapulmonary colonization by activating the nuclear factor-κB of local fibroblasts. J Cell Mol Med (2020) 24 7802-13. DOI: 10.1111/jcmm.15411 33. Satoyoshi R, Kuriyama S, Aiba N, Yashiro M, Tanaka M. Asporin activates coordinated invasion of scirrhous gastric cancer and cancer-associated fibroblasts. Oncogene. (2015) 34 650-60. DOI: 10.1038/onc.2013.584 34. Satoyoshi R, Aiba N, Yanagihara K, Yashiro M, Tanaka M. Tks5 activation in mesothelial cells creates invasion front of peritoneal carcinomatosis. Oncogene. (2015) 34 3176-87. DOI: 10.1038/onc.2014.246 35. Sahai E, Astsaturov I, Cukierman E, DeNardo DG, Egeblad M, Evans RM. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer (2020) 20 174-86. DOI: 10.1038/s41568-019-0238-1 36. Chen X, Song E. Turning foes to friends: targeting cancer-associated fibroblasts. Nat Rev Drug Discov (2019) 18 99-115. DOI: 10.1038/s41573-018-0004-1 37. Su S, Chen J, Yao H, Liu J, Yu S, Lao L. CD10(+)GPR77(+) cancer-associated fibroblasts promote cancer formation and chemoresistance by sustaining cancer stemness. Cell. (2018) 172 841-56.e16. DOI: 10.1016/j.cell.2018.01.009 38. Qin JD, Cao ZH, Li XF, Kang XL, Xue Y, Li YL. Effect of ammonium pyrrolidine dithiocarbamate (PDTC) on NF-κB activation and CYP2E1 content of rats with immunological liver injury. Pharm Biol (2014) 52 1460-6. DOI: 10.3109/13880209.2014.898075 39. Németh ZH, Deitch EA, Szabó C, Haskó G. Pyrrolidinedithiocarbamate inhibits NF-kappaB activation and IL-8 production in intestinal epithelial cells. Immunol Lett (2003) 85 41-6. DOI: 10.1016/S0165-2478(02)00208-0 40. Tanaka T, Narazaki M, Kishimoto T. Interleukin (IL-6) immunotherapy. Cold Spring Harb Perspect Biol (2018) 10 a028456. DOI: 10.1101/cshperspect.a028456 41. Murakami M, Kamimura D, Hirano T. Pleiotropy and specificity: insights from the interleukin 6 family of cytokines. Immunity. (2019) 50 812-31. DOI: 10.1016/j.immuni.2019.03.027 42. Seifart C, Plagens A, Dempfle A, Clostermann U, Vogelmeier C, von Wichert P. TNF-alpha, TNF-beta, IL-6, and IL-10 polymorphisms in patients with lung cancer. Dis Markers (2005) 21 157-65. DOI: 10.1155/2005/707131 43. Zhang Y, Liao R, Li H, Liu L, Chen X, Chen H. Expression of cofilin-1 and transgelin in esophageal squamous cell carcinoma. Med Sci Monit (2015) 21 2659-65. DOI: 10.12659/MSM.895242 44. Xu L, Gao Y, Chen Y, Xiao Y, He Q, Qiu H. Quantitative proteomics reveals that distant recurrence-associated protein R-Ras and Transgelin predict post-surgical survival in patients with stage III colorectal cancer. Oncotarget. (2016) 7 43868-93. DOI: 10.18632/oncotarget.9701 45. Elyada E, Bolisetty M, Laise P, Flynn WF, Courtois ET, Burkhart RA. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov (2019) 9 1102-23. DOI: 10.1158/2159-8290.CD-19-0094 46. Wang Z. CAF heterogeneity and dynamics. Nat Cell Biol (2022) 24 1686. DOI: 10.1038/s41556-022-01054-z 47. Galbo PM, Zang X, Zheng D. Molecular features of cancer-associated fibroblast subtypes and their implication on cancer pathogenesis, prognosis, and immunotherapy resistance. Clin Cancer Res (2021) 27 2636-47. DOI: 10.1158/1078-0432.CCR-20-4226 48. Ni C, Lou X, Yao X, Wang L, Wan J, Duan X. ZIP1+ fibroblasts protect lung cancer against chemotherapy via connexin-43 mediated intercellular Zn2+ transfer. Nat Commun (2022) 13 5919. DOI: 10.1038/s41467-022-33521-4 49. Lew ZX, Zhou HM, Fang YY, Ye Z, Zhong W, Yang XY. Transgelin interacts with PARP1 in human colon cancer cells. Cancer Cell Int (2020) 20 366. DOI: 10.1186/s12935-020-01461-y 50. Tufan AB, Lazarow K, Kolesnichenko M, Sporbert A, von Kries JP, Scheidereit C. TSG101 associates with PARP1 and is essential for PARylation and DNA damage-induced NF-κB activation. EMBO J (2022) 41 e110372. DOI: 10.15252/embj.2021110372 51. Kobayashi H, Enomoto A, Woods SL, Burt AD, Takahashi M, Worthley DL. Cancer-associated fibroblasts in gastrointestinal cancer. Nat Rev Gastroenterol Hepatol (2019) 16 282-95. DOI: 10.1038/s41575-019-0115-0 52. Öhlund D, Handly-Santana A, Biffi G, Elyada E, Almeida AS, Ponz-Sarvise M. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J Exp Med (2017) 214 579-96. DOI: 10.1084/jem.20162024 53. Pan MS, Wang H, Ansari KH, Li XP, Sun W, Fan YZ. Gallbladder cancer-associated fibroblasts promote vasculogenic mimicry formation and tumor growth in gallbladder cancer via upregulating the expression of NOX4, a poor prognosis factor, through IL-6-JAK-STAT3 signal pathway. J Exp Clin Cancer Res (2020) 39 234. DOI: 10.1186/s13046-020-01742-4 54. Chan LC, Li CW, Xia W, Hsu JM, Lee HH, Cha JH. IL-6/JAK1 pathway drives PD-L1 Y112 phosphorylation to promote cancer immune evasion. J Clin Invest (2019) 129 3324-38. DOI: 10.1172/JCI126022 55. Lee JJ, Kim HJ, Yang CS, Kyeong HH, Choi JM, Hwang DE. A high-affinity protein binder that blocks the IL-6/STAT3 signaling pathway effectively suppresses non-small cell lung cancer. Mol Ther (2014) 22 1254-65. DOI: 10.1038/mt.2014.59 56. Li Z, Sun C, Qin Z. Metabolic reprogramming of cancer-associated fibroblasts and its effect on cancer cell reprogramming. Theranostics. (2021) 11 8322-36. DOI: 10.7150/thno.62378 57. Yu H, Königshoff M, Jayachandran A, Handley D, Seeger W, Kaminski N. Transgelin is a direct target of TGF-beta/Smad3-dependent epithelial cell migration in lung fibrosis. FASEB J (2008) 22 1778-89. DOI: 10.1096/fj.07-083857 58. Hu F, Song D, Yan Y, Huang C, Shen C, Lan J. IL-6 regulates autophagy and chemotherapy resistance by promoting BECN1 phosphorylation. Nat Commun (2021) 12 3651. DOI: 10.1038/s41467-021-23923-1 59. Karakasheva TA, Lin EW, Tang Q, Qiao E, Waldron TJ, Soni M. IL-6 mediates cross-talk between tumor cells and activated fibroblasts in the tumor microenvironment. Cancer Res (2018) 78 4957-70. DOI: 10.1158/0008-5472.CAN-17-2268 60. Huang X, Fang J, Lai W, Hu Y, Li L, Zhong Y. IL-6/STAT3 axis activates Glut5 to regulate fructose metabolism and tumorigenesis. Int J Biol Sci (2022) 18 3668-75. DOI: 10.7150/ijbs.68990 61. Zhou C, Bi F, Yuan J, Yang F, Sun S. Gain of UBE2D1 facilitates hepatocellular carcinoma progression and is associated with DNA damage caused by continuous IL-6. J Exp Clin Cancer Res (2018) 37 290. DOI: 10.1186/s13046-018-0951-8 62. Wunderlich CM, Ackermann PJ, Ostermann AL, Adams-Quack P, Vogt MC, Tran ML. Obesity exacerbates colitis-associated cancer via IL-6-regulated macrophage polarisation and CCL-20/CCR-6-mediated lymphocyte recruitment. Nat Commun (2018) 9 1646. DOI: 10.1038/s41467-018-03773-0
--- title: Reduction of BSI associated mortality after a sepsis project implementation in the ER of a tertiary referral hospital authors: - Elena Seminari - Marta Colaneri - Marta Corbella - Annalisa De Silvestri - Alba Muzzi - Stefano Perlini - Ilaria Francesca Martino - Lea Nadia Marvulli - Alessia Arcuri - Marcello Maffezzoni - Rita Minucci - Enrica Bono - Patrizia Cambieri - Piero Marone - Raffaele Bruno journal: Scientific Reports year: 2023 pmcid: PMC10060234 doi: 10.1038/s41598-023-31219-1 license: CC BY 4.0 --- # Reduction of BSI associated mortality after a sepsis project implementation in the ER of a tertiary referral hospital ## Abstract The emergency room (ER) is the first gateway for patients with sepsis to inpatient units, and identifying best practices and benchmarks to be applied in this setting might crucially result in better patient’s outcomes. In this study, we want to evaluate the results in terms of decreased the in-hospital mortality of patients with sepsis of a Sepsis Project developed in the ER. All patients admitted to the ER of our Hospital from the 1st January, 2016 to the 31stJuly 2019 with suspect of sepsis (MEWS score ≥ of 3) and positive blood culture upon ER admission were included in this retrospective observational study. The study comprises of two periods: Period A: From the 1st Jan 2016 to the 31st Dec 2017, before the implementation of the Sepsis project. Period B: From the 1st Jan 2018 to the 31stJul 2019, after the implementation of the Sepsis project. To analyze the difference in mortality between the two periods, a univariate and multivariate logistic regression was used. The risk of in-hospital mortality was expressed as an odds ratio (OR) and a $95\%$ confidence interval ($95\%$ CI). Overall, 722 patients admitted in ER had positive BC on admissions, 408 in period A and 314 in period B. In-hospital mortality was $18.9\%$ in period A and $12.7\%$ in period B ($$p \leq 0.03$$). At multivariable analysis, mortality was still reduced in period B compared to period A (OR 0.64, $95\%$ CI 0.41–0.98; $$p \leq 0.045$$). Having an infection due to GP bacteria or polymicrobial was associated with an increased risk of death, as it was having a neoplasm or diabetes. A marked reduction in in-hospital mortality of patients with documented BSI associated with signs or symptoms of sepsis after the implementation of a sepsis project based on the application of sepsis bundles in the ER. ## Introduction Sepsis is still a major cause of death. Since the emergency room (ER) is the first gateway for patients with sepsis, identifying best practices to be applied might crucially result in better patients’ outcomes. Although some shadows have been cast on the efficacy of sepsis bundles implementation1,2, others widely support its value3,4. The bundles have been demonstrated to improve survival, through modification of the physician’s clinical approach, and quality improvement5,6. As happens with poly-trauma, acute myocardial infarction and stroke7, also in the sepsis case, bundles application might avoid discontinuous and delayed treatments, by providing a standardised approach for timely and appropriate management of patients8. Although some studies found an association between timing of antibiotics and mortality in septic patients9,10, results from other studies have failed to observe it 11,12. Our objective was to retrospectively evaluate the results of a broader project developed in the ER to improve compliance with care bundles and standardise diagnostic and therapeutic intervention for early identification and fast treatment of septic adult patients. The project was focused on ameliorating sepsis diagnosis in association with an antimicrobial stewardship program based on proper blood cultures (BCs) collection. Specifically, the project was first aimed at identifying the potential septic patients immediately on arrival in the ER with a priority colour code (Fig. 1). This was followed by a standardised diagnostic and therapeutic management of the patient according to the MEWS score (Fig. 2).Figure 1Table for assignment of patient priority code at the ER triage. Figure 2Patient management flow chat at the ER. MEWS modified early warning score. First hour—“sepsis six” box is referred to the 3 diagnostic (take three) and 3 therapeutic (give three) steps to be delivered by staff within 1 h. To select only those patients with actual systemic infection, thus limiting the heterogeneity of the enrolled population, we included only the patients with positive BCs. For this purpose, we analysed the clinical outcome, defined as in hospital-all-cause mortality of patients accessing our ER with BSI during two different periods of time, from the 1st January 2016 to the 31st December 2017, and from the 1st January 2018 to the 31st December 2019, respectively. The watershed between these two periods was the Sepsis Project implementation. ## Results In period A 1402 sets of BCs were collected, $29.1\%$ of them were positive while in period B 1060 BC sets were collected, $29.6\%$ were positive (p = ns). Overall, 722 patients admitted in ER had positive BC on admissions, 408 in period A and 314 in period B, and these patients with positive blood culture were included in the present study. Patient’s characteristics in the two periods are summarised in Table 1, both groups were comparable for age, gender, and concomitant diseases, MEWS, and SOFA score. Table 1Patients’ general characteristics. Period APeriod BpAge76 (65–84)76 (67–84)0.6Female188 ($46\%$)220 ($54\%$)0.8Male142 ($45\%$)172 ($55\%$)SOFA3 (2–4)3 (2–5)0.13MEWS4 (3–6)4 (3–5)0.11HIV5 ($1.23\%$)2 ($0.64\%$)0.4CKD35 ($8.58\%$)9 ($2.87\%$)0.001DM72 ($17.65\%$)61 ($19.43\%$)0.5Neoplasms67 ($16.42\%$)41 ($13.06\%$)0.2CHD36 ($8.82\%$)23 ($7.32\%$)0.5ICU stay43 ($10.5\%$)29 ($9.2\%$)0.5Time to BCs collection3.3 (1–5)1.6 (0.5–4)0.04BCs turnaround time (hours)12.4 (10.1–17.1)12.4 (10–17.5)0.8Appropriated antimicrobial treatment266 ($65.2\%$)249 ($80.6\%$) < 0.001Antibiotic treatment started within 1 h27 ($8.8\%$)79 ($25.3\%$) < 0.001 3 h77 ($28.4\%$)101 ($32.4\%$) 6 h70 ($27.1\%$)62 ($19.9\%$) 24 h92 ($32\%$)70 ($22.4\%$)Color code associated mortality Green5 ($2.7\%$)1 ($1.1\%$) < 0.01 Yellow55 ($29.8\%$)30 ($15.2\%$) Red17 ($43.8\%$)9 ($40.9\%$)SOFA sequential organ failure assessment, MEWS modified early warning score, HIV human immunodeficiency virus, CKD chronic kidney disease, DM diabetes mellitus, ICU intensive care unit, CHD chronic hepatic disease, BCs blood cultures. BC turnaround time was 12.4 h (10.1–17.1) in period A and 12.4 h (10–17.5) in period 2 ($$p \leq 0.8$$).Microbiology data are summarized in Table 2.Table 2Bacteria isolated in blood cultures. Period APeriod BTotal bacteria isolated459366GN bacteria258 ($56.2\%$)218 (59.5) Escherichia coli WT130 ($28.32\%$)101 ($27.60\%$) Escherichia coli ESBL36 ($7.84\%$)29($7.92\%$) Klebsiella pneumoniae WT23 ($5.01\%$)16 ($4.37\%$) Klebsiella pneumoniae ESBL4 ($0.87\%$)7($1.91\%$) Klebsiella pneumoniae KPC4 ($0.87\%$)1 ($0.27\%$) Klebsiella spp. WT5 ($1.09\%$)8 ($2.19\%$) Klebsiella spp. ESBL1 ($0.22\%$) Proteus mirabilis13($2.83\%$)14($3.83\%$) Enterobacter WT6($1.31\%$)8 ($2.19\%$) Enterobacter EBSL1 ($0.22\%$)1 ($0.27\%$) Salmonella spp5 ($1.09\%$)2 ($0.55\%$) Serratiamarcescens WT2 ($0.44\%$)1 ($0.27\%$) Citrobacter spp.2 ($0.44\%$)1 ($0.27\%$) Providencia spp WT2 ($0.44\%$) Providencia spp ESBL1 ($0.22\%$) Hafnia alvei WT1 ($0.22\%$) Shigella spp1 ($0.22\%$) Pseudomonas aeruginosa13 ($2.83\%$)17 ($4.64\%$) Pseudomonas spp2 ($0.44\%$)1($0.27\%$) Acinetobacter baumanii1 ($0.27\%$) Acinetobacter spp1 ($0.22\%$)2($0.55\%$) Neisseria meningitidis1 ($0.22\%$)1 ($0.27\%$) Haemophilus influenzae1($0.22\%$)1($0.27\%$) Stenotophomonas maltophilia1 ($0.22\%$)1 ($0.27\%$) Leclercia adecarboxylata1 ($0.22\%$) Morganella morganii1 ($0.22\%$) Achromobacter xylosoxidans2 ($0.55\%$) Shewanella putrefaciens1 ($0.27\%$) Aeromonas caviae1 ($0.27\%$) Bordatella holmesii1 ($0.27\%$)GP bacteria189 (41.2)135 (36.9) Staphylococcus aureus MS45 ($9.8\%$)29 ($7.92\%$) Staphylococcus aureous MR17 ($3.7\%$)8 ($2.19\%$) Coagulase-negative Staphylococcus MS12 ($2.61\%$)8 ($2.19\%$) Coagulase-negative Staphylococcus MR20 ($4.36\%$)7 ($1.91\%$) Streptococcus pneumoniae24 ($5.23\%$)23 ($6.28\%$) Streptococcus gallolyticus5 ($1.09\%$)7 ($1.91\%$) Streptococcus anginosus5 ($1.09\%$)6 ($1.64\%$) Streptococcus agalactiae4 ($0.87\%$)3 ($0.82\%$) Streptococcus constellatus6 ($1.31\%$)1 ($0.27\%$) Streptococcus pyogenes3 ($0.65\%$)1 ($0.27\%$) Streptococcus viridans1 ($0.22\%$) Streptococcus spp12 ($2.61\%$)19 ($5.19\%$) Enterococcus faecalis19 ($4.14\%$)12 ($3.28\%$) Enterococcus faecium3 ($0.65\%$)6 ($1.64\%$) Enterococcus spp.5 ($1.09\%$)2 ($0.55\%$) Listeria monocytogenes5 ($1.09\%$)1($0.27\%$) Lactobacillus spp1 ($0.22\%$) Bacillus spp1 ($0.22\%$) Nocardia spp.1 ($0.22\%$) Aerococcus urinae1 ($0.27\%$) Corynebacterium spp.1 ($0.27\%$) Anaerobes8($1.74\%$)12($3.28\%$) Candida spp4 ($0.87\%$)1 ($0.27\%$)GP Gram positive, GN Gram negative, WT wild type, ESBL extended spectrum beta-lactamase, MS methicillin sensitive, MR methicillin resistant. Gram-negative (GN) bacteria were the principal isolated pathogens (in $56.2\%$ in period A and $59.5\%$ in period B) with Enterobacterales being responsible for $51.6\%$ of BSI episodes in both period A and B. The rate of extended-spectrum beta-lactamase (ESBL) producers was roughly $10\%$ and was stable among the 2 periods. Gram positive (GP) bacteria were documented in $41.2\%$ and $36.9\%$% of patients in period A and B respectively (being Staphylococcus aureus and Streptococcus pneumoniae the most represented). Polymicrobial BSI was recorded in $10.3\%$ and $11.8\%$ in periods A and B, respectively. The bacterial species were equally distributed among the 2 periods ($$p \leq 0.5$$). Among patients included in the present study, in-hospital mortality was $18.9\%$ in period A and $12.7\%$ in period B ($$p \leq 0.03$$). At multivariable analysis, mortality was reduced in period B compared to period A (OR 0.64, $95\%$ CI 0.41–0.98, $$p \leq 0.045$$). Having an infection due to GP bacteria or polymicrobial was associated with an increased risk of death, as it was having a neoplasm or diabetes (Table 3).Table 3Factors associated with the risk of death in univariate and multivariate analysis. Univariate analysisMultivariate analysisOR ($95\%$ CI)P valueOR ($95\%$ CI)P valueAge (per 1 year)1.04 (1.04–1.11)0.0001.04 (1.02–1.05) < 0.001GP bacteria versus GN bacteria2.01 (1.31–3.09)0.0012.47 (1.57–3.9) < 0.001Polimycrobial versus GNB1.86 (0.99–3.49)0.0521.95 (1.01–3.74)0.45Neoplasm2.06 (1.27–3.35)0.0032.06 (1.24–3.42)0.005DM0.42 (0.21–0.79)0.0070.4 (0.2–0.8)0.007Period B versus ACKD0.63 (0.41–0.95)0.39 (0.40–10.1)0.0270.64 (0.41–0.98)0.04DM diabetes, GP Gram positive, GN Gram negative, CKD chronic kidney disease, DM diabetes mellitus, CKD chronic kidney disease. Interestingly, we noticed a significant reduction in mortality according to the colour code of admission. In particular, mortality in patients admitted with the yellow code was $29.5\%$ in period A versus $15.2\%$ in period B (Table 1). Adequate antimicrobial treatment was observed in $65.2\%$ of patients in period A and in $80.6\%$ in period B ($p \leq 0.001$). The timing of antimicrobial therapy has been reported in Table 1, and there were statistically differences between the two periods ($p \leq 0.001$). Neither timing of antimicrobial therapy nor receiving adequate therapy were associated with reduced mortality, possibly because the study was not powered on these secondary endpoints. Twenty-nine patients ($9.2\%$) were admitted in ICU during period B. Age and having a GP BSI were associated with ICU admission. One out of 29 patients admitted to ICU did not receive adequate antimicrobial therapy in ER. Timing of antimicrobial therapy was not associated with ICU admission (Table 4).Table 4Factors associated with the risk of ICU stay in univariate and multivariate analysis. Univariable analysisMultivariable analysisOR ($95\%$ CI)POR ($95\%$ CI)pPeriodo B versus period A0.86 (0.53–1.42)0.560.86 (0.53–1.48)0.661Age0.97 (0.96–0.98) < 0.0010.97 (0.96–0.98)0.000GPB versus GNB2.8 (1.21–6.5)0.021.34 (0.79–2.28)0.279GPB Gram-positive bacteria, GNB Gram-negative bacteria. To evaluate how the project affected the outcome of all patients with suspect of sepsis, the mortality in patients who collected blood cultures (1402 and 1060 in the two periods) was evaluated and was $13.8\%$ and $11.1\%$, in period A and B respectively ($$p \leq 0.0063$$). To evaluate the impact of the antimicrobial use on hospital microbial ecology in the two periods, Clostridium difficile colitis were compared. No difference was observed in cases of Clostridium difficile colitis in the two periods (220 in period A and 232 in period B, p = ns). Moreover the incidence of Enterobacteriaceae carbaenemase resistant (infection/colonization) in the two period incidence was stable (379 in period A and 287 in period B, p = ns). ## Discussion After the implementation of a sepsis project based on the bundles as in the Surviving Sepsis campaign in the ER of our Hospital, a reduction in in-hospital mortality of patients with documented BSI associated with signs or symptoms of sepsis was observed. Although this result is in line with other studies13, some scepticism was also observed 14,15. As a result, the real advantages of sepsis bundles have been questioned, and uncertain data are currently available. The main feature of our study has been the inclusion of those patients with positive BC, rather than all those with a clinical suspicion of sepsis, as the sepsis clinical presentation might frequently resemble that of other morbid conditions16. Although we agree with Fuchs et al. ’s findings that a standardised method of BCs may positively contribute to optimised management of sepsis, and consequently to an improved survival rate of septic patients14, it should be mentioned that BCs collection procedures have been already standardised during period A in our ER. This virtuous practice, which had already been introduced in the period A, and consistently maintained throughout the period B, allowed to attain a positive BCs rate around $30\%$. Since a BCs low yield may lead to prolonged hospitalisation, and broad antibiotic usage17, ours is undoubtedly a remarkable result. The availability of an antimicrobial stewardship program allowed the setting of appropriate treatment in a significant slice of patients ($80\%$) within 24 h. A major advantage for the timely provision of proper antimicrobial treatment, was the short turn-around time of BCs (12.4 h), which allowed the ID specialists to quickly acquire the necessary data to prescribe a proper therapy. However, concerning the timely administration of antimicrobial therapy within one to six hours from admission, our study failed in finding a significant association between the timely antimicrobials administration and favorable clinical outcome, consistently with others18,19, perhaps also because the study was not powered on this secondary objective. Patients with yellow code on admission had the greatest benefit in terms of mortality. This data is intriguing, as it is the most frequently observed presenting pattern in clinical practice, where the application of the bundle finds a more prominent rationale. Possibly, in these patients, a prompt antibiotic therapy might have a more significant impact on septic shock patients’ outcomes20. Moreover, our nurses' and physicians’ attention to sepsis significantly increased, consequently reducing their operating variability. This certainly played a key role, underlying the importance of standardising clinical practice. In our view, since no standardised guidelines are applicable to every healthcare setting, it is of paramount value that each hospital develops and closely adheres to its own internal procedures, which best reflect its own internal organisation. Furthermore, reporting the obtained results help comparing the different strategies. Finally, regarding the microbiological aetiology of the observed BSI, the higher death rate associated with GP rather than GN microorganisms in our analyses, is not universally recognized21. However, over recent years, multidrug-resistant patterns in GP bacteria have resulted in difficult-to-treat infections, which in turn caused a current, overall increase of mortality22. Moreover, Staphylococcus aureus and coagulase-negative staphylococci, which were mostly isolated in our patients, are the leading cause of serious infections, such as endocarditis, with, furthermore, an increasing, high rate of methicillin resistance. Our results confirm the opinion that, although recent global attention has focused on the issue of multidrug resistance (MDR) in GN bacteria, GPs are also a serious concern. Our study has several limitations. Firstly, it is a single-centre setting, with a relatively small number of patients and secondly, due to its retrospective nature. Moreover, the implementation of the Sepsis Project was associated with a mortality improvement, rather than being representative of causal factors. There is uncertainty about the mortality benefit because it cannot be definitively stated if this is due to increased physicians' and nurses’ awareness of severe sepsis, the Sepsis Project implementation, or other, unrelated determinants. However, in summary, the difference in terms of saved human lives between period A and period B, pre and post Sepsis Project implementation respectively, has been overwhelming, and we can assume that this result came from an overall improvement in sepsis management and establishing a quality pathway, from the ER front door toward ward-admission, which we believe that it’s crucial to report. ## Data source and study design This is a retrospective observational study, conducted at our Hospital, Fondazione IRCCS Policlinico San Matteo of Pavia, Northern Italy. The study was approved by the local Research Ethics Committee Foundation (P-20200109218, prot 20210015825). All patients admitted to the ER of our Hospital from the 1st January, 2016 to the 31st July 2019 were identified through electronic records and their medical data were retrospectively collected. The study comprises of two periods: Period A: From the 1st Jan 2016 to the 31st Dec 2017 introduction of BC collection in ER $\frac{24}{24}$ h. Period B: From the 1st Jan 2018 to the 31st Jul 2019 introduction of the Sepsis project in ER. ## The sepsis project The aim of the sepsis project implementation in the ER was to standardise diagnostic and therapeutic intervention for early identification and fast treatment of septic adult patients. Due to the knowledge that only a multifaceted approach might be effective, a multidisciplinary team was assembled. It was composed of a medical direction member and one specialist doctor for each of the following fields: emergency medicine, resuscitation and anesthesiology, infectious diseases (ID), and microbiology. A fundamental culture change from the ER staff was the desired objective and this team was first accountable for raising awareness of the existing problem, and the consequent demand for improvement. The specific roles and responsibilities of these specialists have been thoroughly defined and a diagnostic and therapeutic plan has been set up. The strength of the sepsis project has been to combine practical and educational interventions. Practically, a “sepsis pathway” has been designed, to identify the potential septic patients immediately on arrival in the ER, as it happens for stroke and myocardial infarction. This “on door” identification has been conceived to minimise the waste of valuable time. In more detail, a triage nurse collects a brief anamnesis and firstly assigns to the patient a priority colour code, according to the MEWS score (Fig. 1). A MEWS score ≥ 3 with signs or symptoms of infections prompts a yellow priority code and led directly to the ER evaluation box, where a combined nurse and physician’s intervention has been organized as follows. A nurse, assigned to the examination room, reassesses the patient’s vital signs, finds proper venous access, and performs laboratory tests and 2 sets of BCs. A physician visits the patient and prescribes treatments (fluids, antimicrobial therapy, dopamine if necessary), alerts, if necessary, other consultants (resuscitation and anesthesiology and/or ID specialists,) and transfers the patient to the most suitable department. The color code included the follows criteria: red color when the MEWS score is ≥ 9 and the patient is in immediate risk for life, yellow code for MEWS ≥ 3 and < 9, green code for MES < 3.Moreover, requiring blood tests have been simplified by generating sepsis-specific panels and a summary algorithm has been designed. Particularly, the “First Hour- Sepsis six” bundle is referred to the 3 diagnostic (take three) and 3 therapeutic (give three) steps to be delivered by staff within 1 h (Fig. 2). Specific training of the nurses has been focused on the correct BCs pre-analytical phase, and the automated BC system BacT/ALERT (bioMérieux SA, Marcy-l’Etoile, France) had been placed in the ER23. Workshops and conferences have been organised and conducted every week for a year, focusing on the recognition, monitoring, and management of septic patients. To ensure that improvements were sustainable in the long term, recruitment of new members of the team had followed a similar practice. To achieve greater staff compliance, minimising potential oversights, poster formats of the Sepsis bundles algorithm were displayed in all the ER areas. ## Blood cultures BCs were processed as previously described15. Positive BCs were defined according to Weinstein 8. Coagulase-negative staphylococci, aerobic and anaerobic diphtheroids, Micrococcus spp., Bacillus spp., and viridans streptococci were considered contaminants if only one bottle was positive, and susceptibility testing was not performed. An alert system was implemented in case of positive BCs that consisted of a phone call to the ward in charge of the patient and e-mail was automatically sent to the ID consultant physicians. The organisms are identified through Gram stain on smears and by Matrix-Assisted Laser Desorption Ionization time-of-flight (MALDI-TOF) (Bruker Daltonics GmbH, Bremen, Germany) or by biochemical tests using Phoenix 100 (BD) automated system N-MIC/ID or P-MIC/ID panel GmbH, Bremen, Germany). The antimicrobial susceptibility is tested using Phoenix 100 (BD).A real-time PCR technology GeneXpert system (Cepheid, Sunnyvale, CA), performed according to the manufacturer's instructions, is used to detect methicillin/oxacillin resistance for BCs growing Gram-positive (GP). A multiplex nested PCR FilmArray enables rapid (1 h) and accurate detection of 24 pathogens (bacteria and yeasts) and 3 antibiotic resistance genes and rapid immune-chromatographic tests are used for the determination of resistance markers. ## Antimicrobial stewardship The ER staff has been taught to consult an on-call 24 h a day ID specialist to set the most appropriate antimicrobial regimen, once microbiological results are available. Besides, online internal guidelines have been provided for empirical treatment of lung, urinary tract, abdominal and skin and soft tissue infections. For carbapenems, linezolid, tigecycline, and daptomycin and 4th generation cephalosporin an ID consultation was considered mandatory. According to the in vitro sensitivity of the isolated microorganisms, an empirical treatment has been considered appropriate if started within the first 24 h from admission, if effective according to the in vitro results, adequate to the site of infection, and administered at the appropriate dose and schedule. To evaluate the impact of antimicrobial use in hospital microbial ecology Clostridium difficile infection registered in the two periods were compared. No difference was observed in cases of Clostridium difficile infection in the two periods (220 in period A and 232 in period B). Moreover the incidence of Enterobacteralescarbapenemase resistant (infection/colonization) in the two period incidence was stable (379 in period A and 287 in period B). ## Inclusion criteria and patients’ characteristics All the patients who fulfilled the following inclusion criteria were then recruited into the study:Patients with suspect of sepsis with a MEWS score ≥ of 3 upon admission in the ER andpositive BC(s). ## Variables The available data for both the considered periods included demographics (sex and age), co-morbidities (cancer, diabetes, hypertension, chronic kidney disease and chronic liver disease), and microbiological data (type of microorganism causing the infection with specific susceptibility profiles) and the severity scores (Sequential Organ Failure Assessment [SOFA] and modified early warning scoring [MEWS])24. Differently, appropriateness and timing of antimicrobial therapy were retrospectively collected only for period B. ## Outcomes of the study The primary outcome of the study was in-hospital all-cause mortality. The study aimed to evaluate the effects of the sepsis project implementation on patients’ in-hospital all-cause mortality, by comparing mortality rates between the 2 considered periods (both A and B period). Secondary outcomes included intensive care unit (ICU) admission rate in the two periods. Moreover, the impact of appropriate and timely antibiotic administration (within 1 h, within 3 h, within 6 h and within 24 h since ER admission) on in-hospital mortality and ICU admission was evaluated in period B only. ## Accordance statement The study protocol was written in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. ## Approval statement The SePs Study was approved by the Ethical Committee of Fondazione IRCCS Policlinico San Matteo, PAvia (Comitato Etico Pavia, no. prot. P-20200109218, prot 20210015825). All participants have signed an informed consent for data collection. ## Statistical methods Continuous variables were expressed as a median and interquartile range. Qualitative variables were expressed as they absolute value accompanied by a percentage. The median values for the continuous variables from both groups were compared using the Mann–Whitney U test. Proportions were compared using the chi-squared test. To analyze the difference in mortality between the two periods, an univariate and multivariate logistic regression was used. The risk of in-hospital mortality was expressed as an odds ratio (OR) and a $95\%$ confidence interval ($95\%$ CI). The multivariate analysis included the variables that demonstrated differences with a $p \leq 0.1$ in the univariate analysis. ## References 1. Marik PE, Farkas JD, Spiegel R. POINT: Should the surviving sepsis campaign guidelines be retired?. Yes. Chest. (2019) 155 12-14. DOI: 10.1016/j.chest.2018.10.008 2. Klompas M, Calandra T, Singer M. Antibiotics for sepsis—Finding the equilibrium. JAMA J. Am. Med. Assoc. (2018) 320 1433-1434. DOI: 10.1001/jama.2018.12179 3. Levy MM, Rhodes A, Evans LE. Counterpoint: Should the surviving sepsis campaign guidelines be retired?. No. Chest. (2019) 155 14-17. DOI: 10.1016/j.chest.2018.10.012 4. Daniels R, Nutbeam T, McNamara G, Galvin C. The sepsis six and the severe sepsis resuscitation bundle: A prospective observational cohort study. Emerg. Med. J. (2011) 28 507-512. DOI: 10.1136/emj.2010.095067 5. Ferrer R, Artigas A, Levy MM. Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain. JAMA-J. Am. Med. Assoc. (2008) 299 2294. DOI: 10.1001/jama.299.19.2294 6. Nguyen HB, Corbett SW, Steele R. Implementation of a bundle of quality indicators for the early management of severe sepsis and septic shock is associated with decreased mortality. Crit. Care Med. (2007) 299 2294-2303. DOI: 10.1001/jama.299.19.2294 7. Seymour CW, Rea TD, Kahn JM, Walkey AJ, Yealy DM, Angus DC. Severe sepsis in pre-hospital emergency care: Analysis of incidence, care, and outcome. Am. J. Respir. Crit. Care Med. (2012) 186 1264-1271. DOI: 10.1164/rccm.201204-0713OC 8. Santarossa M, Kilber EN, Wenzler E, Albarillo FS, Sterk EJ. BundlED Up: A narrative review of antimicrobial stewardship initiatives and bundles in the emergency department. Pharmacy (2019) 7 145. DOI: 10.3390/pharmacy7040145 9. Ferrer R, Martin-Loeches I, Phillips G. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: Results from a guideline-based performance improvement program. Crit. Care Med. (2014) 42 1749-1755. DOI: 10.1097/CCM.0000000000000330 10. Rhodes A, Evans LE, Alhazzani W. Surviving sepsis campaign: International guidelines for management of sepsis and septic shock: 2016. Intensive Care Med. (2017) 43 304-377. DOI: 10.1007/s00134-017-4683-6 11. Nygård ST, Langeland N, Flaatten HK, Fanebust R, Haugen O, Skrede S. Aetiology, antimicrobial therapy and outcome of patients with community acquired severe sepsis: A prospective study in a Norwegian university hospital. BMC Infect. Dis. (2014) 14 121. DOI: 10.1186/1471-2334-14-121 12. de Groot B, Ansems A, Gerling DH. The association between time to antibiotics and relevant clinical outcomes in emergency department patients with various stages of sepsis: A prospective multi-center study. Crit. Care (2015) 19 194. DOI: 10.1186/s13054-015-0936-3 13. Raith EP, Udy AA, Bailey M. Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA-J. Am. Med. Assoc. (2017) 317 290-300. DOI: 10.1001/jama.2016.20328 14. Fuchs A, Tufa TB, Hörner J, Hurissa Z, Nordmann T, Bosselmann M, Abdissa S, Fuchs A, Tufa TB, Hörner J, Hurissa Z, Nordmann T, Bosselmann M, Abdissa S, Sorsa A, Orth HM, Jensen BO, MacKenzie C, Pfeffer K, Kaasch AJ, Bode JG, Häussinger D, Feldt Torsa AFT. Clinical and microbiological characterization of sepsis and evaluation of sepsis scores. PLoS ONE (2021) 16 e0247646. DOI: 10.1371/journal.pone.0247646 15. Roberts FJ, Geere IW. A three-year study of positive blood cultures, with emphasis on prognosis. Rev. Infect. Dis. (1991) 13 34-46. DOI: 10.1093/clinids/13.1.34 16. Klein Klouwenberg PMC, Cremer OL, van Vught LA. Likelihood of infection in patients with presumed sepsis at the time of intensive care unit admission: A cohort study. Crit. Care (2015) 19 319. DOI: 10.1186/s13054-015-1035-1 17. Darby JM, Linden P, Pasculle W. Utilization and diagnostic yield of blood cultures in a surgical intensive care unit. Crit. Care Med. (1997). DOI: 10.1097/00003246-199706000-00016 18. Abe T, Kushimoto S, Tokuda Y. Implementation of earlier antibiotic administration in patients with severe sepsis and septic shock in Japan: A descriptive analysis of a prospective observational study. Crit. Care (2019) 23 360. DOI: 10.1186/s13054-019-2644-x 19. Castaño P, Plaza M, Molina F. Antimicrobial agent prescription: A prospective cohort study in patients with sepsis and septic shock. Trop. Med. Int. Health (2019) 24 175-184. DOI: 10.1111/tmi.13186 20. Seok H, Song J, Jeon JH. Timing of antibiotics in septic patients: A prospective cohort study. Clin. Microbiol. Infect. (2020) 26 1495-1500. DOI: 10.1016/j.cmi.2020.01.037 21. Mouncey PR, Osborn TM, Power GS. Trial of early, goal-directed resuscitation for septic shock for the ProMISe Trial Investigators. N. Engl. J. Med. (2015) 372 1301-1311. DOI: 10.1056/NEJMoa1500896 22. Papadimitriou-Olivgeris M, Kolonitsiou F, Karamouzos V. Molecular characteristics and predictors of mortality among Gram-positive bacteria isolated from bloodstream infections in critically ill patients during a 5-year period (2012–2016). Eur. J. Clin. Microbiol. Infect. Dis. (2020) 39 863-869. DOI: 10.1007/s10096-019-03803-9 23. Mariani B, Corbella M, Seminari E. Evaluation of a model to improve collection of blood cultures in patients with sepsis in the emergency room. Eur. J. Clin. Microbiol. Infect. Dis. (2018) 37 241-246. DOI: 10.1007/s10096-017-3122-5 24. Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified early warning score in medical admissions. QJM-Mon. J. Assoc. Phys. (2001) 94 521-526. DOI: 10.1093/qjmed/94.10.521
--- title: Possible contribution of phosphate to the pathogenesis of chronic kidney disease in dolphins authors: - Nourin Jahan - Hiroyuki Ohsaki - Kiyoko Kaneko - Asadur Rahman - Takeshi Nishiyama - Makoto Koizumi - Shuichiro Yamanaka - Kento Kitada - Yuki Sugiura - Kenji Matsui - Takashi Yokoo - Takayuki Hamano - Makoto Kuro-o - Takuya Itou - Miwa Suzuki - Keiichi Ueda - Akira Nishiyama journal: Scientific Reports year: 2023 pmcid: PMC10060237 doi: 10.1038/s41598-023-32399-6 license: CC BY 4.0 --- # Possible contribution of phosphate to the pathogenesis of chronic kidney disease in dolphins ## Abstract This study aimed to investigate whether phosphate contributes to the pathogenesis of chronic kidney disease (CKD) in dolphins. Renal necropsy tissue of an aged captive dolphin was analyzed and in vitro experiments using cultured immortalized dolphin proximal tubular (DolKT-1) cells were performed. An older dolphin in captivity died of myocarditis, but its renal function was within the normal range until shortly before death. In renal necropsy tissue, obvious glomerular and tubulointerstitial changes were not observed except for renal infarction resulting from myocarditis. However, a computed tomography scan showed medullary calcification in reniculi. Micro area X-ray diffractometry and infrared absorption spectrometry showed that the calcified areas were primarily composed of hydroxyapatite. In vitro experiments showed that treatment with both phosphate and calciprotein particles (CPPs) resulted in cell viability loss and lactate dehydrogenase release in DolKT-1 cells. However, treatment with magnesium markedly attenuated this cellular injury induced by phosphate, but not by CPPs. Magnesium dose-dependently decreased CPP formation. These data support the hypothesis that continuous exposure to high phosphate contributes to the progression of CKD in captive-aged dolphins. Our data also suggest that phosphate-induced renal injury is mediated by CPP formation in dolphins, and it is attenuated by magnesium administration. ## Introduction As humanity enters a super-aging society, the number of patients with chronic kidney disease (CKD) is dramatically increasing1. Although the pathophysiology of age-related CKD is likely due to multiple factors2, the potential role of phosphate, which accelerates aging, has been recently attracting attention3–5. In mammals, a small increase in blood phosphate and calcium concentrations due to a transient increase after a meal may induce a risk of calcium phosphate precipitation6,27. However, when the blood phosphate concentrations increase, fibroblast growth factor 23 is secreted from bone and acts on the αKlotho and fibroblast growth factor receptor complex in the proximal tubule to promote phosphate excretion into the urine7–9. Furthermore, calcium phosphate precipitated in the blood is quickly adsorbed by fetuin-A and does not form large clumps10. The adsorbed calcium phosphate is dispersed in the blood as microscopic colloidal calciprotein particles (CPPs). CPPs sequester phosphate and calcium preventing ectopic calcification caused by the precipitation of calcium phosphate into extraosseous tissues11. However, CPP agglomeration was recently shown to induce chronic inflammation and calcification in vascular tissues which contribute to the development of CKD12,13. Formed CPPs bind to toll-like receptor 4 expressed on tubular cells and are taken up into the tubules, causing tubular cell injury and inducing fibrosis of the renal interstitium13. Interestingly, in Dahl salt-sensitive hypertensive rats with normal blood phosphate concentrations, a phosphate binding agent does not alter serum phosphate concentrations but shows renoprotective effects by reducing urinary phosphorus excretion and suppressing CPP formation in renal tissue12. These data suggest that even if blood phosphate concentrations are maintained within the normal range, an increase in phosphate urinary excretion causes renal tissue injury by intratubular CPP formation12,13. Excessive phosphate intake has been reported to induce CKD not only in humans but also in mammals across species14,15. Furthermore, the International Renal Interest Society, which was created to advance the scientific understanding of kidney disease in small animals, recommends feeding a low-phosphate diet16. However, no reports have examined the relationship between CKD and phosphate in cetaceans, which are marine mammals. In particular, the modern captive dolphin society is facing a super-aging population, and cases of deaths with declining kidney function have been often reported17. Indeed, the average lifespan of wild bottlenose dolphins is reported to be 20–30 years old18. However, some individuals have been reported to live to be older than 50 years old in captivity with proper treatment for infectious diseases and adequate nutrition19. Captive dolphins eat fish and squid as staple foods, both of which contain high levels of animal phosphate20. Therefore, we hypothesized that captive dolphins are at increased risk of developing CKD due to phosphate as they age. Clinical studies have indicated a possible relationship between the risk of phosphate-induced progression of CKD and serum magnesium concentrations21. We also hypothesized that magnesium attenuates phosphate-induced renal injury. To test these hypotheses, we investigated the presence of phosphate in components of renal necropsy tissue in an aged dolphin, and examined the toxicity of phosphate and the effects of magnesium in immortalized cultured dolphin tubular (DolKT-1) cells. ## Pathological findings and necropsy tissue sampling An older dolphin (estimated to be > 50 years old) in captivity died of myocarditis and other causes. The heart, kidneys, lungs, liver, spleen, pancreas, testes, and diaphragmatic lymph nodes were removed. Dissected tissues were fixed with paraformaldehyde and part of the renal tissue were frozen for analyses. A histopathological examination by a pathologist showed that the mitral valve had numerous bacterial clusters and pyogenic inflammation, which indicated that bacterial endocarditis had occurred. Focal necrosis had occurred in the left ventricle and left kidney, which suggested infarction due to bacteria and thrombus from the mitral valve. The lungs showed focal interstitial fibrosis, which was thought to be an old change. Pyogenic enteritis was also observed in the intestines. The histopathological diagnosis was bacterial endocarditis, left ventricular infarction, left renal infarction, and suppurative enteritis, while there was no major change in the liver, spleen, adrenal gland, testes, or lymph nodes. ## Computed tomography (CT) scan A computed tomographic (CT) scan of the removed right kidney showed many high-density areas in the reniculi in the medullary portion, which could be considered calcification (Fig. 1A). Similarly, high-density areas were observed in a series of cross-sectional movies of the reniculi taken using micro-CT (Supplemental Movie Data).Figure 1CT scan and histological findings in necropsy tissues in a dolphin. Representative CT image of the right kidney (A). A CT scan shows many high-density areas in the reniculi in the medullary portion of the kidney. Histology with HE staining (B,C original magnification, × 40 and 100, respectively) and Von Kossa (D,E original magnification, × 100 and 400, respectively) staining, respectively. There is no obvious abnormality in the renal tissues or vessels except infarcted area, but minor glomerular sclerosis and interstitial fibrosis can be seen. Von Kossa staining is positive in part of the medullary region. CT computed tomography, HE hematoxylin and eosin. ## Micro area X-ray diffractometry and infrared absorption (IR) spectroscopy Dried medullary tissues were analyzed with microbeam X-rays and IR spectroscopy. As shown in Supplementary Figs. S1A and S1B, micro area X-ray analysis determined high-density area to contain hydroxyapatite (calcium phosphate). On the other hand, IR spectroscopy data showed that absorption of reniculi tissues of high-density area in the medulla was observed at 1457, 1040, 873, 606, and 567 cm−1 (green line, Supplementary Fig. S1C). When the absorption wave number was compared with reported IR data, this renal tissue was shown to contain hydroxyapatite. Interestingly, in tissues of normal- or low-density area, absorption at 1040 cm−1 was smaller, and those at 873, 606, and 567 cm−1 was not observed (red line). These data indicate that reniculi medullary tissues contain hydroxyapatite (calcium phosphate). ## Histological findings Hematoxylin and eosin (HE) staining of formalin-fixed renal tissue showed no obvious abnormalities in the renal tissue or vessels except infarcted area, but some parts of the glomerulus and tubulointerstitium appeared to have sclerosis and fibrosis, respectively (Fig. 1B,C). However, Von Kossa staining showed positive staining in a part of the medullary region (Fig. 1D,E). ## Effects of phosphate and magnesium on DolKT-1 cell morphology DolKT-1 cells were exposed to two different concentrations of phosphate at 1.5 and 2 mM. The treatment with 1.5 mM phosphate resulted in moderate shrinkage and a rounded shape of cells. However, 2 mM phosphate for 72 h induced dramatic morphological changes, such as cell shrinkage, rounding, and loss of cell attachment to the substratum compared with the control treatment (0.9 mM phosphate and 0.8 mM magnesium). We also examined the effect of magnesium on phosphate-induced DolKT-1 cellular changes. We found that pretreatment with 2 mM magnesium completely abolished the phosphate-induced morphological changes (Fig. 2A).Figure 2Effects of phosphate and magnesium on DolKT-1 cell calcification. Morphological features of cells exposed to 0.9 mM phosphate and 0.8 mM magnesium (control), 1.5 mM phosphate, 2 mM phosphate, 1.5 mM phosphate + 2 mM magnesium, and 2 mM phosphate + 2 mM magnesium (A). The images were taken after 72 h of exposure. Magnification, × 42. Von Kossa staining shows calcium deposition in each experimental group at 3, 5, and 7 days of exposure. Images are representative of three separate experiments (B). Quantitative analysis of Von Kossa staining was performed using morphometric analysis by Image J (Image J bundled with 64-bit Java 8; https://imagej.nih.gov/ij/download.html?fbclid=IwAR060tXklMhoVowfvHzwq9sNxID7_IkQBrPxqD6Ej4fN68jqlGbYma40eFc) on the pooled results of three separate experiments (C). $$n = 4$$ for each group in each culture condition. *** $P \leq 0.001$ vs. control; #$P \leq 0.05$, ###$P \leq 0.001$ vs. 2 mM phosphate, respectively. DolKT-1 cells Dolphin proximal tubular cells. ## Effects of phosphate and magnesium on DolKT-1 cell calcification Von Kossa staining showed that phosphate dose-dependently increased calcium deposition in DolKT-1 cells (Fig. 2B). Dense calcification was observed after 2 mM phosphate exposure for 3–7 days. Morphometric analysis of calcium deposition showed that phosphate-induced calcification was completely abolished by the addition of magnesium. Concomitant treatment with magnesium caused a significant reduction in calcification within 3 days of intervention ($P \leq 0.05$, Fig. 2C). Similar inhibition was also observed on day 5 and 7 ($P \leq 0.001$, respectively). ## Effects of phosphate and magnesium on DolKT-1 cell viability The cell viability was analyzed by the water-soluble tetrazolium salt-1 (WST-1) assay following 48 h of exposure to phosphate and concomitant treatment with magnesium. Phosphate dose-dependently decreased cell viability in DolKT-1 cells ($P \leq 0.005$, Fig. 3A). However, concomitant treatment with magnesium markedly attenuated the phosphate-induced reduction in cell viability. Figure 3Effects of phosphate and magnesium on DolKT-1 cell viability and injury. Cell viability was quantified by the WST-1 assay after 48 h of exposure with various experimental groups and expressed as the fold change with the live cells in the control group (A). $$n = 4$$ or 5 for each group. Cytotoxicity was detected by LDH assay after 48 h of exposure (B). $$n = 5$$ for each group. * $P \leq 0.05$, $P \leq 0.01$ vs. control; #$P \leq 0.05$, ###$P \leq 0.001$ vs. 2 mM phosphate, respectively. WST-1 water-soluble tetrazolium salt-1, LDH lactate dehydrogenase. ## Effects of phosphate and magnesium on DolKT-1 cellular injury Cellular injury was evaluated by lactate dehydrogenase (LDH) release in DolKT-1 cells (Fig. 3B). Treatment with phosphate dose-dependently elevated LDH release ($P \leq 0.05$). However, concomitant treatment with magnesium markedly attenuated the phosphate-induced LDH release. These data suggested that magnesium protected the DolKT-1 cells against phosphate-induced cytotoxicity. ## Effects of phosphate on DolKT-1 cell apoptosis To detect apoptosis, flow cytometry was performed by counting annexin V- and propidium iodide (PI)-stained positive cells. The proportion of apoptotic cells (annexin V-positive and PI-negative cells indicated early apoptosis, and annexin V-positive and PI-positive cells indicated late apoptosis) was not significantly changed by phosphate or magnesium (Fig. 4A,B).Figure 4Effects of phosphate on DolKT-1 cell apoptosis and mitochondrial function. Apoptotic cells were determined using flowcytometry with annexin V/PI double staining (A). Data were analyzed by CytExpert ver. 2.3 (https://www.beckman.com/flow-cytometry/research-flow-cytometers/cytoflex/software). The bar graph shows the percentage of apoptotic cells in each experimental group, $$n = 4$$ for each group (B). DolKT-1 cells were pretreated with 2 mM phosphate for 36 h and mitochondrial function was measured by flux analysis. Oxygen consumption rate (C), proton leak (D), ATP production (E), and basal respiration (F). $$n = 2$$ for each group. n.s not significant, PI propidium iodide. ## Effects of phosphate on mitochondrial function in DolKT-1 cells Flux analysis was performed to investigate mitochondrial dysfunction and damage in DolKT-1 cells. The oxygen consumption rate was measured (Fig. 4C). Treatment with 2 mM phosphate did not result in any significant difference in proton leak, which is an indicator of mitochondrial damage (Fig. 4D), ATP production (Fig. 4E), or basal respiration (Fig. 4F). ## Effects of CPPs on DolKT-1 cell damage To investigate whether phosphate-induced cell injury is mediated through CPP formation, we centrifuged the high phosphate- and calcium-containing medium at 16,000×g for 2 h to precipitate the CPPs and removed the supernatant. We, then treated the cells with supernatant and CPPs for 24 h. We found that CPPs significantly decreased cell viability ($P \leq 0.0001$, Fig. 5A) and increased LDH release ($P \leq 0.05$, Fig. 5B), while these changes were not observed by treatment with only the supernatant. These data suggested that CPPs were responsible for causing cell damage. Figure 5Effects of CPPs on DolKT-1 cell damage. Cell viability of DolKT-1 cells was measured by using a WST-1 method. Cells were treated for 24 h with the control medium, the supernatant of the high phosphate plus high calcium-containing medium after centrifugation, or CPP suspension (A). $$n = 6$$ for each group. LDH release was measured after treatment with control solution, supernatant, or CPPs for 24 h (B). $$n = 5$$ for each group. $P \leq 0.01$, *$P \leq 0.001$ vs. control. CPP calciprotein particles. ## Effects of magnesium on CPP-induced DolKT-1 cell damage The administration of CPPs resulted in a significant reduction in cell viability ($P \leq 0.0001$, Fig. 6A). Supplementation with magnesium did not attenuate the CPP-induced reduction in DolKT-1 cell viability. The administration of CPPs significantly increased LDH release ($P \leq 0.0001$, Fig. 6B), which was not changed by treatment with magnesium. Figure 6Effects of magnesium on CPP-induced DolKT-1 cell damage. The cell viability was measured by the WST-1 method after 48 h of exposure in the experimental groups (A). Data are expressed as the fold change level of the live cells in the control group. $$n = 8$$ for each group. LDH release was measured in the control, CPP, and CPP + magnesium groups after 24 h of exposure (B). $$n = 5$$ for each group. * $P \leq 0.001$ vs. control. ## Effects of magnesium on CPP formation To investigate the mechanism by which magnesium attenuates phosphate-induced cell injury, we evaluated CPP formation by measuring the absorbance when concentrations of high phosphate (5 mM), high calcium (6 mM), and high magnesium (2 or 5 mM) were mixed together in the absence or presence of fetal bovine serum (FBS). In the presence of FBS, the 5 mM phosphate + 6 mM calcium group showed increased CPP formation from the initial time points and gradually increased over time. At each time point, magnesium significantly decreased CPP formation induced by high phosphate and calcium concentrations (Supplementary Fig. S2A). However, magnesium did not change CPP formation in the absence of FBS (Supplementary Fig. S2B). ## Discussion During high phosphate loading, CPP formation in the tubular lumen is greatly affected by the amount of phosphate reached per single nephron, independent of changes in blood phosphate concentrations22. In humans, the number of nephrons declines with age, with approximately $50\%$ fewer nephrons in the 70s than in the 20s23. There have been no reports examining changes in the nephron number in detail in dolphins. However, a diet of phosphate-rich fish and squid for many years may increase the risk of intratubular CPP formation, accompanied by a reduction in the number of nephrons as they age. In the present study, we analyzed renal tissue from an older dolphin that had died, and its renal function had been diagnosed as normal by monthly blood tests. Surprisingly, the medullary portion of reniculi tissue showed marked calcified lesions due to calcium phosphate (hydroxyapatite) accumulation. Further in vitro experiments using cultured dolphin tubular cells also showed that CPPs actually injured dolphin tubular cells. To the best of our knowledge, this is the first report to show that the risk of phosphate-induced CKD increases with aging in captive dolphins. In the captive dolphin in which a necropsy was performed, a pathologist diagnosed the cause of death as bacterial endocarditis, and identified the site of infarction in the left ventricle and kidney caused by the resulting bacteria and blood clots. Blood tests showed that blood urea nitrogen, creatinine, and phosphate concentrations were within the normal range until just before death (Supplementary Table S1). However, a detailed analysis using CT scans showed the presence of a high-density area in many reniculi in medullary tissue, which strongly indicated calcification of renal tissue. Interestingly, micro-domain X-ray diffraction analyses of the medullary tissue showed that it contained a large amount of hydroxyapatite. Further analyses by IR spectroscopy confirmed that the peak observed in the calcified area was hydroxyapatite. Histological examination showed Von Kossa-positive staining with calcification in renal medulla. In the present study using immortalized cultured dolphin tubular DolKT-1 cells, we found that phosphate changed cellular morphology and calcium deposition, which were associated with a marked reduction in cell viability. Moreover, phosphate also caused cytotoxicity as indicated by LDH release in the culture medium. To further determine the mechanism responsible for the phosphate-induced cytotoxicity, we examined the effects of phosphate on cell apoptosis and mitochondrial function. However, phosphate did not alter cell apoptosis or mitochondrial function in dolphin tubular cells, suggesting that alternative mechanisms may be involved. In this regard, Fujimura et al.24 reported that phosphate-induced mitochondrial damage in human proximal tubular cells was suppressed by the appropriate function of autophagy. Recent studies in human proximal tubular HK-2 cells have shown that phosphate is involved in cell damage by activating multiple intracellular signaling pathways13,25,26. Further detailed studies are required to determine if similar molecular mechanisms are involved in dolphin tubular cell injury. In mammals, a small increase in blood phosphate and calcium concentrations due to a transient increase after a meal may induce a risk of calcium phosphate precipitation6,27. To prevent amorphous calcium phosphate precipitates from being in a crystallized form, which may be responsible for ectopic calcification, a mineral-binding protein fetuin-A binds together and forms soluble colloidal particles called CPPs10. Although the covering of mineral crystals by fetuin-A is thought to prevent ectopic calcification, CPPs undergo a topological change from amorphous CPP1 to crystalline CPP2 in a high-phosphorus environment28. Several studies have shown that CPP2 formation induces calcification, inflammation and oxidative stress29, and renal injury26. Recently, Kunishige et al.26 showed that CPPs were incorporated into human proximal tubular HK-2 cells and caused the disruption of lysosomal homeostasis, autophagic flux, and plasma membrane integrity without causing oxidative stress. Furthermore, Shiizaki et al.13 reported that CPPs formed in the proximal tubule lumen induced inflammation and cell death, leading to tubular injury and interstitial fibrosis. In the present study, supernatant centrifuged from the culture medium did not alter cell viability or LDH release during treatment with high phosphate concentrations, while purified CPPs induced cell viability loss and cytotoxicity. These data indicate that dolphin tubular damage caused by high phosphate concentrations is mediated, at least in part, by formed CPPs. Recent cohort studies in patient with CKD have shown that low serum magnesium concentrations increase the risk of end-stage kidney disease caused by high serum phosphate concentrations21. This finding suggests a close relationship between magnesium deficiency and phosphate renal toxicity. Studies using $\frac{5}{6}$ nephrectomized mice showed that a low-magnesium diet reduced α-klotho expression in the kidney and significantly worsened tubulointerstitial fibrosis induced by a high-phosphate diet30. In the present study, phosphate-induced changes in cultured dolphin tubular cell morphology, Von Kossa-positive calcification, and cell damage were prominently suppressed by magnesium administration. These data suggest that magnesium has protective effects against phosphate-induced dolphin tubular cell injury. However, our data also showed that these effects of magnesium were not observed in CPP-induced dolphin tubular cell injury. Interestingly, the addition of magnesium to phosphate significantly inhibited the formation of CPPs, while magnesium administration to CPPs did not affect the concentration of CPPs per se. These data support the hypothesis that magnesium attenuates phosphate-induced injury of dolphin tubular cells through the inhibition of CPP formation. Recently, an open-label, randomized, controlled trial by Sakaguchi et al.31 showed that magnesium oxide administration improved coronary artery calcification in patients with renal failure. Nevertheless, magnesium-induced intervention human clinical studies on the improvement of the prognosis of CKD have not been conducted yet. Aquatic geochemistry studies have indicated that magnesium stabilizes the amorphous calcium phosphate phase possibly resulting from the combination of multiple mechanisms. In particular, the direct precipitation of apatite in seawater upon the addition of dissolved inorganic phosphate is inhibited by magnesium ions32. In the body, hydroxyapatite- and protein-containing CPPs are major drivers of calcification29,33. The transition from calcium- and phosphate-containing amorphous or primary CPP1 towards crystalline or secondary CPP2 is key in the development of calcification28,33,34. Additionally, magnesium may delay the formation of secondary CPP2, thereby preventing phosphate-induced calcification35,36. The present study suggests that, as in humans, magnesium may be effective to attenuate the progression of renal injury in dolphins. Therefore, future studies need to carefully investigate the benefit of magnesium administration to captive dolphins with CKD complications. A limitation of this study is that we could not examine the cytotoxic effects of CPP1 and CPP2 in DoIKT-1 cells and the effect of magnesium on CPP1 and CPP2 formation separately. The necessary technology to measure CPP1 and CPP2 separately by combining different characterization techniques, such as turbidimetry, dynamic light scattering, infrared spectroscopy, and scanning electron microscopy, is not available at our institution. Currently, synthesizing pure CPP1 and CPP2 is also difficult. In the future, we will establish such technologies to examine these effects. In conclusion, we analyzed necropsy renal tissue from an older dolphin with normal renal function and found that the medullary portion of reniculi tissue had marked calcified lesions due to calcium phosphate (hydroxyapatite) accumulation. Further in vitro experiments in cultured dolphin tubular cells showed that phosphate-damaged tubular cells through the formation of CPP. The Inhibition of CPP formation by magnesium administration significantly attenuated the phosphate-induced dolphin tubular cell injury. These data suggest that dolphins are at increased risk of developing CKD due to phosphate accumulation in the kidney as they age. Further studies are required to determine the possible relationship between renal function and urinary phosphate excretion or plasma CPP concentrations in aged captive dolphins. ## Methods In the present study, we did not perform any experiments on humans and use of human samples. This study was conducted using veterinarian-observed dolphin health examination data and postmortem necropsy samples. Therefore, no painful medication, anesthesia, or sampling was performed on the live dolphin. Experimental protocols (Protocol No. 21601) were approved by the Animal experimentation Ethics Committee at Kagawa University. All procedures in this study were carried out in compliance with the Fundamental Guidelines for Proper Conduct of Animal Experiments and Related Activities in Academic Research Institutions under the jurisdiction of the Ministry of Education, Culture, Sports, Science, and Technology as well as WAZA (World Association of Zoos and Aquariums). We also followed the ethical guidelines for the Conduct of Research on Animals by Zoos and Aquariums and the guidelines for animal experiments of Kagawa University. All dolphins in Ocean Expo Park (OEP; Kunigami-gun, Japan) have been housed following category 1 animal handling business, which is standard for housing and exhibiting animals approved by Okinawa Prefecture (OEP; No. 643), as previously described37 in detail. The health of the dolphins was monitored monthly by veterinarians on the basis of blood chemistry and behavior. Dolphins were maintained in outdoor pools with sea water sterilized by pressure filtration using polyester fiber and silica sand. ## Sample collection for necropsy and CT scan A male Indo-Pacific bottlenose dolphin, estimated to be approximately 50 years old, has been kept at OEP for 43 years since May 1, 1975. Renal function remained in the normal range until 2015 (Supplementary Table S1). Beginning in October 2017, this dolphin presented with symptoms, such as anorexia, and blood tests suspected infection. Therefore, antibiotics were administered. During the following 6 months, the dolphin alternated between anorexia and temporary recovery. However, the general condition worsened later, and despite treatment with antibiotics, corticosteroids, and intravenous drip, the dolphin developed respiratory failure and died on March 22, 2018. A complete necropsy was immediately performed, and several tissues including the kidneys were dissected. Dissected tissues were fixed with paraformaldehyde and part of the right kidney tissues were frozen for analyses. Thereafter, CT (Asteion Super 4 Edition, Toshiba Medical Systems Co., Ohtawara, Japan and SOMATOM Scope, Siemens Healthineers Japan, Tokyo, Japan) and micro-CT scans (Latheta LCT-200, Hitachi Aloka Medical Ltd., Tokyo, Japan) were performed on the removed right kidney and reniculi, respectively, and some of the tissues were dried completely in a dryer for 3 days. Dried samples were used for analysis by microarea X-ray diffractometry and IR spectrometry. ## Histological analysis Renal tissues were dissected and fixed with $10\%$ buffered paraformaldehyde, embedded in paraffin, and sectioned into 3-μm-thick slices. The sections were then stained with hematoxylin and eosin (HE), or Von Kossa reagent38. ## Micro area X-ray diffractometry Dried medullary tissues were analyzed with microbeam X-rays at several locations. In this study, micro-area X-ray diffractometer (RINT-RAPID II Rigaku, Tokyo, Japan) with a microscope was used as previously described39. The analytical conditions were as follows: target, Cu; filter, Ni; voltage, 40 kV; current, 36 mA; and collimator diameter, 100 µm. The diffraction patterns obtained were compared with the data that were registered in the database of the Joint Committee on Powder Diffraction Standards (JCPDS). ## IR spectroscopy After X-ray analysis, tissues were ground to powder and, then analyzed with IR spectroscopy40. IR spectra of the powders were recorded using a KBr tablet and IR spectrometer (FT/IR-4200 Jasco Tokyo, Japan). ## Cell line DolKT-1 cells were obtained from the Department of Marine Science and Resources, College of Bioresource Science, Nihon University, Kanagawa, Japan. The establishment procedure of this cell line was described elsewhere41. ## Cell culture DolKT1 cells were cultured in Dulbecco’s modified eagle medium (DMEM) (Cat# 11885084; Gibco, Grand Island, NY) supplemented with $10\%$ FBS (Nichirei Biosciences, Tokyo, Japan), $1\%$ insulin-transferrin-selenium (ITS-G 100X, # 41400045, Thermo Fisher, Waltham, MA), 50 U streptomycin/mL and 50 mg penicillin/mL (Life Technologies, Van Allen Way Carlsbad, CA). The cells were grown at 37 °C in a humidified atmosphere with $5\%$ CO2. We tested for mycoplasma on a regular basis. The cells were passaged at $90\%$ confluency and exposed to different experimental conditions. The culture medium was changed with $1\%$ FBS (if not mentioned separately) for 18 h before the experiments. All experiments were performed between passages 24 and 30. ## Treatment protocol DMEM (phosphate and magnesium concentrations in the medium were 0.9 and 0.8 mM, respectively) with $1\%$ FBS was set as the control. In the treatment groups, the control medium was supplemented with NaH2PO4 and Na2HPO4 at a 1:2 proportion to reach the final phosphate concentration of 1.5 mM phosphate, and 2 mM phosphate respectively. MgCl2 was used to raise the magnesium concentration up to 2 mM. The pH of the medium was maintained at 7.4 in each case. The experiments conducted in this study were repeated at least three times. ## Cell morphological analysis Cell morphology was examined by using an inverted light-phase contrast microscope (Olympus FSX100; Olympus Corporation, Center Valley, PA). ## Von Kossa staining Calcium deposition of the cultured cells was detected by Von Kossa staining. The cells were seeded in the 24-well plates at 1.5 × 105 cells/mL with a regular DMEM culture medium. After reaching $70\%$ confluence, the cultured medium was switched to $5\%$ FBS-containing medium for 18 h. Subsequently, cells were cultured for 3, 5, or 7 days in accordance with our treatments containing $5\%$ FBS. With regard to staining, we mostly followed a previously reported protocol42 with some modifications. Briefly, the cells were washed two times with phosphate-buffered saline (PBS) followed by fixation with $4\%$ paraformaldehyde for 20 min. Then again cells were washed with PBS two times and once with water. After this washing, $2\%$ silver nitrate solution was added and exposed to ultraviolet light for 30 min. After washing again with water, $5\%$ sodium thiosulfate was added and kept for 3 min. After washing with water, hematoxylin was added for 10 min to counterstain the nuclei. Finally, after three times washing with water, calcification was observed by an Olympus FSX100 microscope (Olympus Corporation) at a magnification of × 42. ## Cell viability Cells were seeded on 24-well tissue culture plates at 1.5 × 105 cells/mL and allowed to grow up to $70\%$ confluence, and then switched to $1\%$ FBS medium for 18 h. The intervention was performed for 48 h, and cell viability was then measured using a WST-1 assay kit in accordance with the manufacturer's protocol (Takara Bio, Otsu, Japan). Briefly, 50 µL of WST-1 reagents were added to each well of 500 µL of cell culture medium and incubated for 2 h, and the absorbance was measured with a microplate reader (Corona Multi Grating Microplate Reader SH-9000Lab; Hitachi High-Tech Science Corporation, Tokyo, Japan) at a wavelength of 480 nm. ## LDH assay LDH concentrations in the medium were measured as an indicator of cell injury. An LDH cytotoxicity assay kit (Item no. 601170, Cayman Chemical, East Ellsworth Road Ann Arbor, MI) was used in accordance with the manufacturer's protocol. Briefly, the cell supernatant was removed after centrifugation, mixed with LDH reaction solution (reagents provided with the kit), and incubated at 37 °C for 30 min. The absorbance was measured at 490 nm with a microplate reader (Corona Multi Grating Microplate Reader SH-9000Lab, Hitachi High-Tech Science Corporation). ## Apoptosis assay To detect apoptosis, flow cytometry was performed by counting annexin V- and PI- positive cells in line with the manufacturer’s instructions (catalog no #ab14085; Abcam, Cambridge, UK). Briefly, cells were cultured for 48 h in accordance with treatments and then trypsinized to obtain single cells. After washing with PBS twice, the cells were resuspended in a binding buffer with annexin V and PI for 5 min at room temperature. Apoptosis-positive cells were detected by using flow cytometry (BD FACS Canto II; BD Biosciences, San Jose, CA). Cells that were annexin V-positive and PI-negative were considered apoptotic. At least 20,000 cells were analyzed in each sample. ## Mitochondrial oxygen consumption Mitochondrial oxygen consumption was measured by the Seahorse XFp Analyser (Agilent Technologies, Santa Clara, CA) with the Seahorse XFp Cell Mito Stress Test Kit (Agilent Technologies) in accordance with the manufacturer’s protocol. Briefly, cells were seeded into an Agilent Seahorse XFp well plate at a density of 1.5 × 105 cells/mL and kept in a culture medium in the incubator for 24 h. While cells were near confluent, the culture medium was replaced with experimental medium for 36 h. One hour before the measurement, the medium was washed again and replaced with Seahorse XF Assay Medium supplemented with 2 mM l-glutamine, 1 mM sodium pyruvate, and 10 mM glucose, and cells were placed in a non-CO2 incubator. The kit components that had to be calibrated for final concentrations in the wells were 2 μM oligomycin, 1 μM carbonyl cyanide- 4-phenylhydrazone (FCCP), 0.5 μM antimycin A, and 0.5 μM rotenone by using a loaded assay cartridge. After baseline measurement, preloaded inhibitors were released consecutively into each well in a calibration chamber. The machine recorded oxygen concentration in pmol/min in every 4 min. The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were normalized to total cellular protein content. The total protein concentration was determined by a standard colorimetric protein assay after each experiment. ## CPP preparation CPPs were prepared by mixing CaCl2 (final concentration was 5 mM), NaH2PO4 and Na2HPO4 in 1:2 proportion (final concentration was 6 mM) in 25 mL of regular DMEM supplemented with $5\%$ fetal bovine serum and $1\%$ streptomycin/penicillin. These concentrations were adopted from a previous study26. This medium was incubated at 37 °C for 24 h and centrifuged at 16,000×g for 2 h. Subsequently, the supernatant was removed and precipitated CPPs were mixed with 5 mL of regular DMEM containing $10\%$ FBS to make a CPP suspension that was later used as treatment. ## CPP-induced cell viability and cytotoxicity DolKT-1 cells were incubated with or without CPPs, and CPPs in combination with 2 mM magnesium for 24 h. The WST-1 assay was performed to measure cell viability as stated above. To avoid the background of CPP turbidity, we also measured absorbance at a wavelength of 630 nm and subtracted it from the absorbance at a wavelength of 480 nm. A cytotoxicity assay was performed for CPP-exposed cells for 24 h by following the same protocols described above. ## Statistical analysis All statistical analyses were performed with GraphPad Prism (ver., 5.0, https://www.graphpad.com). Data are presented as the mean ± standard error of the mean. One-way analysis of variance (ANOVA) followed by the Newman–Keuls multiple-comparison test was performed for all one-factor data to compare values in the control medium with those intervention groups. Comparison of two groups was performed using Students t test (parametric). ## Supplementary Information Supplementary Information. Supplementary Video 1. The online version contains supplementary material available at 10.1038/s41598-023-32399-6. ## References 1. Bikbov B. Global, regional, and national burden of chronic kidney disease, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet (2020.0) 395 709. DOI: 10.1016/S0140-6736(20)30045-3 2. Weinstein JR, Anderson S. The aging kidney: Physiological changes. Adv. Chronic Kidney Dis. (2010.0) 17 302. DOI: 10.1053/j.ackd.2010.05.002 3. Kuro-o M. A potential link between phosphate and aging—Lessons from Klotho-deficient mice. Mech. Ageing Dev. (2010.0) 131 270. DOI: 10.1016/j.mad.2010.02.008 4. Yang ZY. Examining the association between serum phosphate levels and leukocyte telomere length. Sci. Rep. (2020.0) 10 1-7. PMID: 31913322 5. McClelland R. Accelerated ageing and renal dysfunction links lower socioeconomic status and dietary phosphate intake. Aging (2016.0) 8 1135-1149. DOI: 10.18632/aging.100948 6. Kuro-O M. Phosphate as a pathogen of arteriosclerosis and aging. J. Atheroscler. Thromb. (2021.0) 28 RV17045. DOI: 10.5551/jat.RV17045 7. Razzaque MS, Lanske B. The emerging role of the fibroblast growth factor-23–klotho axis in renal regulation of phosphate homeostasis. J. Endocrinol. (2007.0) 194 1-10. DOI: 10.1677/JOE-07-0095 8. Czaya B, Faul C. The role of fibroblast growth factor 23 in inflammation and anemia. Int. J. Mol. Sci. (2019.0) 20 4195. DOI: 10.3390/ijms20174195 9. Takeshita A, Kawakami K, Furushima K, Miyajima M, Sakaguchi K. Central role of the proximal tubular αklotho/FGF receptor complex in FGF23-regulated phosphate and Vitamin D metabolism. Sci. Rep. (2018.0) 8 6917. DOI: 10.1038/s41598-018-25087-3 10. Kuro-o M. Calciprotein particle (CPP): A true culprit of phosphorus woes?. Nefrologia (2014.0) 34 1-4. PMID: 24336683 11. Voelkl J, Egli-Spichtig D, Alesutan I, Wagner CA. Inflammation: A putative link between phosphate metabolism and cardiovascular disease. Clin. Sci. (Lond.) (2021.0) 135 201. DOI: 10.1042/CS20190895 12. Wang Q. Urinary phosphate-containing nanoparticle contributes to inflammation and kidney injury in a salt-sensitive hypertension rat model. Commun. Biol. (2020.0) 2020 1-12 13. Shiizaki K. Calcium phosphate microcrystals in the renal tubular fluid accelerate chronic kidney disease progression. J. Clin. Investig. (2021.0) 131 e145693. DOI: 10.1172/JCI145693 14. Mackay EM, Oliver J. Renal damage following the ingestion of a diet containing an excess of inorganic phosphate. J. Exp. Med. (1935.0) 61 319. DOI: 10.1084/jem.61.3.319 15. 15.Rollins, A. Dietary guidelines for dogs with chronic kidney disease (CKD). TVP.https://todaysveterinarypractice.com/nutrition/diet-dogs-ckd-chronic-kidney-disease/ (2018). 16. 16.IRIS Kidney—About IRIS. http://www.iriskidney.com/education/about/index.html. 17. Venn-Watson SK, Jensen ED, Ridgway SH. Evaluation of population health among bottlenose dolphins (. J. Am. Vet. Med. Assoc. (2011.0) 238 356-360. DOI: 10.2460/javma.238.3.356 18. DeMaster DP, Drevenak JK. Survivorship patterns in three species of captive cetaceans. Mar. Mamm. Sci. (1988.0) 4 297-311. DOI: 10.1111/j.1748-7692.1988.tb00539.x 19. Hohn AA, Fernandez S. Biases in dolphin age structure due to age estimation technique. Mar. Mamm. Sci. (1999.0) 15 1124-1132. DOI: 10.1111/j.1748-7692.1999.tb00881.x 20. Le-Bert CR. Comparison of potential dietary and urinary risk factors for ammonium urate nephrolithiasis in two bottlenose dolphin (. Am. J. Physiol. Renal Physiol. (2018.0) 315 F231-F237. DOI: 10.1152/ajprenal.00606.2017 21. Sakaguchi Y. Magnesium modifies the association between serum phosphate and the risk of progression to end-stage kidney disease in patients with non-diabetic chronic kidney disease. Kidney Int. (2015.0) 88 833-842. DOI: 10.1038/ki.2015.165 22. Rubio-Aliaga I, Krapf R. Phosphate intake, hyperphosphatemia, and kidney function. Pflugers Arch. (2022.0) 474 935. DOI: 10.1007/s00424-022-02691-x 23. Denic A. The substantial loss of nephrons in healthy human kidneys with aging. J. Am. Soc. Nephrol. (2017.0) 28 313-320. DOI: 10.1681/ASN.2016020154 24. Fujimura R. Autophagy protects kidney from phosphate-induced mitochondrial injury. Biochem. Biophys. Res. Commun. (2020.0) 524 636-642. DOI: 10.1016/j.bbrc.2020.01.137 25. Richter B. High phosphate-induced progressive proximal tubular injury is associated with the activation of Stat3/Kim-1 signaling pathway and macrophage recruitment. FASEB J. (2022.0) 36 e22407. DOI: 10.1096/fj.202200167RR 26. Kunishige R. Calciprotein particle-induced cytotoxicity via lysosomal dysfunction and altered cholesterol distribution in renal epithelial HK-2 cells. Sci. Rep. (2020.0) 10 1-18. DOI: 10.1038/s41598-020-77308-3 27. Boskey AL, Posner AS. Formation of hydroxyapatite at low supersaturation. J. Phys. Chem. (1976.0) 80 40-45. DOI: 10.1021/j100542a009 28. Pasch A. Nanoparticle-based test measures overall propensity for calcification in serum. J. Am. Soc. Nephrol. (2012.0) 23 1744-1752. DOI: 10.1681/ASN.2012030240 29. Aghagolzadeh P. Calcification of vascular smooth muscle cells is induced by secondary calciprotein particles and enhanced by tumor necrosis factor-α. Atherosclerosis (2016.0) 251 404-414. DOI: 10.1016/j.atherosclerosis.2016.05.044 30. Sakaguchi Y. Low magnesium diet aggravates phosphate-induced kidney injury. Nephrol. Dial. Transplant. (2019.0) 34 1310-1319. DOI: 10.1093/ndt/gfy358 31. Sakaguchi Y. A randomized trial of magnesium oxide and oral carbon adsorbent for coronary artery calcification in predialysis CKD. J. Am. Soc. Nephrol. (2019.0) 30 1073-1085. DOI: 10.1681/ASN.2018111150 32. Martens CS, Harriss RC. Inhibition of apatite precipitation in the marine environment by magnesium ions. Geochim. Cosmochim. Acta (1970.0) 34 621-625. DOI: 10.1016/0016-7037(70)90020-7 33. Viegas CSB. Chronic kidney disease circulating calciprotein particles and extracellular vesicles promote vascular calcification: A role for GRP (Gla-Rich Protein). Arterioscler. Thromb. Vasc. Biol. (2018.0) 38 575-587. DOI: 10.1161/ATVBAHA.117.310578 34. Sage AP, Lu J, Tintut Y, Demer LL. Hyperphosphatemia-induced nanocrystals upregulate the expression of bone morphogenetic protein-2 and osteopontin genes in mouse smooth muscle cells in vitro. Kidney Int. (2011.0) 79 414-422. DOI: 10.1038/ki.2010.390 35. Ter Braake AD. Calciprotein particle inhibition explains magnesium-mediated protection against vascular calcification. Nephrol. Dial. Transplant. (2020.0) 35 765-773. DOI: 10.1093/ndt/gfz190 36. Gelli R, Pucci V, Ridi F, Baglioni P. A study on biorelevant calciprotein particles: Effect of stabilizing agents on the formation and crystallization mechanisms. J. Colloid Interface Sci. (2022.0) 620 431-441. DOI: 10.1016/j.jcis.2022.04.025 37. Suzuki A. Comparison of the gut microbiota of captive common bottlenose dolphins. J. Appl. Microbiol. (2019.0) 126 31-39. DOI: 10.1111/jam.14109 38. Michaux A. Missense mutation of VKORC1 leads to medial arterial calcification in rats. Sci. Rep. (2018.0) 8 1-11. DOI: 10.1038/s41598-018-31788-6 39. Kaneko K. Analysis of urinary calculi obtained from a patient with idiopathic hypouricemia using micro area X-ray diffractometry and LC-MS. Urol. Res. (2005.0) 33 415-421. DOI: 10.1007/s00240-005-0480-8 40. Kaneko K. Proteomic analysis of a rare urinary stone composed of calcium carbonate and calcium oxalate dihydrate: A case report. Int. J. Urol. (2014.0) 21 341-346. DOI: 10.1111/iju.12282 41. Suzuki M. Two isoforms of aquaporin 2 responsive to hypertonic stress in the bottlenose dolphin. J. Exp. Biol. (2016.0) 219 1249-1258. DOI: 10.1242/jeb.132811 42. Priante G. Human proximal tubular cells can form calcium phosphate deposits in osteogenic culture: Role of cell death and osteoblast-like transdifferentiation. Cell Death Discov. (2019.0) 5 57. DOI: 10.1038/s41420-019-0138-x
--- title: 'Dietary riboflavin intake in relation to psychological disorders in Iranian adults: an observational study' authors: - Parisa Rouhani - Mohammad Amoushahi - Ammar Hassanzadeh Keshteli - Parvane Saneei - Hamid Afshar - Ahmad Esmaillzadeh - Peyman Adibi journal: Scientific Reports year: 2023 pmcid: PMC10060244 doi: 10.1038/s41598-023-32309-w license: CC BY 4.0 --- # Dietary riboflavin intake in relation to psychological disorders in Iranian adults: an observational study ## Abstract Findings of earlier investigations on association between dietary riboflavin intake and psychological disorders are contradictory. Therefore, the relation between dietary riboflavin intake and depression, anxiety, and psychological distress was assessed in Iranian adults. In this cross-sectional study, dietary intakes of 3362 middle-aged adults were collected using a validated dish-based food frequency questionnaire. Daily intake of riboflavin for each participant was calculated by summing up the amount of riboflavin contents of all foods and dishes. Hospital Anxiety and Depression Scale (HADS) and General Health Questionnaire (GHQ), as validated questionnaires among Iranians, have been applied to assess depression, anxiety, and psychological distress. After considering potential confounders, adults in the top energy-adjusted quartile of riboflavin intake, compared to the bottom quartile, had decreased odds of depression (OR = 0.66; $95\%$CI 0.49, 0.88), anxiety (OR = 0.64; $95\%$CI 0.44, 0.94) and high psychological distress (OR = 0.65; $95\%$CI 0.48, 0.89). Stratified analysis by sex revealed that men in the forth quartile of riboflavin intake, compared with those in the first quartile, had respectively 51 and $55\%$ lower odds of depression (OR = 0.49; $95\%$CI 0.29, 0.83) and anxiety (OR = 0.45; $95\%$CI 0.21, 0.95). In women, riboflavin intake was significantly associated with lower odds of psychological distress (OR = 0.67; $95\%$CI 0.46, 0.98). An inverse relation was observed between dietary riboflavin intake and chance of psychological disorders in Iranian adults. High intake of riboflavin decreased the chance of depression and anxiety in men and high psychological distress in women. More prospective studies are needed to confirm these findings. ## Introduction The prevalence of mental diseases such as depression, anxiety, and psychological distress has been growing worldwide1. These disorders are associated with decreased quality of life and fatality2,3. Depression and anxiety have respectively a global prevalence of $4.7\%$ and $7.3\%$4,5. A national survey in Iran showed that $20.0\%$ and $20.8\%$ of adults suffered from anxiety and depression, respectively6,7. Depression manifests itself physically and emotionally in a variety of ways, including: feelings of worthlessness, overwhelm, misery, loss of confidence, muscle discomfort, and headaches. All of these symptoms contribute to decreased efficiency and functional capacities of the affected individuals8. Anxiety, as another common mental disorder, causes excessive worry, fear and has negative effects on health status, and family relationships9,10. Mental disorders exacerbate the consequences of a wide variety of medical conditions, contribute to disability, and increase mortality11. Based on previous reports, mental disorders had a high social and economic costs on societies and healthcare systems every year12,13. Depression and anxiety contribute between 30 and $50\%$ of the worldwide costs of mental health14. Several non-modifiable and modifiable factors including genetics, environmental factors (such as life events, low social support, financial problems), and lifestyle behaviors, including diet, might play critical roles in incidence of depression and anxiety15. Notably, the role of food intakes and dietary patterns in development of mental diseases has been recognized16–18. Earlier studies demonstrated inverse associations between consumption of dairy products, vegetables, fruits, olive oil and phytochemicals and depression risk19,20. Moreover, inadequate intake of micronutrients including magnesium, potassium, zinc, and B vitamins was positively associated with depression21. The association of these micronutrients with mental health might be explained by their contribution to neural function and synthesis of several neurotransmitters22,23. Riboflavin, known as vitamin B2, is a member of B-group vitamins. This nutrient, as a water-soluble and heat-stable vitamin and an antioxidant, can be involved in metabolizing macronutrients into glucose24. Riboflavin could possibly affect mental state of individuals through its two important coenzymes, flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN)25–27. Several previous studies have examined the relation between vitamin B6, B12, and folate with depression or anxiety in Iranian adults28,29. In case of riboflavin, the association between this vitamin and depression has been investigated in East Asia populations30–32; however, to the best of our knowledge, no previous study has investigated the relation between riboflavin consumption and common psychological disorders, especially anxiety and psychological distress, in Iranian adults. Therefore, the present study aimed to examine the association between riboflavin consumption and prevalence of depression, anxiety and psychological distress in a large group of Iranian adults. ## Study design and participants The current cross-sectional study was conducted in a retrospective manner on the Study on the Epidemiology of Psychological-Alimentary Health and Nutrition (SEPAHAN) data33. This two-phase study was conducted on a group of general Iranian adults working in 50 health care centers affiliated with Isfahan University of Medical Sciences (IUMS). As shown in Supplementary Fig. 1, in the first phase of SEPAHAN, a comprehensive self-administered questionnaire on socio-demographic characteristics, dietary behaviors, and dietary intakes was delivered to 10,087 individuals aged 18–71 years; 8691 of them returned the completed questionnaires (response rate: $86.16\%$). In the second phase, another set of questionnaires was distributed among the same participants to obtain data on psychological distress and mental health (response rate: $61.85\%$). After merging data from these two phases, the completed data of 4669 participants were available. There was no significant difference in demographic data between those who returned completed questionnaires and those who did not. ## Exclusion criteria Some university teaching hospitals and research centers were not included in order to reduce the conflict of interest in research. In addition, adults with a total energy intake outside of the range of 800 and 4200 kcal/day (as under-reporters and over-reporters), those with missing dietary or psychological data, and those who participated in only one phase of the study, participants who suffer from aggressive disease such as cardiovascular, respiratory, neurologic diseases were excluded. However, individuals with mild metabolic disorders such as abdominal obesity, hyperlipidemia, or hypertension were included in the current study. These exclusions resulted in data of 3362 adults for the current analysis. ## Dietary intake assessment Long-term dietary intakes were evaluated by a validated Willet-format 106-item dish-based semi-quantitative food frequency questionnaire (DS-FFQ) that was specifically designed for Iranian adults34. Detailed information about the design, foods included, and validity of this tool has been published elsewhere34. The questionnaire included five different dishes and foods categories: [1] dairy products (dairies, butter and cream, 9 items); [2] mixed dishes (canned or cooked, 29 items); [3] grain products and grains (different types of potato, biscuits, bread, and cakes, 10 items); [4] fruits and vegetables and (22 items); and [5] miscellaneous food items and beverages (including fast foods, nuts, beverages, desserts, and sweets, 36 items). The questionnaire was completed by a self-administered method. A one-page written instruction was provided for participants to complete the questionnaire. Participants were asked to report their dietary intakes of mixed dishes and foods in the previous year according to multiple frequency choices differing from ‘never or less than once a month’ to ‘12 or more times per day’. Considering the portion size of each food or dish in the questionnaire and the reported frequency of that item, all foods and dishes were converted to grams per day, using household measures35. Then, all gram/day values were input into the Nutritionist IV software to calculate daily energy and nutrient intakes. Dietary riboflavin intake for each individual was calculated by summing up the amount of vitamin B2 in all foods and dishes. The validation study of this DS-FFQ showed that reasonably valid and reliable dietary intakes could be provided by this tool34. Additionally, our prior investigations revealed that this DS-FFQ has an acceptable level of validity and reliability for assessing foods and dietary intakes in relation to various diseases36–38. ## Assessment of psychological disorders Anxiety and depression were defined by the validated Iranian version of Hospital Anxiety and Depression Scale (HADS)39. HADS is a 14-item scale which includes two subscales: depression and anxiety. Each item comprises a four-point scale; greater scores suggest higher level of depression and anxiety. Score ranges for depression and anxiety are between 0 and 21. In the current study, values of 8 or higher for each subscale were considered as having depression or anxiety, whereas scores of 0–7 were considered as normal status39. A validation study on 167 Iranian adults revealed reasonable validity of the translated version of HADS for measurement of mental health39. Furthermore, psychological distress was screened by the validated Iranian version of General Health Questionnaire (GHQ)40. This questionnaire contains 12 items with a 4-point rating scale (less than usual, no more than usual, rather more than usual, or much more than usual). Total score for each individual was calculated through a bimodal method [0-0-1-1] and ranged from 0 to 12; higher scores were related to higher degree of psychological distress. In the current study, having a score of 4 or more was defined as high psychological distress40. The validity of this questionnaire was reasonable based on a validation study on 748 Iranian adults41. ## Assessment of other variables Data on confounders including age, sex, marital status, education levels, smoking, number of family members, house possession, disease history, current use of anti-psychotic medications (including sertraline, nortriptyline, fluoxetine, amitriptyline or imipramine, citalopram, and fluvoxamine) and dietary supplements (including intake of iron, calcium, and other dietary supplements) was collected through a self-administered questionnaire. Data on weight (kg) and height (cm) were measured using a self-reported questionnaire. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. The validity of the self-reported anthropometric measures was investigated in pilot study on 200 individuals. Correlation coefficients between self-reported height, weight, BMI, and waist circumference (WC) and measured values were respectively 0.83, 0.95, 0.70, and 0.60 ($P \leq 0.001$ for all)42; these robust correlations indicated that the self-reported values of anthropometric indices could provide valid measures in this population. To determine physical activity level of participants, the validated General Practice Physical Activity Questionnaire (GPPAQ) was applied43. Then, participants were categorized into two groups: physically active (≥ 1 h/week) and physically inactive (< 1 h/week). ## Statistical analysis First, energy-adjusted dietary intake of riboflavin was obtained through the residual method44. To examine the association of exposure and outcome of the interest, participants were classified into energy-adjusted quartiles of riboflavin intake. Then, data on socio-demographic variables were reported as mean ± SD or percentage. The differences across quartiles of riboflavin were assessed using analysis of variance (ANOVA) or chi-square test. Analysis of covariance (ANCOVA) was applied to examine dietary intakes of participants across quartiles of riboflavin. Crude and multivariable-adjusted models were used to evaluate the association between riboflavin and psychological health status. Confounding variables were determined based on previously published investigations33,38. Age (continuous), sex (male/ female), and energy intake (continuous) were adjusted in the first model. More adjustments were done for physical activity (< 1 h/week/ ≥ 1 h/week), house possession (yes/ no), number of family members (≥ 4/ < 4), history of diabetes (yes/no), smoking (current smoker/former smoker/ non-smoker), marital status (single/widow or divorced/married), education (under diploma/ diploma/ above diploma/ bachelors and above), and anti-psychotic medications intake (yes/ no) and dietary supplements (yes/ no) in the second model. In the third model, intakes of iron (continuous), thiamin (continuous), fat (continuous), and n-3 fatty acids (continuous) were additionally adjusted. In the last model, further adjustment for BMI (continuous) was done. All odds ratios were calculated based on the first quartile of riboflavin intake as the reference category. Quartiles of riboflavin intake were considered as an ordinal variable in the logistic regression models to estimate the trend of odds ratios across these categories. Stratified analyses were done to obtain odds ratios for psychological disorders in different categories of sex (men/women) and BMI (< 25/ ≥ 25 kg/m2). All statistical analyses were conducted by the use of SPSS software (version 20; SPSS Inc, Chicago IL). $P \leq 0.05$ was considered as significant level. ## Ethical approval and consent to participate All methods were performed in accordance with the STROBE guidelines and regulations. All methods were carried out in accordance with relevant guidelines and regulations. Each participant signed an informed written consent. The Bioethics Committee of Isfahan University of Medical Sciences has ethically approved the SEPAHAN project (no. 189069). ## Results The study participants consisted of 3362 middle-aged adults (1959 women and 1403 men) with a mean weight of 68.7 kg. General characteristics of the study population across energy-adjusted quartiles of dietary riboflavin intake are provided in Table 1. Individuals in the fourth quartile of riboflavin intake in comparison to the first quartile were more likely to have type 2 diabetes and obesity, higher age, weight, and BMI.Table 1General characteristics of study participants across energy-adjusted quartiles of riboflavin intake ($$n = 3362$$).Quartiles of riboflavin intakePaQ1($$n = 840$$)(< 1.6 mg/d)Q2($$n = 841$$)(1.6–1.82 mg/d)Q3($$n = 841$$)(1.83–2.09 mg/d)Q4($$n = 840$$)(> 2.09 mg/d)Age (years)35.55 ± 7.8235.92 ± 7.7436.15 ± 7.8037.52 ± 7.97 < 0.001Weight (kg)68.07 ± 12.7968.03 ± 14.3167.99 ± 12.2370.52 ± 13.16 < 0.001BMI (kg/m2)24.65 ± 3.7224.58 ± 3.9224.87 ± 3.8225.51 ± 3.74 < 0.001Female (%)57.457.260.857.70.40Marital status (%)0.48 Married80.382.380.883.3 Single18.016.217.714.6 Divorced/widow1.71.51.42.1Education (%)0.94 Under diploma12.411.112.512.2 Diploma27.627.825.426.9 Above diploma and under master's52.753.055.252.9 Master's and above7.38.16.97.9Family members (%)0.18 ≤ 485.886.889.387.3 > 414.213.210.712.7House possession (yes) (%)52.460.955.963.9 < 0.001Diabetes (%)2.31.31.12.50.07Anti-depressants medications useb (%)5.26.75.25.10.46Dietary supplement usec (%)29.829.030.930.40.85Smokers (%)13.315.212.414.30.36Physically activity (%)0.18 < 1 h/week85.787.588.685.5 ≥ 1 h/week14.312.511.414.5Obesed (%)42.642.742.751.2 < 0.001All values are means ± standard deviation (SD), unless indicated.aObtained from ANOVA for continuous variables and chi-square test for categorical variables.bAnti-depressants medications include nortriptyline, amitriptyline or imipramine, fluoxetine, citalopram, fluvoxamine and sertraline.cDietary supplements include iron, calcium, vitamins and other dietary supplements.dBMI ≥ 25(kg/m2). Dietary intakes of selected nutrients and food groups of study participants across energy-adjusted quartiles of dietary riboflavin intake are presented in Table 2. Individuals in the top quartile of riboflavin intake had significantly higher intake of protein, carbohydrate, dietary fiber, thiamin, pyridoxine, iron, vitamin C, whole grain, fruit, vegetable, and dairy (low fat and high fat) in comparison to those in the bottom quartile. Whereas, subjects in the last quartile of riboflavin in comparison to those in the reference quartile had lower consumption of energy, fat, vitamin E, red meat, refined grain, omega-3 fatty acids, nuts, soy and legumes. Table 2Dietary intakes of selected nutrients and food groups across energy-adjusted quartiles of riboflavin intake ($$n = 3362$$).Quartiles of riboflavin intakePaQ1($$n = 840$$)(< 1.6 mg/d)Q2($$n = 841$$)(1.6–1.82 mg/d)Q3($$n = 841$$)(1.83–2.09 mg/d)Q4($$n = 840$$)(> 2.09 mg/d)Energy (kcal/d)2559.29 ± 29.882249.15 ± 29.372229.52 ± 29.122499.72 ± 29.33 < 0.001Nutrients Proteins (% of energy)13.79 ± 0.0814.62 ± 0.0815.08 ±.0815.77 ± 0.08 < 0.001 Fats (% of energy)39.71 ± 0.2338.19 ± 0.2236.92 ± 0.2235.25 ± 0.22 < 0.001 Carbohydrates (% of energy)47.77 ± 0.2848.60 ± 0.2849.54 ± 0.2850.60 ± 0.28 < 0.001 Dietary fiber (g/d)20.70 ± 0.2022.53 ± 0.2023.40 ± 0.2023.73 ± 0.20 < 0.001 Omega-3 fatty acids (g/d)1.76 ± 0.021.77 ± 0.021.76 ± 0.021.67 ± 0.02 < 0.001 Vitamin B1 (mg/d)1.55 ± 0.021.79 ± 0.021.94 ± 0.022.09 ± 0.02 < 0.001 Vitamin B6 (mg/d)1.95 ± 0.012.00 ± 0.011.99 ± 0.011.98 ± 0.01 < 0.001 Iron (mg/d)16.74 ± 0.1217.64 ± 0.1118.15 ± 0.1117.89 ± 0.11 < 0.001 Vitamin C (mg/d)88.78 ± 1.89101.12 ± 1.86104.48 ± 1.84111.98 ± 1.85 < 0.001 Vitamin E (mg/d)24.93 ± 0.1922.60 ± 0.1920.73 ± 0.1917.66 ± 0.19 < 0.001Food groups (g/d) Red meat94.05 ± 1.4285.37 ± 1.4075.31 ± 1.3960.22 ± 1.39 < 0.001 Whole grains21.79 ± 2.7536.42 ± 2.7046.48 ± 2.6864.68 ± 2.69 < 0.001 Refined grains436.05 ± 6.02398.75 ± 5.90385.41 ± 5.86353.12 ± 5.89 < 0.001 Fruit280.85 ± 8.43318.43 ± 8.27328.74 ± 8.20341.61 ± 8.25 < 0.001 Vegetables210.56 ± 4.31232.67 ± 4.22248.40 ± 4.19264.22 ± 4.21 < 0.001 Nuts, soy and legumes63.98 ± 1.3358.85 ± 1.3057.69 ± 1.2948.71 ± 1.30 < 0.001 Low fat dairy152.94 ± 7.39243.53 ± 7.25334.64 ± 7.20597.30 ± 7.24 < 0.001 High fat dairy13.82 ± 0.6415.23 ± 0.6315.28 ± 0.6214.45 ± 0.62 < 0.001All values are means ± standard error (SE); energy intake is adjusted for age and sex; all other values are adjusted for age, sex and energy intake.aObtained from ANCOVA. As depicted in Fig. 1, participants in the top category of riboflavin intake compared to the bottom category had a significant lower prevalence of depression ($24.5\%$ vs. $32.6\%$; $$P \leq 0.01$$), anxiety ($11.6\%$ vs. $16.3\%$; $$P \leq 0.04$$), and distress ($19.2\%$ vs. $25.5\%$; $$P \leq 0.01$$).Figure 1Prevalence of depression, anxiety and high psychological distress across energy-adjusted quartiles of dietary riboflavin intake. Multivariable-adjusted odds ratios for depression, anxiety, and distress across quartiles of riboflavin intake are provided in Table 3. Compared to the lowest quartile, adults in the highest quartile of riboflavin intake had a $34\%$ decreased odds of depression (OR = 0.66; $95\%$CI 0.54, 0.83). This association remained significant, even after adjusting for all potential confounders (OR = 0.66; $95\%$CI 0.49, 0.88). Greater intake of riboflavin was also associated with $33\%$ lower odds of anxiety (OR = 0.67; $95\%$CI 0.48, 0.95). This relation strengthened after controlling for all cofounders (OR = 0.64; $95\%$CI 0.44, 0.94). Compared to the lowest quartile, individuals in the highest quartile of riboflavin intake had a decreased odds of distress either in crude (OR = 0.69, $95\%$CI 0.55, 0.87) or in fully-adjusted model (OR = 0.65, $95\%$CI 0.48, 0.89); such that, the top quartile of riboflavin intake in comparison to the bottom quartile was associated with $35\%$ decreased odds of distress. A significant trend was additionally observed across quartiles of riboflavin intake and chance of all psychological disorders ($P \leq 0.05$ for all models).Table 3Multivariable-adjusted odds ratios and $95\%$ confidence intervals for depression, anxiety and psychological distress across energy-adjusted quartiles of riboflavin intake in whole population ($$n = 3362$$).Quartiles of riboflavin intakePtrendQ1($$n = 840$$)(< 1.6 mg/d)Q2($$n = 841$$)(1.6–1.82 mg/d)Q3($$n = 841$$)(1.83–2.09 mg/d)Q4($$n = 840$$)(> 2.09 mg/d)Depression Crude1 (Ref.)0.87 (0.71–1.08)0.78 (0.63–0.96)0.66 (0.54–0.83) < 0.001 Model 11 (Ref.)0.85 (0.68–1.07)0.73 (0.57–0.91)0.66 (0.52–0.83) < 0.001 Model 21 (Ref.)0.83 (0.65–1.05)0.73 (0.58–0.93)0.65 (0.51–0.83) < 0.001 Model 31 (Ref.)0.82 (0.64–1.05)0.73 (0.56–0.94)0.65 (0.49–0.86)0.01 Model 41 (Ref.)0.82 (0.63–1.05)0.71 (0.55–0.92)0.66 (0.49–0.88)0.01Anxiety Crude1 (Ref.)0.78 (0.59–1.03)0.77 (0.59–1.02)0.67 (0.51–0.89)0.01 Model 11 (Ref.)0.70 (0.52–0.95)0.69 (0.51–0.93)0.68 (0.51–0.92)0.01 Model 21 (Ref.)0.67 (0.49–0.92)0.73 (0.53–0.99)0.67 (0.49–0.93)0.03 Model 31 (Ref.)0.65 (0.47–0.91)0.70 (0.50–0.97)0.65 (0.45–0.94)0.04 Model 41 (Ref.)0.66 (0.47–0.92)0.68 (0.48–0.95)0.64 (0.44–0.94)0.03Psychological distress Crude1 (Ref.)0.94 (0.76–1.18)0.79 (0.63–0.99)0.69 (0.55–0.87)0.01 Model 11 (Ref.)0.95 (0.75–1.21)0.78 (0.61–0.99)0.75 (0.58–0.96)0.01 Model 21 (Ref.)0.92 (0.71–1.18)0.78 (0.61–1.01)0.73 (0.56–0.94)0.01 Model 31 (Ref.)0.87 (0.67–1.13)0.73 (0.55–0.95)0.66 (0.49–0.89)0.01 Model 41 (Ref.)0.87 (0.67–1.13)0.71 (0.54–0.94)0.65 (0.48–0.89)0.01Model 1: Adjusted for age, sex and energy intake; Model 2: Further adjustment for physical activity, smoking, marital status, education, household size, house possession, diabetes, use of anti-depressant medications and dietary supplements; Model 3: Additional controlling for dietary intakes of fat, n-3 fatty acids, iron, thiamin; Model 4: Further adjusted for BMI. Multivariable-adjusted odds ratios for psychological disorders across different categories of riboflavin intake, stratified by sex, are presented in Table 4. After controlling all confounding variables, the highest level of riboflavin intake, compared to the lowest level, was respectively linked to $51\%$ and $55\%$ significant reduced odds of depression (OR = 0.49, $95\%$CI 0.29, 0.83) and anxiety (OR = 0.45, $95\%$CI 0.21, 0.95) in men. However, no relation was observed between intake of riboflavin and distress among men. Women in the highest quartile of riboflavin intake, compared to those in the lowest quartile, had a $33\%$ significant decreased odds of distress (OR = 0.67, $95\%$CI 0.46, 0.98). However, no relation was observed between riboflavin intake and depression or anxiety disorders among female participants. Table 4Multivariable-adjusted odds ratios and $95\%$ confidence intervals for depression, anxiety and psychological distress across energy-adjusted quartiles of riboflavin consumption, stratified by sex. Quartiles of riboflavin intakePtrendQ1 (< 1.6 mg/d)Q2 (1.6–1.82 mg/d)Q3 (1.83–2.09 mg/d)Q4 (> 2.09 mg/d)Male participants ($$n = 1403$$) Depression Crude1 (Ref.)0.67 (0.47–0.95)0.66 (0.46–0.95)0.50 (0.34–0.72) < 0.001 Model 11 (Ref.)0.67 (0.45–1.00)0.66 (0.44–1.00)0.48 (0.32–0.73)0.01 Model 21 (Ref.)0.66 (0.43–1.00)0.66 (0.43–1.01)0.49 (0.32–0.76)0.01 Model 31 (Ref.)0.66 (0.43–1.01)0.65 (0.42–1.02)0.53 (0.32–0.87)0.01 Model 41 (Ref.)0.65 (0.42–1.01)0.59 (0.37–0.94)0.49 (0.29–0.83)0.01 Anxiety Crude1 (Ref.)0.48(0.29–0.82)0.57(0.34–0.95)0.47(0.28–0.80)0.01 Model 11 (Ref.)0.44(0.24–0.81)0.53(0.30–0.97)0.46(0.25–0.83)0.02 Model 21 (Ref.)0.40(0.21–0.76)0.50(0.27–0.93)0.50(0.27–0.92)0.03 Model 31 (Ref.)0.39 (0.20–0.76)0.51 (0.26–0.98)0.48 (0.23–0.98)0.05 Model 41 (Ref.)0.42 (0.21–0.82)0.47 (0.23–0.93)0.45 (0.21–0.95)0.03 Psychological distress Crude1 (Ref.)0.80 (0.55–1.16)0.68 (0.46–1.01)0.58 (0.39–0.87)0.01 Model 11 (Ref.)0.85 (0.56–1.31)0.75 (0.48–1.16)0.70 (0.45–1.08)0.09 Model 21 (Ref.)0.83 (0.53–1.30)0.70 (0.44–1.12)0.70 (0.43–1.10)0.09 Model 31 (Ref.)0.81 (0.51–1.27)0.67 (0.41–1.09)0.68 (0.40–1.15)0.11 Model 41 (Ref.)0.84 (0.52–1.34)0.61 (0.37–1.02)0.58 (0.33–1.01)0.03Female participants ($$n = 1959$$) Depression Crude1 (Ref.)1.02 (0.79–1.33)0.82 (0.63–1.07)0.78 (0.60–1.02)0.03 Model 11 (Ref.)0.95 (0.72–1.26)0.76 (0.57–1.00)0.76 (0.57–1.00)0.02 Model 21 (Ref.)0.93 (0.69–1.24)0.76 (0.56–1.01)0.72 (0.53–0.97)0.01 Model 31 (Ref.)0.921 (0.67–1.24)0.74 (0.54–1.02)0.70 (0.50–1.00)0.02 Model 41 (Ref.)0.90 (0.66–1.23)0.75 (0.54–1.03)0.74 (0.52–1.06)0.06 Anxiety Crude1 (Ref.)0.96 (0.69–1.33)0.86 (0.62–1.20)0.79 (0.56–1.10)0.14 Model 11 (Ref.)0.83(0.59–1.17)0.76 (0.54–1.07)0.79(0.55–1.11)0.14 Model 21 (Ref.)0.80 (0.55–1.16)0.80 (0.55–1.16)0.76 (0.52–1.10)0.17 Model 31 (Ref.)0.76 (0.51–1.11)0.77 (0.52–1.13)0.74 (0.47–1.14)0.20 Model 41 (Ref.)0.75 (0.50–1.11)0.75 (0.50–1.12)0.74 (0.47–1.15)0.21 Psychological distress Crude1 (Ref.)1.04 (0.79–1.37)0.82 (0.62–1.09)0.75 (0.56–1.00)0.02 Model 11 (Ref.)1.00 (0.75–1.34)0.78 (0.58–1.05)0.77 (0.57–1.04)0.03 Model 21 (Ref.)0.96 (0.70–1.30)0.80 (0.59–1.08)0.74 (0.54–1.01)0.03 Model 31 (Ref.)0.89 (0.65–1.22)0.71 (0.52–1.00)0.63 (0.44–0.91)0.01 Model 41 (Ref.)0.87 (0.63–1.21)0.71 (0.51–1.00)0.67 (0.46–0.98)0.02All values are odds ratios and $95\%$ confidence intervals. Model 1: Adjusted for age and energy intake; Model 2: Further adjustment for physical activity, smoking, marital status, education, household size, house possession, diabetes, use of anti-depressant medications and dietary supplements; Model 3: Additional controlling for dietary intakes of fat, n-3 fatty acids, iron, thiamin; Model 4: Further adjusted for BMI. Multivariable-adjusted odds ratios for psychological disorders across quartiles of riboflavin intake, stratified by BMI categories, are reported in Table 5. In normal-weight participant (BMI < 25 kg/m2), the top quartile of riboflavin intake compared to the bottom quartile was associated with a significant $38\%$ decrease in depression odds (OR = 0.62, $95\%$CI 0.41, 0.94), in fully-adjusted model. Also, participants in the top quartile of riboflavin intake were marginally less likely to have distress, in comparison to those in the bottom quartile (OR = 0.66, $95\%$CI 0.41, 1.02). Among overweight/obese individuals (BMI ≥ 25 kg/m2), no association was observed between riboflavin consumption and anxiety in fully-adjusted model. The highest level of riboflavin intake, as compared to the lowest level, was associated with a marginally significant decrease in odds of depression, in fully adjusted model (OR = 0.66; $95\%$ CI 0.44, 1.00). Higher riboflavin intake was also associated with a $42\%$ decreased odds of distress (OR = 0.58; $95\%$ CI 0.38, 0.90), after adjustment for confounders. Table 5Multivariable-adjusted odds ratios and $95\%$ confidence intervals for depression, anxiety and high psychological distress across energy-adjusted quartiles of riboflavin intake, stratified by BMI.Quartiles of riboflavin intakePtrendQ1 (< 1.6 mg/d)Q2 (1.6–1.82 mg/d)Q3 (1.83–2.09 mg/d)Q4 (> 2.09 mg/d)Normal weight participants (BMI < 25 kg/m2) ($$n = 1856$$) Depression Crude1 (Ref.)0.99 (0.74–1.31)0.74 (0.55–1.00)0.63 (0.46–0.86)0.01 Model 11 (Ref.)1.00 (0.74–1.36)0.72 (0.52–0.98)0.67 (0.48–0.94)0.01 Model 21 (Ref.)0.96 (0.70–1.33)0.74 (0.53–1.03)0.64 (0.45–0.92)0.01 Model 31 (Ref.)0.94 (0.67–1.32)0.71 (0.50–1.01)0.62 (0.41–0.94)0.01 Anxiety Crude1 (Ref.)0.97 (0.66–1.43)0.97 (0.66–1.43)0.63 (0.40–0.98)0.07 Model 11 (Ref.)0.98 (0.65–1.47)0.88 (0.58–1.33)0.66 (0.41–1.04)0.08 Model 21 (Ref.)0.94 (0.60–1.46)1.01 (0.66–1.57)0.64 (0.39–1.06)0.17 Model 31 (Ref.)0.91 (0.58–1.44)0.98 (0.61–1.55)0.63 (0.35–1.11)0.21 Psychological distress Crude1 (Ref.)1.13 (0.84–1.52)0.77 (0.56–1.05)0.64 (0.46–0.90)0.01 Model 11 (Ref.)1.19 (0.86–1.64)0.76 (0.54–1.07)0.71 (0.50–1.01)0.01 Model 21 (Ref.)1.14 (0.81–1.61)0.79 (0.56–1.13)0.73 (0.50–1.06)0.03 Model 31 (Ref.)1.09 (0.76–1.55)0.73 (0.50–1.07)0.66 (0.43–1.02)0.02Overweight/obese participants (BMI ≥ 25 kg/m2) ($$n = 1506$$) Depression Crude1 (Ref.)0.79 (0.57–1.09)0.84 (0.61–1.16)0.73 (0.54–1.00)0.09 Model 11 (Ref.)0.73 (0.51–1.04)0.73 (0.51–1.04)0.66 (0.47–0.92)0.03 Model 21 (Ref.)0.72 (0.50–1.04)0.72 (0.50–1.04)0.65 (0.45–0.92)0.03 Model 31 (Ref.)0.73 (0.50–1.06)0.73 (0.50–1.08)0.66 (0.44–1.00)0.07 Anxiety Crude1 (Ref.)0.66 (0.43–0.99)0.64 (0.42–0.97)0.68 (0.46–1.00)0.06 Model 11 (Ref.)0.51 (0.32–0.82)0.54 (0.34–0.85)0.66 (0.43–1.00)0.09 Model 21 (Ref.)0.50 (0.31–0.82)0.52 (0.32–0.85)0.66 (0.42–1.03)0.11 Model 31 (Ref.)0.49 (0.30–0.81)0.50 (0.30–0.83)0.61 (0.36–1.01)0.07 Psychological distress Crude1 (Ref.)0.77 (0.54–1.09)0.81 (0.57–1.15)0.75 (0.54–1.05)0.15 Model 11 (Ref.)0.73 (0.50–1.06)0.78 (0.54–1.14)0.77 (0.54–1.10)0.25 Model 21 (Ref.)0.67 (0.45–1.00)0.74 (0.50–1.09)0.68 (0.46–0.99)0.09 Model 31 (Ref.)0.64 (0.42–0.96)0.68 (0.45–1.02)0.58 (0.38–0.90)0.03All values are odds ratios and $95\%$ confidence intervals. Model 1: Adjusted for age, sex and energy intake; Model 2: Further adjustment for physical activity, smoking, marital status, education, household size, house possession, diabetes, use of anti-depressant medications and dietary supplements; Model 3: Additional controlling for dietary intakes of fat, n-3 fatty acids, iron, thiamin. ## Discussion This population-based study revealed that dietary riboflavin intake was inversely linked to psychological disorders in Iranian adults. This association was independent from several potential confounders. Moreover, stratified analysis revealed that greater riboflavin intake from diet was linked to lower chance of depression and anxiety in men. In addition, an inverse association was seen between dietary intake of riboflavin and psychological distress in women. In addition, the associations between riboflavin intake and depression and psychological distress were independent from BMI categories in adults. To our knowledge, this is one of the first studies which investigated the association between dietary intake of riboflavin and psychological disorders in Iranian adults. The prevalence of depression, anxiety, and other psychological disorders has dramatically risen across the world1,3. Along with other chronic conditions such as obesity45, cardiovascular disease46, diabetes47, and cancers48, these mental disorders have become global health issues and imposed substantial economic burden on healthcare systems49. As a result, prevention strategies for these disorders are crucial50. The findings of current study suggested that high intake of riboflavin from foods might be helpful for general adult population to prevent these conditions. People should be advised to consume more dietary sources of riboflavin such as dairy products, leafy vegetables, legumes, liver, kidneys, yeast, and mushrooms51. Some prior studies have reported an inverse association between vitamin B252,53 and other B-vitamins with psychological disorders in various nations28,29. The current study showed that higher intake of riboflavin was protectively related to lower chance of depression. Similarly, a cross-sectional study in adult population revealed a reduced odds of depression in relation to the highest intake of riboflavin53, although stratified analysis based on sex was not conducted in the mentioned study. Another cross-sectional investigation on psychiatric in-patients showed a significant inverse relationship between riboflavin intake and endogenous depression52. In addition, a cross-sectional study on 6517 adolescents demonstrated that riboflavin intake was inversely associated with depressive symptoms in girls, but not in boys30. In line with our findings, a meta-analysis on six epidemiologic investigations revealed a significant inverse linkage between riboflavin intake and depression; however, five of these investigations were conducted in East Asian countries54. A cross-sectional study on 314 HIV-infected adults revealed that about $26\%$ of participants suffered from depression55. More than $67\%$ of individuals consumed B-vitamins less than estimated average requirements (EAR) and low consumption of riboflavin was connected to depression risk in women, but not in men. Although the exact mechanism remains unclear, it was proposed that female gonadal hormones might change the serotoninergic activity in the brain, alter regulation of monoamines levels such as serotonin, and result in higher levels of depressive symptoms56. However, stratified analysis in the current study revealed a significant inverse relation between dietary riboflavin intake and depression in men, but not in women. Subgroup analysis by sex has additionally documented the inverse association only in women. A prospective birth-cohort study conducted on 636 British women has documented a non-significant inverse relationship between riboflavin intake and psychological distress. In the mentioned cohort GHQ-28, multiple 24-h recalls and a 5-day food record were applied to measure psychological distress and dietary vitamin B2 assessment57. More prospective investigations in this regard are needed. A possible concern in interpreting findings of epidemiologic studies is reverse causation, particularly when the study design is cross-sectional. The reverse causation refers to the fact that symptoms of depression might lead to changes in dietary intakes of individuals58. Such that, a longitudinal investigation demonstrated the existence of bidirectional relationships between food intakes and depression59, especially in case of meat, dairy products, and vegetable intake59. The analysis of the current study revealed significant differences in some nutrients and vitamins between categories of riboflavin intake. In other words, those who had more riboflavin intake also had more other nutrients and vitamins intake or had a healthier dietary pattern. Therefore, this healthier dietary intake might result in reduction of psychological disorders odds. Several probable mechanisms might elucidate the observed associations between vitamin B2 and mental health. FMN and FAD are two crucial rate-limiting flavoprotein coenzymes which derive from riboflavin60. Flavoproteins are co-factors in the metabolism of essential fatty acids in brain lipids51. The action of riboflavin coenzymes in the re-methylation and trans-sulphuration of homocysteine may also explain the benefits of riboflavin on mental health61,62, as an inverse relation among homocysteine and circulating concentrations of riboflavin was previously reported in National Health and Nutrition Examination Survey (NHANES) and Framingham Offspring cohort63,64. Thus, high level of homocysteine may mediate the connection between riboflavin deficiency and high risk of depression. The current study has several strengths. A considerable population of adults was investigated by the use of validated questionnaires for assessment of dietary intakes, physical activity, and psychological disorders. Several potential confounders were also considered in the current analyses. However, some limitations should be acknowledged when interpreting the findings. This study cannot infer a causal relationship between riboflavin intake and psychological disorders due to the cross-sectional design of the study. Further studies with prospective design are needed to establish causality. The study was conducted on non-academic personnel of a medical university with different socioeconomic levels; although the sample was somehow representative of general adult population, extrapolating our findings to other populations should be done with caution. Finally, although a validated self-administered FFQ was applied, misclassification of subjects was inevitable. In conclusion, the current study found that dietary riboflavin intake was inversely associated with chance of psychological disorders in Iranian adults. High intake of riboflavin decreased the chance of depression and anxiety in men and high psychological distress in women. More prospective studies are needed to confirm these findings. ## Supplementary Information Supplementary Figure 1. The online version contains supplementary material available at 10.1038/s41598-023-32309-w. ## References 1. Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V. The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013. Int. J. Epidemiol. (2014.0) 43 476-493. DOI: 10.1093/ije/dyu038 2. Batty GD, McIntosh AM, Russ TC, Deary IJ, Gale CR. Psychological distress, neuroticism, and cause-specific mortality: Early prospective evidence from UK Biobank. J. Epidemiol. Community Health. (2016.0) 70 1136-1139. DOI: 10.1136/jech-2016-207267 3. Investigators EM, Alonso J, Angermeyer M, Bernert S, Bruffaerts R, Brugha T. Prevalence of mental disorders in Europe: Results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr. Scand. (2004.0) 109 21-27. DOI: 10.1111/j.1600-0047.2004.00327.x 4. Baxter AJ, Scott KM, Vos T, Whiteford HA. Global prevalence of anxiety disorders: A systematic review and meta-regression. Psychol. Med. (2013.0) 43 897-910. DOI: 10.1017/S003329171200147X 5. Ferrari A, Somerville A, Baxter A, Norman R, Patten S, Vos T. Global variation in the prevalence and incidence of major depressive disorder: A systematic review of the epidemiological literature. Psychol. Med. (2013.0) 43 471-481. DOI: 10.1017/S0033291712001511 6. Tahan M, Saleem T, Zygoulis P, Pires LVL, Pakdaman M, Taheri H. A systematic review of prevalence of depression in Iranian patients. Neuropsychopharmacol. Hung. (2020.0) 22 16-22. PMID: 32329749 7. Hajebi A, Motevalian SA, Rahimi-Movaghar A, Sharifi V, Amin-Esmaeili M, Radgoodarzi R. Major anxiety disorders in Iran: Prevalence, sociodemographic correlates and service utilization. BMC Psychiatry (2018.0) 18 261. DOI: 10.1186/s12888-018-1828-2 8. Burns J, Birrell E. Enhancing early engagement with mental health services by young people. Psychol. Res. Behav. Manag. (2014.0) 7 303. DOI: 10.2147/PRBM.S49151 9. Munir S, Takov V. Generalized Anxiety Disorder. StatPearls (2021.0) 10. Rose GM, Tadi P. Social Anxiety Disorder. StatPearls (2021.0) 11. Alexopoulos GS. Depression in the elderly. Lancet (2005.0) 365 1961-1970. DOI: 10.1016/S0140-6736(05)66665-2 12. 12.Alshawwa, I. A., Elkahlout, M., El-Mashharawi, H. Q. & Abu-Naser, S. S. An Expert System for Depression Diagnosis (2019). 13. 13.Brody, D. J., Pratt, L. A. & Hughes, J. P. Prevalence of depression among adults aged 20 and over: United States, 2013–2016 (2018). 14. Mathers CD. History of global burden of disease assessment at the World Health Organization. Arch. Public Health. (2020.0) 78 1-13. DOI: 10.1186/s13690-020-00458-3 15. Saveanu RV, Nemeroff CB. Etiology of depression: Genetic and environmental factors. Psychiatr. Clin. (2012.0) 35 51-71 16. Murakami K, Sasaki S. Dietary intake and depressive symptoms: A systematic review of observational studies. Mol. Nutr. Food Res. (2010.0) 54 471-488. DOI: 10.1002/mnfr.200900157 17. Liu Z-M, Ho SC, Xie YJ, Chen Y-J, Chen Y-M, Chen B. Associations between dietary patterns and psychological factors: A cross-sectional study among Chinese postmenopausal women. Menopause (2016.0) 23 1294-1302. DOI: 10.1097/GME.0000000000000701 18. Abshirini M, Siassi F, Koohdani F, Qorbani M, Mozaffari H, Aslani Z. Dietary total antioxidant capacity is inversely associated with depression, anxiety and some oxidative stress biomarkers in postmenopausal women: A cross-sectional study. Ann. Gen. Psychiatry (2019.0) 18 1-9. DOI: 10.1186/s12991-019-0225-7 19. Sánchez-Villegas A, Doreste J, Schlatter J, Pla J, Bes-Rastrollo M, Martínez-González M. Association between folate, vitamin B6 and vitamin B12 intake and depression in the SUN cohort study. J. Hum. Nutr. Diet. (2009.0) 22 122-133. DOI: 10.1111/j.1365-277X.2008.00931.x 20. Sánchez-Villegas A, Delgado-Rodríguez M, Alonso A, Schlatter J, Lahortiga F, Majem LS. Association of the Mediterranean dietary pattern with the incidence of depression: The Seguimiento Universidad de Navarra/University of Navarra follow-up (SUN) cohort. Arch. Gen. Psychiatry (2009.0) 66 1090-1098. DOI: 10.1001/archgenpsychiatry.2009.129 21. Sánchez-Villegas A, Pérez-Cornago A, Zazpe I, Santiago S, Lahortiga F, Martínez-González MA. Micronutrient intake adequacy and depression risk in the SUN cohort study. Eur. J. Nutr. (2018.0) 57 2409-2419. DOI: 10.1007/s00394-017-1514-z 22. Mikkelsen K, Stojanovska L, Apostolopoulos V. The effects of vitamin B in depression. Curr. Med. Chem. (2016.0) 23 4317-4337. DOI: 10.2174/0929867323666160920110810 23. Xu Y, Wang C, Klabnik J, M O'Donnell J. Novel therapeutic targets in depression and anxiety: Antioxidants as a candidate treatment. Curr. Neuropharmacol. (2014.0) 12 108-119. DOI: 10.2174/1570159X11666131120231448 24. 24.Rivlin, R. S. & Pinto, J. T. Riboflavin (vitamin B2). 255–73 (2001). 25. Mahabadi N, Bhusal A, Banks SW. Riboflavin Deficiency. StatPearls (2020.0) 26. Peechakara BV, Gupta M. Vitamin B2 (Riboflavin). StatPearls (2020.0) 27. Wolak N, Zawrotniak M, Gogol M, Kozik A, Rapala-Kozik M. Vitamins B1, B2, B3 and B9—occurrence, biosynthesis pathways and functions in human nutrition. Mini. Rev. Med. Chem. (2017.0) 17 1075-1111. DOI: 10.2174/1389557516666160725095729 28. Kafeshani M. Higher vitamin B6 intake is associated with lower depression and anxiety risk in women but not in men: A large cross-sectional study. Int. J. Vitamin Nutr. Res. (2019.0) 90 484-492. DOI: 10.1024/0300-9831/a000589 29. Mozaffari H, Mofrad MD, Surkan PJ, Askari M, Azadbakht L. Associations between dietary intake of B-vitamins and psychological disorders among Iranian women: A cross-sectional study. Public Health Nutr. (2021.0) 24 1787-1797. DOI: 10.1017/S1368980020002943 30. Murakami K, Miyake Y, Sasaki S, Tanaka K, Arakawa M. Dietary folate, riboflavin, vitamin B-6, and vitamin B-12 and depressive symptoms in early adolescence: The Ryukyus Child Health Study. Psychosom. Med. (2010.0) 72 763-768. DOI: 10.1097/PSY.0b013e3181f02f15 31. Murakami K, Mizoue T, Sasaki S, Ohta M, Sato M, Matsushita Y. Dietary intake of folate, other B vitamins, and ω-3 polyunsaturated fatty acids in relation to depressive symptoms in Japanese adults. Nutrition (2008.0) 24 140-147. DOI: 10.1016/j.nut.2007.10.013 32. Woo J, Lynn H, Lau W, Leung J, Lau E, Wong S. Nutrient intake and psychological health in an elderly Chinese population. Int. J. Geriatr. Psychiatry (2006.0) 21 1036-1043. DOI: 10.1002/gps.1603 33. Adibi P, Keshteli AH, Esmaillzadeh A, Afshar H, Roohafza H, Bagherian-Sararoudi R. The study on the epidemiology of psychological, alimentary health and nutrition (SEPAHAN): Overview of methodology. J. Res. Med. Sci. (2012.0) 17 S292-S298 34. Keshteli AH, Esmaillzadeh A, Rajaie S, Askari G, Feinle-Bisset C, Adibi P. A dish-based semi-quantitative food frequency questionnaire for assessment of dietary intakes in epidemiologic studies in Iran: Design and development. Int. J. Prev. Med. (2014.0) 5 29. PMID: 24554989 35. Ghaffarpour M, Houshiar-Rad A, Kianfar H. The manual for household measures, cooking yields factors and edible portion of foods. Tehran: Nashre Olume Keshavarzy. (1999.0) 7 42-58 36. Saneei P, Hajishafiee M, Keshteli AH, Afshar H, Esmaillzadeh A, Adibi P. Adherence to Alternative Healthy Eating Index in relation to depression and anxiety in Iranian adults. Br. J. Nutr. (2016.0) 116 335-342. DOI: 10.1017/S0007114516001926 37. Haghighatdoost F, Azadbakht L, Keshteli AH, Feinle-Bisset C, Daghaghzadeh H, Afshar H. Glycemic index, glycemic load, and common psychological disorders. Am. J. Clin. Nutr. (2016.0) 103 201-209. DOI: 10.3945/ajcn.114.105445 38. Anjom-Shoae J, Sadeghi O, Keshteli AH, Afshar H, Esmaillzadeh A, Adibi P. Legume and nut consumption in relation to depression, anxiety and psychological distress in Iranian adults. Eur. J. Nutr. (2020.0) 59 3635-3645. DOI: 10.1007/s00394-020-02197-1 39. Montazeri A, Vahdaninia M, Ebrahimi M, Jarvandi S. The Hospital Anxiety and Depression Scale (HADS): Translation and validation study of the Iranian version. Health Qual. Life Outcomes (2003.0) 1 14. DOI: 10.1186/1477-7525-1-14 40. Schmitz N, Kruse J, Heckrath C, Alberti L, Tress W. Diagnosing mental disorders in primary care: The General Health Questionnaire (GHQ) and the Symptom Check List (SCL-90-R) as screening instruments. Soc. Psychiatry Psychiatr. Epidemiol. (1999.0) 34 360-366. DOI: 10.1007/s001270050156 41. Montazeri A, Harirchi AM, Shariati M, Garmaroudi G, Ebadi M, Fateh A. The 12-item General Health Questionnaire (GHQ-12): Translation and validation study of the Iranian version. Health Qual. Life Outcomes (2003.0) 1 1-4. DOI: 10.1186/1477-7525-1-1 42. Aminianfar S, Saneei P, Nouri M, Shafiei R, Hassanzadeh-Keshteli A, Esmaillzadeh A. Validation study of self-reported anthropometric indices among the staff of the Isfahan University of Medical Sciences, Isfahan, Iran. J. Isfahan Med. Sch. (2015.0) 33 1318-1327 43. Promoting N. Creating Built or Natural Environments that Encourage and Support Physical Activity: Scope (2006.0) 44. Brown CC, Kipnis V, Freedman LS, Hartman AM, Schatzkm A, Wacholder S. Energy adjustment methods for nutritional epidemiology: The effect of categorization. Am. J. Epidemiol. (1994.0) 139 323-338. DOI: 10.1093/oxfordjournals.aje.a117000 45. Markowitz S, Friedman MA, Arent SM. Understanding the relation between obesity and depression: Causal mechanisms and implications for treatment. Clin. Psychol. Sci. Pract. (2008.0) 15 1. DOI: 10.1111/j.1468-2850.2008.00106.x 46. Van der Kooy K, Van Hout H, Marwijk H, Marten H, Stehouwer C, Beekman A. Depression and the risk for cardiovascular diseases: Systematic review and meta analysis. Int. J. Geriatr. Psychiatry (2007.0) 22 613-626. DOI: 10.1002/gps.1723 47. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: A meta-analysis. Diabetes Care (2008.0) 31 2383-2390. DOI: 10.2337/dc08-0985 48. Ma X, Yu H. Cancer issue: Global burden of cancer. Yale J. Biol. Med. (2006.0) 79 85. PMID: 17940618 49. 49.WHOMethods and Data Sources for Global Burden of Disease Estimates 2000–20192020World Health Organization. Methods and Data Sources for Global Burden of Disease Estimates 2000–2019 (2020.0) 50. Saxena S, Jané-Llopis E, Hosman C. Prevention of mental and behavioural disorders: Implications for policy and practice. World Psychiatry (2006.0) 5 5. PMID: 16757984 51. Kennedy DO. B vitamins and the brain: Mechanisms, dose and efficacy—a review. Nutrients (2016.0) 8 68. DOI: 10.3390/nu8020068 52. Carney M, Ravindran A, Rinsler M, Williams D. Thiamine, riboflavin and pyridoxine deficiency in psychiatric in-patients. Br. J. Psychiatry (1982.0) 141 271-272. DOI: 10.1192/bjp.141.3.271 53. Park S-J, Choi JH, Lee JY, Lee C, Lee H-J. Association between nutrient intakes and prevalence of depressive disorder in Korean adults: 2014 Korean National Health and Nutrition Examination Survey. J. Nutr. Health. (2018.0) 51 414-422. DOI: 10.4163/jnh.2018.51.5.414 54. Wu Y, Zhang L, Li S, Zhang D. Associations of dietary vitamin B1, vitamin B2, vitamin B6, and vitamin B12 with the risk of depression: A systematic review and meta-analysis. Nutr. Rev. (2022.0) 80 351-366. DOI: 10.1093/nutrit/nuab014 55. Poudel-Tandukar K. Dietary B vitamins and depression in persons with human immunodeficiency virus infection: The positive living with HIV (POLH) study. J. Nutr. Sci. Vitaminol. (2016.0) 62 388-396. DOI: 10.3177/jnsv.62.388 56. Yary T. The association between dietary intake of folate and physical activity with psychological dimensions of depressive symptoms among students from Iran. BioMed. Res. Int. (2013.0) 2013 Kafeshani. DOI: 10.1155/2013/582693 57. Mishra GD, McNaughton SA, O’Connell MA, Prynne CJ, Kuh D. Intake of B vitamins in childhood and adult life in relation to psychological distress among women in a British birth cohort. Public Health Nutr. (2009.0) 12 166-174. DOI: 10.1017/S1368980008002413 58. Flegal KM, Graubard BI, Williamson DF, Cooper RS. Reverse causation and illness-related weight loss in observational studies of body weight and mortality. Am. J. Epidemiol. (2011.0) 173 1-9. DOI: 10.1093/aje/kwq341 59. Elstgeest LE, Visser M, Penninx BW, Colpo M, Bandinelli S, Brouwer IA. Bidirectional associations between food groups and depressive symptoms: Longitudinal findings from the Invecchiare in Chianti (InCHIANTI) study. Br. J. Nutr. (2019.0) 121 439-450. DOI: 10.1017/S0007114518003203 60. Rucker RB, Zempleni J, Suttie JW, McCormick DB. Handbook of Vitamins (2007.0) 61. Gerhard GT, Malinow MR, DeLoughery TG, Evans AJ, Sexton G, Connor SL. Higher total homocysteine concentrations and lower folate concentrations in premenopausal black women than in premenopausal white women. Am. J. Clin. Nutr. (1999.0) 70 252-260. DOI: 10.1093/ajcn.70.2.252 62. McCormick DB. Two interconnected B vitamins: Riboflavin and pyridoxine. Physiol. Rev. (1989.0) 69 1170-1198. DOI: 10.1152/physrev.1989.69.4.1170 63. Ganji V, Kafai MR. Demographic, health, lifestyle, and blood vitamin determinants of serum total homocysteine concentrations in the third National Health and Nutrition Examination Survey, 1988–1994. Am. J. Clin. Nutr. (2003.0) 77 826-833. DOI: 10.1093/ajcn/77.4.826 64. Jacques PF, Bostom AG, Wilson PW, Rich S, Rosenberg IH, Selhub J. Determinants of plasma total homocysteine concentration in the Framingham offspring cohort. Am. J. Clin. Nutr. (2001.0) 73 613-621. DOI: 10.1093/ajcn/73.3.613
--- title: High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension authors: - Bastien Lechat - Kelly A. Loffler - Amy C. Reynolds - Ganesh Naik - Andrew Vakulin - Garry Jennings - Pierre Escourrou - R. Doug McEvoy - Robert J. Adams - Peter G. Catcheside - Danny J. Eckert journal: NPJ Digital Medicine year: 2023 pmcid: PMC10060245 doi: 10.1038/s41746-023-00801-2 license: CC BY 4.0 --- # High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension ## Abstract Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood. This study uses in-home monitoring of 15,526 adults with ~180 nights per participant with an under-mattress sleep sensor device, plus ~30 repeat blood pressure measures. OSA severity is defined from the mean estimated apnea–hypopnoea index (AHI) over the ~6-month recording period for each participant. Night-to-night variability in severity is determined from the standard deviation of the estimated AHI across recording nights. Uncontrolled hypertension is defined as mean systolic blood pressure ≥140 mmHg and/or mean diastolic blood pressure ≥90 mmHg. Regression analyses are performed adjusted for age, sex, and body mass index. A total of 12,287 participants ($12\%$ female) are included in the analyses. Participants in the highest night-to-night variability quartile within each OSA severity category, have a 50–$70\%$ increase in uncontrolled hypertension likelihood versus the lowest variability quartile, independent of OSA severity. This study demonstrates that high night-to-night variability in OSA severity is a predictor of uncontrolled hypertension, independent of OSA severity. These findings have important implications for the identification of which OSA patients are most at risk of cardiovascular harm. ## Introduction Obstructive sleep apnea (OSA) is a common clinical sleep disorder characterized by repetitive upper airway collapse during sleep. OSA is estimated to affect approximately one billion people globally1,2. Untreated OSA is associated with a wide range of adverse health and safety consequences including increased risk of hypertension3, cardiovascular disease4, depression5, reduced quality of life6, traffic accidents7, and all-cause mortality4. Recent evidence indicates that there is considerable night-to-night variation in OSA severity2,8,9. This has raised concerns regarding OSA misdiagnosis and possible misdirected management and care2,8,9. Night-to-night variation in OSA may explain, at least in part, why a single-night diagnosis of OSA shows inconsistent relationships with important health outcomes and responses to treatment10–12. Indeed, emerging evidence indicates that high night-to-night variability in OSA severity may be an important contributor to cardiovascular diseases such as atrial fibrillation13,14. Similarly, blood pressure varies widely from day-to-day in some people, and high day-to-day variability in blood pressure is associated with atrial fibrillation, OSA, all-cause mortality, and cardiovascular events, independent of mean blood pressure15–21. Whether night-to-night changes in OSA severity contribute to blood pressure variability is unknown. To investigate the potential clinical importance of night-to-night variation in OSA severity on cardiovascular risk, this study was designed to determine the potential association between night-to-night variability in OSA severity and blood pressure using a validated under-mattress sleep monitor2,22,23 and a clinically validated home-blood pressure monitor. We hypothesized that high variability in OSA severity would be a strong predictor of hypertension and blood pressure variability. This study includes data from 12,287 adults monitored over ~180 nights using an under-mattress sleep sensor device accompanied by ~30 repeat blood pressure measurements. Here we use the under-mattress sleep sensor technology to estimate OSA severity (mean apnea–hypopnea-index; AHI) and OSA variability (standard deviation of AHI over the recording period). People in the highest variability quartile within each OSA severity category have a 50–$70\%$ increased likelihood of uncontrolled hypertension compared to the lowest variability quartile, regardless of OSA severity. We conclude that high variability in OSA severity is an independent predictor of uncontrolled hypertension and this has major implications for identification of patients most at risk of cardiovascular harm. Furthermore, this study highlights the unique and important insights that multi-night, in-home, non-invasive monitoring of OSA severity can yield. ## Participant characteristics Of the 15,526 users in the database, 1377 ($8.9\%$) and 1482 (9.5 %) were excluded because they had <28 nights of sleep recordings or an average use of <4 times a week, respectively. A further 346 ($2.2\%$) were excluded due to missing body mass index (BMI) data and 34 ($0.2\%$) as they were <18 or >90 years old. The characteristics of the remaining 12,287 users are summarized in Table 1. The characteristics of the current participants were similar to a recent report that included data from >65,000 people to investigate multi-night OSA prevalence and single-night disease misclassification2. However, the proportion of males was slightly higher, and mean BMI and AHI values were ~1 kg/m2 and 3 events/h higher, respectively, in the current study sample (Supplementary Table 1). Most users resided in Europe ($69\%$) and North America ($27\%$). Participants had a mean (±SD) of 181 ± 69 overnight sleep recordings and median [IQR] 29 [12, 81] repeat blood pressure recordings. A total of 910,836 direct blood pressure entries were acquired during the recording period. $75\%$ of the total blood pressure measurements were acquired as single time point measurements and $25\%$ were acquired as the mean of three consecutive measurements. Table 1Baseline participant characteristics. OverallNo OSAMild OSAModerate OSASevere OSAn12,2874529 ($36.9\%$)4233 ($34.4\%$)2256 ($18.4\%$)1269 ($10.3\%$)Age (years)50 ± 1244 ± 1150 ± 1155 ± 1157 ± 11BMI (kg/m2)28 ± 627 ± 528 ± 530 ± 532 ± 6SexMale10,868 ($88\%$)3804 ($84\%$)3770 ($89\%$)2082 ($92\%$)1212 ($96\%$)Female1419 ($12\%$)725 ($16\%$)463 ($11\%$)174 ($8\%$)57 ($4\%$)Number of nights (n)181 ± 69179 ± 69182 ± 69183 ± 67181 ± 70Use per week, (nights/week)6 ± 16 ± 16 ± 16 ± 16 ± 1Apnea–hypopnea-index (events/h)Mean13 ± 142 ± 19 ± 321 ± 445 ± 14SD6 ± 43 ± 16 ± 210 ± 315 ± 5Number of BP entries m[IQR]29 [12, 81]24 [11, 67]30 [12, 86]34 [14, 95]35 [14, 89]Systolic BP (mmHg)Mean127 ± 12124 ± 12127 ± 12129 ± 11133 ± 12SD9 ± 38 ± 39 ± 310 ± 310 ± 4Diastolic BP (mmHg)Mean82 ± 880 ± 882 ± 883 ± 885 ± 9SD6 ± 26 ± 26 ± 26 ± 27 ± 2HypertensionNo9820 ($80\%$)3922 ($87\%$)3376 ($80\%$)1700 ($75\%$)822 ($65\%$)Yes2467 ($20\%$)607 ($13\%$)857 ($20\%$)556 ($25\%$)447 ($35\%$)Data are reported as mean ± standard deviation (SD) for continuous variables and n (%) for categorical variables. Mean and SD for the apnea–hypopnea index values are calculated as the group average and standard deviation of the estimated apnea–hypopnea-index over the entire recording period available for each individual participant. BMI body mass index, BP blood pressure. Mean systolic and diastolic blood pressure were 9 and 5 mmHg higher in people with severe versus no OSA (Table 1). Linear associations between OSA, OSA variability, and systolic and diastolic blood pressures (as a continuous variable), controlled for age, sex, and BMI also revealed that blood pressure was consistently higher (all p-values < 0.001) for participants with severe and variable OSA compared to controls and those with low night-to-night variability in OSA severity (see Tables 2 and 3, Supplementary Figs. 1 and 2). Individual examples of matched blood pressure and AHI measurements in people with and without high night-to-night variability in OSA severity and different OSA severity categories are displayed in Fig. 1 with additional examples shown in Supplementary Fig. 3.Table 2β coefficients (mean and $95\%$ confidence interval) of the association between obstructive sleep apnea (OSA) severity with diastolic and systolic blood pressure. OSA severityNo OSAMild OSAModerate OSASevere OSABlood pressureSystolic0 (ref)1.15 (0.68, 1.61)1.92 (1.34, 2.51)3.32 (2.58, 4.05)Diastolic0 (ref)1.81 (1.48, 2.15)2.43 (2.00, 2.85)3.24 (2.70, 3.78)OSA severity categories were defined using standard clinical cut-offs of the apnea–hypopnea index (<5 = no OSA, ≥5 and <15 = mild, ≥15 and <30 = moderate and ≥30 events/h sleep = severe OSA). Models were adjusted for age, sex, and BMI.Table 3β coefficients (mean and $95\%$ confidence interval) of the association between quartiles of night-to-night variability in obstructive sleep apnea (OSA) severity with diastolic and systolic blood pressure. OSA variabilityQuartile 1Quartile 2Quartile 3Quartile 4Blood pressureSystolic0 (ref)0.32 (−0.23, 0.86)1.42 (0.82, 2.02)2.06 (1.22, 2.89)Diastolic0 (ref)1.16 (0.76, 1.56)2.26 (1.82, 2.69)2.41 (1.80, 3.02)OSA variability was defined as average standard deviation of the apnea–hypopnea index and categorized using quartiles. Models were adjusted for age, sex, and BMI and mean apnea–hypopnea-index. Fig. 1Nightly variation in systolic and diastolic blood pressure and estimated apnea–hypopnea index. Night-to-night variation in systolic (orange) and diastolic (blue) blood pressure and estimated apnea–hypopnea index (AHI; green) for participants with mild OSA (mean AHI > 5 but <15 events/h sleep) and either low (a) or high night-to-night variability (c) in AHI. Participants with moderate-to-severe OSA (mean AHI > 15 events) and either low (b) or high (d) night-to-night variability in AHI. e and f tracings represent a zoomed-in 14-day period of examples c and d (high night-to-night AHI variability) to show the temporal relationship between AHI and blood pressure variability. Darker lines are smoothed averaged over 7 days and light lines are raw data. Note: the temporal association between low night-to-night variability in AHI and blood pressure in panels a and c—and between high night-to-night variability in AHI and blood pressure in panels (b) and (d). ## OSA and uncontrolled hypertension OSA severity (estimated AHI mean 75th vs. 25th centile: 16.8 vs. 3.0 events/h; OR [$95\%$ CI], 1.54 [1.38, 1.72]) and higher variability in OSA severity (estimated AHI SD 75th vs. 25th centile: 8.2 vs. 3.4; 1.63 [1.46, 1.83]) were associated with increased uncontrolled hypertension likelihood in separate models after controlling for age, BMI, and sex. Both associations were nonlinear, and marginal probability plots are shown in Fig. 2a, b. There were no significant interactions between sex and OSA severity or sex and OSA variability (p-values = 0.75 and 0.73, respectively). When OSA severity and variability were combined into one model, the variance inflation factors (a measure of collinearity) for OSA severity and variability were below 5 (2.81 and 2.74), supporting the combination of both metrics into a single model. The likelihood ratio test indicated that a model with both OSA severity and variability provided a superior model fit than a model with OSA severity alone (p-value < 0.001). In this combined model, OSA severity was associated with a $7\%$ increase in uncontrolled hypertension likelihood (1.07 [1.00, 1.14]). Furthermore, OSA variability independent of OSA severity was associated with a $51\%$ increase (1.51 [1.31, 1.75]) in uncontrolled hypertension likelihood. Fig. 2Association between mean apnea–hypopnea index, apnea–hypopnea index variability and hypertension. Marginal probability of hypertension in relation to: a mean apnea–hypopnea index and b apnea–hypopnea index variability. Categorical analysis of OSA severity confirmed that mild, moderate, and severe OSA were associated with increased uncontrolled hypertension likelihood compared to the no OSA group (Fig. 3). Participants with mild, moderate, and severe OSA were at $43\%$ (OR $95\%$CI; 1.43 [1.26–1.61]), $62\%$ (1.62 [1.40–1.87]) and $122\%$ (2.22 [1.88–2.62]) increased uncontrolled hypertension likelihood versus no OSA. Direct comparisons between the association of OSA severity, as measured over multiple nights with the Withings Sleep Analyzer (WSA), with hypertension in the current study versus single-night assessment in the Sleep Heart Health Study are presented in Supplementary Table 2. Furthermore, analysis of OSA variability by quartiles within each traditionally defined OSA severity category, indicated that higher night-to-night variability in OSA severity was associated with greater uncontrolled hypertension likelihood (Fig. 4).Fig. 3Association between obstructive sleep apnea (OSA) severity with uncontrolled hypertension. Odds ratio (Estimate and $95\%$ confidence interval) of the association between obstructive sleep apnea (OSA) severity with hypertension. This model is controlled for age, sex, and BMI. OSA category was defined using standard clinical cut-offs of the apnea–hypopnoea index where <5 = no OSA, ≥5 and <15 = mild, ≥15 and <30 = moderate and ≥30 events/h sleep = severe OSA).Fig. 4Association between nightly variation in obstructive sleep apnea severity with uncontrolled hypertension. Odds ratio (mean and $95\%$ confidence interval) of the association between obstructive sleep apnea severity as measured by the mean apnea–hypopnea index (AHI), and night–night variability in AHI, measured by the SD of AHI, with uncontrolled hypertension. Mild OSA is defined as an AHI between 5 and 15 events/h. Moderate/severe OSA is defined as an AHI > 15 events/h. Within each OSA severity category, the lowest quartiles of variability in AHI are taken as the reference. After excluding blood pressure entries not taken during the morning, 5383 ($43.7\%$) participants remained for the sensitivity analysis. Of these, 1170 ($21.7\%$) cases of hypertension were observed. In separate models, high average AHI (OR [$95\%$ CI]; 1.51 [1.29, 1.77]) and high AHI variability (1.58 [1.33, 1.88]) were associated with an increased risk of hypertension. In the combined model, average AHI was associated with a $14\%$ increase in hypertension risk (1.14 [1.02, 1.27]), and variability in AHI was associated with a $36\%$ increase in hypertension risk (1.36 [1.09, 1.69]). Model fit was superior in the combined model than in the AHI-mean-only model ($$p \leq 0.029$$). Quartile analysis of AHI variability within the OSA severity category also suggested increased hypertension risk for participants with variable AHI, although some results became non-significant likely due to the smaller sample size (Supplementary Table 3). Findings were also similar when the model was additionally adjusted for the number of blood pressure entries (Supplementary Tables 4 and 5) and average total sleep time (Supplementary Tables 6 and 7). Three alternative cut-offs to define uncontrolled hypertension also did not change the main findings (Supplementary Tables 8 and 9). ## Mediation analysis Mediation analysis was consistent with a mediating effect of OSA night-to-night variability in the association between OSA severity and uncontrolled hypertension (Supplementary Table 10). A 1-unit increase in AHI mean was associated with a $0.9\%$ increase in the probability of having uncontrolled hypertension (similar effect size as observed in Fig. 2a). The increase was due to both a direct effect of mean AHI on uncontrolled hypertension ($0.4\%$ increase), but also via an increase in night-to-night AHI variability (indirect effect, $0.5\%$). About $55\%$ ($\frac{0.5}{0.9}$) of the effect of mean AHI on uncontrolled hypertension is mediated by an increase in AHI variability. There are important limitations associated with this mediation analysis, as highlighted in the Supplementary Discussion. A potential mediating effect of night-to-night variability in OSA severity in the association between OSA severity and uncontrolled hypertension was investigated using a model depicted in Supplementary Fig. 4. The model was estimated using a diagonally weighted least-squares approach with a probit link function and all associations were adjusted for age, BMI, and sex. The analysis was conducted using the Lavaan package46 in R programming language. ## OSA and blood pressure variability Similar to the OSA and uncontrolled hypertension findings, quartile analyses indicated an association between OSA variability, OSA severity categories, and greater blood pressure variability over the ~6 months recording period. In the combined model, an increase in AHI variability was associated with a ~0.27 mmHg increase in diastolic blood pressure variability (75th vs. 25th; β [$95\%$ CI], 0.27 [0.19, 0.35]) and a ~0.37 mmHg increases in systolic blood pressure variability (0.37 [0.26, 0.48]) independent of mean AHI. In the combined model, mean AHI was not associated with blood pressure variability. In separate models, mean AHI (systolic: 0.31 [0.24, 0.37]; diastolic: 0.24 [0.19, 0.28]) and AHI variability (systolic: 0.41 [0.34, 0.48]; diastolic: 0.31 [0.26, 0.36]) were associated with increased blood pressure variability. The combined model provided a superior model fit compared to a model with mean AHI alone (p-value < 0.001). Quartile analyses also indicated an association between OSA severity categories, OSA variability, and greater blood pressure variability. Severe OSA was associated with ~1 mmHg increase in systolic and ~0.7 mmHg increase in diastolic blood pressure variability compared to no OSA (Supplementary Table 11). Furthermore, compared to the lowest quartile, the highest quartile of OSA variability was associated with an increase in both systolic (~0.5 to 1 mmHg) and diastolic (~0.4 to 0.8 mmHg) blood pressure variability independent of OSA severity category (Supplementary Table 12). Limiting the analyses to only morning blood pressure recordings did not change any of the main findings (Supplementary Table 13). ## Discussion The current findings demonstrate an association between high night-to-night variability in OSA severity with increased uncontrolled hypertension risk and blood pressure variability. Even after accounting for the OSA severity category, OSA variability itself was a strong independent predictor of both uncontrolled hypertension and blood pressure variability. These new findings provide important insight into the different manifestations of OSA and its consequences and highlight the need to consider the importance of night-to-night variability in disease severity. The current multi-night recordings to quantify OSA with a simple non-invasive under-mattress sensor in the home, also support previously documented associations between OSA severity and hypertension risk from conventional single-night polysomnography recordings3,24,25. Accordingly, there is considerable potential to incorporate new simplified monitoring approaches to aid current single-night diagnostics. The current findings are in accordance with emerging evidence that indicates an association between cardiovascular disease and night-to-night variability in OSA severity13,14. A key mechanism through which OSA and cardiovascular risk may be related is via increased day-to-day blood pressure variability20,21,26. Blood pressure variability has been associated with an increased risk of cardiovascular events, all-cause mortality, vascular organ damage, atrial fibrillation, and dementia15–19,27. Consistent with the current findings of increased blood pressure in those with severe or variable OSA, day-to-day blood pressure variation of similar magnitude to the current study is associated with increased all-cause mortality and non-fatal and fatal cardiovascular events28. The increased risk of hypertension and blood pressure variability may reflect a direct physiological increased risk from people with a “variable OSA phenotype”. The multifaceted nature of OSA pathophysiology may place some people more prone to night-to-night variation in OSA severity. These concepts, and the potential underlying mechanisms, warrant further investigation. Our findings also highlight the important new information that multi-night monitoring of OSA and blood pressure can yield. Current one-size-fits-all clinical care approaches based on a single-night diagnostic study may not be appropriate for people with high internight variability and may explain, at least in part, the heterogeneity in prior treatment trials12,29,30. More in-depth assessment of hypoxemia, OSA endotypes, insomnia, and sleep fragmentation may be valuable in more complex manifestations of OSA31–33. The findings of this study also warrant future prospective trials to investigate the effects of different sleep patterns and night-to-night variability in OSA severity and the effects of different therapies on other key cardiovascular and health consequences such as mental health, sleepiness, workplace and traffic accident, and cognitive impairment and their potential interactions. The volume of physiological data available from this study is substantially greater than previous cohort studies that have investigated OSA and its consequences3,24,25. The large sample size allows for greater precision around estimates of OSA severity and uncontrolled hypertension risk for increased power to detect relationships with available exposure variables. The non-invasive monitoring technology used in this study allowed us to identify relationships between variability in OSA severity and blood pressure in a large number of participants over a prolonged period, which is simply not feasible with conventional sleep monitoring approaches. Data were also collected in the participant’s home environment, more directly relevant to real-world risk exposure conditions compared to clinical laboratory sleep study settings. However, AHI derived from the under-mattress sensor includes fewer input variables in which to detect respiratory events compared to conventional polysomnography. Accordingly, the possible contribution of other physiological aspects of OSA (e.g., hypoxia) that may contribute to hypertension risk and blood pressure variability requires further consideration. However, validation studies versus gold standard polysomnography in over 150 participants2,22 support the device performance characteristics. Nonetheless, the potential effects of comorbid insomnia, medications, and other clinical covariates on device accuracy remain to be investigated. Despite these potential unknown influences, OSA prevalence estimates using, non-contact multi-night data yield very similar findings to previously published literature1,2. Similarly, misclassification rates and AHI variability are comparable to data derived from multi-night in-laboratory polysomnography and other home sleep apnea tests2,8,9. The effect size of the association between the estimated AHI and uncontrolled hypertension detected in the current study is also similar to existing epidemiological trials such as the Sleep Heart Health Study, the Wisconsin cohort, and the HypnoLaus cohort25,34,35. While the magnitude of the effect size was comparable between studies, it is important to note that, unlike the current investigation, prior epidemiological studies also included the use of antihypertensive medications in the definition of hypertension. Thus, these findings provide support that the multi-night mean AHI estimates derived in the current study provide comparable, and potentially superior insight, into key health outcomes such as hypertension versus traditional single-night but more complex polysomnography approaches. Instructions on the timing of blood pressure measurements were not given to participants. Thus, some of the variability in blood pressure may reflect circadian changes across the day28. However, the main study findings remained in sensitivity analyses when limited to morning blood pressure entries. Accordingly, circadian influences appear unlikely to have been a major confounder in the current study. Indeed, consistent with previous findings, variability in systolic blood pressure is a significant predictor of death when blood pressure is measured in the morning or evening, or both16. The number of clinical covariates available in this study was also somewhat limited. Thus, the potential impact of uncontrolled confounding behavioral and lifestyle factors (diet, exercise, alcohol, caffeine, tobacco use, and medications)36,37 and treatment status to influence the current findings remain to be investigated. Like many digital health innovations and consumer-data research, there is a balance between data volume and the ability to capture all clinically relevant variables. Conceptually, these data could also be synchronized via electronic health records databases, and via the development of new digital health tools to automatically capture more diverse aspects of a person’s health. Multimodal inputs to predictive algorithms (i.e., using health data from different sources) have been recently shown to better predict health outcomes compared to single-source approaches across 12 predictive tasks, including 10 distinct chest pathology diagnoses, hospital length-of-stay, and 48 h mortality predictions38. Similarly, the field of sleep medicine may benefit from multi-input clinical data where daytime symptoms, overall clinical history, and multi-sensor recordings could be used to better predict health outcomes and treatment response. The decision of the users to purchase a WSA and monitor their blood pressure may represent a selection bias towards those who had preexisting sleep and/or cardiovascular problems. Indeed, users were predominantly male, clearly indicating a sex-specific participation bias. Nonetheless, while no sex differences were detected in the current study in relation to the detected associations with uncontrolled hypertension, the pathophysiology of OSA differs in women versus men39. Thus, it will be important to investigate whether the potential consequences of night-to-night variability in OSA severity are comparable between sexes in a larger cohort with more comparable numbers of women. Despite these methodological considerations, the identification of strong associations between blood pressure variability, uncontrolled hypertension, and high night-to-night variability in OSA severity provides further support for the unique and important insights that multi-night non-invasive monitoring can yield regarding clinical end-points and potential health risks. ## Participants Data were acquired retrospectively from 15,526 participants from a consumer-user database of people aged between 18 and 90 years who purchased an under-mattress sleep sensor device WSA and reported blood pressure measurements on at least five separate occasions. Blood pressure measurements were acquired using an FDA-registered Withings blood pressure cuff monitor in the participant’s home, synchronized directly to the Withings database (both devices are CE-medical IIa certified). Data were collected between 1 July 2020 and 30 March 2021. Further inclusion criteria included ≥28 nights of WSA recordings and an average use of ≥4 times per week. All participants provided written consent through the Withings app for their deidentified data to be used for research purposes when signing up for a Withings account and the current study was approved by the Flinders University Human Research Ethics Committee. ## Monitoring equipment The WSA is a nearable sleep monitoring device placed under-the-mattress that detects body movements, respiratory rate, heart rate, snoring, and cessation of breathing episodes. These signals are used to estimate the AHI using automated algorithms that show good agreement with in-laboratory polysomnography-derived AHI with high predictive performance to classify mild ($89\%$ sensitivity and $75\%$ specificity), moderate-to-severe ($88\%$ sensitivity and $88\%$ specificity) and severe OSA ($86\%$ sensitivity and $91\%$ specificity)2,22,23. The estimated AHI also has minimal bias with in-laboratory polysomnography-derived AHI when the AHI is considered as a continuous variable22. The Withings blood pressure monitor used in conjunction with the WSA comes with an instruction booklet that outlines how to take a blood pressure measurement. The user is instructed to [1] rest for ≥5 min before taking a measurement, [2] be seated in a comfortable position and in a quiet area with legs uncrossed, feet flat on the floor, and back/arm supported [3] not speak during the measurements, and [4] perform the measurement on their left arm. The user has the option of performing a single measurement or taking three consecutive measurements to acquire an average blood pressure value. ## Effect of OSA on uncontrolled hypertension and potential confounders Hypertension exposure variables were the mean estimated AHI (hereafter referred to as “OSA severity”) and the average standard deviation of estimated AHI (hereafter termed “OSA variability”). OSA severity and variability were calculated for each participant derived from all available nights for each individual. OSA severity categories were defined using standard clinical cut-offs40 (<5 = no OSA, ≥5 and <15 = mild, ≥15 and <30 = moderate and ≥30 events/h sleep = severe OSA), and OSA variability was defined within each OSA severity category. Uncontrolled hypertension was defined as a mean systolic blood pressure ≥140 mmHg or mean diastolic blood pressure ≥90 mmHg41 across the monitoring period as these thresholds are most commonly applied in previously published papers on the relationship between uncontrolled hypertension and OSA severity3,25,34,35. However, alternate definitions of uncontrolled hypertension were also included in sensitivity analyses. Only participants with five or more independent blood pressure measurements across the assessment period were included in the analysis. Systolic and diastolic blood pressure variability was defined as the standard deviation of the systolic and diastolic blood pressure across all measurements during the recording period. All first-time users of the WSA are prompted to enter their age, sex, height, and weight from which body mass index (BMI) is calculated. These data were either self-reported and manually entered or, in the case of weight, acquired via Withings scales, a device fed directly into the online Withings database. ## Statistical analysis Odds ratio (ORs) and $95\%$ confidence interval (CIs) were determined using logistic-regression models to assess the association between OSA severity, OSA variability, and uncontrolled hypertension. Linear regression was used to assess the association between OSA severity, OSA variability, and blood pressure variability. Arbitrary cut-offs for continuous variables were omitted in favor of restricted cubic spline transformations, which are better suited to non-linearity. Thus, ORs (or β coefficients) for continuous variables were used to compare the 75th percentile to that of the 25th percentile, using the 25th percentile as the reference. OSA severity and variability were first included in separate models (Model 1A and 1B), and then in a combined model (Model 2). All models also included age, BMI, and sex. Likelihood ratios were used to compare predictive performance between models. Collinearity was assessed using variance inflation factors. Interactions between OSA severity and OSA variability and sex were also examined. Although the primary analysis examined OSA severity as a continuous measure, secondary analyses also examined OSA severity categorized according to standard clinical cut-offs and quartiles. Logistic and linear regression was performed in the R programming language, using the rms modeling package42. ## Sensitivity analyses Four sensitivity analyses were conducted to further validate our findings. Firstly, given that blood pressure may vary during the day, we performed a sensitivity analysis where only morning blood pressure, measured between 6 a.m. and 12 p.m., entries were included. Secondly, the analysis was repeated by further adjusting for the number of blood pressure entries to control for potential biases associated with a variable number of blood pressure entries for each participant. Third, total sleep time has been shown to influence AHI certainty43, and was therefore included as a confounder in the third sensitivity analysis. Finally, alternative definitions of hypertension were also investigated, including the home-blood pressure cut-offs of the European/NICE guidelines44,45 (systolic blood pressure/diastolic blood pressure ≥ $\frac{135}{85}$), the AHA guidelines [33, 34] (≥$\frac{130}{80}$) and stage 2 hypertension of the NICE guidelines44 (≥$\frac{150}{95}$). ## Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. ## Supplementary information Supplementary material REPORTING SUMMARY The online version contains supplementary material available at 10.1038/s41746-023-00801-2. ## References 1. Benjafield AV. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir. Med. (2019.0) 7 687-698. DOI: 10.1016/S2213-2600(19)30198-5 2. Lechat B. Multinight prevalence, variability, and diagnostic misclassification of obstructive sleep apnea. Am. J. Respir. Crit. Care Med. (2022.0) 205 563-569. DOI: 10.1164/rccm.202107-1761OC 3. Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N. Engl. J. Med. (2000.0) 342 1378-1384. DOI: 10.1056/NEJM200005113421901 4. Kendzerska T, Gershon AS, Hawker G, Leung RS, Tomlinson G. Obstructive sleep apnea and risk of cardiovascular events and all-cause mortality: a decade-long historical cohort study. PLoS Med. (2014.0) 11 e1001599. DOI: 10.1371/journal.pmed.1001599 5. Lang CJ. Associations of undiagnosed obstructive sleep apnea and excessive daytime sleepiness with depression: an Australian Population Study. J. Clin. Sleep Med. (2017.0) 13 575-582. DOI: 10.5664/jcsm.6546 6. Appleton SL. Undiagnosed obstructive sleep apnea is independently associated with reductions in quality of life in middle-aged, but not elderly men of a population cohort. Sleep Breath. (2015.0) 19 1309-1316. DOI: 10.1007/s11325-015-1171-5 7. Teran-Santos J, Jimenez-Gomez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. Cooperative Group Burgos-Santander. N. Engl. J. Med. (1999.0) 340 847-851. DOI: 10.1056/NEJM199903183401104 8. Punjabi NM, Patil S, Crainiceanu C, Aurora RN. Variability and misclassification of sleep apnea severity based on multi-night testing. Chest (2020.0) 158 365-373. DOI: 10.1016/j.chest.2020.01.039 9. Roeder M. Night-to-night variability of respiratory events in obstructive sleep apnoea: a systematic review and meta-analysis. Thorax (2020.0) 75 1095-1102. DOI: 10.1136/thoraxjnl-2020-214544 10. Sforza E, Pichot V, Martin MS, Barthelemy JC, Roche F. Prevalence and determinants of subjective sleepiness in healthy elderly with unrecognized obstructive sleep apnea. Sleep Med. (2015.0) 16 981-986. DOI: 10.1016/j.sleep.2015.03.010 11. Kendzerska T. Cardiovascular consequences of obstructive sleep apnea in women: a historical cohort study. Sleep Med. (2020.0) 68 71-79. DOI: 10.1016/j.sleep.2019.08.021 12. 12.Lechat, B. et al. Single night diagnosis of sleep apnea contributes to inconsistent cardiovascular outcome findings. Chesthttps://www.sciencedirect.com/science/article/abs/pii/S0012369223001575?via%3Dihub (2023). 13. Linz D. Nightly sleep apnea severity in patients with atrial fibrillation: potential applications of long-term sleep apnea monitoring. Int. J. Cardiol. Heart Vasc. (2019.0) 24 100424. PMID: 31763438 14. Linz D. Variability of sleep apnea severity and risk of atrial fibrillation: the VARIOSA-AF study. JACC Clin. Electrophysiol. (2019.0) 5 692-701. DOI: 10.1016/j.jacep.2019.03.005 15. Mehlum MH. Blood pressure variability and risk of cardiovascular events and death in patients with hypertension and different baseline risks. Eur. Heart J. (2018.0) 39 2243-2251. DOI: 10.1093/eurheartj/ehx760 16. Stevens SL. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ (2016.0) 354 i4098. DOI: 10.1136/bmj.i4098 17. Yoo JE. Blood pressure variability and the risk of dementia: a Nationwide Cohort Study. Hypertension (2020.0) 75 982-990. DOI: 10.1161/HYPERTENSIONAHA.119.14033 18. Lee SR. Blood pressure variability and incidence of new-onset atrial fibrillation: a Nationwide Population-Based Study. Hypertension (2020.0) 75 309-315. DOI: 10.1161/HYPERTENSIONAHA.119.13708 19. Clark D. Visit-to-visit blood pressure variability, coronary atheroma progression, and clinical outcomes. JAMA Cardiol. (2019.0) 4 437-443. DOI: 10.1001/jamacardio.2019.0751 20. Kansui Y. Impact of obstructive sleep apnea on long-term blood pressure variability in Japanese men: a cross-sectional study of a work-site population. Hypertens. Res. (2018.0) 41 957-964. DOI: 10.1038/s41440-018-0096-3 21. Pengo MF. Effect of continuous positive airway pressure on blood pressure variability in patients with obstructive sleep apnea. J. Clin. Hypertens. (Greenwich) (2016.0) 18 1180-1184. DOI: 10.1111/jch.12845 22. Edouard P. Validation of the Withings Sleep Analyzer, an under-the-mattress device for the detection of moderate-severe sleep apnea syndrome. J. Clin. Sleep. Med. (2021.0) 17 1217-1227. DOI: 10.5664/jcsm.9168 23. Yang RY, Bendjoudi A, Buard N, Boutouyrie P. Pneumatic sensor for cardiorespiratory monitoring during sleep. Biomed. Phys. Eng. Express (2019.0) 5 055014. DOI: 10.1088/2057-1976/ab3ac9 24. Li X. Associations of sleep-disordered breathing and insomnia with incident hypertension and diabetes. The Hispanic Community Health Study/Study of Latinos. Am. J. Respir. Crit. Care Med. (2021.0) 203 356-365. DOI: 10.1164/rccm.201912-2330OC 25. Heinzer R. Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. Lancet Respir. Med. (2015.0) 3 310-318. DOI: 10.1016/S2213-2600(15)00043-0 26. Shiina K. Obstructive sleep apnea as possible causal factor for visit-to-visit blood pressure variability. Circ. J. (2016.0) 80 1787-1794. DOI: 10.1253/circj.CJ-16-0302 27. Cremer A. Short-term blood pressure variability, arterial stiffness, and cardiovascular events: results from the Bordeaux cohort. J. Hypertens. (2021.0) 39 947-951. DOI: 10.1097/HJH.0000000000002735 28. Johansson JK, Niiranen TJ, Puukka PJ, Jula AM. Prognostic value of the variability in home-measured blood pressure and heart rate: the Finn-Home Study. Hypertension (2012.0) 59 212-218. DOI: 10.1161/HYPERTENSIONAHA.111.178657 29. McEvoy RD. CPAP for prevention of cardiovascular events in obstructive sleep apnea. N. Engl. J. Med. (2016.0) 375 919-931. DOI: 10.1056/NEJMoa1606599 30. Drager LF. Sleep apnea and cardiovascular disease: lessons from recent trials and need for team science. Circulation (2017.0) 136 1840-1850. DOI: 10.1161/CIRCULATIONAHA.117.029400 31. Azarbarzin A. The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study. Eur. Heart J. (2019.0) 40 1149-1157. DOI: 10.1093/eurheartj/ehy624 32. Lechat B. New and emerging approaches to better define sleep disruption and its consequences. Front. Neurosci. (2021.0) 15 751730. DOI: 10.3389/fnins.2021.751730 33. Lechat B. Co-morbid insomnia and obstructive sleep apnoea is associated with all-cause mortality. Eur. Respir. J. (2021.0) 60 2101958. DOI: 10.1183/13993003.01958-2021 34. Nieto FJ. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. JAMA (2000.0) 283 1829-1836. DOI: 10.1001/jama.283.14.1829 35. Young T. Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch. Intern. Med. (1997.0) 157 1746-1752. DOI: 10.1001/archinte.1997.00440360178019 36. Burgos-Sanchez C. Impact of alcohol consumption on snoring and sleep apnea: a systematic review and meta-analysis. Otolaryngol. Head Neck Surg. (2020.0) 163 1078-1086. DOI: 10.1177/0194599820931087 37. Varol Y, Anar C, Tuzel OE, Guclu SZ, Ucar ZZ. The impact of active and former smoking on the severity of obstructive sleep apnea. Sleep Breath. (2015.0) 19 1279-1284. DOI: 10.1007/s11325-015-1159-1 38. Soenksen LR. Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit. Med. (2022.0) 5 149. DOI: 10.1038/s41746-022-00689-4 39. Won CHJ. Sex differences in obstructive sleep apnea phenotypes, the multi-ethnic study of atherosclerosis. Sleep (2020.0) 43 1-8. DOI: 10.1093/sleep/zsz274 40. Epstein LJ. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J. Clin. Sleep Med. (2009.0) 5 263-276. DOI: 10.5664/jcsm.27497 41. Chobanian AV. The Seventh Report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA (2003.0) 289 2560-2572. DOI: 10.1001/jama.289.19.2560 42. 42.Harrell, E. F. Regression Modeling Strategies (Springer International Publishing, Cham, Switzerland, 2015). 43. Thomas RJ, Chen S, Eden UT, Prerau MJ. Quantifying statistical uncertainty in metrics of sleep disordered breathing. Sleep Med. (2020.0) 65 161-169. DOI: 10.1016/j.sleep.2019.06.003 44. Jones NR, McCormack T, Constanti M, McManus RJ. Diagnosis and management of hypertension in adults: NICE guideline update. Br. J. Gen. Pract. (2020.0) 70 90-91. DOI: 10.3399/bjgp20X708053 45. Stergiou GS. 2021 European Society of Hypertension practice guidelines for office and out-of-office blood pressure measurement. J. Hypertens. (2021.0) 39 1293-1302. DOI: 10.1097/HJH.0000000000002843 46. Rosseel Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. (2012.0) 48 1-36. DOI: 10.18637/jss.v048.i02
--- title: A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases authors: - Alexandros Rekkas - David van Klaveren - Patrick B. Ryan - Ewout W. Steyerberg - David M. Kent - Peter R. Rijnbeek journal: NPJ Digital Medicine year: 2023 pmcid: PMC10060247 doi: 10.1038/s41746-023-00794-y license: CC BY 4.0 --- # A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases ## Abstract Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: [1] definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; [2] identification of relevant databases; [3] development of a prediction model for the outcome(s) of interest; [4] estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; [5] presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments. ## Introduction Treatment effects often vary substantially across individual patients, causing overall effect estimates to be inaccurate for a significant proportion of the patients at hand1,2. Understanding this heterogeneity of treatment effects (HTE) has been crucial for both personalized (or precision) medicine and comparative effectiveness research, giving rise to a wide range of approaches for its discovery, evaluation and application in clinical practice. A common approach to evaluating HTE in clinical trials is through subgroup analyses. However, as these analyses are rarely adequately powered, they can lead to false conclusions of absence of HTE or exaggerate its presence3,4. In addition, patients differ in multiple characteristics simultaneously, resulting in much richer HTE compared to the heterogeneity explored with regular one-variable-at-a-time subgroup analyses. Baseline risk is a summary score inherently related to treatment effect that can be used to represent the variability in patient characteristics3,5–8. For example, an invasive coronary procedure—compared to medical treatment—improves survival in patients with myocardial infarction at high (predicted) baseline risk but not in those at low baseline risk9. It has also been shown that high-risk patients with pre-diabetes benefit substantially more from a lifestyle modification program than low-risk patients10. The recently proposed Predictive Approaches to Treatment effect Heterogeneity (PATH) statement provides systematic guidance on the application of risk-based methods for the assessment of HTE in randomized controlled trial (RCT) data11,12. After risk-stratifying patients using an existing or an internally derived prediction model, risk stratum-specific estimates of relative and absolute treatment effect are evaluated. Several methods for predictive HTE analysis have been adapted for use in observational data, but risk-based methods are still not readily available and have been highlighted as an important future research need12. The Observational Health Data Science and Informatics (OHDSI) collaborative has established a global network of data partners and researchers that aim to bring out the value of health data through large-scale analytics by mapping local databases to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)13,14. A standardized framework applying current best practices for comparative effectiveness studies within the OHDSI setting has been proposed15. This framework was successfully implemented in the Large-scale Evidence Generation and Evaluation across a Network of Databases for Hypertension (LEGEND-HTN) study. In this study, average effects of all first-line hypertension treatment classes were estimated for a total of 55 outcomes across a global network of nine observational databases16. LEGEND-HTN found benefit for patients treated with thiazide or thiazide-like diuretics compared to angiotensin-converting enzyme (ACE) inhibitors in terms of three main outcomes of interest, i.e., acute myocardial infarction (MI), hospitalization with heart failure, and stroke. Thiazide or thiazide-like diuretics also had a better safety profile compared to ACE inhibitors which, according to that study, makes them an attractive option for first-line treatment of hypertension. However, as already pointed out, overall (average) effect estimates may not be applicable to large portions of the target population due to strong variability of important patient characteristics. A risk-based analysis of treatment effect heterogeneity can add further insights to the results of LEGEND-HTN, both in understanding how treatment effects evolve with increasing baseline outcome risk and in identifying patient subgroups, which could be targeted with a certain treatment. Hereto, we focus on the three main outcomes of LEGEND-HTN (acute MI, hospitalization with heart failure, and stroke) and nine safety outcomes (hyponatremia, hypotension, acute renal failure, angioedema, kidney disease, cough, hyperkalemia, hypokalemia, and gastrointestinal bleeding). For our analyses, we develop a systematic framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases, extending the existing methodology from the RCT setting. The suggested framework is also implemented in an open-source, publicly available R-package. It is highly scalable and can be easily implemented across a network of observational databases mapped to OMOP-CDM. ## Overview The proposed framework defines 5 distinct steps: [1] definition of the research aim; [2] identification of the databases within which the analyses will be performed; [3] prediction of outcomes of interest; [4] estimation of absolute and relative treatment effects within risk strata; [5] presentation of the results. We developed an open-source R-package for the implementation of the proposed framework and made it publicly available (https://github.com/OHDSI/RiskStratifiedEstimation). An overview of the entire framework can be found in Fig. 1.Fig. 1Framework overview. Illustration of the framework’s application on two observational databases, preferably mapped to OMOP-CDM. As a demonstration, we evaluated treatment effect heterogeneity of thiazide or thiazide-like diuretics compared to ACE inhibitors using acute MI risk quarter-specific effect estimates, both on the relative and on the absolute scale. We focused on three efficacy outcomes (acute MI, hospitalization with heart failure, and ischemic or hemorrhagic stroke) and nine safety outcomes (acute renal failure, kidney disease, cough, hyperkalemia, hypokalemia, gastrointestinal bleeding, hyponatremia, hypotension, and angioedema). We used data from three US-based claims databases. ## Step 1: General definition of the research aim We considered the following research aim: “compare the effect of thiazide or thiazide-like diuretics (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T$$\end{document}T) to the effect of ACE inhibitors (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document}C) in patients with established hypertension with respect to 12 outcomes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O_1, \ldots,O_{12}$$\end{document}O1,…,O12)”. The required cohorts are:Treatment cohort: Patients receiving any drug within the class of thiazide or thiazide-like diuretics with at least one year of follow-up before treatment initiation and a recorded hypertension diagnosis within that year. Comparator cohort: Patients receiving any drug within the ACE inhibitor class with at least one year of follow-up before treatment initiation and a recorded hypertension diagnosis within that year. Outcome cohorts: We considered three efficacy and nine safety outcome cohorts. These were patients in the database with a diagnosis of: acute MI; hospitalization with heart failure; ischemic or hemorrhagic stroke (efficacy outcomes); acute renal failure; kidney disease; cough; hyperkalemia; hypokalemia; gastrointestinal bleeding; hyponatremia; hypotension; angioedema (safety outcomes). All cohort definitions were identical to the ones used in the multinational LEGEND-HTN study16. More information can be found in the Supplementary Results (Sections A and B) and Supplementary Tables 1–19. The typical research aim is: “to compare the effect of treatment to a comparator treatment in patients with a disease with respect to outcomes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O_1, \ldots,O_n$$\end{document}O1,…,On”. We use a comparative cohort design. This means that at least three cohorts of patients need to be defined at this stage of the framework:A single treatment cohort (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T$$\end{document}T), which includes patients with disease receiving the target treatment of interest. A single comparator cohort (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document}C), which includes patients with disease receiving the comparator treatment. One or more outcome cohorts (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O_1, \ldots,O_n$$\end{document}O1,…,On) that contain patients developing the outcomes of interest ## Step 2: Identification of the databases For our demonstration we used data from three US claims databases, namely IBM® MarketScan® Commercial Claims and Encounters (CCAE), IBM® MarketScan® Multi-State Medicaid (MDCD), and IBM® MarketScan® Medicare Supplemental Beneficiaries (MDCR). More information on the included databases can be found in Supplementary Results Section D. Our analyses included a total of 355,826 (CCAE), 54,835 (MDCD), and 37,882 (MDCR) patients initiating treatment with thiazide or thiazide-like diuretics and 930,629 (CCAE), 106,492 (MDCD), and 105,852 (MDCR) patients initiating treatment with ACE inhibitors (Table 1). Patient characteristics are available in Supplementary Tables 20–22. Adequate numbers of patients were included in all strata of predicted acute MI risk (Supplementary Table 23).Table 1Sample sizes. Number of patients, person years and events for the three efficacy outcomes of the study across the three databases after excluding patients with prior outcomes. Thiazides or thiazide-like diureticsAce inhibitorsOutcomePatientsPerson yearsOutcomesPatientsPerson yearsOutcomesCCAEAcute myocardial infarction355,826204,593405930,369584,1671813Hospitalization with heart failure355,528204,451389930,629584,5411492Stroke354,446203,792425923,604579,7361636MDCDAcute myocardial infarction54,83521,44076106,49251,481440Hospitalization with heart failure54,35421,290212105,00550,878835Stroke54,25921,179149104,41050,334562MDCRAcute myocardial infarction37,88224,642161105,85274,990732Hospitalization with heart failure37,61724,509277105,13474,6541196Stroke37,24824,267261102,50272,705977 Including in our analyses multiple databases representing the population of interest potentially increases the generalizability of results. Furthermore, the cohorts should preferably have adequate sample size with adequate follow-up time to ensure precise effect estimation, even within smaller risk strata. Other relevant issues such as the depth of data capture (the precision at which measurements, lab tests, conditions are recorded) and the reliability of data entry should also be considered. In our analyses, we used data from IBM® MarketScan® Commercial Claims and Encounters (CCAE), IBM® MarketScan® Medicaid (MDCD), and IBM® MarketScan® Medicare Supplemental Beneficiaries (MDCR). The New England Institutional Review Board (IRB) has determined that studies conducted in these databases are exempt from study-specific IRB review, as these studies do not qualify as human subjects research. ## Step 3: Prediction We internally developed separate prediction models for 2-year acute MI risk in each of the three databases. The prediction models were fitted on the propensity score-matched (1:1) subset of the entire study population, using a caliper of 0.2 and after excluding patients having the outcome at any time prior to treatment initiation. We considered a large set of candidate predictors containing patients’ demographic information (age, sex), disease and medication history, and the Charlson comorbidity index (Romano adaptation) measured in the year prior to treatment initiation. As all three databases are mapped to OMOP-CDM, coding of all predictors was uniform across databases. This enables the development of the prediction models for acute MI risk in a uniform fashion across databases. However, due to the differences in data capture among databases, we cannot expect that all covariates will be present in all databases. We developed the prediction models using LASSO logistic regression with 3-fold cross validation for hyper-parameter selection. In Supplementary Table 24 we show the available sample sizes on which the prediction models were developed, while in Supplementary Tables 25–27 we show the 20 selected covariates with the largest coefficients in each database. The models had moderate discriminative ability (internally validated) in CCAE and MDCD and lower discriminative ability in MDCR (Table 2).Table 2Prediction performance. Discriminative ability (c-statistic) of the derived prediction models for acute MI in the matched set (development set), the treatment cohort, the comparator cohort, and the entire population in CCAE, MDCD, and MDCR. Values in parentheses are cross-validated $95\%$ confidence intervals. Matched population is the propensity score-matched subset in each database on which the prediction models were developed. Treatment population is the set of patients receiving thiazide or thiazide-like diuretics in each database, while comparator population is the set of patients receiving ACE inhibitors. Finally, entire population refers to the combined set of treatment and comparator patients. PopulationCCAEMDCDMDCRMatched0.73 (0.71, 0.74)0.76 (0.73, 0.79)0.65 (0.62, 0.68)Treatment0.73 (0.71, 0.75)0.82 (0.77, 0.86)0.66 (0.62, 0.70)Comparator0.70 (0.67, 0.71)0.74 (0.71, 0.76)0.66 (0.64, 0.68)Entire population0.71 (0.70, 0.72)0.76 (0.74, 0.78)0.66 (0.64, 0.68) For our risk-based approach to adequately evaluate treatment effect heterogeneity, a well performing prediction model assigning patient-level risk for the outcome of interest needs to be available, either from literature or internally developed from the data at hand. For internally developing a risk prediction model we adopt a standardized framework focused on observational data that ensures adherence to existing guidelines25–27. We use the derived prediction model to separate the patient population into risk strata, within which treatment effects on both the relative and the absolute scale will be assessed. For the development of the risk prediction model, we first need to define a target cohort of patients, i.e., the set of patients on whom the prediction model will be developed. In our case, the target cohort is generated by pooling the already defined treatment and comparator cohorts. We develop the prediction model on the propensity score-matched (1:1) subset of the pooled sample to avoid differentially fitting between treatment arms, thus introducing spurious interactions with treatment28,29. We also need to define a set of patients that experience the outcome of interest, i.e., the outcome cohort. Finally, we need to decide the time frame within which the predictions will be carried out, i.e., the patients’ time at risk. Subsequently, we can develop the prediction model. It is important that the prediction models display good discriminative ability to ensure that risk-based subgroups are accurately defined. A performance overview of the derived prediction models including discrimination and calibration both in the propensity score-matched subset, the entire sample and separately for treated and comparator patients should also be reported. ## Step 4: Estimation In each database, we used patient-level predictions of the internally derived acute MI risk prediction model to stratify the patients into three acute MI risk groups RG-1, RG-2, and RG-3 (patients below $1\%$ risk, patients between $1\%$ and $1.5\%$ risk, and patients above $1.5\%$ risk). Within risk groups, in order to account for observed confounding, we further stratified the patients into five propensity score strata. Propensity score models were developed within each risk group separately using the same approach as in step 3 (LASSO logistic regression with a large set of predefined covariates). Risk group-specific relative treatment effects were estimated by averaging over the hazard ratio estimates derived from Cox regression models fitted in each propensity score stratum. Similarly, risk group-specific absolute treatment effects were estimated by averaging over the differences in Kaplan-Meier estimates in each propensity score stratum at 2 years after treatment initiation. In all databases we found adequate overlap of the propensity score distributions across the risk groups, except for high-risk patients in CCAE (acute MI risk above $1.5\%$). Hence, the propensity scores should be able to adjust for observed confounding, except for high-risk CCAE patients (Fig. 2). The covariate balance plots comparing covariate standardized mean differences before and after adjustment with the propensity scores confirmed strong imbalances for CCAE patients with acute MI predicted risk above $1.5\%$ (Fig. 3). Owing to very limited overlap of the preference score distributions (Fig. 2) and persisting imbalances after stratification on the propensity scores (Fig. 3), we do not present the results for patients at risk above $1.5\%$ for acute MI in CCAE. Additionally, a small number of characteristics remained slightly imbalanced even after stratification on the propensity scores for the two lower acute MI risk groups of MDCD (Fig. 3). Therefore, results from analyses in this database should be interpreted with caution. Fig. 2Preference score distributions within strata of predicted acute MI risk. RG-1 represents patients with acute MI risk lower than $1\%$; RG-2 represents patients with acute MI risk between $1\%$ and $1.5\%$; RG-3 represents patients with acute MI risk larger than $1.5\%$. The preference score is a transformation of the propensity score that adjusts for prevalence differences between populations. The percentages in each figure represent the amount of preference score overlap between treatment arms. Higher overlap of the preference score distributions indicates that patients in the target and the comparator cohorts are more similar in terms of the predicted probability of receiving treatment (thiazide or thiazide-like diuretics).Fig. 3Covariate balance. Patient characteristic balance for thiazide or thiazide-like diuretics and ACE inhibitors before and after stratification on the propensity scores. RG-1 represents patients with acute MI risk lower than $1\%$; RG-2 represents patients with acute MI risk between $1\%$ and $1.5\%$; RG-3 represents patients with acute MI risk larger than $1.5\%$. Each point represents the standardized difference of means for a single covariate before (x-axis) and after (y-axis) stratification. A commonly used rule of thumb suggests that standardized mean differences above 0.1 after propensity score adjustment indicate insufficient covariate balance. Finally, the distribution of the estimated relative risks with regard to a total of 76 negative control outcomes (Supplementary Results, Section C) showed no evidence of residual confounding, except for CCAE (Fig. 4)17–19. Hazard ratios for CCAE (Fig. 4, panel a) were often significantly larger than 1 (true effect size). This suggests significant negative effects of thiazide or thiazide-like diuretics compared to ACE inhibitors on causally unrelated outcomes, indicating unresolved differences between the two treatment arms. Therefore, results from CCAE should be interpreted with caution, as residual confounding may still be present, despite our propensity score adjustment. The results of the risk-stratified negative control analyses for each database can be found in Supplementary Figs. 1–3.Fig. 4Systematic error. Effect size estimates for the negative controls (true hazard ratio = 1) in a CCAE, b MDCD, and c MDCR databases. Estimates below the diagonal dashed lines are statistically significant (different from the true effect size; alpha = 0.05). A well-calibrated estimator should include the true effect size within the $95\%$ confidence interval, $95\%$ of times. We estimate treatment effects (both on the relative and the absolute scale) within risk strata defined using the prediction model of step 3. We often consider four risk strata, but fewer or more strata can be considered depending on the available power for accurately estimating stratum-specific treatment effects. Effect estimation may be focused on the difference in outcomes for a randomly selected person from the risk stratum (average treatment effect) or for a randomly selected person from the treatment cohort within the risk stratum receiving the treatment under study (average treatment effect on the treated). Any appropriate method for the analysis of relative and absolute treatment effects can be considered, as long as the this is done consistently in all risk strata. Common statistical metrics are odds ratios or hazard ratios for relative scale estimates and differences in observed proportions or differences in Kaplan-Meier estimates for absolute scale estimates, depending on the problem at hand. We estimate propensity scores within risk strata which we then use to match patients from different treatment cohorts or to stratify them into groups with similar propensity scores or to weigh each patient’s contribution to the estimation process30. Prior to analyzing results, it is crucial to ensure that all diagnostics are passed in all risk strata. The standard diagnostics we carry out include analysis of the overlap of propensity score distributions and calculation of standardized mean differences of the covariates before and after propensity score adjustment. Finally, we use effect estimates for a large set of negative control outcomes—i.e., outcomes known to not be related with any of the exposures under study—to evaluate the presence of residual confounding not accounted for by propensity score adjustment17–19. ## Step 5: Presentation of results On average, thiazide or thiazide-like diuretics were beneficial compared to ACE inhibitors for all outcomes, except for hospitalization with heart failure in CCAE and stroke in MDCD (Table 3). The hazard ratios are in line with, but not equal to, those reported in the LEGEND-HTN study, mainly because of restricting time at risk to two years. Table 3Relative effect estimates. Hazard ratio estimates for the overall treatment effect of thiazide or thiazide-like diuretics compared to ACE inhibitors. Values in brackets are $95\%$ confidence intervals. OutcomeCCAEMDCDMDCRAcute myocardial infarction0.86 (0.77, 0.97)0.60 (0.46, 0.77)0.82 (0.68, 0.98)Hospitalization with heart failure0.99 (0.88, 1.12)0.84 (0.71, 0.99)0.83 (0.72, 0.95)Stroke0.87 (0.78, 0.97)0.87 (0.71, 1.06)0.90 (0.78, 0.95) For the primary outcomes (acute MI, hospitalization with heart failure and stroke) relative treatment effect estimates of thiazide or thiazide-like diuretics versus ACE inhibitors varied substantially across risk groups, but no clear trends indicating an association between risk and relative treatment effect estimates were observed (Fig. 5).Fig. 5Relative treatment effects for main outcomes. Treatment effect heterogeneity for the main outcomes on the relative scale (hazard ratios) of thiazide or thiazide-like diuretics compared to ACE inhibitors within strata of predicted acute MI risk. In a we present treatment effects on the relative scale for acute MI within groups of predicted acute MI risk across all three databases. In b we present treatment effects on the relative scale for hospitalization with heart failure within groups of predicted acute MI risk across all three databases. In c we present treatment effects on the relative scale for stroke (both ischemic and hemorrhagic) within groups of predicted acute MI risk across all three databases. RG-1 represents the group of patients with acute MI risk below $1\%$; RG-2 represents the group of patients with acute MI risk between $1\%$ and $1.5\%$; RG-3 represents the group of patients with acute MI risk larger than $1.5\%$. Hazard ratios estimated in CCAE, MDCD, and MDCR are represented by blue, green, and orange circles, respectively. The bars represent $95\%$ confidence intervals. Values below 1 favor thiazide or thiazide-like diuretics, while values above 1 favor ACE inhibitors. For acute MI, hazard ratios showed an increasing trend with increasing baseline acute MI risk in MDCD and CCAE, implying larger benefit on the relative scale for patients in the lower risk groups. This was less pronounced in MDCR (Fig. 5; panel a). For hospitalization with heart failure, hazard ratios were similar across all acute MI risk strata in MDCD, with a slightly decreasing trend favoring thiazide or thiazide-like diuretics (Fig. 5; panel b). In MDCR, these hazard ratios were very similar to MDCD for patients at acute MI risk higher than $1\%$. For patients below $1\%$ acute MI risk, hazard ratios were close to 1 (negligible relative treatment effects) in all three databases. Finally, for stroke, the hazard ratios indicated a beneficial effect of thiazide or thiazide-like diuretics in all databases, but we found no clear trends in hazard ratios across acute MI risk groups (Fig. 5; panel c). Absolute treatment effects (risk reduction) for acute MI and hospitalization with heart failure tended to increase with increasing acute MI risk (Fig. 6; panels a and b). This was most evident in MDCD, where the absolute benefits for acute MI were $0.25\%$ ($0.03\%$ to $0.48\%$; $95\%$ CI) and $1.57\%$ ($0.49\%$ to $2.65\%$; $95\%$ CI) in the lowest and the highest acute MI risk group, respectively. Similarly, in MDCR these absolute benefits were −$0.04\%$ (−$0.40\%$ to $0.32\%$; $95\%$ CI) and $0.70\%$ ($0.04\%$ to $1.37\%$; $95\%$ CI), respectively. For hospitalization with heart failure, these absolute benefits were −$0.07\%$ (−$0.50\%$ to $0.36\%$; $95\%$ CI) and $2.31\%$ ($0.22\%$ to $4.39\%$; $95\%$ CI), respectively, in MDCD and −$0.05\%$ (−$0.59\%$ to $0.49\%$; $95\%$ CI) and $0.97\%$ (−$0.16\%$ to $2.09\%$; $95\%$ CI), respectively, in MDCR. In CCAE, we found negligible treatment effects on the absolute scale for all three outcomes. Finally, for stroke, the differences on the absolute scale were small in all risk groups and databases (Fig. 6; panel c).Fig. 6Absolute treatment effects for main outcomes. Treatment effect heterogeneity for the main outcomes on the absolute scale of thiazide or thiazide-like diuretics compared to ACE inhibitors within strata of predicted acute MI risk. In a we present treatment effects on the absolute scale for acute MI within groups of predicted acute MI risk across all three databases. In b we present treatment effects on the absolute scale for hospitalization with heart failure within groups of predicted acute MI risk across all three databases. In c we present treatment effects on the absolute scale for stroke (both ischemic and hemorrhagic) within groups of predicted acute MI risk across all three databases. RG-1 represents the group of patients with acute MI risk below $1\%$; RG-2 represents the group of patients with acute MI risk between $1\%$ and $1.5\%$; RG-3 represents the group of patients with acute MI risk larger than 1.5. Absolute treatment effects estimated in CCAE, MDCD, and MDCR are represented by blue, green, and orange circles, respectively. The bars represent $95\%$ confidence intervals. Values above 0 favor thiazide or thiazide-like diuretics, while values below 0 favor ACE inhibitors. Across all databases and all risk groups (Fig. 7), thiazide or thiazide-like diuretics reduced the risk for angioedema, cough, hyperkalemia, and hypotension, but were associated with increased risk of hypokalemia and hyponatremia. For cough and hypokalemia, the relative treatment effect tended to decrease with increasing MI risk (hazard ratios moving closer to 1).Fig. 7Relative treatment effects for safety outcomes. Treatment effect heterogeneity for the safety outcomes on the relative scale (hazard ratios) of thiazide or thiazide-like diuretics compared to ACE inhibitors within strata of predicted acute MI risk. Panels present treatment effects on the relative scale for a acute renal failure, b angioedema, c cough, d gastrointestinal bleeding, e hyperkalemia, f hypokalemia, g hyponatremia, h hypotension, and i kidney disease within groups of predicted acute MI risk across all three databases. RG-1 represents the group of patients with acute MI risk below $1\%$; RG-2 represents the group of patients with acute MI risk between $1\%$ and $1.5\%$; RG-3 represents the group of patients with acute MI risk larger than $1.5\%$. Hazard ratios estimated in CCAE, MDCD, and MDCR are represented by blue, green, and orange circles, respectively. Bars represent $95\%$ confidence intervals. Values below 1 favor thiazide or thiazide-like diuretics, while values above 1 favor ACE inhibitors. The absolute benefit for angioedema of thiazide or thiazide-like diuretics was negligible, despite the large treatment effect estimated on the relative scale (Fig. 8; panel b). The absolute risk increase of hypokalemia was large with thiazide or thiazide diuretics—as expected based on the effect estimates on the relative scale—across all risk strata (Fig. 8; panel f). This effect remained relatively constant across acute MI risk groups in MDCR, fluctuating between −$4.13\%$ and −$3.25\%$. Similar effects on the absolute scale were observed in CCAE, where effect estimates were close to −$5\%$ for all patients below $1.5\%$ risk of acute MI. A much larger hypokalemia risk increase with thiazide or thiazide-like diuretics was observed in MDCD, where the absolute effect estimates evolved from −$9.89\%$ (−$11.23\%$ to −$8.54\%$; $95\%$ CI) in patients below $1\%$ acute MI risk to −$15.58\%$ (−$23.78\%$ to −$7.38\%$; $95\%$ CI) in patients above $1.5\%$ acute MI risk. The absolute benefit estimates of thiazide or thiazide-like diuretics for cough ranged between $3.05\%$ and $3.77\%$ in CCAE, and between $2.32\%$ and $3.73\%$ in MDCR (Fig. 8; panel c). In MDCD, we observed a small risk increase of cough with thiazide or thiazide-like diuretics in patients at high acute MI baseline risk (−$1.82\%$ with a $95\%$ CI from −$7.82\%$ to $4.17\%$). Finally, we observed a small risk increase of hyponatremia with thiazide or thiazide diuretics, which was more substantial in patients with high acute MI risk in MDCR (−$1.91\%$ with a $95\%$ CI from −$3.43\%$ to −$0.38\%$).Fig. 8Absolute treatment effects for safety outcomes. Treatment effect heterogeneity for the safety outcomes on the absolute scale of thiazide or thiazide-like diuretics compared to ACE inhibitors within strata of predicted acute MI risk. Panels present treatment effects on the absolute scale for a acute renal failure, b angioedema, c cough, d gastrointestinal bleeding, e hyperkalemia, f hypokalemia, g hyponatremia, h hypotension, and i kidney disease within groups of predicted acute MI risk across all three databases. RG-1 represents the group of patients with acute MI risk below $1\%$; RG-2 represents the group of patients with acute MI risk between $1\%$ and $1.5\%$; RG-3 represents the group of patients with acute MI risk larger than $1.5\%$. Absolute treatment effects estimated in CCAE, MDCD, and MDCR are represented by blue, green, and orange circles, respectively. The bars represent $95\%$ confidence intervals. Values above 0 favor thiazide or thiazide-like diuretics, while values below 0 favor ACE inhibitors. In the presence of a positive treatment effect and a well-discriminating prediction model we expect an increasing pattern of the differences in the absolute scale, even if treatment effects remain constant on the relative scale across risk strata. Owing to this scale-dependence of treatment effect heterogeneity, results should be assessed both on the relative and the absolute scale. ## Interpretation The overall benefits of thiazide or thiazide-like diuretics compared to ACE inhibitors that were observed in MDCR, in terms of acute MI and hospitalization with heart failure, were mainly driven by patients with predicted acute MI risk above $1.5\%$. Even in MDCD, where benefit on the absolute scale was observed across all acute MI risk strata, treatment effects were much larger in patients with predicted acute MI risk above $1.5\%$. In CCAE, where the majority of the patients had a predicted acute MI risk below $1\%$, we found negligible treatment effects. This provides further support for the similarity of the effect of thiazide or thiazide-like diuretics compared to ACE inhibitors in patients at low risk of acute MI. Even though LEGEND-HTN found beneficial effects of thiazide or thiazide-like diuretics over ACE inhibitors in terms of several safety outcomes, there are still safety concerns when prescribing thiazide or thiazide-like diuretics. The hypokalemia and hyponatremia risk increase with thiazide or thiazide-like diuretics was not negligible in any of the acute MI risk strata. On the other hand, ACE inhibitor-related cough risk increase was also present in all databases and acute MI risk groups. Provided that absolute benefits of thiazide or thiazide-like diuretics for the main outcomes (acute MI, hospitalization with heart failure, and stroke) were mainly observed in patients at high acute MI risk, the prescribing physician has to carefully weigh benefits and harms for individual patients. Note that any conclusions drawn are for demonstration purposes only and should be interpreted under this very limited setting. ## Sensitivity analyses As a sensitivity analysis, we evaluated treatment effect heterogeneity of thiazide or thiazide-like diuretics compared to ACE inhibitors in patients with or without prior cardiovascular disease. We defined the set of patients without prior cardiovascular disease as the patients that had no occurrence in their medical history of any of the following conditions: heart valve disorder or transplanted heart valve, coronary artery disease, cardiac dysfunction, heart block, unstable angina, atrial fibrillation, myocardial infarction, ventricular arrhythmia or cardiac arrest, ischemic heart disease, myocarditis or pericarditis, cardiomyopathy, cardiomegaly, heart failure, or stroke (ischemic or hemorrhagic). If patients had any of these conditions recorded in their medical history, they were assigned to the group with prior cardiovascular disease. We repeated our analyses using the exact same settings for both groups of patients. In patients without prior cardiovascular disease, the estimates of the relative effect of thiazide or thiazide-like diuretics compared to ACE inhibitors on acute MI were similar to the original analyses—hazard ratios 0.90 (0.79 to 1.02; $95\%$ CI), 0.52 (0.36 to 0.74; $95\%$ CI), and 0.83 (0.65 to 1.05; $95\%$ CI) in CCAE, MDCD, and MDCR respectively. In patients with prior cardiovascular disease the effect of thiazide or thiazide-like diuretics was stronger in CCAE—hazard ratio 0.73 (0.55 to 0.95; $95\%$ CI)—but weaker in MDCD and MDCR—hazard ratios 0.78 (0.51 to 1.16; $95\%$ CI) and 0.88 (0.66 to 1.15; $95\%$ CI), respectively. In both sets of sensitivity analyses, risk-stratified results showed trends comparable to the original analysis (Supplementary Figs. 4–11). ## Discussion In this study we develop a risk-based framework for the assessment of treatment effect heterogeneity in large observational databases. Our framework fills a gap identified in the literature after the development of guidelines for performing such analyses in the RCT setting11,12. As an additional contribution we provide the software for implementing this framework in practice and make it publicly available. We made our software compatible to databases mapped to OMOP-CDM, which allows researchers to easily implement our framework in a global network of healthcare databases. In our case study we demonstrate the use of our framework for the evaluation of treatment effect heterogeneity of thiazide or thiazide-like diuretics compared to ACE inhibitors on three efficacy and nine safety outcomes. We propose that this framework is implemented any time treatment effect estimation in high-dimensional observational data is undertaken. In recent years, several methods for the analysis of treatment effect heterogeneity have been developed in the RCT setting20. However, low power and restricted prior knowledge on the mechanisms of variation in treatment effect are often inherent in RCTs, which are usually adequately powered only for the analysis of the primary outcome. Observational databases contain a large amount of information on treatment assignment and outcomes of interest, while also capturing key patient characteristics. They contain readily available data on patient sub-populations of interest on which no RCT has focused before either due to logistical or ethical reasons. However, observational databases can be susceptible to biases, poorly measured outcomes and missingness, which may obscure true HTE or falsely indicate it when there is none21. Therefore, inferences on both overall treatment effect estimates and HTE need to rely on strong—often unverifiable—assumptions, despite the advancements and guidance on best practices. When evaluating treatment effect heterogeneity using a risk-based approach these issues may be compounded, mainly because of the risk of conflating confounding and effect modification. Well-designed observational studies on average replicate RCT results, even though often differences in magnitude may occur22. Our framework is in line with the recently suggested paradigm of high-throughput observational studies using consistent and standardized methods for improving reproducibility in observational research19. However, more empirical research comparing analyses of observational data and RCTs is required to assess the conditions under which different approaches for evaluating treatment effect heterogeneity provide credible results. Our software package can help support this research. Our framework highlights the scale dependency of HTE and how it relates to baseline risk. Treatment effect is mathematically determined by baseline risk, if we assume a constant non-zero effect size23. Patients with low baseline risk can only experience minimal benefits, before their risk is reduced to zero. In contrast, high-risk patients can potentially have much larger absolute benefits. This becomes evident when evaluating the safety of thiazide or thiazide-like diuretics on angioedema and cough, both adverse events linked to treatment with ACE inhibitors. For angioedema, the substantial relative risk increase with ACE inhibitors only translated in a small risk increase on the absolute scale due to the limited baseline angioedema risk. Conversely, despite the small relative cough risk increase of ACE inhibitors, the large baseline cough risk resulted in larger absolute risk differences, compared to the other considered outcomes. For patients with comorbidities the Guidelines of the American College of Cardiology often recommend initiation of treatment with ACE inhibitors, e.g., for patients with stable ischemic heart disease or patients with preserved ejection fraction24. Since these are patients with more severe medical conditions there may be a potential interaction of baseline acute MI risk with the propensity of receiving a thiazide or a thiazide-like diuretic. We do not formally test for that interaction, however, we observed that with increasing acute MI baseline risk, the overlap of the propensity score distributions decreases and the propensity score distributions for each treatment arm become more skewed, especially in CCAE and MDCD (Fig. 2). This could potentially result in unobserved confounding being present even after propensity score adjustment. Indeed, in CCAE, negative control analyses showed evidence of residual confounding and therefore results should be interpreted with caution. In risk-stratified negative control analyses we observed more evidence of residual confounding in patients with higher acute MI risk, which was, however, not identified in the other two databases. The application of our framework in the case study is for demonstration purposes and there are several limitations to its conclusions. First, risk groups defined in each database were not defined using a universal prediction model, but using internally developed prediction models in each database. Future research could explore model combination or transfer learning methods for the development of universal risk prediction models. Second, death could be a competing risk. We could expand our framework in the future to potentially support sub-distribution hazard ratios and cumulative incidence reductions. Third, we only used the databases readily available to us and not all the available databases mapped to OMOP-CDM. Therefore, the generalizability of our results still needs to be explored in future studies. These studies should also address the particular aspects of the databases at hand, such as their sampling frame, the completeness of the data they capture and many other aspects that were not assessed in our demonstration. Fourth, we did not correct for multiplicity when presenting the results. We are interested in presenting trends in the data rather than detecting specific subgroups with significant treatment effects. The implementation of our framework, however, generates all the relevant output required for a researcher to correct for multiple testing, if that is required. In conclusion, the case study demonstrates the feasibility of our framework for risk-based assessment of treatment effect heterogeneity in large observational data. It is easily applicable and highly informative whenever treatment effect estimation in high-dimensional observational data is of interest. ## Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. ## Supplementary information Supplemental Material REPORTING SUMMARY The online version contains supplementary material available at 10.1038/s41746-023-00794-y. ## References 1. Rothwell PM. Can overall results of clinical trials be applied to all patients?. Lancet (1995.0) 345 1616-1619. DOI: 10.1016/S0140-6736(95)90120-5 2. Kravitz RL, Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q. (2004.0) 82 661-687. DOI: 10.1111/j.0887-378X.2004.00327.x 3. Hayward RA, Kent DM, Vijan S, Hofer TP. Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Med. Res. Methodol. (2006.0) 6 18. DOI: 10.1186/1471-2288-6-18 4. 4.Kent, D. M., Steyerberg, E. & Klaveren, D. van. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ k4245 10.1136/bmj.k4245 (2018). 5. Rothwell PM, Mehta Z, Howard SC, Gutnikov SA, Warlow CP. From subgroups to individuals: general principles and the example of carotid endarterectomy. Lancet (2005.0) 365 256-265. DOI: 10.1016/S0140-6736(05)70156-2 6. Kent DM, Hayward RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA (2007.0) 298 1209-1212. DOI: 10.1001/jama.298.10.1209 7. Kent DM, Alsheikh-Ali A, Hayward RA. Competing risk and heterogeneity of treatment effect in clinical trials. Trials (2008.0) 9 30. DOI: 10.1186/1745-6215-9-30 8. Kent DM, Rothwell PM, Ioannidis JP, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials (2010.0) 11 85. DOI: 10.1186/1745-6215-11-85 9. Thune JJ. Simple risk stratification at admission to identify patients with reduced mortality from primary angioplasty. Circulation (2005.0) 112 2017-2021. DOI: 10.1161/CIRCULATIONAHA.105.558676 10. Sussman JB, Kent DM, Nelson JP, Hayward RA. Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program. BMJ (2015.0) 350 h454-h454. DOI: 10.1136/bmj.h454 11. Kent DM. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement. Ann. Intern. Med. (2019.0) 172 35. DOI: 10.7326/M18-3667 12. Kent DM. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Ann. Intern. Med. (2019.0) 172 W1. DOI: 10.7326/M18-3668 13. Hripcsak G. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. (2015.0) 216 574. PMID: 26262116 14. Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J. Am. Med. Inform. Assoc. (2012.0) 19 54-60. DOI: 10.1136/amiajnl-2011-000376 15. Ryan PB, Schuemie MJ, Gruber S, Zorych I, Madigan D. Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system. Drug Saf. (2013.0) 36 59-72. DOI: 10.1007/s40264-013-0099-6 16. Suchard MA. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet (2019.0) 394 1816-1826. DOI: 10.1016/S0140-6736(19)32317-7 17. Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat. Med. (2014.0) 33 209-218. DOI: 10.1002/sim.5925 18. Schuemie MJ, Hripcsak G, Ryan PB, Madigan D, Suchard MA. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proc. Natl Acad. Sci. USA (2018.0) 115 2571-2577. DOI: 10.1073/pnas.1708282114 19. Schuemie MJ, Ryan PB, Hripcsak G, Madigan D, Suchard MA. Improving reproducibility by using high-throughput observational studies with empirical calibration. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. (2018.0) 376 20170356. DOI: 10.1098/rsta.2017.0356 20. Rekkas A. Predictive approaches to heterogeneous treatment effects: a scoping review. BMC Med. Res. Methodol. (2020.0) 20 264. DOI: 10.1186/s12874-020-01145-1 21. Varadhan R, Segal JB, Boyd CM, Wu AW, Weiss CO. A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research. J. Clin. Epidemiol. (2013.0) 66 818-825. DOI: 10.1016/j.jclinepi.2013.02.009 22. Anglemyer A, Horvath HT, Bero L. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials. Cochrane Database Syst. Rev. (2014.0) 2014 MR000034. PMID: 24782322 23. Dahabreh IJ, Hayward R, Kent DM. Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence. Int. J. Epidemiol. (2016.0) 45 2184-2193. PMID: 27864403 24. Whelton PK. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension (2018.0) 71 e13-e115. PMID: 29133356 25. Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J. Am. Med. Inform. Assoc. (2018.0) 25 969-975. DOI: 10.1093/jamia/ocy032 26. Collins GS, Reitsma JB, Altman DG, Moons K. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. (2015.0) 13 1. DOI: 10.1186/s12916-014-0241-z 27. Moons KGM. Transparent reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann. Intern. Med. (2015.0) 162 W1-W73. DOI: 10.7326/M14-0698 28. Burke JF, Hayward RA, Nelson JP, Kent DM. Using internally developed risk models to assess heterogeneity in treatment effects in clinical trials. Circ. Cardiovasc. Qual. Outcomes (2014.0) 7 163-169. DOI: 10.1161/CIRCOUTCOMES.113.000497 29. Klaveren D, van, Balan TA, Steyerberg EW, Kent DM. Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting. J. Clin. Epidemiol. (2019.0) 114 72-83. DOI: 10.1016/j.jclinepi.2019.05.029 30. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar. Behav. Res. (2011.0) 46 399-424. DOI: 10.1080/00273171.2011.568786
--- title: Association between familial aggregation of chronic kidney disease and its incidence and progression authors: - Jae Young Kim - Sung-youn Chun - Hyunsun Lim - Tae Ik Chang journal: Scientific Reports year: 2023 pmcid: PMC10060248 doi: 10.1038/s41598-023-32362-5 license: CC BY 4.0 --- # Association between familial aggregation of chronic kidney disease and its incidence and progression ## Abstract This study aimed to examine the association between familial aggregation of chronic kidney disease (CKD) and risk of CKD development and its progression. This nationwide family study comprised 881,453 cases with newly diagnosed CKD between 2004 and 2017 and 881,453 controls without CKD matched by age and sex, using data from the Korean National Health Insurance Service with linkage to the family tree database. The risks of CKD development and disease progression, defined as an incident end-stage renal disease (ESRD), were evaluated. The presence of any affected family member with CKD was associated with a significantly higher risk of CKD with adjusted ORs ($95\%$ CI) of 1.42 (1.38–1.45), 1.50 (1.46–1.55), 1.70 (1.64–1.77), and 1.30 (1.27–1.33) for individuals with affected parents, offspring, siblings, and spouses, respectively. In Cox models conducted on patients with predialysis CKD, risk of incident ESRD was significantly higher in those with affected family members with ESRD. The corresponding HRs ($95\%$ CI) were 1.10 (1.05–1.15), 1.38 (1.32–1.46), 1.57 (1.49–1.65), and 1.14 (1.08–1.19) for individuals listed above, respectively. Familial aggregation of CKD was strongly associated with a higher risk of CKD development and disease progression to ESRD. ## Introduction Chronic kidney disease (CKD) is a growing public health problem not only in South Korea but also worldwide1,2. Patients with CKD have a substantially higher risk of cardiovascular disease and mortality even in the early stages of CKD3–5. While patients with CKD are five to ten times more likely to die than progress to end-stage renal disease (ESRD), those who survive may ultimately require dialysis treatment or kidney transplantation6. These interventions put an exorbitant economic burden on many countries, cost billions of dollars to treat patients with ESRD, and incur substantial financial costs in preventing CKD and its complications7. Family health history has become increasingly recognized as the most useful tool for risk assessment of common chronic diseases8. A growing body of literature suggests that individuals with an affected first-degree relative have a higher risk of various cancers9, stroke10, type 2 diabetes11, and cardiovascular diseases12,13. Furthermore, associations between family history and kidney diseases have also been reported, but these studies have largely focused on some Mendelian disorders, such as polycystic kidney disease and Alport syndrome. While the causes of CKD are diverse, there is a paucity of large population-based cohort studies that have examined the familiar contributions to the broader spectrum of CKD to date. Furthermore, there are significant shortcomings in these few studies that examined whether CKD aggregates within family, which result from small sample size, data acquired via hospital records or registries, information derived from questionnaires, and definition of study variables focused mainly on its later stage (i.e., ESRD)14–19. Therefore, this study aimed to determine the association between family history of CKD and risk of incident CKD and its progression to ESRD in a large nationwide population-based cohort using data from the Korean National Health Insurance Service (NHIS) database linked to the family tree database to better inform the field. ## Data source and study population Data were obtained from the Korean NHIS database linked to the nationwide family tree database. Since the NHIS covers compulsory health insurance for all citizens in Korea as a single-payer national health system, all medical records of covered inpatient and outpatient visits are centralized in the NHIS database20,21. The family tree database provides details on family relationships and degree of kinship (grandparents, parents, offspring, full siblings, and spouses) for the entire population, which was created using a new family code system, health insurance eligibility, and resident register data. The methods for constructing the database have been described previously, in which parents and grandparents are matched for more than $95\%$ of those who were born between 2010 and 201722. To construct the study population of this nationwide case–control study, we first identified all 983,736 adult patients (age ≥ 18 years) having a diagnosis of CKD recorded between 1 January 2004 and 31 December 2017. To restrict the cohort with newly diagnosed patients with CKD, 93,506 patients who had claims for CKD during a washout period of two years from 1 January 2002 to 31 December 2003 were excluded. Ascertainment of CKD was based on the International Statistical Classification of Disease and Related Health Problems, Tenth Revision (ICD-10) code of N18, and index date was defined as the date of the first diagnosis of CKD. Furthermore, to minimize errors in the estimation of familial risk associated with the more common causes of CKD, 8777 patients with claims for hereditary kidney diseases such as polycystic kidney disease (ICD-10 codes Q61.1 and Q61.2; $$n = 7140$$), medullary cystic kidney disease (ICD-10 code Q61.5; $$n = 248$$), Fabry disease (ICD-10 codes E75.2 and N08.4; $$n = 1028$$), and Alport syndrome (ICD-10 code Q87.8; $$n = 361$$) were excluded. After exclusion, a total of 881,453 patients with incident CKD were included in the study. For each case, we randomly assigned index date to controls as the same date of the matched cases. And then we matched each case of patients with one control by age and sex at the time of index date, who did not have a diagnosis of CKD from 1 January 2002 to a randomly assigned index date drawn from the corresponding dates in the CKD cases. Therefore, the final study population comprised 1,762,906 participants, which included 881,453 cases with CKD and 881,453 matched controls without CKD (Fig. S1). This study complied with the Declaration of Helsinki and was approved by the Institutional Review Board of NHIS Ilsan Hospital, which waived the requirement for informed consent due to the use of deidentified data. ## Data collection and covariables We considered age, sex, residential area, income level, and comorbidities such as hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and dyslipidemia to be potential confounders or to potentially affect familial associations. Thus, they were included as covariables23. Baseline data on sociodemographic information such as age, sex, residential area, and income level were collected before the index date. Comorbidities (e.g., hypertension (I10 ~ 13, I15), diabetes (E10 ~ 14), ischemic heart disease (I20 ~ 25), cerebrovascular disease (I60 ~ 69), and dyslipidemia (E78.0 ~ 78.5)) were assessed using the ICD-10 coding algorithms, which were ascertained by the presence of at least two or more diagnostic codes up to two years before the index date. The presence of affected family members with CKD, along with or without ESRD, was assessed using the nationwide claims database in conjunction with the family tree database at the time of the index date. ## Exposure and outcome ascertainment The exposure of interest was a familial aggregation of CKD. A family was defined as a group of individuals related to each other by blood or by at least one common blood relative, including first-degree relatives (i.e., parents and offspring), full siblings, and spouses. The outcomes of interest were incident CKD and CKD progression, with CKD progression being defined as an incident ESRD. ESRD was defined as receipt of long-term dialysis or a kidney transplant, identified by specific insurance codes (called V code) or dialysis-related intervention codes24. NHIS provides special insurance benefits for patients with ESRD who receive a kidney transplant or require maintenance dialysis for a minimum of 3-month duration. Once a patient has a specific code related to ESRD (e.g., V001 for hemodialysis, V003 for peritoneal dialysis, and V005 for kidney transplant), it is carried forward in medical records and claims created for that patient. Therefore, ESRD diagnoses based on claims are considered reliable. ## Statistical analysis Multivariable logistic regression models with adjustment for age, sex, residential area, income level, and comorbidities such as hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and dyslipidemia were used to examine the association between having an affected family member with CKD and the risk of incident CKD. The risk of CKD was expressed as odds ratios (OR) with $95\%$ confidence intervals (CI). Next, Cox proportional hazards models with the presence of an affected individual with ESRD as a predictor were conducted to assess the risk of an incident ESRD among patients with predialysis CKD, adjusting for all covariables that were used to construct the multivariable logistic regression models as above. For this analysis, patients with a prior diagnosis of ESRD at any time before the index date were excluded. Follow-up began on the index date and continued until the occurrence of ESRD, death, or 31 December 2017 (study end date), whichever came first. The risk of ESRD was represented as hazard ratios (HR) with $95\%$ CI. To further address the potential influence of unmeasured confounding on the analyses, we performed additional sensitivity analyses using the E-value methodology. The E-value represents the minimum magnitude of association required between unmeasured confounder and both the exposure and outcome, conditional on measured covariables, to fully attenuate the observed exposure-outcome relationship25. Each E-value was calculated using a publicly available online calculator26. All models were explored for individuals with an affected first-degree relative of any kinship and for individual kinship (e.g., parents, offspring, and full siblings) in the overall cohort and subpopulation stratified by sex. Furthermore, spouses were also used as controls to account for contributions from shared environmental factors to phenotypic variance. Data from descriptive analyses were summarized using mean (standard deviation (SD)) or numbers (proportions), as appropriate. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC). ## Baseline characteristics of study population A total of 1,762,906 participants who met the eligibility criteria were included in the study. The baseline characteristics of the participants are shown in Table 1. The mean age of the study participants was 64.2 (SD, 16.0) years. Among them, $57.1\%$ were male, $57.9\%$ had hypertension, and $33.6\%$ had diabetes. In the overall cohort, $7.7\%$ and $3.1\%$ of participants had at least one affected family member with CKD or ESRD, respectively: 35,455 ($2.0\%$) with affected parents, 32,841 ($1.9\%$) with an affected offspring, 18,609 ($1.1\%$) with an affected sibling, and 54,056 ($3.1\%$) with an affected spouse for CKD and 13,687 ($0.8\%$) with affected parents, 14,985 ($0.9\%$) with an affected offspring, 8750 ($0.5\%$) with an affected sibling, and 19,110 ($1.1\%$) with an affected spouse for ESRD.Table 1Baseline characteristics of the study participants. OverallStudy participantsCasesMatched controlsCharacteristics($$n = 1$$,762,906)($$n = 881$$,453)($$n = 881$$,453)Age, years64.2 (16.0)64.2 (16.0)64.2 (16.0)Age intervals < 40 years142,935 (8.1)71,467 (8.1)71,468 (8.1) 40–49 years172,707 (9.8)86,354 (9.8)86,353 (9.8) 50–59 years293,100 (16.6)146,550 (16.6)146,550 (16.6) 60–69 years386,470 (21.9)193,234 (21.9)193,236 (21.9) 70–79 years476,544 (27.1)238,246 (27.1)238,298 (27.1) ≥ 80 years291,150 (16.5)145,602 (16.5)145,548 (16.5)Sex Men1,006,266 (57.1)503,177 (57.1)503,089 (57.1) Women756,640 (42.9)378,276 (42.9)378,364 (42.9)Residential area Metropolitan717,193 (40.7)369,612 (41.9)347,581 (39.4) Large city429,804 (24.4)213,604 (24.3)216,200 (24.5) Small city and rural area615,909 (34.9)298,237 (33.8)317,672 (36.1)Income quartiles First quartile (lowest)420,551 (23.9)224,921 (25.5)195,630 (22.2) Second quartile329,474 (18.7)162,918 (18.5)166,556 (18.9) Third quartile432,303 (24.5)210,633 (23.9)221,670 (25.1) Fourth quartile (highest)580,578 (32.9)282,981 (32.1)297,597 (33.8)Comorbidities Hypertension1,021,250 (57.9)658,096 (74.7)363,154 (41.2) Diabetes592,795 (33.6)426,989 (48.4)165,806 (18.8) Ischemic heart disease288,602 (16.4)206,601 (23.4)82,001 (9.3) Cerebrovascular disease254,739 (14.5)177,895 (20.2)76,844 (8.7) Dyslipidemia681,790 (38.7)458,702 (52.0)223,088 (25.3)Affected family member CKD135,353 (7.7)80,666 (9.2)54,687 (6.2) ESRD55,334 (3.1)34,299 (3.9)21,035 (2.4)Data are presented as means (standard deviation) or numbers (percentages).CKD chronic kidney disease, ESRD end-stage renal disease. Overall, age, sex, residential area, and income level were generally similar across the groups, but comorbid conditions were more prevalent in patients with CKD. Additionally, cases were more likely to have an affected family member with CKD ($9.2\%$ vs. $6.2\%$) or ESRD ($3.9\%$ vs. $2.4\%$) than age- and sex-matched controls. ## Risks of CKD in individuals with affected relatives with CKD In logistic regression models adjusted for sociodemographic data and comorbidities, the presence of any affected family member with CKD was associated with a significantly higher risk of CKD (Table 2 and Fig. 1). Overall, adjusted OR ($95\%$ CI) for individuals with affected first-degree relatives with CKD was 1.46 (1.43–1.49): specifically, 1.42 (1.38–1.45) for individuals with affected parents and 1.50 (1.46–1.55) for individuals with affected offspring, respectively. Additionally, having an affected sibling or spouse was associated with a higher risk of CKD, with OR ($95\%$ CI) of 1.70 (1.64–1.77) and 1.30 (1.27–1.33) in individuals with affected siblings and spouses, respectively. Of note, further subgroup analyses confirmed the strong and consistent association between familial aggregation of CKD and risk of CKD in both men and women (Fig. S2).Table 2Association between familial aggregation of CKD and risk of CKD.Type of affected family memberStudy participants, number (%)OR ($95\%$ CI)Cases($$n = 881$$,453)Matched controls($$n = 881$$,453)First-degree relatives41,441 (4.7)26,704 (3.0)1.46 (1.43–1.49) Parents21,413 (2.4)14,042 (1.6)1.42 (1.38–1.45) Father10,637 (1.2)7464 (0.9)1.32 (1.28–1.37) Mother11,645 (1.3)6960 (0.8)1.52 (1.46–1.58) Offspring20,142 (2.3)12,699 (1.4)1.50 (1.46–1.55)Sibling12,108 (1.4)6501 (0.7)1.70 (1.64–1.77)Spouse30,920 (3.5)23,136 (2.6)1.30 (1.27–1.33) Husband15,992 (1.8)12,116 (1.4)1.27 (1.23–1.31) Wife14,928 (1.7)11,020 (1.3)1.32 (1.28–1.36)All models were adjusted for age, sex, residential area, income level, and comorbidities such as hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and dyslipidemia. CKD chronic kidney disease, OR odds ratio, CI confidence interval. Figure 1Risks of CKD in individuals having affected relatives with CKD. All models were adjusted for age, sex, residential area, income level, and comorbidities such as hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and dyslipidemia. CKD, chronic kidney disease; CI, confidence interval. ## Risks of ESRD in patients with predialysis CKD with affected relatives with ESRD In this study, we aimed to examine the association between familial aggregation of ESRD and the risk of incident ESRD among patients with predialysis CKD. For this analysis, of the 881,453 patients with CKD, 66,318 patients with a prior diagnosis of ESRD were excluded, and the analysis was conducted on a total of 815,135 patients with non-dialysis dependent CKD, among whom 31,512 ($3.9\%$) individuals had at least one affected family member with ESRD. During a mean follow-up of 3.9 (SD, 3.6) years (3,207,497 person-years of follow-up), a total of 126,483 ($15.5\%$) incident ESRD events occurred: 6512 and 119,971 events in patients with and without an affected family member with ESRD, respectively. In Cox regression models, the risks of incident ESRD were significantly higher in individuals with affected first-degree relatives, parents, offspring, siblings, and spouses with a corresponding HR ($95\%$ CI) of 1.22 (1.17–1.26), 1.10 (1.05–1.15), 1.38 (1.32–1.46), 1.57 (1.49–1.65), and 1.14 (1.08–1.19), respectively. Although these higher observed risks were markedly attenuated in individuals with an affected father, the small sample size in this group makes this association less reliable (Table 3 and Fig. 2). Similar to findings in the overall cohort, individuals with any affected family member with ESRD tended to have a higher risk of ESRD across subgroups stratified by sex, but the associations of affected parents were much attenuated in female patients with CKD (Fig. S3).Table 3Association between familial aggregation of ESRD and risk of incident ESRD.Type of affected family memberEvent number/patient number (%)HR ($95\%$ CI)With affected family memberWithout affected family memberFirst-degree relatives$\frac{3447}{16}$,588 (20.8)123,$\frac{036}{798}$,547 (15.4)1.22 (1.17–1.26)Parents$\frac{1858}{8235}$ (22.6)124,$\frac{625}{806}$,900 (15.4)1.10 (1.05–1.15)Father$\frac{795}{3797}$ (20.9)125,$\frac{688}{811}$,338 (15.5)1.03 (0.96–1.11)Mother$\frac{1099}{4555}$ (24.1)125,$\frac{384}{810}$,580 (15.5)1.15 (1.09–1.23)Offspring$\frac{1593}{8371}$ (19.0)124,$\frac{890}{806}$,764 (15.5)1.38 (1.32–1.46)Sibling$\frac{1613}{5425}$ (29.7)124,$\frac{870}{809}$,710 (15.4)1.57 (1.49–1.65)Spouse$\frac{1687}{10}$,266 (16.4)124,$\frac{796}{804}$,869 (15.5)1.14 (1.08–1.19)Husband$\frac{828}{5110}$ (16.2)51,$\frac{572}{345}$,210 (14.9)1.10 (1.03–1.18)Wife$\frac{859}{5156}$ (16.7)73,$\frac{224}{459}$,659 (15.9)1.15 (1.07–1.23)All models were adjusted for age, sex, residential area, income level, and comorbidities such as hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and dyslipidemia. HR hazard ratio, CI confidence interval, ESRD end-stage renal disease. Figure 2Risks of ESRD in patients with predialysis CKD having affected relatives with ESRD. All models were adjusted for age, sex, residential area, income level, and comorbidities such as hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and dyslipidemia. CKD, chronic kidney disease; CI, confidence interval; ESRD, end-stage renal disease. To further substantiate our findings, we calculated E-values to assess the potential influence of unmeasured confounders on the association between familial aggregation of CKD and its incidence and progression. Given that point estimates and upper CIs of each E-value were seemingly remote beyond those of confounders that were measured, it is less likely that unmeasured confounders exist that can overcome the associations observed in this study (Tables S1 and S2). ## Discussion This nationwide population-based study of 1.76 million people in South Korea showed a strong familial aggregation of CKD such that individuals with an affected family member with CKD had a higher risk of incident CKD. Furthermore, once CKD had been diagnosed, family history of ESRD was also associated with a significantly higher risk of disease progression to ESRD. Thus, these findings reveal that the family history of kidney disease may be useful to early identify individuals at high risk of CKD and accurately classify patients’ risk of ESRD among patients with CKD. There has been accumulating evidence that CKD has a familial predisposition. Over 30 years ago, Ferguson et al.27 reported that a family history of CKD was associated with a substantially higher risk of ESRD. A year later, Seaquist et al. found that there is a striking concordance of diabetic nephropathy between siblings with type 1 diabetes28. Additionally, Lei et al.29 showed that the risk of ESRD was significantly higher in individuals with any family history of renal disease, and these associations could not be completely explained by clustering of other known risk factors for ESRD within the family, such as diabetes and hypertension. Notably, these observations have inspired several studies to search for genes contributing to the risk of a wider range of kidney diseases in the twenty-first century. For example, genome-wide association studies have identified many genetic regions associated with renal traits, such as diabetic nephropathy, estimated glomerular filtration rate, and albuminuria30–35. As another notable discovery, the polymorphisms in the APOL1 (apolipoprotein L1) gene were found in 2010, which conferred very high risks of hypertensive nephrosclerosis and focal global glomerulosclerosis in a recessive manner36–38. Interestingly, the higher prevalence of such a pathogenic APOL1 allele in Black Americans has been recognized as one of the plausible explanations responsible for the higher burden of CKD in this population than in White Americans39,40. Recently, the familial risk of CKD and ESRD has also been confirmed in several large observational studies. A cross-sectional, population-based cohort study including 87,849 Taiwanese patients with ESRD found that there was an association between having an affected first-degree relative with ESRD and the development of ESRD with a relative risk of 2.46 ($95\%$ CI, 2.32–2.62)19. Furthermore, Zhang et al. more recently reported similar findings in European patients with an earlier stage of CKD, including 1,862 CKD cases of 155,911 study participants, noting that the risk of CKD in individuals with an affected first-degree member was three times higher than that in the general population (recurrence risk ratio 3.04, $95\%$ CI 2.26–4.09)41. Their study is particularly noble given that most studies examining familial contributions to kidney diseases have largely focused on the advanced stages, ESRD. As an extension of these studies, our study additionally confirmed that individuals with an affected family member with CKD are not only far more likely to develop CKD but also exhibit faster disease progression to ESRD. Interestingly, a significantly higher risk for incident CKD was found in individuals with parents with CKD irrelevant to parent’s sex. However, higher risk for ESRD was observed only in those with affected mothers with ESRD. The reason for this finding is uncertain but we suspect that our database may have intrinsic flaws that may explain the discrepancy. The Korean NHIS database contains information recorded since 2002, and patients with kidney disease who died before 2002 were not included in this database. Eventually, affected individuals with parents who died before 2002 may have been misrepresented as CKD individuals with healthy parents. Furthermore, since the prevalence of CKD is generally higher in women, whereas mortality is higher in men42,43, a number of deceased fathers with CKD may not have been accounted in this study. Thus, cautious interpretation is required making a conclusion that fathers are less associated with offspring’s kidney disease compared to mothers. In addition, it is possible that sex of the affected siblings and offspring may have also affected the family aggregation. However, we did not examine the risk regarding sex of relatives due to the absence of information on sex distinction (i.e., brother, sister, son, and daughter) in our dataset. Hence, future studies are needed to ascertain these important issues. Nonetheless, to the best of our knowledge, this is the largest study conducted to date examining more than 1.7 million people in Korea, providing strong statistical power. While the underlying mechanisms responsible for these associations await further investigation, the study findings suggest that a family history of CKD or dialysis is associated with an increased incidence of CKD and disease progression to ESRD and can be used to identify individuals at high risk of both kidney diseases. A higher risk of kidney disease with an affected family member indicates that CKD is a hereditable condition. However, it is more evident that shared environment and shared genes likely contribute to kidney disease. Assuming that spouses share the family environment but not close genetic similarity with other family members, they have been used to estimate the relative contribution of shared environmental factors to susceptibility to kidney disease19,41,44. In this regard, we also found that individuals having affected spouses with CKD were associated with a $30\%$ higher risk of CKD. Likewise, among patients with CKD, those with affected spouses with ESRD were also associated with a $14\%$ increased risk of ESRD. These findings are further supported by the aforementioned studies, which consistently showed that individuals with an affected spouse with CKD or ESRD were associated with a higher burden of each kidney disease. Accordingly, it should be emphasized that both genetic and shared environmental factors might be considered to better understand the complex nature of the familial contribution to kidney disease45. The validity of this study is strengthened by the use of the Korean NHIS database linked to the nationwide family tree database, which contains information on the entire population of South Korea. Given that the awareness of CKD by both patients themselves and other family members is likely to be low (i.e., when compared to awareness of other catastrophic illnesses such as ischemic heart disease, stroke, or malignancy that are often included in family history questionnaires), it seems to be more valid to ascertain an affected family member based on the nationwide family tree database rather than on questionnaire17. However, this study has several limitations. First, we ascertained CKD entirely relied on ICD-10 codes due to the lack of relevant laboratory data such as estimated glomerular filtration rate and albuminuria, which might not precisely capture the disease burden. Hence, the study results may underestimate the true prevalence of CKD in this population. Second, residual confounding might still be a limitation as we did not capture complete data on potential risk factors such as blood pressure, obesity, health behaviors (e.g., smoking status), or medications (e.g., use of angiotensin-converting enzyme inhibitors and/or angiotensin-receptor blockers), some of which have been associated with CKD outcomes23. Therefore, we could not assume that all measured covariables were sufficient to adjust for all biases. Nonetheless, we tried to address this shortcoming, at least in part by vigorous adjustment for measured covariables such as sociodemographic data and various comorbidities. Furthermore, sensitivity analysis using E-value estimation indicated that contribution of unmeasured confounding to this association was less likely. Third, given the observational nature of our study design, we could not infer the causality of the observed associations between familial aggregation of CKD and disease occurrence and progression. Finally, our findings may not be generalizable to populations outside of South Korea, given the genetic architecture, social factors, environmental exposures, national healthcare policies, and chronic disease burden, including kidney disease, which may be distinct from other countries. In conclusion, this national family cohort study of the Korean population revealed that a family history of CKD was associated with a significantly higher risk of not only CKD but also ESRD. While more accurate and readily applicable genetic testing is not currently available in routine clinical practice, these intriguing findings provide useful information suggesting that ascertainment of affected family members with CKD or ESRD is useful to early identify individuals at high risk of CKD, which is also valid to predict disease progression in patients with CKD. Hence, it should be emphasized that a detailed family history of kidney diseases should be taken as a part of clinical care to screen and treat high risk individuals in a timely manner. ## Supplementary Information Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-32362-5. ## References 1. 1.National Health IS. Health Insurance Statistics. .https://www.nhis.or.kr/nhis/together/wbhaec06400m01.do?mode=view&articleNo=137170&article.offset=0&articleLimit=10 (2019). 2. Glassock RJ, Warnock DG, Delanaye P. The global burden of chronic kidney disease: Estimates, variability and pitfalls. Nat. Rev. Nephrol. (2017.0) 13 104-114. DOI: 10.1038/nrneph.2016.163 3. Thomas B. Global cardiovascular and renal outcomes of reduced GFR. J. Am. Soc. Nephrol. (2017.0) 28 2167-2179. DOI: 10.1681/ASN.2016050562 4. Matsushita K. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: A collaborative meta-analysis. Lancet (2010.0) 375 2073-2081. DOI: 10.1016/S0140-6736(10)60674-5 5. van der Velde M. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int. (2011.0) 79 1341-1352. DOI: 10.1038/ki.2010.536 6. Webster AC, Nagler EV, Morton RL, Masson P. Chronic kidney disease. Lancet (2017.0) 389 1238-1252. DOI: 10.1016/S0140-6736(16)32064-5 7. Jha V. Chronic kidney disease: Global dimension and perspectives. Lancet (2013.0) 382 260-272. DOI: 10.1016/S0140-6736(13)60687-X 8. Ginsburg GS, Wu RR, Orlando LA. Family health history: Underused for actionable risk assessment. Lancet (2019.0) 394 596-603. DOI: 10.1016/S0140-6736(19)31275-9 9. Goldgar DE, Easton DF, Cannon-Albright LA, Skolnick MH. Systematic population-based assessment of cancer risk in first-degree relatives of cancer probands. J. Natl. Cancer Inst. (1994.0) 86 1600-1608. DOI: 10.1093/jnci/86.21.1600 10. Flossmann E, Schulz UGR, Rothwell PM. Systematic review of methods and results of studies of the genetic epidemiology of ischemic stroke. Stroke (2004.0) 35 212-227. DOI: 10.1161/01.STR.0000107187.84390.AA 11. Meigs JB, Cupples LA, Wilson PW. Parental transmission of type 2 diabetes: The Framingham Offspring Study. Diabetes (2000.0) 49 2201-2207. DOI: 10.2337/diabetes.49.12.2201 12. Earle K, Walker J, Hill C, Viberti G. Familial clustering of cardiovascular disease in patients with insulin-dependent diabetes and nephropathy. N. Engl. J. Med. (1992.0) 326 673-677. DOI: 10.1056/NEJM199203053261005 13. Lloyd-Jones DM. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: A prospective study of parents and offspring. JAMA (2004.0) 291 2204-2211. DOI: 10.1001/jama.291.18.2204 14. O'Dea DF, Murphy SW, Hefferton D, Parfrey PS. Higher risk for renal failure in first-degree relatives of white patients with end-stage renal disease: A population-based study. Am. J. Kidney Dis. (1998.0) 32 794-801. DOI: 10.1016/S0272-6386(98)70135-0 15. Freedman BI. Population-based screening for family history of end-stage renal disease among incident dialysis patients. Am. J. Nephrol. (2005.0) 25 529-535. DOI: 10.1159/000088491 16. McClellan WM. Individuals with a family history of ESRD are a high-risk population for CKD: Implications for targeted surveillance and intervention activities. Am. J. Kidney Dis. (2009.0) 53 100. DOI: 10.1053/j.ajkd.2008.07.059 17. McClellan WM. Association of family history of ESRD, prevalent albuminuria, and reduced GFR with incident ESRD. Am. J. Kidney Dis. (2012.0) 59 25-31. DOI: 10.1053/j.ajkd.2011.09.018 18. Skrunes R, Svarstad E, Reisæter AV, Vikse BE. Familial clustering of ESRD in the Norwegian population. Clin. J. Am. Soc. Nephrol. (2014.0) 9 1692-1700. DOI: 10.2215/CJN.01680214 19. Wu HH. Family aggregation and heritability of ESRD in Taiwan: A population-based study. Am. J. Kidney Dis. (2017.0) 70 619-626. DOI: 10.1053/j.ajkd.2017.05.007 20. Kwon S. Thirty years of national health insurance in South Korea: Lessons for achieving universal health care coverage. Health Policy Plan. (2009.0) 24 63-71. DOI: 10.1093/heapol/czn037 21. Song SO. Background and data configuration process of a nationwide population-based study using the Korean national health insurance system. Diabetes Metab. J. (2014.0) 38 395-403. DOI: 10.4093/dmj.2014.38.5.395 22. Kim Y, Hong H, Cho K, Park JH. Family tree database of the National Health Information Database in Korea. Epidemiol. Health (2019.0) 41 e2019040. DOI: 10.4178/epih.e2019040 23. Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic kidney disease. Lancet (2021.0) 398 786-802. DOI: 10.1016/S0140-6736(21)00519-5 24. Kim JE. Statin initiation and all-cause mortality in incident statin-naïve dialysis patients. Atherosclerosis (2021.0) 337 59-65. DOI: 10.1016/j.atherosclerosis.2021.08.026 25. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: Introducing the E-value. Ann. Intern. Med. (2017.0) 167 268-274. DOI: 10.7326/M16-2607 26. Haneuse S, VanderWeele TJ, Arterburn D. Using the E-value to assess the potential effect of unmeasured confounding in observational studies. JAMA (2019.0) 321 602-603. DOI: 10.1001/jama.2018.21554 27. Ferguson R, Grim CE, Opgenorth TJ. A familial risk of chronic renal failure among blacks on dialysis?. J. Clin. Epidemiol. (1988.0) 41 1189-1196. DOI: 10.1016/0895-4356(88)90023-6 28. Seaquist ER, Goetz FC, Rich S, Barbosa J. Familial clustering of diabetic kidney disease. Evidence for genetic susceptibility to diabetic nephropathy. N. Engl. J. Med. (1989.0) 320 1161-1165. DOI: 10.1056/NEJM198905043201801 29. Lei HH, Perneger TV, Klag MJ, Whelton PK, Coresh J. Familial aggregation of renal disease in a population-based case-control study. J. Am. Soc. Nephrol. (1998.0) 9 1270-1276. DOI: 10.1681/ASN.V971270 30. O'Seaghdha CM, Fox CS. Genome-wide association studies of chronic kidney disease: What have we learned?. Nat. Rev. Nephrol. (2011.0) 8 89-99. DOI: 10.1038/nrneph.2011.189 31. Köttgen A. New loci associated with kidney function and chronic kidney disease. Nat. Genet. (2010.0) 42 376-384. DOI: 10.1038/ng.568 32. Köttgen A. Multiple loci associated with indices of renal function and chronic kidney disease. Nat. Genet. (2009.0) 41 712-717. DOI: 10.1038/ng.377 33. Liu C. Genetic association for renal traits among participants of African ancestry reveals new loci for renal function. PLoS Genet. (2011.0) 7 e1002264. DOI: 10.1371/journal.pgen.1002264 34. Pezzolesi MG. Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes. Diabetes (2009.0) 58 1403-1410. DOI: 10.2337/db08-1514 35. Böger CA. CUBN is a gene locus for albuminuria. J. Am. Soc. Nephrol. (2011.0) 22 555-570. DOI: 10.1681/ASN.2010060598 36. Genovese G. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science (2010.0) 329 841-845. DOI: 10.1126/science.1193032 37. Freedman BI. The apolipoprotein L1 (APOL1) gene and nondiabetic nephropathy in African Americans. J. Am. Soc. Nephrol. (2010.0) 21 1422-1426. DOI: 10.1681/ASN.2010070730 38. Freedman BI, Skorecki K. Gene-gene and gene-environment interactions in apolipoprotein L1 gene-associated nephropathy. Clin. J. Am. Soc. Nephrol. (2014.0) 9 2006-2013. DOI: 10.2215/CJN.01330214 39. Derose SF. Racial differences in estimated GFR decline, ESRD, and mortality in an integrated health system. Am. J. Kidney Dis. (2013.0) 62 236-244. DOI: 10.1053/j.ajkd.2013.01.019 40. Norris KC. Hemodialysis disparities in African Americans: The deeply integrated concept of race in the social fabric of our society. Semin. Dial. (2017.0) 30 213-223. DOI: 10.1111/sdi.12589 41. Zhang J, Thio CHL, Gansevoort RT, Snieder H. Familial aggregation of CKD and heritability of kidney biomarkers in the general population: The Lifelines Cohort Study. Am. J. Kidney Dis. (2021.0) 77 869-878. DOI: 10.1053/j.ajkd.2020.11.012 42. Mauvais-Jarvis F. Sex and gender: Modifiers of health, disease, and medicine. Lancet (2020.0) 396 565-582. DOI: 10.1016/S0140-6736(20)31561-0 43. Nitsch D. Associations of estimated glomerular filtration rate and albuminuria with mortality and renal failure by sex: A meta-analysis. BMJ (2013.0) 346 f324. DOI: 10.1136/bmj.f324 44. Tsai J. Prevalence and risk factors for CKD in spouses and relatives of hemodialysis patients. Am. J. Kidney Dis. (2010.0) 55 856-866. DOI: 10.1053/j.ajkd.2009.12.021 45. Drawz PE, Sedor JR, Hostetter TH. Family history and kidney disease. Am. J. Kidney Dis. (2012.0) 59 9-10. DOI: 10.1053/j.ajkd.2011.11.012
--- title: Free androgen index (FAI)’s relations with oxidative stress and insulin resistance in polycystic ovary syndrome authors: - Leili Rahmatnezhad - Lida Moghaddam-Banaem - Tahereh Behrouzi Lak - Afshin Shiva - Javad Rasuli journal: Scientific Reports year: 2023 pmcid: PMC10060256 doi: 10.1038/s41598-023-31406-0 license: CC BY 4.0 --- # Free androgen index (FAI)’s relations with oxidative stress and insulin resistance in polycystic ovary syndrome ## Abstract This study aimed to determine the levels of the free androgen index (FAI) and its association with oxidative stress and insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). This cross-sectional study was performed on 160 women aged 18–45 years, visiting gynecology clinics of Urmia in northwestern Iran during 2020–2021 who were diagnosed with PCOS and exhibited one of the four phenotypes of PCOS. All the participants underwent clinical examinations, paraclinical tests, and ultrasounds. FAI cut-off point was considered to be $5\%$. The significance level was set at < 0.05. Among the 160 participants, the prevalence of the four phenotypes was as follows: phenotype A: $51.9\%$, phenotype B: $23.1\%$, phenotype C: $13.1\%$, and phenotype D: $11.9\%$. High FAI was detected in 30 participants ($18.75\%$). Additionally, It was found that phenotype C had the highest FAI levels among the PCOS phenotypes, with a significant difference between phenotypes A and C (p value = 0.03). IR was observed in 119 ($74.4\%$) of the participants, and the median (interquartile range: IQR) of malondialdehyde (MDA) levels among the participants was 0.64 (0.86) μM/L. In linear regression, the PCOS phenotype (standard beta = 0.198, p-value = 0.008), follicle-stimulating hormone (FSH) levels (standard beta = 0.213, p-value = 0.004), and MDA levels (standard beta = 0.266, p-value < 0.001) were significantly related to the FAI level, but the homeostatic model assessment for insulin resistance (HOMA-IR) was not statistically associated with FAI. Thus, in this study, PCOS phenotypes and MDA levels (an indicator of stress oxidative) were significantly related to FAI, but HOMA-IR (the indicator of IR) was not associated with it. ## Introduction Polycystic ovary syndrome (PCOS) is an endocrine metabolic disorder, which afflicts $7\%$ to $10\%$ of women of reproductive age1. It is a multisystem endocrine disorder, which is characterized by clinical and biochemical abnormalities such as menstrual irregularities, hyperandrogenism, infertility, hyperinsulinemia, and multiple ovarian cysts2. According to the Rotterdam criteria, PCOS is defined by the presence of two of the following criteria (by ruling out other etiologies): reduced or absent ovulation, clinical or biochemical hyperandrogenism, and polycystic ovaries3. According to the Rotterdam criteria for PCOS, there are four different phenotypes for this syndrome: (A): oligomenorrhoea + polycystic ovaries (PCO) + hyperandrogenism, (B): oligomenorrhoea + hyperandrogenism, (C): hyperandrogenism + PCO, and (D): oligomenorrhea + PCO. High concentrations of testosterone, total cholesterol, and LDL cholesterol in phenotype A increase the risk of cardiovascular diseases, type 2 diabetes, or metabolic syndrome4. This disorder is initially characterized by irregular menstrual cycles with no ovulation5. The absence of ovulation is one of the main characteristics of PCOS, and its spectrum ranges from normal ovulation to long-term ablation6. After oligomenorrhea, hirsutism is the second most common symptom of PCOS7. Hyperandrogenism is also a proven factor in the development of this syndrome with a prevalence of 60–$80\%$, and insulin resistance (IR) is another known factor contributing to the development of PCOS, which is observed in 50–$80\%$ of patients8. Hyperandrogenism in PCOS leads to hirsutism, acne, and alopecia. Androgens may also inhibit follicular growth and thus prevent ovulation7. IR and the resulting compensatory hyperinsulinism are associated with hyperandrogenemia in several ways. Insulin increases androgen synthesis from theca cells, either directly or by increasing the theca cell response to the circulating luteinizing hormone (LH)9. Oxidative stress, which is defined as an imbalance resulting from the excessive formation of oxidants in the presence of limited antioxidant defenses, is actively involved in the etiology of the syndrome in addition to hormonal disturbances, insulin signaling defects, and adipose tissue dysfunction10. It has been shown that oxidative stress affects the ovarian phases and leads to hormonal disorders that disturb the hormonal conditions, causing the disorder to become more severe11. Malondialdehyde (MDA) is a marker of chronic oxidative stress12. Biomarkers of oxidative stress have the potential to be used in the evaluation of the risk of oxidative damage and related diseases13,14. Among the products of lipid peroxidation are malondialdehyde (MDA) and hydroxyl radicals, which accumulate from damaged intracellular and cell wall polyunsaturated fatty acids, along with an increase in ROS. It is therefore likely that serum MDA levels could serve as a measure of lipid peroxidation as well as damage to the membrane and DNA of cells15. Thus, MDA can be employed as an indicator in assessing the effectiveness of antioxidant therapy12. Functional ovarian hyperandrogenism is a type of PCOS characterized by increased circulating levels of ovarian-derived androgens. IR is the most common etiologic factor in women with functional ovarian hyperandrogenism. It increases oxidative stress and worsens antioxidant status. Oxidative stress is directly related to IR and testosterone levels, thereby contributing to endocrine and biochemical changes in women with functional ovarian hyperandrogenism16. Given the importance of FAI in diagnosing functional ovarian hyperandrogenism as a type of PCOS and its association with increased oxidative stress and other individual characteristics and considering that no study has been conducted in this field on different PCOS phenotypes, the present research aimed to investigate the prevalence of high FAI levels in PCOS and its association with oxidative stress and insulin resistance in PCOS patients. ## Results Out of 168 women with PCOS who met the inclusion criteria for this study, 160 gave their consent to participate in the study. The prevalence of the four phenotypes and characteristics of the participants are shown in Table 1.Table 1Demographic, clinical, and paraclinical characteristics of the participants ($$n = 160$$).Characteristic (Qualitative)GroupingN (%)Characteristic (Quantitative)Median (IQR)Marital statusSingle96 [60]Age (years)24 [7]Married64 [40]Height (cm)165 [6]Weight (kg)66.5 (14.75)JobEmployed14 (8.8)BMI (kg/m2)24.61 (5.38)Housewife57 (35.6)Age at menarche (years)12 [1]University student51 (31.9)FSH (IU/L)2.73 (1.47)Highschool student19 (11.9)Total testosterone (ng/ml)0.65 (0.58)Self-employment19 (11.9)SHBG (nmol/L)31.3 (14.58)EducationIlliterate5 (3.1)LH (IU/L)7.45 (5.9)Under diploma33 (20.6)MDA (μM/L)0.64 (0.86)Diploma29 ($18.1\%$)FBS (mg/dl)80 [2]College education93 ($58.1\%$)FI (μU/dl)19.65 (16.37)HOMA3.88 (3.26)Economic statusPoor (expenditure more than income)44 ($27.5\%$)Hb (g/dl)12.30 (0.30)Good (expenditure less than and equal to income)116 ($72.5\%$)TSH (mU/L)3.20 (1.04)Physical activityNo (< 90 min per week)66 ($41.3\%$)T4 (nmol/L)1.07 [24]Yes (> = 90 min per week)94 ($58.8\%$)T3 (nmol/L)1.20 (0.85)PCOS PhenotypeA83 ($51.9\%$)PLT (N/mm3)209.50 (66.75)B37 ($23.1\%$)FAI2.04 (2.48)C21 ($13.1\%$)WHR0.81 (.02)D19 ($11.9\%$)FAILow (< $5\%$) (n:130)130 ($81.25\%$)High (> = $5\%$) (n:30)30 ($18.75\%$)Insulin Resistance (IR)No (HOMA < 2.5)41 ($25.6\%$)Yes (HOMA > = 2.5)119 ($74.4\%$)BMINormal (< 25)85 ($53.1\%$)Overweight/obese (> = 25)75 ($46.9\%$)Ovarian cystsN < 27 ($4.4\%$)N > = 2153 ($95.6\%$)BMI: Body mass index, FSH: Follicle Stimulating Hormone, SHBG: Sex Hormone Binding Globulin, LH: Luteinizing Hormone, MDA: Malonaldehyde, FBS: Fasting Blood Sugar, FI: Fasting Insulin, HOMA-IR: Homeostasis Model Assessment of Insulin Resistance, Hb: Hemoglobin, TSH: Thyroid Stimulating Hormone, T4: Thyroxin, T3: Triiodothyronine, PLT: Platelets, FAI: Free Androgen Index, WHR: Waist-to-Hip Ratio. According to Rotterdam criteria, out of the 160 women studied, 83 ($51.9\%$) had phenotype A, which was the most prevalent phenotype compared to the others. Considering the FAI cut-off point of $5\%$, 30 patients ($18.75\%$) had High FAI. In addition, 85 patients ($53.1\%$) had normal body mass index (BMI). The median and interquartile range (IQR) of FAI in the participants was 2.04 (2.48) the median and IQR of MDA levels in the participants were 0.64 (0.86). The relationships between FAI and demographic, clinical, and paraclinical characteristics of the participants are provided in Table 2.Table 2The relationships between FAI and demographic, clinical, and paraclinical characteristics of the participants ($$n = 160$$).Parameter (quantitative)FAICorrelation coefficient (r)P-value (Spearman's test)Age (years)0.030.7Height(cm)0.020.76Weight(kg)0.020.78Age at menarche (years)0.160.03FSH (IU/L)0.100.18LH (IU/L)0.100.2MDA (μM/L)0.30 < 0.001Hb (g/dl)-0.030.64TSH (mU/L)0.040.57PLT (N/mm3)0.010.88BMI (kg/m2)0.0060.93HOMA0.170.02WHR0.050.5Parameter (Qualitative)GroupingFAI Median(IQR)P-valueEducationIlliterate (n:5)4.17 (4.07)0.17 ■Under diploma/Diploma (n:62)2.04 (2.17)College education (n:93)1.96 (2.61)Number of ovarian cysts < 2 (n:7)2.87 (4.31)0.63⬜ > = 2 (n:153)2.03 (2.3)JobHousewife (n:57)2.14 (2.73)0.79⬜Employed (n:103)2.03 (2.4)PCOS phenotype△A (n:83)1.75 (1.99)△0.03 ■B (n:37)2.46 (3.67)C (n:21)3.05 (4.99)D (n:19)2.03 (4.24)Marital statusSingle (n:96)1.94 (2.5)0.27⬜Married (n:64)2.31 (2.62)Physical activityNo (< 90 min per week) (n:66)2.04 (2.22)0.32⬜Yes (> = 90 min per week) (n:94)2 (2.75)Economic groupPoor (expenditure more than income) (n:44)1.96 (2.02)0.71⬜Good (expenditure less than income) (n:116)2.07 (2.81)BMI group (kg/m2)Normal (< 25) (n:85)2.09 (2.42)0.86⬜Overweight/obese (> = 25) (n:75)2.03 (2.58)FSH: Follicle Stimulating Hormone, LH: Luteinizing Hormone, MDA: Malonaldehyde, Hb: Hemoglobin, TSH: Thyroid Stimulating Hormone, PLT: Platelets, BMI: Body Mass Index, HOMA-IR: Homeostasis Model Assessment of Insulin Resistance, WHR: Waist-to-Hip Ratio. ⬜ Mann–Whitney U Test, ■ Kruskal–Wallis test. △ Pairwise comparisons were conducted by the Bonferroni correction for multiple tests. Significant results were: Phenotypes A & C: P – value = 0.007.Significant values are in bold. Regarding the relationship between FAI and demographic, clinical, and paraclinical variables in the participants, the following parameters were significantly associated with FAI: age at menarche ($r = 0.16$), MDA level ($r = 0.30$), and HOMA-IR (homeostatic model assessment for insulin resistance) ($r = 0.17$) (Table 2). It was found that phenotype C had the highest FAI levels among the PCOS phenotypes, with a significant difference between phenotypes A and C. The relationship between the FAI groups and the demographic, clinical, and paraclinical characteristics of the participants are shown in Table 3.Table 3The relationship between high FAI (cut-off point $5\%$) and the demographic, clinical, and paraclinical characteristics of the participants ($$n = 160$$).Parameter (Quantitative)High FAI ($$n = 30$$) Median (IQR)Normal FAI ($$n = 130$$) Median (IQR)P-value (Mann–Whitney Test)Age (years)23.5 (7.5)24 [7]0.2Height(cm)165 [8]165 [6]0.91Weight(kg)64.5 (15.5)67 (15.75)0.74age at menarche (years)12 [0]12 [1]0.03FSH (IU/L)3.1 (3.48)2.6 (1.3)0.01LH (IU/L)8.95 (9.7)7.2 (5.36)0.13MDA (μM/L)0.6 (1.25)0.68 (0.89)0.35Hb (g/dl)12.1 (0.73)12.3 (0.6)0.72TSH (mU/L)3.2 (0.85)3.16 (1.08)0.46T4 (nmol/L)1.08 (0.23)1.06 (0.3)0.52PLT (N/mm3)198 [45]216.5 [68]0.52BMI (kg/m2)24.50 (6.01)24.61 (5.24)0.82HOMA (nmol/L)4.82 (2.97)3.74 (3.29)0.2WHR0.81 (0.03)0.81 (0.02)0.67Parameter (Qualitative)GroupingHigh FAI ($$n = 30$$) N (%)Normal FAI ($$n = 130$$) N (%)P-value (Chi-Square Tests)EducationIlliterate2 [40]3 [60]0.3Under diploma and Diploma9 (14.5)53 (85.5)college education19 (20.4)74 (79.6)Number of ovarian cysts groupN < 22 (28.6)5 (71.4)0.61N > = 228 (18.3)125 (81.7)PCOS phenotypeA9 (10.8)74 (89.2)0.05B9 (24.3)28 (75.7)C7 (33.3)14 (66.7)D5 (26.3)14 (73.7)Marital statusSingle15 (15.6))81 (84.4)0.22Married15 (23.4)49 (76.6)ExerciseNo (< 90 min per week)15 (22.7)51 (77.3)0.3Yes (> = 90 min per week)15 [16]79 [84]Economic statusWeek (expenditure more than income)8 (18.2)36 (81.8)1Good (expenditure less than income)22 [19]94 [81]JobHousewife12 (21.1)45 (78.9)0.67Employed18 (17.5)85 (82.5)HirsutismYes127 (82.5)27 (17.5)0.08No3 [50]3 [50]FSH: Follicle Stimulating Hormone, LH: Luteinizing Hormone, MDA: Malonaldehyde, Hb: Hemoglobin,TSH: Thyroid Stimulating Hormone, T4: Thyroxin, PLT: Platelets, BMI: Body Mass Index, HOMA-IR: Homeostasis Model Assessment of Insulin Resistance, WHR: Waist-to-Hip Ratio. Significant values are in bold. Age at menarche and follicle-stimulating hormone (FSH) were significantly associated with high FAI (cut-off point $5\%$). The highest FAI rate was observed in phenotype C patients ($33.3\%$). Linear regression analyses were used to control confounding factors as shown in Table 4.Table 4Linear regression analyses to assess the factors effective on FAI in the study participants ($$n = 160$$).VariableStandardized coefficients betaP-valuePCOS phenotype0.1980.008FSH (IU/L)0.2130.004MDA (μM/L)0.266 < 0.001HOMA-IR0.1390.06FSH: Follicle Stimulating Hormone, MDA: Malonaldehyde, HOMA-IR: Homeostasis Model Assessment of Insulin. Significant values are in bold. In linear regression, FAI was entered as the independent variable and its possible effective factors as independent variables. The results showed that PCOS phenotypes, FSH, and MDA levels were significantly related to FAI. ## Discussion The present study was conducted on 160 women with PCOS in Urmia Province in the northwest of Iran, in order to determine the FAI level and its association with oxidative stress and IR in PCOS patients. The most prevalent PCOS phenotype in this study was phenotype A ($51.9\%$), which was consistent with the results of the study by Vaggopoulos et al. They showed that the prevalence of phenotype A was higher than the other phenotypes17. However, the study by Alawia et al. reported different findings from our study. They found that the prevalence of phenotype D was higher than the other phenotypes18, which could be due to genetic factors, lifestyle, eating habits, and differences in the number of participants. In the present study, considering a cut-off point of $5\%$ for FAI, 30 participants ($18.75\%$) had high FAI. According to the results of the present study, the rate of high FAI among different phenotypes of PCOS was as follows: phenotype A ($10.8\%$), phenotype B ($24.3\%$), phenotype C ($33.3\%$), and phenotype D ($26.3\%$), with phenotype C exhibiting the highest rate of high FAI. Moreover, the FAI level was significantly different between the four phenotype groups in our study, and the highest level was observed among phenotype C patients. This finding is inconsistent with the results of Głuszak's study. Their study indicated elevated levels of total testosterone, androstenedione, and significantly higher levels of total cholesterol and LDL cholesterol in the phenotype A group4, which could be due to differences in the participants' characteristics and lifestyles. According to the results, there was a statistically significant relationship between FAI and MDA, which was in line with the results of the study by Desai et al.19. According to their findings, IR, hyperandrogenism, dyslipidemia, and obesity associated with PCOS increased MDA levels while decreasing antioxidant enzyme levels. According to their research, oxidative stress leads to cell damage and activates the transcription of pro-inflammatory cytokines such as tumor necrosis factor-alpha, which is a known mediator of IR. This pro-inflammatory state may also contribute to IR and hyperandrogenism19. Moreover, the results of our study were in line with those of the study by Yuan et al. which was conducted in order to investigate the relationship between sex hormone binding globulin (SHBG) and oxidized low-density lipoprotein (ox-LDL), total oxidant status, total antioxidant capacity, oxidative stress index, and MDA and to evaluate the effect of oxidative stress on SHBG expression. Their findings showed that oxidative stress inhibits the expression and secretion of SHBG in laboratory conditions and may be an important factor in increasing the incidence of hyperandrogenemia in PCOS20. We also found a statistically significant direct correlation between FAI and HOMA-IR, which was in line with the results of the study by Garzia et al. They stated that the insulin-induced increase in androgens is primarily due to the direct effect on the steroidogenesis of the ovarian theca cells as well as the inhibitory effect on the production of insulin-like growth factor 1 (IGF-1) binding protein by the liver21. In this study, a statistically significant relationship was observed between MDA and HOMA. The findings of our research were in line with those of the study by Uçkan et al., which aimed to investigate the relationship between the oxidant-antioxidant status, endothelial dysfunction, lipid metabolism, and the risk of metabolic syndrome in women with PCOS. They reported a positive correlation between MDA, BMI, and HOMA-IR in the PCOS patient group22. Furthermore, our findings were in line with those of the study by Fatima et al., which was conducted in order to evaluate the relationship between PCOS and oxidative stress as well as the relationship between oxidative stress biomarkers and insulin parameters. The results of their study showed a positive correlation between oxidative stress and insulin parameters in PCOS23. There was a statistically significant relationship between FAI and age at menarche, which was in agreement with the results of the study by Asanidze et al. They reported that around $50\%$ of adolescents with PCOS (according to Rotterdam and NIH criteria) have biochemical hyperandrogenism, but the cut-off values were not reported in the study24. Our findings were also consistent with those of the research by Valeria Calcaterra et al. which reported the earlier onset of menstruation in adolescents with PCOS9. In our work, there was no link between hirsutism and the level of FAI, which contradicted the findings of the study by Chanukvadze et al. In their study, which was conducted to investigate the relationship between clinical symptoms and biochemical markers of hyperandrogenism, they reported a positive correlation between the hirsutism score and FAI and a negative correlation between the hirsutism score and SHBG25, which could be due to genetic factors, lifestyle, eating habits, and differences in the number of participants. Multivariate analysis (linear regression) was used to assess the predictive ability of independent variables and to adjust and control the effects of confounding variables. In linear regression, FAI was entered as the independent variable and its possible effective factors as independent variables. The results showed that the phenotype of PCOS, FSH, MDA, and HOMA-IR levels were significantly associated with FAI. According to these results, PCOS phenotype C and high FSH and MDA levels led to an increase in FAI, which should be taken into account in PCOS management and treatment programs. ## Conclusion The prevalence of PCOS phenotype A was higher than the other phenotypes in our study. The four PCOS phenotypes were significantly different in terms of FAI, and the highest rate of FAI was observed in phenotype C. PCOS phenotypes, FSH, HOMA-IR, MDA, and age at menarche were related to FAI. Therefore, it seems necessary to pay attention to FSH, HOMA-IR, MDA levels, and PCOS phenotypes when diagnosing PCOS for better management of androgens in PCOS patients and the other complications of this disorder; however, further research is suggested. ## Ethical approval The ethics committee of Tarbiat Modares University approved all the procedures of this study (ethical approval no: IR.TMU.REC.1397.235). Moreover, all the methods were performed in accordance with the relevant guidelines and regulations. This cross-sectional study was carried out from November 2020 to June 2021 on 160 women aged 18–45 years who had a PCOS diagnosis and visited the obstetrics and gynecology clinics of Urmia City (a city with a population of 736,224, according to the census of 2016, located in northwestern Iran). The sampling method used in this study was convenient sampling. All eligible women who met the inclusion criteria were enrolled in the study until the sample size was reached. Inclusion criteria were 18 to 45 years of age; diagnosis of PCOS by a gynecologist (based on clinical, laboratory, and imaging findings); no current pregnancy; not receiving infertility treatment, hormonal medications, or any medications other than over the counter (OTC) painkillers in the last three months; interval of more than four years from the onset of menarche; and the absence of severe underlying diseases such as malignancy and thalassemia affecting menstrual cycles, known endocrinopathies such as Cushing's syndrome, untreated thyroid disorders, and other similar conditions. ## Study procedures The purpose and protocol of the study were explained and informed consent was obtained from all the participants before enrolment. A researcher-made questionnaire of demographic/reproductive/medical characteristics was completed by the researcher for all the patients. The questionnaire included age, level of education, occupation, marital status, number of pregnancies, number of abortions, number of children, age at menarche, economic status (poor, average, or wealthy as stated by the participant), physical activity (regular exercises or inactive as stated by the participant), diet (the common diet or a specific diet such as vegetarianism), and history of illnesses. After entering the study, relevant clinical examinations, paraclinical tests, and ultrasounds were performed for all the participants as follows: Anthropometric measurements were performed using standard protocols and calibrated instruments. Height without shoes was measured with a standard tape attached to the wall. Weight was measured with light clothes and without shoes using a Seka 755 scale with an accuracy of 500 g. BMI was calculated using the following formula: weight (kg)/height2 (m). Waist circumference was measured with a standard measuring tape parallel to the umbilicus, and hip circumference was measured with a measuring tape at the largest diameter of the hip area. The waist-to-hip ratio (WHR) was measured as the waist/hip circumference. Clinical signs of hyperandrogenism (acne, oily skin, and hirsutism) were assessed by observation and physical examination. All clinical findings were evaluated by a gynecologist. The diagnosis of hirsutism was based on taking a history and performing a clinical examination with the modified Freeman-Galloway rating score, which examines coarse terminal hairs in nine areas of the body including the upper lip, chin, chest, upper and lower abdomen, arms, and thighs. The severity of hirsutism in each section was scored from 0 to 426. In the summation of scores, women with a level ≥ 4 were considered as hirsute27. Venous blood samples of the participants were collected in fasting conditions (after fasting for 10–12 h) to measure the following indices: Fasting blood sugar (FBS), fasting insulin levels (FIL), total serum testosterone, thyroid stimulating hormone (TSH), T4 (thyroxine), T3 (triiodothyronine), MDA, SHBG, FSH, LH, hemoglobin (Hb), and platelets (PLT). The serum levels of FSH, LH, total testosterone, insulin, and SHBG were measured using the enzyme-linked immunosorbent assay (ELISA) (Demeditec Diagnostics GmbH, German). The serum levels of FBS were measured using electro-chemical luminescent technique kits (E-411, Roche Company Germany, immunoassay technique). MDA levels were measured using thiobarbituric acid (TBA) with a TBARS kit (KA1381) (Abnova, Taiwan). ## Hyperandrogenism Hyperandrogenism was defined based on serum levels of male hormones (total serum testosterone, SHBG, FAI) and clinical signs (acne, oily skin, hirsutism, and male pattern hair loss). FAI was calculated as (total testosterone)/SHBG × 10028, and the cut-off point of FAI was set at $5\%$ so that the values above $5\%$ were considered high FAI29. ## PCOS phenotypes These phenotypes were identified in the study participants based on history, clinical examination, and paraclinical tests using hyperandrogenism (H), ovulatory dysfunction (OD), and PCO as follows: Phenotype A: OD + PCO + H; phenotype B: OD + H; phenotype C: H + PCO; phenotype D: OD + PCO. ## IR IR was assessed using HOMA-IR, which was calculated as follows: fasting insulin (mg/dL) × fasting blood glucose/405 (μu/mL)30. HOMA-IR (the cut-off value for IR) ≥ 2.5 was considered an indicator of IR according to the previous studies1,31–34. ## BMI It was calculated using the following formula: weight (kg)/[height (m)]235. ## PCO PCO was defined as finding 10 or more immature follicles in each ovary and/or an ovarian volume of more than 10 cm3 in ultrasound36. ## Menstrual disorders/OD These disorders included amenorrhea, oligomenorrhea, hypomenorrhea, hypermenorrhea, and irregular menstrual intervals and were defined based on the participants' history. Menstrual disorders were diagnosed as oligomenorrhea when menstrual cycles lasted more than 35 days or occurred less than nine times a year. ## Data management and analysis Data were entered into the computer and analyzed by IBM® SPSS® Software version 26. The statistical significance level was set at < 0.05. Qualitative variables were compared by the K2 test. Quantitative variables in two groups were analyzed by the Mann–Whitney U test due to the non-normal distribution of variables. Quantitative variables in more than two groups were analyzed by the Kruskal–Wallis test due to the non-normal distribution of variables. Linear regression analysis was used to determine effective factors on FAI. ## References 1. Sobti SH, Rupali D, Sunil R. Metabolic syndrome and insulin resistance in PCOS phenotypes. Int. J. Reprod. Contracept. Obstet. Gynecol. (2017.0) 6 5067-5073. DOI: 10.18203/2320-1770.ijrcog20175027 2. Sandhu JK. Oxidative stress in polycystic ovarian syndrome and the effect of antioxidant N-Acetylcysteine on ovulation and pregnancy rate. Cureus (2021.0) 13 34660086. DOI: 10.7759/cureus.17887 3. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Hum. Reprod. (2004.0) 19 41-47. DOI: 10.1093/humrep/deh098 4. Głuszak O. Phenotype and metabolic disorders in polycystic ovary syndrome. ISRN Endocrinol. (2012.0) 12 569862 5. Ruta K, Shinde K, Rahul K, Mrudula K. Contemporary and traditional perspectives of polycystic ovarian syndrome (PCOS). IOSR J. Dental Med. Sci. (IOSR-JDMS) (2014.0) 13 89-98. DOI: 10.9790/0853-13968998 6. Diamanti-Kandarakis E. Polycystic ovarian syndrome: Pathophysiology, molecular aspects and clinical implications. Expert Rev. Mol. Med. (2008.0) 30 18230193. DOI: 10.1017/S1462399408000598 7. Lowenstein EJ. Diagnosis and management of the dermatologic manifestations of the polycystic ovary syndrome. Dermatol. Ther. (2006.0) 19 210-223. DOI: 10.1111/j.1529-8019.2006.00077.x 8. Teede H, Deeks A, Moran L. Polycystic ovary syndrome: A complex condition with psychological, reproductive and metabolic manifestations that impacts on health across the lifespan. BMC Med. (2010.0) 30 20591140. DOI: 10.1186/1741-7015-8-41 9. Calcaterra V. Polycystic ovary syndrome in insulin-resistant adolescents with obesity: The role of nutrition therapy and food supplements as a strategy to protect fertility. Nutrients (2021.0) 13 34071499. DOI: 10.3390/nu13061848 10. Papalou O, Victor VM, Diamanti-Kandarakis E. Oxidative stress in polycystic ovary syndrome. Curr. Pharm. Des. (2016.0) 22 2709-2722. DOI: 10.2174/1381612822666160216151852 11. 11.Pinkerton, J. V. Polycystic Ovary Syndrome (PCOS) Hyper androgenic Chronic An ovulation; Stein-Leventhal Syndrome. http://www.merckmanuals.com/professional/gynecology-and-obstetrics/menstrual-abnormalities/polycystic-ovary-syndrome-pcos (2023). 12. Nawrocka-Rutkowska J. Assessment of the parameters of oxidative stress depending on the metabolic and anthropometric status indicators in women with PCOS. Life (Basal) (2022.0) 12 5207512. DOI: 10.3390/life12020225 13. Mohammadi M. Oxidative stress and polycystic ovary syndrome: A brief review. Int. J. Prev. Med. (2019.0) 10 31198521. DOI: 10.4103/ijpvm.IJPVM_576_17 14. Marrocco I, Altieri F, Peluso I. Measurement and clinical significance of biomarkers of oxidative stress in humans. Oxid. Med. Cell Longev. (2017.0) 2017 28698768. DOI: 10.1155/2017/6501046 15. Enechukwu CI. Oxidative stress markers and lipid profiles of patients with polycystic ovary syndrome in a Nigerian tertiary hospital. Obstet. Gynecol. Sci. (2019.0) 62 335-343. DOI: 10.5468/ogs.2019.62.5.335 16. Suresh S, Vijayakumar T. Correlations of insulin resistance and serum testosterone levels with LH:FSH ratio and oxidative stress in women with functional ovarian hyperandrogenism. Indian J. Clin. Biochem. (2015.0) 30 345-350. DOI: 10.1007/s12291-014-0447-z 17. Vaggopoulos V. The prevalence of phenotypic subgroups in Greek women with polycystic ovarian syndrome. Clin. Exp. Obstet. Gynecol. (2013.0) 40 253-256. PMID: 23971253 18. Elasam AN. The prevalence and phenotypic manifestations of polycystic ovary syndrome (PCOS) among infertile Sudanese women: A cross - sectional study. BMC Women’s Health (2022.0) 22 35562723. DOI: 10.1186/s12905-022-01762-6 19. Desai V. Oxidative stress in non-obese women with polycystic ovarian syndrome. J. Clin. Diagn. Res. (2014.0) 8 25177558 20. Sun Y. Oxidative stress promotes hyperandrogenism by reducing sex hormone-binding globulin in polycystic ovary syndrome. Fertil. Steril. (2021.0) 116 1641-1650. DOI: 10.1016/j.fertnstert.2021.07.1203 21. Garzia E. Hyperandrogenism and menstrual imbalance are the best predictors of metformin response in PCOS patients. Reprod. Biol. Endocrinol. (2022.0) 20 34983571. DOI: 10.1186/s12958-021-00876-0 22. Uçkan K, Demir H, Turan K, Sarıkaya E, Demir C. Role of oxidative stress in obese and nonobese PCOS patients. Int. J. Clin. Pract. (2022.0) 2022 35685525. DOI: 10.1155/2022/4579831 23. Fatima Q. Evaluation of antioxidant defense markers in relation to hormonal and insulin parameters in women with polycystic ovary syndrome (PCOS): A case-control study. Diabetes Metab. Syndr. (2019.0) 13 1957-1961. DOI: 10.1016/j.dsx.2019.04.032 24. Asanidze E. Challenges in diagnosis of polycystic ovary syndrome in adolescence. Gynecol. Endocrinol. (2021.0) 37 819-822. DOI: 10.1080/09513590.2021.1943344 25. Chanukvadze D, Kristesashvili J, Kvashilava N. Correlation of biochemical markers and clinical signs of hyperandrogenism in women with polycystic ovary syndrome (PCOS) and women with non-classic congenital adrenal hyperplasia (NCAH). Iran J. Reprod. Med. (2012.0) 10 307-314. PMID: 25246891 26. Spritzer PM, Marchesan LB, Santos BR, Fighera TM. Hirsutism, normal androgens and diagnosis of PCOS. Diagnostics (Basel) (2022.0) 12 36010272. DOI: 10.3390/diagnostics12081922 27. Teede HJ. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum. Reprod. (2018.0) 33 1602-1618. DOI: 10.1093/humrep/dey256 28. 28.. Beckett, G. Lothian Guideline for the Diagnosis of Polycystic Ovary Syndrome (PCOS) in Primary Care. PD-GEN-A-LGPCOS Version 1.1. https://static1.squarespace.com/static/53b1670ee4b0be242b013ed7/t/543ee6cbe4b0e172550236ac/1413408459277/038_Guideline (2013). 29. Al Kindi MK, Al Essry FS, Al Essry FS, Mula-Abed WA. Validity of Serum testosterone, free androgen index, and calculated free testosterone in women with suspected hyperandrogenism. Oman Med. J. (2012.0) 6 471-474. DOI: 10.5001/omj.2012.112 30. Sun YF. High BMI and Insulin resistance are risk factors for spontaneous abortion in patients with polycystic ovary syndrome undergoing assisted reproductive treatment: A systematic review and meta-analysis. Front. Endocrinol. (2020.0) 11 33343510. DOI: 10.3389/fendo.2020.592495 31. Zheng Y, Yin G, Chen F, Lin L, Chen Y. Evaluation of triglyceride glucose index and homeostasis model of insulin resistance in patients with polycystic ovary syndrome. Int. J. Womens Health. (2022.0) 14 1821-1829. DOI: 10.2147/IJWH.S387942 32. Sahmay S. Serum AMH levels and insulin resistance in women with PCOS. Eur. J. Obstet. Gynecol. Reprod. Biol. (2018.0) 224 159-164. DOI: 10.1016/j.ejogrb.2018.03.007 33. Reyes-Muñoz E. Association of obesity and overweight with the prevalence of insulin resistance, pre-diabetes and clinical–biochemical characteristics among infertile Mexican women with polycystic ovary syndrome: A cross-sectional study. BMJ Open (2016.0) 6 27449893. DOI: 10.1136/bmjopen-2016-012107 34. Bahadur A. Correlation of homeostatic model assessment-insulin resistance, anti-Mullerian hormone, and BMI in the characterization of polycystic ovary syndrome. Cureus (2021.0) 13 34336524. DOI: 10.7759/cureus.16047 35. 35.Department of health and human services centers for disease control and prevention (CDC). https://www.cdc.gov/nccdphp/dnpao/growthcharts/training/bmiage/page5_1.html (2023). 36. El Hayek S, Bitar L, Hamdar LH, Mirza FG, Daoud G. Poly cystic ovarian syndrome: An updated overview. Front. Physiol. (2016.0) 7 27092084. DOI: 10.3389/fphys.2016.00124
--- title: A predictive model of response to metoprolol in children and adolescents with postural tachycardia syndrome authors: - Bo-Wen Xu - Qing-You Zhang - Xue-Ying Li - Chao-Shu Tang - Jun-Bao Du - Xue-Qin Liu - Hong-Fang Jin journal: World Journal of Pediatrics year: 2023 pmcid: PMC10060270 doi: 10.1007/s12519-022-00677-4 license: CC BY 4.0 --- # A predictive model of response to metoprolol in children and adolescents with postural tachycardia syndrome ## Abstract ### Background The present work was designed to explore whether electrocardiogram (ECG) index-based models could predict the effectiveness of metoprolol therapy in pediatric patients with postural tachycardia syndrome (POTS). ### Methods This study consisted of a training set and an external validation set. Children and adolescents with POTS who were given metoprolol treatment were enrolled, and after follow-up, they were grouped into non-responders and responders depending on the efficacy of metoprolol. The difference in pre-treatment baseline ECG indicators was analyzed between the two groups in the training set. Binary logistic regression analysis was further conducted on the association between significantly different baseline variables and therapeutic efficacy. Nomogram models were established to predict therapeutic response to metoprolol. The receiver-operating characteristic curve (ROC), calibration, and internal validation were used to evaluate the prediction model. The predictive ability of the model was validated in the external validation set. ### Results Of the 95 enrolled patients, 65 responded to metoprolol treatment, and 30 failed to respond. In the responders, the maximum value of the P wave after correction (Pcmax), P wave dispersion (Pd), Pd after correction (Pcd), QT interval dispersion (QTd), QTd after correction (QTcd), maximum T-peak-to-T-end interval (Tpemax), and T-peak-to-T-end interval dispersion (Tped) were prolonged (all $P \leq 0.01$), and the P wave amplitude was increased ($P \leq 0.05$) compared with those of the non-responders. In contrast, the minimum value of the P wave duration after correction (Pcmin), the minimum value of the QT interval after correction (QTcmin), and the minimum T-peak-to-T-end interval (Tpemin) in the responders were shorter ($P \leq 0.01$, < 0.01 and < 0.01, respectively) than those in the non-responders. The above indicators were screened based on the clinical significance and multicollinearity analysis to construct a binary logistic regression. As a result, pre-treatment Pcmax, QTcmin, and Tped were identified as significantly associated factors that could be combined to provide an accurate prediction of the therapeutic response to metoprolol among the study subjects, yielding good discrimination [area under curve (AUC) = 0.970, $95\%$ confidence interval (CI) 0.942–0.998] with a predictive sensitivity of $93.8\%$, specificity of $90.0\%$, good calibration, and corrected C-index of 0.961. In addition, the calibration curve and standard curve had a good fit. The accuracy of internal validation with bootstrap repeated sampling was 0.902. In contrast, the kappa value was 0.769, indicating satisfactory agreement between the predictive model and the results from the actual observations. In the external validation set, the AUC for the prediction model was 0.895, and the sensitivity and specificity were $90.9\%$ and $95.0\%$, respectively. ### Conclusions A high-precision predictive model was successfully developed and externally validated. It had an excellent predictive value of the therapeutic effect of metoprolol on POTS among children and adolescents. ### Supplementary Information The online version contains supplementary material available at 10.1007/s12519-022-00677-4. ## Introduction Postural tachycardia syndrome (POTS) is a common syndrome that significantly affects the quality of life among children and adolescents [1, 2]. POTS shows a $6.8\%$ prevalence in Chinese children [3]. It is characterized by chronic orthostatic intolerance with symptoms including palpitations, dizziness, chest tightness, gastrointestinal symptoms, etc. [ 1, 2]. The pathogenesis of POTS is unclear but mainly involves increased catecholamine contents and sympathetic hyperfunction, hypovolemia, excessive vasodilation, and immune abnormalities [4]. Empirical and nonselective beta-adrenoceptor blocker treatment among all children and adolescents with POTS has limited efficacy. For instance, metoprolol was previously depicted to achieve a low effective rate of only around $50\%$ among POTS in children [5]. Since metoprolol blocks β-adrenoceptors within cardiovascular tissues to reduce excessive sympathetic nerve activity in POTS, it would be only effective for POTS patients with high plasma catecholamine levels and sympathetic activity as the main pathogenesis [6]. Therefore, if we can effectively predict POTS patients with elevated pre-treatment plasma catecholamine levels or increased sympathetic activity, individualized treatment with beta-adrenoceptor blockers would have a favorable therapeutic efficacy in such instances of POTS. To predict whether high sympathetic activity exists among pediatric POTS before treatment, the investigators showed that the pre-treatment plasma norepinephrine levels [7], copeptin levels [8], C-type natriuretic peptide levels [9], baseline values of pre-treatment 24-hour heart rate variability [10], QTcd [11], and heart rate variation [12] during head-up tilt testing (HUTT) had a specific predictive value for the effectiveness of metoprolol in treating POTS. However, detecting plasma norepinephrine [7], plasma copeptin [8], and C-type natriuretic peptide [9] is complex and requires blood collection through an invasive procedure. The 24-hour heart rate variability examination [10] is time-consuming, and HUTT [12] is expensive, and requires specialized equipment. QTcd [11], as a single ECG index, has low predictive specificity and sensitivity in predicting the therapeutic response to beta-adrenoceptor blockers. Therefore, it is essential to identify valuable indicators or models that are easy to use and inexpensive with high sensitivities and specificities in predicting the effectiveness of metoprolol among pediatric POTS. Electrocardiographic waveforms can reflect the effects of sympathetic and vagal interactions on the heart. Sympathetic excitation is associated with elevated heart rate, prolonged P wave duration, enhanced P wave amplitude, shortened QT interval, low flattened or inverted T wave, extended T-peak-to-T-end interval (Tpe interval), and high dispersion in ECG [13–16]. Therefore, the study was conducted to comprehensively analyze the pre-treatment ECG indices associated with sympathetic nervous function in POTS children to determine if the integrated indices derived from ECG would successfully predict the effectiveness of metoprolol on pediatric POTS to improve the effective rate of individualized therapy with metoprolol among pediatric patients with POTS. ## Source of data and participants We screened 147 patients with POTS, of whom 137 ($93.2\%$) were eligible. The study consisted of a training set and an external validation set. In the training set, a retrospective analysis of 102 POTS among children and adolescents receiving metoprolol treatment was conducted, and the patients were followed up at the Department of Pediatrics, Peking University First Hospital, China, between January 2012 and December 2020. Seven children with POTS were excluded from this study because of lost to follow up or inadequate information during the follow-up. The loss to follow-up rate was $6.9\%$. Therefore, we enrolled 95 POTS children receiving oral metoprolol treatment in the training set. In addition, the present study enrolled another 42 patients who met the inclusion criteria and received metoprolol treatment between January 2021 and June 2022 in the external validation set. Figure 1 shows the flowchart of the patients inclusion. Fig. 1Flowchart of the inclusion of participants in the study. POTS postural tachycardia syndrome The diagnostic criteria for POTS included [1] symptoms related to predisposing factors, including the extended standing position or a fast change from supine to upright; [2] symptoms of orthostatic intolerance, including headache, dizziness, blurred vision, fatigue, palpitation, chest tightness, syncope following the upright position, exercise intolerance, and hand tremors; [3] positive standing or HUTT test results, and [4] exclusion of cardiovascular, nervous, and metabolic diseases [17]. Inclusion criteria were: [1] children and adolescents aged 5–18 years; [2] those diagnosed with POTS in Peking University First Hospital, China; [3] all received a standard assessment, including complete medical history, physical/neurological examination, baseline laboratory assessment, 12-lead ECG, echocardiography, standing test, or HUTT, and [4] the patients receiving the treatment with metoprolol. Exclusion criteria were: [1] cardiac syncope or orthostatic intolerance due to cardiac, neurologic, or metabolic diseases; [2] patients without sinus rhythm on ECG; [3] those without complete medical records or data, and [4] those who were lost to follow-up. The Ethics Committee of Peking University First Hospital, China approved the investigation. Written informed consent was obtained from the parents/guardians of the participants in this study. ## Demographic and clinical data We secured medical records of all the patients through the Medical Recording Management Digital System (Kaihua, Beijing, China), including demographic data [age, sex, and body mass index (BMI)], detailed medical history (previous medical history, history of present illness, family history, or any allergic history), physical examination, hemodynamic parameters [supine heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP)], and the results of laboratory investigations (blood biochemistry, echocardiography, 24-hour Holter monitoring, cranial CT, or MRI). ## Symptom score The symptom score (SS) was recorded for all the patients. SS was obtained based on the orthostatic intolerance symptom frequency, including syncope, dizziness, chest tightness, headache, palpitations, sweating, gastrointestinal symptoms, blurred vision or amaurosis, hand tremors, and difficulties in attention centralization. Symptom frequency was scored as follows [18, 19]: 0 point represented no appearance of orthostatic intolerance symptoms; 1 point indicated symptoms once a month; 2 points represented symptoms 2–4 times a month, 3 points represented symptoms 2–7 times a week, and 4 points indicated symptom frequency greater than once per day. The symptom scores were evaluated to obtain the overall SS for each patient. ## Standing and basic head-up tilt tests The procedure for standing test [20]: the patient was in a dimly lit and quiet environment within an appropriate temperature and rested under a supine position for a 10-minute period until the heart rate was stable. Then, the patient was requested to lie upright and stand for a 10-minute period. HR and blood pressure (BP) were dynamically recorded with a monitor (Dash 2000, General Electric Company, NY, USA) throughout this procedure to visualize and record whether the subject showed any discomfort during the test. If the patient could not tolerate the test, it was immediately stopped. The procedure for basic HUTT [11, 21]: any drug and diets possibly affecting autonomic nervous function, such as tea and coffee, which may influence autonomic nervous function, were discontinued for over five half-lives before the test. The patients were required to fast and abstain for 4 hours before the test. The examination was scheduled from 8:00 to 11:00 am in a quiet environment at appropriate room temperature and dim light. The HUTT was performed using a tilting bed (SHUT-100A, Standard, Jiangsu-HUT-821, Juchi Company, Beijing, China). ECG and heart rate were continuously monitored with a multi-lead ECG monitor (General Electric, NY, USA). Hemodynamic changes were determined with the non-invasive continuous BP monitor Finapres Medical system-FMS (FinometerPRO, FMS, Amsterdam, The Netherlands). The patient was kept in a supine and quiet position for 10–30 minutes, followed by continuous measurement of basic ECG and BP. When the HR, BP, and ECG data were kept stable, the patient was tilted at 60°. The above indicators were continuously recorded until a positive response was achieved or after 45 minutes of the test. Once a positive response occurred, the patient was again placed supine. ## Electrocardiography All patients were required to complete the 12-lead ECG by electrocardiography (FX-7402, Fukuda, Japan). ECG was recorded after the patient was stable in the supine position for at least 10 min in the quiet room. Then, quiet and comfortable breathing was encouraged for the patients within this trial. The paper determined the ECG results at 1 mV/cm (25 mm/s), followed by scanner digitization. ECG parameters were determined with Image-Pro Plus version 6.0.0.260 on a high-resolution computer screen at a threefold magnification of the stored digitized ECG data. ECG parameters were selected based on the ECG during sinus rhythm. We selected the horizontal line at the beginning of the Q wave as the isoelectric baseline to measure the amplitude of each waveform of the electrocardiogram, and if the beginning of the Q wave was not clear, we used the TP segment or PR segment for measurement [22–24]. P wave amplitude represents the distance from the isoelectric line position of lead II to the apex of the P wave. The P wave duration indicated the period from the beginning to the end of the P wave. The QT interval represented the duration between the QRS wave beginning and the end of the T wave, while the Tpe interval was the time from the apex of the T wave to its end in ECG. Thus, the T wave apex was the intersection between the highest vertical peak of the upright T wave and the isoelectric line or the lowest vertical valley within the inverted T wave with the isoelectric line. The T wave endpoint became the intersection of the descending branch through the isoelectric line. Pmax, Pmin, QTmax, QTmin, Tpemax, and Tpemin were determined in 12-lead ECG and expressed in milliseconds. Based on the Bazett formula, the corrected Pmax (Pcmax = Pmax/RR$\frac{1}{2}$), Pmin (Pcmin = Pmin/RR$\frac{1}{2}$), QTmin (QTcmin = QTmin/RR$\frac{1}{2}$), and QTmax (QTcmax = QTmax/RR$\frac{1}{2}$) were obtained after correcting the heart rate. Indicators representing dispersion (including Pd, Pcd, QTd, QTcd, and Tped) were obtained by calculating the difference between the maximum and minimum values in 12-lead ECG. Measurements are listed as the mean values of three-to-five consecutive R–R cycles. Medical history and laboratory results were reviewed and documented by a dedicated investigator for all the patients. Another investigator independently proofread the records. ## Treatment, follow-up, and outcome In this study, all the children and adolescents diagnosed with POTS were provided health education to allow them and their families to understand POTS fully, avoid predisposing factors, enhance water and salt intake, and strengthen autonomic nervous function exercises (limb compression movements and orthostatic training). Metoprolol was given to children and adolescents who still showed symptoms of orthostatic intolerance after receiving standardized health education and physical treatment. Metoprolol was initially provided at 0.5 mg/kg twice a day, and the course of treatment was 3 [2, 4] months according to the tolerance of the patient (maximum, 2 mg/kg/day) [25]. The POTS patients received a follow-up of 3 months after metoprolol therapy and were documented by trained personnel. In addition, follow-up was performed by hospital visits and telephone follow-up. Medication compliance, frequency of orthostatic intolerance symptoms, and side effects were carefully monitored during the follow-up period. The cases were grouped into responder and non-responder groups based on the effectiveness of metoprolol treatment at follow-up. Pre-treatment SS was evaluated after the cases were initially diagnosed as POTS. SS after treatment was determined after the follow-up. A decrease of $50\%$ or more in SS after treatment relative to SS before treatment was considered effective and ineffective if the decrease was less than $50\%$ [26]. ## Univariate regression The normally distributed measurement data were represented as mean ± SD. If the data were not within a normal distribution, they were expressed by the median and interquartile range (IQR). For continuous variables, normality was tested using the Kolmogorov–Smirnov test. Intergroup differences were compared with Student’s t test or the Mann–Whitney U test based on the normal distribution of continuous variables. For categorical variables, the difference was tested using the χ2 test, and categorical variables were expressed as values and percentages (%). $P \leq 0.05$ (two-sided) was considered statistically significant. ## Establishment of the predictive model Factors that showed statistical significance during univariable regression were screened according to clinical significance and multicollinearity analysis. The significant variables were incorporated into a binary logistic regression, where two-sided P values < 0.05 were used to identify independent factors. The combination of factors most accurately associated with metoprolol efficacy was determined by backward stepwise analysis. Their odds ratios (ORs) and $95\%$ confidence intervals (CIs) were calculated. A nomogram was created based on the binary logistic regression model. ## Evaluation and validation The area under the curve (AUC) was used to evaluate the nomogram discrimination in the study, providing the model accuracy. The calibration curve and Hosmer–Lemeshow goodness-of-fit test were applied to assess the goodness-of-fit between the predicted model and the observed data. Then, the corrected C-index was calculated, which was the consistency of the model. Bootstrap repeated sampling (1000 bootstrap resamplings) was performed for internal validation to reduce the over-fitting bias of the nomogram model and determine accuracy and kappa values. The model was used to assess the score of children with POTS in the validation set to predict the efficacy of metoprolol, and ROC curves and calibration curves were plotted to verify the accuracy and reproducibility of the prediction model. Statistical analysis was conducted with SPSS 24.0 (IBM, New York, USA) and R software (version: 4.2.0, Fig. http://www.R-project.org). ## Clinical features and hemodynamic parameters for subjects in the training set In this study, 95 children (44 boys and 51 girls) with POTS in the training set were analyzed. The responder group consisted of 33 ($50.8\%$) boys and 32 ($49.2\%$) girls, and the non-responder group consisted of 11 boys ($36.7\%$) and 19 girls ($63.3\%$). Data of sex, age, BMI, supine HR, SBP, DBP, and pre-treatment SS between the two groups did not significantly differ ($P \leq 0.05$). Table 1 represents the patient clinical features. Table 1Demographic and hemodynamic parameters between responders and non-responders to metoprolol in children with POTS in the training setItemsRespondersNon-responderst/Z/χ2 valueP valueNumber n (%)65 (68.4)30 (31.6)NANASex (M/F)$\frac{33}{3211}$/191.6420.200Age (y)12.0 (10.0, 13.0)13.0 (11.0, 13.0) −0.9190.358BMI (kg/cm2)20.2 (16.9, 23.8)19.0 (16.9, 20.9) −0.9930.321Supine HR (bpm)77.0 (70.5, 87.5)76.5 (71.0, 87.3) −0.3570.721SBP (mmHg)114.8 ± 13.1113.8 ± 8.50.3760.708DBP (mmHg)70.0 (61.5, 75.5)68.0 (65.8, 73.0) −0.2040.838Pre-treatment SS (points)7.0 (4.0, 12.0)7.5 (4.0, 10.0) −0.2690.788POTS postural tachycardia syndrome, M/F male/female, BMI body mass index, HR heart rate, bpm beats per minutes, SBP systolic blood pressure, DBP diastolic blood pressure, SS symptom score, NA not available ## Model development in the training set In univariable analysis, baseline Pcmax, Pd, Pcd, QTd, QTcd, Tpemax, and Tped in responders were elevated compared with those in non-responders (all $P \leq 0.01$); P wave amplitude in responders was enhanced compared with that in non-responders ($P \leq 0.05$), and Pcmin, QTcmin, and Tpemin in responders were reduced compared with those in non-responders (all $P \leq 0.01$, Table 2).Table 2Comparison of baseline ECG parameters between responders and non-responders to metoprolol in children with POTS in the training setItemsRespondersNon-responderst/Z valueP valueP wave amplitude (mV)0.119 (0.100, 0.132)0.109 (0.081, 0.133) −1.9740.048Pcmax (ms)117.2 ± 13.5104.5 ± 13.14.292 < 0.01Pcmin (ms)61.6 ± 10.870.9 ± 8.7 −4.133 < 0.01Pd (ms)48.9 ± 10.629.5 ± 7.98.965 < 0.01Pcd (ms)55.6 ± 11.633.6 ± 8.99.157 < 0.01QTcmax (ms)452.1 ± 28.9441.5 ± 23.41.7680.080QTcmin (ms)369.7 ± 30.3393.0 ± 23.2 −3.736 < 0.01QTd (ms)70.8 (52.2, 89.1)38.6 (32.2, 46.1) −5.501 < 0.01QTcd (ms)78.1 (62.5, 102.3)43.3 (37.6, 57.3) −5.476 < 0.01Tpemax (ms)109.7 (100.0, 119.3)96.7 (88.4, 104.2) −4.328 < 0.01Tpemin (ms)50.5 ± 10.059.9 ± 9.3 −4.385 < 0.01Tped (ms)56.7 (51.2, 70.1)36.8 (31.4, 41.3) −6.870 < 0.01ECG electrocardiogram, POTS postural tachycardia syndrome, Pcmax the maximum value of P wave duration in 12 leads of ECG after correction, Pcmin the minimum value of P wave duration in 12 leads of ECG after correction, Pd P wave duration dispersion, Pcd Pd after correction, QTcmax the maximum value of QT interval in 12 leads of ECG after correction, QTcmin the minimum value of QT interval in 12 leads of ECG after correction, QTd QT interval dispersion QTcd QTd after correction, Tpemax the maximum value of T-peak-to-T-end interval in 12 leads of ECG, Tpemin the minimum value of T-peak-to-T-end interval in 12 leads of ECG, Tped T-peak-to-T-end dispersion Multicollinearity analysis was performed between the above parameters, and the parameters Pcmax, QTcmin, and Tped which were not correlated with each other were used for the subsequent analysis (Supplemental Table 1). The binary logistic regression indicated that pre-treatment Pcmax, QTcmin, and Tped were the variables associated with the effectiveness of metoprolol therapy in POTS patients (Supplemental Table 2). The AUCs of baseline Pcmax, QTcmin, and Tped in predicting the response to metoprolol were 0.777 ($95\%$ CI 0.671–0.883), 0.738 ($95\%$ CI 0.633–0.843), and 0.940 ($95\%$ CI 0.887–0.993), respectively. In cases of the greatest Youden index, pre-treatment baseline cutoffs for Pcmax, QTcmin, and Tped were 109 ms, 382.5 ms, and 45.6 ms, respectively, yielding sensitivities of $76.9\%$, $70.8\%$, and $92.3\%$ and specificities of $76.7\%$, $76.7\%$, and $90.0\%$, respectively, for predicting the treatment efficacy (Table 3).Table 3The cut-off value of predictor variables predicting metoprolol efficacy in children with POTS in the training setVariablesAUC$95\%$ CIP valueCut-off valueSensitivitySpecificityPcmax0.7770.671–0.883 < 0.01 ≥ 109 ms$76.9\%$$76.7\%$QTcmin0.7380.633–0.843 < 0.01 ≤ 382.5 ms$70.8\%$$76.7\%$Tped0.9400.887–0.993 < 0.01 ≥ 45.6 ms$92.3\%$$90.0\%$Combined Prediction0.9700.942–0.998 < 0.01 ≥ $0.26693.8\%$$90.0\%$POTS postural tachycardia syndrome, ECG electrocardiogram, Pcmax the maximum value of P wave duration in 12 leads of ECG after correction, QTcmin the minimum value of QT interval in 12 leads of ECG after correction, Tped T-peak-to-T-end dispersion, AUC area under receiver-operating characteristic curve, CI confidence interval To further improve the predictive values, these variables were combined to predict the efficacy of metoprolol. A model was constructed based on logistic regression as follows: Y = Logit (P) = − 2.258 + 0.073 × Pcmax − 0.040 × QTcmin + 0.208 × Tped, where P was the probability value to predict the efficacy of metoprolol. Pcmax, QTcmin, and Tped were taken as the actual measured values. The AUC for combined prediction was 0.970 ($95\%$ CI 0.942–0.998, Fig. 2). Moreover, the optimum prediction cut-off $Y = 0.266$ ($$P \leq 0.566$$) was determined using the Youden index, which generated a predictive sensitivity and specificity of $93.8\%$ and $90.0\%$, respectively (Table 3).Fig. 2An ROC analysis of the nomogram for evaluating the effectiveness of metoprolol in POTS in children in the training set. The y-axis represents the sensitivity to predict the response to metoprolol; the x-axis represents the specificity to predict the response to metoprolol. The 45° reference line of the chart indicates that the sensitivity and the specificity are equal to $50\%$. The area under the curve was 0.970 with a $95\%$ confidence interval of 0.942–0.998. ROC receiver-operating characteristic curve, POTS postural tachycardia syndrome, AUC area under the curve ## Establishment of a nomogram in the training set A nomogram model was constructed based on binary logistic regression analysis (Fig. 3). The model revealed that the effective rate of metoprolol treatment increased with the prolongation of Pcmax and Tped and the shortening of QTcmin before treatment. Fig. 3Nomogram for predicting the efficacy of metoprolol in children with POTS in the training set. To estimate the response to metoprolol, the patient score for each axis is marked, a line perpendicular to the point axis is drawn, and the points for all variables are summed. Next, the sum is marked on the total point axis, and a line perpendicular to the probability axis is drawn. POTS postural tachycardia syndrome, Pcmax the maximum value of P wave duration in 12 leads of electrocardiogram after correction, ms millisecond, QTcmin the minimum value of QT interval in 12 leads of electrocardiogram after correction, Tped T-peak-to-T-end dispersion ## Evaluation of the model in the training set The ROC curve developed by the predictive model was the same as described earlier in the HL test, and the χ2 of the prediction model was 0.9196 ($$P \leq 0.6314$$ > 0.05). The corrected C-index was 0.961 after evaluation. The calibration curve of the nomogram was drawn (Fig. 4). The calibration curve and standard curve had a good fit. Fig. 4Calibration of the nomogram for predicting the efficacy of metoprolol in children with POTS in the training set. The x-axis shows the predicted probability of metoprolol response, and the y-axis shows the observed probability of metoprolol response. The ideal line means that the predicted and actual probabilities of the model agree perfectly. The apparent line indicates the actual performance of the prediction model in the training set. The bias-corrected line indicates the performance of the prediction model in the training set after the correction of the over-fitting situation. The calibration curve and standard curve have a good fit. POTS postural tachycardia syndrome ## Internal validation and external validation Finally, we performed internal validation by bootstrap repeated sampling, having an accuracy of $90.2\%$ and a kappa value of 0.769. In the validation set of children with POTS, the AUC for the prediction model was 0.895, and the sensitivity and specificity were $90.9\%$ and $95.0\%$, respectively. The ROC curves and calibration curves are shown in Figs. 5 and 6.Fig. 5An ROC analysis of the nomogram for evaluating the effectiveness of metoprolol in POTS in children in the external validation set. The y-axis represents the sensitivity to predict the response to metoprolol; the x-axis represents the specificity to predict the response to metoprolol. The 45° reference line of the chart indicates that the sensitivity and the specificity are equal. The AUC was 0.895, and the sensitivity and specificity were $90.9\%$ and $95\%$, respectively. ROC receiver-operating characteristic curve, POTS postural tachycardia syndrome, AUC area under the curveFig. 6Calibration of the nomogram for predicting the efficacy of metoprolol in children with POTS in the external validation set. The x-axis shows the predicted probability of metoprolol response, and the y-axis shows the observed probability of metoprolol response. The ideal line means that the predicted and actual probabilities of the model agree perfectly. The apparent line indicates the actual performance of the prediction model in the validation set. The bias-corrected line indicates the performance of the prediction model in the validation set after the correction of the overfitting situation. The calibration curve and standard curve have a good fit. POTS postural tachycardia syndrome ## Discussion In this study, pre-treatment Pcmax, QTcmin, and Tped could be used to predict the effectiveness of metoprolol on pediatric POTS. A combined prediction of the three indices could more accurately predict the efficacy of metoprolol. A nomogram model was developed to predict the response to metoprolol among pediatric patients. Our constructed model exhibited excellent discrimination. The AUC was 0.970 ($95\%$ CI 0.942–0.998), yielding a predictive sensitivity and specificity of $93.8\%$ and $90.0\%$, respectively, at the $Y = 0.266$ ($$P \leq 0.566$$) cutoffs. The corrected C-index was 0.961. The results indicated that it was well calibrated, and the internally validated model was not overfitted with good clinical application value. In the external validation, the sensitivity and specificity of the prediction model were $90.9\%$ and $95.0\%$, respectively, which were similar to the results obtained in the training set, indicating that this predictive model had good practical value in the application. Pediatric POTS is a chronic syndrome that strongly affects the quality of life of patients. Current studies have reported that pathogenesis, including excessive sympathetic function or plasma catecholamine levels, impaired limb muscle pump function, vascular endothelial dysfunction, or effective circulating hypovolemia, could be involved in developing POTS [27]. Recently, Zhang et al. revealed that baseline norepinephrine was remarkably elevated among some cases with POTS, depicting a positive relationship with the severity of clinical manifestations [7]. In addition, the norepinephrine elimination mechanism is impaired among POTS patients with significant sympathetic activation [28, 29]. Therefore, attention has been given to the role of hyperadrenaline status in developing POTS. Metoprolol is a beta-adrenoceptor blocker that inhibits sympathetic nerve function [6]. However, due to the complex pathogenesis of POTS, empirical and nonselective use of metoprolol in pediatric POTS usually shows poor effectiveness [5]. Therefore, if the sympathetic hyperactivity or plasma hyperadrenaline status as the primary pathogenesis in patients with POTS can be effectively predicted before treatment, individualized, targeted treatment with β-adrenoceptor blockers would vastly improve the effectiveness of POTS. Many scholars have conducted relevant studies in the past, but they have their own limitations in terms of price, operability, index stability, and so on. It is indispensable to find convenient, easily accessible, non-invasive, simple, and affordable indicators to efficiently predict high sympathetic activity in pediatric POTS before treatment, so that individualized metoprolol treatment can be implemented among POTS patients. Electrocardiogram waveforms can reflect the effect of sympathetic nerves on the heart, and the autonomic nervous system primarily affects the depolarization and repolarization processes of the myocardium by secreting neurotransmitters and changing the ion distribution on the myocardial cell membrane surface [30–32]. The P wave is a potential change produced by atrial depolarization. During sympathetic stimulation, the action potential of the atrial muscle cell is shortened; the slope of phase 0 increases; the refractory period is compressed; the automaticity is elevated; the triggering activity is enhanced, and the P wave voltage, P wave maximum time, and P wave dispersion can be significantly increased [12, 33]. Hooper et al. [ 34] observed a significant and positive increase in P wave duration when rats inhaled allyl isothiocyanate (AITC), an experimental agent exciting the sympathetic nerves. Cheema et al. [ 12] observed that epinephrine infusion prolonged P wave duration in healthy volunteers. In contrast, atropine-based parasympathetic blockade resulted in a reduced P wave duration with excellent reproducibility among subjects (intraclass correlation coefficient 0.99). The above studies indicated that P wave changes were closely related to autonomic nervous function. In this study, the responders showed longer Pcmax, increased Pd, increased P wave amplitude, and shorter Pcmin before metoprolol treatment than the non-responders, indicating that the responders to metoprolol in children with POTS were more likely to have high sympathetic activity before treatment than the non-responders. Therefore, individualized metoprolol treatment would show a better effect on those cases. QT interval is the total duration of depolarization and repolarization of the myocardial cells, equivalent to the end of phase 0 to phase 3 of the action potential and is mainly formed by changing the ion transport across the membrane, which in turn affects the cardiac electrical activity and is modulated by an autonomic nervous system. Sympathetic stimulation causes elevated heart rate, shortened QT interval, and increased QTd; after vagus nerve activation, the heart rate decreases, while the QT interval is prolonged [13, 35]. According to Huang et al. [ 36], sympathetic denervation in a canine model significantly decreased HR and prolonged QT duration. Kittnar et al. [ 37] found that patients with diabetes and cardiac autonomic neuropathy were likely to have tachycardia, QRS, and QT interval shortening, often due to elevated sympathetic tone. Previous studies have shown significant changes within the QTc interval over 24 hours in healthy adults, with subjects awake with shorter QTc than at night [38], consistent with the circadian profile of circulating catecholamine concentrations [39]. The above studies depict that the QT interval is closely related to autonomic function. In this study, metoprolol responders had increased QTd and shorter QTcmin before treatment than non-responders, indicating that such indices would greatly value the individualized metoprolol treatment strategy. The Tpe interval refers to the duration between the T wave apex and end in ECG, represents the time from the submembranous myocardial repolarization end to the medial M cell repolarization end within the center during cardiac repolarization, and reflects the ECG index of the transmural ventricular repolarization dispersion. Tpe dispersion tends to reflect the spatial discordance of transmural dispersion due to inconsistent repolarization of the three layers of the ventricular wall at different sites. Yagishita et al. [ 40] reported that the stellate ganglia and the heart were exposed in Yorkshire pigs. Sympathetic nerve stimulation was performed unilaterally or bilaterally, and a significant prolongation of the Tpe interval was observed. Tanabe et al. [ 41] observed surface electrocardiogram changes before and after epinephrine infusion in 13 cases depicting long QT syndrome (LQTS) type 1 (LQT1) and 6 showing LQTS type 2 (LQT2), and found that epinephrine significantly enhanced the mean Tpe interval and dispersion during LQT1 and LQT2. The increased mean Tpe interval and dispersion caused by epinephrine were more significant among LQT1 cases than LQT2 cases, which could explain the greater sensitive clinical characteristics of LQT1 patients during sympathetic stimulation. Studies indicate that the Tpe interval and its changes are closely associated with high catecholamine levels and sympathetic nerve activity. In this study, Tpemax and Tped in responders were longer before metoprolol treatment than in non-responders; Tpemin was shorter than that in non-responders, and Tpe interval dispersion was enhanced, indicating that responders to metoprolol had high sympathetic activity before treatment. At the same time, metoprolol treatment was more effective in these patients, further verifying the hypothesis that baseline Tpe dispersion could be used as an early predictive indicator of the effectiveness of metoprolol treatment in POTS. Therefore, P wave duration and amplitude, QT interval, and Tpe dispersion could reflect autonomic function. When sympathetic nerves are excited, heart rate increases; P wave amplitude increases; P wave duration is prolonged, and QT interval shortens. Simultaneously, T waves show inversion or flatness; the Tpe interval is prolonged, and the dispersion increases [12–15]. Based on the clinical significance of the ECG indicators and the results of multicollinearity analysis, pre-treatment Pcmax, QTcmin, Tped from ECG, and demographic characteristics (gender, age, and BMI) were examined through binary logistic regression using the backward stepwise method. A nomogram model to predict the effect of metoprolol treatment in pediatric and adolescent POTS was constructed according to pre-treatment ECG indicators. The model depicted that pre-treatment Pcmax, QTcmin, and Tped showed a better C-index level in the prediction and presented a better correlation with the actual occurrence. The internal validation and external validation also revealed that the nomogram model could effectively predict the effective rate and depicted a better clinical application value. The present work provided the first nomogram model to predict the effectiveness of metoprolol in pediatric POTS. This predictive model depicted good accuracy and consistency in both samples built into the model and samples validated externally. The measurement of ECG indicators has advantages, such as noninvasiveness, easy operation, and good cost-effectiveness. Meanwhile, the nomogram, a simple visualized graph, enables its application in clinical practice to be more convenient [42]. The above model would provide clinicians with a personalized metoprolol treatment strategy for children and adolescents with POTS. However, there were some limitations to the study. This was a retrospective and single-center-based study. Therefore, prospectively randomized and controlled studies are needed in the future to assist in predicting early optimization in children with POTS. In conclusion, in the present pilot study, for the first time, we developed a high-precision nomogram model to assist clinicians during the early decision of metoprolol therapy for children and adolescents suffering from POTS, which is significant in improving the therapeutic ability of POTS among children and adolescents. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOC 67 kb) ## References 1. Vernino S, Bourne KM, Stiles LE, Grubb BP, Fedorowski A, Stewart JM. Postural orthostatic tachycardia syndrome (POTS): state of the science and clinical care from a 2019 National Institutes of Health Expert Consensus Meeting—part 1. Auton Neurosci (2021.0) 235 102828. DOI: 10.1016/j.autneu.2021.102828 2. Bryarly M, Phillips LT, Fu Q, Vernino S, Levine BD. Postural orthostatic tachycardia syndrome: JACC focus seminar. J Am Coll Cardiol (2019.0) 73 120-128. DOI: 10.1016/j.jacc.2018.11.059 3. Lin J, Han Z, Li X, Ochs T, Zhao J, Zhang X. Risk factors for postural tachycardia syndrome in children and adolescents. PLoS One (2014.0) 9 e113625. DOI: 10.1371/journal.pone.0113625 4. Zadourian A, Doherty TA, Swiatkiewicz I, Taub PR. Postural orthostatic tachycardia syndrome: prevalence, pathophysiology, and management. Drugs (2018.0) 78 983-994. DOI: 10.1007/s40265-018-0931-5 5. Deng X, Zhang Y, Liao Y, Du J. Efficacy of beta-blockers on postural tachycardia syndrome in children and adolescents: a systematic review and meta-analysis. Front Pediatr (2019.0) 7 460. DOI: 10.3389/fped.2019.00460 6. Raj SR, Black BK, Biaggioni I, Paranjape SY, Ramirez M, Dupont WD. Propranolol decreases tachycardia and improves symptoms in the postural tachycardia syndrome: less is more. Circulation (2009.0) 120 725-734. DOI: 10.1161/CIRCULATIONAHA.108.846501 7. Zhang Q, Chen X, Li J, Du J. Orthostatic plasma norepinephrine level as a predictor for therapeutic response to metoprolol in children with postural tachycardia syndrome. J Transl Med (2014.0) 12 249. DOI: 10.1186/s12967-014-0249-3 8. Zhao J, Du S, Yang J, Lin J, Tang C, Du J. Usefulness of plasma copeptin as a biomarker to predict the therapeutic effectiveness of metoprolol for postural tachycardia syndrome in children. Am J Cardiol (2014.0) 114 601-605. DOI: 10.1016/j.amjcard.2014.05.039 9. Lin J, Han Z, Li H, Chen SY, Li X, Liu P. Plasma C-type natriuretic peptide as a predictor for therapeutic response to metoprolol in children with postural tachycardia syndrome. PLoS One (2015.0) 10 e0121913. DOI: 10.1371/journal.pone.0121913 10. Wang Y, Zhang C, Chen S, Liu P, Wang Y, Tang C. Heart rate variability predicts therapeutic response to metoprolol in children with postural tachycardia syndrome. Front Neurosci (2019.0) 13 1214. DOI: 10.3389/fnins.2019.01214 11. Wang Y, Sun Y, Zhang Q, Zhang C, Liu P, Wang Y. Baseline corrected QT interval dispersion is useful to predict effectiveness of metoprolol on pediatric postural tachycardia syndrome. Front Cardiovasc Med (2022.0) 8 808512. DOI: 10.3389/fcvm.2021.808512 12. Wang S, Zou R, Cai H, Wang Y, Ding Y, Tan C. Heart Rate and heart rate difference predicted the efficacy of metoprolol on postural tachycardia syndrome in children and adolescents. J Pediatr (2020.0) 224 110-114. DOI: 10.1016/j.jpeds.2020.05.017 13. Cheema AN, Ahmed MW, Kadish AH, Goldberger JJ. Effects of autonomic stimulation and blockade on signal-averaged P wave duration. J Am Coll Cardiol (1995.0) 26 497-502. DOI: 10.1016/0735-1097(95)80028-F 14. Imamoglu EY, Oztunc F, Eroglu AG, Onal H, Guzeltas A. Dispersion of the P wave as a test for cardiac autonomic function in diabetic children. Cardiol Young (2008.0) 18 581-585. DOI: 10.1017/S1047951108003089 15. Smith AH, Norris KJ, Roden DM, Kannankeril PJ. Autonomic tone attenuates drug-induced QT prolongation. J Cardiovasc Electrophysiol (2007.0) 18 960-964. DOI: 10.1111/j.1540-8167.2007.00901.x 16. Amoozgar H, Hosseiniasl M. T-peak to T-end abnormality in pediatric patients with syncope. Iran J Pediatr (2012.0) 22 385-391. PMID: 23400391 17. Brignole M, Moya A, de Lange FJ, Deharo JC, Elliott PM, Fanciulli A. 2018 ESC Guidelines for the diagnosis and management of syncope. Eur Heart J (2018.0) 39 1883-1948. DOI: 10.1093/eurheartj/ehy037 18. Rea NA, Campbell CL, Cortez MM. Quantitative assessment of autonomic symptom burden in Postural tachycardia syndrome (POTS). J Neurol Sci (2017.0) 377 35-41. DOI: 10.1016/j.jns.2017.03.032 19. Zhang Q, Liao Y, Tang C, Du J, Jin H. Twenty-four-hour urinary sodium excretion and postural orthostatic tachycardia syndrome. J Pediatr (2012.0) 161 281-284. DOI: 10.1016/j.jpeds.2012.01.054 20. Tao CY, Li HX, Li XY, Tang CS, Jin HF, Du JB. Hemodynamic changes in standing-up test of children and adolescents with postural tachycardia syndrome. Beijing Da Xue Xue Bao Yi Xue Ban (2019.0) 51 414-421. PMID: 31209411 21. Plash WB, Diedrich A, Biaggioni I, Garland EM, Paranjape SY, Black BK. Diagnosing postural tachycardia syndrome: comparison of tilt testing compared with standing hemodynamics. Clin Sci (Lond) (2013.0) 124 109-114. DOI: 10.1042/CS20120276 22. 22.Philip P, Rajeev M, Rahul K, Peter AN. Podrid's real—world ECGs. In: The basics: a master's approach to the art and practice of clinical ECG interpretation, vol 1. Minneapolis, Minnesota, USA: Cardiotext Publishing, LLC. 2013. 23. Uberoi A, Jain NA, Perez M. Early repolarization in an ambulatory clinical population. Circulation (2011.0) 124 2208-2214. DOI: 10.1161/CIRCULATIONAHA.111.047191 24. Aizawa Y, Sato A, Watanabe H. Dynamicity of the J-wave in idiopathic ventricular fibrillation with a special reference to pause-dependent augmentation of the J-wave. J Am Coll Cardiol (2012.0) 59 1948-1953. DOI: 10.1016/j.jacc.2012.02.028 25. Liao Y, Du J. Pathophysiology and individualized management of vasovagal syncope and postural tachycardia syndrome in children and adolescents: an update. Neurosci Bull (2020.0) 36 667-681. DOI: 10.1007/s12264-020-00497-4 26. Chen L, Wang L, Sun J, Qin J, Tang C, Jin H. Midodrine hydrochloride is effective in the treatment of children with postural orthostatic tachycardia syndrome. Circ J (2011.0) 75 927-931. DOI: 10.1253/circj.CJ-10-0514 27. Thanavaro JL, Thanavaro KL. Postural orthostatic tachycardia syndrome: diagnosis and treatment. Heart Lung (2011.0) 40 554-560. DOI: 10.1016/j.hrtlng.2009.12.014 28. Crnosija L, Krbot Skoric M, Lovric M, Junakovic A, Miletic V, Alfirev RS. Differences in neurohumoral and hemodynamic response to prolonged head-up tilt between patients with high and normal standing norepinephrine forms of postural orthostatic tachycardia syndrome. Auton Neurosci (2017.0) 205 110-114. DOI: 10.1016/j.autneu.2017.05.007 29. Crnosija L, Krbot Skoric M, Adamec I, Lovric M, Junakovic A, Mismas A. Hemodynamic profile and heart rate variability in hyperadrenergic versus nonhyperadrenergic postural orthostatic tachycardia syndrome. Clin Neurophysiol (2016.0) 127 1639-1644. DOI: 10.1016/j.clinph.2015.08.015 30. Novak P. Autonomic disorders. Am J Med (2019.0) 132 420-436. DOI: 10.1016/j.amjmed.2018.09.027 31. Lujan HL, Rivers JP, DiCarlo SE. Complex and interacting influences of the autonomic nervous system on cardiac electrophysiology in conscious mice. Auton Neurosci (2016.0) 201 24-31. DOI: 10.1016/j.autneu.2016.08.017 32. Rosenberg AA, Weiser-Bitoun I, Billman GE, Yaniv Y. Signatures of the autonomic nervous system and the heart's pacemaker cells in canine electrocardiograms and their applications to humans. Sci Rep (2020.0) 10 9971. DOI: 10.1038/s41598-020-66709-z 33. Chen LY, Ribeiro ALP, Platonov PG, Cygankiewicz I, Soliman EZ, Gorenek B. P wave parameters and indices: a critical appraisal of clinical utility, challenges, and future research-a consensus document endorsed by the international society of electrocardiology and the international society for holter and noninvasive electrocardiology. Circ Arrhythm Electrophysiol (2022.0) 15 e010435. PMID: 35333097 34. Hooper JS, Taylor-Clark TE. Irritant inhalation evokes P wave morphological changes in spontaneously hypertensive rats via reflex modulation of the autonomic nervous system. Front Physiol (2021.0) 12 642299. DOI: 10.3389/fphys.2021.642299 35. Krasemann T, Strompen C, Blumenberg J, Gehrmann J, Burkhardtsmaier G, Vogt J. Changes of the corrected QT interval in healthy boys and girls over day and night. Eur Heart J (2009.0) 30 202-208. DOI: 10.1093/eurheartj/ehn452 36. Bing H, Lilei Y, Bo H, Songyun W, Zhibing L, Kai L. Sympathetic denervation of heart and kidney induces similar effects on ventricular electrophysiological properties. EuroIntervention (2015.0) 11 598-604. DOI: 10.4244/EIJV11I5A119 37. Kittnar O. Electrocardiographic changes in diabetes mellitus. Physiol Res (2015.0) 64 S559-S566. DOI: 10.33549/physiolres.933230 38. Molnar J, Zhang F, Weiss J, Ehlert FA, Rosenthal JE. Diurnal pattern of QTc interval: how long is prolonged? Possible relation to circadian triggers of cardiovascular events. J Am Coll Cardiol (1996.0) 27 76-83. DOI: 10.1016/0735-1097(95)00426-2 39. Barnes P, FitzGerald G, Brown M, Dollery C. Nocturnal asthma and changes in circulating epinephrine, histamine, and cortisol. N Engl J Med (1980.0) 303 263-267. DOI: 10.1056/NEJM198007313030506 40. Yagishita D, Chui RW, Yamakawa K, Rajendran PS, Ajijola OA, Nakamura K. Sympathetic nerve stimulation, not circulating norepinephrine, modulates T-peak to T-end interval by increasing global dispersion of repolarization. Circ Arrhythm Electrophysiol (2015.0) 8 174-185. DOI: 10.1161/CIRCEP.114.002195 41. Tanabe Y, Inagaki M, Kurita T, Nagaya N, Taguchi A, Suyama K. Sympathetic stimulation produces a greater increase in both transmural and spatial dispersion of repolarization in LQT1 than LQT2 forms of congenital long QT syndrome. J Am Coll Cardiol (2001.0) 37 911-919. DOI: 10.1016/S0735-1097(00)01200-6 42. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med (2015.0) 162 55-63. DOI: 10.7326/M14-0697
--- title: 'Spinal pseudoarthrosis following osteoporotic vertebral fracture: prevalence, risk factors, and influence on patients’ activities of daily living 1 year after injury' authors: - Norimitsu Wakao - Yoshihito Sakai - Tsuyoshi Watanabe - Naoaki Osada - Takaya Sugiura - Hiroki Iida - Yuto Ozawa - Kenta Murotani journal: Archives of Osteoporosis year: 2023 pmcid: PMC10060279 doi: 10.1007/s11657-023-01236-8 license: CC BY 4.0 --- # Spinal pseudoarthrosis following osteoporotic vertebral fracture: prevalence, risk factors, and influence on patients’ activities of daily living 1 year after injury ## Abstract ### Purpose To investigate the prevalence and risk factors and influence of pseudoarthrosis on activities of daily living (ADL) of patients with osteoporotic vertebral fracture (OVF). ### Methods Spinal pseudoarthrosis is defined as the presence of a cleft in the vertebral body on a lateral X-ray image in the sitting position at 1 year after admission. Of the total 684 patients treated for OVF between January 2012 and February 2019 at our institution, 551 patients (mean age, 81.9 years; a male-to-female ratio, 152:399) who could be followed up to 1 year were included in this study. Prevalence, risk factors, and influence of pseudoarthrosis on the ADL of patients as well as fracture type and location were investigated. Pseudoarthrosis was set as the objective variable. Total bone mineral density, skeletal muscle mass index, sex, age, history of osteoporosis treatment, presence of dementia, vertebral kyphosis angle, fracture type (presence of posterior wall injury), degree of independence before admission, history of steroid use, albumin level, renal function, presence of diabetes, and diffuse idiopathic skeletal hyperostosis were set as explanatory variables for multivariate analysis of the influence of pseudoarthrosis on the walking ability and ADL independence before and 1 year after OVF. ### Results In total, 54 ($9.8\%$) patients were diagnosed with pseudarthrosis 1 year after injury (mean age, 81.3 ± 6.5 years; male-to-female ratio, 18:36). BKP was performed in nine patients who did not develop pseudoarthrosis after 1 year. In the multivariate analysis, only the presence of posterior wall injury was significantly correlated with the presence of pseudoarthrosis (OR = 2.059, $$p \leq 0.039$$). No significant difference was found between the pseudarthrosis group and the non-pseudarthrosis group in terms of walking ability and ADL independence at 1 year. ### Conclusions The prevalence of pseudoarthrosis following OVF was $9.8\%$, and its risk factor was posterior wall injury. The BKP group was not included in the pseudoarthrosis group, which may have led to an underestimation of the prevalence of pseudoarthrosis. ### Summary The prevalence, risk factors, and influence of spinal pseudoarthrosis on patients’ ADL following osteoporotic vertebral fracture (OVF) were investigated. Pseudoarthrosis occurs in $9.8\%$ 1 year after the injury in patients with OVF. Posterior wall injury was the risk factor of pseudoarthrosis. ## Introduction The prevalence of osteoporotic vertebral fractures (OVFs) is increasing with the increase in elderly population [1–4]. Bone fusion and stabilization are achieved naturally in most OVF cases [5, 6]. However, orthopedic doctors occasionally come across patients in whom early stabilization has not been achieved [7–13], and the technical terms for this pathology have not been defined clearly. The Japanese clinical guideline for OVFs was revised in 2012, and pseudoarthrosis of the spine was defined as the lack of visible signs of healing 12 months after the onset of the fracture (Fig. 1). In long-term follow-up of patients with OVFs, pseudoarthrosis developed in many cases 1 year after injury without any adverse complications [10, 14], and the clinical significance and frequency of spinal pseudoarthrosis remain unclear. This study aimed to clarify the clinical significance of vertebral pseudoarthrosis by investigating the rate of spinal pseudoarthrosis following OVFs, risk factors for pseudoarthrosis, and influence of pseudoarthrosis on walking ability and independence in daily living after 1 year in patients treated at our institution. Fig. 1Images of patients with pseudoarthrosis of L1 fracture. MR T1-weighted image (A), T2-weighted image (B), and STIR image (C) at injury. D shows lateral radiogram at injury. E shows lateral radiogram 1 year after the injury ## Materials and methods Eligible subjects were patients with acute OVFs who were hospitalized and treated in our institution between January 2012 and February 2019. In our institution, patients with acute OVFs are treated as inpatients (Fig. 2). After hospitalization, the treatment strategy for OVFs is as follows: [1] bed rest and rehabilitation on the bed until patients wear a made-to-order brace, [2] bed rest until the pain during body movements is relieved (whether or not the patient can change positions by himself/herself), [3] wearing a brace of sufficient length and starting walking training if the pain during body movement is improved, [4] starting or continuing osteoporosis treatment, [5] balloon kyphoplasty (BKP) should be considered if pain with movements does not improve after 2–4 weeks, and vertebral body damage is considered severe on imaging (T1, diffuse low on magnetic resonance imaging (MRI); T2, fluid accumulation, posterior wall injury on MRI) [15].Fig. 2Baseline characteristics and flowchart of treatment for OVF The survey items were as follows: [1] presence of pseudoarthrosis (Fig. 1) (spinal pseudoarthrosis is defined as the presence of a cleft in the vertebral body on a lateral X-ray image in the sitting position at 1 year after admission); [2] ADL assessment included walking ability (independent/assisted/unable to walk) before and 1 year after injury and level of ADL independence (independent/non-independent: The level of independence in daily living was determined based on the classification of care level evaluated by long-term care insurance system in Japan, which all seniors 65 years of age and older are enrolled in.); and [3] patient background factors such as whole-body bone mass measured by dual-energy X-ray absorptiometry, skeletal muscle mass index, sex, age, history of osteoporosis treatment before admission, presence of dementia (The presence or absence of dementia was determined by the mini-mental state examination (MMSE); a score of 27 or higher was considered normal, and a score of less than 27 was diagnosed as dementia.), kyphotic angle of the fractured vertebrae, presence of posterior wall injury on MRI, degree of independence before admission, history of steroid medication, albumin level, renal function (eGFR), diabetes, and diffuse idiopathic skeletal hyperostosis (DISH). The following data were also obtained: degree of independence, steroid history, albumin level, eGFR, presence of diabetes treatment, and presence of DISH (fusion of consecutive four vertebrae on frontal and lateral X-ray images of the vertebrae). ## Statistical analysis From these survey items, we first investigated the prevalence of pseudoarthrosis. Then, the influence of pseudoarthrosis on walking ability was investigated by comparing the pseudoarthrosis group and the non-pseudoarthrosis group (Mann–Whitney U test). Factors influencing pseudoarthrosis were investigated by univariate and multivariate logistic analyses with pseudoarthrosis as the objective variable and the above survey items as explanatory variables. Items with $p \leq 0.10$ in the univariate logistic analysis were selected, and multivariate logistic analysis was performed using these variables as explanatory variables. SAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used, and the significance level was set at $$p \leq 0.05.$$ ## Results Of the 684 patients treated for OVF between January 2012 and February 2019, 551 patients (mean age, 81.8 ± 7.6 years; male-to-female ratio, 152:399) were followed up 1 year after the injury and eligible in this study. BKP was performed in nine patients who had poor pain relief after conservative treatment and hospitalization and developed advanced vertebral damage on imaging. These patients did not develop pseudoarthrosis after 1 year. The mean duration of bed rest after admission for the 542 patients who did not undergo BKP was 6.3 days. No patients developed osteoporotic-delayed vertebral collapse (ODVC) and paralysis requiring surgical treatment by 1 year. Among all patients, 54 ($9.8\%$) were diagnosed with pseudoarthrosis after 1 year (mean age, 81.3 ± 6.5 years; male-to-female ratio, 18:36) (Table 1). Fracture location with and without pseudoarthrosis is shown in Fig. 3. Fractures occurred more frequently in the thoracolumbar site and less frequently in the upper thoracic and lower lumbar spine. The number of pseudoarthrosis was also higher in the thoracolumbar site but could not be statistically significant due to differences of number of occurrences. The walking ability of the patients before and 1 year after injury in the pseudoarthrosis and non-pseudoarthrosis groups is shown in Fig. 4. Although a certain number of patients in each group had decreased walking ability and became unable to walk, the presence of a pseudoarthrosis did not significantly affect the decrease in walking ability ($$p \leq 0.52$$). Similarly, the results for ADL independence are shown in Fig. 5. Although a certain number of patients in each group showed a decrease in ADL independence, as with the walking ability, the presence of a pseudoarthrosis had no significant effect on the decrease in ADL independence ($$p \leq 0.48$$). The results of the univariate and multivariate analyses of factors influencing pseudoarthrosis are shown in Table 2. In the univariate analysis, the vertebral kyphosis angle in patients with fracture at admission (OR = 0.968, $$p \leq 0.009$$) and posterior wall injury (OR = 2.561, $$p \leq 0.005$$) were significant (OR = 2.059, $$p \leq 0.039$$); however, only posterior wall injury was significantly correlated with the presence of pseudoarthrosis in the multivariate analysis. Figure 6 shows the administration of osteoporosis medication before injury and 1 year after injury. Table 1Comparison of pseudoarthrosis and non-pseudoarthrosis groupsFig. 3Fracture location and pseudoarthrosisFig. 4Results of walking capability before and 1 year after injury in the pseudoarthrosis group and non-pseudoarthrosis groupFig. 5Results of autonomy before and 1 year after injury in the pseudoarthrosis group and non-pseudoarthrosis groupTable 2Results of univariate and multivariate analysesAbbreviation: SMI; Skeletal muscle mass index eGFR; estimated glomerular filtration rate,DISH; diffuse idiopathic skeletal hyperostosisFig. 6Osteoporosis medication before and 1 year after injury ## Discussion Based on the results of this study, the pseudoarthrosis rate was $9.8\%$. Compared with previous reports in Japan, the frequency was slightly low [7, 8, 10, 14, 16]. This result was possibly influenced by two factors. First, our hospitalization policy for all patients with OVFs has led to good outcomes of conservative therapy. Inpatient treatment for OVFs is available at a few facilities and is especially difficult in high care units in emergency hospitals. This may naturally lead to missed cases, delayed diagnosis, inadequate initial treatment, and inappropriate orthotic prescriptions. Second, BKP was performed in nine cases [15]. These nine patients had vertebral stabilization after 1 year and did not belong to the pseudoarthrosis group. Even considering these differences in study backgrounds, it is confirmed that pseudoarthrosis occurs 1 year after OVF in approximately 10–$20\%$ of cases. Non-union, fusion failure, and pseudoarthrosis following OVFs are considered factors with poor functional prognosis; indeed, some clinical studies have investigated patients with residual clefts in the vertebral body after a certain period of conservative treatment [7, 10, 12, 13, 16, 17]. However, in long-term follow-up of fracture cases, there are patients who developed pseudoarthrosis after 1 year without any adverse complications. When spine surgeons reviewed cases of ODVC requiring major surgical treatment [9], all cases involved vertebral instability several months after the injury, and vertebral instability does not necessarily result in adverse events such as intractable pain and paralysis. ODVC occurs within 6 months after the injury, and non-fused vertebrae at 1 year after injury (pseudoarthrosis) may not result in significant adverse events. Based on these clinical questions and characteristics, this study focused on two clinical influences of pseudoarthrosis: walking ability and independence in daily living after 1 year. The results showed that pseudoarthrosis did not have a significant negative effect on both walking ability and independence in daily living. This is new knowledge that has not been reported previously. Although previous studies have mentioned back pain and health-related quality of life and have reported inferior outcomes in the pseudoarthrosis group compared with the non-pseudoarthrosis group, influences on walking ability or independence in daily living were not investigated. The results of this study are of great clinical significance from the viewpoint that the actual functional prognosis after 1 year is more important in the case group of patients aged 80 years, since diseases other than OVFs have a diverse effect. The results of the multivariate analysis showed that only posterior wall injury significantly correlated with the presence of pseudoarthrosis, which was also consistent with previous reports [7, 8, 10, 13, 14]. Recently, MR images in the early phase of OVFs predict pseudoarthrosis [14, 15], and diffuse low on T1-weighted images and diffuse low with fluid retention on T2-weighted images may be useful prognostic factors for poorer clinical outcome following OVFs. Our study focused on examining more confounding factors and examined in detail patient factors that have not been examined in many previous case studies. The result that only the posterior vertebral wall injury on MRI was significant is very interesting and reinforces the possibility that the severity of the fracture and the limitation of conservative treatment are determined at the time of diagnosis, as has been reported in the past. The authors plan to add MRI signal changes as one of the explanatory variables in the future. Patients with OVF and posterior wall injuries are predisposed to have difficulty obtaining subsequent vertebral fusion, and, if OVF-derived symptoms do not improve after careful conservative treatment, minimally invasive surgical intervention, including BKP, is necessary before severe vertebral deformity or neurological manifest develops [15]. ## Strength and limitation of this study The study included many OVF cases, and 551 of 684 ($80.6\%$) were functionally evaluated 1 year after injury. The number of cases and the small number of dropouts are strengths of the study, which increase the reliability of the analysis results but also have limitations. First, the BKP group, which is supposed to have a poor functional prognosis, was not included in the pseudoarthrosis group. This may have led to an underestimation of the prevalence of pseudoarthrosis in this study. Second, assessment of the effect of pseudoarthrosis on the patient was limited. Essentially, there were not many cases in which OVF caused significant functional decline 1 year after the injury. Specifically, cases in which functional assessment at 1 year was not possible (133 cases, $19.4\%$ in this study) probably included deaths or cases in which patients had to be transferred or institutionalized to nursing homes due to functional decline. Furthermore, proof of functional decline due to OVF must exclude the influence of other diseases as much as possible. In the present study, the patients were evaluated in terms of walking ability and independence in daily living, but more detailed evaluation indices (pain, walking speed, walking distance, etc.) would have made a difference. Third, the method of evaluating pseudoarthrosis was limited. There is no uniform method for cleft evaluation 1 year after injury, and the pseudoarthrosis rate would be higher if computed tomography or functional radiographic imaging with anterior and posterior bending were used as evaluation methods. This study employed lateral radiography in the sitting position and a method of imaging that is thought to have the lowest pseudoarthrosis diagnosis rate, which may have missed true pseudoarthrosis. ## Conclusions Spinal pseudoarthrosis, which occurs at a certain rate after OVFs, was investigated based on the revised clinical guideline for OVFs in 2012. Pseudoarthrosis was observed in $9.8\%$ of all cases. The influence of pseudoarthrosis was examined in terms of two aspects: walking ability and independence in daily living, but no difference was found between the pseudoarthrosis group and the non-pseudoarthrosis group. The inclusion of nine patients who underwent BKP in the non-pseudarthrosis group might have led to bias. The only significant risk factor that affected pseudoarthrosis was posterior wall injury following OVF. ## References 1. 1.Bengner U, Johnell O, Redlund-Johnell I (1988) Changes in the incidence of fracture of the upper end of the humerus during a 30-year period. A study of 2125 fractures. Clin Orthop Relat Res 179–182 2. Cauley JA, Zmuda JM, Wisniewski SR, Krishnaswami S, Palermo L, Stone KL, Black DM, Nevitt MC. Bone mineral density and prevalent vertebral fractures in men and women. Osteoporos Int (2004.0) 15 32-37. DOI: 10.1007/s00198-003-1462-8 3. Kanis JA, Johnell O, Oden A, Borgstrom F, Zethraeus N, De Laet C, Jonsson B. The risk and burden of vertebral fractures in Sweden. Osteoporos Int (2004.0) 15 20-26. DOI: 10.1007/s00198-003-1463-7 4. O'Neill TW, Felsenberg D, Varlow J, Cooper C, Kanis JA, Silman AJ. The prevalence of vertebral deformity in European men and women: the European vertebral osteoporosis study. J Bone Miner Res (1996.0) 11 1010-1018. DOI: 10.1002/jbmr.5650110719 5. 5.Lee YL, Yip KM (1996) The osteoporotic spine. Clin Orthop Relat Res 91–97 6. Silverman SL. The clinical consequences of vertebral compression fracture. Bone (1992.0) 13 S27-31. DOI: 10.1016/8756-3282(92)90193-Z 7. Hoshino M, Nakamura H, Terai H, Tsujio T, Nabeta M, Namikawa T, Matsumura A, Suzuki A, Takayama K, Takaoka K. Factors affecting neurological deficits and intractable back pain in patients with insufficient bone union following osteoporotic vertebral fracture. Eur Spine J (2009.0) 18 1279-1286. DOI: 10.1007/s00586-009-1041-6 8. 8.Tsujio T, Nakamura H, Terai H, et al. (2011) Characteristic radiographic or magnetic resonance images of fresh osteoporotic vertebral fractures predicting potential risk for nonunion: a prospective multicenter study. Spine (Phila Pa 1976) 36:1229–1235 9. Wakao N, Takeuchi M, Riew DK, Hirasawa A, Imagama S, Kawanami K, Matsuo T, Murotani K, Deie M. Effect of an intensive conservative therapy with daily teriparatide administration and rehabilitation for osteoporotic delayed vertebral collapse and paralysis. Medicine (Baltimore) (2018.0) 97 e10906. DOI: 10.1097/MD.0000000000010906 10. Muratore M, Ferrera A, Masse A, Bistolfi A. Osteoporotic vertebral fractures: predictive factors for conservative treatment failure. A systematic review Eur Spine J (2018.0) 27 2565-2576. DOI: 10.1007/s00586-017-5340-z 11. 11.Heini PF (2005) The current treatment--a survey of osteoporotic fracture treatment. Osteoporotic spine fractures: the spine surgeon's perspective. Osteoporos Int 16 Suppl 2:85–92 12. McKiernan F, Jensen R, Faciszewski T. The dynamic mobility of vertebral compression fractures. J Bone Miner Res (2003.0) 18 24-29. DOI: 10.1359/jbmr.2003.18.1.24 13. Takahashi S, Hoshino M, Takayama K. Time course of osteoporotic vertebral fractures by magnetic resonance imaging using a simple classification: a multicenter prospective cohort study. Osteoporos Int (2017.0) 28 473-482. DOI: 10.1007/s00198-016-3737-x 14. 14.Inose H, Kato T, Ichimura S, et al. (2020) Risk factors of nonunion after acute osteoporotic vertebral fractures: a prospective multicenter cohort study. Spine (Phila Pa 1976) 45:895–902 15. 15.Hoshino M, Takahashi S, Yasuda H, et al. (2019) Balloon kyphoplasty versus conservative treatment for acute osteoporotic vertebral fractures with poor prognostic factors: propensity score matched analysis using data from two prospective multicenter studies. Spine (Phila Pa 1976) 44:110–117 16. 16.Matsumoto T, Hoshino M, Tsujio T, et al. (2012) Prognostic factors for reduction of activities of daily living following osteoporotic vertebral fractures. Spine (Phila Pa 1976) 37:1115–1121 17. 17.Inose H, Kato T, Shirasawa S, Takahashi S, Hoshino M, Yamato Y, Matsukura Y, Hirai T, Yoshii T, Okawa A (2021) Time course of acute vertebral fractures: a prospective multicenter cohort study. J Clin Med 10:
--- title: 'The influence of cyclooxygenase inhibitors on kynurenic acid production in rat kidney: a novel path for kidney protection?' authors: - Izabela Zakrocka - Wojciech Załuska journal: Pharmacological Reports year: 2023 pmcid: PMC10060280 doi: 10.1007/s43440-023-00460-w license: CC BY 4.0 --- # The influence of cyclooxygenase inhibitors on kynurenic acid production in rat kidney: a novel path for kidney protection? ## Abstract ### Background Kidney diseases have become a global health problem, affecting about $15\%$ of adults and being often under-recognized. Immunological system activation was shown to accelerate kidney damage even in inherited disorders. The kynurenine pathway is the main route of tryptophan degradation. A metabolite of kynurenine (KYN), kynurenic acid (KYNA), produced by kynurenine aminotransferases (KATs), was reported to affect fluid and electrolyte balance as a result of natriuresis induction. The accumulation of KYNA was shown in patients with impaired kidney function and its level was related to the degree of kidney damage. Cyclooxygenase (COX) inhibitors are well-known analgesics and most of them demonstrate an anti-inflammatory effect. Their main mechanism of action is prostaglandin synthesis blockade, which is also responsible for their nephrotoxic potential. Since the KYN pathway is known to remain under immunological system control, the purpose of this study was to analyze the effect of 9 COX inhibitors on KYNA production together with KATs’ activity in rat kidneys in vitro. ### Methods Experiments were carried out on kidney homogenates in the presence of L-KYN and the selected compound in 6 various concentrations. ### Results Among the examined COX inhibitors only acetaminophen did not change KYNA production in rat kidneys in vitro. Additionally, acetaminophen did not affect the activity of KAT I and KAT II, whereas acetylsalicylic acid and ibuprofen inhibited only KAT II. The remaining COX inhibitors decreased the activity of both KATs in rat kidneys in vitro. ### Conclusion Our study provides novel mechanisms of COX inhibitors action in the kidney, with possible implications for the treatment of kidney diseases. ## Introduction Kidney diseases have become a global health burden, significantly increasing cardiovascular and all-cause morbidity and mortality [1]. Chronic kidney disease (CKD), predominantly caused by diabetes mellitus, is known to affect more than 800 million individuals worldwide [2]. Due to the higher number of CKD risk factors, especially obesity, and its complications, CKD is predicted to be the fifth cause of death since 2040 [2]. The increasing prevalence of kidney disorders and their impact on global health indicate the need for exploring kidney damage mechanisms and nephroprotection methods. Immune system dysregulation has been implicated in the pathogenesis of various kidney disorders, especially in different types of glomerulonephritis [3], acute kidney injury (AKI) [4], and CKD [5]. Interestingly, according to recently published studies, the role of inflammation in the course of metabolic or congenital diseases, like autosomal dominant polycystic kidney disease (ADPKD) [6] has been pointed out. Nonselective blockade of prostaglandin (PG) synthesis through cyclooxygenase (COX) inhibition has been reported to slow down the growth of cysts in the animal model of ADPKD [7]. Kynurenine (KYN) pathway is the main route of tryptophan degradation. Constitutive enzyme called tryptophan 2,3-dioxygenase (TDO) converts tryptophan to KYN in the liver, whereas another enzyme, indoleamine 2,3-dioxygenase (IDO), is known to be activated in response to inflammatory stimuli in various tissues [8]. In further steps, KYN is a source of biologically active compounds with pleiotropic effects [9]. Most data about the biological role of the KYN pathway are available from neurological studies, indicating the role of KYN metabolites in the pathogenesis of neurodegenerative diseases or epilepsy [10]. However, less is known about the peripheral KYN pathway activity and the mechanisms of its regulation. A tryptophan metabolite, kynurenic acid (KYNA), is produced from KYN by kynurenine aminotransferases (KATs) [11]. KAT I and KAT II are the most thoroughly analyzed KAT isoenzymes. The antagonism towards α7-nicotinic receptors and all types of ionotropic glutamatergic receptors is known to be the predominant mechanism of KYNA’s action [12]. Additionally, KYNA was shown to affect G protein-coupled receptor (GPR)-35 and aryl hydrocarbon receptor (AhR) activity [13]. In particular, the modulation of AhR activity has recently generated a lot of interest. Although AhR activation is required for kidney development and for the maintenance of normal kidney function, increased AhR activity in the kidney was found in CKD animal models and in patients with CKD [14]. The bifunctional role of AhR, especially as a regulator of oxidative reactions, has been widely studied [15]. AhR activation was found to increase the expression of COX-2 [16], whereas in the AhR knockout mice model of diabetic nephropathy, COX-2 activity and PG production was significantly lower, together with decreased lipid peroxidation, oxidative stress level, and extracellular matrix accumulation [17]. On the other hand, some nonselective COX inhibitors, diclofenac [18] and sulindac [19] have been presented as AhR ligands that decreased renal perfusion and promoted kidney damage in healthy subjects and patients with impaired kidney function, respectively. In previous studies, KYNA was reported to have a natriuretic [20] and chronotropic negative effect [21] in the animal model of hypertension. However, there is a growing body of evidence suggesting the relationship between KYNA level and the degree of kidney damage. Higher KYNA serum concentration, together with KYN and quinolinic acid, were related to CKD severity and the concentration of inflammatory markers [22]. Previously two classes of drugs, angiotensin-converting enzyme (ACE) inhibitors [23] and angiotensin II type 1 receptor blockers (ARBs) [24] were shown to inhibit KYNA production in rat kidneys in vitro. Based upon previous findings that KYN pathway activity is under the influence of the immune system and that COX inhibitors are known to impair kidney function, the goal of this study was to determine the effect of the most commonly used COX inhibitors, called nonsteroidal anti-inflammatory drugs (NSAIDs): acetylsalicylic acid, diclofenac, ibuprofen, indomethacin, meloxicam, naproxen, nimesulide, piroxicam, and acetaminophen, on KYNA production and KATs activity in rat kidney in vitro. ## Animals Presented experiments were performed on 28 male Wistar rats housed in the Experimental Medicine Center, Lublin, Poland. Animals were kept in the laboratory minimum of 7 days before planned tests were performed. Rats weighing 150–200 g were stored under standard laboratory conditions (temperature 21 °C ± 1 °C, 55 ± $5\%$ humidity, 12 h light/dark cycle) with chow and water available ad libitum. All experiments were performed between 7 a.m. and 1 p.m. The study was carried out in accordance with the European Directive $\frac{2010}{63}$/EU on the protection of animals used for scientific purposes. Animal tissues were obtained based on the Local Ethics Committee for Animal Experiments in Lublin approval (No. $\frac{32}{2014}$ of 13 June 2014). ## Chemical substances L-Kynurenine sulfate salt (K3750), acetaminophen (A7085), acetylsalicylic acid (A5376), diclofenac sodium salt (D6899), ibuprofen (I4883), indomethacin (I7378), meloxicam sodium salt hydrate (M3935), naproxen sodium (M1275), nimesulide (N1016), piroxicam (P5654), reagents for Krebs Ringer buffer preparation: sodium chloride (S7653), potassium chloride (P9333), magnesium sulfate heptahydrate (M7506), calcium chloride anhydrous (C1016), sodium phosphate monobasic dihydrate [71,505], sodium phosphate dibasic (S0876), glucose (G8270); dimethyl sulfoxide (DMSO) (D1435); reagents for KATs analysis: Trizma base (T1503), acetic acid (A6283), pyridoxal 5′-phosphate hydrate (P9255), 2-mercaptoethanol (M3148), sodium pyruvate (P2256), and D-glutamine (D9003) were obtained from Sigma-Aldrich. Substances needed to perform high-performance liquid chromatography (HPLC) were purchased from J.T. Baker Chemicals and from Sigma-Aldrich. Most tested drugs were dissolved in DMSO, whereas naproxen was administered in an aqueous solution. DMSO was given to adequate control samples and its concentration was not higher than $5\%$ [25]. ## The procedure of KYNA synthesis analysis in rat kidney in vitro In the first step, after the animals were decapitated, rat kidneys were harvested and immediately put on ice. Afterward whole kidneys were weighed and homogenized in prepared oxygenated Krebs–Ringer buffer at pH 7.4 (1:4; w/v). 100 µL of kidney homogenate was put into test tubes, pre-filled with oxygenated Krebs–Ringer buffer (containing 800 μL in every tube). Then, the homogenate was incubated for 2 h at 37 °C together with 10 µM L-KYN (50 µL) and one of the tested COX inhibitors: acetaminophen, acetylsalicylic acid, diclofenac, ibuprofen, indomethacin, meloxicam, naproxen, nimesulide or piroxicam (50 µL). L-KYN concentration used in this experimental procedure was higher than in KATs activity analysis, due to lower basal KYNA production by kidney homogenates in vitro. Six various drug concentrations were analyzed in the study: 1 μM, 10 μM, 50 μM, 100 μM, 500 μM, and 1 mM. At least six independent kidney samples were used for each experiment in this part of the study. Control samples, instead of the drug solution, contained an equal volume of a drug solvent (50 µL of DMSO or water). The reaction was stopped by adding 1 N HCl (100 μL per sample) on ice. After that, all samples were centrifuged (15,133×g, 15 min), and the supernatants were collected and subjected to the HPLC analysis (Thermo Fisher Scientific HPLC system, ESA catecholamine HR-80, 3 μm, C18 reverse-phase column, mobile phase 250 mM zinc acetate, 25 mM sodium acetate, $5\%$ acetonitrile, pH 6.2, flow rate 1.0 mL/min; fluorescence detector parameters: excitation 344 nm, emission 398 nm), and KYNA level was quantified fluorometrically. To achieve comparable results, each experiment was repeated twice. Tissues from 14 animals were used in this part of the study. ## The procedure of KATs activity analysis in rat kidney in vitro KAT I and KAT II activity in rat kidneys in vitro was analyzed based on procedures previously presented in a study by Gramsbergen et al. [ 26]. In brief, kidneys were homogenized in dialysate buffer (1:9; w/v) prepared from 5 mM Tris–acetate buffer (pH 8.0), 50 μM pyridoxal 5′-phosphate and 10 mM 2-mercaptoethanol. The homogenate was centrifuged (15,133×g, 15 min) and then collected supernatant was dialyzed against 4 L of the dialysate buffer for 12 h at 8 °C with the use of cellulose membrane dialysis tubing. Afterward, the obtained enzyme sample was incubated for 2 h at 37 °C with L-KYN (2 μM) and the tested drugs at 6 different concentrations (1 μM, 10 μM, 50 μM, 100 μM, 500 μM, and 1 mM). L-KYN concentration used in this analysis was equal to plasma KYN concentration, as reported by Pawlak et al. [ 27], and was sufficient to obtain appropriate enzymatic activity. The optimal pH was set at 9.5 and 7.0 for KAT I or KAT II activity analysis, respectively. Glutamine (2 mM), a KAT I inhibitor, was added to samples to measure KAT II’s activity. The reaction was terminated by moving all samples into the ice-cold bath. Finally, all samples were centrifuged and analyzed by HPLC, as described in the previous section. All assays were carried out in triplicates to get comparable results. To analyze KAT’s activity, kidneys from 14 rats were used. ## Statistical analysis Presented data are shown as mean ± standard deviation (SD). The one-way analysis of variance (one-way ANOVA) followed by Tukey’s multiple comparison test was applied to analyze differences between tested COX inhibitors. The half-maximal inhibitory concentrations values (IC50) were assessed by fitting the experimental data to a four-parameter logistic equation. Statistical analyses were done with the use of GraphPad Prism 6. Values of $p \leq 0.05$ were considered to be statistically significant. ## The influence of COX inhibitors on KYNA production in rat kidney in vitro Basal production of KYNA in rat kidney homogenate in the presence of 10 μM KYN was 4.52 ± 1.73 pmol/mg tissue. Diclofenac and indomethacin were the strongest KYNA production inhibitors among the examined drugs. Diclofenac decreased KYNA synthesis by $64\%$ with IC50 of 230 μM (F5,30 = 39.05, $p \leq 0.0001$) (Fig. 1). Indomethacin showed similar inhibitory activity with IC50 of 246 μM (F5,30 = 2.952, $$p \leq 0.0284$$). Naproxen displayed lower inhibitory activity with IC50 of 464 μM (F5,30 = 15.14, $p \leq 0.0001$). Similarly, nimesulide and piroxicam showed inhibitory activity with IC50 value of 473 μM (F5,30 = 31.87, $p \leq 0.0001$) and 474 μM (F5,30 = 18.12, $p \leq 0.0001$), respectively. Acetylsalicylic acid (F5,30 = 12.78, $p \leq 0.0001$), ibuprofen (F5,30 = 13.34, $p \leq 0.0001$) and meloxicam (F5,30 = 9.126, $p \leq 0.0001$) were the weakest KYNA synthesis inhibitors, with IC50 exceeding 1 mM. Acetaminophen did not change KYNA production in rat kidney in vitro (F5,30 = 2.081, $$p \leq 0.0998$$).Fig. 1The effect of COX inhibitors on KYNA production in rat kidney in vitro. ANOVA followed by Tukey’s multiple comparison test. The data are shown as a percentage of control KYNA production, mean ± SD, $$n = 6$$, veh.—vehicle, $p \leq 0.05$, $p \leq 0.01$, *$p \leq 0.001$ ## The influence of COX inhibitors on KAT I activity in rat kidney in vitro Standard KAT I activity in rat kidney in vitro in the presence of 2 µM KYN was 52.18 ± 18.43 pmol of KYNA/mg tissue. Nimesulide was the strongest inhibitor of KAT I in the kidney with IC50 of 128 µM (F5,12 = 58.59, $p \leq 0.0001$) (Fig. 2). Meloxicam and indomethacin decreased less KAT I activity in rat kidney homogenates in vitro, with IC50 of 310 µM (F5,12 = 33.06, $p \leq 0.0001$) and 503 µM (F5,12 = 32.92, $p \leq 0.0001$), respectively. Diclofenac (F5,12 = 41.55, $p \leq 0.0001$), piroxicam (F5,12 = 19.67, $p \leq 0.0001$) and naproxen (F5,12 = 3.746, $$p \leq 0.0283$$) displayed higher IC50 values above 1 mM. Acetaminophen (F5,12 = 0.5384, $$p \leq 0.7439$$), acetylsalicylic acid (F5,12 = 0.3192, $$p \leq 0.8920$$), and ibuprofen (F5,12 = 2.765, $$p \leq 0.0691$$) did not affect the activity of KAT I in rat kidney in vitro. Fig. 2The effect of COX inhibitors on KAT I activity in rat kidney in vitro. ANOVA followed by Tukey’s multiple comparison test. The data are shown as a percentage of control KAT I activity, mean ± SD, $$n = 3$$, veh.—vehicle, $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$ ## Influence of COX inhibitors on KAT II activity in rat kidney in vitro The mean KYNA production in rat kidneys in vitro by KAT II under 2 µM L-KYN was 91.05 ± 30.47 pmol/mg tissue. With IC50 of 155 μM, meloxicam was the most potent KAT II inhibitor in rat kidneys in vitro (F5,12 = 58.62, $p \leq 0.0001$) (Fig. 3). Similar inhibitory activity was displayed by diclofenac with IC50 of 191 µM (F5,12 = 85.60, $p \leq 0.0001$). Indomethacin and naproxen blocked KAT II with IC50 equal to 420 µM (F5,12 = 40.98, $p \leq 0.0001$) and 504 μM (F5,12 = 19.58, $p \leq 0.0001$), respectively. Piroxicam (F5,12 = 18.85, $p \leq 0.0001$), ibuprofen (F5,12 = 22.17, $p \leq 0.0001$), nimesulide (F5,12 = 3.480, $$p \leq 0.0356$$) and acetylsalicylic acid (F5,12 = 10.69, $$p \leq 0.0004$$) were the weakest KAT II inhibitors, with IC50 values exceeding 1 mM. Acetaminophen was not shown to inhibit KAT II activity in rat kidneys in vitro (F5,12 = 1.419, $$p \leq 0.2858$$).Fig. 3The effect of COX inhibitors on KAT II activity in rat kidney in vitro. ANOVA followed by Tukey’s multiple comparison test. The data are shown as a percentage of control KAT II activity, mean ± SD, $$n = 3$$, veh.—vehicle, $p \leq 0.05$, $p \leq 0.01$ ## Discussion The presented study demonstrates for the first time the influence of various COX inhibitors on KYNA production in rat kidneys in vitro. We found that most of the drugs under examination, except acetaminophen, decrease KYNA production in rat kidneys in vitro. Diclofenac was the strongest KYNA synthesis inhibitor in the kidney, whereas other COX inhibitors displayed lower inhibitory capacity (indomethacin > naproxen > nimesulide > piroxicam > acetylsalicylic acid > ibuprofen > meloxicam). In addition to that, significant KAT I inhibition by tested COX inhibitors, apart from acetaminophen, acetylsalicylic acid, and ibuprofen, was shown (nimesulide > meloxicam > indomethacin > diclofenac > piroxicam > naproxen). Similarly, all analyzed drugs, except acetaminophen, reduced KAT II activity in rat kidneys in vitro, with meloxicam and diclofenac as the strongest KAT II inhibitors, and other COX inhibitors presenting lower KAT II inhibitory activity (indomethacin > naproxen > piroxicam > ibuprofen > nimesulide > acetylsalicylic acid). PGs are major metabolites of arachidonic acid produced by COX, responsible for triggering inflammatory responses [28]. The main PG, PGE2, was shown to be involved in renal hemodynamics, renin release, and tubular sodium with water absorption [29, 30]. Increased PG production was observed in various kidney diseases, especially in diabetic kidney disease [31], glomerulonephritis [32] or ADPKD [33]. COX-1 isoenzyme is claimed to be constitutively expressed, whereas COX-2 expression is induced under inflammatory conditions [34], and also in chronic sodium deficiency or excessive ultrafiltration [35]. Interestingly, it was pointed out that COX-2 is also constitutively expressed in the kidney, suggesting that its inhibition can significantly impair kidney function [36]. NSAIDs were shown to inhibit both COX isoenzymes activity [37]. Chronic inflammation was reported to be tightly connected with kidney diseases, especially CKD. Schefold et al. indicated a correlation between IDO activation, disease severity and inflammatory parameters [22]. Similar observations were made by Pawlak et al., who observed higher tryptophan degradation and KYN production in CKD patients, together with increased oxidative stress parameters [38]. Among KYN metabolites KYNA was recently shown to be positively correlated with CKD severity in adults with ADPKD [39]. Furthermore, Dąbrowski et al. reported that only plasma KYNA concentration was correlated with the level of procalcitonin and lactate level in patients with septic shock and AKI, predicting their survival [40]. Based on these findings, it should be concluded that modulating KYNA synthesis can be an interesting tool for the prevention and treatment of kidney diseases. Based on available studies, which have given a novel insight into the inflammatory pathogenesis of kidney diseases, the inhibition of KYNA synthesis by COX inhibitors can improve kidney function, especially in ADPKD. PGs, as well as other inflammation markers, were shown to stimulate cell proliferation, the growth of cysts, and fluid secretion in primary cultured ADPKD cells [41]. Nonselective COX inhibition by sulindac [7] and selective COX-2 blockade by celecoxib [42] were reported to decrease cysts volume in animal models of ADPKD. An interesting view on the association between KYN pathway activity and ADPKD severity was recently shown by Klawitter et al., who found KYNA to be positively correlated with disease severity, expressed as height-adjusted kidney volume and estimated glomerular filtration rate [39]. According to this study, inhibition of KYNA synthesis should be considered a novel method of slowing ADPKD progression. Angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type 1 receptor blockers (ARBs) are already known to have an anti-inflammatory effect, to decrease kidney damage in ADPKD [43] and lower KYNA synthesis in the kidney [23, 24]. Based on our study, COX inhibitors, through KYNA production inhibition, can be considered as potential drugs for ADPKD treatment and other diseases of immune-mediated origin. Adding to that, there are available reports suggesting kidney protection by COX inhibitors in an animal model of diabetes [44] or sepsis-induced AKI [45]. In the presented study, among tested COX inhibitors naproxen, diclofenac and indomethacin have been shown as the strongest inhibitors of KYNA synthesis in the kidney. Moreover, these drugs caused significant inhibition of KAT II, the crucial enzyme involved in KYNA production. Since the aforementioned drugs are known to cause kidney damage, especially through impaired renal hemodynamics [46], the results of our study indicate a novel possible mechanism of nephrotoxicity caused by selected COX inhibitors. In previous studies, KYNA was found to attenuate kidney injury in an animal model of heat stroke [47] or ischemia reperfusion-induced AKI [48]. In this manner, the inhibition of KYNA production by COX inhibitors should be considered potentially toxic. Adding to that, other risks, including increased mortality, should be considered in relation to the administration of COX inhibitors. In a systematic review published by Asghar and Jamali, an analysis was performed to stratify cardiovascular and renal risks of meloxicam use compared with other NSAIDs [49]. Interestingly, meloxicam demonstrated a lower risk of vascular complications and no risk of renal episodes, adversely to other COX inhibitors, indomethacin, diclofenac, naproxen, and ibuprofen, which were shown to increase the risk of renal side effects and all-cause mortality [49]. Similarly, in a Danish nationwide cohort study by Schmidt et al. diclofenac posed the highest cardiovascular risk, compared with other NSAIDs and acetaminophen [50]. On the other hand, weaker KYNA synthesis inhibitors, acetylsalicylic acid, ibuprofen and meloxicam should be considered as less nephrotoxic drugs. Indeed, long-term acetylsalicylic acid administration was reported to not affect kidney function in a randomized controlled trial in patients with diabetes mellitus [51]. COX-3 isoenzyme, a splice variant of COX-1, was previously considered as a place of action of acetaminophen, explaining different properties of this drug [52]. However, since COX-3 was not found in humans, other mechanisms, including the inhibition of peroxidase (POX) site by acetaminophen was suggested [52]. Similarly in our study, acetaminophen showed distinct effect on KYNA synthesis in the kidney than the rest of COX inhibitors. Since acetaminophen did not change the activity of KATs and KYNA production, it can be suggested that this drug has weaker effect on kidney function impairment compared with the other COX inhibitors being analyzed, as suggested by [53]. Partly similar results indicating the effect of COX inhibitors on KYNA production were shown previously, although not in kidney tissue. Decreased KYNA content in the rat brain after meloxicam administration was presented in a study by Schwieler et al., whereas diclofenac and indomethacin elevated KYNA levels [54]. Similarly, diclofenac increased KYNA concentration in the rat brain following tail ischemia, suggesting KYNA increase being responsible for the analgesic effect of diclofenac [55]. It should be emphasized that the different effects of diclofenac on KYNA production, compared to our study, can be related to various experimental protocols and the type of organs studied. Recently, Savitz et al. presented a randomized, placebo-controlled, crossover study on 20 healthy volunteers, indicating that the acute administration of ibuprofen increases serum KYNA concentration, although 5 h after drug administration [56]. Our study has its limitation. We have shown the effect of 9 COX inhibitors in 6 different concentrations, reaching up to 1 mM to check how efficiently these drugs can influence KYNA production in a dose-dependent manner. After oral administration, selected drugs showed inhibitory effect at concentrations exceeding those reported in the rat serum [57]. However, most available studies on COX inhibitors pharmacokinetics were performed on isolated rat kidneys, using various concentrations of drugs ranging from 12 to 120 µM in the ibuprofen study [58] up to 10 mM in the acetaminophen study [59]. ## Conclusions The presented study indicates a novel mechanism of action of COX inhibitors. The reduction of KYNA production and KATs activity in the kidney by COX inhibitors provides another pathway for the prevention and treatment of inflammatory-mediated kidney diseases. The potentially unfavorable effect of COX inhibitors, related to the inhibition of KYNA synthesis and KATs activity in the kidney should also be considered. ## References 1. Darlington O, Dickerson C, Evans M, McEwan P, Sörstadius E, Sugrue D. Costs and healthcare resource use associated with risk of cardiovascular morbidity in patients with chronic kidney disease: evidence from a systematic literature review*. Adv Ther* (2021.0) 38 994-1010. DOI: 10.1007/s12325-020-01607-4 2. Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2022.0) 12 7-11. DOI: 10.1016/j.kisu.2021.11.003 3. Sethi S, De Vriese AS, Fervenza FC. Acute glomerulonephritis. Lancet (2022.0) 399 1646-1663. DOI: 10.1016/S0140-6736(22)00461-5 4. Aranda-Rivera AK, Srivastava A, Cruz-Gregorio A, Pedraza-Chaverri J, Mulay SR, Scholze A. Involvement of Inflammasome Components in Kidney Disease. Antioxidants (2022.0) 11 246. DOI: 10.3390/antiox11020246 5. Yuan Q, Tang B, Zhang C. Signaling pathways of chronic kidney diseases, implications for therapeutics. Signal Transduct Target Ther (2022.0) 7 182. DOI: 10.1038/s41392-022-01036-5 6. 6.Agborbesong E, Li LX, Li L, Li X. Molecular Mechanisms of Epigenetic Regulation, Inflammation, and Cell Death in ADPKD. Front Mol Biosci 2022; 9:922428. 10.3389/fmolb.2022.922428. 7. Zhang M, Srichai MB, Zhao M, Chen J, Davis LS, Wu G. Nonselective cyclooxygenase inhibition retards cyst progression in a murine model of autosomal dominant polycystic kidney disease. Int J Med Sci (2019.0) 16 180-188. DOI: 10.7150/ijms.27719 8. Wang Q, Liu D, Song P, Zou MH. Tryptophan-kynurenine pathway is dysregulated in inflammation and immune activation. Front Biosci (2015.0) 20 1116-1143. DOI: 10.2741/4363 9. Jamshed L, Debnath A, Jamshed S, Wish JV, Raine JC, Tomy GT. An emerging cross-species marker for organismal health: tryptophan-kynurenine pathway. Int J Mol Sci (2022.0) 23 6300. DOI: 10.3390/ijms23116300 10. Hestad K, Alexander J, Rootwelt H, Aaseth JO. The role of tryptophan dysmetabolism and quinolinic acid in depressive and neurodegenerative diseases. Biomolecules (2022.0) 12 998. DOI: 10.3390/biom12070998 11. Rossi F, Miggiano R, Ferraris DM, Rizzi M. The synthesis of kynurenic acid in mammals: an updated kynurenine aminotransferase structural KATalogue. Front Mol Biosci (2019.0) 6 7. DOI: 10.3389/fmolb.2019.00007 12. Schwarcz R. Kynurenines and glutamate: multiple links and therapeutic implications. Adv Pharmacol (2016.0) 76 13-37. DOI: 10.1016/bs.apha.2016.01.005 13. Moroni F, Cozzi A, Sili M, Mannaioni G. Kynurenic acid: a metabolite with multiple actions and multiple targets in brain and periphery. J Neural Transm (2012.0) 119 133-139. DOI: 10.1007/s00702-011-0763-x 14. 14.Curran CS, Kopp JB. Aryl Hydrocarbon Receptor Mechanisms Affecting Chronic Kidney Disease. Front Pharmacol 2022; 13:782199. 10.3389/fphar.2022.782199. 15. Liu J-R, Miao H, Deng D-Q, Vaziri ND, Li P, Zhao Y-Y. Gut microbiota-derived tryptophan metabolism mediates renal fibrosis by aryl hydrocarbon receptor signaling activation. Cell Mol Life Sci (2021.0) 78 909-922. DOI: 10.1007/s00018-020-03645-1 16. Yang Y, Liu K, Chen Y, Gong Y, Liang Y. Indoleamine 2,3-dioxygenase (IDO) regulates Th17/Treg immunity in experimental IgA nephropathy. Folia Biol (Praha) (2019.0) 65 101-108. PMID: 31464185 17. Lee W-J, Liu S-H, Chiang C-K, Lin S-Y, Liang K-W, Chen C-H. Aryl hydrocarbon receptor deficiency attenuates oxidative stress-related mesangial cell activation and macrophage infiltration and extracellular matrix accumulation in diabetic nephropathy. Antioxid Redox Signal (2016.0) 24 217-231. DOI: 10.1089/ars.2015.6310 18. Bass SE, Sienkiewicz P, MacDonald CJ, Cheng RYS, Sparatore A, Del Soldato P. Novel dithiolethione-modified nonsteroidal anti-inflammatory drugs in human hepatoma hepg2 and colon LS180 cells. Clin Cancer Res (2009.0) 15 1964-1972. DOI: 10.1158/1078-0432.CCR-08-1870 19. Ciolino HP, MacDonald CJ, Memon OS, Bass SE, Yeh GC. Sulindac regulates the aryl hydrocarbon receptor-mediated expression of Phase 1 metabolic enzymes in vivo and in vitro. Carcinogenesis (2006.0) 27 1586-1592. DOI: 10.1093/carcin/bgi359 20. Bądzyńska B, Zakrocka I, Sadowski J, Turski WA, Kompanowska-Jezierska E. Effects of systemic administration of kynurenic acid and glycine on renal haemodynamics and excretion in normotensive and spontaneously hypertensive rats. Eur J Pharmacol (2014.0) 743 37-41. DOI: 10.1016/j.ejphar.2014.09.020 21. Bądzyńska B, Zakrocka I, Turski WA, Olszyński KH, Sadowski J, Kompanowska-Jezierska E. Kynurenic acid selectively reduces heart rate in spontaneously hypertensive rats. Naunyn Schmiedebergs Arch Pharmacol (2020.0) 393 673-679. DOI: 10.1007/s00210-019-01771-7 22. Schefold JC, Zeden JP, Fotopoulou C, Von Haehling S, Pschowski R, Hasper D. Increased indoleamine 2,3-dioxygenase (IDO) activity and elevated serum levels of tryptophan catabolites in patients with chronic kidney disease: A possible link between chronic inflammation and uraemic symptoms. Nephrol Dial Transplant (2009.0) 24 1901-1908. DOI: 10.1093/ndt/gfn739 23. Zakrocka I, Kocki T, Turski WA. The effect of three angiotensin-converting enzyme inhibitors on kynurenic acid production in rat kidney in vitro. Pharmacol Reports (2017.0) 69 536-541. DOI: 10.1016/j.pharep.2017.01.023 24. Zakrocka I, Targowska-Duda KM, Wnorowski A, Kocki T, Jóźwiak K, Turski WA. Angiotensin II type 1 receptor blockers decrease kynurenic acid production in rat kidney in vitro. Naunyn Schmiedebergs Arch Pharmacol (2019.0) 392 209-217. DOI: 10.1007/s00210-018-1572-7 25. Sanmartín-Suárez C, Soto-Otero R, Sánchez-Sellero I, Méndez-Álvarez E. Antioxidant properties of dimethyl sulfoxide and its viability as a solvent in the evaluation of neuroprotective antioxidants. J Pharmacol Toxicol Methods (2011.0) 63 209-215. DOI: 10.1016/j.vascn.2010.10.004 26. Gramsbergen JBP, Schmidt W, Turski WA, Schwarcz R. Age-related changes in kynurenic acid production in rat brain. Brain Res (1992.0) 588 1-5. DOI: 10.1016/0006-8993(92)91337-E 27. Pawlak D, Tankiewicz A, Buczko W. Kynurenine and its metabolites in the rat with experimental renal insufficiency. J Physiol Pharmacol (2001.0) 52 755-766. PMID: 11785771 28. Wang T, Fu X, Chen Q, Patra JK, Wang D, Wang Z. Arachidonic acid metabolism and kidney inflammation. Int J Mol Sci (2019.0) 20 3683. DOI: 10.3390/ijms20153683 29. Breyer MD, Breyer RM. Prostaglandin E receptors and the kidney. Am J Physiol Physiol (2000.0) 279 F12-23. DOI: 10.1152/ajprenal.2000.279.1.F12 30. Kotnik P, Nielsen J, Kwon T-H, Kržišnik C, Frøkiær J, Nielsen S. Altered expression of COX-1, COX-2, and mPGES in rats with nephrogenic and central diabetes insipidus. Am J Physiol Physiol (2005.0) 288 F1053-F1068. DOI: 10.1152/ajprenal.00114.2004 31. 31.Guan Y, Davis L, Breyer MD, Hao C-M. Cyclooxygenase-2 contributes to diabetic nephropathy through glomerular EP4 receptor. Prostaglandins Other Lipid Mediat 2022;159:106621. doi:10.1016/j.prostaglandins.2022.106621. 32. Aringer I, Artinger K, Kirsch AH, Schabhüttl C, Jandl K, Bärnthaler T. Blockade of prostaglandin E 2 receptor 4 ameliorates nephrotoxic serum nephritis. Am J Physiol Physiol (2018.0) 315 F1869-F1880. DOI: 10.1152/ajprenal.00113.2018 33. Lannoy M, Valluru MK, Chang L, Abdela-Ali F, Peters DJM, Streets AJ. The positive effect of selective prostaglandin E2 receptor EP2 and EP4 blockade on cystogenesis in vitro is counteracted by increased kidney inflammation in vivo. Kidney Int (2020.0) 98 404-419. DOI: 10.1016/j.kint.2020.02.012 34. 34.Faki Y, Er A. Different Chemical Structures and Physiological/Pathological Roles of Cyclooxygenases. Rambam Maimonides Med J 2021;12:e0003. doi:10.5041/RMMJ.10426. 35. Lipsky PE, Brooks P, Crofford LJ, DuBois R, Graham D, Simon LS. Unresolved Issues in the role of cyclooxygenase-2 in normal physiologic processes and disease. Arch Intern Med (2000.0) 160 913-920. DOI: 10.1001/archinte.160.7.913 36. Yang T, Liu M. Regulation and function of renal medullary cyclooxygenase-2 during high salt loading. Front Biosci (2017.0) 22 128-136. DOI: 10.2741/4476 37. Cabassi A, Tedeschi S, Perlini S, Verzicco I, Volpi R, Gonzi G. Non-steroidal anti-inflammatory drug effects on renal and cardiovascular function: from physiology to clinical practice. Eur J Prev Cardiol (2020.0) 27 850-867. DOI: 10.1177/2047487319848105 38. Pawlak K, Domaniewski T, Mysliwiec M, Pawlak D. The kynurenines are associated with oxidative stress, inflammation and the prevalence of cardiovascular disease in patients with end-stage renal disease. Atherosclerosis (2009.0) 204 309-314. DOI: 10.1016/j.atherosclerosis.2008.08.014 39. Klawitter J, Jackson MJ, Smith PH, Hopp K, Chonchol M, Gitomer BY. Kynurenines in polycystic kidney disease. J Nephrol (2022.0). DOI: 10.1007/s40620-022-01361-6.10.1007/s40620-022-01361-6 40. Dabrowski W, Kocki T, Pilat J, Parada-Turska J, Malbrain MLNG. Changes in plasma kynurenic acid concentration in septic shock patients undergoing continuous veno-venous haemofiltration. Inflammation (2014.0) 37 223-234. DOI: 10.1007/s10753-013-9733-9 41. 41.Aukema HM. Prostaglandins as potential targets for the treatment of polycystic kidney disease. Prostaglandins, Leukot Essent Fat Acids 2021;164:102220. doi:10.1016/j.plefa.2020.102220. 42. Monirujjaman M, Aukema HM. Cyclooxygenase 2 inhibition slows disease progression and improves the altered renal lipid mediator profile in the Pkd2WS25/− mouse model of autosomal dominant polycystic kidney disease. J Nephrol (2019.0) 32 401-409. DOI: 10.1007/s40620-018-00578-8 43. Ta MH, Harris DC, Rangan GK. Role of interstitial inflammation in the pathogenesis of polycystic kidney disease. Nephrology (2013.0) 18 317-330. DOI: 10.1111/nep.12045 44. Liu Y-W, Zhu X, Cheng Y-Q, Lu Q, Zhang F, Guo H. Ibuprofen attenuates nephropathy in streptozotocin-induced diabetic rats. Mol Med Rep (2016.0) 13 5326-5334. DOI: 10.3892/mmr.2016.5150 45. Zhao S, Cui L, Zheng X, Ji Y, Yu C. Meloxicam alleviates sepsis-induced kidney injury by suppression of inflammation and apoptosis via upregulating GPNMB. Appl Bionics Biomech (2022.0) 2022 1-11. DOI: 10.1155/2022/1790104 46. Baker M, Perazella MA. NSAIDs in CKD: Are They Safe?. Am J Kidney Dis (2020.0) 76 546-557. DOI: 10.1053/j.ajkd.2020.03.023 47. Hsieh YC, Chen RF, Yeh YS, Lin MT, Hsieh JH, Chen SH. Kynurenic acid attenuates multiorgan dysfunction in rats after heatstroke. Acta Pharmacol Sin (2011.0) 32 167-174. DOI: 10.1038/aps.2010.191 48. Arora S, Kaur T, Kaur A, Singh AP. Glycine aggravates ischemia reperfusion-induced acute kidney injury through N-methyl-D-aspartate receptor activation in rats. Mol Cell Biochem (2014.0) 393 123-131. DOI: 10.1007/s11010-014-2052-0 49. Asghar W, Jamali F. The effect of COX-2-selective meloxicam on the myocardial, vascular and renal risks: a systematic review. Inflammopharmacology (2015.0) 23 1-16. DOI: 10.1007/s10787-014-0225-9 50. 50.Schmidt M, Sørensen HT, Pedersen L. Diclofenac use and cardiovascular risks: series of nationwide cohort studies. BMJ 2018;362:k3426. doi:10.1136/bmj.k3426. 51. 51.Okada S, Morimoto T, Ogawa H, Sakuma M, Soejima H, Nakayama M, et al. Is Long-Term Low-Dose Aspirin Therapy Associated with Renal Dysfunction in Patients with Type 2 Diabetes? JPAD2 Cohort Study. PLoS One 2016;11:e0147635. doi:10.1371/journal.pone.0147635. 52. McCrae JC, Morrison EE, MacIntyre IM, Dear JW, Webb DJ. Long-term adverse effects of paracetamol—a review. Br J Clin Pharmacol (2018.0) 84 2218-2230. DOI: 10.1111/bcp.13656 53. Alchin J, Dhar A, Siddiqui K, Christo PJ. Why paracetamol (acetaminophen) is a suitable first choice for treating mild to moderate acute pain in adults with liver, kidney or cardiovascular disease, gastrointestinal disorders, asthma, or who are older. Curr Med Res Opin (2022.0) 38 811-825. DOI: 10.1080/03007995.2022.2049551 54. Schwieler L, Erhardt S, Erhardt C, Engberg G. Prostaglandin-mediated control of rat brain kynurenic acid synthesis—opposite actions by COX-1 and COX-2 isoforms. J Neural Transm (2005.0) 112 863-872. DOI: 10.1007/s00702-004-0231-y 55. Edwards SR, Mather LE. Diclofenac increases the accumulation of kynurenate following tryptophan pretreatment in the rat: a possible factor contributing to its antihyperalgesic effect. Inflammopharmacology (2003.0) 11 277-292. DOI: 10.1163/156856003322315622 56. Savitz J, Ford BN, Kuplicki R, Khalsa S, Teague TK, Paulus MP. Acute administration of ibuprofen increases serum concentration of the neuroprotective kynurenine pathway metabolite, kynurenic acid: a pilot randomized, placebo-controlled, crossover study. Psychopharmacology (2022.0) 239 3919-3927. DOI: 10.1007/s00213-022-06263-w 57. Yuan J, Ma H, Cen N, Zhou A, Tao H. A pharmacokinetic study of diclofenac sodium in rats. Biomed Reports (2017.0) 7 179-182. DOI: 10.3892/br.2017.942 58. Cox PGF, Moons WM, Russel FGM, Ginneken CAM. Renal handling and effects of S(+)-ibuprofen and R(-)-ibuprofen in the rat isolated perfused kidney. Br J Pharmacol (1991.0) 103 1542-1546. DOI: 10.1111/j.1476-5381.1991.tb09824.x 59. Trumper L, Monasterolo LA, Ochoa E, Elias MM. Tubular effects of acetaminophen in the isolated perfused rat kidney. Arch Toxicol (1995.0) 69 248-252. DOI: 10.1007/s002040050166
--- title: 'Trends in incidence and mortality risk for acromegaly in Norway: a cohort study' authors: - Camilla M. Falch - Nicoleta C. Olarescu - Jens Bollerslev - Olaf M. Dekkers - Ansgar Heck journal: Endocrine year: 2022 pmcid: PMC10060282 doi: 10.1007/s12020-022-03275-6 license: CC BY 4.0 --- # Trends in incidence and mortality risk for acromegaly in Norway: a cohort study ## Abstract ### Purpose Recent data have shown a decreasing overall mortality in acromegaly over the last decades. However, cancer incidence and cancer-related mortality still appear to be increased. Our aim was to obtain updated epidemiological data from Norway in a clinically well-defined cohort with complete register-based follow-up. ### Methods Patients diagnosed with acromegaly from South-Eastern Norway between 1999–2019 ($$n = 262$$) and age and sex matched population controls (1:100) were included ($$n = 26$$,200). Mortality and cancer data were obtained from the Norwegian Cause of Death and Cancer Registry. Mortality and cancer incidence were compared by Kaplan–Meier analyses and Cox regression; we report hazard ratios (HRs) with $95\%$ confidence intervals ($95\%$ CI). ### Results Median age at diagnosis was 48.0 years (interquartile range (IQR): 37.6–58.0). Mean annual acromegaly incidence rate was 4.7 ($95\%$ CI 4.2–5.3) cases/106 person-years, and the point prevalence [2019] was 83 ($95\%$ CI 72.6–93.5) cases/106 persons. Overall mortality was not increased in acromegaly, HR 0.8 ($95\%$ CI 0.5–1.4), cancer-specific and cardiovascular-specific mortality was also not increased (HR: 0.7 ($95\%$ CI 0.3–1.8) and 0.8 ($95\%$ CI: 0.3–2.5) respectively). The HR for all cancers was 1.45 (1.0–2.1; $$p \leq 0.052$$). ### Conclusion In this large cohort study, covering the period 1999–2019, patients were treated with individualized multimodal management. Mortality was not increased compared to the general population and comparable with recent registry studies from the Nordic countries and Europe. Overall cancer risk was slightly, but not significantly increased in the patients. ## Introduction Acromegaly is a chronic disease caused by excessive secretion of growth hormone (GH) most often from a somatotroph pituitary adenoma, with subsequently increased levels of insulin-like growth factor 1 (IGF-1) [1]. Based on recent studies, the estimated prevalence is 28–137 cases/million inhabitants and the incidence varies between 2–11 cases/million per year [2–5], hence acromegaly is considered an orphan disease as defined by the European Union (European Medicines Agency, 02.09.2019). The clinical manifestations of acromegaly are caused by systemic effects related to the prolonged exposure to GH/IGF-1 excess (musculoskeletal, cardiovascular and metabolic comorbidities) and the local tumor extension (visual-field defects, cranial-nerve palsy and hypopituitarism) [1]. The metabolic complications, including insulin resistance and diabetes mellitus, increase the risk of cardiovascular-related morbidity and mortality [1, 6]. Further, increased levels of IGF-1 has been associated with increased risk for several malignancies [7], and studies have suggested GH and IGF-1 to facilitate a tumor microenvironment and neoplastic growth in the colon [8, 9]. Surgery is the only curative treatment. However, due to delayed diagnosis most patients with acromegaly present with macroadenomas (65–$79\%$) and frequently invasive tumors [10]. Thus, the cure rate is disappointingly low when performed as primary treatment in clinical practice [11–15]. Treatment algorithms with focus on improving the surgical cure rate by preoperative medical treatment have been developed during the last two decades. The purpose of this individualized approach was to improve therapeutic outcomes and reduce the need for complicated and costly long-term medical therapy [10, 13, 15, 16]. The concept is based on relatively small prospective studies showing an improvement in surgical cure rate in newly diagnosed patients with acromegaly following somatostatin analogue (SSA) pretreatment, as compared to direct surgery [4, 17–19]. However, as the few RCT’s on the topic mostly provide short-time observation on the primary endpoint being cure rate, the concept is still debatable and well-planned studies with longer postoperative observation are warranted. Before the millennium, mortality in patients with acromegaly was reported to be increased by two- to three- fold compared to the general population. However, since the millennium overall mortality rates have declined [2]. This change has been ascribed to the modern, multimodal therapy [2, 20]. According to a large meta-analysis, malignancies have become the leading cause of death [2]. Recent studies suggest that the type of cancers related to mortality in acromegaly are diverse, and not restricted to those traditionally associated with acromegaly, such as colorectal and thyroid cancer [2]. Thus, studies indicate that when mortality in acromegaly declines by modernized treatment, causes of death in acromegaly shift towards ageing and environmental factors, similar as in the general population [2]. Recent meta-analyses have shown an elevated risk of cancer in patients with acromegaly [21]. However, conflicting results regarding cancer risk in patients with acromegaly have been described, including population based studies from the United Kingdom and Germany that showed a lower cancer incidence in patients when compared to controls (standardized incidence ratio (SIR) 0.76 ($95\%$ confidence interval (CI) 0.60–0.95) and 0.75 ($95\%$ CI: 0.55–1.00), respectively) [22, 23]. The aim of the present single center cohort study was to investigate incidence, prevalence, overall mortality and the risk of cancer in a clinically well-defined cohort of patients with acromegaly with complete register-based follow-up. ## Study design and population Oslo University Hospital (OUS) is the tertiary referral center for patients with acromegaly in the South-Eastern Health Region of Norway, which is the regional health authority for about 3 million inhabitants, approximately $56\%$ of the total Norwegian population (South-Eastern Norway Regional Health Authority, 16.11.2020). Between August 1999 and December 2019, 262 patients with newly diagnosed acromegaly were managed at OUS (Section of Specialized Endocrinology). This comprises patients included in the Preoperative Treatment of Acromegaly (POTA) study between 1999 and 2005 [4, 17, 18], and thereafter patients from our internal pituitary quality registry. The included patients underwent a standardized diagnostic work up, as established by the POTA protocol [4, 17, 18]. The patients were followed prospectively, and clinical, biochemical and radiological findings were recorded during a standardized set of serial visits, from diagnosis at baseline and following the treatment on a yearly basis. Patients with invasive and/or macroadenomas were usually offered primary SSA treatment for an individualized time period (in general minimum 6 months), before subsequent transsphenoidal surgery. Treatment was carefully tailored by a multidisciplinary approach according to the most recent recommendations [10, 12–16]. Treatment information during the time of follow-up including surgical procedures, medical treatment (1st generation SSAs, Pasireotide, dopamine agonists (DAs), GH receptor antagonists (GHRAs)) and radiotherapy was recorded consecutively during regular visits. During the study period, different assays for GH and IGF-1 were used, and we used morning GH levels (μg/L) for the statistical analysis as described previously [24]. IGF-1 is presented as the ratio of measured IGF-1 values, divided by the age-specific upper limit of normal (IGF-1/ULN). A control cohort was obtained from the general population of the South-Eastern Health Region of Norway by the National Population Register and the Norwegian Tax Administration. The comparison cohort consisted of 100 age - and gender matched persons for every patient ($$n = 26$$,200). Date of diagnosis was considered the index date for the acromegaly patients and the matched controls. Demographics for the total population of the South-Eastern Health Region of Norway were obtained by The National Statistical Institute of Norway (National Statistical Institute of Norway, 04.11.2020). Follow-up started at the date of diagnosis and the matched index date for the control cohort members. The follow-up period ended at the time of death or at the end of study (December 31, 2019). ## Cause of death and cancer data We received data regarding cause and date of death, and cancer diagnosis and localization from the Norwegian Cause of Death Registry (Norwegian Cause of Death Registry, 03.05.2022) and Cancer Registry of Norway (Cancer Registry of Norway, 02.05.2022). The cancer diagnoses were coded according to the International Classification of Diseases 10th Revision (ICD-10), and categorized into major cancer groups. Registry entries defined as benign by the Cancer registry of Norway (including pituitary adenomas) were excluded from the cancer analyses. International rules for multiple primary cancers (ICD-0 third edition) were used to define multiple primary neoplasms when reporting the data on cancer diagnoses and incidences [25]. For estimation of cancer incidence, only cancer diagnoses established after the index date were considered. However, all established cancer diagnoses between date of birth and end of follow-up were considered, when describing cancer events over time in relation to date of acromegaly diagnosis or the corresponding index date in the control cohort. Cancer events were adjusted for persons at risk per year. ## Statistical analyses We estimated the annual incidence rate of acromegaly per 106 persons-years based on the total population of South-Eastern Health Region of Norway for each calendar year, and the mean annual incidence rate. The point prevalence was estimated per million inhabitants in 2019, the last year of the study. Kaplan–Meier analysis was used to generate survival curves and Cox regression was used for time to event analysis. Using the comparison cohort as a reference, hazard ratios (HRs) with $95\%$ CIs for mortality were estimated. Additionally, we divided the study period into three periods, and HRs for the periods 1999–2005, 2006–2012 and 2013–2019 were estimated, to analyze the potential change in mortality over time. To investigate cause-specific mortality, cause of death was categorized into main groups according to the leading causes of death observed in the acromegaly cohort; cardiovascular (any cardiovascular death), cancer (any cancer death) and other (any death that was not cardiovascular or cancer). Cox regression was used to investigate potential risk factors for mortality for predefined baseline characteristics (age, sex, tumor size, IGF-1/ULN values and first treatment modality). As only the first treatment was considered and this was close to baseline, immortal time bias was not considered an issue. We calculated and compared cancer incidence in the case and the control cohorts. Data are presented as median (interquartile range (IQR)) for continuous measures, and n (%) for categorical variables. For comparison of medians, Wilcoxon rank-sum test was used. Statistical analyses were executed by using STATA version 16.1. ## Patient characteristics A total of 262 patients with acromegaly and 26,200 age- and sex-matched controls were included, $50.4\%$ women. The mean follow-up time was 9.2 (SD: 6.0, range (0.0–20.4)) years. The median age at diagnosis was 48.0 years (37.6–58.0) and was constant over time. There was no major difference in median age at diagnosis between men and women (49.0 (37.8–59.2) and 47.7 (37.5–57.2), respectively ($$p \leq 0.700$$)). In the acromegaly cohort the median baseline GH and IGF-1/ULN were 7.9 (3.4–18.8) μg/L and 2.5 (1.8–3.4), respectively. At baseline, the median IGF-1/ULN was 2.4 (1.6–3.1) in women and 2.6 (1.9–3.5) in men. Radiological assessments were available for 229 patients, of whom $21\%$ had microadenomas (<10 mm) and $79\%$ had macroadenomas (≥10 mm). As a first treatment, $48\%$ received surgery and $44\%$ received 1st generation SSAs (remaining listed in Table 1). Six percent was treated with radiotherapy at some time point, and $8\%$ underwent multiple surgeries. At the last visit, median GH had decreased to 1.1 (0.4–2.8) μg/L and median IGF-1/ULN to 0.9 (0.7–1.1). Of a total of 240 patients with available IGF-1/ULN values and with more than at least one year follow-up time, $62\%$ were in remission (defined as IGF-1/ULN levels < 1). Medical treatment for acromegaly (including 1st generation SSAs, Pasireotide, DAs, GHRAs, or medical combination treatment) was received by $49\%$ of patients, and $51\%$ did not receive any treatment. Due to pituitary deficiency, $32\%$ received hormone replacement therapy at the last visit (Table 1).Table 1Patients’ characteristics of the acromegaly cohortTotalN = 262BaselineAge at diagnosis48.0 (37.6–58.0) Women47.7 (37.5–57.2) Men49.0 (37.8–59.2)Sex (Women)132 ($50\%$)Growth hormone (μg/L)a7.9 (3.4–18.8)IGF-1/ULNb2.5 (1.8–3.4) Women2.4 (1.6–3.1) Men2.6 (1.9–3.5)Tumor size (mm)c15 (10–20) Macroadenoma180 ($79\%$) Microadenoma49 ($21\%$)TreatmentFirst treatment Surgery125 ($48\%$) 1st generation SSAs114 ($44\%$) DAs10 ($4\%$) Otherd11 ($4\%$)Radiotherapye16 ($6\%$)Multiple surgeriesf21 ($8\%$) Two19 ($7\%$) Three2 ($1\%$)Last visitgGrowth hormone (μg/L)h1.1 (0.4–2.8)IGF-1/ULNi0.9 (0.7–1.1)Biochemical remissionj149 ($62\%$)Current therapyk 1st generation SSAs92 ($36\%$) Otherl32 ($13\%$) None131 ($51\%$)Substitution therapym No163 ($68\%$) Yes78 ($32\%$)Baseline is the visit at the department when acromegaly diagnosis was established. Last visit is the last visit at the department before end of studyIGF-1/ULN Insulin-like growth factor 1 upper limit of normal, SSAs somatostatin analogs, DAs dopamine agonistsaN = 164bN = 206cN = 229dN = 260 and includes growth hormone receptor antagonists, medical combination treatment, no treatment and censored (due to lack of follow-up)eN = 262fN = 256gN = 248, patients with follow-up time < 1 year were excluded.hN = 207.iN = 240.jN = 240 and biochemical remission was defined by IGF-1/ULN levels < 1.kN = 241.lIncludes DAs, growth hormone receptor antagonists, Pasireotide and medical combination treatment.mN = 241 and includes patients that received hormonal therapy due to pituitary insufficiency. ## Incidence and prevalence The point prevalence of acromegaly was 83 ($95\%$ CI: 72.6–93.5) cases/106 in 2019. The mean annual incidence rate was 4.7 ($95\%$ CI: 4.2–5.3) cases/106 persons, and remained constant over time. ## Mortality During the follow-up period, 14 patients ($5\%$) with acromegaly died; 5 from cancer, 3 from cardiovascular disease and 6 from other causes (including multiple sclerosis, unspecified ileus, unspecified diabetes mellitus with renal complications, unexplained instantaneous death and missing). The overall mortality risk in acromegaly was not increased compared to the general population: HR 0.83 ($95\%$ CI: 0.49–1.41), $$p \leq 0.501$$ (Fig. 1).Fig. 1Mortality. Kaplan–Meier analysis of observed mortality rates among patients with acromegaly (dashed line) and matched controls (solid line), including number at risk for patients with acromegaly and controls for the study period For patients diagnosed between 1999–2005, 2006–2012 and 2013–2019 the HRs for death were 0.88 ($95\%$ CI: 0.42–1.85), 0.86 ($95\%$ CI: 0.39–1.92) and 0.52 ($95\%$ CI: 0.07–3.74), respectively (Fig. 2).Fig. 2Mortality over three time periods. HRs ($95\%$ CIs) of mortality for patients diagnosed with acromegaly within time periods 1999–2005, 2006–2012 and 2013–2019 Cancer-specific and cardiovascular-specific mortality risks were not clearly increased in patients with acromegaly (HR 0.74 ($95\%$ CI: 0.31–1.79) and 0.80 ($95\%$ CI: 0.26–2.48), respectively). Age was the only factor at baseline associated with mortality risk in acromegaly (HR 1.16 ($95\%$ CI 1.07–1.26)), whereas sex, IGF-1/ULN levels, tumor size at diagnosis, and first treatment modality (surgery or SSAs) did not influence mortality risk (Table 2).Table 2Baseline characteristics and mortality risk in patients with acromegalyDeadN = 14AliveN = 248HR$95\%$ CIP-valueAge at diagnosis67.8 (59.2–76.8)47.1 (37.4–56.7)1.161.07–1.29<0.001Sex (Women)10 ($71\%$)122 ($49\%$)2.210.44–11.150.336IGF-1/ULN2.5 (1.7–3.0)2.5 (1.8–3.4)0.640.32–1.280.208Tumor size (mm)17 (15–23)14 (10–20)1.040.98–1.100.169First treatment (surgery)5 ($38\%$)120 ($50\%$)0.920.17–5.070.921Baseline characteristics of the patients that died and the patients alive during the study period. Risk of mortality is given in HRs, $95\%$ CI and p-values in patients that died during the study period compared to alive patients (Cox regression).IGF-1/ULN Insulin-like growth factor I upper limit of normal. ## Cancer Of the patients with acromegaly, 28 ($10.7\%$) developed cancer following the diagnosis of acromegaly, as compared to 2063 ($7.9\%$) of the matched controls after the corresponding index date. The HR for all cancers was 1.45 ($95\%$ CI: 1.0–2.1), $$p \leq 0.052.$$ There were 3 cases of thyroid cancer ($1.1\%$) in the acromegaly cohort and 19 ($0.1\%$) in the control cohort. The HR for thyroid cancers was increased in patients with acromegaly as compared to the controls (HR: 17.0 ($95\%$ CI: 5.0–58.1), $p \leq 0.001$). We found no increased risk for other cancers, however, there was a borderline significant increased risk of prostate cancer in male patients with acromegaly (HR 2.05 ($95\%$ CI: 0.97–3.37), $$p \leq 0.060$$) (Table 3). Figure 3 illustrates all established cancer diagnoses in the study adjusted for persons at risk per year. There was a total of 48 cancer diagnoses in the acromegaly cohort, and 3211 in the control cohort, when including both cancers diagnosed before and after the diagnosis of acromegaly and index date, respectively. There was an increased rate of cancer diagnoses in the period around acromegaly diagnosis (±2 years) (Fig. 3).Table 3Cancer risk in patients with acromegaly and matched controlsCancerIncidences after study inclusionAcromegalyControlcohortcohortN = 262N = 26200HR$95\%$ CIP-valueOverall28 ($10.7\%$)2063 ($7.9\%$)1.451.00–2.100.052 Localized11 ($4.2\%$)776 ($3.0\%$)1.520.84–2.760.168 Non-localized12 ($4.6\%$)845 ($3.2\%$)1.500.85–2.660.162 Missing5 ($1.9\%$)442 ($1.7\%$)Breast1 ($0.4\%$)259 ($1.0\%$)0.400.06–2.860.362Prostate7 ($2.7\%$)356 ($1.4\%$)2.050.97–4.370.060Colorectal5 ($1.9\%$)265 ($1.0\%$)2.050.84–4.970.113Thyroid3 ($1.1\%$)19 ($0.1\%$)16.984.96–58.10<0.001Kidney1 ($0.4\%$)53 ($0.2\%$)1.860.26–13.510.537Hematological3 ($1.1\%$)171 ($0.7\%$)1.870.59–5.850.285Lung2 ($0.8\%$)202 ($0.8\%$)1.070.27–4.330.920Other6 ($2.3\%$)738 ($2.8\%$)0.880.39–1.970.755Cancer incidences in patients with acromegaly and matched controls. Risk of cancer is given in HRs, $95\%$ CI and p-values in patients compared to controls, and estimated for cancer incidences after study inclusion (Cox regression).Fig. 3Cancer incidence rate over time in patients with acromegaly and matched controls. Cancer diagnoses are presented per 100 person years in the control and acromegaly cohort distributed in relation to time of acromegaly diagnosis/index date (Year 0). The time categories cover two years each, e.g., “0” covers the time interval from time of diagnosis/index date until two years after diagnosis, and “−2” covers two years before diagnosis until time diagnosis/index date ## Discussion In the present prospective, single center cohort study from the South-Eastern Region of Norway, we found a constant incidence rate of acromegaly over time, and the mortality for patients diagnosed in the last two decades was persistently not elevated. There was a trend towards increased cancer incidence in patients with acromegaly. No acromegaly-related risk factors for death could be identified. The prevalence and incidence of acromegaly in the present study are in line with previous estimates from the Nordic countries [4, 5, 26–29], indicating a good coverage of the patient population. We did not demonstrate increased mortality in patients with acromegaly compared to the general population, in contrast to the slightly elevated rates in Sweden and Denmark [3, 26, 30, 31]. Possible explanations may be that the present analyses are based on a more recent cohort (1999–2019) than the analyses from Sweden (1987–2013 [3, 31] and 1991–2011 [30]) and Denmark (1991–2010 [26]). This is supported by the decline in mortality in Sweden in the patients diagnosed more recently [31], and is coinciding with the broader availability of effective medication for acromegaly and improved surgical and radiation techniques, enabling multi-modal, individualized treatment to patients with acromegaly. Although the overall mortality in the most recent Swedish publication was increased, the mortality in patients with biochemical control was not elevated, in contrast to non-controlled patients [30]. Due to few events, no firm conclusion on the most recently diagnosed patients could be drawn, as indicated by a broad CI in our study. However, similar trends with improved survival have been demonstrated previously in meta-analyses [2, 20]. In the present study, we could not identify any baseline characteristics with significant influence on mortality, except for age, as expected. In order to avoid immortal time bias, only baseline characteristics were included in our analysis [32]. The majority of our patients had pituitary macroadenomas, and almost half of them received SSAs as a primary treatment, because the probability for surgical cure was considered to be low in many of these cases. This practice was regularly implemented in the patients diagnosed in the early 2000s, when the first randomized study on preoperative SSA treatment was initiated [4]. This change in treatment, together with the modernized and individualized approach over the last twenty years, had an important effect on mortality. When mortality in acromegaly decreases towards the background population, causes of patient death shift towards causes in the general population [2]. Thus, the leading cause of death in the Norwegian population at present is malignancies, with cardiovascular disease as the second, in accordance with our findings in the patients with acromegaly (Norwegian Cause of Death Registry, 10.06.2021). We observed an equal gender distribution and median age at diagnosis, in contrast to a meta-analysis demonstrating a moderate female predominance ($53\%$), and a higher age at diagnosis in women as compared to men [33]. In comparison, a recent review found that acromegaly was more prevalent in women than in men, and women were older at diagnosis [34]. However, in the Nordics, the gender distribution was equal, and age at diagnosis for men and women was similar [26–28, 33, 34]. Studies derived from national registries, like the Danish and Swedish studies and the present study, are representative for a large, unselected populations, and are thus little prone to selection bias. Cancer incidence increased with age in the control cohort as expected, whereas, in the acromegaly cohort cancer incidence peaked markedly around the time of acromegaly (Fig. 3). This could be ascribed to surveillance bias occurring after a cancer diagnosis resulting in the detection of acromegaly, or vice versa: Newly diagnosed acromegaly may prompt cancer screening or suspicion. In order to reduce surveillance bias when estimating cancer incidence and risk, we excluded cases of malignancy established before the diagnosis of acromegaly, in accordance with the recent Swedish population-based study [35]. In contrast to the registry-based cohort study from Denmark [21], we did not exclude cancers established one year after the acromegaly diagnosis in order to avoid discharging cancers that could be associated with acromegaly, and possibly reflect biologic effects of GH excess in the years before delayed acromegaly diagnosis. Similarly to our results, the referred studies from Scandinavia found an overall elevated cancer risk in patients with acromegaly, and the meta-analysis in the Danish publication added further support to these findings [21, 35]. Accordingly, a nationwide cohort study from Italy demonstrated an overall increased cancer risk in patients compared to the general population [36]. However, this is in contrast with the population based studies from the United Kingdom and Germany, where no increased cancer incidence in patients with acromegaly compared to the general population were demonstrated [22, 23]. Of all five thyroid cancer cases in our patient cohort, two were established before the diagnosis of acromegaly and three after. Only two of these five cases where diagnosed close to the diagnosis (+/− 2 years) of acromegaly. However, we cannot exclude that the elevated thyroid cancer risk in our study can be related to surveillance bias. As described, data on thyroid cancer in patients with acromegaly are controversial and the absolute numbers are low [21, 35, 36]. Interestingly, we found a borderline significant increased risk of prostate cancer in men with acromegaly. These findings are similar to the above mentioned Danish meta-analysis [21], but in contrast to the studies from the United Kingdom and Germany [22, 23]. Previous studies have shown that increased IGF-1 levels were associated with increased risk of prostate cancer [7]. For cancer categories with low incidence, no finite conclusions can be drawn, as there is a considerable risk for type 2 error. The strengths of this study is that it is a single center study with patients followed according to a standardized management course and long follow-up interval for of up to 20 years, in combination with data from national health-registries, and the comparison with a large matched cohort based on the general population. Despite the single center design, the study cohort is population based representing unselected cases from over 3 million inhabitants. Although the study covers a large geographical area and population followed up to two decades, the absolute numbers of death and cancers were low. ## Conclusion The incidence, prevalence, gender distribution and age at diagnosis of acromegaly in South-Eastern Norway is similar to data from the other Nordic countries. The mortality rates in the patient cohort, that has received modern multimodal therapies and active surveillance for acromegaly-related complications, was not different from the background population. As in other recent European studies, we found a trend towards an increased overall cancer risk. Cardiovascular- and cancer-related mortality were not different as compared to the general population. ## Author contributions All authors contributed to the formulation of the scientific question and study design. All authors participated in material acquisition and preparation. C.M.F. and O.M.D. performed the statistical analyses. All authors contributed to the data interpretation. C.M.F. and A.H. wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and accepted the submission of the final manuscript. ## References 1. Melmed S, Bronstein MD, Chanson P, Klibanski A, Casanueva FF, Wass JAH. A Consensus Statement on acromegaly therapeutic outcomes. Nat. Rev. Endocrinol. (2018.0) 14 552-61.. DOI: 10.1038/s41574-018-0058-5 2. Bolfi F, Neves AF, Boguszewski CL, Nunes-Nogueira VS. Mortality in acromegaly decreased in the last decade: a systematic review and meta-analysis. Eur. J. Endocrinol. (2018.0) 179 59-71. DOI: 10.1530/EJE-18-0255 3. Esposito D, Ragnarsson O, Granfeldt D, Marlow T, Johannsson G, Olsson DS. Decreasing mortality and changes in treatment patterns in patients with acromegaly from a nationwide study. Eur. J. Endocrinol. (2018.0) 178 459-69.. DOI: 10.1530/EJE-18-0015 4. Carlsen SM, Lund-Johansen M, Schreiner T, Aanderud S, Johannesen O, Svartberg J. Preoperative octreotide treatment in newly diagnosed acromegalic patients with macroadenomas increases cure short-term postoperative rates: a prospective, randomized trial. J. Clin. Endocrinol. Metab. (2008.0) 93 2984-2990. DOI: 10.1210/jc.2008-0315 5. Aagaard C, Christophersen AS, Finnerup S, Rosendal C, Gulisano HA, Ettrup KS. The prevalence of acromegaly is higher than previously reported: changes over a three-decade period. Clin. Endocrinol (2022.0) 97 773-782. DOI: 10.1111/cen.14828 6. Esposito D, Olsson DS, Franzén S, Miftaraj M, Nåtman J, Gudbjörnsdottir S. Effect of diabetes on morbidity and mortality in patients with acromegaly. J. Clin. Endocrinol. Metab. (2022.0) 107 2483-92.. DOI: 10.1210/clinem/dgac400 7. Renehan AG, Zwahlen M, Minder C, O’Dwyer ST, Shalet SM, Egger M. Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis. Lancet (2004.0) 363 1346-1353. DOI: 10.1016/S0140-6736(04)16044-3 8. Chesnokova V, Zonis S, Zhou C, Recouvreux MV, Ben-Shlomo A, Araki T. Growth hormone is permissive for neoplastic colon growth. Proc. Natl Acad. Sci. USA (2016.0) 113 E3250-E3259. DOI: 10.1073/pnas.1600561113 9. Kasuki L, Maia B, Gadelha MR. Acromegaly and colorectal neoplasm: an update. Front Endocrinol. (2022.0) 13 924952. DOI: 10.3389/fendo.2022.924952 10. Bollerslev J, Heck A, Olarescu NC. Management of endocrine disease: individualised management of acromegaly. Eur. J. Endocrinol. (2019.0) 181 R57-r71. DOI: 10.1530/EJE-19-0124 11. Bex M, Abs R, T’Sjoen G, Mockel J, Velkeniers B, Muermans K. AcroBel–the Belgian registry on acromegaly: a survey of the ‘real-life’ outcome in 418 acromegalic subjects. Eur. J. Endocrinol. (2007.0) 157 399-409. DOI: 10.1530/EJE-07-0358 12. Bollerslev J, Fougner SL, Berg JP. New directions in pharmacological treatment of acromegaly. Expert Opin. Investigational drugs (2009.0) 18 13-22. DOI: 10.1517/13543780802554357 13. Fleseriu M, Hoffman AR, Katznelson L. American Association of Clinical Endocrinologists and American College of Endocrinology Disease State clinical review: management of acromegaly patients: what is the role of pre-operative medical therapy?. Endocr. Pract. Off. J. Am. Coll. Endocrinol. Am. Assoc. Clin. Endocrinologists (2015.0) 21 668-673 14. Giustina A, Chanson P, Bronstein MD, Klibanski A, Lamberts S, Casanueva FF. A consensus on criteria for cure of acromegaly. J. Clin. Endocrinol. Metab. (2010.0) 95 3141-3148. DOI: 10.1210/jc.2009-2670 15. Katznelson L, Laws ER, Melmed S, Molitch ME, Murad MH, Utz A. Acromegaly: an endocrine society clinical practice guideline. J. Clin. Endocrinol. Metab. (2014.0) 99 3933-3951. DOI: 10.1210/jc.2014-2700 16. Losa M, Bollerslev J. Pros and cons in endocrine practice: pre-surgical treatment with somatostatin analogues in acromegaly. Endocrine (2016.0) 52 451-457. DOI: 10.1007/s12020-015-0853-x 17. Carlsen SM, Svartberg J, Schreiner T, Aanderud S, Johannesen O, Skeie S. Six-month preoperative octreotide treatment in unselected, de novo patients with acromegaly: effect on biochemistry, tumour volume, and postoperative cure. Clin. Endocrinol. (2011.0) 74 736-743. DOI: 10.1111/j.1365-2265.2011.03982.x 18. Fougner SL, Bollerslev J, Svartberg J, Oksnes M, Cooper J, Carlsen SM. Preoperative octreotide treatment of acromegaly: long-term results of a randomised controlled trial. Eur. J. Endocrinol. (2014.0) 171 229-235. DOI: 10.1530/EJE-14-0249 19. Nunes VS, Correa JM, Puga ME, Silva EM, Boguszewski CL. Preoperative somatostatin analogues versus direct transsphenoidal surgery for newly-diagnosed acromegaly patients: a systematic review and meta-analysis using the GRADE system. Pituitary (2015.0) 18 500-508. DOI: 10.1007/s11102-014-0602-9 20. Dekkers OM, Biermasz NR, Pereira AM, Romijn JA, Vandenbroucke JP. Mortality in acromegaly: a metaanalysis. J. Clin. Endocrinol. Metab. (2008.0) 93 61-67. DOI: 10.1210/jc.2007-1191 21. Dal J, Leisner MZ, Hermansen K, Farkas DK, Bengtsen M, Kistorp C. Cancer incidence in patients with acromegaly: a cohort study and meta-analysis of the literature. J. Clin. Endocrinol. Metab. (2018.0) 103 2182-2188. DOI: 10.1210/jc.2017-02457 22. Orme SM, McNally RJ, Cartwright RA, Belchetz PE. Mortality and cancer incidence in acromegaly: a retrospective cohort study. United Kingdom Acromegaly Study Group. J. Clin. Endocrinol. Metab. (1998.0) 83 2730-2734. PMID: 9709939 23. Petroff D, Tönjes A, Grussendorf M, Droste M, Dimopoulou C, Stalla G. The incidence of cancer among acromegaly patients: results from the German Acromegaly Registry. J. Clin. Endocrinol. Metab. (2015.0) 100 3894-3902. DOI: 10.1210/jc.2015-2372 24. Olarescu NC, Heck A, Godang K, Ueland T, Bollerslev J. The metabolic risk in patients newly diagnosed with acromegaly is related to fat distribution and circulating adipokines and improves after treatment. Neuroendocrinology (2016.0) 103 197-206. DOI: 10.1159/000371818 25. 25.Working Group Report. International rules for multiple primary cancers (ICD-0 third edition). Eur. J. Cancer Prev. 14(4), 307–308 (2016) 26. Dal J, Feldt-Rasmussen U, Andersen M, Kristensen LO, Laurberg P, Pedersen L. Acromegaly incidence, prevalence, complications and long-term prognosis: a nationwide cohort study. Eur. J. Endocrinol. (2016.0) 175 181-190. DOI: 10.1530/EJE-16-0117 27. Tjornstrand A, Gunnarsson K, Evert M, Holmberg E, Ragnarsson O, Rosen T. The incidence rate of pituitary adenomas in western Sweden for the period 2001-2011. Eur. J. Endocrinol. (2014.0) 171 519-526. DOI: 10.1530/EJE-14-0144 28. Agustsson TT, Baldvinsdottir T, Jonasson JG, Olafsdottir E, Steinthorsdottir V, Sigurdsson G. The epidemiology of pituitary adenomas in Iceland, 1955-2012: a nationwide population-based study. Eur. J. Endocrinol. (2015.0) 173 655-664. DOI: 10.1530/EJE-15-0189 29. Kauppinen-Mäkelin R, Sane T, Reunanen A, Välimäki MJ, Niskanen L, Markkanen H. A nationwide survey of mortality in acromegaly. J. Clin. Endocrinol. Metab. (2005.0) 90 4081-4086. DOI: 10.1210/jc.2004-1381 30. Arnardóttir S, Järås J, Burman P, Berinder K, Dahlqvist P, Erfurth EM. Long-term outcomes of patients with acromegaly: a report from the Swedish Pituitary Register. Eur. J. Endocrinol. (2022.0) 186 329-39.. DOI: 10.1530/EJE-21-0729 31. Esposito D, Ragnarsson O, Granfeldt D, Marlow T, Johannsson G, Olsson DS. Decreasing mortality and changes in treatment patterns in patients with acromegaly from a nationwide study. Eur. J. Endocrinol. (2019.0) 180 X1-X3. DOI: 10.1530/EJE-18-0015e 32. Dekkers OM, Groenwold RHH. When observational studies can give wrong answers: the potential of immortal time bias. Eur. J. Endocrinol. (2021.0) 184 E1-e4. DOI: 10.1530/EJE-20-1124 33. Dal J, Skov BG, Andersen M, Feldt-Rasmussen U, Feltoft CL, Karmisholt J. Sex differences in acromegaly at diagnosis: a nationwide cohort study and meta-analysis of the literature. Clin. Endocrinol. (2021.0) 94 625-35.. DOI: 10.1111/cen.14392 34. Lenders NF, McCormack AI, Ho KKY. Management of endocrine disease: does gender matter in the management of acromegaly?. Eur. J. Endocrinol. (2020.0) 182 R67-r82. DOI: 10.1530/EJE-19-1023 35. Esposito D, Ragnarsson O, Johannsson G, Olsson DS. Incidence of benign and malignant tumors in patients with acromegaly is increased: a nationwide population-based study. J. Clin. Endocrinol. Metabol (2021.0) 106 3487-3496 36. Terzolo M, Reimondo G, Berchialla P, Ferrante E, Malchiodi E, De Marinis L. Acromegaly is associated with increased cancer risk: a survey in Italy. Endocr. Relat. Cancer (2017.0) 24 495-504. DOI: 10.1530/ERC-16-0553
--- title: Body surface area-based kidney length percentiles misdiagnose small kidneys in children with overweight/obesity authors: - Pierluigi Marzuillo - Gemma Carreras-Badosa - José-María Martínez-Calcerrada - Stefano Guarino - Pier Luigi Palma - Delfina Petrone - Emanuele Miraglia del Giudice - Judit Bassols - Abel López-Bermejo journal: Pediatric Nephrology (Berlin, Germany) year: 2022 pmcid: PMC10060296 doi: 10.1007/s00467-022-05718-8 license: CC BY 4.0 --- # Body surface area-based kidney length percentiles misdiagnose small kidneys in children with overweight/obesity ## Abstract ### Background We evaluated the diagnostic performance of height-, age- and body surface area (BSA)-based kidney length (KL) percentiles in the identification of at least one small kidney (KL < 3rd) and in the prediction of reduced estimated glomerular filtration rate (eGFR) and/or elevated blood pressure (BP) in children with and without overweight (OW)/obesity(OB). ### Methods In this cross-sectional study, 744 apparently healthy children (mean age 8.3 years) were recruited in a primary care setting. Clinical data were collected, and serum creatinine and KL were measured. Height-, age- and BSA-based percentiles of KL were calculated and the association of at least one small kidney per subject with reduced eGFR and/or elevated BP was explored by logistic regression. ### Results Two hundred fifty-seven out of seven hundred forty-four ($34.5\%$) subjects were OW/OB and 127 ($17.1\%$) had reduced eGFR or elevated BP. In separate analyses in children with OW/OB, the KL percentiles calculated on the basis of BSA were lower compared with height- and age-based KL percentiles. Consequently, the prevalence of a small kidney was significantly higher when evaluating percentiles of KL based on BSA compared with other percentiles. In logistic regression analysis, a small kidney was significantly associated with reduced eGFR and/or elevated BP only when using height-based KL percentiles. The KL percentiles according to BSA for the ideal weight (iBSA) showed similar performance compared with height-based percentiles. No differences in the diagnostic performance of different percentiles were found in children with normal weight. ### Conclusions BSA-based percentiles underestimate KL in children with OW/OB. In these subjects, the use of height-based or iBSA-based percentiles should be preferred. ### Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information ### Supplementary Information The online version contains supplementary material available at 10.1007/s00467-022-05718-8. ## Introduction It is common practice to normalize kidney length (KL) by body surface area (BSA), based on the Mosteller [1] or Du Bois and Du Bois [2] formulae, using height and weight measurements. However, the kidneys only clear the extracellular water and not the fat mass. It has been suggested to use ideal body weight to calculate BSA, rather than actual body weight [3], but this is rarely practiced. Since in children and adolescents with overweight (OW) or obesity (OB) the actual weight would lead to a higher BSA than BSA using ideal body weight, we hypothesized that KL percentiles would be underestimated in this group of patients despite the fact that they represent a peculiar category of subjects in whom the size of abdominal organs may increase consistently with their BMI [4–6]. Children with OB, in fact, have longer kidneys than their normal weight (NW) counterparts [5]. Normal limits of KL based on height for this group to avoid unnecessary evaluation for nephromegaly have thus been calculated [5]. In view of the OW/OB rates among children and adolescents worldwide with about one-third of children presenting with OW or OB [7, 8], it is important to know the degree of misdiagnosing kidney hypoplasia when the actual weight to calculate BSA is used, because this could lead to unnecessary investigations to rule out chronic kidney disease [9, 10]. We, therefore, conducted a study to identify the most appropriate method to calculate KL percentiles in children with OW/OB. To achieve this aim, we evaluated the diagnostic performance of height-, age- and BSA-based KL percentiles [11] in the identification of at least one small kidney (KL < 3rd) and in the prediction of reduced estimated glomerular filtration rate (eGFR; < 90 mL/min/1.73 m2) and/or elevated BP (> 95th percentile for age and sex). We also estimated the economic and biological costs derived from a potential small kidney misdiagnosis in children and adolescents with OW/OB. ## Methods In this study, 744 apparently healthy school-age children (coming from the general population and without a prior diagnosis of disease) were seen in a primary care setting (primary care centers of Girona and Figueres, both regions in Northeastern Spain) and recruited between 2009 and 2015 in a prospective longitudinal study of obesity and cardiovascular risk factors in children [12, 13]. Children were invited to participate in the study during their routine healthy visits in the primary care centers. These visits are part of the protocol of the Childhood Health Program established by the Catalan Public Health Agency. During the visit, the pediatrician explained the study to the families, and those interested to participate were contacted by the study investigators. Informed written consent was signed by the parents before the enrollment. Inclusion criteria were the availability of ultrasound of both kidneys and the anthropometrical parameters allowing the calculation of height-, age-, and BSA-based KL percentiles [11]. In case of major known congenital anomalies (abnormal liver, kidney, or thyroid functions), evidence of chronic or acute illness, or prolonged use of medication, the patients were excluded from the study. The study was approved by the Institutional Review Board of Dr. Josep Trueta Hospital and was carried out according to the Declaration of Helsinki. ## Assessment of subjects Subjects were weighed on a calibrated scale and their height was measured with a Harpenden stadiometer with an accuracy of 0.1 cm and 0.05 kg, respectively. Body-mass index (BMI) was calculated as weight (in kilograms) divided by the square of height (in meters). Age- and sex-adjusted percentiles for BMI were calculated using regional normative data [14]. The study subjects were grouped according to their BMI into normal weight (NW) if BMI was less than 85th percentile, OW if BMI between 85th to less than 95th percentile and OB if BMI was 95th percentile or greater [15]. BSA was calculated as follows: BSA (m2) = square root of [height (cm) × weight (kg)/3600] [1]. Also, ideal BSA (iBSA) was calculated computing into the above-mentioned equation the ideal weight of the subject as follows: iBSA (m2) = square root of [height (cm) × ideal weight (kg)/3600]. The ideal body weight was the weight at the same percentile as the height, for the same age and gender [16]. Systolic and diastolic blood pressures (SBP and DBP) were measured using an electronic sphygmomanometer (Dinamap Pro 100, GE Healthcare, Chalfont St. Giles, UK) after a 10 min rest on the right arm for three consecutive times with the child in the supine position. The average of the two most similar measurements was used in the analysis. Elevated BP was defined by SBP or DBP > 95th percentile for age, height, and gender [17] for this dataset. KL was measured by high-resolution ultrasonography (MyLabTM25, Esaote, Firenze, Italy) as previously reported [13]. The studies were conducted by an experienced pediatric technician using a 3.5–5 MHz convex transducer. KL was measured as the distance between the upper and the lower pole for each kidney on images taken longitudinally. The measures were taken with the child placed in the supine position and the technician situated on the right side of the child. Thereafter, all KL measurements were taken from the right and left flank of the child. Averages of three measurements were used in the study. The intra-observer error of KL measurement was $2.5\%$ and the inter-observer error was $0\%$, as all ultrasound measurements were performed by the same observer who was unaware of the clinical and laboratory characteristics of the subjects. Intra-subject coefficient of variation for ultrasound measurements was less than $6\%$. The percentage of coefficient of variation for each sample was calculated in a smaller subset of children ($$n = 10$$) as the standard deviation of the 3 independent measurements, divided by the mean and multiplied by 100. Height-, age- and BSA-based percentiles of both kidneys on the basis of the normative values provided by Obrycki et al. were calculated [11]. Blood samples were obtained in the morning after an overnight fast. Serum creatinine concentrations were routinely assessed in the clinical laboratory of the Hospital using the enzymatic method (COBAS 702, Roche Diagnostics, IN, USA). eGFR was calculated by the pediatric version of the FAS-equation: [eGFR = 107.3/(Scr/Q)] where $Q = 0.0270$ × Age + 0.232921. We defined the eGFR as being reduced when it was < 90 mL/min/1.73 m2 [18]. The FAS-equation has previously shown the ability to better select children and adolescents with OW/OB with reduced eGFR and worse cardiometabolic profile [19]. This formula, in fact, eliminates a “potential bias” related to the higher stature of children with OW/OB compared with NW subjects matched for age and gender [19]. ## Classifications A kidney with length < 3rd percentile was defined as “small kidney” in this manuscript. Subjects were also classified as having or not OW/OB, reduced eGFR and/or elevated BP as defined above. ## Cost analysis The direct costs that would have been incurred for a misdiagnosis of small kidney by BSA-based percentiles compared with the height-based percentiles were calculated. We used the reimbursement of the Catalan Health System to estimate these costs as follows: blood sample collection (€9.00), creatinine measurement (€0.93), urinalysis (€2.96), nephrological follow-up visit (€80.00), Tc99m DMSA renal scintigraphy (€53.00), and cystography (€139.00) [20]. To calculate the X-ray dose exposure, we used an estimated mean dose of 0.30 mSv for the Tc99m DMSA renal scintigraphy and an estimated mean dose of 1.85 mSv for cystography [21]. ## Statistical analysis P values < 0.05 were considered significant. Differences for continuous variables were analyzed with the independent sample t-test for normally distributed variables and with the Mann–Whitney test in case of non-normality. All the data are presented as mean ± standard deviation scores (SDS). Qualitative variables were compared using the chi-squared test. Univariate and multivariate logistic regression models were used to explore associations with reduced eGFR and/or elevated BP of a small kidney according to height-, age-, and BSA-based percentiles [11]. We computed into the multivariate logistic regression analyses the parameters showing significant differences ($p \leq 0.05$) in univariate analysis. The Stat-Graph XVII software for Windows was used for all statistical analyses except for logistic regression models, which were carried out with SPSS 25 software for Windows. ## General characteristics Participation in the study was $70\%$ out of all invited families. We enrolled 744 children, 395 ($53.1\%$ boys), with a mean age of 8.3 years (age range: 3.2–14.8 years). The general characteristics of the enrolled subjects are shown in Table 1. Out of these 744 subjects, 257 ($34.5\%$) were OW/OB and 127 ($17.1\%$) had reduced eGFR or elevated BP. Specifically, four children showed both eGFR reduction and elevated BP, 77 only eGFR reduction and 46 only elevated BP. On the basis of weight status, 41 out of 257 ($15.9\%$) children with OW/OB showed eGFR reduction and/or elevated BP compared with 86 out of 487 ($17.6\%$) children with normal weight (NW) ($$p \leq 0.55$$). Children with OW/OB were older compared with those with NW (9.0 ± 2.0 years vs. 7.9 ± 1.9 years; $p \leq 0.001$).Table 1General characteristics of the enrolled patients. Continuous variables are presented as median and interquartile range, if not normally distributed, and as mean and standard deviation, if normally distributedAge (years), mean (SD)8.3 (2.0)Gender (female), No. (%) 349 (46.9)Puberty (yes), No. (%) 99 (13.3)BMI-SDS, median (IQR)0.4 (2.3)Height-SDS, median (IQR)0.5 (1.6)BSA (m2), median (IQR)1.1 (0.4)Waist/height ratio0.5 (0.1)SBP-SDS, mean (SD)0.6 (0.9)DBP-SDS, median (IQR)0.1 (0.9)Creatinine (mg/dL), median (IQR)0.5 (0.12)eGFR (mL/min/1.73 m2), median (IQR)99.4 (23.4)BMI, body mass index; BSA, body surface area; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; IQR, interquartile range; SBP, systolic blood pressure; SD, standard deviation; SDS, standard deviation scores ## Comparison of performance of height-, age- and BSA-based KL percentiles When evaluating the global population (including subjects with NW, OW and OB), the KL percentiles calculated on the basis of BSA were significantly lower compared with height- and age-based percentiles. The results were similar for both kidneys (Figs. 1A and B). Moreover, the prevalence of a small kidney was significantly higher when evaluating KL by BSA-based percentiles compared with height- and age-based percentiles (Figs. 2A and B).Fig. 1Comparison of the kidney length percentiles calculated on the basis of height, age and BSA in all children and in those with overweight/obesity or with normal weight. Abbreviations: BSA, body surface area; OB, obesity; OW, overweight; NW, normal weightFig. 2Prevalence of a small kidney comparing height-, age-, and BSA-based percentiles in all children and in those with overweight/obesity or with normal weight. Abbreviations: BSA, body surface area; OB, obesity; OW, overweight; NW, normal weight Restricting the analysis to the children with OW/OB, for both kidneys, the KL percentiles calculated on the basis of BSA were lower compared with height- and age-based KL percentiles (Figs. 1C and D), and therefore the prevalence of a small kidney was significantly higher when evaluating KL by BSA-based percentiles compared with height- and age-based percentiles (Figs. 2C and D). When the analysis was limited to the children with NW, the KL percentiles calculated on the basis of height, age and BSA (Figs. 1E and F) and the prevalence of a small kidney on the basis of height, age and BSA percentiles (Figs. 2E and F) were similar for both kidneys. An exploratory analysis of the prediction of reduced eGFR and/or elevated BP by a small kidney on the basis of height-, age- and BSA-based KL percentile calculations was performed (Table 2). We found that, for the global population and in univariate analysis, a small kidney on the basis of height, age and BSA percentiles was significantly associated with reduced eGFR and/or elevated BP (Table 2). In multivariate analysis, only a small kidney on the basis of height persisted significantly associated with reduced eGFR and/or elevated BP (Table 2). Assessing separately children with OW/OB, a small kidney was significantly associated with reduced eGFR and/or elevated BP only when calculated on the basis of height (Table 2). On the other hand, among subjects with NW, in univariate analysis, a small kidney calculated on the basis of height-, age-, or BSA-derived KL percentiles was significantly associated with reduced eGFR and/or elevated BP (Table 2). None of them persisted as significant in multivariate analysis (Table 2).Table 2Exploratory analyses of the prediction of reduced eGFR and/or elevated blood pressure by a small kidney (at least one kidney with length < 3th percentile) on the basis of height-, age- and BSA-derived percentilesGlobal populationChildren with overweight/obesityChildren with normal weightUnivariate analysisMultivariate analysisUnivariate analysisMultivariate analysisUnivariate analysisMultivariate analysisKL percentilesOR$95\%$CIpOR$95\%$CIpOR$95\%$CIpOR$95\%$CIpOR$95\%$CIpOR$95\%$CIpHeight-based5.21.6–16.80.0065.01.2–20.30.022.91.02–8.30.042.91.02–8.30.046.21.5–26.20.011.80.4–8.30.47Age-based1.91.01–4.00.051.00.4–2.30.92.20.7–7.50.19n.an.an.a2.31.01–5.20.041.10.4–2.80.8BSA-based2.11.1–4.10.030.90.4–2.10.91.90.8–4.10.11n.an.an.a4.41.3–14.40.013.70.6–24.00.17 ## Comparison of performance of height-, age- and iBSA-based KL percentiles in children with OW/OB For both kidneys, the KL percentiles calculated on the basis of iBSA were significantly lower only when compared with age-based percentiles (Figs. 3A and B). The prevalence of a small kidney was similar when evaluating KL by age-based and iBSA-based percentiles (Figs. 3C and D).Fig. 3Comparison of the right and left kidney length percentiles and prevalence of a small kidney on the basis of height, age, and iBSA percentiles in children with overweight/obesity. Abbreviations: iBSA, ideal body surface area; OB, obesity; OW, overweight; NW, normal weight In univariate analysis, a small kidney on the basis of both height and iBSA was significantly associated with reduced eGFR and/or elevated BP. None of them persisted as significant in multivariate analysis (Table 3).Table 3Exploratory analyses of the prediction of reduced eGFR and/or elevated blood pressure by a small kidney (at least one kidney with length < 3th percentile) on the basis of height-, age- and iBSA-derived percentiles in children with OW/OBUnivariate analysisMultivariate analysisKL percentilesOR$95\%$CIpOR$95\%$CIpHeight-based2.91.02–8.30.041.90.4–100.5Age-based2.20.7–7.50.19n.an.an.aiBSA-based2.71.1–7.30.041.70.3–100.6 ## Estimate of economic and biological costs derived from a misdiagnosis of a small kidney obtained by BSA-based KL percentiles in children with OW/OB On the basis of height-based KL percentiles, 18 out of 257 ($7\%$) subjects had a small kidney (three with both small kidneys and 15 with one small kidney). On the basis of age-based KL percentiles 14 out of 247 ($5.7\%$) subjects had a small kidney (three with both small kidneys and 11 with one small kidney). On the basis of BSA-based KL percentiles, 46 out of 257 ($17.9\%$) subjects had a small kidney (16 with both small kidneys and 30 with one small kidney). Because the height-based KL percentiles showed the best diagnostic performance, these percentiles were used as the gold standard in the present analysis. Therefore, compared with height-based percentiles, when using the BSA-based percentiles, 28 out of 257 ($10.9\%$) children received a misdiagnosis of a small kidney with a potential inappropriate indication to undergo further biochemical and instrumental exams. Considering the performing of blood sample collection with creatinine measurement, urinalysis, nephro-urological visit, Tc99m DMSA renal scintigraphy and cystography for each of these 28 subjects, the cumulative direct economic and biological costs due to small kidney misdiagnosis were respectively 7976.92 € (284.89 € for each patient) and 81.7 mSv (2.15 mSv for each patient, equivalent to 107.5 chest X-rays for each patient). ## Discussion This study investigates the diagnostic performance of BSA-based percentiles of KL compared with height- and age-based percentiles. Our data indicate that the BSA-based percentiles underestimate KL in children and adolescents with OW/OB yielding a misdiagnosis of a small kidney in $11\%$ of these subjects. A small kidney is associated with reduced eGFR and/or hypertension [9, 22, 23]. This association has been also shown in our population (Table 2). Interestingly, not all percentiles performed similarly in predicting reduced eGFR and/or elevated BP. In the global population, in univariate analysis, a small kidney according to all the percentiles (height-, age- and BSA-based) was significantly associated with reduced eGFR and/or elevated BP. However, in multivariate analysis, only a small kidney calculated on the basis of height-based percentiles showed a significant association with reduced eGFR and/or elevated BP. Similar findings were obtained in children with OW/OB. Moreover, among children with NW, all the percentiles performed equally in the identification of a small kidney. In these subjects, in fact, in univariate analysis, a small kidney calculated on the basis of both height- and age- and BSA-based percentiles was significantly associated with reduced eGFR and/or elevated BP while in multivariate analysis, none of them remained significant. Usually the right kidney is smaller than the left one [24, 25]; we confirmed this finding also in our pediatric population. In fact, the right KL percentiles both for age, height, BSA and iBSA and both in children with NW and OW/OB were lower compared with the left KL percentile (Fig. 1). Despite this difference, the BSA-based percentiles underestimated the KL of both kidneys compared with other percentiles. We wish to emphasize that the percentage of subjects showing elevated BP and/or reduced eGFR is quite high ($17\%$) in our study group. This could be explained by the cross-sectional design of the study, with measurement of the parameters only in a 1-day clinical evaluation. In fact, evidence indicates that when repeating the BP measurements in different visits, the percentage of elevated BP significantly decreases from 11.4 to $2.2\%$ [26]. Similarly, one serum creatinine measurement is not sufficient to correctly identify the subjects with reduced eGFR levels because the creatinine measurement is influenced by several factors, such as dietary intake, body composition and muscle mass [27]. Moreover, as stated in the KDIGO chronic kidney disease guidelines, the serum creatinine measurement over a 3-month period is mandatory to correctly identify patients with chronic kidney disease [10]. The presence of a small kidney predicted the presence of reduced eGFR and/or elevated BP in our study group despite the overestimation of the subjects with these conditions. This adds further evidence to the association between small kidney and reduced eGFR and/or elevated BP and reinforces the importance of a correct interpretation of KL using the most appropriate percentiles according to weight status. Our data indicate that when evaluating children with OW/OB, the most appropriate percentiles are those calculated on the basis of height. This finding is in line with the results of Obrycki et al. [ 11] who showed — with different methods and starting from a different research question — that the most significant predictor of KL was statural height. Differently from the above-mentioned paper [11], we tested the performance of the different percentiles [11] toward the diagnosis of small kidney after the classification of population in NW and OW/OB. In addition, we showed that in children with OW/OB, as an alternative, also the BSA-based percentiles can be used but it is important to calculate the subject’s BSA on the basis of ideal weight (iBSA). In this manner, a similar performance compared with height-based percentiles could be obtained in the identification of a small kidney. In fact, differences among the percentiles of KL on the basis of iBSA, height, and age were minimal (Fig. 3) and the prevalence of a small kidney was similar when using height- and iBSA-based KL percentiles (Fig. 3). Moreover, height- and iBSA-based KL percentiles performed equally well in the identification of reduced eGFR and/or elevated blood pressure by a small kidney (Table 3). Correct identification of a small kidney is also important because it can reflect congenital anomalies of the kidney and urinary tract. The current clinical practice relies on performing a Tc99m DMSA renal scintigraphy and cystography in the case of a small kidney [9, 28]. Our cost analysis in case of misdiagnosis of a small kidney using BSA-based percentile to evaluate KL in children with OW/OB indicates both non-negligible economic and biological costs further underlining the importance of a correct selection of KL percentiles to be used in clinical practice in case of children with OW/OB. Moreover, the reduction of X-ray exposure is mandatory in children because evidence exists linking X-ray exposure, especially if occurring early in life, and increased risk of cancer during the life of the subjects [29]. Finally, we wish to emphasize that the prevalence of $34.5\%$ of OW/OB found among Spanish children and adolescents in our study is in line with published data about children and adolescents of the same nation indicating a prevalence of OW/OB of $35.3\%$ [30]. Limitations of our study include the cross-sectional design with the lack of a second collection of parameters within the following 3 months in subjects with elevated BP and/or reduced eGFR and the lack of availability of data regarding proteinuria. Unfortunately, low agreement between kidney volume and KL has been demonstrated [31–33]. As a future perspective, it could be interesting to study the performance of kidney volume to precisely assess kidney size in children and adolescents with OW/OB. In the meantime, when evaluating KL in children with OW/OB, the KL percentiles could be evaluated on the basis of height or iBSA. In conclusion, our study shows that the BSA-based percentiles can underestimate the KL percentiles when evaluating children with OW/OB. The most appropriate method to evaluate KL percentiles in these subjects is the use of height-based percentiles. Alternatively, also iBSA-based percentiles — by calculating BSA on the basis of ideal weight — can be used with a similar diagnostic performance in the detection of a small kidney and in the identification of subjects with elevated BP and/or reduced eGFR. ## Supplementary Information Below is the link to the electronic supplementary material. Graphical Abstract (PPTX 3525 KB) ## References 1. Mosteller R. Simplified calculation of body-surface area. N Engl J Med (1987.0) 317 1098-1098. DOI: 10.1056/NEJM198710223171717 2. Du Bois D, Du Bois E. A formula to estimate the approximate surface area if height and weight be known. 1916 - PubMed. Arch Intern Med (1916.0) 17 863-871 3. Livingston EH, Lee S. Body surface area prediction in normal-weight and obese patients. Am J Physiol - Endocrinol Metab (2001.0) 281 586-591. DOI: 10.1152/AJPENDO.2001.281.3.E586/ASSET/IMAGES/LARGE/H10910507003.JPEG 4. Lane PH, Belsha CW, Plummer J, Steinhardt GF, Lynch RE, Wood EG. Relationship of renal size, body size, and blood pressure in children. Pediatr Nephrol (1998.0) 12 35-39. DOI: 10.1007/S004670050399 5. Pantoja Zuzuárregui JR, Mallios R, Murphy J. The effect of obesity on kidney length in a healthy pediatric population. Pediatr Nephrol (2009.0) 24 2023-2027. DOI: 10.1007/S00467-009-1202-1 6. Dinkel E, Ertel M, Dittrich M, Peters H, Berres M, Schulte-Wissermann H. Kidney size in childhood sonographical growth charts for kidney length and volume. Pediatr Radiol (1985.0) 15 38-43. DOI: 10.1007/BF02387851 7. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA (2014.0) 311 806-814. DOI: 10.1001/JAMA.2014.732 8. Ahrens W, Pigeot I, Pohlabeln H, De Henauw S, Lissner L, Molnár D, Moreno LA, Tornaritis M, Veidebaum T, Siani A. Prevalence of overweight and obesity in European children below the age of 10. Int J Obes (Lond) (2014.0) 38 S99-S107. DOI: 10.1038/IJO.2014.140 9. Guarino S, Capalbo D, Martin N, Campana G, Rambaldi PF, Miraglia Del Giudice E, La Manna A, Marzuillo P. In children with urinary tract infection reduced kidney length and vesicoureteric reflux predict abnormal DMSA scan. Pediatr Res (2020.0) 87 779-784. DOI: 10.1038/s41390-019-0676-1 10. Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl (2012.0) 33 1-150 11. Obrycki Ł, Sarnecki J, Lichosik M, Sopińska M, Placzyńska M, Stańczyk M, Mirecka J, Wasilewska A, Michalski M, Lewandowska W, Dereziński T, Pac M, Szwarc N, Annusewicz K, Rekuta V, Ažukaitis K, Čekuolis A, Wierzbicka A, Jankauskiene A, Kalicki B, Jobs K, Tkaczyk M, Feber J, Litwin M. Kidney length normative values in children aged 0–19 years - a multicenter study. Pediatr Nephrol (2022.0) 37 1075-1085. DOI: 10.1007/S00467-021-05303-5 12. Xargay-Torrent S, Puerto-Carranza E, Marcelo I, Mas-Parés B, Gómez-Vilarrubla A, Martínez-Calcerrada JM, de Zegher F, Ibáñez L, López-Bermejo A, Bassols J. Estimated glomerular filtration rate and cardiometabolic risk factors in a longitudinal cohort of children. Sci Rep (2021.0) 11 1-9. DOI: 10.1038/s41598-021-91162-x 13. Lizarraga-Mollinedo E, Martínez-Calcerrada JM, Padrós-Fornieles C, Mas-Pares B, Xargay-Torrent S, Riera-Pérez E, Prats-Puig A, Carreras-Badosa G, de Zegher F, Ibáñez L, Bassols J, López-Bermejo A. Renal size and cardiovascular risk in prepubertal children. Sci Rep (2019.0) 9 1-8. DOI: 10.1038/s41598-019-41757-2 14. Carrascosa A, Fernández JM, Fernández C, Ferrández A, López-Siguero JP, Sánchez E, Sobradillo B, Yeste D. Spanish growth studies 2008. New anthropometric standards Endocrinol Nutr (2008.0) 55 484-506. DOI: 10.1016/S1575-0922(08)75845-5 15. 15.Centers for Disease Control and Prevention Defining Childhood Weight Status \| Overweight & Obesity \| CDC. https://www.cdc.gov/obesity/basics/childhood-defining.html. Accessed 11 May 2022 16. 16.(2004) A health professional’s guide to using growth charts. Paediatr Child Health 9:174–176. 10.1093/PCH/9.3.174 17. Lurbe E, Cifkova R, Cruickshank JK, Dillon MJ, Ferreira I, Invitti C, Kuznetsova T, Laurent S, Mancia G, Morales-Olivas F, Rascher W, Redon J, Schaefer F, Seeman T, Stergiou G, Wühl E, Zanchetti A, Europea S. Management of high blood pressure in children and adolescents: recommendations of the European Society of hypertension. An Pediatr (Barc) (2010.0) 73 51.e1-28. DOI: 10.1016/J.ANPEDI.2010.04.001 18. Levey AS, Eckardt KU, Dorman NM, Christiansen SL, Hoorn EJ, Ingelfinger JR, Inker LA, Levin A, Mehrotra R, Palevsky PM, Perazella MA, Tong A, Allison SJ, Bockenhauer D, Briggs JP, Bromberg JS, Davenport A, Feldman HI, Fouque D, Gansevoort RT, Gill JS, Greene EL, Hemmelgarn BR, Kretzler M, Lambie M, Lane PH, Laycock J, Leventhal SE, Mittelman M, Morrissey P, Ostermann M, Rees L, Ronco P, Schaefer F, St Clair Russell J, Vinck C, Walsh SB, Weiner DE, Cheung M, Jadoul M, Winkelmayer WC. Nomenclature for kidney function and disease: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney Int (2020.0) 97 1117-1129. DOI: 10.1016/J.KINT.2020.02.010 19. Di Bonito P, Licenziati MR, Campana G, Chiesa C, Pacifico L, Manco M, Miraglia Del Giudice E, Di Sessa A, Baroni MG, Marzuillo P, Valerio G. Prevalence of mildly reduced estimated GFR by height- or age-related equations in young people with obesity and its association with cardiometabolic risk factors. J Ren Nutr (2021.0) 31 586-592. DOI: 10.1053/J.JRN.2020.11.005 20. 20.Departament de salut (2020) ORDRE SLT/63/2020, de 8 de març, per la qual s’aproven els preus públi. https://www.clc.cat/pdf/module_28/10015462/Ordre-SLT____TFsk9YiZzVZ9MSh7fWvy_es.pdf. Accessed 16 May 2022 21. Revised guidelines on management of antenatal hydronephrosis. Indian Pediatr (2013.0) 50 215-231. DOI: 10.1007/S13312-013-0064-6 22. Isert S, Müller D, Thumfart J. Factors associated with the development of chronic kidney disease in children with congenital anomalies of the kidney and urinary tract. Front Pediatr (2020.0) 8 298. DOI: 10.3389/FPED.2020.00298 23. Peters C, Rushton HG. Vesicoureteral reflux associated renal damage: congenital reflux nephropathy and acquired renal scarring. J Urol (2010.0) 184 265-273. DOI: 10.1016/j.juro.2010.03.076 24. Moëll H. Size of normal kidneys Acta radiol (1956.0) 46 640-645. DOI: 10.3109/00016925609171457 25. Nahm AM, Ritz E. Errors in ultrasonographic measurements of the kidney. Nephrol Dial Transplant (2000.0) 15 549-550. DOI: 10.1093/ndt/15.4.549 26. Chiolero A, Cachat F, Burnier M, Paccaud F, Bovet P. Prevalence of hypertension in schoolchildren based on repeated measurements and association with overweight. J Hypertens (2007.0) 25 2209-2217. DOI: 10.1097/HJH.0B013E3282EF48B2 27. Vega MW, Swartz SJ, Devaraj S, Poyyapakkam S. Elevated serum creatinine: but is it renal failure?. Pediatrics (2020.0) 146 e20192828. DOI: 10.1542/PEDS.2019-2828/37011 28. La Scola C, De Mutiis C, Hewitt IK, Puccio G, Toffolo A, Zucchetta P, Mencarelli F, Marsciani M, Dall'Amico R, Montini G. Different guidelines for imaging after first UTI in febrile infants: yield, cost, and radiation. Pediatrics (2013.0) 131 e665-e671. DOI: 10.1542/peds.2012-0164 29. Kleinerman RA. Cancer risks following diagnostic and therapeutic radiation exposure in children. Pediatr Radiol (2006.0) 36 121-125. DOI: 10.1007/s00247-006-0191-5 30. Bravo-Saquicela DM, Sabag A, Rezende LFM, Rey-Lopez JP. Has the prevalence of childhood obesity in Spain plateaued? A systematic review and meta-analysis. Int J Environ Res Public Health (2022.0) 19 5240. DOI: 10.3390/ijerph19095240 31. Restrepo JM, Torres-Canchala L, Viáfara LM, Agredo MA, Quintero AM, Filler G. Renal length z-score for the detection of dysfunction in children with solitary functioning kidney. Acta Paediatr Int J Paediatr (2021.0) 110 652-658. DOI: 10.1111/APA.15425 32. Restrepo JM, Torres-Canchala L, Cadavid JCA, Ferguson M, Villegas A, Ramirez O, Rengifo M, Filler G. Renal volume of five-year-old preterm children are not different than full-term controls. J Pediatr (Rio J) (2022.0) 98 282-288. DOI: 10.1016/j.jped.2021.06.008 33. Torres-Canchala L, Rengifo M, Filler G, Arias JC, Ramirez O, Restrepo JM. Low agreement between kidney volume and kidney length z-scores. Pediatr Nephrol (2021.0) 36 1525-1532. DOI: 10.1007/s00467-020-04836-5
--- title: Acute and chronic kidney complications in children with type 1 diabetes mellitus authors: - Giulio Rivetti - Brenden E. Hursh - Emanuele Miraglia del Giudice - Pierluigi Marzuillo journal: Pediatric Nephrology (Berlin, Germany) year: 2022 pmcid: PMC10060299 doi: 10.1007/s00467-022-05689-w license: CC BY 4.0 --- # Acute and chronic kidney complications in children with type 1 diabetes mellitus ## Abstract Children with type 1 diabetes mellitus (T1DM) have an increased risk of developing kidney involvement. Part of the risk establishes at the beginning of T1DM. In fact, up to $65\%$ of children during T1DM onset may experience an acute kidney injury (AKI) which predisposes to the development of a later chronic kidney disease (CKD). The other part of the risk establishes during the following course of T1DM and could be related to a poor glycemic control and the subsequent development of diabetic kidney disease. In this review, we discuss the acute and chronic effects of T1DM on the kidneys, and the implications of these events on the long-term prognosis of kidney function. ## Introduction Children with type 1 diabetes mellitus (T1DM) can present with kidney involvement both in the acute setting presenting with acute kidney injury (AKI), as well as tubular damage and in the chronic setting presenting with diabetic kidney disease (DKD) [1–6]. It has been shown that during the onset of T1DM, an AKI—associated or not with tubular damage biomarkers—can occur in $43.8\%$ of patients [1]. Moreover, separately evaluating patients with diabetic ketoacidosis (DKA), the prevalence of AKI significantly increases up to $65\%$ [1, 2]. A further increase of prevalence of AKI up to $81\%$ has been observed in cases of recurrent DKA episodes [7, 8]. Although both AKI and tubular damage are reversible, they have been associated with an increased risk of future chronic kidney disease (CKD) [2]. Furthermore, patients with T1DM may eventually develop DKD in 15–$20\%$ of cases, which in susceptible patients begins soon after the disease onset and may accelerate during adolescence [4]. For these reasons, kidney health for children with T1DM must not be neglected at each step of illness in order to avoid the development of CKD and a possible future progression towards kidney failure. In this review, we discuss the acute and chronic effects of T1DM on the kidneys, and the implications of these events on the long-term prognosis of kidney function. ## Acute setting According to the Kidney Disease/Improving Global Outcome (KDIGO), AKI can be defined by an increase of the serum creatinine or a reduction of the urine output [9]. Since diabetic patients usually present polyuria and polydipsia during the onset of the disease, the urine output criterion seems to be less reliable for the diagnosis of AKI. In fact, only $15\%$ of patients with AKI at T1DM onset met the urinary output KDIGO criteria [1]. Nevertheless, clinicians should keep in mind that serum creatinine measurement in DKA is challenging, as high acetoacetate, glucose, and HbA1c levels may lead to a falsely elevated measured creatinine by interference with its measurement [10–13]. This effect is most pronounced with the Jaffe method at low creatinine concentrations, but is still observed with other enzymatic assay testing [10, 11]. ## Pathophysiology of kidney damage During the T1DM onset, the main pathophysiological mechanism is represented by hyperglycemia, which causes osmotic polyuria [1, 2]. The osmotic polyuria, in turn, leads to dehydration, hypovolemia, and kidney hypoperfusion, which causes tubular damage. This leads to an adaptive fall in glomerular filtration rate, due to the vasoconstriction which is an attempt to compensate the failure to reabsorb filtered solutes [14] with further deterioration of kidney perfusion [1, 2, 8]. A delayed therapeutic intervention may lead to the persistence of glomerular vasoconstriction, which may result in an acute tubular necrosis (described in about $30\%$ of patients with T1DM onset [1]), thus shifting AKI from functional to intrinsic [15, 16]. For this reason, there has been a recent focus on the investigation of early tubular AKI biomarkers, in order to diagnose functional AKI in a timely manner, and to hopefully prevent the evolution to intrinsic AKI [3, 17]. When T1DM onset is complicated by DKA, acidosis could lead to further deterioration of tubular damage [1] with a further negative effect on kidney perfusion. Moreover, if impaired consciousness is present, the compensatory polydipsia disappears, which further deteriorates hydration status and kidney function. Transient (< 48 h) AKI episodes are associated with better outcomes than persistent (2–7 days) ones, but still are not without consequences compared to no AKI [18, 19]. ## AKI with DKA AKI and DKA seem to be intriguingly interconnected. The more severe the DKA, the higher the probability of developing AKI [2, 7]. Lower bicarbonate [1, 2, 7, 20], higher heart rate (HR) [1, 2, 20], higher ketones [1], and higher chloride levels [21] (which are all factors related to DKA) [22–24] have been associated with AKI development at T1DM onset. Other factors associated with AKI in this setting are kidney length > 2 standard deviation scores (SDS), Ht ≥ $45\%$ [1], male gender [7], higher corrected serum Na [1, 2, 20, 25], higher blood urea nitrogen, higher serum K+, and higher blood glucose levels [20]. These factors, however, do not present the same diagnostic value at all AKI stages. For example, ketones, HR > 2 SDS, Ht ≥ $45\%$, higher corrected serum Na levels, and kidney length > 2 SDS are associated with severe AKI but not with mild AKI [1]. Moreover, it has been shown that older age, recurrent DKA episodes, increased acidosis severity, increased time to anion gap normalization, and increased initial glucose are associated with a prolonged AKI recovery [7]. Patients with family history of T1DM have a significantly shorter duration of polyuria and polydipsia before T1DM diagnosis and lower prevalence of AKI when compared with those without T1DM family history [1]. This may reflect that their parents are aware of the signs and symptoms heralding T1DM [1]. This highlights that a prompt diagnosis might reduce the risk of developing AKI. The evidence regarding the risks of developing AKI in patients with known T1DM is poor. Yang et al. found that longer duration of T1DM was an independent predictor of severe AKI in pediatric DKA with T1DM [8]. Moreover, we can assume that the patients with the greatest risk of developing AKI are those with recurrent DKA who, in turn, present a prolonged AKI recovery [7], itself associated with increased risk of CKD [26]. Therefore, in recurrent DKA, the risk of AKI could be higher because of (i) the higher T1DM duration and (ii) possible previous AKI episodes which could reduce the “functional reserve of the kidneys.” ## AKI without DKA The current literature principally focuses on the incidence of AKI during DKA, since during T1DM onset the development of AKI is deeply related to DKA severity. Nevertheless, as about one-fifth of patients with T1DM onset without DKA develops AKI [1], we want to highlight that it is extremely important to pay heed to kidney function and focus on a proper rehydration in this group of patients, who are generally considered at a lower risk of severe presentation of diabetes and of severe complications of initial diabetes therapy. The DiAKIdney is the only study in which this population is described, identifying serum chloride level as the most important risk factor for AKI in patients without DKA [1]. ## Prevalence and risk factors for tubular damage in children at T1DM onset and in those with known T1DM No universal definition of tubular damage has been provided. The fractional excretion of sodium is an important and easily available marker to help identify the kind of kidney involvement. On the other side, the tubular reabsorption of phosphate can be supportive in identifying a tubular involvement. Piani et. al reported that DKA is characterized by markers of reversible tubular injury and that the degree of injury is associated with elevated copeptin and serum uric acid (SUA) levels in children with T1DM [3]. In fact, during the onset of T1DM, high levels of copeptin and SUA are associated with the presence of tubular injury markers, such as neutrophil gelatin–associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), chitinase 3-like 1 (YKL-40), interleukin 18 (IL-18), and monocyte chemoattractant protein-1 (MCP-1) [3]. Aminoaciduria can also be considered a marker of tubular dysfunction, since an increased excretion of amino acids has been reported during DKA [27–29]. Recently, Melena et al. demonstrated that DKA is associated with a profound aminoaciduria, suggestive of proximal tubular dysfunction, similarly to Fanconi syndrome [17]. In fact, it has been hypothesized that a proximal tubular injury and the consequent aminoaciduria may serve as a marker of early functional and structural damage in the kidney and may represent an early indicator of kidney disease development and progression in T1DM. In particular, during DKA, it was found that the concentration of urine histidine, threonine, tryptophan, and leucine per creatinine are higher at 0–8 h and then significantly decrease over 3 months. Moreover, in patients with severe DKA, there is significantly elevated urinary excretion of leucine compared to those who experience mild DKA, who in turn have a lower excretion of tryptophan compared to those who have a moderate DKA [17]. However, a general tubular damage (either associated to subclinical or overt AKI) can be observed in up to $73.5\%$ of patients at T1DM onset [1]. Indeed, high glucose states such as DKA have been shown to induce a proximal tubular degeneration [20]. Moreover, around $30\%$ of patients with T1DM onset present with increased tubular biomarkers without a real kidney dysfunction (subclinical AKI) [30, 31] and, on the other hand, almost $12\%$ of patients may have a functional loss that occurs in the absence of detectable kidney damage, based on biomarkers (hemodynamic AKI) [1, 31]. Many risk factors have been identified for each combination between kidney and tubular involvement. The highest serum creatinine at T1DM onset/basal creatinine (HC/BC) ratio indicating a more severe AKI was significantly associated with acute tubular necrosis [1]. Also, kidney length > 2 SDS was significantly associated with hemodynamic AKI, while lower serum phosphorus levels and higher HC/BC ratio were significantly associated with subclinical AKI [1]. Lastly, although there is no study dealing with the incidence of tubular damage in patients with known diabetes, we could assume that patients with recurrent DKA are at higher risk of developing tubular damage, such as already hypothesized for AKI risk. ## Clinical management The clinical management of DKA is fully described in the ISPAD 2018 guidelines [32]. Fluid replacement is of paramount importance, especially as we consider kidney health, and it should begin before starting insulin therapy. As pointed out before, AKI can occur during the acute onset of T1DM, due to acidosis, dehydration, and hypovolemia. Considering that an episode of AKI is independently associated with an increased risk of CKD [1] and hypertension [33], and that in turn hypertension is an important risk factor to develop DKD (that eventually leads to CKD), we can realize how important it is to quickly initiate a safe rehydration therapy during T1DM onset. Hence, we suggest that a “therapeutic compromise” between a too slow fluid replacement therapy (that could lead to an AKI and all of the further consequences described) and a too rapid replacement (that on the other hand could lead to a cerebral edema) should be found. Laskin et al. suggested to give fluids to patients with AKI secondary to volume depletion while quickly shifting to more restrictive strategies in those who do not respond to volume and have decreasing urinary output [34]. The data deriving from the study of Kuppermann et al., however, are reassuring about the risk of neurologic outcomes in patients with DKA [35]. In fact, neither the rate of administration nor the sodium content of intravenous fluids significantly influenced neurological outcomes in children with DKA [35]. Future expert panels should identify the best treatment modalities, taking into account the kidney health of patients with T1DM onset. Screening for DKD should begin at 11 years with 2–5 years diabetes duration [5]. A regular annual follow-up is important to identify a rapid or slow progression to microalbuminuria, as well as cases of regression to normo-albuminuria. Furthermore, longitudinal follow-up of albumin excretion is also important to identify patients with progressive small increases of the urinary albumin excretion within the normal range, which might be a prelude to the development of microalbuminuria. Patients with DKD eventually develop hypertension, in fact 10 years after the onset of the disease an increase of BP can occur [4]. The relationship between hypertension and DKD can be explained by the retention of concentrated sodium and subsidiary blood vessel resistance [50]. Various pediatric diabetes clinical practice guidelines suggest measuring BP at least annually or twice-yearly for children with T1DM [51]. As indicated in the current pediatric hypertension guidelines [52], oscillometric devices may be used for BP screening in children and adolescents. If elevated BP is reported by the oscillometric readings, confirmatory measurements should be obtained by auscultation. An appropriately sized cuff should be used for accurate BP measurement and if the initial BP is elevated (≥ 90th percentile), two additional oscillometric or auscultatory BP measurements should be performed at the same visit and averaged [52]. To confirm the diagnosis of hypertension according to casual BP measurements, a subsequent ambulatory BP monitoring (ABPM) should be performed [52]. Considering that even a mild episode of AKI stage 1 can double the risk of CKD [53], an anticipation of the need for screening for DKD and a closer follow-up for this cohort of patients, especially in the case of recurrent DKA, could be required. In fact, according to the KDIGO guidelines, after an AKI episode a follow-up is necessary in order to detect the development of proteinuria and hypertension which herald CKD [9]. Urine albumin–creatinine ratio (Ua:CR) measured after AKI is a strong and potentially modifiable risk factor for more rapid loss of kidney function [54]. Stoumpos et al. reported that even among patients who had severe AKI requiring dialysis, those who had a post-AKI eGFR level greater than 60 mL/min/1.73 m2 had a low risk of accelerated loss of kidney function [55]. However, Hsu et al. showed that proteinuria is more significantly associated with a subsequent loss of kidney function than post-AKI eGFR level [54]. This, in our opinion, underlines the importance of an adequate proteinuria monitoring. It has been recommended that patients with AKI should be monitored through the evaluation of:BP and Ua:CR, (on the first urine of the morning taken on rising) 12 months after AKI.Annual BP and Ua:CR for life. Serum creatinine if previous measurement elevated or if proteinuria or raised BP develops [14]. The clinical management of DKD is fully described in the ISPAD 2018 guidelines [5]. In summary, management is focused on achieving excellent glycemic management with HbA1c $7\%$ or less [4]. Lifestyle modifications in the form of weight loss, dietary changes, and increased physical activity are not only useful for glycemic management, but they also aid in preventing the development of hypertension and treating existing hypertension. When hypertension is confirmed, pharmacologic treatment should be considered in addition to lifestyle modification. Moreover, antiproteinuric drugs represent a pivotal treatment in cases of microalbuminuria, and renin–angiotensin–aldosterone system (RAAS) inhibitors are considered the mainstay of treatment for DKD. In fact, pharmacological renoprotective treatment with angiotensin-converting enzyme inhibitors (ACE-I) is indicated for all patients with persistent microalbuminuria regardless of BP measurements [56–58]. The principal mechanism of kidney protection by RAAS inhibitors is to reduce intraglomerular pressure and glomerular hyperfiltration [59, 60], and in addition to ameliorate angiotensin II-induced oxidative stress, inflammation, and fibrosis [61]. ACE-I are recommended for use in children and adolescents with hypertension and microalbuminuria [5]. Therapy with ACE-I is known to reduce the progression to overt nephropathy by $62\%$ and increase the regression to normo-albuminuria three-fold compared with placebo [58]. Many studies have also confirmed that treatment with ACE-I may lead to the reduction [62, 63] or normalization [64, 65] of microalbuminuria and the preservation of normal GFR [66]. If this drug is not tolerated (e.g., due to cough), an angiotensin receptor blocker (ARB) can be used; indeed, this latter class is considered to have similar effects on lowering BP and decreasing albuminuria [67]. Many studies completed in hypertensive children showed that ACE-I (such as enalapril [68], lisinopril [69], and ramipril [70]) and ARBs (such as irbesartan [71], telmisartan [72], valsartan [73], candesartan [74], and losartan [75]) had few adverse effects. Since RAAS inhibition has been shown to improve the prognosis in patients with DKD [76], we agree that persistent microalbuminuria should be treated regardless of BP measurements, using the lowest effective dose for the treatment progressively increased up to a maximum safe dose until the regression of microalbuminuria is achieved. This latter was defined by a reduction of $50\%$ or more in the albumin excretion rate from one 2-year period to the next [77]. The combination of ACE-I and ARBs has been shown to have additional renal protective effects in albuminuric adults with diabetes [78], even if it is not recommended in pediatric DKD, partly because of the increased risk of acute-on-chronic kidney impairment and hyperkalaemia [79]. Lastly, we can summarize the three main targets of management in the chronic setting:To obtain the best glycemic control maintaining HbA1c levels at $7\%$ or lessTo maintain BP in the normal range, defined by systolic and diastolic BP values < 90th percentile (on the basis of age, sex, and height percentiles)To detect microalbuminuria early on and achieve regression of microalbuminuria. ## Chronic setting In the past, chronic kidney involvement in patients with T1DM was defined as diabetic nephropathy. However, in 2020, the KDIGO guidelines suggested avoiding the term diabetic nephropathy because there is still no consensus definition [36]. For this reason, and because the term diabetic nephropathy is technically a histopathological diagnosis [37], in this manuscript we use the term DKD which indicates the clinical syndrome related to a chronic kidney involvement in patients with T1DM [6]. Therefore, DKD can be considered a clinical manifestation of the histopathological anomalies of diabetic nephropathy and the previous definitions of diabetic nephropathy from a practical point of view are overlapping with the term DKD [4, 5]. Many authors proposed their own definition of DKD, and almost all of them agreed with the ISPAD 2018 guidelines [5], in which the beginning of DKD is associated with the development of microalbuminuria (albumin excretion rate between 30 and 300 mg/24 h or 20 and 200 μg/min in a 24-h or timed urine collection). For example, in 2008, Bogdanovic stated that a clinically detectable DKD begins with the development of microalbuminuria [4]. This definition was lately confirmed by Parkins et al. suggesting that DKD can be defined by the development of microalbuminuria or by loss in GFR in patients affected by T1DM [38]. Particular attention should be paid to this latter criterion for the definition of DKD. In fact, a substantial proportion of patients with T1DM have a kidney function loss without an overt proteinuria or even with normo-albuminuria. This particular form of DKD is called nonproteinuric diabetic kidney disease and is defined by an eGFR < 60 mL/min/1.73 m2 and a urine albumin to creatinine ratio (Ua:CR) ≤ 300 mg/g creatinine [39]. We can finally summarize all these findings as we attempt to give a universal definition of DKD, which can be considered as a microvascular complication of the diabetes characterized by the development of microalbuminuria/proteinuria or by the reduction of eGFR in patients with T1DM. ## Pathophysiology of kidney damage DKD is a dynamic process that can affect the kidney function and morphology over the years, and is sustained by a continuous exposure of the kidneys to high blood glucose levels [6, 40]. In fact, hyperglycemia causes an abnormal homeostasis in blood flow and a vascular permeability in the glomerulus. The increased blood flow and intracapillary pressure eventually leads to a decreased nitric oxide production on the efferent side of the glomerular capillaries, causing an increased sensitivity to angiotensin II with profibrotic effects. At the beginning, the increased permeability can be reversible, but under the continuous triggering effect of hyperglycemia, the lesions become irreversible [41]. DKD determines changes in the kidney structure over years and is schematically divided in 5 stages. Hyperfiltration: with the onset of diabetes hyperglycemia usually determines kidney hemodynamic changes that end up with the constriction of the efferent arteriole and a glomerular hypertension that eventually determine a glomerular hypertrophy. In fact, during this first phase, there is an increased kidney size and increase of the eGFR by 20–$40\%$ [42]. Microalbuminuria can be present during this phase, but it is usually reversible with insulin treatment and there is no evidence of histological lesions in glomeruli or vascular structure [4].Silent: during this phase, there is a thickening of the glomerular basal membrane and a mesangial matrix expansion caused by the production of reactive oxygen species [42] that is typically related to the high glucose exposure of these tissues. In fact, microalbuminuria can be present in this stage, but only during the periods of poor metabolic control or with exercise. Incipient: about 7–10 years after the diagnosis, microalbuminuria appears in $\frac{1}{3}$ of the patients. Microalbuminuria is considered as the very first clinical sign of DKD and is often associated with established significant glomerular damage: during this phase there is an increase in blood pressure (BP) (about 3 mmHg/year), albeit still within the conventional age-corrected normal range. In fact, in adolescents, microalbuminuria can be preceded by an increase of the nocturnal systolic BP [43].Overt: this stage is characterized by an overt proteinuria (> 0.5 g/24 h), a steady rise of BP, an increased albumin excretion rate and the decline of glomerular filtration rate (GFR) by about 10 ml/min year. This stage occurs 10 to 15 years after T1DM onset and is highly predictive of subsequent progress to kidney failure, if left untreated [41].Kidney failure: the final stage is characterized by uremia and can occur in up to $40\%$ of T1DM patients usually 10 years after the appearance of proteinuria [4]. With a worse patient compliance to insulin therapy and higher blood glucose levels, the terminal phase (and the need of a kidney function replacement therapy) is reached more quickly. ## Prevalence and risk factors for diabetic kidney disease Approximately 20 to $30\%$ of people with T1DM have microalbuminuria (and consequently DKD) after a mean diabetes duration of 15 years and the overall incidence of kidney failure is reported to be 4 to $17\%$ at 20 to 30 years from T1DM diagnosis [41]. Many risk factors are related to the development of DKD and they can be divided in non-modifiable and modifiable risk factors as well as in factors predisposing to progression to CKD (Fig. 1) [4, 44]. The long-term glycemic control is the most important factor for the development and severity of complications in T1DM. A causal relationship between chronic hyperglycemia and diabetic microvascular complications has been demonstrated [4].Fig. 1Risk factors for development of diabetic kidney disease. The development of diabetic kidney disease is a dynamic process and is the result of cumulative kidney insults. Classically, non-modifiable and modifiable risk factors and progression factors can be identified. In addition, recent evidence indicates the importance of the potential kidney damage as consequence of the severity of T1DM onset The level of albuminuria instead has been shown to predict the progression to CKD stage 5 [45, 46], and it is associated with an increased risk of macrovascular disease [47]. Translating the evidence about the relationship between AKI and CKD, in our opinion AKI itself could be considered a risk factor for DKD. The results of the study by Huang et al. [ 48] reinforce our hypothesis. They, indeed, demonstrated how each episode of AKI during DKA can be associated with a hazard ratio of 1.56 for development of microalbuminuria that can increase by more than fivefold if four or more episodes of AKI occur [48]. In addition to the AKI-related CKD risk, it has been proven that an AKI is independently associated with a $22\%$ increase of the odds of developing hypertension which is in turn related to DKD [33]. DKD, in turn, eventually leads to CKD (Fig. 2). In fact, high BP and alterations in circadian rhythm have been associated with the risk of developing nephropathy and retinopathy in youth with T1DM [49].Fig. 2The relationship between AKI, hypertension, diabetic kidney disease, and chronic kidney disease. AKI, while it can be totally reversible, may also lead to subclinical damage which predisposes to hypertension (HT), diabetic kidney disease (DKD), and chronic kidney disease (CKD). HT itself could determine progression toward DKD or CKD ## Conclusions: kidney health in T1DM patients, an integrated overview Part of the future risk of developing CKD in T1DM is established at the onset of diabetes. Indeed, up to $65\%$ of the patients at T1DM onset can develop AKI, which in turn is associated with an increased risk of CKD. The more severe the AKI episode, the higher the associated risk of developing CKD and kidney failure [53]. As a preventive measure, a higher parental awareness to the red flags of T1DM should be provided by Pediatricians in order to facilitate an early diagnosis of T1DM reducing the risk of AKI at T1DM onset and then of later CKD [1]. In addition to the “first hit” to kidneys at T1DM onset, during the years of the illness additional hits can further deteriorate the kidney function such as recurrent DKA with recurrent concomitant AKI. AKI, however, can also develop in non-diabetes-related conditions such as acute gastroenteritis or community acquired pneumonia [80, 81]. Therefore, T1DM patients should be carefully informed about the importance of dehydration prevention by adequate hydration in case of the common acute illnesses of childhood. Moreover, poor glycemic control predisposes to DKD development with subsequent risk of CKD, indicating the importance of an adequate compliance to T1DM treatment. Finally, in patients with T1DM regular follow-up visits are important to identify the possible onset of microalbuminuria or hypertension to start a timely and adequate treatment. These conditions, in fact, can facilitate both onset and progression of CKD. ## Key summary points At T1DM onset, AKI occurs in $\frac{1}{5}$ of patients without DKA and $\frac{2}{3}$ of patients with DKA.With more severe onset of T1DM, there is higher risk of AKI and subsequent increased risk of CKD.DKD is a microvascular chronic complication of T1DM. It can occur in almost $\frac{1}{5}$ of patients. Poor glycemic control and previous AKI episodes increase the risk of developing DKD.Regular T1DM follow-up visits are important to identify the possible onset of microalbuminuria or hypertension. Timely and adequate treatment with renin–angiotensin–aldosterone system inhibitors should be considered. ## Multiple Choice Questions (answers can be found following the reference list) Acute kidney injury in children at T1DM onset…… could manifest in about $\frac{2}{3}$ of children with DKA… is extremely rare… is usually not reversible… could manifest in about of $\frac{2}{3}$ of children without DKAIn AKI pathophysiology for patients at the onset of T1DM, all of the following factors are involved with the exception ofosmotic polyuriadelayed T1DM diagnosisacidosisolder age at T1DM onsetThe presence of diabetic kidney disease may be indicated by all the following parameters with the exception of:urine albumin to creatinine ratio > 30 mg/g creatinineeGFR < 60 mL/min/1.73 m.2urine albumin to creatinine ratio > 300 mg/g creatinineglycosuriaThe first-choice pharmacological treatment for hypertension in children with T1DM isangiotensin-converting enzyme inhibitorscalcium channel blockersdiureticsbeta-blockersThe risk of developing DKD increases in case of:poor glycemic controlprevious AKI episodeuntreated hypertensionall of the above ## References 1. Marzuillo P, Iafusco D, Zanfardino A, Guarino S, Piscopo A, Casaburo F, Capalbo D, Ventre M, Arienzo MR, Cirillo G, De Luca Picione C, Esposito T, Montaldo P, Di Sessa A, Miraglia del Giudice E. Acute kidney injury and renal tubular damage in children with type 1 diabetes mellitus onset. J Clin Endocrinol Metab (2021.0) 106 e2720-e2737. DOI: 10.1210/clinem/dgab090 2. Hursh BE, Ronsley R, Islam N, Mammen C, Panagiotopoulos C. Acute kidney injury in children with type 1 diabetes hospitalized for diabetic ketoacidosis. JAMA Pediatr (2017.0) 171 e170020. DOI: 10.1001/jamapediatrics.2017.0020 3. Piani F, Melena I, Severn C, Chung LT, Vinovskis C, Cherney D, Pyle L, Roncal-Jimenez CA, Lanaspa MA, Rewers A, van Raalte DH, Obeid W, Parikh C, Nelson RG, Pavkov ME, Nadeau KJ, Johnson RJ, Bjornstad P. Tubular injury in diabetic ketoacidosis: results from the diabetic kidney alarm study. Pediatr Diabetes (2021.0) 22 1031-1039. DOI: 10.1111/PEDI.13259 4. Bogdanović R. Diabetic nephropathy in children and adolescents. Pediatr Nephrol (2008.0) 23 507-525. DOI: 10.1007/s00467-007-0583-2 5. Donaghue K, Marcovecchio M, Wadwa R, Chew E, Wong TY, Calliari LE, Zabeen B, Salem MA, Craig ME. ISPAD Clinical Practice Consensus Guidelines 2018: microvascular and macrovascular complications in children and adolescents. Pediatr Diabetes (2018.0) 19 262-274. DOI: 10.1111/PEDI.12742 6. Lin YC, Chang YH, Yang SY, Wu KD, Chu TS. Update of pathophysiology and management of diabetic kidney disease. J Formos Med Assoc (2018.0) 117 662-675. DOI: 10.1016/J.JFMA.2018.02.007 7. Al Khalifah R, Al-Eyadhy A, Musibeeh N, Alshalawi A, Alanazi N, Alhboob A, Hassan G, Temsah MH, Alhaboob AAN, Raina R, Alhasan K. Risk factors, outcomes, and predictors of resolution of acute kidney injury in children with diabetic ketoacidosis. Pediatr Nephrol (2022.0). DOI: 10.1007/s00467-022-05578-2 8. Yang E, Lee H, Oh K, Kim C. Acute kidney injury in pediatric diabetic ketoacidosis. Indian J Pediatr (2021.0) 88 568-573. DOI: 10.1007/s12098-020-03549-9 9. 9.(2012) KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl 1:1–138. 10.1038/kisup.2012.1 10. Kemperman FAW, Weber JA, Gorgels J, Van Zanten AP, Krediet RT, Arisz L. The influence of ketoacids on plasma creatinine assays in diabetic ketoacidosis. J Intern Med (2000.0) 248 511-517. DOI: 10.1046/j.1365-2796.2000.00768.x 11. Greenberg N, Roberts WL, Bachmann LM, Wright EC, Dalton RN, Zakowski JJ, Miller WG. Specificity characteristics of 7 commercial creatinine measurement procedures by enzymatic and jaffe method principles. Clin Chem (2012.0) 58 391-401. DOI: 10.1373/clinchem.2011.172288 12. Martinez Herrada A, Shein SL, Rotta AT. Methodologic challenges in the diagnosis of acute kidney injury in children with diabetic ketoacidosis. Pediatr Crit Care Med (2019.0) 20 589. DOI: 10.1097/PCC.0000000000001926 13. Feldman-Kiss D, Li D, Cleve R, Sinclair G, Dubland JA, Li W. Interference of ketone bodies on laboratory creatinine measurement in children with DKA: a call for change in testing practices. Pediatr Nephrol (2022.0) 37 1347-1353. DOI: 10.1007/S00467-021-05324-0 14. Rees L, Bockenhauer D, Webb NJA, Punaro MG, Rees L, Bockenhauer D, Webb NJA, Punaro MG. Acute kidney injury. Paediatric Nephrology (2019.0) 414 15. 15.Prasad Devarajan M Acute kidney injury in children: clinical features, etiology, evaluation, and diagnosis - UpToDate. https://www.uptodate.com/contents/acute-kidney-injury-in-children-clinical-features-etiology-evaluation-and-diagnosis. Accessed 22 May 2021 16. Devarajan P. Acute kidney injury: still misunderstood and misdiagnosed. Nat Rev Nephrol (2017.0) 13 137-138. DOI: 10.1038/nrneph.2017.9 17. Melena I, Piani F, Tommerdahl KL, Severn C, Chung LT, MacDonald A, Vinovskis C, Cherney D, Pyle L, Roncal-Jimenez CA, Lanaspa MA, Rewers A, van Raalte DH, Cara-Fuentes G, Parikh CR, Nelson RG, Pavkov ME, Nadeau KJ, Johnson RJ, Bjornstad P. Aminoaciduria and metabolic dysregulation during diabetic ketoacidosis: results from the diabetic kidney alarm (DKA) study. J Diabetes Complications (2022.0) 36 108203. DOI: 10.1016/J.JDIACOMP.2022.108203 18. Chawla LS, Bellomo R, Bihorac A, Goldstein SL, Siew ED, Bagshaw SM, Bittleman D, Cruz D, Endre Z, Fitzgerald RL, Forni L, Kane-Gill SL, Hoste E, Koyner J, Liu KD, Macedo E, Mehta R, Murray P, Nadim M, Ostermann M, Palevsky PM, Pannu N, Rosner N, Wald R, Zarbock A, Ronco C, Kellum JA. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol (2017.0) 13 241-257. DOI: 10.1038/NRNEPH.2017.2 19. Kellum JA, Sileanu FE, Murugan R, Lucko N, Shaw AD, Clermont G. Classifying AKI by Urine output versus serum creatinine level. J Am Soc Nephrol (2015.0) 26 2231-2238. DOI: 10.1681/ASN.2014070724 20. Huang SK, Huang CY, Lin CH, Cheng BW, Chiang YT, Lee YC, Yeh SN, Chan CI, Chua WK, Lee YJ, Ting WH. Acute kidney injury is a common complication in children and adolescents hospitalized for diabetic ketoacidosis. PLoS One (2020.0) 15 e0239160. DOI: 10.1371/JOURNAL.PONE.0239160 21. Baalaaji M, Jayashree M, Nallasamy K, Singhi S, Bansal A. Predictors and outcome of acute kidney injury in children with diabetic ketoacidosis. Indian Pediatr (2018.0) 55 311-314. DOI: 10.1007/s13312-018-1274-8 22. Cashen K, Petersen T. Diabetic ketoacidosis. Pediatr Rev (2019.0) 40 412-420. DOI: 10.1542/PIR.2018-0231 23. Dhatariya KK, Glaser NS, Codner E, Umpierrez GE. Diabetic ketoacidosis. Nat Rev Dis Prim (2020.0) 6 40. DOI: 10.1038/S41572-020-0165-1 24. Nyenwe EA, Kitabchi AE. The evolution of diabetic ketoacidosis: an update of its etiology, pathogenesis and management. Metabolism (2016.0) 65 507-521. DOI: 10.1016/J.METABOL.2015.12.007 25. Weissbach A, Zur N, Kaplan E, Kadmon G, Gendler Y, Nahum E. Acute kidney injury in critically Ill children admitted to the PICU for diabetic ketoacidosis. A retrospective study. Pediatr Crit Care Med (2019.0) 20 e10-e14. DOI: 10.1097/PCC.0000000000001758 26. Bhatraju PK, Zelnick LR, Chinchilli VM, Moledina DG, Coca SG, Parikh CR, Garg AX, Hsu CY, Go AS, Liu KD, Ikizler TA, Siew ED, Kaufman JS, Kimmel PL, Himmelfarb J, Wurfel MM. Association between early recovery of kidney function after acute kidney injury and long-term clinical outcomes. JAMA Netw Open (2020.0) 3 e202682. DOI: 10.1001/JAMANETWORKOPEN.2020.2682 27. Hammeke M, Bear R, Lee R, Goldstein M, Halperin M. Hyperchloremic Metabolic acidosis in diabetes mellitus: a case report and discussion of the pathophysiologic mechanisms. Diabetes (1978.0) 27 16-20. DOI: 10.2337/DIAB.27.1.16 28. Suzuki D, Kumagai N, Kondo T, Matsumoto Y, Katsuyuki Y, Nakajima Y, Ito T, Ikezumi Y. Transient Fanconi syndrome caused by diabetic ketoacidosis in type 1 diabetes mellitus. Jpn J Pediatr Nephrol (2020.0) 33 191-195. DOI: 10.3165/JJPN.CR.2020.0174 29. Szabó A, Kenesei É, Körner A, Miltényi M, Szücs L, Nagy I. Changes in plasma and urinary amino acid levels during diabetic ketoacidosis in children. Diabetes Res Clin Pract (1991.0) 12 91-97. DOI: 10.1016/0168-8227(91)90085-R 30. Ostermann M, Bellomo R, Burdmann EA, Doi K, Endre ZH, Goldstein SL, Kane-Gill SL, Liu KD, Prowle JR, Shaw AD, Srisawat N, Cheung M, Jadoul M, Winkelmayer WC, Kellum JA. Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference for Conference Participants. Kidney Int (2020.0) 98 294-309. DOI: 10.1016/j.kint.2020.04.020 31. Vanmassenhove J, Van Biesen W, Vanholder R, Lameire N. Subclinical AKI: ready for primetime in clinical practice?. J Nephrol (2019.0) 32 9-16. DOI: 10.1007/S40620-018-00566-Y/FIGURES/2 32. Wolfsdorf JI, Glaser N, Agus M, Fritsch M, Hanas R, Rewers A, Sperling MA, Codner E. ISPAD Clinical Practice Consensus Guidelines 2018: diabetic ketoacidosis and the hyperglycemic hyperosmolar state. Pediatr Diabetes (2018.0) 19 155-177. DOI: 10.1111/pedi.12701 33. Hsu CY, Hsu RK, Jang J, Ordonez JD. Elevated BP after AKI. J Am Soc Nephrol (2016.0) 27 914-923. DOI: 10.1681/ASN.2014111114 34. Laskin BL, Goebel J. Acute kidney injury in children admitted with diabetic ketoacidosis: finding the sweet spot of fluid management. JAMA Pediatr (2017.0) 171 12-13. DOI: 10.1001/jamapediatrics.2017.0009 35. Kuppermann N, Ghetti S, Schunk J, Stoner M, Rewers A, McManemy JK, Myers SR, Nigrovic LE, Garro A, Brown KM, Quayle KS, Trainor JL, Tzimenatos L, Bennett JE, DePiero AD, Kwok MY, Perry CS 3rd, Olsen CS, Casper TC, Dean JM, Glaser NS. Clinical Trial of fluid infusion rates for pediatric diabetic ketoacidosis. N Engl J Med (2018.0) 378 E793-E794. DOI: 10.1056/NEJMOA1716816 36. Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int (2020.0) 98 S1-S115. DOI: 10.1016/j.kint.2020.06.019 37. Umanath K, Lewis JB. Update on diabetic nephropathy: core curriculum 2018. Am J Kidney Dis (2018.0) 71 884-895. DOI: 10.1053/J.AJKD.2017.10.026 38. Perkins BA, Bebu I, De Boer IH, Molitch M, Tamborlane W, Lorenzi G, Herman W, White NH, Pop-Busui R, Paterson AD, Orchard T, Cowie C, Lachin JM. Risk factors for kidney disease in type 1 diabetes. Diabetes Care (2019.0) 42 883-890. DOI: 10.2337/dc18-2062 39. Mamanouchi Y, Furuichi K, Hoshino J, Ubara Y, Wada T. Nonproteinuric diabetic kidney disease. Clin Exp Nephrol (2020.0) 24 573-581. DOI: 10.1007/S10157-020-01881-0 40. Rodríguez E, Arias-Cabrales C, Bermejo S, Sierra A, Burballa C, Soler MJ, Barrios C, Pascual J. Impact of Recurrent acute kidney injury on patient outcomes. Kidney Blood Press Res (2018.0) 43 34-44. DOI: 10.1159/000486744 41. Papadopoulou-Marketou N, Chrousos GP, Kanaka-Gantenbein C. Diabetic nephropathy in type 1 diabetes: a review of early natural history, pathogenesis, and diagnosis. Diabetes Metab Res Rev (2017.0) 33 e2841. DOI: 10.1002/DMRR.2841 42. Sun H-J, Wu Z-Y, Cao L, Zhu M-Y, Liu TT, Guo L, Lin Y, Nie XW, Bian JS. Hydrogen sulfide: recent progression and perspectives for the treatment of diabetic nephropathy. Molecules (2019.0) 24 2857. DOI: 10.3390/MOLECULES24152857 43. Lurbe E, Redon J, Kesani A, Pascual JM, Tacons J, Alvarez V, Batlle D. Increase in nocturnal blood pressure and progression to microalbuminuria in type 1 diabetes. N Engl J Med (2002.0) 347 797-805. DOI: 10.1056/NEJMOA013410 44. 44.Elena A, Stroescu B, Tanasescu MD, Diaconescu A, Raducu L, Balan DG, Mihai A, Tanase M, Stanescu II, Ionescu D (2018) Diabetic nephropathy: a concise assessment of the causes, risk factors and implications in diabetic patients. Rev Chim 69:3118. 10.21767/2472-5056.100029 45. Mogensen CE. Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. N Engl J Med (1984.0) 310 356-360. DOI: 10.1056/NEJM198402093100605 46. Lambers Heerspink HJ, Gansevoort RT, Brenner BM, Cooper ME, Parving HH, Shahinfar S, de Zeeuw D. Comparison of different measures of urinary protein excretion for prediction of renal events. J Am Soc Nephrol (2010.0) 21 1355-1360. DOI: 10.1681/ASN.2010010063 47. Fox CS, Matsushita K, Woodward M, Bilo HJ, Chalmers J, Heerspink HJ, Lee BJ, Perkins RM, Rossing P, Sairenchi T, Tonelli M, Vassalotti JA, Yamagishi K, Coresh J, de Jong PE, Wen CP, Nelson RG. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis. Lancet (2012.0) 380 1662-1673. DOI: 10.1016/S0140-6736(12)61350-6 48. Huang JX, Casper TC, Pitts C, Myers S, Loomba L, Ramesh J, Kuppermann N, Glaser N. Association of acute kidney injury during diabetic ketoacidosis with risk of microalbuminuria in children with type 1 diabetes. JAMA Pediatr (2021.0) 176 169-175. DOI: 10.1001/JAMAPEDIATRICS.2021.5038 49. Mateo-Gavira I, Vílchez-López FJ, García-Palacios MV, Carral-San Laureano F, Jiménez-Carmona S, Aguilar-Diosdado M. Nocturnal blood pressure is associated with the progression of microvascular complications and hypertension in patients with type 1 diabetes mellitus. J Diabetes Complications (2016.0) 30 1326-1332. DOI: 10.1016/J.JDIACOMP.2016.05.021 50. Wagnew F, Eshetie S, Kibret GD, Zegeye A, Dessie G, Mulugeta H, Alemu A. Diabetic nephropathy and hypertension in diabetes patients of sub-Saharan countries: a systematic review and meta-analysis. BMC Res Notes (2018.0) 11 565. DOI: 10.1186/S13104-018-3670-5 51. Wherrett DK, Ho J, Huot C, Legault L, Nakhla M, Rosolowsky E. Type 1 diabetes in children and adolescents. Can J Diabetes (2018.0) 42 S234-S246. DOI: 10.1016/J.JCJD.2017.10.036 52. Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK, Gidding SS, Goodwin C, Leu MG, Powers ME, Rea C, Samuels J, Simasek M, Thaker VV, Urbina EM. Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics (2017.0) 140 e20171904. DOI: 10.1542/PEDS.2017-1904 53. Coca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int (2012.0) 81 442-448. DOI: 10.1038/ki.2011.379 54. Hsu CY, Chinchilli VM, Coca S, Devarajan P, Ghahramani N, Go AS, Hsu RK, Ikizler TA, Kaufman J, Liu KD, Parikh CR, Reeves WB, Wurfel M, Zappitelli M, Kimmel PL, Siew ED. Post-Acute kidney injury proteinuria and subsequent kidney disease progression: the assessment, serial evaluation, and subsequent sequelae in acute kidney injury (ASSESS-AKI) study. JAMA Intern Med (2020.0) 180 402-410. DOI: 10.1001/JAMAINTERNMED.2019.6390 55. Stoumpos S, Mark PB, McQuarrie EP, Traynor JP, Geddes CC. Continued monitoring of acute kidney injury survivors might not be necessary in those regaining an estimated glomerular filtration rate >60 mL/min at 1 year. Nephrol Dial Transplant (2017.0) 32 81-88. DOI: 10.1093/NDT/GFW413 56. 56.Diabetes - microalbuminuria in children and young people with diabetes mellitus. https://starship.org.nz/guidelines/diabetes-microalbuminuria-in-children-and-young-people-with-diabetes-mellitus/. Accessed 12 May 2022 57. Cooper ME. Pathogenesis, prevention, and treatment of diabetic nephropathy. Lancet (1998.0) 352 213-219. DOI: 10.1016/S0140-6736(98)01346-4 58. Barnett AH, Bilous R, Bojestig M, Crepaldi G. Should all patients with type 1 diabetes mellitus and microalbuminuria receive angiotensin-converting enzyme inhibitors? A meta-analysis of individual patient data. Ann Intern Med (2001.0) 134 370-379. DOI: 10.7326/0003-4819-134-5-200103060-00009 59. Zatz R, Dunn BR, Meyer TW, Anderson S, Brenner BM. Prevention of diabetic glomerulopathy by pharmacological amelioration of glomerular capillary hypertension. J Clin Invest (1986.0) 77 1925-1930. DOI: 10.1172/JCI112521 60. Anders H-J, Davis JM, Thurau K. Nephron protection in diabetic kidney disease. N Engl J Med (2016.0) 375 2096-2098. DOI: 10.1056/NEJMCIBR1608564 61. Benigni A, Cassis P, Remuzzi G. Angiotensin II revisited: new roles in inflammation, immunology and aging. EMBO Mol Med (2010.0) 2 247-257. DOI: 10.1002/EMMM.201000080 62. Cook J, Daneman D, Spino M, Sochett E, Perlman K, Balfe JW. Angiotensin converting enzyme inhibitor therapy to decrease microalbuminuria in normotensive children with insulin-dependent diabetes mellitus. J Pediatr (1990.0) 117 39-45. DOI: 10.1016/S0022-3476(05)82441-2 63. Rudberg S, Aperia A, Freyschuss U, Persson B. Enalapril reduces microalbuminuria in young normotensive type 1 (insulin-dependent) diabetic patients irrespective of its hypotensive effect. Diabetologia (1990.0) 33 470-476. DOI: 10.1007/BF00405108 64. Rudberg S, Østerby R, Bangstad HJ, Dahlquist G, Persson B. Effect of angiotensin converting enzyme inhibitor or beta blocker on glomerular structural changes in young microalbuminuric patients with type I (insulin-dependent) diabetes mellitus. Diabetologia (1999.0) 42 589-595. DOI: 10.1007/S001250051199 65. Yüksel H, Darcan Ş, Kabasakal C, Cura A, Mir S, Mavi E. Effect of enalapril on proteinuria, phosphaturia, and calciuria in insulin-dependent diabetes. Pediatr Nephrol (1998.0) 12 648-650. DOI: 10.1007/S004670050520 66. Mathiesen ER, Hommel E, Hansen HP, Smidt UM, Parving HH. Randomised controlled trial of long term efficacy of captopril on preservation of kidney function in normotensive patients with insulin dependent diabetes and microalbuminuria. BMJ (1999.0) 319 24-25. DOI: 10.1136/BMJ.319.7201.24 67. Lacourcière Y, Bélanger A, Godin C, Hallé JP, Hallé JP, Ross S, Wright N, Marion J. Long-term comparison of losartan and enalapril on kidney function in hypertensive type 2 diabetics with early nephropathy. Kidney Int (2000.0) 58 762-769. DOI: 10.1046/J.1523-1755.2000.00224.X 68. Wells T, Frame V, Soffer B, Shaw W, Zhang Z, Herrera P, Shahinfar S. A double-blind, placebo-controlled, dose-response study of the effectiveness and safety of enalapril for children with hypertension. J Clin Pharmacol (2002.0) 42 870-880. DOI: 10.1177/009127002401102786 69. Soffer B, Zhang Z, Miller K, Vogt BA, Shahinfar S. A double-blind, placebo-controlled, dose-response study of the effectiveness and safety of lisinopril for children with hypertension. Am J Hypertens (2003.0) 16 795-800. DOI: 10.1016/S0895-7061(03)00900-2 70. Wühl E, Trivelli A, Picca S, Litwin M, Peco-Antic A, Zurowska A, Testa S, Jankauskiene A, Emre S, Caldas-Afonso A, Anarat A, Niaudet P, Mir S, Bakkaloglu A, Enke B, Montini G, Wingen AM, Sallay P, Jeck N, Berg U, Caliskan S, Wygoda S, Hohbach-Hohenfellner K, Dusek J, Urasinski T, Arbeiter K, Neuhaus T, Gellermann J, Drozdz D, Fischbach M, Möller K, Peruzzi L, Mehls O, Schaefer F. Strict blood-pressure control and progression of renal failure in children. N Engl J Med (2009.0) 361 1639-1650. DOI: 10.1056/NEJMOA0902066 71. Sakarcan A, Tenney F, Wilson JT, Stewart JJ, Adcock KG, Wells TG, Vachharajani NN, Hadjilambris OW, Slugg P, Ford NF, Marino MR. The pharmacokinetics of irbesartan in hypertensive children and adolescents. J Clin Pharmacol (2001.0) 41 742-749. DOI: 10.1177/00912700122010645 72. Wells TG, Portman R, Norman P, Haertter S, Davidai G, Wang F. Safety, efficacy, and pharmacokinetics of telmisartan in pediatric patients with hypertension. Clin Pediatr (Phila) (2010.0) 49 938-946. DOI: 10.1177/0009922810363609 73. Flynn JT, Meyers KEC, Neto JP, De Paula Meneses R, Zurowska A, Bagga A, Mattheyse L, Shi V, Gupte J, Solar-Yohay S, Han G. Efficacy and safety of the angiotensin receptor blocker valsartan in children with hypertension aged 1 to 5 years methods pediatric hypertension. Hypertension (2008.0) 52 222-228. DOI: 10.1161/HYPERTENSIONAHA.108.111054 74. Schaefer F, Van De Walle J, Zurowska A, Gimpel C, van Hoeck K, Drozdz D, Montini G, Bagdasorova IV, Sorof J, Sugg J, Teng R, Hainer JW. Efficacy, safety and pharmacokinetics of candesartan cilexetil in hypertensive children from 1 to less than 6 years of age. J Hypertens (2010.0) 28 1083-1090. DOI: 10.1097/HJH.0B013E328336B86B 75. Webb NJA, Wells TG, Shahinfar S, Massaad R, Dankner WM, Lam C, Santoro EP, McCrary Sisk C, Blaustein RO. A randomized, open-label, dose-response study of losartan in hypertensive children. Clin J Am Soc Nephrol (2014.0) 9 1441-1448. DOI: 10.2215/CJN.11111113 76. Andrésdóttir G, Jensen ML, Carstensen B, Parving HH, Hovind P, Hansen TW, Rossing P. Improved prognosis of diabetic nephropathy in type 1 diabetes. Kidney Int (2015.0) 87 417-426. DOI: 10.1038/KI.2014.206 77. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JM, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med (2003.0) 23 2285-2293. DOI: 10.1056/NEJMoa021835 78. Jacobsen P, Andersen S, Rossing K, Jensen BR, Parving HH. Dual blockade of the renin-angiotensin system versus maximal recommended dose of ACE inhibition in diabetic nephropathy. Kidney Int (2003.0) 63 1874-1880. DOI: 10.1046/J.1523-1755.2003.00940.X 79. Tobe SW, Clase CM, Gao P, McQueen M, Grosshennig A, Wang X, Teo KK, Yusuf S, Mann JF. Cardiovascular and renal outcomes with telmisartan, ramipril, or both in people at high renal risk: results from the ONTARGET and TRANSCEND studies. Circulation (2011.0) 123 1098-1107. DOI: 10.1161/CIRCULATIONAHA.110.964171 80. Marzuillo P, Baldascino M, Guarino S, Perrotta S, Miraglia del Giudice E, Nunziata F. Acute kidney injury in children hospitalized for acute gastroenteritis: prevalence and risk factors. Pediatr Nephrol (2021.0) 36 1627-1635. DOI: 10.1007/s00467-020-04834-7 81. Marzuillo P, Pezzella V, Guarino S, Di Sessa A, Baldascino M, Polito C, Miraglia del Giudice E, Nunziata F. Acute kidney injury in children hospitalized for community acquired pneumonia. Pediatr Nephrol (2021.0) 36 2883-2890. DOI: 10.1007/s00467-021-05022-x
--- title: Acute kidney injury and diabetic kidney disease in children with acute complications of diabetes authors: - Jolanta Soltysiak - Izabela Krzysko-Pieczka - Anna Gertig-Kolasa - Ewa Mularz - Bogda Skowrońska - Danuta Ostalska-Nowicka - Jacek Zachwieja journal: Pediatric Nephrology (Berlin, Germany) year: 2022 pmcid: PMC10060302 doi: 10.1007/s00467-022-05735-7 license: CC BY 4.0 --- # Acute kidney injury and diabetic kidney disease in children with acute complications of diabetes ## Abstract ### Background Diabetic ketoacidosis (DKA) and hyperglycaemia without ketoacidosis are common acute complications of diabetes. Their association with acute kidney injury (AKI) and diabetic kidney disease (DKD) was studied. ### Methods The study group consisted of 197 children with type 1 diabetes with average diabetes duration of 8.08 ± 2.32 years. The medical history of the patients was retrospectively reviewed. The number of children with severe hyperglycaemia, DKA and AKI was assessed. The association with the risk of chronic kidney disease (CKD) was analysed. ### Results AKI was found in $14\%$ of cases hospitalised for DKA and $8\%$ of cases hospitalised for hyperglycaemia. Patients with AKI showed a significantly increased corrected sodium (141.23 ± 5.09 mmol/L, $$p \leq 0.035$$). Patients with AKI in DKA showed a significant increase in WBC (20.73 ± 8.71 × 103/µL, $$p \leq 0.0009$$). Follow-up analysis after a minimum of 5 years of diabetes revealed that a single episode of DKA was found in 63 patients and a single episode of AKI in 18 patients. Two or more episodes of DKA were found in 18 patients, and nine cases were complicated by AKI. These patients showed a significant increase in urinary albumin excretion (44.20 ± 64.21 mg/24 h), the highest values of eGFR and the worst glycaemic control. ### Conclusions Diabetic children can develop AKI in the course of DKA and hyperglycaemia without ketoacidosis, which is associated with volume depletion and reflected by corrected sodium concentration. AKI in DKA seems to be complicated by stress and inflammation activation. AKI and poor glycaemic control with repeated DKA episodes can magnify the risk of progression to DKD. ### Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information ### Supplementary Information The online version contains supplementary material available at 10.1007/s00467-022-05735-7. ## Introduction Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD), with a high risk of dialysis and mortality [1]. Given the growing incidence of type 1 and type 2 diabetes in children and adolescents, DKD represents a significant public health problem [2, 3]. The pathophysiology of DKD is complex and multifactorial. Chronic and acute hyperglycaemia associated with diabetes leads to glomerular hypertrophy, glomerulosclerosis, tubulointerstitial inflammation and fibrosis. These result in the natural history in DKD of glomerular hyperfiltration, progressive albuminuria, declining glomerular filtration rate (GFR) and ultimately, kidney failure [1]. The earliest alterations in the kidney structure are apparent within 1.5–2 years of type 1 diabetes diagnosis, in the form of a thickening of the glomerular basement membrane. Mesangial volume expansion is detectable within 5–7 years after diabetes diagnosis and then increased albuminuria can also occur [4]. Although DKD is considered a glomerular disease, a growing body of evidence suggests that tubular-interstitial injury may be the first alteration in DKD [5]. The common risk factors for DKD include age, age at onset, duration of diabetes, genetics, gender, glycaemic control, blood pressure, cholesterol levels and smoking [6]. Another risk factor for CKD is acute kidney injury (AKI). Paediatric AKI is associated with increased morbidity and mortality [7–9]. For those who survive AKI, recent data suggest that they likely have permanent kidney damage. These findings have challenged the previous belief that AKI was a completely reversible event [10]. AKI can also occur in diabetes [11]. Acute hyperglycaemic events, chronic poor glycaemic control, and diabetic ketoacidosis (DKA) can lead to AKI [11, 12]. Hyperglycaemia has been shown to induce kidney inflammation and tubulopathy, and poor glycaemic control can lead to polyuria with resultant volume contraction and hypovolemia, which is subsequently associated with the development of pre-renal AKI [13, 14]. DKA is a common and severe acute complication of diabetes. It is characterised by a combination of hyperglycaemia, metabolic acidosis and the production of ketone bodies [15–18]. DKA is currently the leading cause of hospitalisation, morbidity and mortality in youth with type 1 diabetes [15, 19, 20]. Severe hyperglycaemia associated with DKA leads to osmotic diuresis, dehydration and significant pre-renal AKI [17, 18, 21]. In a study by Hursh et al., up to $64\%$ of children with type 1 diabetes hospitalised for DKA developed AKI [15]. AKI is currently defined by the Kidney Disease Improving Global Outcomes (KDIGO) consensus classification based on conventional serum creatinine and urine output (UO) criteria [22]. However, despite the strict standards of AKI and the marked intravascular volume depletion that occurs in DKA, kidney injury in DKA in children has not been systematically studied. Moreover, the impact of DKA on AKI and chronic diabetic kidney injury has not been studied. The primary objective of this study was to determine the proportion of children with type 1 diabetes hospitalised for the disease that developed severe hyperglycaemia, DKA and AKI. As a secondary objective, we wanted to determine whether developing DKA and AKI was associated with an increased risk for CKD in children with type 1 diabetes for more than 5 years. ## Study design and participants The study group consisted of 197 adolescents with type 1 diabetes (104 girls and 93 boys) with a mean age of 14.69 ± 2.64 years and with a duration of diabetes of more than 5 years, who were hospitalised in the Department of Paediatric Diabetes and Obesity at Poznan University of Medical Sciences, Poland (Fig. 1). The data was collected in 2019 and 2020. All patients were Caucasian. The medical history of the patients and detailed information, including gender, age, height, weight, body mass index and pre-existing CKD, were retrospectively reviewed based on electronic hospital records. We analysed data obtained at the onset of diabetes and later acute complications, as well as at planned medical control after a minimum of 5 years of diabetes duration. The planned hospitalisation needed to have taken place a minimum of 3 months after the last episode of acute complications of diabetes. However, because in most cases acute complications of diabetes were at the onset of the disease, the observational period was almost as long as the duration of diabetes. No patients had CKD for reasons besides diabetes. Fig. 1The flow diagram of the study The cases hospitalised due to acute complications of diabetes were divided into the following groups:N — cases with hyperglycaemia without DKA or AKIDKA — cases with DKA without AKIAKI — cases with AKI On admission, because of acute complications, biological parameters, including pH, HCO3, serum creatinine, blood glucose (Glu), serum sodium (Na), haematocrit (HCT) and white blood cell (WBC) count, were collected. The serum creatinine was assessed within 24 h of admission. Corrected sodium (cNa) during DKA episodes was calculated using the following formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Corrected Na}=\hspace{0.17em}[(\mathrm{Glucose }(\mathrm{mg}/\mathrm{dL})/18)-5.6]\times 0.36+\mathrm{Serum Na}$$\end{document}CorrectedNa=[(Glucose(mg/dL)/18)-5.6]×0.36+SerumNa For our patients’ treatment and fluid regimen, we follow the International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines. Nevertheless, although the resuscitation bolus consisted of $0.9\%$ saline in all our DKA patients, subsequent rehydration in most patients was carried out with balanced salt solution (Optilyte) with the initial maintenance rate of fluid administration as indicated in the guidelines [23]. The biochemical criteria for the diagnosis of DKA were. Hyperglycaemia (blood glucose > 11 mmol/L [200 mg/dL])Venous pH < 7.3 or serum bicarbonate < 15 mmol/LKetonemia or ketonuria [23] The severity of DKA was categorised by the degree of acidosis:Mild — venous pH < 7.3 or serum bicarbonate < 15 mmol/LModerate — pH < 7.2 or serum bicarbonate < 10 mmol/LSevere — pH < 7.1 or serum bicarbonate < 5 mmol/L [23] AKI was defined according to the KDIGO Clinical Practice Guideline by any of the following:Increase in serum creatinine by ≥ 0.3 mg/dL within 48 hIncrease in serum creatinine to ≥ 1.5 times baseline, known or presumed to have occurred within the prior 7 daysUrine volume < 0.5 mL/kg/h for 6 h [24] In acute complications of diabetes, the full age spectrum (FAS) equation (eGFRFAS) was used for estimating the glomerular filtration rate [25]. eGFRFAS was calculated using the following formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{eGFR}}_{\mathrm{FAS}}=107.3/(\mathrm{SCr}/\mathrm{Q})$$\end{document}eGFRFAS=107.3/(SCr/Q) SCrserum creatinineQvalues [= median serum creatinine in mg/dL] for the FAS equation, according to height-specific healthy populations Because no study participants had available baseline serum creatinine values before admission at the onset of diabetes or acute complications, we used an estimated GFR of 120 mL/min/1.73 m2 to calculate an expected baseline creatinine level (EBC). A GFR of 120 mL/min/1.73 m2 was selected based on previously established standards in paediatric AKI studies [26, 27]. Stage 1 AKI occurred if a creatinine value was 1.5 times to less than two times the EBC, stage 2 AKI occurred if a creatinine value was two to less than three times the EBC, and stage 3 AKI occurred if a creatinine value was three times the EBC. KDIGO AKI UO criteria were not used because the recording of hourly UO rates was inconsistent among cases. During planned medical control after a minimum of 5 years of diabetes duration, the patients were divided into the following groups:0 — patients who had never had DKA or AKIDKA1 — patients who had a single episode of DKA without AKIAKI1 — patients who had a single episode of AKI with or without DKADKAM — patients who had multiple (two or more) episodes of DKA, including episodes of DKA complicated with AKI The urinary albumin excretion (UAE), cystatin C, glycosylated haemoglobin (HbA1c) total cholesterol, triglycerides and serum uric acid (UA) were collected. UAE was assessed by 24-h urine collection; kidney function was estimated by glomerular filtration rate (eGFR) according to the Filler formula based on cystatin C [28, 29]; long-term glycaemic control was based on haemoglobin A1c (HbA1c) levels [30]; serum glucose, serum creatinine in blood samples and the examination of albuminuria were measured by an automated biochemical analyser, Alinity c (ABBOTT, USA); WBCs were measured by a blood routine analyser XN-1000 (SYSMEX, Japan); pH was tested in arterial blood by a blood gas analyser ABL 835 (RADIOMETER, Denmark), and HbA1c was measured by glycosylated haemoglobin analyser Alinity c (ABBOTT, USA). ## Statistical analysis Statistical analysis was performed using Statistica ver. 8 (StatSoft, Tulsa, OK) and MedCalc. The statistical analysis results of the studied parameters were normally distributed and expressed as the mean ± standard deviation (SD). Continuous variables were tested using the analysis of variance Scheffe post hoc tests. The level of statistical significance was $p \leq 0.05.$ ### Fig. 2) Retrospective analysis revealed 234 hospitalisations due to acute complications of diabetes. Among them were 98 cases of hyperglycaemia without DKA or AKI. Diabetic ketoacidosis was diagnosed in 127 cases ($54\%$ hospitalisations). In 18 cases, it was complicated with AKI (AKIDKA; $14\%$ of cases patients were hospitalised for DKA). In 109 cases of DKA, an episode of AKI occurred. AKI was diagnosed in 27 cases in total. Apart from 18 episodes of AKI in DKA, nine episodes of AKI occurred in children with hyperglycaemia without DKA (AKIN; $8\%$ of cases hospitalised for hyperglycaemic events). The highest prevalence of AKI was noted in patients at the onset of diabetes ($\frac{22}{27}$ AKI in total). All AKI cases were diagnosed on admission to the hospital. In 21 cases, AKI was in stage 1, and in six cases, it was in stage 2. None were treated in the paediatric intensive care unit (PICU). The presence of AKI did not influence PICU admission. All AKI cases were resolved during the first week of hospitalisation. Analysis of biochemical parameters during acute complications revealed that patients with AKI showed a significantly increased concentration of corrected sodium (141.23 ± 5.09 mmol/L) when compared to patients who only had DKA. Patients with DKA or AKI showed a significant increase in WBC when compared to group N. The seriousness of acidosis based on pH was comparable in the AKI and DKA groups, whereas the concentration of HCO3 was even increased in the AKI group. Interestingly, the comparison of children with AKI revealed that patients with AKIDKA showed significantly higher WBC levels than children with AKIN. Group AKIN presented the highest mean concentration of glucose, but it was not significant (Table 2). ### Fig. 3) Among the 197 patients with diabetes, 91 had DKA ($46\%$). A single episode of DKA was diagnosed in 73 patients ($37\%$), usually at the onset of the disease. In 63 patients, it was a single episode of DKA without AKI (group DKA1). In 18 cases (group AKI1), a single episode of AKI was diagnosed (in 10 cases during DKA and in eight cases during severe hyperglycaemia). Two or more episodes of DKA were found in 18 patients ($9\%$, group DKAM). In group DKAM there were 54 episodes of DKA in total, including nine episodes of AKI in eight patients. A girl who had seven episodes of DKA showed two episodes of AKI. In 98 patients ($49\%$), no episodes of DKA or AKI occurred (group 0). The UAE was significantly increased in group DKAM (44.20 ± 64.21 mg/24 h) compared to groups 0, DKA1, and AKI1. The AKI1 subgroup showed an increase in UAE levels, but it was not significant. The eGFR did not differ much among the studied groups. However, the highest values were in DKAM. Group DKAM had the worst glycaemic control and the highest levels of lipids (total cholesterol and triglycerides). The duration of diabetes was significantly increased only in AKI1, and these patients were the youngest group at the onset of diabetes. ## Discussion This study revealed that among all acute complications of diabetes, around half of the patients had DKA, mostly a single episode. It usually occurred at the onset of the disease. This is consistent with other reports, which show frequencies of DKA at the beginning of diabetes from approximately 15 to $70\%$, depending on the age at diagnosis, origin, ethnicity, and access to medical care. The risk of DKA in established type 1 diabetes is estimated to be between 1 and $10\%$ in each patient every year, similar to the findings of our study, in which $9\%$ of established patients showed two or more episodes of DKA [31, 32]. However, our study revealed that only $14\%$ of children hospitalised for DKA developed AKI. This is much less than what other authors have reported. Hursh et al. presented that $64.2\%$ of children hospitalised for DKA had AKI, mostly stage 2 [15]. Baalaaji et al. showed AKI in $35.4\%$ of children with DKA admitted to a single PICU. In contrast, Myers et al. showed that $43.0\%$ of children with DKA had AKI (1359 episodes) [33, 34]. In a recently published study by Al Khalifah et al., the AKI incidence reached $80.75\%$ of all children with DKA. However, none of these studies investigated the development of AKI during hyperglycaemia without ketoacidosis. Baalaaji used the pRIFLE classification based on decreased estimated creatinine clearance [27]. Using the Schwartz formula, Hursh, Myers and Al Khalifah used serum creatinine measurements using the KDIGO criteria and eGFR of 120 mL/min/1.73 m2 to calculate an expected baseline creatinine level. In this study, we used the FAS formula as it has more validity for patients with different heights or ages. Nevertheless, the percentage of AKI in DKA was still low compared to other studies. In the present study, all patients with AKI showed a significantly increased corrected sodium, considered a good indicator of dehydration in diabetes. In many studies, the corrected sodium was increased in AKI patients and usually correlated with the severity of AKI. All studies analysed AKI in diabetic ketoacidosis [15, 16, 21, 33, 34]. Interestingly, our study revealed that AKI can also occur in children without DKA during severe hyperglycaemia, and it represents $8\%$ of cases of hyperglycaemic events. All AKIN episodes were established at the onset of diabetes and were in stage 1. These children presented the highest mean levels of glucose. In the setting of diabetes, as shown by others, the extracellular volume depletion and pre-renal AKI are commonly induced by glucosuria and osmotic diuresis because of poorly controlled diabetes [15, 35]. Severe hyperglycaemia seems to be enough to cause AKI. In our study, children with AKI during DKA above increased corrected sodium also showed the highest WBC concentration (20.73 ± 8.71 × 103/µL). An increased WBC was noticed in an earlier study in diabetic adults, in which AKI patients showed a WBC of 16.51 × 103/µL vs. 9.38 × 103/µL in DKA without AKI [36]. Leucocytosis is very common in hyperglycaemic crises, but its origin is still unknown [23]. It seems to respond to metabolic stress during DKA without apparent infection [37]. Significant dehydration, haemoconcentration and hyperglycaemia lead to the release of catecholamine and cortisol from adrenal glands, increasing leucocyte levels [38–40]. Moreover, a lack of insulin and a lack of its possible anti-inflammatory effect can stimulate the production of neutrophils in bone marrow [41]. In addition to increased WBC, the elevation of cytokines such as TNF-α and IL-6 can also occur in DKA [42]. These cytokines, along with IL-1β, regulate the production of acute-phase proteins [43]. In DKA, increased reactive oxygen species production leads to increased cytokine levels and the emergence of growth factor receptors [44]. Cytokines released during DKA may result in capillary perturbation and thus may contribute to developing acute clinical complications (i.e., cerebral or pulmonary oedema). The pathophysiology of these complications remains uncertain, but they likely involve some capillary perturbation that begins before the management of DKA and is accentuated by it [45]. In other words, increased WBC may reflect the severity of stress and inflammatory activation during DKA, resulting in capillary perturbation and AKI. The combination of poor glycaemic control with pre-renal AKI and inflammatory activation during DKA can intensify AKI in DKA. In our study, the most severe AKI (stage 2) was diagnosed only in patients with DKA. AKI is an independent factor associated with more extended hospital stays and a higher mortality rate for children [46]. However, AKI is also associated with long-term health outcomes. In a review that included 13 cohort studies of adults with AKI, a single episode of AKI was associated with an increased risk of developing CKD, with a pooled adjusted hazard ratio of 8.8 ($95\%$ CI, 2.1–25.5) [11]. In the present study, children with AKI (groups AKI1 and DKAM) showed the highest concentration of albuminuria during planned medical control. Moreover, in group DKAM, changes in eGFR were noticed and were highest, reflecting the tendency to hyperfiltration, which is often the initial sign of DKD [4]. The development of DKD is associated with many alterations in the structure of multiple kidney compartments, which can start very early, even within 1.5–2 years of diabetes diagnosis. It is paralleled by capillary and tubular basement membrane thickening. Other glomerular changes include the loss of endothelial fenestrations, mesangial matrix expansion and loss of podocytes with effacement of foot processes. The longer the duration of diabetes, the higher the risk of DKD [4]. In the present study, the mean diabetes duration was 8 years. However, only patients with AKI or more than two episodes of DKA with poor glycaemic control showed changes in UAE. This emphasises that AKI and repeated episodes of DKA with poor glycaemic control are an essential risk for chronic kidney injury. In particular, the AKI1 group had the longest duration of diabetes (10.60 ± 2.61 years). This finding highlights that the duration of diabetes is an important risk factor for CKD. Interestingly, the DKAM group showed the highest increase in HbA1c and lipids compared to other groups. In other words, children with repeated DKA showed the worst glycaemic control with the highest risk of CKD and progression to DKD. The AKI incidence in this study might be an underestimation because it is possible that in the DKAM group, there were more episodes of AKI than were diagnosed. A limitation of this study is the relatively small subgroup with AKI and a short follow-up period. It was also a single-centre study, and further cohort studies are needed to clarify the impact of AKI and DKA on DKD in children. ## Conclusions Acute complications of diabetes mellitus are risk factors for AKI. This can occur in children with DKA and those with hyperglycaemia without ketoacidosis. AKI incidences in diabetes are associated with volume depletion reflected by corrected sodium concentration. In children with DKA, AKI incidences seem to be complicated by stress and inflammation activation, reflected by increased WBC. AKI and repeated DKA with poor glycaemic control can magnify CKD and progression to DKD. Prospective longitudinal studies are needed to better understand the risk factors and long-term implications of AKI and DKA in children with diabetes. ## Supplementary Information Below is the link to the electronic supplementary material. Graphical Abstract (PPTX 95 KB)Supplementary file2 (DOCX 33 KB) ## References 1. Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol (2017.0) 12 2032-2045. DOI: 10.2215/CJN.11491116 2. 2.Afkarian M (2015) Diabetic kidney disease in children and adolescents. Pediatr Nephrol 30:65–74; quiz 70–71. 10.1007/s00467-014-2796-5 3. Pulgaron ER, Delamater AM. Obesity and type 2 diabetes in children: epidemiology and treatment. Curr Diab Rep (2014.0) 14 508. DOI: 10.1007/s11892-014-0508-y 4. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl (2013.0) 3 1-163 5. Makris K, Spanou L. Acute kidney injury: definition, pathophysiology and clinical phenotypes. Clin Biochem Rev (2016.0) 37 85-98. PMID: 28303073 6. Harjutsalo V, Groop PH. Epidemiology and risk factors for diabetic kidney disease. Adv Chronic Kidney Dis (2014.0) 21 260-266. DOI: 10.1053/j.ackd.2014.03.009 7. 7.Duzova A, Bakkaloglu A, Kalyoncu M, Poyrazoglu H, Delibas A, Ozkaya O, Peru H, Alpay H, Soylemezoglu O, Gur-Guven A, Bak M, Bircan Z, Cengiz N, Akil I, Ozcakar B, Uncu N, Karabay-Bayazit A, Sonmez F; Turkish Society for Pediatric Nephrology Acute Kidney Injury Study Group (2010) Etiology and outcome of acute kidney injury in children. Pediatr Nephrol 25:1453–1461. 10.1007/s00467-010-1541-y 8. Alkandari O, Eddington KA, Hyder A, Gauvin F, Ducruet T, Gottesman R, Phan V, Zappitelli M. Acute kidney injury is an independent risk factor for pediatric intensive care unit mortality, a longer length of stay and prolonged mechanical ventilation in critically ill children: a two-center retrospective cohort study. Crit Care (2011.0) 15 R146-R157. DOI: 10.1186/cc10269 9. Schneider J, Khemani R, Grushkin C, Bart R. Serum creatinine as stratified in the RIFLE score for acute kidney injury is associated with mortality and length of stay for children in the pediatric intensive care unit. Crit Care Med (2010.0) 38 933-939. DOI: 10.1097/CCM.0b013e3181cd12e1 10. Askenazi DJ, Feig DI, Graham NM, Hui-Stickle S, Goldstein SL. 3–5 year longitudinal follow-up of pediatric patients after acute renal failure. Kidney Int (2006.0) 69 184-189. DOI: 10.1038/sj.ki.5000032 11. Coca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int (2012.0) 81 442-448. DOI: 10.1038/ki.2011.379 12. Piani F, Reinicke T, Borghi C, Tommerdahl KL, Cara-Fuentes G, Johnson RJ, Bjornstad P. Acute kidney injury in pediatric diabetic kidney disease. Front Pediatr (2021.0) 9 668033. DOI: 10.3389/fped.2021.668033 13. Vallon V. Do tubular changes in the diabetic kidney affect the susceptibility to acute kidney injury?. Nephron Clin Pract (2014.0) 127 133-138. DOI: 10.1159/000363554 14. Gordillo R, Ahluwalia T, Woroniecki R. Hyperglycemia and acute kidney injury in critically ill children. Int J Nephrol Renovasc Dis (2016.0) 9 201-204. DOI: 10.2147/IJNRD.S115096 15. Hursh BE, Ronsley R, Islam N, Mammen C, Panagiotopoulos C. Acute kidney injury in children with type 1 diabetes hospitalised for diabetic ketoacidosis. JAMA Pediatr (2017.0) 171 e170020. DOI: 10.1001/jamapediatrics.2017.0020 16. 16.Orban JC, Maiziere EM, Ghaddab A, Van Obberghen E, Ichai C (2014) Incidence and characteristics of acute kidney injury in severe diabetic ketoacidosis. PLoS One 9:e110925. 10.1371/journal.pone.0110925 17. Castellanos L, Tuffaha M, Koren D, Levitsky LL. Management of diabetic ketoacidosis in children and adolescents with type 1 diabetes mellitus. Paediatr Drugs (2020.0) 22 357-367. DOI: 10.1007/s40272-020-00397-0 18. Gilbert RE. Proximal tubulopathy: prime mover and key therapeutic target in diabetic kidney disease. Diabetes (2017.0) 66 791-800. DOI: 10.2337/db16-0796 19. Realsen J, Goettle H, Chase HP. Morbidity and mortality of diabetic ketoacidosis with and without insulin pump care. Diabetes Technol Ther (2012.0) 14 1149-1154. DOI: 10.1089/dia.2012.0161 20. Razavi Z. Frequency of ketoacidosis in newly diagnosed type 1 diabetic children. Oman Med J (2010.0) 25 114-117. DOI: 10.5001/omj.2010.31 21. Al Khalifah R, Al-Eyadhy A, Musibeeh N, Alshalawi A, Alanazi N, Alhboob A, Hassan G, Temsah MH, Alhaboob AAN, Raina R, Alhasan K. Risk factors, outcomes, and predictors of resolution of acute kidney injury in children with diabetic ketoacidosis. Pediatr Nephrol (2022.0). DOI: 10.1007/s00467-022-05578-2 22. Ricci Z, Romagnoli S. Acute kidney injury: diagnosis and classification in adults and children. Contrib Nephrol (2018.0) 193 1-12. DOI: 10.1159/000484956 23. 23.Wolfsdorf JI, Glaser N, Agus M, Fritsch M, Hanas R, Rewers A, Sperling MA, Codner E (2018) ISPAD Clinical Practice Consensus Guidelines 2018: diabetic ketoacidosis and the hyperglycemic hyperosmolar state. 10.1111/pedi.12701 24. Volume2/Issue 1/March. Kidney Int Suppl (2012.0) 2 8-12. DOI: 10.1038/kisup.2012.7 25. Pottel H, Hoste L, Dubourg L, Ebert N, Schaeffner E, Eriksen BO, Melsom T, Lamb EJ, Rule AD, Turner ST, Glassock RJ, De Souza V, Selistre L, Mariat C, Martens F, Delanaye P. An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dial Transplant (2016.0) 31 798-806. DOI: 10.1093/ndt/gfv454 26. 26.Basu RK, Kaddourah A, Terrell T, Mottes T, Arnold P, Jacobs J, Andringa J, Goldstein SL; Prospective Pediatric AKI Research Group (ppAKI) (2015) Assessment of worldwide acute kidney injury, renal angina and epidemiology in critically ill children (AWARE): study protocol for a prospective observational study. BMC Nephrol 16:24. 10.1186/s12882-015-0016-6 27. Akcan-Arikan A, Zappitelli M, Loftis LL, Washburn KK, Jefferson LS, Goldstein SL. Modified RIFLE criteria in critically ill children with acute kidney injury. Kidney Int (2007.0) 71 1028-1035. DOI: 10.1038/sj.ki.5002231 28. Filler G, Lepage N. Should the Schwartz formula for estimation of GFR be replaced by cystatin C formula?. Pediatr Nephrol (2003.0) 18 981-985. DOI: 10.1007/s00467-003-1271-5 29. Perrin N, Berg UB. Estimated glomerular filtration rates cannot replace measured GFR in type 1 diabetes patients with hyperfiltration. Acta Paediatr (2015.0) 104 730-737. DOI: 10.1111/apa.12993 30. Rewers M, Pihoker C, Donaghue K, Hanas R, Swift P, Klingensmith GJ. Assessment and monitoring of glycemic control in children and adolescents with diabetes. Pediatr Diabetes (2009.0) 10 71-81. DOI: 10.1111/j.1399-5448.2009.00582.x 31. Hanas R, Lindgren F, Lindblad B. A 2-yr national population study of pediatric ketoacidosis in Sweden: predisposing conditions and insulin pump use. Pediatr Diabetes (2009.0) 10 33-37. DOI: 10.1111/j.1399-5448.2008.00441.x 32. 32.Maahs DM, Hermann JM, Holman N, Foster NC, Kapellen TM, Allgrove J, Schatz DA, Hofer SE, Campbell F, Steigleder-Schweiger C, Beck RW, Warner JT, Holl RW; National Paediatric Diabetes Audit and the Royal College of Paediatrics and Child Health, the DPV Initiative, and the T1D Exchange Clinic Network (2015) Rates of diabetic ketoacidosis: international comparison with 49,859 pediatric patients with type 1 diabetes from England, Wales, the U.S., Austria, and Germany. Diabetes Care 38:1876–1882.10.2337/dc15-0780 33. Baalaaji M, Jayashree M, Nallasamy K, Singhi S, Bansal A. Predictors and outcome of acute kidney injury in children with diabetic ketoacidosis. Indian Pediatr (2018.0) 55 311-314. DOI: 10.1007/s13312-018-1274-8 34. 34.Myers SR, Glaser NS, Trainor JL, Nigrovic LE, Garro A, Tzimenatos L, Quayle KS, Kwok MY, Rewers A, Stoner MJ, Schunk JE, McManemy JK, Brown KM, DePiero AD, Olsen CS, Casper TC, Ghetti S, Kuppermann N; Pediatric Emergency Care Applied Research Network (PECARN) DKA FLUID Study Group (2020) Frequency and risk factors of acute kidney injury during diabetic ketoacidosis in children and association with neurocognitive outcomes. JAMA Netw Open 3:e2025481. 10.1001/jamanetworkopen.2020.25481 35. Schrier RW. ARF, AKI, or ATN?. Nat Rev Nephrol (2010.0) 6 125. DOI: 10.1038/nrneph.2010.1 36. Chen J, Zeng H, Ouyang X, Zhu M, Huang Q, Yu W, Ling L, Lan HY, Xu A, Tang Y. The incidence, risk factors, and long-term outcomes of acute kidney injury in hospitalized diabetic ketoacidosis patients. BMC Nephrol (2020.0) 21 48. DOI: 10.1186/s12882-020-1709-z 37. Flood RG, Chiang VW. Rate and prediction of infection in children with diabetic ketoacidosis. Am J Emerg Med (2001.0) 19 270-273. DOI: 10.1053/ajem.2001.24473 38. Karavanaki K, Karanika E, Georga S, Bartzeliotou A, Tsouvalas M, Konstantopoulos I, Fotinou A, Papassotiriou I, Karayianni C. Cytokine response to diabetic ketoacidosis (DKA) in children with type 1 diabetes (T1DM). Endocr J (2011.0) 58 1045-1053. DOI: 10.1507/endocrj.ej11-0024 39. Hansen TK, Thiel S, Wouters PJ, Chrstiansen JS, Vanden Berghe G. Intensive insulin therapy exerts anti-inflammatory effects in critically ill patients and counteracts the adverse effect of low mannose-binding lectin levels. J Clin Endocrinol Metab (2003.0) 88 1082-1088. DOI: 10.1210/jc.2002-021478 40. Dandona P, Aljada A, Mohanty P. The anti-inflammatory and potential antiatherogenic effect of insulin: a new paradigm. Diabetologia (2002.0) 45 924-930. DOI: 10.1007/s00125-001-0766-5 41. Moradi S, Kerman SR, Rohani F, Salari F. Association between diabetes complications and leukocyte counts in Iranian patients. J Inflam Res (2012.0) 5 7-11. DOI: 10.2147/JIR.S26917 42. Goebel MU, Mills PJ, Irwin MR, Ziegler MG. Interleukin-6 and tumor necrosis factor-a production after acute psychological stress, exercise and infused isoproterenol: differential effects and pathways. Psychosom Med (2000.0) 62 591-598. DOI: 10.1097/00006842-200007000-00019 43. Suffredini AF, Fiantazzi G, Badolto R, Oppenheimer J, O’Grady N. New insights into the biology of acute phase response. J Clin Immunol (1999.0) 19 203-214. DOI: 10.1023/a:1020563913045 44. Kitabchi AE, Stentz FB, Umpierrez GE. Diabetic ketoacidosis induces in vivo activation of T-lymphocytes. Biochem Biophys Res Commun (2004.0) 315 404-407. DOI: 10.1016/j.bbrc.2004.01.065 45. Hoffman WH, Burek CL, Waller JL, Fisher LE, Khichi M, Mellick LB. Cytokine response to diabetic ketoacidosis and its treatment. Clin Immunol (2003.0) 108 175-181. DOI: 10.1016/s1521-6616(03)00144-x 46. Uber AM, Sutherland SM. Acute kidney injury in hospitalized children: consequences and outcomes. Pediatr Nephrol (2020.0) 35 213-220. DOI: 10.1007/s00467-018-4128-7
--- title: 'Markers of arterial stiffness and urinary metabolomics in young adults with early cardiovascular risk: the African-PREDICT study' authors: - Wessel L. du Toit - Ruan Kruger - Lebo F. Gafane-Matemane - Aletta E. Schutte - Roan Louw - Catharina M. C. Mels journal: Metabolomics year: 2023 pmcid: PMC10060307 doi: 10.1007/s11306-023-01987-y license: CC BY 4.0 --- # Markers of arterial stiffness and urinary metabolomics in young adults with early cardiovascular risk: the African-PREDICT study ## Abstract ### Introduction Increased exposure to risk factors in the young and healthy contributes to arterial changes, which may be accompanied by an altered metabolism. ### Objectives To increase our understanding of early metabolic alterations and how they associate with markers of arterial stiffness, we profiled urinary metabolites in young adults with cardiovascular disease (CVD) risk factor(s) and in a control group without CVD risk factors. ### Methods We included healthy black and white women and men ($$n = 1202$$), aged 20–30 years with a detailed CVD risk factor profile, reflecting obesity, physical inactivity, smoking, excessive alcohol intake, masked hypertension, hyperglycemia, dyslipidemia and low socio-economic status, forming the CVD risk group ($$n = 1036$$) and the control group ($$n = 166$$). Markers of arterial stiffness, central systolic blood pressure (BP) and pulse wave velocity were measured. A targeted metabolomics approach was followed by measuring amino acids and acylcarnitines using a liquid chromatography-tandem mass spectrometry method. ### Results In the CVD risk group, central systolic BP (adjusted for age, sex, ethnicity) was negatively associated with histidine, arginine, asparagine, serine, glutamine, dimethylglycine, threonine, GABA, proline, methionine, pyroglutamic acid, aspartic acid, glutamic acid, branched chain amino acids (BCAAs) and butyrylcarnitine (all P ≤ 0.048). In the same group, pulse wave velocity (adjusted for age, sex, ethnicity, mean arterial pressure) was negatively associated with histidine, lysine, threonine, 2-aminoadipic acid, BCAAs and aromatic amino acids (AAAs) (all P ≤ 0.044). In the control group, central systolic BP was negatively associated with pyroglutamic acid, glutamic acid and dodecanoylcarnitine (all P ≤ 0.033). ### Conclusion In a group with increased CVD risk, markers of arterial stiffness were negatively associated with metabolites related to AAA and BCAA as well as energy metabolism and oxidative stress. Our findings may suggest that metabolic adaptations may be at play in response to increased CVD risk to maintain cardiovascular integrity. ### Supplementary Information The online version contains supplementary material available at 10.1007/s11306-023-01987-y. ## Introduction Exposure to risk factors contributes to premature cardiovascular disease (CVD) development, a global and growing health burden (Rodgers et al., 2019; Stewart et al., 2017; World Health Organisation, 2022a). This predisposition increases as the exposure to CVD related risk factors increase. These may include obesity, physical inactivity, tobacco and alcohol use, elevated blood pressure (BP), hyperglycemia, dyslipidemia and low socio-economic status (SES), or a combination of these risk factors (Banks et al., 2019; Cercato & Fonseca, 2019; Kjeldsen, 2018; Lavie et al., 2019; Matheus et al., 2013; Nelson, 2013; Piano, 2017; Rosengren et al., 2019; Schultz et al., 2018). In the young and healthy, increased exposure to CVD risk factors may already affect the structure and function of large arteries which may lead to the development of CVD later in life (Bruno et al., 2020; Laurent et al., 2019). These early vascular alterations are reflected by markers of arterial stiffness such as central systolic BP and aortic pulse wave velocity, the gold standard measurement for arterial stiffness (Pauca et al., 2001; Townsend et al., 2015; Van Bortel et al., 2012). Additionally, these early changes may also be accompanied by altered metabolism before the manifestation of clinical CVD (Polonis et al., 2020). Identifying these early metabolic alterations and how they associate with markers of arterial stiffness in the young and healthy may lead to biomarker or pathway discovery normally masked by advanced CVD and age. The discovery of novel biomarkers and related pathways may also lead to the identification of new targets for therapeutic interventions and the development of preventative strategies. Metabolomics enables the identification of altered pathways or profiles through the quantification of multiple metabolites simultaneously (Barallobre-Barreiro et al., 2013; Kordalewska & Markuszewski, 2015; Lewis et al., 2008; McGarrah et al., 2018; Ussher et al., 2016). In this regard, we have previously demonstrated specific urinary metabolomic profiles and pathways associated with CVD risk, including altered aromatic amino acid (AAA), and branched chain amino acid (BCAA) metabolism, energetics, and oxidative stress within the African Prospective study on Early Detection and Identification of Cardiovascular disease and Hypertension (African-PREDICT) cohort (aged 20–30 years) (du Toit et al., 2022; Mels et al., 2019). However, it remains unclear whether these metabolic pathways associate with vascular alterations in the presence of CVD risk factors. Therefore, we aimed to investigate the associations between markers of arterial stiffness (central systolic BP and pulse wave velocity) with urinary metabolites in young adults stratified by the presence or absence of CVD risk factors (obesity, physical inactivity, smoking, excessive alcohol intake, masked hypertension, hyperglycemia, dyslipidemia and low SES). ## Study design and population This study forms part of the African-PREDICT study. Details of the study were previously published (Schutte et al., 2019). In short, the African-PREDICT study is aimed at investigating early CVD-related pathophysiology by tracking young (aged 20–30 years) apparently healthy black and white adults over time (Schutte et al., 2019). Recruitment of participants were done on a voluntary basis from the North-West Province of South Africa. During screening participants were included if they were normotensive (clinic BP was < $\frac{140}{90}$ mmHg) (Mancia et al., 2013), uninfected with the human immunodeficiency virus, not diagnosed with chronic diseases or using medication for chronic diseases (self-reported), not pregnant or lactating (self-reported). This study was approved by the Health Research Ethics Committee of the North-West University (NWU-00411-20-A1) and adhered to the principles set out in the Declaration of Helsinki. All participants provided written informed consent. The full baseline cohort of 1202 young adults stratified by the presence or absence of CVD risk factors (obesity—≥ 0.55 waist-to-height ratio, physical inactivity—< 600 metabolic equivalents (METs) for moderate and/or vigorous intensity physical activity, smoking—≥ 11 ng/mL cotinine and self-reported smoking, excessive alcohol intake—≥ 49 U/L gamma-glutamyl transferase (GGT) and self-reported drinking, masked hypertension—normal clinic BP and 24 h/day/night BP classified as hypertensive, hyperglycemia—≥ $5.7\%$ glycated haemoglobin (HbA1c), dyslipidemia—> 3.4 mmol/L low density lipoprotein cholesterol (LDL) and low SES) were cross-sectionally analysed (Fig. 1).Fig. 1Grouping of participants according to the presence/absence of cardiovascular disease risk factors. Cardiovascular disease risk group criteria and sources: Obese (Amirabdollahian & Haghighatdoost, 2018; Yoo, 2016)—≥ 0.55 waist-to-height ratio; Physically inactive (Keating et al., 2019; World Health Organisation, 2022b)—< 600 METs for moderate and/or vigorous intensity physical activity; Smoking (Kim, 2016; Raja et al., 2016)—≥ 11 ng/mL cotinine & self-reported smoking; Excessive alcohol intake (Agarwal et al., 2016; Jastrzebska et al., 2016; Puukka et al., 2006)—≥ 49 U/L GGT & self-reported drinking; Masked hypertensive (Anstey et al., 2018)—normal clinic BP & 24 h/day/night BP classified as hypertensive; Hyperglycemic (Sherwani et al., 2016)—≥ $5.7\%$ HbA1c; Dyslipidemic (Nelson, 2013; Pagana et al., 2020)—> 3.4 mmol/L LDL; Low socio-economic (Patro et al., 2012)—low SES. CVD cardiovascular disease ## Questionnaire data Demographic data were collected using a General Health and Demographic Questionnaire. Data obtained included age, sex, ethnicity, education level, employment information, household income, medication use, smoking and alcohol consumption (used in conjunction with cotinine and GGT respectively, as criteria for the CVD risk group). From the demographic information, SES was calculated using a point system adapted from Kuppuswamy's Socioeconomic Status Scale 2010 (Patro et al., 2012) for a South African environment. Participants were scored in three categories: skill level (classified according to the South African Standard Classification of Occupation), education and income. These three factors were used to categorise participants into socio-economic classes (low, middle, high) and used as criteria for the CVD risk group. Furthermore, a SES score was determined. Physical activity data were collected using the Global Physical Activity Questionnaire. Data obtained included sedentary behaviour, moderate and vigorous intensity physical activity. From the physical activity information, the METs were calculated, where one MET is defined as the energy cost of sitting quietly and is equivalent to a caloric consumption of 1 kCal/kg/hour; 4 METs is assigned to moderate intensity physical activity and 8 METs is assigned to vigorous intensity physical activity (World Health Organisation, 2022b). The METs were used as criteria for the CVD risk group (< 600 METs for moderate and/or vigorous intensity physical activity—physically inactive) (Keating et al., 2019; World Health Organisation, 2022b). The average of the three variables was used in this study. Dietary data were collected using a 24 h dietary recall questionnaire. The questionnaire was completed three times, once on site and twice within 7 days (Steinfeldt et al., 2013). Participants answered the questionnaire using a standardised dietary collection kit containing example pictures, packages, measurement tools and food models. Data obtained included protein intake that were coded according to the South African Medical Research Council’s (SAMRC) food composition tables (Wolmarans et al, 2010) and the SAMRC’s food quantities manual (Langenhoven et al., 1991) in grams. Protein intake were used to adjust the metabolomics data, since this may influence amino acid concentrations in the body (Wu, 2016). ## Anthropometric measurements Anthropometric measurements were taken in accordance with the guidelines of the International Society for the Advancement of Kinanthropometry (International Society for the Advancement of Kinanthropometry, 2001) to obtain height (m), determined by the SECA 213 Portable Stadiometer (SECA, Hamburg, Germany), weight (kg), using the SECA 813 Electronic Scales (SECA, Hamburg, Germany) and waist circumference (cm), using the Lufkin Steel Anthropometric Tape (W606 PM; Lufkin, Apex, USA). Body mass index (BMI) (weight (kg)/height(m2)) and waist-to-height ratio were then calculated. Waist-to-height ratio were used as criteria for the CVD risk group (≥ 0.55 waist-to-height ratio—obese) (Amirabdollahian & Haghighatdoost, 2018; Yoo, 2016). ## Cardiovascular measurements Clinic BP measurements were obtained using the Dinamap Procare 100 Vital Signs Monitor (GE Medical Systems, Milwaukee, USA) (Reinders et al., 2006) apparatus with appropriately sized cuffs and the participant in the upright sitting position. Participants were requested to rest for a 5 min period before and between each measurement and not to have exercised, smoked or eaten for the last 30 min prior to commencement of the measurements. Measurements were taken in duplicate at the left and right brachial artery. The mean of the second measurements at the right and left arm were used to calculate mean arterial pressure. Ambulatory BP measurements were obtained over 24 h using the Card(X)plore (Meditech, Budapest, Hungary) apparatus with appropriately sized cuffs. The device measured BP in 30 min intervals during daytime (6 a.m. to 10 p.m.) and hourly during the night (10 p.m. to 6 a.m.). The mean successful inflation rate over the 24 h period was $88\%$. Furthermore, ambulatory BP together with clinic BP measurements were used to identify participants with masked hypertension (normal clinic BP and 24 h (SBP ≥ 130 mmHg and/or DBP ≥ 80 mmHg) /day (SBP ≥ 135 mmHg and/or DBP ≥ 85 mmHg) /night (SBP ≥ 120 mmHg and/or DBP ≥ 70 mmHg) BP classified as hypertensive) which was used as criteria for the CVD risk group (Anstey et al., 2018). Central systolic BP and pulse wave velocity were obtained using the SphygmoCor® XCEL device (AtCor Medical Pty. Ltd., Sydney, Australia) (Pauca et al., 2001; Townsend et al., 2015; Van Bortel et al., 2012). Participants were requested to be in a supine relaxed position for approximately 5 min before the measurement commenced. Central systolic BP (pulse wave analysis) was determined by placing a brachial cuff on the right upper arm. Pulse wave velocity was determined by locating the right carotid artery, identifying the strongest pulse point though palpation and measuring it with a tonometer. The femoral pulse was measured using a femoral cuff placed around the thigh, while $80\%$ of the distance between the carotid pulse point and upper femoral cuff was calculated and used to measure pulse wave velocity (transit-distance method). Measurements were taken in duplicate. The mean of the measurements was used in this study. ## Biochemical analyses Blood and spot urine samples were obtained by a registered nurse from fasted participants. The biological samples were immediately prepared and aliquoted into cryovials and stored at -80ºC until analysis. The Cobas Integra® 400 plus (Roche, Basel, Switzerland) were used to analyse GGT, the lipid profile (total cholesterol, high-density lipoprotein cholesterol, LDL and triglycerides) and C-reactive protein in serum samples. Glucose levels in sodium fluoride plasma samples and HbA1c in EDTA whole blood samples were also analysed using the Cobas Integra® 400 plus (Roche, Basel, Switzerland). Cotinine was analysed from serum samples using the Immulite (Siemens, Erlangen, Germany) apparatus. Serum peroxides as a measure of reactive oxygen species (ROS), were analysed in serum samples (Hayashi et al., 2007) using Synergy H4 hybrid microplate reader (BioTek, Winooski, VT, USA). Metabolomics data (30 amino acids and 9 acylcarnities) were analysed using a liquid chromatography-tandem mass spectrometry method on an Agilent© system (1200 series LC front end coupled to a 6410 series triple quadrupole mass analyser) with electrospray ionisation source operated in positive ionisation mode. Details of this method were previously published (du Toit et al., 2022). In short, urine samples were randomised to be prepared and analysed in batches of 20 samples per batch, together with 3 quality control urine samples and an additional in-house standard mixture (consisting of all analysed metabolites), to monitor data integrity. Sample preparation started with defrosting urine samples overnight, after which an isotope mixture containing various amino acid and acylcarnities isotopes were added to a predetermined volume of urine. Thereafter the urine samples were further processed and stored (− 80 °C) until analysis. Before analysis the samples were again defrosted and processed for metabolite separation using a Zorbax SB-Aq 80 Å StableBond column (Agilent©, 2.1 mm × 100 mm × 1.8 μ; cat# 828700-914) and Zorbax Eclipse Plus C18 guard column (Agilent©, 2.1 mm × 5 mm, 1.8 μm, cat# 821725-901) with specific run order times and parameters. Regarding data prepossessing, a peak intensity filter was applied to remove features with areas below the limit of quantification (LOQ cut-off of area < 750). Metabolomics data were then normalised to the added isotope internal standards. Furthermore, spectral data matrices were individually inspected for each batch to ensure good data quality. Altogether, the data proved to be good quality with no batch effects visible. Biochemical variables used as criteria for the CVD risk group include, GGT (≥ 49 U/L GGT and self-reported alcohol use—excessive alcohol intake) (Agarwal et al., 2016; Jastrzebska et al., 2016; Puukka et al., 2006), LDL (> 3.4 mmol/L LDL—dyslipidemic) (Nelson, 2013; Pagana et al., 2020), HbA1c (≥ $5.7\%$ HbA1c—hyperglycemic) (Sherwani et al., 2016) and cotinine (≥ 11 ng/mL cotinine and self-reported tobacco use—smoking) (Kim, 2016; Raja et al., 2016). ## Statistical analyses Statistical analyses were performed with IBM®, SPSS® version 27 (IBM Corporation, Armonk, New York). Variables were tested for normality and logarithmically transformed if skewed. Logged variables included physical activity, cotinine, GGT, triglycerides, C-reactive protein, ROS, protein intake and the metabolomics data. Data is reported as mean (normally distributed variables) or geometric mean (logarithmically transformed variables) with $95\%$ confidence intervals. Grouping of participants were performed according to the presence of CVD risk factor(s), forming the CVD risk group and the control group (Fig. 1). The characteristics between the control and CVD risk group were compared using the Chi-square test to compare categorical variables and ANCOVA to compare continuous variables. In the ANCOVAs, adjustment was made for sex and ethnicity, for pulse wave velocity further adjustment was made using mean arterial pressure, and for the metabolomics data further adjustment was made for protein intake (as part of a sensitivity analysis). P-values for comparing metabolomics data between the control and CVD risk group were adjusted for multiple comparisons to lower the false discovery rate using the Benjamini–Hochberg procedure (q-value). Multivariable adjusted regression analyses were performed to determine associations between central systolic BP and pulse wave velocity with the metabolomics data in the control and CVD risk group. The basic model included age, sex and ethnicity with additional adjustment for pulse wave velocity using mean arterial pressure. Furthermore, as part of the multiple regression analysis, a sensitivity analysis was performed in the control and CVD risk group taking into consideration protein intake as a covariate. The data underlying this article are available in the article and in its online supplementary material. ## Results The demographics and cardiovascular risk factor comparison between the control and CVD risk group are shown in Table 1. As expected, the CVD risk group showed a worse CVD risk profile compared to the control group (all P ≤ 0.003). Markers of arterial stiffness revealed higher central systolic BP in the CVD risk group compared to the control group ($$P \leq 0.003$$), with no difference in pulse wave velocity between the groups ($$P \leq 0.858$$). Markers of inflammation (C-reactive protein) and oxidative stress (serum peroxides) were also higher in the CVD risk group compared to the control group (P ≤ 0.009). The metabolomics comparison (Supplementary Table 1) indicated higher creatine, tyrosine and phenylalanine in the CVD risk group compared to the control group (all P ≤ 0.044). We additionally adjusted the metabolomics data for protein intake, since this may influence amino acid levels in the body (Wu, 2016). After the adjustment phenylalanine lost significance (Supplementary Table 2). Furthermore, after performing the Benjamini–Hochberg procedure the differences between all metabolites lost statistical significance. Table 1Comparisons of demographics and cardiovascular risk factors between the control and cardiovascular disease risk groupControl groupCVD risk groupP-valueN1661036Age (years)24.8 (24.3; 25.3)24.5 (24.3; 24.7)0.238Sex, female [n (%)]108 (65.1)516 (49.8) < 0.001Ethnicity, black [n (%)]39 (23.5)567 (54.7) < 0.001Cardiovascular disease risk factorsWeight (kg)65.4 (62.9; 67.9)72.2 (71.2; 73.1) < 0.001Waist circumference (cm)74.7 (72.9; 76.5)81.0 (80.3; 81.7) < 0.001Height (m)1.69 (1.68; 1.70)1.68 (1.68; 1.69)0.758Body mass index (kg/m2)23.0 (22.2; 23.9)25.4 (25.1; 25.7) < 0.001Waist-to-height ratio0.44 (0.43; 0.45)0.48 (0.48; 0.49) < 0.001Physical activity (kCal/kg/day)271 (240; 309)206 (195; 219) < 0.001Cotinine (ng/ml)1.15 (0.83; 1.58)4.32 (3.80; 4.90) < 0.001Self-reported smoking [n (%)]0 [0]286 (27.6) < 0.001γ-glutamyl transferase (U/L)13.1 (12.0; 14.5)19.2 (18.6; 20.0) < 0.001Self-reported alcohol use [n (%)]69 (41.6)597 (58.1) < 0.00124 h Systolic BP (mmHg)114 (113; 115)117 (117; 118) < 0.00124 h Diastolic BP (mmHg)67 (66; 68)69 (69; 69) < 0.001MAP (mmHg)93 (92; 94)94 (94; 95)0.128Central systolic BP (mmHg)106 (105; 108)108 (108; 109)0.003Pulse wave velocity (m/s)6.33 (6.20; 6.46)6.34 (6.29; 6.39)0.858Masked hypertensive, [n (%)]0 [0]206 (20.1) < 0.001HbA1c (%)5.26 (5.21; 5.30)5.33 (5.31; 5.35)0.003Glucose (mmol/L)3.85 (3.69; 4.02)4.13 (4.07; 4.19)0.002Total cholesterol (mmol/L)3.29 (3.12; 3.47)3.83 (3.76; 3.90) < 0.001HDL cholesterol (mmol/L)1.17 (1.11; 1.23)1.16 (1.13; 1.18)0.734LDL cholesterol (mmol/L)2.03 (1.88; 2.17)2.51 (2.45; 2.57) < 0.001Triglycerides (mmol/L)0.56 (0.52; 0.62)0.75 (0.72; 0.78) < 0.001C-reactive protein (mg/L)0.59 (0.47; 0.74)0.95 (0.87; 1.05) < 0.001ROS, mg/L H2O234.3 (30.9; 38.0)39.4 (38.0; 40.7)0.009Socio-economic status score23.5 (22.7; 24.3)20.2 (19.9; 20.5) < 0.001Protein intake (g)69.7 (64.6; 74.1)66.5 (64.6; 67.6)0.224Tests used: Chi-square tests and ANCOVAs (adjusted for sex and ethnicity with additional adjustment for pulse wave velocity using mean arterial pressure). Data are presented as mean or geometric mean with $95\%$ confidence intervals. Bold values denote P ≤ 0.05. Cardiovascular disease risk group criteria: Obese—≥ 0.55 waist-to-height ratio; Physically inactive—< 600 METs for moderate and/or vigorous intensity physical activity; Smoking—≥ 11 ng/mL cotinine AND self-reported smoking; Excessive alcohol intake—≥ 49 U/L GGT and self-reported drinking; Masked hypertensive—normal clinic BP and 24 h/day/night BP classified as hypertensive; Hyperglycemic—≥ $5.7\%$ HbA1c; Dyslipidemic—> 3.4 mmol/L LDL; Low socio-economic—low SESγ gamma, MAP mean arterial pressure, BP blood pressure, HbA1c glycated haemoglobin, HDL high-density lipoprotein, LDL low-density lipoprotein, ROS reactive oxygen species, CVD cardiovascular disease Using adjusted regression models, we determined whether central systolic BP (adjusted for age, sex and ethnicity) and pulse wave velocity (adjusted for age, sex, ethnicity and mean arterial pressure) were associated with the metabolomics data in the control and CVD risk group (Fig. 2 and Supplementary Table 3A-D). In the CVD risk group, we found negative associations between central systolic BP and histidine, arginine, asparagine, serine, glutamine, dimethylglycine, threonine, GABA, proline, valine, methionine, pyroglutamic acid, leucine/isoleucine, aspartic acid, glutamic acid and butyrylcarnitine (all P ≤ 0.048). Additionally, we found negative associations between pulse wave velocity and histidine, lysine, threonine, valine, tyrosine, leucine/isoleucine, phenylalanine, tryptophan and 2-aminoadipic acid (all P ≤ 0.044). In the control group, negative associations were found between central systolic BP and pyroglutamic acid, glutamic acid and dodecanoylcarnitine (all P ≤ 0.033).Fig. 2Multi-variable adjusted regression analysis with central systolic blood pressure or pulse wave velocity as the dependent variable, with the metabolomics data in the control and cardiovascular disease risk group. Test used: Multiple linear regressions. β coefficients are presented—separate models. Central systolic BP, adjusted for age, sex, ethnicity; pulse wave velocity, adjusted for age, sex, ethnicity, mean arterial pressure. Cardiovascular disease risk group criteria: Obese—≥ 0.55 waist-to-height ratio; Physically inactive—< 600 METs for moderate and/or vigorous intensity physical activity; Smoking—≥ 11 ng/mL cotinine & self-reported smoking; Excessive alcohol intake—≥ 49 U/L GGT & self-reported drinking; Masked hypertensive—normal clinic BP & 24 h/day/night BP classified as hypertensive; Hyperglycemic—≥ $5.7\%$ HbA1c; Dyslipidemic—> 3.4 mmol/L LDL; Low socio-economic—low SES. Metabolite concentration expressed as arbitrary units. BP blood pressure, CVD cardiovascular disease ## Sensitivity analysis We additionally performed a sensitivity analysis in the control and CVD risk group and included protein intake as an additional covariate in the multiple regression model (Supplementary Table 4A–D). This was done since protein intake may influence amino acid levels in the body (Wu, 2016). After the additional adjustment for protein intake the associations between central systolic BP and arginine ($$P \leq 0.052$$), GABA ($$P \leq 0.052$$) and leucine/isoleucine ($$P \leq 0.063$$) in the CVD risk group lost significance. All the other associations remained significant. ## Discussion Comparing the metabolite concentrations between the control and CVD risk group revealed no statistically significant differences, this could be explained in part by the young age and apparent healthy nature of the research participants. However, when considering the multi-variate adjusted regression analysis, negative associations were found between central systolic BP and pulse wave velocity with metabolites associated with AAA and BCAA metabolism, energy metabolism and oxidative stress in a study population consisting of young apparently healthy black and white men and women with one or more CVD risk factors. In contrast to other metabolomic studies, which were mostly conducted in aged adults and in those with overt vascular complications and established CVD, such as arterial stiffness, coronary artery disease, peripheral artery disease and hypertension (Koh et al., 2018; Li et al., 2019; Menni et al., 2015; Paapstel et al., 2016; Polonis et al., 2020; Zagura et al., 2015), our findings highlight the early metabolic changes associated with markers of arterial stiffness in individuals at risk for the development of CVD. ## Aromatic and branched chain amino acid metabolism In the CVD risk group, inverse associations were found between pulse wave velocity and the AAAs, phenylalanine and tyrosine. This is in contrast to a previous metabolomic study conducted in aged men (66 years) with peripheral artery disease in which positive associations were found between pulse wave velocity and the AAAs (Zagura et al., 2015). Phenylalanine, an essential amino acid, is metabolised to tyrosine, a precursor for the synthesis of catecholamines such as dopamine, norepinephrine, and epinephrine (Motiejunaite et al., 2021) (Fig. 3A). This metabolic pathway is controlled by the rate limiting enzyme, tyrosine hydroxylase, which is regulated by feedback inhibition by the respective catecholamines (Dickson & Briggs, 2013; Motiejunaite et al., 2021). These catecholamines activate different adrenergic receptor(s), which lead to specific effects depending on the type of receptors activated, the location of receptors (blood vessels, heart, brain) and signaling cascades activated (Motiejunaite et al., 2021; Sorriento et al., 2011). In a young study population without CVD, but with increased CVD risk, the inverse associations found between pulse wave velocity with phenylalanine and tyrosine may suggest that the catecholamine synthesis, and hence binding to the adrenergic receptors are decreased via a negative feedback mechanism to maintain vasodilation. The negative feedback mechanism may thus result in the higher phenylalanine and tyrosine levels observed in this group. When considering that pulse wave velocity was similar in both groups and within normal ranges (Van Bortel et al., 2012) this may be an adaptive response to maintain the elasticity of the central arteries despite the increased CVD risk. Fig. 3Markers of arterial stiffness relate to altered aromatic amino acid and branched chain amino acid metabolism within the cardiovascular disease risk group. Within the CVD risk group, central systolic BP and pulse wave velocity showed inverse associations with metabolites linked to AAA and BCAA metabolism. Phenylalanine and tyrosine feed into the catecholamine pathway, producing dopamine, norepinephrine and epinephrine which is implicated in vascular tone. Tryptophan is metabolised through the kynurenine pathway which is induced by inflammation and when nitric oxide is unable to inhibit this pathway. Kynurenine is implicated in vascular tone; the downstream metabolites is linked to oxidative stress and inflammation. The BCAAs, through mTOR activation, preserve cardiovascular integrity and enable cardiovascular adaptation; increased activation causes oxidative stress and inflammation. Metabolites associated with markers of arterial stiffness are indicated in bold and italic. IDO indoleamine-2,3-dioxygenase, KMO kynurenine 3-monooxygenase; NAD+ nicotinamide adenine dinucleotide, mTOR mammalian target of rapamycin Tryptophan, an essential amino acid, was also inversely associated with pulse wave velocity in the CVD risk group. Uncontrolled tryptophan metabolism has been associated with numerous vascular complications and CVD such as atherosclerosis, endothelial dysfunction, heart disease and hypertension (Ramprasath et al., 2021; Song et al., 2017). Tryptophan metabolism occurs mainly via the kynurenine pathway (> $95\%$ of tryptophan metabolism), where tryptophan is converted to formylkynurenine by two rate-limiting enzymes, tryptophan 2,3-dioxygenase (TDO) (hepatic) and indoleamine-2,3-dioxygenase (IDO) (extrahepatic, such as endothelial and vascular smooth muscle cells) (Badawy, 2019; Ramprasath et al., 2021; Song et al., 2017) (Fig. 3B). Usually, TDO governs basal tryptophan metabolism, while IDO increases tryptophan metabolism under inflammatory and oxidative conditions (decreased nitric oxide (NO)) (Badawy, 2019; Ramprasath et al., 2021; Song et al., 2017). In a young study population without CVD, but with increased CVD risk, ROS levels and the inflammatory marker C-reactive protein were higher (but still within normal ranges) (Forman et al., 2016; Pagana et al., 2020) when compared to the control group. Since kynurenine was shown to induce arterial relaxation (Badawy, 2019; Ramprasath et al., 2021; Song et al., 2017), we hypothesise that the inverse association between pulse wave velocity and tryptophan, may reflect an activated kynurenine pathway with consequent increased vasodilation to maintain vascular tone at physiological levels. However, increased flux through the one branch of the kynurenine pathway i.e. metabolism of kynurenine by kynurenine 3-monooxygenase (KMO) to ultimately produce nicotinamide adenine dinucleotide (NAD +), may lead to the formation of pro-oxidative and pro-inflammatory metabolites such as 3-hydroxykynurenine, xanthurenic acid, 3-hydroxyanthranilic acid and quinolinic acid (Badawy, 2019; Ramprasath et al., 2021; Song et al., 2017). The accumulation of these metabolites may result in a vicious cycle of IDO activation and consequently vascular complications and CVD (Badawy, 2019; Ramprasath et al., 2021; Song et al., 2017). Therefore, inhibiting KMO activity might be beneficial in CVD with increased inflammation (Badawy, 2019; Phillips et al., 2019). This in turn will result in increased kynurenine and downstream metabolites, kynurenic acid and anthranilic acid (through different branches) which has antioxidant properties (Francisco-Marquez et al., 2016; Lugo-Huitrón et al., 2011). Furthermore, in this group with CVD risk, both central systolic BP and pulse wave velocity associated inversely with the BCAAs (leucine/isoleucine and valine), and with butyrylcarnitine (central systolic BP only), a product of BCAA metabolism (Fig. 3C). Branched-chain amino acids, along with other nutrient signals such as insulin, lead to mammalian target of rapamycin (mTOR) activation (Laplante & Sabatini, 2012; Sciarretta et al., 2018). Physiologically, this activation is essential for the preservation of cardiovascular integrity and enable cardiovascular adaptation to mechanical stress (Sciarretta et al., 2018). In a young study population without CVD, but with increased CVD risk, central and 24 h BP were higher when compared to the control group. We therefore hypothesise that the inverse association between central systolic BP, pulse wave velocity and the BCAAs, may reflect mTOR activation with consequent cardiovascular adaptation to maintain cardiovascular integrity. However, deregulation of mTOR have also been associated with CVD (Chong et al., 2011; Dyachok et al., 2016; Laplante & Sabatini, 2012; Sciarretta et al., 2018; Zhenyukh et al., 2018), it is therefore essential that activation of mTOR remain in a compensatory state, preventing deregulated activation and the pathological consequences leading to CVD. ## Cardiovascular energy metabolism In the CVD risk group, inverse associations of central systolic BP and pulse wave velocity were found with several amino acids related to energy producing pathways such as glycolysis (the primary energy-producing mechanism in endothelial cells) and the citric acid cycle including, histidine, threonine, BCAAs (associated with central systolic BP and pulse wave velocity); serine, glutamic acid, glutamine, dimethylglycine, methionine, arginine, aspartic acid, asparagine, proline, GABA (associated with central systolic BP); AAAs, lysine, and 2-aminoadipic acid (associated with pulse wave velocity). All of these amino acids feed into glycolysis or the citric acid cycle on different levels such as pyruvate, acetyl-CoA or various citric acid cycle intermediates to produce the reducing agents flavin adenine dinucleotide (FADH2) and NADH, which subsequently enters the electron transport chain to generate adenosine triphosphate (ATP) (Akram, 2014) (Fig. 4). Although ATP is considered the major energy source in the cell, it is also a potent extracellular signalling molecule which can be released from all major cell types in the vessel wall to act as an autocrine or paracrine (Ralevic & Dunn, 2015; Wu et al., 2022). Binding of ATP to the different purinergic receptors causes an overall influx of Ca2+ into vascular smooth muscle cells or endothelial cells with consequent activation of endothelial NO synthase and NO production (Ralevic & Dunn, 2015; Wu et al., 2022). In a young study population without CVD, but with increased CVD risk, the inverse associations found between central systolic BP, pulse wave velocity and various amino acids related to ATP production, we hypothesise that more amino acids may be made available for ATP production and the consequent binding to the receptors in an attempt to maintain vascular tone in the presence of CVD risk factors. However, if the CVD risk persist over time, this may lead to detrimental consequences as chronic increases in ATP may potentiate hypertension and atherosclerosis (Huang et al., 2021; Ralevic & Dunn, 2015; Wu et al., 2022; Zhao et al., 2019). Therefore, it is essential that the extracellular concentration of this nucleotide is tightly regulated. Fig. 4Markers of arterial stiffness relate to altered energetics within the cardiovascular disease risk group. Within the CVD risk group, central systolic BP and pulse wave velocity showed inverse associations with metabolites linked to glycolysis and the citric acid cycle. Glycolysis is the primary energy-producing mechanism in the vasculature. This pathway uses amino acids as substrate to generate ATP. Metabolites associated with markers of arterial stiffness are indicated in bold and italic. ATP adenosine triphosphate, NAD+ nicotinamide adenine dinucleotide, FAD flavin adenine dinucleotide ## Oxidative stress Several metabolites which may serve as precursors for the y-glutamyl cycle, were inversely associated with central systolic BP and pulse wave velocity in the CVD risk group. These included histidine, threonine (associated with central systolic BP and pulse wave velocity); serine, proline, arginine, glutamic acid, glutamine, methionine, dimethylglycine and pyroglutamic acid (associated with central systolic BP) (Fig. 5).Fig. 5Markers of arterial stiffness relate to oxidative stress within the cardiovascular disease risk group. Within the CVD risk group, central systolic BP and pulse wave velocity showed inverse associations with metabolites linked to the methionine and y-glutamyl cycle. These pathways are important in producing the antioxidant glutathione to alleviate oxidative stress. Increased oxidative stress will in turn affect the NO bioavailability and cause vascular damage (fibrosis). Metabolites associated with markers of arterial stiffness are indicated in bold and italic The y-glutamyl cycle produces glutathione from glutamine (with histidine, arginine, proline, and glutamic acid as precursors), cysteine (with methionine and serine as precursors) and glycine (with threonine, proline, serine, and dimethylglycine as precursors) (Durante, 2019; Lushchak, 2012). Glutathione is important in maintaining a healthy redox state by serving as an antioxidant (Durante, 2019; Holeček, 2020; Lushchak, 2012). Our finding of an inverse association between markers of arterial stiffness with pyroglutamic acid levels, an intermediate of the y-glutamyl cycle, along with the precursors for glutathione, may suggest that more amino acids may be made available to produce glutathione (Durante, 2019; Gueta et al., 2020; Lushchak, 2012; Venkataraman et al., 2019), possibly due to increased oxidative stress. As stated above, we indicated elevated ROS levels in the CVD risk group. Additionally, some of the precursors for the y-glutamyl cycle such as methionine and cysteine may also contribute to an oxidative environment, through pro-inflammatory, pro-oxidant and pro-atherogenic effects (Fu et al., 2019; Ganguly & Alam, 2015; Garlick, 2006; Rehman et al., 2020; Xiao et al., 2015). This oxidative environment may decrease the bioavailability of NO with consequent vascular damage (fibrosis) (Cyr et al., 2020; Stakos et al., 2010; Zhao et al., 2015). ## Strengths and limitations The cross-sectional design of this study prevents us from inferring causal relationships. A major strength of our study was that we focused on high-level metabolomics data in a young apparently healthy population from African and European descent without CVD, thereby minimizing the influence of age and disease on the metabolism. We are also among the first to present this type of findings in a multi-ethnic cohort, which is especially limited in Africa. ## Conclusion In conclusion, in a young study population without CVD, but with increased CVD risk, markers of arterial stiffness were inversely associated with metabolites linked to AAA and BCAA metabolism, energy metabolism and oxidative stress. These pathways may be regulated as an adaptive response to maintain cardiovascular integrity in the presence of CVD risk factors. However, with continued exposure to CVD risk factors these pathways may become dysregulated as previously implicated in CVD. ## Recommendations Longitudinal studies investigating the associations between markers of arterial stiffness and urinary metabolites. Metabolomic studies focusing on individual CVD risk factors and how this translates to markers of arterial stiffness. Furthermore, future studies to test the associations found in this hypothesis-generating study by investigating the relevant pathways in a targeted manner. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 149 kb) ## References 1. Agarwal S, Fulgoni VL, Lieberman HR. Assessing alcohol intake & its dose-dependent effects on liver enzymes by 24-h recall and questionnaire using NHANES 2001–2010 data. Journal of Nutrition (2016.0) 15 1-12. DOI: 10.1186/s12937-016-0180-y 2. Akram M. Citric acid cycle and role of its intermediates in metabolism. Cell Biochemistry and Biophysics (2014.0) 68 475-478. DOI: 10.1007/s12013-013-9750-1 3. Amirabdollahian F, Haghighatdoost F. Anthropometric indicators of adiposity related to body weight and body shape as cardiometabolic risk predictors in british young adults: Superiority of waist-to-height ratio. Journal of Obesity (2018.0) 1 1-15. DOI: 10.1155/2018/8370304 4. Anstey DE, Muntner P, Bello NA, Pugliese DN, Yano Y, Kronish IM, Reynolds K, Schwartz JE, Shimbo D. Diagnosing masked hypertension using ambulatory blood pressure monitoring, home blood pressure monitoring, or both?. Hypertension (2018.0) 72 1200-1207. DOI: 10.1161/HYPERTENSIONAHA.118.11319 5. Badawy AA. Tryptophan metabolism: A versatile area providing multiple targets for pharmacological intervention. Egyptian Journal of Basic and Clinical Pharmacology (2019.0) 9 1-48. DOI: 10.32527/2019/101415 6. Banks E, Joshy G, Korda RJ, Stavreski B, Soga K, Egger S, Day C, Clarke NE, Lewington S, Lopez AD. Tobacco smoking and risk of 36 cardiovascular disease subtypes: Fatal and non-fatal outcomes in a large prospective Australian study. BMC Medicine (2019.0) 17 1-18. DOI: 10.1186/s12916-019-1351-4 7. Barallobre-Barreiro J, Chung YL, Mayr M. Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease. Revista Española De Cardiología (2013.0) 66 657-661. DOI: 10.1016/j.rec.2013.04.009 8. Bruno RM, Nilsson PM, Engström G, Wadström BN, Empana JP, Boutouyrie P, Laurent S. Early and supernormal vascular aging: Clinical characteristics and association with incident cardiovascular events. Hypertension (2020.0) 76 1616-1624. DOI: 10.1161/HYPERTENSIONAHA.120.14971 9. Cercato C, Fonseca FA. Cardiovascular risk and obesity. Diabetology & Metabolic Syndrome (2019.0) 11 1-15. DOI: 10.1186/s13098-019-0468-0 10. Chong ZZ, Shang YC, Maiese K. Cardiovascular disease and mTOR signaling. Trends in Cardiovascular Medicine (2011.0) 21 151-155. DOI: 10.1016/j.tcm.2012.04.005 11. Cyr AR, Huckaby LV, Shiva SS, Zuckerbraun BS. Nitric oxide and endothelial dysfunction. Critical Care Clinics (2020.0) 36 307-321. DOI: 10.1016/j.ccc.2019.12.009 12. Dickson PW, Briggs GD. Tyrosine hydroxylase: Regulation by feedback inhibition and phosphorylation. Advances in Pharmacology and Pharmacy (2013.0) 68 13-21. DOI: 10.1016/b978-0-12-411512-5.00002-6 13. du Toit WL, Kruger R, Gafane-Matemane LF, Schutte AE, Louw R, Mels CMC. Urinary metabolomics profiling by cardiovascular risk factors in young adults: The African prospective study on early detection and identification of cardiovascular disease and hypertension study. Journal of Hypertension (2022.0) 40 1545-1555. DOI: 10.1097/hjh.0000000000003182 14. Durante W. The emerging role of l-glutamine in cardiovascular health and disease. Nutrients (2019.0) 11 1-16. DOI: 10.3390/nu11092092 15. Dyachok J, Earnest S, Iturraran EN, Cobb MH, Ross EM. Amino acids regulate mTORC1 by an obligate two-step mechanism. Journal of Biological Chemistry (2016.0) 291 22414-22426. DOI: 10.1074/jbc.M116.732511 16. Forman HJ, Bernardo A, Davies KJ. What is the concentration of hydrogen peroxide in blood and plasma?. Archives of Biochemistry and Biophysics (2016.0) 603 48-53. DOI: 10.1016/j.abb.2016.05.005 17. Francisco-Marquez M, Aguilar-Fernández M, Galano A. Anthranilic acid as a secondary antioxidant: Implications to the inhibition of OH production and the associated oxidative stress. Computational and Theoretical Chemistry (2016.0) 1077 18-24. DOI: 10.1016/j.comptc.2015.09.025 18. Fu X, Cate SA, Dominguez M, Osborn W, Özpolat T, Konkle BA, Chen J, López JA. Cysteine disulfides (cys-ss-x) as sensitive plasma biomarkers of oxidative stress. Scientific Reports (2019.0) 9 1-9. DOI: 10.1038/s41598-018-35566-2 19. Ganguly P, Alam SF. Role of homocysteine in the development of cardiovascular disease. Nutrition (2015.0) 14 1-10. DOI: 10.1186/1475-2891-14-6 20. Garlick PJ. Toxicity of methionine in humans. Journal of Nutrition (2006.0) 136 1722-1725. DOI: 10.1093/jn/136.6.1722S 21. Gueta I, Perach Ovadia Y, Markovits N, Schacham YN, Epsztein A, Loebstein R. Is pyroglutamic acid a prognostic factor among patients with suspected infection? A prospective cohort study. Scientific Reports (2020.0) 10 1-16. DOI: 10.1038/s41598-020-66941-7 22. Hayashi I, Morishita Y, Imai K, Nakamura M, Nakachi K, Hayashi T. High-throughput spectrophotometric assay of reactive oxygen species in serum. Mutation Research (2007.0) 631 55-61. DOI: 10.1016/j.mrgentox.2007.04.006 23. Holeček M. Histidine in health and disease: Metabolism, physiological importance, and use as a supplement. Nutrients (2020.0) 12 1-20. DOI: 10.3390/nu12030848 24. Huang Z, Xie N, Illes P, Di Virgilio F, Ulrich H, Semyanov A, Verkhratsky A, Sperlagh B, Yu SG, Huang C, Tang Y. From purines to purinergic signalling: Molecular functions and human diseases. Signal Transduction and Targeted Therapy (2021.0) 6 1-20. DOI: 10.1038/s41392-021-00553-z 25. International Society for the Advancement of Kinanthropometry. International standards for anthropometric assessment. Australia; 2001. 26. Jastrzebska I, Zwolak A, Szczyrek M, Wawryniuk A, Skrzydlo-Radomanska B, Daniluk J. Biomarkers of alcohol misuse: Recent advances and future prospects. Przeglad Gastroenterologiczny (2016.0) 11 78-89. DOI: 10.5114/pg.2016.60252 27. Keating XD, Zhou K, Liu X, Hodges M, Liu J, Guan J, Phelps A, Castro-Pinero J. Reliability and concurrent validity of Global Physical Activity Questionnaire (GPAQ): A systematic review. International Journal of Environmental Research and Public Health (2019.0) 16 1-27. DOI: 10.3390/ijerph16214128 28. Kim S. Overview of cotinine cutoff values for smoking status classification. International Journal of Environmental Research and Public Health (2016.0) 13 1-15. DOI: 10.3390/ijerph13121236 29. Kjeldsen SE. Hypertension and cardiovascular risk: General aspects. Pharmacological Research (2018.0) 129 95-99. DOI: 10.1016/j.phrs.2017.11.003 30. Koh AS, Gao F, Liu J, Fridianto KT, Ching J, Tan RS, Wong JI, Chua SJ, Leng S, Zhong L, Keng BM, Huang FQ, Yuan JM, Koh WP, Kovalik JP. Metabolomic profile of arterial stiffness in aged adults. Diabetes and Vascular Disease Research (2018.0) 15 74-80. DOI: 10.1177/1479164117733627 31. Kordalewska M, Markuszewski MJ. Metabolomics in cardiovascular diseases. Journal of Pharmaceutical and Biomedical Analysis (2015.0) 113 121-136. DOI: 10.1016/j.jpba.2015.04.021 32. Langenhoven M, Conradie P, Wolmarans P, Faber M. Medical Research Council food quantities manual (1991.0) 33. Laplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell (2012.0) 149 274-293. DOI: 10.1016/j.cell.2012.03.017 34. Laurent S, Boutouyrie P, Cunha PG, Lacolley P, Nilsson PM. Concept of extremes in vascular aging: from early vascular aging to supernormal vascular aging. Hypertension (2019.0) 74 218-228. DOI: 10.1161/HYPERTENSIONAHA.119.12655 35. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary behavior, exercise, and cardiovascular health. Circulation Research (2019.0) 124 799-815. DOI: 10.1161/CIRCRESAHA.118.312669 36. Lewis GD, Asnani A, Gerszten RE. Application of metabolomics to cardiovascular biomarker and pathway discovery. Journal of the American College of Cardiology (2008.0) 52 117-123. DOI: 10.1016/j.jacc.2008.03.043 37. Li C, He J, Li S, Chen W, Bazzano L, Sun X, Shen L, Liang L, Shen Y, Gu X, Kelly TN. Novel metabolites are associated with augmentation index and pulse wave velocity: Findings from the Bogalusa Heart study. American Journal of Hypertension (2019.0) 32 547-556. DOI: 10.1093/ajh/hpz046 38. Lugo-Huitrón R, Blanco-Ayala T, Ugalde-Muñiz P, Carrillo-Mora P, Pedraza-Chaverrí J, Silva-Adaya D, Maldonado PD, Torres I, Pinzón E, Ortiz-Islas E, López T, García E, Pineda B, Torres-Ramos M, Santamaría A, La Cruz VP. On the antioxidant properties of kynurenic Acid: Free radical scavenging activity and inhibition of oxidative stress. Neurotoxicology and Teratology (2011.0) 33 538-547. DOI: 10.1016/j.ntt.2011.07.002 39. Lushchak VI. Glutathione homeostasis and functions: potential targets for medical interventions. Journal of Amino Acids (2012.0). DOI: 10.1155/2012/736837 40. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Bohm M, Christiaens T, Cifkova R, De Backer G, Dominiczak A, Galderisi M, Grobbee DE, Jaarsma T, Kirchhof P, Kjeldsen SE, Laurent S, Manolis AJ, Nilsson PM, Ruilope LM. 2013 ESH/ESC guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Journal of Hypertension (2013.0) 7 1281-1357. DOI: 10.1097/01.hjh.0000431740.32696.cc 41. Matheus AS, Tannus LR, Cobas RA, Palma CC, Negrato CA, Gomes MB. Impact of diabetes on cardiovascular disease: An update. International Journal of Hypertension (2013.0) 2013 1-15. DOI: 10.1155/2013/653789 42. McGarrah RW, Crown SB, Zhang G-F, Shah SH, Newgard CB. Cardiovascular metabolomics. Circulation Research (2018.0) 122 1238-1258. DOI: 10.1161/CIRCRESAHA.117.311002 43. Mels CM, Delles C, Louw R, Schutte AE. Central systolic pressure and a nonessential amino acid metabolomics profile: The African prospective study on the early detection and identification of cardiovascular disease and hypertension. Journal of Hypertension (2019.0) 37 1157-1166. DOI: 10.1097/HJH.0000000000002040 44. Menni C, Mangino M, Cecelja M, Psatha M, Brosnan MJ, Trimmer J, Mohney RP, Chowienczyk P, Padmanabhan S, Spector TD, Valdes AM. Metabolomic study of carotid–femoral pulse-wave velocity in women. Journal of Hypertension (2015.0) 33 791-796. DOI: 10.1097/HJH.0000000000000467 45. Motiejunaite J, Amar L, Vidal-Petiot E. Adrenergic receptors and cardiovascular effects of catecholamines. Annales D'endocrinologie (2021.0) 82 193-197. DOI: 10.1016/j.ando.2020.03.012 46. Nelson RH. Hyperlipidemia as a risk factor for cardiovascular disease. Primary Care (2013.0) 40 195-211. DOI: 10.1016/j.pop.2012.11.003 47. Paapstel K, Kals J, Eha J, Tootsi K, Ottas A, Piir A, Zilmer M. Metabolomic profiles of lipid metabolism, arterial stiffness and hemodynamics in male coronary artery disease patients. IJC Metabolic and Endocrine (2016.0) 11 13-18. DOI: 10.1016/j.ijcme.2016.05.001 48. Pagana KD, Pagana TJ, Pagana TN. Mosby’s® diagnostic and laboratory test reference (2020.0) 49. Patro B, Jeyashree K, Gupta P. Kuppuswamy’s Socioeconomic Status Scale 2010-the need for periodic revision. Indian Journal of Pediatrics (2012.0) 79 395-396. DOI: 10.1007/s12098-011-0517-7 50. Pauca AL, O’Rourke MF, Kon ND. Prospective evaluation of a method for estimating ascending aortic pressure from the radial artery pressure waveform. Hypertension (2001.0) 38 932-937. DOI: 10.1161/hy1001.096106 51. Phillips RS, Iradukunda EC, Hughes T, Bowen JP. Modulation of enzyme activity in the kynurenine pathway by kynurenine monooxygenase inhibition. Frontiers in Molecular Biosciences (2019.0) 6 1-9. DOI: 10.3389/fmolb.2019.00003 52. Piano MR. Alcohol's effects on the cardiovascular system. Alcohol Research (2017.0) 38 219-241. PMID: 28988575 53. Polonis K, Wawrzyniak R, Daghir-Wojtkowiak E, Szyndler A, Chrostowska M, Melander O, Hoffmann M, Kordalewska M, Raczak-Gutknecht J, Bartosińska E, Kaliszan R, Narkiewicz K, Markuszewski MJ. Metabolomic signature of early vascular aging (EVA) in hypertension. Frontiers in Molecular Biosciences (2020.0) 7 1-11. DOI: 10.3389/fmolb.2020.00012 54. Puukka K, Hietala J, Koivisto H, Anttila P, Bloigu R, Niemelä O. Age-related changes on serum GGT activity and the assessment of ethanol intake. Alcohol and Alcoholism (2006.0) 41 522-527. DOI: 10.1093/alcalc/agl052 55. Raja M, Garg A, Yadav P, Jha K, Handa S. Diagnostic methods for detection of cotinine level in tobacco users: A review. Journal of Clinical and Diagnostic Research (2016.0) 10 4-6. DOI: 10.7860/JCDR/2016/17360.7423 56. Ralevic V, Dunn WR. Purinergic transmission in blood vessels. Autonomic Neuroscience (2015.0) 191 48-66. DOI: 10.1016/j.autneu.2015.04.007 57. Ramprasath T, Han YM, Zhang D, Yu CJ, Zou MH. Tryptophan catabolism and inflammation: A novel therapeutic target for aortic diseases. Frontiers in Immunology (2021.0) 12 1-11. DOI: 10.3389/fimmu.2021.731701 58. Rehman T, Shabbir MA, Inam-Ur-Raheem M, Manzoor MF, Ahmad N, Liu ZW, Ahmad MH, Siddeeg A, Abid M, Aadil RM. Cysteine and homocysteine as biomarker of various diseases. Food Science and Nutrition (2020.0) 8 4696-4707. DOI: 10.1002/fsn3.1818 59. Reinders A, Reggiori F, Shennan AH. Validation of the DINAMAP ProCare blood pressure device according to the international protocol in an adult population. Blood Pressure Monitoring (2006.0) 11 293-296. DOI: 10.1097/01.mbp.0000217998.96967.fb 60. Rodgers JL, Jones J, Bolleddu SI, Vanthenapalli S, Rodgers LE, Shah K, Karia K, Panguluri SK. Cardiovascular risks associated with gender and aging. Journal of Cardiovascular Development and Disease (2019.0) 6 1-18. DOI: 10.3390/jcdd6020019 61. Rosengren A, Smyth A, Rangarajan S, Ramasundarahettige C, Bangdiwala SI, AlHabib KF, Avezum A, Bengtsson Boström K, Chifamba J, Gulec S, Gupta R, Igumbor EU, Iqbal R, Ismail N, Joseph P, Kaur M, Khatib R, Kruger IM, Lamelas P. Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study. Lancet Global Health (2019.0) 7 748-760. DOI: 10.1016/s2214-109x(19)30045-2 62. Schultz WM, Kelli HM, Lisko JC, Varghese T, Shen J, Sandesara P, Quyyumi AA, Taylor HA, Gulati M, Harold JG, Mieres JH, Ferdinand KC, Mensah GA, Sperling LS. Socioeconomic status and cardiovascular outcomes: Challenges and interventions. Circulation (2018.0) 137 2166-2178. DOI: 10.1161/circulationaha.117.029652 63. Schutte AE, Gona PN, Delles C, Uys AS, Burger A, Mels CM, Kruger R, Smith W, Fourie CM, Botha S, Lammertyn L, van Rooyen JM, Gafane-Matemane LF, Mokwatsi GG, Breet Y, Kruger HS, Zyl TV, Pieters M, Zandberg L. The African prospective study on the early detection and identification of cardiovascular disease and hypertension (African-PREDICT): Design, recruitment and initial examination. The European Journal of Preventive Cardiology (2019.0) 26 458-470. DOI: 10.1177/2047487318822354 64. Sciarretta S, Forte M, Frati G, Sadoshima J. New insights into the role of mTOR signaling in the cardiovascular system. Circulation Research (2018.0) 122 489-505. DOI: 10.1161/circresaha.117.311147 65. Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c test in diagnosis and prognosis of diabetic patients. Biomarker Insights (2016.0) 11 95-104. DOI: 10.4137/BMI.S38440 66. Song P, Ramprasath T, Wang H, Zou MH. Abnormal kynurenine pathway of tryptophan catabolism in cardiovascular diseases. Cellular and Molecular Life Sciences (2017.0) 74 2899-2916. DOI: 10.1007/s00018-017-2504-2 67. Sorriento D, Trimarco B, Iaccarino G. Adrenergic mechanism in the control of endothelial function. Translational Medicine UniSa (2011.0) 1 213-228. PMID: 23905034 68. Stakos DA, Tziakas DN, Chalikias GK, Mitrousi K, Tsigalou C, Boudoulas H. Associations between collagen synthesis and degradation and aortic function in arterial hypertension. American Journal of Hypertension (2010.0) 23 488-494. DOI: 10.1038/ajh.2010.2 69. Steinfeldt L, Anand J, Murayi T. Food reporting patterns in the USDA automated multiple-pass method. Procedia Food Science (2013.0) 2 145-156. DOI: 10.1016/j.profoo.2013.04.022 70. Stewart J, Manmathan G, Wilkinson P. Primary prevention of cardiovascular disease: A review of contemporary guidance and literature. JRSM Cardiovascular Disease (2017.0) 6 1-9. DOI: 10.1177/2048004016687211 71. Townsend RR, Wilkinson IB, Schiffrin EL, Avolio AP, Chirinos JA, Cockcroft JR, Heffernan KS, Lakatta EG, McEniery CM, Mitchell GF, Najjar SS, Nichols WW, Urbina EM, Weber T. Recommendations for improving and standardizing vascular research on arterial stiffness: A scientific statement from the American Heart Association. Hypertension (2015.0) 66 698-722. DOI: 10.1161/hyp.0000000000000033 72. Ussher JR, Elmariah S, Gerszten RE, Dyck JR. The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. Journal of the American College of Cardiology (2016.0) 68 2850-2870. DOI: 10.1016/j.jacc.2016.09.972 73. Van Bortel LM, Laurent S, Boutouyrie P, Chowienczyk P, Cruickshank JK, De Backer T, Filipovsky J, Huybrechts S, Mattace-Raso FU, Protogerou AD, Schillaci G, Segers P, Vermeersch S, Weber T. Expert consensus document on the measurement of aortic stiffness in daily practice using carotid-femoral pulse wave velocity. Journal of Hypertension (2012.0) 30 445-458. DOI: 10.1097/HJH.0b013e32834fa8b0 74. Venkataraman SS, Regone R, Ammar HM, Govindu RR. Pyroglutamic acidemia: An underrecognized and underdiagnosed cause of high anion gap metabolic acidosis: A case report and review of literature. Cureus (2019.0) 11 1-5. DOI: 10.7759/cureus.5229 75. Wolmarans P, Danster N, Dalton A, Rossouw K, Schönfeldt H. Condensed food composition tables for South Africa (2010.0) 76. World Health Organisation (2022a). Cardiovascular diseases.. Retrieved May 12, 2022a, from https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). 77. World Health Organisation (2022b). Global Physical Activity Questionnaire (GPAQ): analysis guide. Retrieved May 28, 2022b, from https://www.who.int/ncds/surveillance/steps/resources/GPAQ_Analysis_Guide.pdf. 78. Wu G. Dietary protein intake and human health. Food and Function (2016.0) 7 1251-1265. DOI: 10.1039/c5fo01530h 79. Wu XM, Zhang N, Li JS, Yang ZH, Huang XL, Yang XF. Purinergic receptors mediate endothelial dysfunction and participate in atherosclerosis. Purinergic Signalling (2022.0). DOI: 10.1007/s11302-021-09839-x 80. Xiao Y, Su X, Huang W, Zhang J, Peng C, Huang H, Wu X, Huang H, Xia M, Ling W. Role of S-adenosylhomocysteine in cardiovascular disease and its potential epigenetic mechanism. International Journal of Biochemistry and Cell Biology (2015.0) 67 158-166. DOI: 10.1016/j.biocel.2015.06.015 81. Yoo EG. Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Korean Journal of Pediatrics (2016.0) 59 425-431. DOI: 10.3345/kjp.2016.59.11.425 82. Zagura M, Kals J, Kilk K, Serg M, Kampus P, Eha J, Soomets U, Zilmer M. Metabolomic signature of arterial stiffness in male patients with peripheral arterial disease. Hypertension Research (2015.0) 38 840-846. DOI: 10.1038/hr.2015.71 83. Zhao TV, Li Y, Liu X, Xia S, Shi P, Li L, Chen Z, Yin C, Eriguchi M, Chen Y, Bernstein EA, Giani JF, Bernstein KE, Shen XZ. ATP release drives heightened immune responses associated with hypertension. Science Immunology (2019.0) 4 1-35. DOI: 10.1126/sciimmunol.aau6426 84. Zhao Y, Vanhoutte PM, Leung SW. Vascular nitric oxide: Beyond eNOS. Journal of Pharmacological Sciences (2015.0) 129 83-94. DOI: 10.1016/j.jphs.2015.09.002 85. Zhenyukh O, González-Amor M, Rodrigues-Diez RR, Esteban V, Ruiz-Ortega M, Salaices M, Mas S, Briones AM, Egido J. Branched-chain amino acids promote endothelial dysfunction through increased reactive oxygen species generation and inflammation. Journal of Cellular and Molecular Medicine (2018.0) 22 4948-4962. DOI: 10.1111/jcmm.13759
--- title: Urinary complement proteins are increased in children with IgA vasculitis (Henoch-Schönlein purpura) nephritis authors: - Rachael D. Wright - Julien Marro - Sarah J. Northey - Rachel Corkhill - Michael W. Beresford - Louise Oni journal: Pediatric Nephrology (Berlin, Germany) year: 2022 pmcid: PMC10060309 doi: 10.1007/s00467-022-05747-3 license: CC BY 4.0 --- # Urinary complement proteins are increased in children with IgA vasculitis (Henoch-Schönlein purpura) nephritis ## Abstract ### Background Children with immunoglobulin A vasculitis (IgAV Henoch-Schönlein purpura) frequently encounter nephritis (IgAV-N) with 1–$2\%$ risk of kidney failure. The pathophysiology of IgAV-N is not fully understood with speculation that complement may contribute. The aim of this study was to identify whether urinary complement proteins are increased in children with IgAV-N. ### Methods A cross-sectional prospective cohort of children with IgAV were recruited together with controls including healthy children and children with systemic lupus erythematosus (SLE). Patients were subdivided according to the presence of nephritis. Urinary C3, C4, C5, and C5a were measured by enzyme-linked immunosorbent assay (ELISA) and corrected for urinary creatinine. ### Results The study included 103 children; 47 with IgAV (37 IgAV without nephritis, IgAVwoN; 10 IgAV-N), 30 SLE and 26 healthy children. Urinary complement C3, C4, and C5 were all statistically significantly increased in all children with IgAV compared to SLE patients (all $p \leq 0.05$). In patients with IgAV-N, urinary complement C3, C4, C5, C5a were all statistically significantly increased compared to IgAVwoN (C3 14.65 μg/mmol [2.26–20.21] vs. 2.26 μg/mmol [0.15–3.14], $$p \leq 0.007$$; C4 6.52 μg/mmol [1.30–9.72] vs. 1.37 μg/mmol [0.38–2.43], $$p \leq 0.04$$; C5 1.36 μg/mmol [0.65–2.85] vs. 0.38 μg/mmol [0.03–0.72], $$p \leq 0.005$$; C5a 101.9 ng/mmol [15.36–230.0] vs. 18.33 ng/mmol [4.27–33.30], $$p \leq 0.01$$). Using logistic regression, the urinary complement components produced an outstanding ability to discriminate between patients with and without nephritis in IgAV (AUC 0.92, $p \leq 0.001$). ### Conclusions Children with IgAV-N have evidence of increased complement proteins present in their urine that may indicate a pathological role and may allow treatment stratification. ### Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information ### Supplementary Information The online version contains supplementary material available at 10.1007/s00467-022-05747-3. ## Introduction Immunoglobulin A vasculitis (IgAV), formerly known as Henoch-Schönlein purpura, is the most common form of vasculitis in children with an estimated incidence of around 20 cases per 100,000 children per year. It is caused by deposition of aberrantly glycosylated IgA in tissues leading to activation of an autoimmune response [1]. IgAV is self-limiting in 50–$70\%$ of cases and most patients will make a complete and full recovery with no intervention. Kidney involvement (termed IgAV nephritis – IgAV-N) is the most damaging consequence of IgAV as it is the only organ affected that is associated with long-term morbidity and carries a 1–$2\%$ risk of progression to kidney failure [2, 3]. There are unmet needs in understanding the pathophysiology of IgAV-N and kidney outcomes have not demonstrated any improvement over time [4]. Histologically, glomerular IgA deposition is universally seen and in $90\%$ of cases it is co-located with complement C3. There is growing interest in the role of the complement pathway in IgA-related kidney diseases [1, 5] and in patients with IgA nephropathy (IgAN), reports demonstrate elevated serum concentrations of C3d, C4d, C5b-9, mannose-binding lectin, and mannose-associated serine protease-1 [6–8], such that selective therapeutic inhibition of the lectin and alternative complement pathways are under current evaluation. In other proteinuric kidney diseases, complement activation products are reported to be increased in patients with focal glomerular sclerosis and diabetic nephropathy but not in heavy proteinuria associated with minimal change disease, suggesting that the findings may have an active pathological role in certain inflammatory kidney diseases [9]. In children with IgAV-N, there are limited corresponding data available, apart from histological evidence of glomerular complement deposition (C3, C4 and C5b-9) [10, 11]. The aim of this study was to identify whether urine complement proteins are present in children with IgAV, how they compare to another form of glomerulonephritis and whether they are able to distinguish patients with IgAV-N. ## Study cohort A cross-sectional cohort of patients was recruited for this study. A one-off urine sample was collected at the time of appointment from a cohort of patients with IgA vasculitis and healthy controls obtained as part of the IgA Vasculitis Study (REC: 17/NE/0390) and a cohort of children with SLE and healthy controls from participants within the UK JSLE Cohort Study and Repository (REC: 6/Q$\frac{1502}{77}$). Informed consent was obtained from all subjects, and if subjects were under the age of 16 years, consent was obtained from a parent and/or legal guardian and assent was obtained from the subject as appropriate. A cohort of age- and sex-matched healthy control participants were used for comparison. For data analysis purposes, healthy controls recruited as part of both studies were pooled as a single control population. ## Nephritis classification Patients within the disease categories (IgAV and SLE) were subdivided according to the presence of nephritis. Nephritis in children with IgAV (termed IgAV-N group) was defined as a urinary albumin to creatinine ratio (ACR) > 30 mg/mmol Cr at the time of sampling. Patients with IgAV who had a normal urine dipstick and/or a urine ACR < 30 mg/mmol Cr at the time of sampling were considered not to have any significant nephritis (termed IgAVwoN). Nephritis in patients with SLE was defined according to the British Isles Lupus Assessment Group (BILAG) 2004 index [12, 13]. Patients with a BILAG score of A or B in their kidney domain (new or worsening hypertension, proteinuria, reduced kidney function, nephrotic syndrome, active urinary sediment and/or active histological evidence of nephritis) were considered to have had nephritis (lupus nephritis, termed LN group) while those with a BILAG score of E in their kidney domain (no previous evidence of nephritis; termed SLEwoLN) were considered not to have LN. ## Laboratory methods Enzyme-linked immunosorbent assay (ELISA) kits were purchased to detect C3, C4, C5, and C5a (Bio-Techne, Abingdon, UK) and were run per the manufacturer’s instructions. Briefly, urine samples were collected as a clean catch sample and immediately transferred to the onsite laboratory for processing. They were spun at 300 g for 5 min to remove any particulate matter and loaded neat onto plates to analyse levels of complement protein in each sample. Urine infection was excluded in all samples prior to analysis either through a urine dipstick that was negative for nitrites and leucocytes or through formal microbiological examination. All urine complement concentrations were corrected for concentration using urinary creatinine measured by the Clinical Chemistry Laboratory at Alder Hey Children's NHS Foundation Trust using the Abbott Enzymatic Creatinine assay on the Abbott Architect Ci8200 (Abbott, Illinois, USA). ## Statistical analysis Urinary complement levels were normalised to urinary creatinine in all instances. Data are expressed as median [range] unless otherwise stated. Statistical analysis was performed using GraphPad Prism 7.01 software programme to compare between the disease cohorts IgAV, SLE and healthy controls and between the subgroups with nephritis (IgAVwoN, IgAV-N, SLEwoLN, LN, healthy controls). Data normality was assessed using Shapiro–Wilk test. Statistical significance was evaluated using Kruskal–Wallis test with Dunn’s post hoc test. Univariate and logistic regression analyses were performed, and receiver operating characteristic (ROC) curves were generated to evaluate the ability of each urinary complement protein to predict the patients with nephritis. The area under the curve (AUC) value was used to assess the strength of the test in distinguishing patients with IgAV-N and defined accordingly (AUC 0.7–0.8 considered acceptable, 0.8–0.9 considered excellent, 0.9–1.0 considered outstanding). A p value of less than 0.05 was considered statistically significant. ## Patient demographic data and baseline characteristics The study included 103 children, consisting of 77 patients with an inflammatory disease and 26 participants who were age- and sex-matched healthy controls. A total of 47 children with IgAV contributed and of these 37 participants were grouped as IgAVwoN and 10 participants were classed as IgAV-N. There were 30 children with SLE consisting of 15 SLE participants without any history of ever having had nephritis (SLEwoLN) and 15 with a history of LN. The demographic data and baseline characteristics are presented in Table 1. As expected, there was an increased proportion of male patients with IgAV and an increased proportion of female patients with SLE, consistent with the overall gender distribution seen for these conditions. SLE patients were significantly older than those with IgAV and healthy controls, consistent with the expected overall age distribution of these conditions. The healthy control cohort was reasonably well-matched in terms of demographics. Patients with IgAV-N had a significantly increased urinary albumin-to-creatinine ratio (UACR) compared to IgAVwoN, SLEwoLN, and LN. Similar types of immunosuppressant therapies were used within the IgAV and SLE cohorts; however, as expected, these were used more frequently in patients with SLE.Table 1Demographic and baseline data for patients included in this study ## Urinary complement concentration in all patients with IgAV Urinary complement concentrations (C3, C4, C5 and C5a) were assessed in all patients with IgAV and compared to patients with SLE and the healthy control group. The urinary complement C3 concentration was statistically significantly increased in all patients with IgAV compared to patients with SLE (median: IgAV 2.74 μg/mmol [0.15–44.5], SLE 1.52 μg/mmol [0.09–9.66]); $$p \leq 0.021$$), as shown in Fig. 1A. There was no statistically significant difference in the urinary C3 concentrations between patients with IgAV and healthy controls (1.98 μg/mmol [0.49–11.0]). Urinary complement C4 concentrations were also statistically significantly increased in patients with IgAV (median: 1.56 μg/mmol [0.38–31.66]) compared to those with SLE (0.87 μg/mmol [0.25–4.01]; $$p \leq 0.001$$) and with the healthy control participants (1.08 μg/mmol [0.37–5.46]; $$p \leq 0.03$$) (Fig. 1B). There were no statistically significant differences seen between SLE patients and healthy control participants. The urinary complement C5 concentrations were statistically significantly increased in IgAV patients (median: 0.51 μg/mmol [0.03–6.16]) compared to patients with SLE (0.20 μg/mmol [0.02–1.90]; $$p \leq 0.008$$) (Fig. 1C). No statistically significant differences were seen between patients with IgAV and healthy control participants (0.28 μg/mmol [0.10–3.02]) or between patients with SLE and healthy controls. Urinary complement C5a was not statistically significantly different between any of the groups. However, there was a trend toward increased levels in the IgAV group (19.69 ng/mmol [4.27–370.9]) to SLE (14.32 ng/mmol [3.92–78.11]; $$p \leq 0.06$$) (Fig. 1D).Fig. 1 Urinary complement concentrations in patients with paediatric inflammatory disease – IgAV and SLE compared to age- and sex-matched controls. Complement concentrations were assessed in urine collected from patients with IgAV, SLE and controls using ELISA. Complement concentrations were normalised to urinary creatinine. ( A) Urinary C3/creatinine, (B) Urinary C4/creatinine, (C) Urinary C5/creatinine, and (D) Urinary C5a/creatinine. $$n = 26$$–47/group. Data are expressed as median and analysed using Kruskal–Wallis test with Dunn’s multiple comparison test. * $P \leq 0.05$ and *$P \leq 0.01$ ## Urinary complement concentration in patients with IgAV nephritis The participants were subdivided according to the presence of nephritis. The concentration of urinary complement proteins (C3, C4, C5 and C5a) was evaluated to see if these could distinguish patients with and without nephritis in IgAV. The urinary complement C3 concentration was statistically significantly increased in patients with IgAV-N (median: 14.65 μg/mmol [2.26–20.21]) compared to patients grouped as IgAVwoN (2.26 μg/mmol [0.15–3.14]; $$p \leq 0.007$$) (Fig. 2A). They were also statistically significantly increased in IgAV-N compared to patients with LN (1.52 μg/mmol [0.54–2.27]; $$p \leq 0.0002$$). The urinary complement C3 concentration was also statistically significantly increased in patients with IgAV-N compared to healthy controls (median: 1.89 μg/mmol [0.49–2.94]; $$p \leq 0.003$$). No other significant differences between the groups were noted. The urinary complement C4 concentration was statistically significantly increased in IgAV-N patients (6.52 μg/mmol [1.30–9.72]) compared to IgAVwoN (1.37 μg/mmol [0.38–2.43]; $$p \leq 0.04$$), and compared to all other groups – SLEwoLN (0.98 μg/mmol [0.47–2.23]; $$p \leq 0.007$$), LN (0.72 μg/mmol [0.25–1.12]; $p \leq 0.0001$) and healthy controls (1.08 μg/mmol [0.37–1.57]; $$p \leq 0.0012$$) (Fig. 2B). No other significant differences between groups were noted. Urinary complement C5 concentration was statistically significantly increased in IgAV-N patients (1.36 μg/mmol [0.65–2.85]) compared to IgAVwoN (0.38 μg/mmol [0.03–0.72]; $$p \leq 0.005$$) and all other groups – SLEwoLN (0.24 μg/mmol [0.02–0.55]; $$p \leq 0.001$$), LN (0.16 μg/mmol [0.05–0.33]; $$p \leq 0.0001$$) and healthy controls (0.28 μg/mmol [0.10–0.51]; $$p \leq 0.002$$) (Fig. 2C). No other significant differences between the groups were noted. Urinary complement C5a concentration was statistically significantly increased in IgAV-N patients (101.9 ng/mmol [15.36–230.0]) compared to patients grouped as IgAVwoN (18.33 ng/mmol [4.27–33.3]; $$p \leq 0.01$$), and all other groups – SLEwoLN (22.81 ng/mmol [3.92–30.79]; $$p \leq 0.03$$), LN (11.28 ng/mmol [6.14–16.88]; $$p \leq 0.0004$$) and healthy controls (14.99 ng/mmol [3.19–29.31]; $$p \leq 0.002$$) (Fig. 2D). No other significant differences between the groups were noted. Interestingly, there were notable outliers seen within the IgAV subgroups (as illustrated in Fig. 2) and these may suggest that certain patients within the nephritis group express a more pronounced complement protein profile. Fig. 2Urinary complement concentrations in patients with paediatric inflammatory disease stratified by kidney involvement – IgAVwoN, IgAV-N, SLEwoLN and LN compared to age- and sex-matched controls. Complement concentrations were assessed in urine collected from patients with IgAVwoN, IgAV-N, SLEwoLN, LN and controls using ELISA. Complement concentrations were normalised to urinary creatinine. ( A) Urinary C3/creatinine, (B) Urinary C4/creatinine, (C) Urinary C5/creatinine, and (D) Urinary C5a/creatinine. $$n = 10$$–37/group. Data are expressed as median and analysed using Kruskal–Wallis test with Dunn’s multiple comparison test. $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$ and *$P \leq 0.0001$ ## Urinary complement concentrations distinguishing patients with IgAV-N Receiver operating characteristic (ROC) curve analyses were used to determine the ability of the complement proteins to discriminate between patients with IgAV-N and those without nephritis. The level of each urinary complement molecule (C3, C4, C5, C5a) was individually excellent at distinguishing between patients with IgAV-N and IgAVwoN (AUC; C3 – 0.085, $$p \leq 0.0009$$; C4 – 0.805, $$p \leq 0.0033$$; C5 – 0.838, $$p \leq 0.0012$$; and C5a – 0.817, $$p \leq 0.0024$$) (Fig. 3). Logistic regression analysis was used to combine all four complement markers into a single biomarker panel and ROC analysis was performed. Combining all four urinary complement components (C3, C4, C5 and C5a) improved the effectiveness of the test to outstanding (AUC; IgAV-N vs. IgAVwoN – 0.919, $p \leq 0.001$) (Fig. 4).Fig. 3Receiver Operating Characteristic (ROC) curve analysis for IgAV-N patient individual complement concentrations compared to IGAVwoN. Urinary complement concentrations were analysed individually for their sensitivity and $100\%$—specificity in order to determine their effectiveness at distinguishing between IgAV-N patients vs. IgAVwoN patientsFig. 4Receiver Operating Characteristic (ROC) curve analysis for IgAV-N patient combined complement concentrations compared to IGAVwoN. Urinary complement concentrations were analysed together using logistic regression analysis for their sensitivity and $100\%$—specificity in order to determine their effectiveness at distinguishing between IgAV-N patients vs. IgAVwoN patients ## Discussion IgAV is a common paediatric condition and the second most frequent reason to perform a kidney biopsy by paediatric nephrologists. It is well recognised that kidney outcomes from IgAV have failed to improve over time and current treatments lack robust evidence to support their use. Due to growing interest in the role of complement in IgA nephropathy, a condition histologically similar to IgAV, this study aimed to evaluate whether urinary complement proteins can be measured in children with IgAV, how they compare to another form of glomerulonephritis, and if they may indicate the presence of nephritis. This study demonstrated that urinary complement C3, C4, C5, and C5a concentrations were all statistically significantly increased in children with IgAV-N. Each urinary complement marker was individually excellent at distinguishing between those with and without nephritis and when combined, using a logistic regression analysis, their ability to discriminate between children with IgAV with or without nephritis was outstanding (AUC – 0.919). Histologically, evidence of complement deposition is well recognised in IgAV-N, with approximately $74\%$ of patients having histological evidence of C3 deposition identified in studies [10]. The complement system is important in linking the innate and adaptive immunity and it can be activated by different routes, known as the classical pathway, mannose-binding lectin pathway and alternative pathway. These specific pathways lead to a common pathway and the production of the membrane attack complex (C5b-9) causing cell lysis, opsonisation and upregulation of C5a, a neutrophil chemoattractant protein. Evidence of activation of the mannose-binding lectin pathway and/or the alternative complement pathway is reported in the skin and systemically in patients with IgAV [14, 15]. To our knowledge, this is the first report of measuring urinary complement in children with IgAV-N. Previous reports on other proteinuric conditions demonstrate that components of the complement system are able to distinguish between patients with membranous nephropathy and diabetic nephropathy independently of the amount of protein loss [7]. In patients with SLE, systemic complement abnormalities are well described but their precise role is not clearly understood, and surprisingly there are few studies measuring urinary complement in patients with LN [16]. Elevated urinary expression of complement is not found in patients with minimal change nephrotic syndrome suggesting that their presence doesn’t seem to be directly related to heavy proteinuria [9]. This study highlights important findings as urinary complement may represent a reliable biomarker in an easily accessible biofluid to stratify patients with IgAV for potential early therapeutic intervention that may mitigate the onset of nephritis. The clear outliers with increased complement expression highlighted in this study give the impression that there may be certain individuals with nephritis who would benefit the most from this intervention. While urine complement products are not routinely measured in clinical laboratories at present, these can be measured in the pre-clinical and pharmaceutical setting and further studies are required to understand the specific role of complement pathway in the pathogenesis of IgAV-N. As the complement pathway is a target of multiple new therapeutic agents currently under evaluation in clinical trials for many inflammatory kidney diseases, gaining insight into their role in IgAV may provide evidence for broader use of these medications [17]. This study does have limitations. Notably, there were assumptions in presuming a negative urine dipstick represented no proteinuria and therefore microalbuminuria may have been missed. Additionally, it did not demonstrate any significant increase in the urinary complement proteins in children with LN, despite the recognised role that complement has in this disease [18]. This may represent limitations in selecting the LN cohort who were defined as having nephritis according to the kidney domain of the BILAG 2004, and patients with chronic residual proteinuria may have been included, a known limitation of the SLE disease activity index [19]. This is supported by the relatively low concentration of urinary protein seen within the LN cohort, which is unusual in acute LN, and the long time elapsed since diagnosis, suggesting that they may have reflected a cohort of children with previous LN and persisting proteinuria. Other limitations of this study include the cross-sectional nature of the study and the relatively small, heterogeneous cohort of patients that may have limited the generalisability of the findings. It would be difficult to determine whether the urinary complement concentrations were merely increased due to the finding of proteinuria, as patients with nephritis were defined according to the presence of proteinuria and recent reports suggest a complex interplay between proteinuria and its potential to upregulate the tubular expression of complement pathway products [8]. Despite these limitations, our exploratory findings report that urinary complement products are measurable in children with IgAV and our data support a potential role for complement as a contributor to the pathophysiology of IgAV-N. Additional large, longitudinal studies are required to observe a large cohort of children from disease presentation to improvement or the evolution of nephritis to determine the timing of when complement may have its onset in the disease process and to identify when complement could be inhibited as a potential therapeutic target. ## Supplementary Information Below is the link to the electronic supplementary material. Graphical Abstract (PPTX 107 KB) ## References 1. Oni L, Sampath S. Childhood IgA vasculitis (Henoch Schonlein Purpura)-advances and knowledge gaps*. Front Pediatr* (2019) 7 257. DOI: 10.3389/fped.2019.00257 2. Rianthavorn P, Chacranon M. Long-term renal outcome in pediatric glomerulonephritis associated with crescent formation. Clin Exp Nephrol (2018) 22 661-667. DOI: 10.1007/s10157-017-1498-2 3. Delbet JD, Hogan J, Aoun B, Stoica I, Salomon R, Decramer S, Brocheriou I, Deschenes G, Ulinski T. Clinical outcomes in children with Henoch-Schonlein purpura nephritis without crescents. Pediatr Nephrol (2017) 32 1193-1199. DOI: 10.1007/s00467-017-3604-9 4. Kawasaki Y, Suyama K, Yugeta E, Katayose M, Suzuki S, Sakuma H, Nemoto K, Tsukagoshi A, Nagasawa K, Hosoya M. The incidence and severity of Henoch-Schonlein purpura nephritis over a 22-year period in Fukushima Prefecture, Japan. Int Urol Nephrol (2010) 42 1023-1029. DOI: 10.1007/s11255-009-9701-3 5. Kaartinen K, Safa A, Kotha S, Ratti G, Meri S. Complement dysregulation in glomerulonephritis. Semin Immunol (2019) 45 101331. DOI: 10.1016/j.smim.2019.101331 6. Rizk DV, Maillard N, Julian BA, Knoppova B, Green TJ, Novak J, Wyatt RJ. The emerging role of complement proteins as a target for therapy of IgA nephropathy. Front Immunol (2019) 10 504. DOI: 10.3389/fimmu.2019.00504 7. Araumi A, Osaki T, Ichikawa K, Kudo K, Suzuki N, Watanabe S, Watanabe M, Konta T. Urinary and plasma proteomics to discover biomarkers for diagnosing between diabetic nephropathy and minimal change nephrotic syndrome or membranous nephropathy. Biochem Biophys Rep (2021) 27 101102. DOI: 10.1016/j.bbrep.2021.101102 8. Ogrodowski JL, Hebert LA, Sedmak D, Cosio FG, Tamerius J, Kolb W. Measurement of SC5b-9 in urine in patients with the nephrotic syndrome. Kidney Int (1991) 40 1141-1147. DOI: 10.1038/ki.1991.326 9. Morita Y, Ikeguchi H, Nakamura J, Hotta N, Yuzawa Y, Matsuo S. Complement activation products in the urine from proteinuric patients. J Am Soc Nephrol (2000) 11 700-707. DOI: 10.1681/ASN.V114700 10. Celik A, Sarioglu S, Kavukçu S. Comparison of clinical, pathological and long-term renal outcomes of children with Henoch-Schonlein purpura nephritis and IgA nephropathy. Int Urol Nephrol (2022) 54 1925-1932. DOI: 10.1007/s11255-021-03063-7 11. Dumont C, Merouani A, Ducruet T, Benoit G, Clermont MJ, Lapeyraque AL, Phan V, Patey N. Clinical relevance of membrane attack complex deposition in children with IgA nephropathy and Henoch-Schonlein purpura. Pediatr Nephrol (2020) 35 843-850. DOI: 10.1007/s00467-019-04445-x 12. Isenberg DA, Rahman A, Allen E, Farewell V, Akil M, Bruce IN, D'Cruz D, Griffiths B, Khamashta M, Maddison P, McHugh N, Snaith M, Teh LS, Yee CS, Zoma A, Gordon C. BILAG 2004. Development and initial validation of an updated version of the British Isles Lupus Assessment Group's disease activity index for patients with systemic lupus erythematosus. Rheumatology (Oxford) (2005) 44 902-906. DOI: 10.1093/rheumatology/keh624 13. Smith EMD, Yin P, Jorgensen AL, Beresford MW. Clinical predictors of proteinuric remission following an LN flare - evidence from the UK JSLE cohort study. Pediatr Rheumatol Online J (2018) 16 14. DOI: 10.1186/s12969-018-0230-4 14. Damman J, Mooyaart AL, Bosch T, Seelen MA, Doorn MBV. Lectin and alternative complement pathway activation in cutaneous manifestations of IgA-vasculitis: a new target for therapy?. Mol Immunol (2022) 143 114-121. DOI: 10.1016/j.molimm.2022.01.011 15. Endo M, Ohi H, Ohsawa I, Fujita T, Matsushita M. Complement activation through the lectin pathway in patients with Henoch-Schonlein purpura nephritis. Am J Kidney Dis (2000) 35 401-407. DOI: 10.1016/s0272-6386(00)70192-2 16. Manzi S, Rairie JE, Carpenter AB, Kelly RH, Jagarlapudi SP, Sereika SM, Medsger TA, Ramsey-Goldman R. Sensitivity and specificity of plasma and urine complement split products as indicators of lupus disease activity. Arthritis Rheum (1996) 39 1178-1188. DOI: 10.1002/art.1780390716 17. Willows J, Brown M, Sheerin NS. The role of complement in kidney disease. Clin Med (Lond) (2020) 20 156-160. DOI: 10.7861/clinmed.2019-0452 18. Bao L, Cunningham PN, Quigg RJ. Complement in lupus nephritis: new perspectives. Kidney Dis (Basel) (2015) 1 91-99. DOI: 10.1159/000431278 19. Ohmura K. Which is the best SLE activity index for clinical trials?. Mod Rheumatol (2021) 31 20-28. DOI: 10.1080/14397595.2020.1775928
--- title: 'Impact of a diabetes disease management program on guideline-adherent care, hospitalization risk and health care costs: a propensity score matching study using real-world data' authors: - Marc Höglinger - Brigitte Wirth - Maria Carlander - Cornelia Caviglia - Christian Frei - Birgitta Rhomberg - Adrian Rohrbasser - Maria Trottmann - Klaus Eichler journal: The European Journal of Health Economics year: 2022 pmcid: PMC10060321 doi: 10.1007/s10198-022-01486-2 license: CC BY 4.0 --- # Impact of a diabetes disease management program on guideline-adherent care, hospitalization risk and health care costs: a propensity score matching study using real-world data ## Abstract ### Objective To evaluate the impact of a DMP for patients with diabetes mellitus in a Swiss primary care setting. ### Methods In a prospective observational study, we compared diabetes patients in a DMP (intervention group; $$n = 538$$) with diabetes patients receiving usual care (control group; $$n = 5050$$) using propensity score matching with entropy balancing. Using a difference-in-difference (DiD) approach, we compared changes in outcomes from baseline [2017] to 1-year ($\frac{2017}{18}$) and to 2-year follow-up ($\frac{2017}{19}$). Outcomes included four measures for guideline-adherent diabetes care, hospitalization risk, and health care costs. ### Results We identified a positive impact of the DMP on the share of patients fulfilling all measures for guideline-adherent care [DiD $\frac{2017}{18}$: 7.2 percentage-points, $p \leq 0.01$; $\frac{2017}{19}$: 8.4 percentage-points, $p \leq 0.001$]. The hospitalization risk was lower in the intervention group in both years, but only statistically significant in the 1-year follow-up [DiD $\frac{2017}{18}$: – 5.7 percentage-points, $p \leq 0.05$; $\frac{2017}{19}$: – 3.9 percentage points, n.s.]. The increase in health care costs was smaller in the intervention than in the control group [DiD $\frac{2017}{18}$: CHF – 852; $\frac{2017}{19}$: CHF – 909], but this effect was not statistically significant. ### Conclusion The DMP under evaluation seems to exert a positive impact on the quality of diabetes care, reflected in the increase in the measures for guideline-adherent care and in a reduction of the hospitalization risk in the intervention group. It also might reduce health care costs, but only a longer follow-up will show whether the observed effect persists over time. ## Background Diabetes mellitus is a tremendous public health issue, and its prevalence is increasing [1]. In Switzerland, the proportion of people with diabetes among over-15-year-olds increased between 2007 and 2017 to $5.4\%$ in men and to $3.5\%$ in women [2]. The treatment of diabetes is complex and requires a careful coordination of measures and of different health professionals providing them. Ill-coordinated care can lead to duplication of services and overuse or, on the other hand, to undertreatment if clinicians do not follow evidence-based guidelines [3, 4]. To overcome poorly coordinated services across involved providers, as well as to strengthen guideline adherence and improve patient outcome, evidence-based disease management programs (DMPs), also called chronic care models or structured treatment programs, have been recommended for the management of patients with chronic conditions such as diabetes [5, 6]. The overarching goal of DMPs is the “optimal” instead of the “maximal” care, being reached by standardization of care and efficient use of resources [5]. The details of a DMP vary between regions and the participating institutions, but it mainly consists of three central parts: (i) evidence-based guidelines (ii) integrated care overcoming barriers between different health professions and institutions to minimize the number of duplicated treatments and (iii) establishment of quality management processes that facilitate the continuous improvement and development of care delivery and guidelines [7]. DMPs have been widely evaluated, but the studies are very heterogeneous [8] and partly used inadequate methodological approaches, such as uncontrolled pre–post-designs [9, 10]. With regard to clinical parameters, a meta-analysis of randomized controlled trials in Europe found only small improvements in the levels of HbA1c, total cholesterol, LDL cholesterol, and blood pressure of diabetes patients in a DMP compared to usual care [11]. As for mortality and costs, a large study that analyzed a nationwide DMP for diabetes in Germany compared intervention to control group, using a propensity score matching strategy. In this 4-year follow-up, the authors found a reduction in overall mortality and in medication and hospital expenditures in the DMP group [12]. Consistently, a systematic review of the effectiveness of DMPs for diabetes patients in Germany concluded that DMPs seem to have a beneficial impact on the mortality and survival time of diabetes patients, but the effects on morbidity, quality of life and monetary outcomes (direct medical costs, cost effectiveness, care expenditures) were inconsistent [13]. For Switzerland, little is known about the impact of structured treatment programs for diabetes mellitus on quality of care and costs in real-world settings. Simulation studies (Markov models) reported that multifactorial interventions (including nephropathy and retinopathy screening, controlling of cardiovascular risk and patient education) may result in yearly savings of 194 million Swiss Francs for the Swiss type 2 diabetes population (285,000 at that time) [14]. A retrospective cohort study using claims data from a large Swiss health insurance company found that the hospitalization risk of diabetes patients was lower if physicians’ guideline adherence was better [15]. Using the same database and a propensity score matching approach, the authors found significantly fewer diabetes-related hospitalizations and lower total healthcare costs (CHF – 778) for patients in integrated care models compared to those in standard models [16]. In addition, an uncontrolled retrospective evaluation of managed diabetes care in a Swiss real-world setting (12 practices from a health provider network) reported improved treatment quality reflected in weight loss, reduction in blood pressure and HbA1c levels [17]. However, evidence for the effect of DMPs in diabetes care in Switzerland is still scarce, particularly with regard to their impact on quality of care [16]. Thus, the aim of this study was to assess the impact of a DMP for diabetes mellitus type 1 and 2 on guideline-adherent care, hospitalization risk (i.e., patient outcome), and health care costs by comparing changes in these outcomes between baseline and years one and two after the introduction of a DMP. Using a difference-in-difference approach with a matched control group (propensity score matching with entropy balancing [18]), our study assesses the impact of the DMP introduction on the intervention group, as far as this is possible using a non-experimental design and real-world data. ## Study design and data We performed a prospective observational study with 2-year follow-up and compared patients with diabetes mellitus enrolled in a DMP with diabetes patients receiving usual care using propensity score kernel matching with entropy balancing [18]. We used a difference-in-difference (DiD) approach [19] and compared changes in outcomes from baseline to 1-year follow-up and from baseline to 2-year follow-up between the DMP (intervention) and the usual care group (control). Analyzing the 1- and 2-year follow-up allows us to assess the robustness of the effect over time and to observe potentially lagged effects of the treatment. The analysis is based on claims data from a large Swiss health insurer (SWICA) with approximately 800,000 insured persons in 2019 (approx. $10\%$ of the Swiss population). ## Study setting In Switzerland, health insurance is mandatory for every resident. There are several different health insurance providers and health care models to choose from. Various contracted insurance models (mostly with shared saving agreements) exist where physician networks collaborate with insurers. Patients joining such networks get rebates on their insurance premiums. Patients are free to visit all physicians in the standard model, but more than $70\%$ of the population choose a managed-care type contract [20]: in case of illness, these patients are obliged to contact first their GP, a telemedicine center, or a GP of choice within the network, who acts as a gatekeeper to more specialized medical care services. However, structured treatment programs are not implemented on a broader scale in Switzerland. Nevertheless, the ‘Medbase’ health care provider has offered a structured DMP for diabetes in some of its primary care practices since 2017. The present study investigated the effect of this DMP in seven ‘Medbase’ practices in the north-eastern part of Switzerland. ## Participants In the DMP group (intervention group), we included 538 patients from the SWICA claims database who were identified as having diabetes mellitus type 1 or 2 using pharmaceutical cost groups (PCGs, at baseline) and who were registered in one of the seven ‘Medbase’ practices that introduced the DMP under investigation. “ Registered” means that they named a particular practice as their medical ‘home’. Only diabetes patients treated with antidiabetic medication can be identified with PCGs, whereas type 2 diabetes patients without oral drug treatment or insulin cannot be identified and were consequently not included in the study. The control group with usual care ($$n = 5050$$) consisted of diabetes patients (again identified by PCGs) from the SWICA claims database not participating in a DMP. All participants had to be insured by SWICA over the whole 3-year analysis period. In addition, members of the intervention group had to be registered continuously in one of the practices with DMP. ## Intervention: disease management program The DMP under assessment consists of the core elements of a DMP [5]: it is evidence-based, interprofessional, and undergoes continuous evaluation and improvement [21]. Treatment is based on the recommendations of the Swiss society of endocrinology and diabetes (SGED) for the treatment of diabetes mellitus type 2 [22] and a central element is the continuous care by the GP, in collaboration with a medical practice assistant qualified in chronic care. Physiotherapists and nutritionists are involved for all aspects of movement and nutrition, respectively. Continuity of GP care is a central element in primary care that might reduce secondary costs [23]. Regular meetings within professional and practice teams ensure professional exchange and team competence. Current treatment and results of examinations are documented in the electronic medical history and are regularly evaluated together with the patient. Treatment goals and measures are adjusted, if necessary, thus ensuring an individual and tailored patient care. For quality assurance, quality circles are held within the physician network to improve and further develop the treatment concept, based on current clinical performance and prescription data [24]. ## Outcome measures Guideline-adherent care was assessed using four performance measures that are identifiable in claims data, the “Four simple performance measures (4SPM)” [15]. They include the measurement of HbA1c, lipid profile, and nephropathy status, as well as examination by the ophthalmologist. We slightly adapted the original measures based on the updated Swiss guidelines and on the suggestions of the involved clinicians. Thus, our outcome measures for guideline-adherent care were (a) at least 2 yearly HbA1c measurements or constant glucose monitoring, (b) yearly lipid profile, (c) yearly nephropathy status or an angiotensin-converting enzyme (ACE) inhibitor therapy, and (d) one examination by the ophthalmologist every 2 years. Hospitalization risk, as a proxy for adverse outcomes, was assessed as the share of patients with at least one hospitalization during the considered year. Lastly, the impact of the DMP on health care costs was assessed with the following outcomes: total health care costs (including all types of health care services and pharmaceuticals), outpatient costs (primary and specialized outpatient health care, physio- and ergotherapy, diagnostics and radiology, nutritionists, hospital outpatient services, pharmaceuticals), and inpatient costs (hospitals, excluding rehab and nursing homes). Costs include all billed health care services that are covered by the compulsory basic health insurance policy and are in Swiss Francs (CHF; official 2017 conversion rate to Euros: 0.85; to US$: 1.02; to British £: 0.76). ## Statistical analysis Difference-in-difference analysis (DiD) [19] was used to determine the effect of the DMP on the outcome parameters. We compare two groups, control and treatment, and two time periods, baseline [2017] and second observation period (2018 or 2019). We independently assess the first-year follow-up (2018 only) as well as the second-year follow-up (2019 only) by comparing them to the baseline year. Analyzing both the 1- and 2-year follow-ups allows us to assess the robustness of the effect over time and to observe potentially lagged effects of the treatment. The estimation equation takes the following form:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\widehat{\delta }}_{DD}=\left({y}_{t1}-{y}_{t2}\right)-\left({y}_{c1}-{y}_{c2}\right)$$\end{document}δ^DD=yt1-yt2-yc1-yc2 where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y$$\end{document}y is any outcome variable, the index t stands for the treatment (DMP) group, c for the control group. The index numbers 1 and 2 stand for the baseline period and the second observation period (year 2018 or 2019). \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\widehat{\delta }}_{DD}$$\end{document}δ^DD is the DiD estimator and, assuming a correct model, corresponds to the Average Treatment Effect on the Treated (ATT), hence capturing the effect of the DMP. The DiD estimator is positive/negative if, for example, a relative increase/decrease is larger in the treatment group than in the control group. We estimated the DiD using a propensity score kernel matching approach with entropy balancing to make the treatment and the control group comparable [18]. While propensity score pairwise-matching has been criticized recently as being inefficient and as producing biased effect estimates [25], kernel matching is considered superior to pairwise matching with regard to efficiency, because it makes use of all cases in the control group by weighting them according to their similarity to the treatment cases. Using an optimization function (i.e., a “kernel”), the entropy-balancing approach produces a (weighted) control group with means and variances of the matching variables identical to those of the treatment group. We used an Epanechnikov-kernel with automatic bandwidth selection as suggested by Huber and colleagues [26]. Our matching variables were gender, age-group, region of residence, type of community (urban/rural, size of community), high vs. low deductible, supplementary outpatient insurance and supplementary hospital insurance, and nine PCGs (Pharmaceutical Cost Groups) as indicators for comorbidities from the baseline year (Table 1). We used t-tests and set an alpha level of $5\%$ to test for statistical significance of group differences and of treatment effects. SEs of the DiD estimators were estimated with bootstrapping (500 replications) which allows matching weights to vary between replications, as suggested by Jann [28]. In addition, bootstrapping is advisable due to the highly skewed distribution of the cost differences [27].Table 1Baseline values 2017: mean values and differences of demographics before (left) and after (right) matching for the control and the treatment groupBefore matchingAfter matchingControl groupTreatment groupDifferenceControl groupTreatment groupDifferenceAge67.0157.26– 9.74c58.7057.41– 1.30 < 20 ($\frac{0}{1}$)0.010.030.02a0.030.03– 0.00 20–39 ($\frac{0}{1}$)0.020.090.06c0.090.09– 0.00 40–59 ($\frac{0}{1}$)0.210.400.19c0.390.390.00 60–79 ($\frac{0}{1}$)0.590.42– 0.17c0.420.420.00 ≥ 80 ($\frac{0}{1}$)0.170.06– 0.10c0.060.060.00Gender Male ($\frac{0}{1}$)0.580.650.06b0.640.640.00Region of residence Zurich area ($\frac{0}{1}$)0.410.40– 0.010.410.410.00 North-Western part of Switzerland ($\frac{0}{1}$)0.210.13– 0.08c0.120.12– 0.00 Eastern part of Switzerland ($\frac{0}{1}$)0.370.470.10c0.480.480.00Type of community Urban, large community ($\frac{0}{1}$)0.340.32– 0.020.330.33– 0.00 Urban, medium-sized community ($\frac{0}{1}$)0.240.470.22c0.470.470.00 Urban, small community ($\frac{0}{1}$)0.110.02– 0.08c0.020.020.00 Peri-urban ($\frac{0}{1}$)0.200.14– 0.06c0.130.130.00 Rural ($\frac{0}{1}$)0.110.05– 0.06c0.050.05– 0.00Health insurance High deductible ($\frac{0}{1}$)0.050.120.07c0.120.12– 0.00 No supplementary outpatient insurances ($\frac{0}{1}$)0.770.71– 0.06b0.710.710.00 Supplementary hospital insurance: private or semi-private ($\frac{0}{10.200.11}$– 0.09c0.110.11– 0.00Pharmacy-based cost groups (indicators for comorbidities) Diabetes type 1 ($\frac{0}{1}$)0.340.370.030.370.37– 0.00 Diabetes type 2 ($\frac{0}{1}$)0.190.270.07c0.270.27– 0.00 Diabetes type 2, hypertension ($\frac{0}{1}$)0.460.36– 0.10c0.370.370.00 Asthma/COPD ($\frac{0}{1}$)0.050.04– 0.010.040.040.00 Mental illness ($\frac{0}{1}$)0.120.07– 0.05c0.070.070.00 Chronic pain ($\frac{0}{1}$)0.050.03– 0.02a0.030.030.00 Heart disease ($\frac{0}{1}$)0.030.01– 0.02c0.010.010.00 Glaucoma ($\frac{0}{1}$)0.050.04– 0.010.040.040.00 Other PCG groups ($\frac{0}{1}$)0.100.05– 0.05c0.050.05– 0.00538 treatment cases in raw data, 530 treatment cases in the matched sample (8 not matched), 5050 control cases. $\frac{0}{1}$ Dummy variables, Values indicate shares of patientsa,b,cStatistically significant difference at 5, 1 and $0.1\%$ level, respectively, based on a t test Analyses were performed using the Stata SE 15 software package (StataCorp. 2015. Stata Statistical Software, College Station, Texas, USA) and the KMATCH-ado [28]. To check the impact of outliers in our analysis, we also conducted a 1, 2, and $5\%$ winsorized analysis for the main outcome total cost, setting values of patients with the highest cost changes between the 2 years to the 99th, 98th, or 95th percentile. ## Results Tables 1 and 2 show background characteristics and outcomes at baseline for the treatment and the control group before and after matching. Before matching, there are significant differences between the groups for most background characteristics. After matching, differences in background characteristics are zero (except for numeric age), and differences in the outcomes are substantially diminished except for a 10 percentage-points higher share of patients with a nephropathy status check (i.e., test for albuminuria) in the treatment relative to the control group. Table 2Baseline values 2017: mean values and differences of primary outcome variables before (left) and after (right) matching for the control and the treatment groupBefore matchingAfter matchingControl groupTreatment groupDifferenceControl groupTreatment groupDifferenceGuideline-adherent care All four measures fulfilled ($\frac{0}{1}$)0.180.200.010.170.200.03 ≥ 2 HbA1c measurements (yearly) ($\frac{0}{1}$)0.810.810.000.800.810.01 Lipid profile (yearly) ($\frac{0}{1}$)0.630.650.020.620.640.02 Nephropathy status (yearly) or ACE ($\frac{0}{1}$)0.390.470.08c0.370.470.10c Ophthalmologist (every two years) ($\frac{0}{1}$)0.670.62– 0.05a0.620.620.00Hospitalization risk ≥ 1 inpatient hospitalization ($\frac{0}{1}$)0.240.19– 0.04a0.190.190.01Health care costs (CHF) Total11,4508783– 2667c92588456– 802 Outpatient82136814– 1400c70476648– 400 Inpatient (excl. rehab and nursing homes)18231340– 483a13231279– 44538 treatment cases in raw data, 530 treatment cases in the matched sample (8 not matched), 5050 control cases. $\frac{0}{1}$ Dummy variables, Values indicate shares of patientsACE Angiotensin-converting enzyme, CHF Swiss Francsa,b,cStatistically significant difference at 5, 1 and $0.1\%$ level, respectively, based on a t test Figure 1 shows the outcome trajectories for the baseline year 2017, and for the 2 follow-up years 2018 and 2019. Figure 2 shows the corresponding difference-in-difference (DiD) estimates. The numbers underlying the DiD estimates are presented in Table 3.Fig. 1Development of the measures for guideline-adherent care, hospitalization risk, and health care costs by treatment and control group: baseline [2017], 1-year follow-up [2018] and 2-year follow-up [2019]. Point estimates with $95\%$ CIsFig. 2Difference-in-difference estimates of outcomes for baseline [2017] versus 2018 and baseline versus 2019. Point estimates with $95\%$ CIsTable 3Measures for guideline-adherent care, hospitalization risk, and health care costs: changes and difference-in-difference estimates [$95\%$ confidence intervals] from baseline [2017] to 2018 (left) and from baseline to 2019 (right)Baseline [2017] versus 2018Baseline [2017] versus 2019Change in control groupChange in treatment groupDiD estimate$95\%$ confidence intervalChanges in control groupChange in treatment groupDiD estimate$95\%$ confidence intervalGuideline-adherent care All four measures fulfilled ($\frac{0}{1}$)0.00540.0770.072b[0.027, 0.12]0.00510.0890.084c[0.036, 0.13] ≥ 2 HbA1c measurements (yearly) or continuous glucose monitoring ($\frac{0}{1}$)0.00760.0340.026[– 0.018, 0.071]– 0.017– 0.023– 0.0061[– 0.05, 0.038] Lipid profile (yearly) ($\frac{0}{1}$)0.0130.0057– 0.0077[– 0.069, 0.053]– 0.0026– 0.0038– 0.0012[– 0.062, 0.06] Nephropathy status (yearly) or ACE ($\frac{0}{1}$)0.00410.0580.054a[0.0089, 0.1]0.00350.0810.078c[0.033, 0.12] Ophthalmologist (every two years) ($\frac{0}{1}$)0.000760.0340.033[– 0.0083, 0.075]– 0.00250.060.063a[0.014, 0.11]Hospitalization risk ≥ 1 inpatient hospitalization ($\frac{0}{1}$)0.017– 0.04– 0.057a[– 0.11, – 0.0074]0.026– 0.013– 0.039[– 0.085, 0.0069]Health care costs (CHF) Total1041190– 852[– 1871, 168]1714806– 909[– 2089, 272] Outpatient281– 28– 309[– 807, 189]694286– 407[– 1034, 219] Inpatient (excl. rehab and nursing homes)473182– 291[– 902, 321]556297– 259[– 923, 404]538 treatment cases in raw data, 530 treatment cases in the matched sample (8 not matched), 5050 control cases. $\frac{0}{1}$ Dummy variables. Values indicate shares of patientsa,b,cStatistically significant difference at 5, 1 and $0.1\%$ level, respectively, based on a t test ## Guideline-adherent care In both follow-up years, the share of patients fulfilling all four performance measures increased much more in the treatment than in the control group [DiD $\frac{2017}{18}$: + 7.2 percentage-points ($95\%$ CI 2.7; 12); DiD $\frac{2018}{19}$: + 8.4 percentage-points ($95\%$ CI 3.6; 13)]. This finding is due to the treatment groups’ higher increase in the share of patients with yearly examination of nephropathy status or intake of ACE inhibitors [DiD $\frac{2017}{18}$: + 5.4 percentage points ($95\%$ CI 0.9; 10); DiD $\frac{2018}{19}$: + 7.8 percentage-points ($95\%$ CI 3.3; 12)] and with ophthalmologic care every 2 years [DiD $\frac{2017}{18}$: + 3.3 percentage-points ($95\%$ CI – 0.8; 7.5), statistically not significant; DiD $\frac{2018}{19}$: + 6.3 percentage-points ($95\%$ CI 1.4; 11)]. There were no systematic differences between treatment and control group in the changes in uptake of two or more HbA1c measurements and in lipid profiles (Figs. 1 and 2, Table 3). ## Hospitalization risk The share of patients with at least one hospitalization per year changed in both follow-ups in favor of the treatment group (Figs. 1 and 2, Table 3): between 2017 and 2018, the control group showed an increase of 1.7 percentage-points in hospitalization risk, the treatment group a decrease of 4 percentage-points, resulting in a DiD of – 5.7 percentage-points in favor of the treatment group ($95\%$ CI – 11; – 0.7). Between 2017 and 2019, the increase in the control group was 2.6 percentage-points and the decrease in the treatment group 1.3 percentage-points, resulting in a non-significant DiD of – 3.9 percentage-points in favor of the treatment group ($95\%$ CI -8.5; 0.7). ## Health care costs All cost outcomes showed negative (not statistically significant) DiD estimates from baseline to 1-year follow-up [2018] and 2-year follow-up [2019], a result of smaller cost increases in the treatment compared to the control group (Figs. 1 and 2, Table 3). Total costs, outpatient and inpatient costs increased less in the treatment compared to the control group in both follow-ups, but the differences were not statistically significant. Total costs, for example, increased from 2017 to 2018 in the control group by CHF 1041 ($\frac{2017}{19}$: 1714) and in the treatment group by CHF 190 ($\frac{2017}{19}$: 806), resulting in a DiD of CHF – 852 ($95\%$ CI – 1871; 168) ($\frac{2017}{19}$: – 909 ($95\%$ CI – 2089; 272)). Results for the winsorized total cost-variable were comparable and even showed statistically significant DiD estimates when winsorizing the 2 or $5\%$ most extreme values, demonstrating that our results are robust and not driven by outlier values.1 ## Discussion In this prospective observational study with 2-year follow-up using a difference-in-difference matching approach, we evaluated the impact of a DMP introduction for diabetes patients in Switzerland on guideline-adherent care, hospitalization risk and health care costs compared to usual care. Adherence to treatment guidelines improved in the treatment group, particularly for the examination of nephropathy status (or intake of ACE inhibitors) and for ensuring regular ophthalmologic examinations. The hospitalization risk, too, changed in favor of the treatment group, indicating that also patients’ health status benefited from the DMP. Health care costs increased substantially less in the treatment compared to the control group. Although this difference was not statistically significant, it accounts for about $10\%$ of the total annual health care costs of CHF 8456 in the intervention group and CHF 9258 in the control group (after matching). In line with this study’s finding of increased guideline adherence and a decrease in hospitalization risk after introducing the DMP, Huber and colleagues [16] found a reduced probability of future hospitalizations for patients in an integrated care model compared to standard care (OR of 0.87; $95\%$ CI 0.79; 0.95). The same authors also reported a clear link between hospitalization risk and physicians’ guideline adherence as measured by the 4SPM [15]. Annual health care costs in our sample of diabetes patients are in a similar range to those found in a study by Huber and colleagues [16], who used claims data of another large Swiss health care insurer for the year 2013 and reported mean annual costs of CHF 9466 for diabetes patients in an integrated care model vs. CHF 10,530 for patients in a standard care model. In addition, the (cost-saving) effect of CHF – 778 for the integrated care model for diabetes patients that they report is similar in range to the effect of the DMP in our study. This is notable, as the two studies used data from different Swiss health insurers, from different years, and they used a somewhat different methodology: pairwise propensity score matching and regression adjustment vs. propensity score kernel matching with entropy balancing combined with difference-in-difference in our study. ## Strengths and limitations The major strength of our study is the analysis of both the quality of care (i.e., guideline adherence and hospitalization risk) and the resulting health care costs. The simultaneous assessment of patient benefit and costs is essential to gain a better understanding of the real value of health care for patients [29]. Furthermore, we analyzed data of a large Swiss health insurer, which adds evidence about the real-world impact of a structured diabetes care approach in primary care in a social health insurance system. Our study has, however, several limitations. It is an observational study and causal inference can, strictly speaking, not be drawn. We addressed the problem of confounding using a DiD approach that removes baseline differences between the treatment and control groups. Using propensity score matching based on entropy balancing, we made the groups comparable with regard to age, gender, comorbidities and place of living. Still, a selection bias may remain because unobserved differences likely influenced the probability of patients being part of the treatment or the control group. A further limitation is that we used pharmacy-based indicators (PCGs) to identify diabetes patients in the claims data and that we had no clinical data about the diagnosis, such as the diabetes type or severity. While PCG-based indicators are widely used and have been shown to be quite valid morbidity measures [30], diabetes patients without antidiabetic therapy are consequently not included in this study. We also do not know whether the diabetes patients enrolled in a practice offering the DMP under evaluation did in fact take full part in the DMP, i.e., made use of all the offered services and consultations. However, as this reflects the “real-world”-situation, our analysis represents the true impact of the DMP under analysis even better. ## Conclusion The DMP under evaluation seems to lead to a better quality of diabetes care at lower health care costs. This has implications for clinicians and managers of health care organizations alike. However, the cost differences are not statistically significant, and the follow-up is short. If the results can be confirmed in a longer follow-up, such structured treatment programs are a good example of value-based health care, as they provide better quality of care at similar or—possibly—even at lower costs. ## References 1. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas, 9th edition. Diabetes Res. Clin. Pract. (2019.0) 157 107843. DOI: 10.1016/j.diabres.2019.107843 2. 2.Swiss Federal Statistical Office: Diabetes, https://www.bfs.admin.ch/bfs/de/home/statistiken/gesundheit/gesundheitszustand/krankheiten/diabetes.html 3. Bovier PA, Sebo P, Abetel G, George F, Stalder H. Adherence to recommended standards of diabetes care by Swiss primary care physicians. Swiss Med. Wkly. (2007.0) 137 173-181. PMID: 17457700 4. Meier R, Muheim L, Senn O, Rosemann T, Chmiel C. The impact of financial incentives to improve quality indicators in patients with diabetes in Swiss primary care: a protocol for a cluster randomised controlled trial. BMJ Open. (2018.0) 8 e023788. DOI: 10.1136/bmjopen-2018-023788 5. Greiner W. Health economic evaluation of disease management programs: the German example. Eur. J. Health Econ. (2005.0) 6 191-196. DOI: 10.1007/s10198-005-0305-2 6. Huber CA, Schwenkglenks M, Rapold R, Reich O. Epidemiology and costs of diabetes mellitus in Switzerland: an analysis of health care claims data, 2006 and 2011. BMC Endocr. Disord. (2014.0) 14 44. DOI: 10.1186/1472-6823-14-44 7. Hunter D, Fairfield G. Disease management. BMJ. (1997.0) 315 50-53. DOI: 10.1136/bmj.315.7099.47 8. Kostial C, Manuwald U, Schulze J, Kugler J, Rothe U. Disease-management-programs in the field of diabetes mellitus with identification of the best practice in Europe: a scoping review. Horm. Metab. Res. (2020.0) 52 149-157. DOI: 10.1055/a-1111-8998 9. Birnbaum S, Braun S. Evaluation von disease management programmen – bewertung der methodik und der ersten ergebnisse aus gesundheitsökonomischer sicht. Z. Evid. Fortbild. Qual. Gesundhwes. (2010.0) 104 85-91. DOI: 10.1016/j.zefq.2009.07.002 10. Linder R, Ahrens S, Köppel D, Heilmann T, Verheyen F. The Benefit and efficiency of the disease management program for type 2 diabetes. Deutsches Aerzteblatt Online. (2011.0). DOI: 10.3238/arztebl.2011.0155 11. Bongaerts BWC, Müssig K, Wens J, Lang C, Schwarz P, Roden M, Rathmann W. Effectiveness of chronic care models for the management of type 2 diabetes mellitus in Europe: a systematic review and meta-analysis. BMJ Open. (2017.0) 7 e013076. DOI: 10.1136/bmjopen-2016-013076 12. Stock S, Drabik A, Büscher G, Graf C, Ullrich W, Gerber A, Lauterbach KW, Lüngen M. German diabetes management programs improve quality of care and curb costs. Health Aff. (2010.0) 29 2197-2205. DOI: 10.1377/hlthaff.2009.0799 13. Fuchs S, Henschke C, Blümel M, Busse R. Disease management programs for type 2 diabetes in Germany. Deutsches Aerzteblatt Online. (2014.0). DOI: 10.3238/arztebl.2014.0453 14. Gozzoli V, Palmer AJ, Brandt A, Spinas GA. Economic and clinical impact of alternative disease management strategies for secondary prevention in type 2 diabetes in the Swiss setting. Swiss Med. Wkly. (2001.0) 131 303-310. PMID: 11584692 15. Huber CA, Brändle M, Rapold R, Reich O, Rosemann T. A set of four simple performance measures reflecting adherence to guidelines predicts hospitalization: a claims-based cohort study of patients with diabetes. PPA. (2016.0). DOI: 10.2147/PPA.S99895 16. Huber CA, Reich O, Früh M, Rosemann T. Effects of integrated care on disease-related hospitalisation and healthcare costs in patients with diabetes, cardiovascular diseases and respiratory Illnesses: a propensity-matched cohort study in Switzerland. Int. J. Integr. Care. (2016.0) 16 11. DOI: 10.5334/ijic.2455 17. Sahli R, Jungi M, Christ E, Adrian G. Chronic care management-programm in der hausärztlichen Praxis. Swiss Med. Forum. (2019.0). DOI: 10.4414/smf.2019.08055 18. Hainmueller J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit. Anal. (2012.0) 20 25-46. DOI: 10.1093/pan/mpr025 19. Heckman JJ, Ichimura H, Todd P. Matching as an econometric evaluation estimator. Rev. Econ. Stud. (1997.0) 64 605-654. DOI: 10.1111/1467-937X.00044 20. 20.Swiss Federal Office of Public Health: Statistik der obligatorischen Krankenversicherung 2019 (2019) 21. 21.Rohrbasser, A., Caviglia, C., Pfänder, M., Schläppi, P.: Betreuungskonzept: Interdisziplinäre Behandlung von Patienten mit Typ 2 Diabetes. Medbase, Winterthur (2017) 22. 22.SGED: Swiss recommendations of the society for endocrinology and diabetes (SGED/SSED) for the treatment of type 2 diabetes mellitus (2020), https://www.sgedssed.ch/fileadmin/user_upload/6_Diabetologie/61_Empfehlungen_Facharzt/2020_Swiss_Recomm_Medis_EN_def.pdf (2020) 23. Barker I, Steventon A, Deeny SR. Association between continuity of care in general practice and hospital admissions for ambulatory care sensitive conditions: cross sectional study of routinely collected, person level data. BMJ. (2017.0). DOI: 10.1136/bmj.j84 24. Rohrbasser A, Harris J, Mickan S, Tal K, Wong G. Quality circles for quality improvement in primary health care: their origins, spread, effectiveness and lacunae– a scoping review. PLoS ONE. (2018.0) 13 e0202616. DOI: 10.1371/journal.pone.0202616 25. King G, Nielsen R. Why propensity scores should not be used for matching. Polit. Anal. (2019.0) 27 435-454. DOI: 10.1017/pan.2019.11 26. Huber M, Lechner M, Steinmayr A. Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behavior. Empir. Econ. (2015.0) 49 1-31. DOI: 10.1007/s00181-014-0847-1 27. Barber JA, Thompson SG. Analysis of cost data in randomized trials: an application of the non-parametric bootstrap. Stat. Med. (2000.0) 19 3219-3236. DOI: 10.1002/1097-0258(20001215)19:23<3219::AID-SIM623>3.0.CO;2-P 28. 28.Jann, B.: KMATCH: Stata module module for multivariate-distance and propensity-score matching, including entropy balancing, inverse probability weighting, (coarsened) exact matching, and regression adjustment., Boston College Department of Economics (2020) 29. 29.Porter, M., Teisberg, E.: Redefining health care. Creating value-based competition on results. Harvard Business School Press, Boston, Massachusetts (2006) 30. Huber CA, Schneeweiss S, Signorell A, Reich O. Improved prediction of medical expenditures and health care utilization using an updated chronic disease score and claims data. J. Clin. Epidemiol. (2013.0) 66 1118-1127. DOI: 10.1016/j.jclinepi.2013.04.011
--- title: Chronic stress targets mitochondrial respiratory efficiency in the skeletal muscle of C57BL/6 mice authors: - Aleksandra Nikolic - Pia Fahlbusch - Natalie Wahlers - Nele-Kathrien Riffelmann - Sylvia Jacob - Sonja Hartwig - Ulrike Kettel - Matthias Dille - Hadi Al-Hasani - Jörg Kotzka - Birgit Knebel journal: 'Cellular and Molecular Life Sciences: CMLS' year: 2023 pmcid: PMC10060325 doi: 10.1007/s00018-023-04761-4 license: CC BY 4.0 --- # Chronic stress targets mitochondrial respiratory efficiency in the skeletal muscle of C57BL/6 mice ## Abstract Episodes of chronic stress can result in psychic disorders like post-traumatic stress disorder, but also promote the development of metabolic syndrome and type 2 diabetes. We hypothesize that muscle, as main regulator of whole-body energy expenditure, is a central target of acute and adaptive molecular effects of stress in this context. Here, we investigate the immediate effect of a stress period on energy metabolism in Musculus gastrocnemius in our established C57BL/6 chronic variable stress (Cvs) mouse model. Cvs decreased lean body mass despite increased energy intake, reduced circadian energy expenditure (EE), and substrate utilization. Cvs altered the proteome of metabolic components but not of the oxidative phosphorylation system (OXPHOS), or other mitochondrial structural components. Functionally, Cvs impaired the electron transport chain (ETC) capacity of complex I and complex II, and reduces respiratory capacity of the ETC from complex I to ATP synthase. Complex I-OXPHOS correlated to diurnal EE and complex II-maximal uncoupled respiration correlated to diurnal and reduced nocturnal EE. Bioenergetics assessment revealed higher optimal thermodynamic efficiencies (ƞ-opt) of mitochondria via complex II after Cvs. Interestingly, transcriptome and methylome were unaffected by Cvs, thus excluding major contributions to supposed metabolic adaptation processes. In summary, the preclinical Cvs model shows that metabolic pressure by Cvs is initially compensated by adaptation of mitochondria function associated with high thermodynamic efficiency and decreased EE to manage the energy balance. This counter-regulation of mitochondrial complex II may be the driving force to longitudinal metabolic changes of muscle physiological adaptation as the basis of stress memory. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00018-023-04761-4. ## Introduction Epidemiological studies show that chronically elevated levels of stress hormones are a risk factor for diabetes, obesity, insulin resistance, and metabolic syndrome [1–5]. Threatening life situations and chronic stress can result in posttraumatic stress syndromes, which is not only a psychic condition, but a predisposition to metabolic disease and thus have a general clinical implication for the pathogenesis of obesity, insulin resistance, or type 2 diabetes to the development and progression of fatty liver [1–5]. Since glucocorticoid (GC) measurements can be used as a stress indicator and GC also contributes to metabolic regulation by promoting hepatic gluconeogenesis, decreasing insulin sensitivity, and inducing adipose tissue lipolysis [1, 3, 5]. However, the stress response is not synonymous with GC response but is a bundle of coordinated factors and processes that act in an energy-dependent manner to adapt to stress. Stress processes, therefore, interfere with energy homeostasis in energy provision, energy consumption, or substrate utilization, bringing the focus to mitochondria. This is further supported by the fact that all components of stress regulation are synthesized and regulated in mitochondria via key pathways [6]. Over $90\%$ of cellular energy production occurs in the mitochondria via oxidative phosphorylation [7], but several essential metabolic pathways, such as ß-oxidation of fatty acids, tricarboxylic acid (TCA) cycle, stress and sex hormone synthesis, as well as urea cycles, are also located in this organelle. In addition, environmental factors, such as stress, can affect mitochondrial activity, and mitochondria themselves act as signal transducing organelles through intermediates derived from the TCA cycle, metabolites, nucleotides or reactive oxygen species (ROS) [8]. These metabolic intermediates affect various cellular metabolic processes and also epigenetic DNA modifications, creating a direct link from mitochondrial function to transcriptional regulation and epigenetic processes [9–12]. Given their crucial role in cell physiology, it is evident that mitochondria have become the first responders to various stressors that affect the homeostasis of the cell and the organism. At least, mitochondrial function is involved in the adaptation of cellular metabolism and the development and progression of metabolic diseases such as obesity, insulin resistance, and type 2 diabetes [13–15]. The skeletal muscle is a key organ of whole body glucose homeostasis and whole-body energy expenditure (EE) is coupled to the capacity of mitochondrial respiration in skeletal muscle [16, 17]. When mitochondria are under pressure to adapt metabolism in response to exacerbating health-threatening situations, there is a gradual decline in mitochondrial organelle function. This can range from changes in electron transport chain (ETC) activity and potential, to functional impairments, like proton leakage, decrease in ATP production, increasing membrane potential, mitophagy, mitochondrial and genomic gene expression, up to changes in cellular metabolic processes and even programmed cell death [18–21]. Additional or sustained high-energy demands can lead to increased organelle activity and further to compensatory increased mitochondrial biogenesis, mitochondrial DNA content and damage, and eventual organelle decline [18, 22]. Moreover, insufficient oxidative capacity due to environmental stressors e.g. insulin resistance can induce mitochondrial stress leading to mitochondrial dysfunction [23–25]. To date, the mechanisms involved in the dysregulation of energy metabolism and mitochondrial function following chronic stress suggested as basis for the metabolic late effects are still under debate [2, 5, 26]. Chronic variable stress (Cvs) affects whole-body energy metabolism but it is tempting to speculate that it acts in a tissue-specific manner on a molecular level. We have previously shown that chronic stress causes hepatic insulin resistance but longitudinal development of improved insulin sensitivity in peripheral tissues [26]. Therefore, it came clear that there have to be tissue-specific mechanisms resulting in long-term metabolic effects of Cvs. Immediately after Cvs, glucose- or lipid uptake and insulin signaling were unaltered in muscle tissues in our initial study [26]. However, measurements of palmitate oxidation, β-oxidation-linked, as well as TCA-cycle-linked respiration show longitudinal effects in muscle tissue, while mitochondrial mass remains unaltered [26]. The data show an overall tendency towards increased energy metabolism in muscle tissue concerning different substrate utilization pathways, which may indicate a general reorganization of mitochondrial metabolic pathways. Here, we follow this observation in detail to account for the molecular mechanisms initiated in muscle directly after Cvs that induce the ongoing molecular adaptation to metabolic stress processes. In the present study, we examined the immediate effects of Cvs intervention on skeletal muscle, specifically M. gastrocnemius. Changes in physical activity, energy expenditure, transcriptome, methylome, proteome, and extracellular flux analysis to determine oxygen consumption rate in isolated skeletal muscle mitochondria, detailed mitochondrial complex capacity and mitochondrial thermodynamics were used to evaluate stress-mediated effects. Our results suggest that mitochondrial disturbance right after Cvs is accompanied by reduced EE and is compensated by an increase in complex II thermodynamic coupling and its efficiency of oxidative phosphorylation. This may indicate an early stage of mitochondrial adaptation to Cvs as a compensatory mechanism to manage the energy balance. ## Experimental animals C57BL/6 male mice (12 weeks old) were housed (5–6 animals per cage) within standard laboratory conditions (12 h light/12 h night cycles, 22–24 °C, with ad libitum access to tap water and standard chow food (R/M-H extrudate: $58\%$ carbohydrates, $9\%$ fat, $33\%$ protein; ssniff Spezialdiäten GmbH, Soest, Germany). All procedures were approved (LANUV, NRW, Germany (81–02.04.2017.A421)) and carried out in accordance with the ‘Principle of laboratory animal care’ (NIH publication No. 85–23, revised 1996) and the German law on the protection of animals. One group of C57BL/6 animals was exposed to our established chronic variable stress (Cvs) protocol [26, 27]. In brief, stressors were: (i) individual caging on a shaker (100 rpm, 1 h); (ii) 30 min restrain; (iii) individual caging without bedding (4 °C, 1 h); (iv) swimming in warm water (30 °C, 20 min); and (v) overnight housing in a large cage. The control group was untreated and only subjected to gentle handling during the weaning and ranking phase until 15 weeks of age. After the mice were sacrificed by CO2 asphyxiation, blood was collected by cardiac puncture, and muscle biopsies (M. gastrocnemius) were immediately removed for isolation of mitochondrial fractions. Muscle tissue for methylome and RNA analyses was snap-frozen in liquid nitrogen and stored at − 80 °C until further processing. ## Body composition Body composition was analyzed ($$n = 6$$ animals per group) using nuclear magnetic resonance (NMR) (Whole Body Composition Analyzer, Echo MRI, TX, USA) to determine fat and lean mass before and after 15 days of the Cvs intervention. Body composition was measured by a sequence of radio pulses followed by nuclear magnetic resonance (NMR) echo recording. The sequence contains several periodic Carr-Purcell-Meiboom-Gill (CPMG) segments separated by pauses of varying duration. The characteristic (relaxation) time scales of the NMR responses (transverse "T2" and longitudinal "T1" relaxation) specific for fat mass and lean mass were analyzed. The final values of these parameters are calculated by linear regression based on the linear combination of the fat mass and lean mass and their respective relaxation rates ($$n = 6$$ animals per group) [28]. ## Indirect calorimetry Animals ($$n = 6$$ animals per group) were assessed for activity, respiratory exchange ratio (RER), and energy expenditure (EE) in metabolic cages (PhenoMaster, TSE Systems, Bad Homburg, Germany). To ensure acclimatization, the animals were placed in the cages 24 h before the start of the measurement recordings. Measurements began at 06:00 am and were taken every 30 min over a 72 h period. Analyses were calculated from data of two complete circadian cycles (48 h) starting with the first light phase. The mean values of 48 h were used. Measurements were performed with a non-invasive infra-red light beam system and the parameters analyzed were real-time feeding and physical activity. Animals had free access to food and water at 22 °C. Total energy expenditure (EE) was derived from oxygen consumption (VO2) and normalized by body surface area [29]. Respiratory exchange ratio (RER) was calculated from oxygen consumption (VO2) and carbon dioxide (VCO2) production. Furthermore, the carbohydrate oxidation (CHO) and fatty acid oxidation (FAO) rates were calculated according to Perronnet and Massicote [30]. The measurements were performed according to the manufacturer's recommendation. ## Plasma analyses Plasma was immediately obtained by centrifugation from collected blood (2000 xg, 10 min, 4 °C). Efficiency of our Cvs protocol was monitored by increased plasma corticosterone levels determined by radioimmunoassay (Corticosterone Double Antibody 125I RIA kit; MP Biomedicals, Orangeburg, NY, USA), increased fasting blood glucose (Contour, Bayer AG, Leverkusen, Germany) after a 6-h fasting as well as increased triglyceride and NEFA levels (Randox Triglycerides (RANDOX Laboratories, Antrim, United Kingdom) and Autokit NEFA C (Wako, Neuss, Germany)) (Supplement Fig. 1). All procedures were performed according to the manufacturer’s protocols. Plasma analyses were analyzed as means ± $95\%$ CI of 6 animals per group. A Shapiro–Wilk analysis confirmed that the sample data were normally distributed, so data were analyses using parametric unpaired t-test. Fig. 1Effect of 15 days of chronic stress intervention (Cvs) on weight change, body composition, and food uptake. A NMR-determined analysis of body composition, consisting of body weight (BW), lean mass, and fat mass, before and after chronic variable stress in control (Ctrl) and Cvs mice ($$n = 6$$/group). B Differences in mean energy intake per gram of body weight (BW) and weight change per convertible energy are presented as mean of 8 measurements taken during the intervention period per group ($$n = 6$$/group). For all analyses of physiological changes, bar graphs represent the mean ± $95\%$ CI. Single measurements of each animal or group are shown as dots. Statistics: one-way ANOVA with Tukey test for multiple comparisons or Mann–Whitney test, $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001$, Ctrl: C57BL/6, Cvs: C57BL/6 after stress intervention ## Enrichment of mitochondria Enrichment of mitochondria [31] was modified for muscle tissue according to [32]. In brief, isolation was based on the differential centrifugation method by 100 mg of M. gastrocnemius minced at a ratio of 1:5 (w/v) in a hypoosmotic buffer (100 mM KCl, 50 mM MOPS, 1 mM EDTA, 5 mM MgSO4, $0.5\%$ fatty-acid free BSA, pH = 7.4) using a homogenizer (TissueRuptorII, Qiagen, Hilden, Germany). The lysate was cleared (600×g, 20 min, 4 °C; (referred as 600×g fraction)) and the resultant supernatant was centrifuged at 10,000×g (20 min, 4 °C) to collect the enriched mitochondria. The pellet was resuspended in isolation buffer without BSA (100 mM KCl, 50 mM MOPS, 1 mM EDTA, 5 mM MgSO4, pH = 7.4) (referred as 10,000×g fraction). Protein concentration was determined using the BCA method. ## Analyses of mitochondria respiration and enzyme activity Mitochondrial respiration experiments were performed by extracellular flux analysis using the Seahorse XFe24 instrument (Agilent, Waldbronn, Germany) as described [31]. 2.5 µg freshly enriched mitochondrial 10,000 xg fraction were added to each well. For electron flow measurement the substrate concentrations were 10 mmol/l pyruvate, 5 mmol/l malate, 10 mmol/l succinate, and 6 µmol/l FCCP. The initial inhibition of complex I was achieved by injection of 2 µmol/l rotenone and the inhibition of complex II was achieved by 10 mmol/l malonate. Complex III was inhibited by the injection of 2 µmol/l antimycin A, followed by the stimulation of complex IV by 100 µmol/l TMPD (N, N, N′, N′-Tetramethyl-1,4-phenylenediamine) in combination with 10 mmol/l ascorbate. The coupling assay was performed in presence of complex I- or complex II-specific substrates pyruvate/malate or succinate in the assay medium. The basal respiration (state 2) was mediated by complex I and complex II substrates. The oxidative phosphorylation (state 3) was initiated by 2 mmol/l ADP injection, the resting respiration rate (state 4o) was initiated by 2 µmol/l oligomycin injection and state 3u was mediated by 6 µmol/l FCCP injection. 2 µmol antimycin A injection induced complex III inhibition (all compounds purchased from Sigma Aldrich/MERCK, Germany). Enriched mitochondria fractions were allowed to equilibrate in the assay setup with two repetitive cycles, where one cycle corresponds to 30 s mix of substrate in the assay wells and 3 min rest of mitochondria in the transient microchamber. Two initial basal oxygen consumption rate (OCR) measurements were used to verify equilibration of mitochondria to the steady state prior to the start of the different assay protocols. In the following analyses, only the second basal measurement was set as basal OCR. With the Agilent Seahorse technology dissolved oxygen and free protons are measured in a transient microchamber every few seconds over a period of 3 min (6 min after ADP injection), then mean oxygen consumption rate (OCR) is calculated for each measurement and depicted as a single data point. All OCR measurements were performed in technical triplicates for $$n = 6$$ per group and normalized by equal protein amount of mitochondrial fraction per well and additionally the use of the mitochondrial content marker citrate synthase (CS) activity [33]. For OCR measurement analyses Wave 2.6.0 (Agilent Technologies, Santa Clara, CA, USA) was used. Calculation of RCR required addition of an arbitrary factor to all readings of state 3 and state 4o, to raise negative state 4o values of individual samples to a positive numeric range, so that they could be used for the calculation. The degree of thermodynamic coupling (q) and the optimal thermodynamic efficiencies (ƞ-opt) were calculated from state 4o and state 3 values as described elsewhere [34]. Activity of citrate synthase and cytochrome c oxidase was measured with the Citrate Synthase Assay Kit (Sigma-Aldrich/MERCK, Germany) and the Cytochrome c Oxidase Assay Kit (Sigma-Aldrich/MERCK, Germany) according to the manufacturer's protocols, using 10 µg or 60 µg of the 10,000×g mitochondrial fraction from five single animals in duplicates, respectively. Membrane integrity was determined from cytochrome c oxidase activity data from six single animals. The enzyme activity of methyltransferases (MTases) and sirtuins (SIRTs; histone deacetylases class III; (SIRT1, SIRT2, SIRT3, SIRT4, SIRT5, SIRT6, SIRT7)) were measured using the MTase Glo Methyltransferase Assay (Promega, Germany) and the SIRT Glo Assay System (Promega, Germany) from 100 ng and 1 µg cell lysates in duplicates from six single animals (600 xg fraction), respectively, according to the manufacturer's protocol. Malondialdehyde (MDA) as a marker for lipid peroxidation was fluorometrically measured from 30 mg of the 600×g fraction in duplicates from five single animals using OxiSelect™ TBARS Assay Kit (MDA Quantification) (Cell Biolabs, Inc., USA) according to manufacturer’s protocol. ## Mitochondrial copy number Genomic DNA was extracted from snap-frozen muscle biopsies using standard technologies (Qiagen, Hilden, Germany). The relative amount of mitochondrial DNA (mtDNA) was determined with gene-specific primers and fluorescent-labeled probes for mitochondrial gene Nd1 and nuclear gene Lpl in triplicates from five animals per group as described [35] by qPCR (Thermo Fisher Scientific, Darmstadt, Germany) ## Proteome analysis The 10,000×g fractions of enriched mitochondria were lysed in loading buffer and subjected to $10\%$ SDS polyacrylamide gel electrophoresis. Coomassie blue-stained protein bands were excised and digested in the gel. The protein-containing gel slices were washed and the proteins were reduced, followed by alkylation. Digestion was performed, and then the eluted peptides were lyophilized. The peptides were reconstituted for mass spectrometry analysis in $1\%$ trifluoroacetic acid (v/v), including index retention time (iRT) peptides (Biognosys, Schlieren, Switzerland). Analyses of the peptides were then performed by LC–MS/MS in a label-free proteome analysis approach with an Ultimate 3000 separation liquid chromatography system combined with an EASY-spray ion source and Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermofisher Scientific, Germany). The peptides were trapped on an Acclaim PepMap C18-LC-column (ID: 75 μm, 2 cm length; Thermofisher Scientific) and separated via EASY-Spray C18 column (ES802; ID: 75 μm, 25 cm length; Thermofisher Scientific). Each LC–MS run lasted 150 min, and MS data were acquired with both data-dependent (DDA) and data-independent (DIA, 34 windows) MS/MS scan approaches. The DDA runs were analyzed using Proteome Discoverer 2.5 software (Thermofisher Scientific) and Sequest HT search (trypsin digestion, max. 2 miscleavages, 5–144 peptide length, max. 10 peptides per spectrum, carbamidomethylation as static and N-terminal acetylation/methionine oxidation as dynamic modifications) against the SwissProt FASTA database (Mus musculus* (TaxID = 10,090, version 2021–07)). The percolator node-based peptide-spectrum match (PSM) analysis was restricted to q-values with 0.01 (strict) and 0.05 (relaxed) false discovery rate (FDR). The proteins were filtered using parsimony principle set to $\frac{0.01}{0.05}$ (strict/relaxed) FDRs. For quantification of proteins prior to interpretation, the DIA runs virtually collecting all fragmented peptides over the retention time, were analyzed via SpectronautTM Pulsar 15 software (Biognosys, Switzerland) set to standard parameter settings and using a self-performed spectral library based on DDA runs, as described [36]. DIA files were processed using Spectronaut with default settings, (PTM localization activated, PEP cutoff set to 0.75, data filtering set to Q-value, and Normalization Strategy set to Local Normalization). A modified Fisher’s exact test (subtracting one entry of hits) was used for functional enrichment analyses to determine if the enrichment in proteomics observed in one pathway is more than a random chance compared to the mouse background. Z-score significance was assessed for estimation plot analysis using the over and under-representation of proteins in each condition with respect to the overall experimental mean were used. For mitochondrial proteome, five animals per group were analyzed, and the mass spectrometry data are available at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD035798. ## Transcriptome analysis Total RNA was extracted with standard procedures from snap-frozen muscle biopsies (Qiagen, Hilden, Germany). Genome-wide expression analysis with $$n = 5$$ samples per condition was performed using Mouse Gene (MTA) arrays (Thermo Fisher Scientific, Darmstadt, Germany) as described starting from 150 ng total RNA [37]. Datasets are available as super series under https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE210510 or transcriptome dataset accession number GSE210365. Gene expression data were further analyzed with Transcriptome Analysis ConsoleTM v.4.01 (Thermo Fisher, Darmstadt, Germany), and knowledge-based transcriptome analyses were performed using IPA® (QIAGEN Inc., www.qiagenbioinformatics.com/ products/ingenuity-pathway-analysis) [38], with 1.2-fold differences, p-value < 0.05). ## Methylome analysis For methylation analysis, genomic DNA was extracted from 20 mg of snap-frozen M. gastrocnemius tissue ($$n = 5$$ animals per condition) using the DNeasy® Blood & Tissue Kit (Qiagen, Hilden, Germany). 500 ng of DNA were shared by ultrasound to an average length of 200 nt (Bioruptor® Pico; Diagenode SA, Seraing, Belgium). DNA was then processed in two steps and subsequently analyzed by Next-Generation Sequencing. First, sample complexity was reduced by enrichment of DNA fragments with methylated cytosines using the EpiXplore™ Methylated DNA Enrichment Kit (Takara Bio Europe, Saint-Germain-en-Lage, France). Next, to facilitate the direct identification of methylated Cs, enriched methylated DNA fragments were subjected to TET-mediated enzymatic conversion of unmethylated cytosines and subsequent library preparation using NEBNext® Enzymatic Methyl-seq Kit (NewEngland BioLabs® Frankfurt, Germany) according to manufacturer's instructions. Libraries were paired-end sequenced on the NextSeq550™ instrument with automated FastQ raw data analyses (Illumina, San Diego, USA). Data are available as superseries or methylome data at NCBI GEO https://www.ncbi.nlm.nih.gov/geo/ dataset accession GSE210509). For methylome analyses, raw Illumina paired-end reads were converted into FastQ format by executing bcl2fastq2 Conversion Software version 2.20 (Illumina, San Diego, USA). FastQ files of methylation analyses were analyzed using the Illumina Dragen® (Dynamic Read Analysis for Genomics) Bio-IT platform v 3.9.5 (Edico Genome, Illumina San Diego) on the Illumina cloud providing predesigned analysis pipelines. Runs were prepared and quality checked using Fast Q toolkit pipeline (v1.0.0.) with a minimum length of 32 bp adapter trimming using the TRUESEQ HT/LT adapters sequence (Illumina San Diego, USA). Settings were: trimming strength: 4 and a quality score < 10 to trim bases at 5’ and 3’ sites before mapping. Paired read FastQ files were then mapped using a Hash table generated by DRAGEN Reference builder (v.3.10.4) based on the mouse genome (GRCm38.p6.genome.fa, including methylation information). Mapping was performed using the DRAGEN Methylation pipeline (version v3.9.5) based on [39] for read conversion (C-to-T and G-to-A), deduplication, sorting, and alignment of bisulfite converted reference genome, genome-wide methyl calling and the calculation of alignment and methylation metrics. The outputs of sequence alignment/map files were further analyzed using the R package MethylKit [40] (BaseSpaceLabs, Illumina San Diego, USA). CpG sites with a minimum read quality of at least 20 and a minimum read coverage of at least 5, as well as a maximum coverage of $99.9\%$, were considered for differential methylation analysis with > $2\%$ difference in conditions. In the single-base CpG resolution analysis, methylation calls were optimized by combining methylated sites from both the forward and reverse reads. Settings were as: 5 × CpG Coverage, $2\%$ methylation difference, and q value of 0.05). ## Statistics For non-OMICS analyses, significant differences between means for variables displaying normal distribution were determined using unpaired two-tailed Student t tests for two variables or one-way ANOVA with Tukey test for more than two groups. A Shapiro–Wilk analysis was used to confirm that the sample were normally distributed. Similarly, variables that did not follow a normal distribution were analyzed with Mann–Whitney test, when appropriate. P values are corrected for multiple testing using the Benjamini–Hochberg (FDR) method. Data are presented as the mean ± $95\%$ confidence interval (CI) unless otherwise stated. All calculations and graphs were made using GraphPad Prism software version 9.4.0 (GraphPad Software Inc., La Jolla, USA). ## Metabolic characteristics after Cvs 12-week-old male C57BL/6 mice were subjected to our 15-day stress protocol, whereas control mice were kept untreated. The comparison of body weight before and after the stress intervention showed a significant decrease of up to 2 g in the stressed animals, whereas the control animals showed an increase of 4 g in body weight (Fig. 1A). Quantification by NMR revealed a significant change in body composition. In detail, in this short phase stress decreased not only the fat mass up to $10\%$ (approx. 0.4 g) but also the lean mass up to $7.5\%$ (approx. 2 g) (Fig. 1A). In contrast, the control animals showed a significant increase in fat mass of up to $10\%$ (approx. 0.3 g) (Fig. 1A) and lean mass of $7.5\%$ (approx. 2 g) (Fig. 1A). The weight change of the animals during the Cvs protocol was not due to food intake. During the stress intervention, the Cvs animals had a $20\%$ higher average energy intake per gram of body weight compared to the control animals (Fig. 1B). Consistent with this, body weight changes per convertible energy (mg/kJ) were significantly different and showed a decrease in the Cvs group compared to controls (Fig. 1B). ## Indirect calorimetry in Cvs mice To investigate the differences observed, the metabolic activity was measured by indirect calorimetry immediately after Cvs. Data were analyzed for light and dark phases to distinguish metabolic phenomena, depending on low (light) and high (dark) activity levels. Compared with control mice, the stressed mice showed an increase in total physical activity over the observation period, which is especially due to increased dark phase activity (light: Cvs = 389; Ctrl = 288; dark: Cvs = 1245; Ctrl = 794) (Fig. 2A). Total energy expenditure (EE) during the entire observation period was significantly lower after Cvs compared to control animals (Cvs = 6.3 ml/min/g0.75; Ctrl = 7.2 ml/min/g0.75) (Fig. 2A). Consistent with higher dark phase physical activity the circadian EE indicated in all animals that the EE rates of the dark were significantly higher (7.9–8.3 ml/min/g0.75) than light period (5.9–6.3 ml/min/g0.75). The overall decrease in EE in stressed mice was solely due to the reduced dark EE (Fig. 2A).Fig. 2Effect of 15 days of Cvs intervention on physical activity, energy expenditure (EE), and substrate utilization. A Detailed differences in activity and energy expenditure (EE) of Cvs mice in comparison to the Ctrl group during light and dark phases, B Respiratory exchange ratio (RER). The dotted lines mark the thresholds for preferential whole-body carbohydrate oxidation (CHO) (VCO2/VO2 = 1.0) or whole-body fatty acid oxidation (FAO) (VCO2/VO2 = 0.7) substrate utilization, (C) CHO and FAO. The pairwise bar graphs represent the mean ± $95\%$ CI over 48 h measurement, dots indicate single animals ($$n = 6$$/group). Graphs separated for light and dark phases depict each light or dark phase measurement for the respective animal ($$n = 6$$/group, with two measurements per phase). Statistics: two-tailed paired t test or one-way ANOVA with Tukey test for multiple comparisons, $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001$, Ctrl C57BL/6, Cvs C57BL/6 after stress intervention, CHO carbohydrate oxidation, FAO fatty acid oxidation Over the observation period, respiratory exchange ratio (RER) indicated a shift in whole body substrate utilization from preferential carbohydrate utilization towards fat oxidation in Cvs mice (Ctrl = 0.95; Cvs = 0.89). In stressed animals, the RER was significantly decreased compared to control mice in both, the light and dark phases, (Fig. 2B), with the greatest decrease in the light phase. Detailed analyses showed that carbohydrate oxidation (CHO) was reduced after Cvs (Fig. 2C). In both groups, CHO was mainly driven in the dark phase (Ctrl: 2.2 mg/min; Cvs: 1.4 mg/min) in contrast to the light phase (Ctrl: 1.4 mg/min; Cvs: 0.6 mg/min) and the Cvs group exhibited a significantly decreased circadian CHO compared to controls (Fig. 2C). In contrast, the average fat oxidation (FAO) increased after Cvs. In both groups, FAO was higher in the light phase than in the dark phase. The observed difference comes from a significantly increased FAO during light phase in Cvs mice (Fig. 2C). So, the Cvs group showed an overall decrease in whole-body substrate utilization, except for resting FAO during light phase. ## Mitochondrial composition assessment To follow this, we analyzed how stress interferes with the status quo of cellular energy metabolism in a label-free proteomic analysis of 10,000×g fractions. Following stress, mitochondrial DNA copy number determined by mtDNA/nDNA ratio showed a small but significant increase in the muscles of the stressed mice (Supplement Fig. 2A). In the enriched mitochondrial fractions, mitochondrial content, as determined by the commonly used valid biomarker citrate synthase activity [41], was not significantly altered within the groups (Supplement Fig. 2B). Mitochondrial membrane integrity analysis, based on cytochrome-c-oxidase activity, showed an average integrity of above $90\%$ with no differences between both groups (Supplement Fig. 2C). In the label-free proteomic analysis, 1090 proteins were identified. Of these, 86 proteins were significantly altered in abundance in Cvs mice (Up-regulated: 73/ Down-regulated: 13; ratio: > 1.5; p-value: < 0.05) (Fig. 3A) (Supplement table 1). Gene ontology analysis of biological processes indicated changes in catabolic processes. In the more detailed analysis of the proteome, the metabolic pathways and the ETC were examined separately and revealed 34 up-regulated and 6 down-regulated proteins followed Cvs (Fig. 3B). The most prominent change with significantly increased protein abundance was the glycolysis pathway (Fig. 3C). In contrast, key catabolic pathways, like ketolysis, glutaminolysis, and branched-chain amino acids metabolism were unaltered by Cvs (Supplement Fig. 3). The proteome data pointed to increased mitochondrial activity, as tricarboxylic acid (TCA) cycle and the ana/cataplerosis pathway proteins showed higher abundances after Cvs (Fig. 3C, D). In addition, fatty acid import into mitochondria, ß-oxidation, and pyruvate import into mitochondria was elevated after stress intervention (Fig. 3E–H).Fig. 3Cvs intervention interferes with metabolic pathway component abundance in enriched muscle mitochondria. A Volcano plot analyses and Gene Ontology (GO) classification of proteome analyses. Volcano plot analysis reveals the significant 73 up-regulated (red) and 13 down-regulated (blue) proteins from label-free proteomic analysis comparing Ctrl and Cvs group ($$n = 5$$ / group). X-axis shows log2-ratio and Y-axis shows − log10 p-value with the dotted line representing the threshold value of statistical significance (student's t-test ($p \leq 0.05$); > 1.5-fold regulation). Significantly enriched pathways (FDR < 0.1) are shown for the GO ontology for biological processes. Functional enrichment was determined with a modified Fisher’s exact test with Benjamini–Hochberg (FDR) for multiple testing correction. B Volcano plot analyses of ETC, mitochondrial and associated pathways derived from proteome analyses. Volcano plot analysis shows the significant 34 up-regulated (red) and 6 down-regulated (blue) proteins corresponding to the analyzed metabolic pathways and ETC from label-free proteomic analysis when comparing the Ctrl and Cvs groups ($$n = 5$$ / group). The significant proteins are shown here with their respective names. X-axis shows log2-ratio and Y-axis shows -log10 p-value with the dotted line representing the threshold value of statistical significance (student's t-test ($p \leq 0.05$); > 1.5-fold regulation). C–H Z-score plots show the over and under-represented proteins of the indicated pathways. Red identifies upregulation, blue identifies downregulation, and white indicates no change of protein abundance to the mean of each condition with respect to the overall experimental mean. The corresponding estimation plots show on the left axis scatter dot plots with mean z-scores, while dots represent each pathway protein as mean of $$n = 5$$/ group. On the right axis the mean ± $95\%$ CI alteration in pathway protein abundance of the Cvs and Ctrl comparison is shown. Dotted lines represent the mean of each group centered on 0. C Glycolysis, D TCA cycle, E ana / cataplerosis F fatty acid import into mitochondria, G ß-oxidation (mitochondria), H pyruvate import into mitochondria. ETC electron transport chain, TCA tricarboxylic acid cycle, Ctrl C57BL/6, Cvs C57BL/6 after stress intervention The proteome data after the Cvs period pointed towards an increase in mitochondrial key metabolic protein components to maintain energy supply. Next, we assessed mitochondrial component compositions about protein abundance differences in sum indicated by estimation plots. There were no significant changes in proteins involved in cristae formation following Cvs (Fig. 4A), and the Coenzyme Q (CoQ) biosynthesis modules (Fig. 4B). The CoQ e donors, i.e. the additional respiratory membrane-bound complexes, showed increased abundances after Cvs (Fig. 4C). A total of 109 of identified proteins could be assigned to the electron transport chain (ETC) (Fig. 4D–H). Complex I and III were unaltered (Fig. 4D, F) in muscle mitochondria derived from Cvs animals. Complex II showed increased abundance between the Cvs and Ctrl groups (Fig. 4E). Complex IV showed differences in subunit abundance of the Mt-Co2 and the Mt-Co3-modules, while Mt-Co1 remained unchanged after stress intervention (Fig. 4G). Although single proteins of complex V F[0] complex and F1 particle were altered, there was no significant overall change (Fig. 4H). In total, the analysis of protein composition of the ETC complexes I to V showed only few changes in single subunit abundances, suggesting that Cvs intervention did not induce substantial changes in ETC composition. Fig. 4Mitochondrial proteome. A–C Z-score plots show the over and under-represented proteins of the indicated pathways. Red identifies upregulation, blue identifies downregulation, and white indicates no change of protein abundance of the mean of each condition with respect to the overall experimental mean. The corresponding estimation plots show on the left axis scatter dot plots with mean z-scores, while dots represent each pathway protein as mean of $$n = 5$$/ group. On the right axis the mean ± $95\%$ CI alteration in pathway protein abundance of the Cvs and Ctrl comparison is shown. Dotted lines represent the mean of each group centered on 0. Plots resulting from z-score analyses and estimation plots: A cristae formation, B CoenzymeQ (CoQ) biosynthesis, and (C) CoQ e donors, D–H Z-score analyses of protein abundance for individual complex I, II, III, IV, and V subunits of electron transport chain. Ctrl C57BL/6, Cvs C57BL/6 after stress intervention ## Cvs interferes with mitochondrial function and EE in muscle Alterations in fuel preferences and the composition of major mitochondrial metabolic pathways, without evidence of modulation of ETC complex proteins or changes of mitochondrial structural components for cristae formation, may indicate altered mitochondrial activity after Cvs. In addition, the abundance of UCP3 at the transcriptional and protein levels was not altered between the two groups (Supplement Fig. 4 A, B). The status of oxidative stress was assessed by proteome analyses of redox-regulating proteins and by measuring the reaction of malondialdehyde with thiobarbituric acid. Here, the proteome and enzyme analysis indicated no changes in redox balance between Cvs and Ctrl groups (Supplement Fig. 4C). First, to determine potential differences in substrate entry to the ETC, and eventual differences in the capacity of such entry on electron transport, basal oxygen consumption rate (OCR) was measured without substrate limitation in uncoupled mitochondria. This was followed by subsequent inhibition of complex I or II using malonate or rotenone in two different experimental setups, respectively, with subsequent complex-specific stimulation of the respective other either by pyruvate/ malate or succinate (Supplement Fig. 5). In both settings, the Cvs group showed significantly decreased OCR values over the time course of the assay (Supplement Fig. 5). Already the basal level in the presence of all substrates to drive complex I as well as complex II in the uncoupled condition, showed lower OCR values ($46\%$ for complex I; $38\%$ for complex II-driven respiration) after Cvs compared with the control group (Fig. 5A/B). Inhibition of complex II under these conditions decreased respiration rate by half in both groups and the complex I-specific electron transport showed a fourfold decrease in OCR in mitochondria following Cvs compared with control animals (Fig. 5A). Inhibition of complex I by rotenone showed a strong reduction in OCR in both groups (Fig. 5B). Forced electron flow either through complex I or II by additional pyruvate/malate or succinate addition in uncoupled Cvs mitochondria* resulted in significant fourfold reduction of OCR compared to controls (Fig. 5A/B).Fig. 5Electron flow and coupling experiments of mitochondrial ETC after Cvs. Respiratory capacity was measured in the enriched mitochondrial fractions of isolated muscle mitochondria from Cvs compared with Ctrl muscles in response to ETC manipulation. A, B Results from electron transport measurements in uncoupled mitochondria (induced by FCCP) specific for complex-I (A) and complex II (B) –driven electron transport. Oxygen consumption rate (OCR) at basal level was measured in the presence of unlimited substrate condition (pyruvate/malate/succinate), complex-specific OCR was measured after inhibition of the respective other using malonate or rotenone and stimulated complex-specific OCR was measured after addition of fresh complex specific substrate, namely pyruvate/ malate or succinate (C) Complex I-specific coupling experiment. OCR was measured using complex I-specific substrate pyruvate and malate, with inhibition of complex II activity by malonate. D Complex II-specific coupling experiment. OCR was measured using complex II-specific substrate succinate, with inhibition of complex I activity by rotenone. ( C/D) Basal respiration (state 2), oxidative phosphorylation (state 3), and maximal uncoupled respiration (state 3u) measures are shown for the complex I- and complex II-specific assays. The OCR values for all experiments were normalized by citrate synthase (CS) activity to adjust for changes in mitochondrial content. Bar graphs represent the mean of $$n = 6$$ animals/ group ± $95\%$ CI. Individual measures for each animal are given as dots. Statistics: Mann–Whitney test, $p \leq 0.05$, $p \leq 0.01$, Ctrl C57BL/6, Cvs C57BL/6 after stress intervention. Electron flow and coupling experiment OCR time courses are given in Supplement Fig. 5A, B To follow the complex I and complex II-independent reduction in electron flows after Cvs, the coupling efficiency of ETC complexes I to IV to ATP synthase (complex V) were measured. Complex I-driven respiration was forced with the specific complex I substrates pyruvate/malate with simultaneous inhibition of complex II by malonate and was significantly lower after Cvs (Fig. 5C; Supplement Fig. 5B). Detailed examination of complex I-driven mitochondrial function revealed a $40\%$ lower OCR in state 2 in Cvs animals compared to controls (Fig. 5C). The initiation of oxidative phosphorylation (state 3) with ADP showed a significant $55\%$ decrease in OCR in the Cvs animals (Fig. 5C). Here, state 3 via complex I, significantly correlated with interindividual light phase EE rates (R2 = 0.405; p-value = 0.0261), but not with the dark phase EE. Maximal uncoupled respiration (state 3u) after FCCP injection was significantly decreased by $42\%$ in mitochondria from Cvs mice (Fig. 5C) but showed no correlations to circadian EE rates. Complex II-specific coupling forced by the specific substrate succinate and simultaneous inhibition of complex I by rotenone revealed a consistently lower OCR over time in Cvs mitochondria* compared to control (Fig. 5D). Complex II-driven function showed a $36\%$ lower respiration in state 2 and $56\%$ lower respiration in state 3 in the Cvs group (Fig. 5D), both without correlations to circadian EE rates. The state3u via complex II (state 3u) showed a $47\%$ reduction following Cvs, and there was a significant intraindividual correlation to both, light and dark EE (light: R2 = 0.549; p-value = 0.0091/ dark: R2 = 0.432; p-value = 0.0278). Overall, states 3 and 3u were reduced for both complex I- and complex II-driven coupling after chronic stress. ## Bioenergetics assessment of mitochondria by respiratory control ratio and calculation of thermodynamic coupling and efficiency For the assessment of the quality of mitochondrial activity, the respiratory control ratio (RCR), a reference value for proton leak was calculated (state 3/state 4o). The Cvs RCRs for neither complex I- nor complex II-driven respiration differ from the controls (Fig. 6A). To determine the thermodynamic coupling of oxidative phosphorylation capacity the q-value was calculated (Fig. 6B). This approached the thermodynamic set point of economic net output power at optimal efficiency. These analyses define the maximal net output flow (ATP) at optimal efficiency (qf = 0.786), maximal net output power (qϱ = 0.910), economic net output flow (qfec = 0.953), and economic net output power at optimal efficiency (qϱec = 0.972) [34]. The oxidative phosphorylation capacity of complex I was unaltered by Cvs. For control animals, the coupling of complex I (> stage qp (= 0.910)) is greater than that for complex II-driven respiration (< stage qp (= 0.910)). The complex II oxidative phosphorylation capacity was significantly enhanced after Cvs compared to controls (Fig. 6B). Moreover, for stressed animals, the coupling of complex II was greater (> stage qpec (= 0.972)) than that for complex I-driven respiration (< stage qfec (= 0.953)). In accordance with that, the efficiency of substrate to energy conversion (ƞ-opt) related to the thermodynamic coupling shows significantly higher levels in Cvs mitochondria with an increase of up to $38.6\%$ compared to the Ctrl group (Fig. 6C).Fig. 6Respiratory control ratio, thermodynamic q value and calculated optimal thermodynamic efficiencies of mitochondrial complex I and II after Cvs. Respiratory control ratio (RCR) was calculated as state 3/state 4o from complex I- and complex II-specific respiration ($$n = 6$$ animals/group). B Calculated thermodynamic coupling q values are shown for complex I- and complex II-dependent respiration. Dotted lines indicate the maximal coupling values of the thermodynamic set points corresponding to maximal net output flow (ATP) at optimal efficiency (qf = 0.786), maximal net output power (qϱ= 0.910), economic net output flow (qfec = 0.953), and economic net output power at optimal efficiency (qϱec = 0.972). ( $$n = 6$$ animals/ group). C Calculated optimal thermodynamic efficiencies (ƞ-opt) of oxidative phosphorylation are shown for complex I- and complex II-dependent respiration. Data are expressed as means ± $95\%$ CI, the dots represent single values for each animal. Statistics: Mann–Whitney test and one-way ANOVA with Tukey test for multiple comparisons, $p \leq 0.05$, Ctrl C57BL/6, Cvs C57BL/6 after stress intervention ## Cvs effects on muscle transcriptome and methylome According to our hypothesis, Cvs interferes directly with metabolism but also initiates a memory effect for metabolic adaptation. The genome-wide DNA methylation patterns in M. gastrocnemius showed a tight correlation between the conditions and thereof no gross alteration directly after chronic stress in muscle (Supplementary Table 2; Supplementary Fig. 6). The only significant variations found on methylation level were the hypomethylation of three intergenic areas including some coding genes like neuroactive Gphn, DDscaml, cell cycle active Sdk1, Mid1, and the nuclear-encoded mitochondrial leucyl-tRNA synthetase Lars2, but methylation differences were low (Supplementary Table 2). Consistently, there were no changes in Sirt and MTase activity (Supplementary Fig. 7). In concordance, there was no gross significant alteration in transcriptome level maintained after the stress phase within Cvs and Ctrl mice even for 1.2-fold expression differences (p-value < 0.05) (Supplementary Table 3; Supplementary Fig. 8). Nevertheless, ND1R1 and 6 mitochondrial tRNAs (mt-Tc, mt-Tt, mt-Tf, mt-Tl2 and the mitochondria-specific iso-acceptors mt-Ts1, mt-Ts2) were in the 10 most suppressed transcripts after Cvs. Mitochondrial tRNAs differ from nuclear tRNAs in sequence, stability, and folding, and are essential for mitochondrial protein synthesis. This may indicate alterations in the translation of mitochondrial-coded transcripts, and may additionally point to the central role of mitochondrial function after Cvs also in the long-term alterations. ## Discussion In the present study, we investigated the acute and adaptive effects of chronic variable stress (Cvs) on energy metabolism in skeletal muscle as the main determinant of whole-body energy expenditure. Analyses of the transcriptome, methylome, and proteome, as well as mitochondrial function in Cvs mice, were compared to controls. We show, that immediately following Cvs (i) body weight, fat- and lean mass decreased despite higher food intake, (ii) nocturnal energy expenditure (EE) is lower despite higher activity, (iii) respiratory exchange ratio (RER) shifted from carbohydrate (CHO) to fatty acid oxidation (FAO), (iv) mitochondrial metabolic capacity was altered with changes in the mitochondrial proteome, and (v) mitochondrial coupling capacity was reduced, but the thermodynamic efficiency increased. The advantage of preclinical animal models over human studies is the tight control of the environment by a standardized laboratory and the uniform genetic background of used mice. Thus, the Cvs effect can be isolated from interaction with exogenous confounding factors. With this, analyses of Cvs in a standardized preclinical model may help to unravel even metabolic fine-tuning effects of cellular adaptation to maintain energy balance, which may be superimposed by confounding effects in more complex and externally influenced systems as clinical studies. Consistent with previous studies, Cvs exposure resulted in immediate activation of the hypothalamic–pituitary–adrenal (HPA) axis [4], with high corticosterone levels, and increased metabolic risk factors such as higher fasting blood glucose, triglycerides, and NEFA levels in plasma. These complex stress-induced alterations are consistent with the well-known pleiotropic effects of corticosterone on glucoregulatory insulin-responsive tissues [42]. So, the system used here mirrors human stress and chronic glucocorticoid (GC) exposure as risk of metabolic syndrome [1–3, 5, 26, 27]. Stress can interfere with food-seeking behavior, including high fat and sugar intake inducing obesity or alcohol-intake interfering with neuropsychiatric function and regulation of satiety hormone action as a futile cycle that may result in a dysfunctional HPA axis [43, 44]. As a result of these complex and integrative processes, GC excess, either endogenous (e.g. Cushing’s syndrome) or exogenous, remodels body composition with central obesity and muscle atrophy [45]. In a mouse model of Cushing syndrome insulin sensitivity and metabolic parameters negatively correlated to the loss of M. gastrocnemius, not M. soleus, mass [46]. Here, we observed a decreased lean and fat mass with a reduction in circadian EE despite increased food intake. Furthermore, Cvs shifts energy utilization towards a greater proportion of FAO in whole-body substrate oxidation, despite relatively carb-heavy standard feed. The Cvs effect is mainly based on increased FAO during the light phase, while CHO is reduced during rest phase, as well as CHO and FAO during the active dark phase. The data may suggest that energy is restored after Cvs intervention by whole-body FAO during periods of low activity to compensate for reduced EE during the active phase. Lipid storage in white adipose tissue (WAT), remodeling or browning of WAT, and uncoupled mitochondrial respiration to thermogenesis in brown adipose tissue (BAT) also mediate systemic influence on FA metabolism and EE. Stress can trigger norepinephrine-mediated ß-adrenergic receptor activation in BAT and WAT, promoting cAMP-mediated lipolysis, browning of WAT, and thermogenesis in BAT [47–49]. Thus, increased lipolysis can provide the FA requirement after Cvs, which is reflected in the decreased fat mass. Also cold exposure is one major driver for thermogenesis in BAT and repetitive periods of even short-term exposure increases total EE coupled to the expression of Pgc1 and Ucp1 in BAT [50]. Even though cold exposure is a component of our Cvs protocol, EE was decrease in our mice after Cvs, so thermogenesis of BAT may not be the primary process, here. In addition, BAT is an endocrine organ and intraorgan crosstalk e.g. with muscle has been shown [51, 52]. Batokines such as FGF21, regulating the expression of thermogenic genes, or IL-6 can be secreted in response to stress [47, 51, 53, 54]. Of note, consistent with observations after acute cold stress [53], previous studies using our stress protocol showed that in the experimental timeframe immediately after Cvs, serum levels of FGF21 and IL-6 were not significantly altered [26, 27]. However, from our previous work, we saw no differences in glucose- and lipid uptake and insulin signaling in muscle tissues in our Cvs model [26]. Although muscle FAO alone may not account for the changes in whole-body EE, there were signs of increased mitochondrial capacity that might affect muscle mitochondrial function. In addition, in the present analyses, proteins of the ana-/ catabolic pathways are increased following stress, which implicates an increased metabolic turnover after Cvs. The enrichment analysis of the differentially expressed proteins revealed that next to strictly mitochondrial proteins also others including metabolic-relevant proteins were enriched, which is consistent with previous reports [55]. Here, the proteome analyses revealed changes in metabolic key enzymes in muscle tissue after Cvs. The rate-limiting enzymes of the glycolysis pathway hexokinase, phosphofructokinase, and pyruvate kinase significantly increased in abundance after Cvs. Also the abundance of 3-oxoacid CoA-transferase 1 of the ketolysis pathway, isocitrate dehydrogenase of the TCA cycle pathway and the glutamic-oxaloacetic transaminase of the glutaminolysis pathway were significantly increased in Cvs muscle. These changes in protein data suggests upregulation of catabolic processes in the M. gastrocnemius after chronic stress intervention. Mitochondrial dynamics including movement, tethering, fusion, and fission events are important for cell viability, senescence, intracellular signaling, mitochondria health, and bioenergetics function [56]. It still remains open if changed ATP demand or supply due to the balance of environmental influences and mitochondrial capacity are cause or consequence of the dynamic processes. However, Cvs may influence this process. Based on our proteomic data central proteins for fusion (Mfn1, Mfn2, as well as the Opa1 protein [56, 57]) or fission (Fis1, Mff, Drp1) and associated recruiting factors (MiD49 and MiD51 [58, 59]), if present in the dataset were unaltered by Cvs. Depending on the circadian rhythm, Cvs causes a greater fluctuation in substrate utilization shown by decreased EE and CHO to save substrates in the activity phase, and recovery of energy demands in resting times by unaltered EE, accompanied by preferential FAO. In addition, we observed a correlation between energy expenditure and mitochondrial capacity. However, there was no evidence for oxidative stress, as ROS production even tended to be lower after Cvs. Taken together, this suggests an imbalance of biogenesis to degradation, which is attempted to be compensated by the mitochondria as well as fuel utilization. The gradual decline of mitochondrial functionality from reversible to irreversible physiological defects is a key event in metabolic diseases. This is due to the fact that mitochondria orchestrate energy homeostasis up to all levels of gene regulation. Mitochondria are the first responders to changing conditions and are an important organelle in triggering catabolic processes that affect cellular homeostasis, as seen here after Cvs. The gradual process in mitochondrial adaptation to altered metabolism follows a sequence from functional alteration, functional impairments with compensatory mitochondrial and genomic gene expression, and mitogenesis, up to organelle exhaustion and mitophagy [18, 20, 21]. GC administration can improve skeletal muscle mitochondria respiration even in a mouse model for Duchenne Muscular Dystrophy [60]. Therefore, the differences in EE and RER seen after Cvs were most likely due to mitochondrial function itself. Defects in mitochondrial oxidative phosphorylation and lower mtDNA copy number are defined risks in diabetes type 2 patients [61]. Clinically, it has been shown that primary mitochondrial dysfunction due to mutations in genes encoding mitochondrial proteins can cause defects in energy metabolism and this may account for GC action directly [4, 18, 41, 62]. As mtDNA is not directly correlated to mitochondrial content [41], an increased mtDNA content in Cvs skeletal muscle compared to Ctrl suggests that compensatory processes have already begun at this stage. Our observation is consistent with previously reported stress and corticosteroids regulation of mitochondrial DNA content and gene expression [18, 19]. In enriched mitochondrial fractions with comparable mitochondrial content, the mitochondrial proteome composition of M. gastrocnemius shows only a few changes in the electron transfer chain (ETC) components after Cvs. There were no hints to structural alterations, key proteins involved in mitochondrial dynamics fission or fusion in the mitochondrial proteome data, although the system used is able to detect such alterations [63]. However, this interpretation is implicative, as morphological alterations of the mitochondria were not determined in the present study. So, the shift in fuel preferences and altered EE could not be explained by reduced lean mass or changes in relevant mitochondrial structural proteins, which may indicate modifications in mitochondrial activity after Cvs. The respiratory profile of muscle mitochondria after and the ability of the ETC to transfer electrons were analyzed in isolated mitochondria of muscle tissue after Cvs. Since the assessment of mitochondrial morphology is implicit based on only proteome analyses, respiration data were normalized by defined protein input and by the accepted marker of mitochondrial content citrate synthase activity [41, 64]. Since both normalization strategies have limitations [33, 65], both approaches were used in combination for our purposes to avoid possible normalization-related misinterpretations. So, the presented results show Cvs effect that is independent of potential alterations in total protein content of enriched fractions or mitochondrial quantity based on citrate synthase activity. The respiratory profile of isolated muscle mitochondria is reduced via both, complex I or II after Cvs. Our results agree with significantly decreased mitochondrial complex I enzyme activity data obtained in patients after corticosterone treatment [66, 67]. Moreover, we found that maximal uncoupled respiration (state 3u) of complex II correlates with whole-body EE Since skeletal muscle is a major contributor to total EE in vivo, this suggests that total EE is dependent on mitochondrial function and coupling. Therefore, it appears that there is a specific mitochondrial impairment involved in GC-induced muscle atrophy [68]. In addition, we observe a reduced electron flow accompanied by significantly decreased mitochondrial coupling independent of NADH (complex I)-linked or FADH2 (complex II)-linked OXPHOS after Cvs. Another limiting factor of ETC capacity could be the electron flux from glycerol via the glycerophosphate dehydrogenase complex (CGpDH) or from fatty acid ß-oxidation via the electron-transferring flavoprotein complex (ETF) to Coenzyme Q as superordinated factors, as upstream bottleneck of electron flux to complex I and complex II [69]. A similar observation with reduced mitochondrial respiration in regard to complex I, II, and ETC was made following diet-induced vitamin D deficiency in M. gastrocnemius of C57BL/6 J mice. Alterations in ETC protein content or citrate synthase activity were excluded and the authors suggested alterations in mitochondrial respiration independent of ETC protein content [70]. The decreased mitochondrial respiration could be explained by decreased basal proton conductance or can also be explained by a change in intrinsic coupling of the respiratory chain activity [71]. It is controversial whether the uncoupling mechanisms of UCP3, the major uncoupling protein expressed in skeletal muscle, affect total EE [72]. Here, there are no differences in UCP3 protein or transcription levels between the Cvs and the control groups. Surprisingly the thermodynamic coupling of mitochondrial phosphorylation is significantly increased in complex II after Cvs, indicating that the rate of energy production is at economic net output power at optimal efficiency. Additionally, the efficiency of both complexes was increased after Cvs. The optimal thermodynamic efficiency (ƞ-opt) has been applied to assess biological processes and has been proposed as a measure of mitochondrial function [73]. According to Stucki [74], the values observed suggest that muscle mitochondria adapt their function to maximize ATP production and to maintain cellular phosphate potential at the expense of the energy conversion efficiency after Cvs. The complex II pathway is set toward maximizing the cellular energy state and cellular integrity. In the Cvs group, the complex II mitochondrial set point is associated with a high degree of coupling and high thermodynamic efficiency. The RCR in both complexes was unaltered after Cvs thus* does not interfere with a higher degree of mitochondrial coupling. As a result, in vivo mitochondrial ATP synthesis might be more efficient through FADH2 (complex II)-linked OXPHOS in Cvs mice. The observation that thermodynamic coupling is increased after Cvs, whereas a correlation of complex II-specific maximal uncoupled respiration with decreased EE, has led to the hypothesis that mitochondria demonstrate functional adaptation with increased efficiency to manage energy balance. This results in a significant increase in economic coupling with the goal of achieving the optimal efficiency of net ATP production rate at the economic benefit to maintain cellular homeostasis processes. According to our hypothesis, Cvs interferes directly with metabolism but also initiates a memory effect for metabolic adaptation. We show that the direct effects on transcriptome and methylome solely initiated by Cvs are limited in muscle. Here, we specifically looked at the status quo after the stress intervention to evaluate any changes present in the transcriptome or even methylome and classify them as persisting after chronic stress. This snapshot of data should provide an indication of molecular changes that are prerequisites for cellular adaptation. Remarkable is the downregulated clock gene Nr1d1 (Rev-erb-α), a transcription factor that is generally involved in energy metabolism featuring maximal oxidative capacity and circadian regulation [75]. Interestingly, most of the significantly repressed transcripts by Cvs are of mitochondrial tRNAs. Specific mitochondrial tRNA mutations have long been linked to type 2 diabetes or metabolic syndrome and diabetes-associated genetic defects [76, 77]. One may speculate that mitochondrial tRNAs are an early target in the decline cascade of mitochondrial function, following stress. Methylome analyses reveal that one of the hypo-methylated genes is the nuclear-encoded mitochondrial tRNA synthase Lars2. Lars2 is expressed in skeletal muscle and controls the translation of mitochondrial-encoded genes via leucyl-tRNA synthetase, the mitochondrial genome stability, and its additional activation improves mitochondrial respiration [78]. In conclusion, our data showed that following Cvs mitochondrial mass is not changed and a correlation occurred of complex II in state 3u with decreased EE. Furthermore, the thermodynamic coupling was increased. This may indicate an early stage of mitochondrial adaptation to Cvs as a compensatory mechanism to manage the energy balance despite advancing atrophy. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 518 KB)Supplementary file2 (XLSX 1903 KB)Supplementary file3 (XLSX 37 KB)Supplementary file4 (XLSX 1791 KB) ## References 1. 1.Beaupere C, Liboz A, Feve B, Blondeau BGuillemain G. (2021) Molecular mechanisms of glucocorticoid-induced insulin resistance, Int J Mol Sci 22. 10.3390/ijms22020623. 2. 2.Mellon SH, Bersani FS, Lindqvist D, Hammamieh R, Donohue D, et al. (2019) Metabolomic analysis of male combat veterans with post traumatic stress disorder, PLoS One 14: e0213839. 10.1371/journal.pone.0213839. 3. van den Berk-Clark C, Secrest S, Walls J, Hallberg E, Lustman PJ. Association between posttraumatic stress disorder and lack of exercise, poor diet, obesity, and co-occuring smoking: a systematic review and meta-analysis. Health Psychol (2018.0) 37 407-416. DOI: 10.1037/hea0000593 4. 4.Kokkinopoulou I, Diakoumi AMoutsatsou P. (2021) Glucocorticoid receptor signaling in diabetes, Int J Mol Sci 22. 10.3390/ijms222011173. 5. 5.Yaribeygi H, Maleki M, Butler AE, Jamialahmadi TSahebkar A. (2022) Molecular mechanisms linking stress and insulin resistance, EXCLI J 21: 317–334. 10.17179/excli2021-4382. 6. 6.Bassi G, Sidhu SKMishra S. (2021) The expanding role of mitochondria, autophagy and lipophagy in steroidogenesis, Cells 10. 10.3390/cells10081851. 7. 7.Chance B, Sies HBoveris A. (1979) Hydroperoxide metabolism in mammalian organs, Physiol Rev 59: 527-605. 10.1152/physrev.1979.59.3.527 8. 8.Bohovych IKhalimonchuk O. (2016) Sending outan SOS: mitochondria as a signaling hub, Front Cell Dev Biol 4: 109. 10.3389/fcell.2016.00109 9. 9.Gao T, Diaz-Hirashi ZVerdeguer F. (2018) Metabolic signaling into chromatin modifications in the regulation of gene expression, Int J Mol Sci 19. 10.3390/ijms19124108. 10. Roubicek DASouza-Pinto NC.. Mitochondria and mitochondrial DNA as relevant targets for environmental contaminants. Toxicology (2017.0) 391 100-108. DOI: 10.1016/j.tox.2017.06.012 11. 11.Sharma J, Kumari R, Bhargava A, Tiwari RMishra PK. (2021) Mitochondrial-induced epigenetic modifications: from biology to clinical translation, Curr Pharm Des 27: 159-176. 10.2174/1381612826666200826165735 12. 12.Wiese MBannister AJ. (2020) Two genomes, one cell: mitochondrial-nuclear coordination via epigenetic pathways, Mol Metab 38: 100942. 10.1016/j.molmet.2020.01.006. 13. Rovira-Llopis S, Banuls C, Diaz-Morales N, Hernandez-Mijares A, Rocha M. Mitochondrial dynamics in type 2 diabetes: pathophysiological implications. Redox Biol (2017.0) 11 637-645. DOI: 10.1016/j.redox.2017.01.013 14. Vezza T, Diaz-Pozo P, Canet F, de Maranon AM, Abad-Jimenez Z. The role of mitochondrial dynamic dysfunction in age-associated type 2 diabetes. World J Mens Health (2022.0) 40 399-411. DOI: 10.5534/wjmh.210146 15. 15.Lewis MT, Kasper JD, Bazil JN, Frisbee JCWiseman RW. (2019) Quantification of mitochondrial oxidative phosphorylation in metabolic disease: application to Type 2 diabetes, International journal of molecular sciences 20. 10.3390/ijms20215271. 16. 16.Lewis MT, Levitsky Y, Bazil JNWiseman RW. (2022) Measuring mitochondrial function: from organelle to organism, Methods Mol Biol 2497: 141-172. 10.1007/978-1-0716-2309-1_10 17. 17.Zumbaugh MD, Johnson SE, Shi THGerrard DE. (2022) Molecular and biochemical regulation of skeletal muscle metabolism, J Anim Sci 100. 10.1093/jas/skac035. 18. 18.Kokkinopoulou IMoutsatsou P. (2021) Mitochondrial glucocorticoid receptors and their actions, Int J Mol Sci 22. 10.3390/ijms22116054. 19. 19.Picard M, McEwen BS, Epel ESSandi C. (2018) An energetic view of stress: focus on mitochondria, Front Neuroendocrinol 49: 72-85. 10.1016/j.yfrne.2018.01.001 20. Weber K, Brück P, Mikes Z, Küpper JH, Klingenspor M. Glucocorticoid hormone stimulates mitochondrial biogenesis specifically in skeletal muscle. Endocrinology (2002.0) 143 177-184. DOI: 10.1210/endo.143.1.8600 21. 21.Yu FLFeigelson P. (1970) A comparative study of RNA synthesis in rat hepatic nuclei and mitochondria under the influence of cortisone, Biochimica et biophysica acta 213: 134-141. 10.1016/0005-2787(70)90014-6 22. 22.Yang M, Ma FGuan M. (2021) Role of steroid hormones in the pathogenesis of nonalcoholic fatty liver disease, Metabolites 11. 10.3390/metabo11050320. 23. Bonnard C, Durand A, Peyrol S, Chanseaume E, Chauvin MA. Mitochondrial dysfunction results from oxidative stress in the skeletal muscle of diet-induced insulin-resistant mice. J Clin Investig (2008.0) 118 789-800. DOI: 10.1172/jci32601 24. Fisher-Wellman KH, Weber TM, Cathey BL, Brophy PM, Gilliam LA. Mitochondrial respiratory capacity and content are normal in young insulin-resistant obese humans. Diabetes (2014.0) 63 132-141. DOI: 10.2337/db13-0940 25. Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab (2008.0) 7 45-56. DOI: 10.1016/j.cmet.2007.10.013 26. Jelenik T, Dille M, Müller-Lühlhoff S, Kabra DG, Zhou Z. FGF21 regulates insulin sensitivity following long-term chronic stress. Mol Metab (2018.0) 16 126-138. DOI: 10.1016/j.molmet.2018.06.012 27. 27.Dille M, Nikolic A, Wahlers N, Fahlbusch P, Jacob S, et al. (2022) Long-term adjustment of hepatic lipid metabolism after chronic stress and the role of FGF21, Biochimica et biophysica acta. Mol Basis Dis 1868: 166286. 10.1016/j.bbadis.2021.166286. 28. Kovner I. Calibration and validation of EchoMRI whole body composition analysis based on chemical analysis of piglets, in comparison with the same for DXA. Int J Body Compos Res (2010.0) 8 17-29. PMID: 21152249 29. 29.Rubner M. ((1883)) Ueber den Einfluss der Körpergrösse auf Stoff- und Kraftwechsel, Zeitschrift für Biologie 19: S. 535–562. S. 535–562. 30. 30.Péronnet FMassicotte D. (1991) Table of nonprotein respiratory quotient: an update, Canadian Journal of Sport Sciences = Journal canadien des sciences du sport 16: 23–29 31. 31.Fahlbusch P, Nikolic A, Hartwig S, Jacob S, Kettel U, et al. (2022) Adaptation of oxidative phosphorylation machinery compensates for hepatic lipotoxicity in early stages of MAFLD, Int J Mol Sci 23. 10.3390/ijms23126873. 32. 32.Bharadwaj MS, Tyrrell DJ, Lyles MF, Demons JL, Rogers GW, et al. (2015) Preparation and respirometric assessment of mitochondria isolated from skeletal muscle tissue obtained by percutaneous needle biopsy, J Vis Exp 10.3791/52350. 33. Groennebaek T, Nielsen J, Jespersen NR, Botker HE, de Paoli FV. Utilization of biomarkers as predictors of skeletal muscle mitochondrial content after physiological intervention and in clinical settings. Am J Physiol Endocrinol Metab (2020.0) 318 E886-E889. DOI: 10.1152/ajpendo.00101.2020 34. 34.Cairns CB, Walther J, Harken AHBanerjee A. (1998) Mitochondrial oxidative phosphorylation thermodynamic efficiencies reflect physiological organ roles, Am J Physiol 274: R1376-1383. 10.1152/ajpregu.1998.274.5.R1376 35. Knebel B, Hartwig S, Haas J, Lehr S, Goeddeke S. Peroxisomes compensate hepatic lipid overflow in mice with fatty liver. Biochem Biophys Acta (2015.0) 1851 965-976. DOI: 10.1016/j.bbalip.2015.03.003 36. Hartwig S, Knebel B, Goeddeke S, Koellmer C, Jacob S. So close and yet so far: mitochondria and peroxisomes are one but with specific talents. Arch Physiol Biochem (2013.0) 119 126-135. DOI: 10.3109/13813455.2013.796994 37. Barbosa DM, Fahlbusch P, Herzfeld de Wiza D, Jacob S, Kettel U. Rhein, a novel Histone Deacetylase (HDAC) inhibitor with antifibrotic potency in human myocardial fibrosis. Sci Rep (2020.0) 10 4888. DOI: 10.1038/s41598-020-61886-3 38. Krämer A, Green J, Pollard J. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics (Oxford, England) (2014.0) 30 523-530. DOI: 10.1093/bioinformatics/btt703 39. 39.Krueger FAndrews SR. (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications, Bioinformatics (Oxford, England) 27: 1571-1572. 10.1093/bioinformatics/btr167 40. Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol (2012.0) 13 R87. DOI: 10.1186/gb-2012-13-10-r87 41. Larsen S, Nielsen J, Hansen CN, Nielsen LB, Wibrand F. Biomarkers of mitochondrial content in skeletal muscle of healthy young human subjects. J Physiol (2012.0) 590 3349-3360. DOI: 10.1113/jphysiol.2012.230185 42. 42.van Bodegom M, Homberg JRHenckens M. (2017) Modulation of the hypothalamic-pituitary-adrenal axis by early life stress exposure, Front Cell Neurosci 11: 87. 10.3389/fncel.2017.00087 43. Pagoto SL, Schneider KL, Bodenlos JS, Appelhans BM, Whited MC. Association of post-traumatic stress disorder and obesity in a nationally representative sample. Obesity (Silver Spring) (2012.0) 20 200-205. DOI: 10.1038/oby.2011.318 44. Blessing EM, Reus V, Mellon SH, Wolkowitz OM, Flory JD. Biological predictors of insulin resistance associated with posttraumatic stress disorder in young military veterans. Psychoneuroendocrinology (2017.0) 82 91-97. DOI: 10.1016/j.psyneuen.2017.04.016 45. 45.Barbot M, Zilio MScaroni C. (2020) Cushing's syndrome: overview of clinical presentation, diagnostic tools and complications, Best Pract Res Clin Endocrinol Metab 34: 101380. 10.1016/j.beem.2020.101380. 46. 46.Uehara M, Yamazaki H, Yoshikawa N, Kuribara-Souta ATanaka H. (2020) Correlation among body composition and metabolic regulation in a male mouse model of Cushing's syndrome, Endocr J 67: 21-30. 10.1507/endocrj.EJ19-0205 47. Cheng L, Wang J, Dai H, Duan Y, An Y. Brown and beige adipose tissue: a novel therapeutic strategy for obesity and type 2 diabetes mellitus. Adipocyte (2021.0) 10 48-65. DOI: 10.1080/21623945.2020.1870060 48. 48.Ikeda K, Maretich P, Kajimura S. (2018) The common and distinct features of Brown and Beige adipocytes, Trends Endocrinol Metab 29: 191-200. 10.1016/j.tem.2018.01.001 49. Vitali A, Murano I, Zingaretti MC, Frontini A, Ricquier D. The adipose organ of obesity-prone C57BL/6J mice is composed of mixed white and brown adipocytes. J Lipid Res (2012.0) 53 619-629. DOI: 10.1194/jlr.M018846 50. 50.Ravussin Y, Xiao C, Gavrilova OReitman ML. (2014) Effect of intermittent cold exposure on brown fat activation, obesity, and energy homeostasis in mice, PLoS One 9: e85876. 10.1371/journal.pone.0085876. 51. 51.Gavalda-Navarro A, Villarroya J, Cereijo R, Giralt M, Villarroya F. (2022) The endocrine role of brown adipose tissue: An update on actors and actions, Rev Endocr Metab Disord 23: 31-41. 10.1007/s11154-021-09640-6 52. 52.Kong X, Yao T, Zhou P, Kazak L, Tenen D, et al. (2018) Brown adipose tissue controls skeletal muscle function via the secretion of myostatin, Cell metabolism 28: 631–643 e633. 10.1016/j.cmet.2018.07.004. 53. Hondares E, Iglesias R, Giralt A, Gonzalez FJ, Giralt M. Thermogenic activation induces FGF21 expression and release in brown adipose tissue. J Biol Chem (2011.0) 286 12983-12990. DOI: 10.1074/jbc.M110.215889 54. 54.Qing H, Desrouleaux R, Israni-Winger K, Mineur YS, Fogelman N, et al. (2020) Origin and function of stress-induced IL-6 in murine models, Cell 182: 372–387 e314. 10.1016/j.cell.2020.05.054. 55. Granata C, Caruana NJ, Botella J, Jamnick NA, Huynh K. High-intensity training induces non-stoichiometric changes in the mitochondrial proteome of human skeletal muscle without reorganisation of respiratory chain content. Nat Commun (2021.0) 12 7056. DOI: 10.1038/s41467-021-27153-3 56. Roca-Portoles ATait SWG.. Mitochondrial quality control: from molecule to organelle. Cell Mol Life Sci (2021.0) 78 3853-3866. DOI: 10.1007/s00018-021-03775-0 57. 57.Liesa M, Palacin MZorzano A. (2009) Mitochondrial dynamics in mammalian health and disease, Physiological reviews 89: 799-845. 10.1152/physrev.00030.2008 58. 58.Loson OC, Song Z, Chen H, Chan DC. (2013) Fis1, Mff, MiD49, and MiD51 mediate Drp1 recruitment in mitochondrial fission, Mol Biol Cell 24: 659-667. 10.1091/mbc.E12-10-0721 59. Palmer CS, Osellame LD, Laine D, Koutsopoulos OS, Frazier AE. MiD49 and MiD51, new components of the mitochondrial fission machinery. EMBO Rep (2011.0) 12 565-573. DOI: 10.1038/embor.2011.54 60. 60.Dubinin MV, Talanov EY, Tenkov KS, Starinets VS, Belosludtseva NV, et al. (2020) The effect of deflazacort treatment on the functioning of skeletal muscle mitochondria in duchenne muscular dystrophy, International journal of molecular sciences 21. 10.3390/ijms21228763. 61. 61.Sangwung P, Petersen KF, Shulman GIKnowles JW. (2020) Mitochondrial dysfunction, insulin resistance, and potential genetic implications, Endocrinology 161. 10.1210/endocr/bqaa017 62. Fisher-Wellman KH, Lin CT, Ryan TE, Reese LR, Gilliam LA. Pyruvate dehydrogenase complex and nicotinamide nucleotide transhydrogenase constitute an energy-consuming redox circuit. Biochem J (2015.0) 467 271-280. DOI: 10.1042/BJ20141447 63. Knebel B, Fahlbusch P, Dille M, Wahlers N, Hartwig S. Fatty liver due to increased de novo lipogenesis: alterations in the hepatic peroxisomal proteome. Front Cell Dev Biol (2019.0) 7 248. DOI: 10.3389/fcell.2019.00248 64. Reichmann H, Hoppeler H, Mathieu-Costello O, von Bergen F, Pette D. Biochemical and ultrastructural changes of skeletal muscle mitochondria after chronic electrical stimulation in rabbits. Pflugers Arch (1985.0) 404 1-9. DOI: 10.1007/BF00581484 65. 65.Franko A, Baris OR, Bergschneider E, von Toerne C, Hauck SM, et al. (2013) Efficient isolation of pure and functional mitochondria from mouse tissues using automated tissue disruption and enrichment with anti-TOM22 magnetic beads, PLoS One 8: e82392. 10.1371/journal.pone.0082392. 66. Mitsui T, Azuma H, Nagasawa M, Iuchi T, Akaike M. Chronic corticosteroid administration causes mitochondrial dysfunction in skeletal muscle. J Neurol (2002.0) 249 1004-1009. DOI: 10.1007/s00415-002-0774-5 67. Mitsui T, Umaki Y, Nagasawa M, Akaike M, Aki K. Mitochondrial damage in patients with long-term corticosteroid therapy: development of oculoskeletal symptoms similar to mitochondrial disease. Acta Neuropathol (2002.0) 104 260-266. DOI: 10.1007/s00401-002-0553-5 68. Romanello V, Guadagnin E, Gomes L, Roder I, Sandri C. Mitochondrial fission and remodelling contributes to muscle atrophy. EMBO J (2010.0) 29 1774-1785. DOI: 10.1038/emboj.2010.60 69. Gnaiger E. Capacity of oxidative phosphorylation in human skeletal muscle: new perspectives of mitochondrial physiology. Int J Biochem Cell Biol (2009.0) 41 1837-1845. DOI: 10.1016/j.biocel.2009.03.013 70. Ashcroft SP, Fletcher G, Philp AM, Jenkinson C, Das S. Diet-induced vitamin D deficiency reduces skeletal muscle mitochondrial respiration. J Endocrinol (2021.0) 249 113-124. DOI: 10.1530/JOE-20-0233 71. Roussel D, Dumas JF, Augeraud A, Douay O, Foussard F. Dexamethasone treatment specifically increases the basal proton conductance of rat liver mitochondria. FEBS Lett (2003.0) 541 75-79. DOI: 10.1016/s0014-5793(03)00307-7 72. 72.Hirschenson J, Melgar-Bermudez EMailloux RJ. (2022) The uncoupling proteins: a systematic review on the mechanism used in the prevention of oxidative stress, Antioxidants (Basel) 11. 10.3390/antiox11020322. 73. Wikstrom M, Springett R. Thermodynamic efficiency, reversibility, and degree of coupling in energy conservation by the mitochondrial respiratory chain. Commun Biol (2020.0) 3 451. DOI: 10.1038/s42003-020-01192-w 74. Stucki JW. The optimal efficiency and the economic degrees of coupling of oxidative phosphorylation. Eur J Biochem (1980.0) 109 269-283. DOI: 10.1111/j.1432-1033.1980.tb04792.x 75. 75.Amador A, Campbell S, Kazantzis M, Lan G, Burris TP, et al. (2018) Distinct roles for REV-ERBalpha and REV-ERBbeta in oxidative capacity and mitochondrial biogenesis in skeletal muscle, PLoS One 13: e0196787. 10.1371/journal.pone.0196787. 76. Wilson FH, Hariri A, Farhi A, Zhao H, Petersen KF. A cluster of metabolic defects caused by mutation in a mitochondrial tRNA. Science (2004.0) 306 1190-1194. DOI: 10.1126/science.1102521 77. Yang L, Guo Q, Leng J, Wang K, Ding Y. Late onset of type 2 diabetes is associated with mitochondrial tRNA(Trp) A5514G and tRNA(Ser(AGY)) C12237T mutations. J Clin Lab Anal (2022.0) 36 e24102. DOI: 10.1002/jcla.24102 78. Feng S, Wan S, Liu S, Wang W, Tang M. LARS2 regulates apoptosis via ROS-mediated mitochondrial dysfunction and endoplasmic reticulum stress in ovarian granulosa cells. Oxid Med Cell Longev (2022.0) 2022 5501346. DOI: 10.1155/2022/5501346
--- title: Addressing Financial Barriers to Health Care Among People Who are Low-Income and Insured in New York City, 2014–2017 authors: - Taylor L. Frazier - Priscilla M. Lopez - Nadia Islam - Amber Wilson - Katherine Earle - Nerisusan Duliepre - Lynna Zhong - Stefanie Bendik - Elizabeth Drackett - Noel Manyindo - Lois Seidl - Lorna E. Thorpe journal: Journal of Community Health year: 2022 pmcid: PMC10060328 doi: 10.1007/s10900-022-01173-6 license: CC BY 4.0 --- # Addressing Financial Barriers to Health Care Among People Who are Low-Income and Insured in New York City, 2014–2017 ## Abstract While health care-associated financial burdens among uninsured individuals are well described, few studies have systematically characterized the array of financial and logistical complications faced by insured individuals with low household incomes. In this mixed methods paper, we conducted 6 focus groups with a total of 55 residents and analyzed programmatic administrative records to characterize the specific financial and logistic barriers faced by residents living in public housing in East and Central Harlem, New York City (NYC). Participants included individuals who enrolled in a municipal community health worker (CHW) program designed to close equity gaps in health and social outcomes. Dedicated health advocates (HAs) were explicitly paired with CHWs to provide health insurance and health care navigational assistance. We describe the needs of 150 residents with reported financial barriers to care, as well as the navigational and advocacy strategies taken by HAs to address them. Finally, we outline state-level policy recommendations to help ameliorate the problems experienced by participants. The model of paired CHW–HAs may be helpful in addressing financial barriers for insured populations with low household income and reducing health disparities in other communities. ### Supplementary Information The online version contains supplementary material available at 10.1007/s10900-022-01173-6. ## Introduction Largely due to economic exploitation and trauma inflicted systematically over centuries in the United States,[1] Black people and other people of color have been prevented from accumulating wealth and entering higher-income occupations in the same ways as white Americans. These inequitable systems persist in the form of social, economic, and cultural barriers to accessing health and social services.[2–5] The health care delivery and financing systems are particularly suboptimal, even punitive, for those with low household incomes.[6] While some research has assessed financial barriers for uninsured individuals with low household incomes,[7–9] financial barriers to health care among insured people with low household incomes, and the strategies to ameliorate them, have not been as well characterized. A limited number of studies have shown that insured families ineligible for Medicaid but still considered to have low household incomes struggle disproportionately with unaffordable co-pays, deductibles, and prescription drug costs.[10–22] However, it is often the case that communities where a high proportion of households live below the federal poverty level (herein, “low-income communities”), and in which a majority of members have Medicaid or other health insurance, have limited access to information about how to obtain free or discounted medical care and other support to help with out-of-pocket medical expenses.[23] These factors contribute to underutilization of medical care and unmet health needs, potentially leading to lower quality of life, stress, depression, and an increased likelihood of emergency department visits and hospitalization.[10–22]. This article describes financial barriers to health care experienced by public housing residents with low household incomes and health insurance in New York City (NYC), using data from participants enrolled in Harlem Health Advocacy Partners (HHAP).[24] Established by the NYC Department of Health and Mental Hygiene (herein, “NYC Health Department”) in 2014, HHAP is a place-based community health worker (CHW) and health advocate (HA) initiative that aims to close equity gaps in health and social outcomes between NYCHA residents in East and Central Harlem and other New Yorkers through health coaching, health navigation, group wellness activities, increased community awareness, and advocacy.[24] Trained HAs were paired with CHWs to provide health care navigational assistance to help residents find, understand, and use affordable/low-cost health insurance and health care. CHWs addressed specific health needs, supported health goal-setting and behavior changes, and addressed social determinants of health. In this paper, we aim to: [1] characterize self-reported financial barriers to health care experienced by insured public housing residents, [2] describe strategies HAs used to assist clients in overcoming financial and related barriers, and [3] highlight broader strategies and policy approaches for ameliorating common financial barriers to accessing health care services for insured individuals with low household incomes. ## Intervention Led by the NYC Health Department, HHAP was developed as a multi-stakeholder partnership with the New York University-City University of New York Prevention Research Center (NYU-CUNY PRC), the Community Service Society of New York (CSS), and the New York City Housing Authority (NYCHA).[24] HHAP is guided by a health equity framework and the evidence supporting the use of community-based CHWs to improve health outcomes.[425–28] HHAP CHWs engage residents with asthma, diabetes, hypertension, and other health conditions, to improve disease management and achieve health goals. CHWs provide health coaching activities, community events, and advocacy training.[24] HHAP strives to hire CHWs who live in public housing and/or the East and Central Harlem neighborhoods to create authentic connections to the community served and a workforce development pipeline through full-time employment. A novel feature of the HHAP model combines CHW assistance with that of HAs, who have technical expertise in health insurance and health system navigation.[24] This component was conceived in response to feedback from the community and evidence in the published literature that many insured residents in low-income communities face health care navigation challenges.[2930] The HA component allows CHWs to refer residents to HAs when there are insurance-related obstacles to achieving their health goals. HAs use their knowledge of both public and private insurance systems and the local health care provision landscape to help residents understand their insurance options, obtain affordable health care, resolve billing issues, and overcome other barriers to getting the health care they need (Fig. 1).[24] CHWs work one-on-one with residents to achieve their health goals and HAs use their specialized training in health system navigation and insurance policy to mitigate barriers to achieving those goals.[24]. Fig. 1Model of Integrated Services Provided to New York City Public Housing Residents through the Harlem Health Advocacy Partners (HHAP) Initiative At HHAP’s inception, CSS was contracted to provide HA services to the program. The history of CSS’s Health Advocates can be traced back to 1998 when the NYC Council provided funding to CSS to establish the Managed Care Consumer Assistance Program in partnership with 26 community-based organizations (CBOs). This program was New York’s first health care Consumer Assistance Program (CAP) and a precursor to federal CAPs, which were authorized in Sect. 1002 of the Patient Protection and Affordable Care Act. CAPs educate consumers about coverage options, enroll consumers into coverage, inform consumers of their rights and responsibilities, file grievances with insurance plans and regulators, and report back to policy makers about problems with existing programs and regulations. CSS has operated New York State’s (NYS’s) CAP, Community Health Advocates (CHA), since 2010. CHA, currently funded by the NYS State Legislature and the NYS Department of Health, is one of the largest CAPs in the country with a live-answer Helpline and a network of 27 CBOs. The inclusion of HA services provides assistance to clients whose health goals are hampered by financial barriers related to their health care and coverage, particularly in light of the subsequent reductions in federal funding for state-based CAPs, which began in 2017.[31]. Because HHAP is place-based, it eliminates the burden of traveling to receive health insurance enrollment or other post-enrollment assistance for clients. HHAP participants can meet with an HA in their homes, a community center, or neighborhood health action center. Unlike other place-based initiatives that train CHWs to identify a client’s issue and refer them to other services for resolution, HAs focus on sustaining relationships with CHWs and clients through advocacy and addressing barriers to health care, while also expanding health insurance literacy.[32–37] Specifically, HAs help residents understand their insurance options, obtain affordable health care, resolve billing issues, and overcome other barriers to getting needed health services (Fig. 2).[24] Some insurance navigation issues can be addressed in a single visit. Other issues require multiple conversations with residents and additional action by HAs. Fig. 2Breakdown of Resolutions by Type of Financial Barrier ## Data Sources Data for this analysis were drawn from two data sources used to conduct formative and program evaluations for HHAP: focus groups done in the planning phase of HHAP and administrative data record review from the first four years of HHAP. At the outset of the project, the NYU-CUNY PRC team conducted focus groups in December 2014 among HHAP-eligible NYCHA residents.[38] Focus group participants were required to meet the following criteria: [1] speak English or Spanish; [2] be aged 35 to 65 years; [3] have a self-reported diagnosis or family history of diabetes, asthma, and/or hypertension; and [4] reside in one of five intervention housing developments. Focus groups lasted approximately 1.5 to 2 h; six focus groups were completed with a total of 55 residents.[38] Discussion topics included barriers and facilitators to disease management, engaging in healthy behaviors, preferred methods of health education and promotion, and acceptability of and motivation to participate in a CHW intervention.[38]. The second data source focused specifically on HHAP participants who were referred to CSS for HA assistance. We reviewed CSS administrative case record notes and compiled case studies from September 1, 2014 through September 30, 2017 to provide further context for problems faced by CSS clients reporting financial barriers to care and the process by which HAs resolved these problems. Within this timeframe, HHAP HAs assisted 591 public housing clients, with 150 individuals ($25\%$) who had insurance reporting financial barriers to care. We identified 244 financial barriers among the 150 clients, with clients having the option to report multiple barriers. ## Data Analysis Recorded focus group sessions were transcribed, and a codebook was developed using the moderator guide as an initial outline of primary codes, followed by secondary and tertiary codes. Three independent coders reviewed and analyzed transcripts through thematic analysis, following a constant comparative approach, discussing and resolving discrepancies until an acceptable level of inter-coder reliability had been established.[39] Atlas.ti 6 was used to code and analyze the data.[3940] Findings related to barriers and facilitators to access to care were highlighted for this paper. Data analysis from the CSS administrative record review was conducted on client cases reporting financial barriers to care. Each CSS client case was coded into one of six categories of financial barriers: [1] Affordability; [2] Outstanding Bills; [3] Non-covered Benefits; [4] Billing Errors; [5] Service Denials; and [6] Eligibility. These codes were created based on major themes presented in the data. Affordability barriers involve cost-sharing, such as deductibles, co-insurance, co-pays, or premiums that prohibit individuals from accessing care. Outstanding Bills include medical bills indicating a balance that clients are responsible for paying. Non-Covered Benefits include services not covered by insurance and cases in which plan benefits were exhausted. Billing Errors include bills sent to clients erroneously, either due to health insurance system or provider error. Service Denials include cases in which clients sought to appeal or otherwise contest pre-authorization or service coverage denials from their health insurer. Eligibility includes the inability to enroll into supplemental health insurance. Resolutions for each financial barrier were coded into one of four categories: [1] Resolved with HA assistance, indicating that an HA directly resolved the issue or provided information towards resolution); [2] Resolved without HA assistance, indicating cases resolved by the client or an administrative body without HA advocacy; [3] Unresolved due to issues outside the scope of HAs, such as an unmet eligibility requirement or a plan contract; and [4] Unresolved due to loss to follow up. HAs participate in weekly case review sessions, and upon closure, cases undergo a quality assurance process to ensure all options available to address the client’s needs were explored. Descriptive analysis was conducted of the codes generated from the CSS database to characterize the proportion and type of barrier experienced by clients as well as the proportion and type of resolutions. In addition to this descriptive analysis, we conducted a content analysis of CSS case files to generate case studies of financial barriers to care and resolution processes and strategies. ## Characterization of Barriers to Health Care Among East and Central Harlem NYCHA Residents During focus groups to explore barriers and facilitators to disease management, residents were asked questions about care management and financial barriers to care. Participants were mostly female ($82\%$), with an average age of 58 years. Participants described affordability issues related to their insurance coverage as a significant barrier to chronic disease management and quality of life. With me, it seems like my copayments are going up higher and higher even though I am taking the same pill. The medicine is just costing more. I don’t know if [it’s] like the insurance I have. You just have to accept it because that is the policy. I feel like each one has a[n] “I am going to get you” part in it. I worked my entire life and then some, and now I have to pay a phenomenal amount of money just because I went to the hospital…. Many participants with low or fixed incomes cited outstanding bills and difficulty covering premiums, co-pays, and other out-of-pocket expenses. Sometimes the [insurance] doesn’t cover the diabetic medication. So, some people can’t get the medication because they are on a fixed income. Participants also expressed frustration with navigating health coverage policies and lack of coordination or consistency among providers, insurers, and pharmacies. I had a doctor that I had for 10 years and he knew me, but when they started this HMO thing, I keep getting a new doctor every 6 months. I don’t get a chance to know my doctor. It’s trying to find the right doctor that can use your insurance. I continue to move until I find someone that I am comfortable [with]. I’ve changed so many times; I’m just used to changing because I want to find one that is comfortable. They don’t give you enough time to know the doctor before he or she is gone. Participants were particularly distressed by the financial consequences of this lack of coordination. A lot of the insurance - they do not accept things like the cuff. They want me to pay out of pocket, which I can’t. It’s hard because you don’t know what the insurance pay[s] for and what it doesn’t pay for. The doctor says you need this, and you need that, and it is like how am I supposed to get it?If you are hospitalized and you need anesthesia, they are working on a team. And then they need your insurance, but then you find out, this guy [was] working on you that was not covered by the insurance. They need to know that certain doctors are covered. They shouldn’t have had him on the team in the first place. And then sometimes my high blood pressure medicine; they were paying for it. Then they said no and that I should be paying something else….I was out of medicine for 3 weeks. Then they give you approval for just one year and you have to do it again. ## Financial Barriers to Care Experienced by CSS Clients Of the 591 HHAP clients served between November 2014 and January 2017, 150 individuals ($25\%$) reported experiencing a financial barrier to care. Although the provision of demographic information was not required to receive HA services, $96\%$ ($$n = 144$$) of the 150 individuals who reported experiencing a financial barrier also reported some demographic information. Of those who reported demographic information, $74\%$ ($$n = 106$$) were female, $50\%$ ($$n = 58$$) identified as Hispanic, and $48\%$ ($$n = 56$$) identified as African American (Table 1). $63\%$ ($$n = 90$$) and $35\%$ ($$n = 51$$) reported speaking English and Spanish at home, respectively. Clients who experienced financial barriers to accessing care ($$n = 150$$) were more likely to be African American ($48\%$ vs. $33\%$) or Hispanic ($50\%$ vs. $40\%$) compared to those who did not experience financial barriers ($$n = 441$$; see Supplement). Table 1Demographics and insurance type among health advocate clients with financial barriers to care, harlem health advocacy partners (HHAP) initiative, sept. 1, 2014–sept. 30, 2017 ($$n = 150$$)DemographicsInsurance TypeMedicaidMedicareOtherN%N%N%N%Age Range 18–455548000120 46–64524917332752815 65 or older494636469400 Did not report44Gender Female106742826706688 Male38267182771411 Did not report6Race/Ethnicity African American56481323407135 Hispanic58501729376447 Other3326713300 Did not report33Language at Home English9063202260671011 Spanish51351529346724 Other320031000 Did not report6Chronic Condition (Self-Reported) No322212381547516 Yes112782220837476 Did not report6Household Income (Self-Reported) Less than 15 K63561930416535 $15,000-$25,0003632514287838 $25,001-$40,000111000982218 $40,001-$60,0003300133267 Did not report37Household Size 1584611194171610 24435920337525 311965554500 4+1310323646431Did not report24 $65\%$ ($$n = 97$$) of clients who experienced financial barriers were enrolled in Medicare through original Medicare or a Medicare Managed Care plan. Of the Medicare enrollees, $26\%$ ($$n = 39$$) were dual-eligible, with Medicaid as their secondary insurance. A total of $23\%$ ($$n = 34$$) of clients had Medicaid as their primary insurance ($17\%$ enrolled in a Medicaid Managed Care plan and $6\%$ enrolled in fee-for-service Medicaid). $6\%$ of clients ($$n = 9$$) had employer-sponsored insurance, $2\%$ ($$n = 3$$) were enrolled in the Essential Plan, the NYS Basic Health Program serving lower income and immigrant populations), and $4\%$ ($$n = 6$$) did not report insurance type. Clients who reported financial barriers experienced a total of 244 financial barriers (ranging from one to three barriers per client). Affordability barriers were the most common and were experienced by $48\%$ of the 150 clients with barriers. This was followed by outstanding bills ($37\%$), non-covered benefit barriers ($27\%$), billing errors ($24\%$), service denials ($15\%$), and eligibility issues ($12\%$). A total of $73\%$ of the 244 financial barriers were resolved by HAs, with at least one barrier (clients often experience more than one) resolved for $79\%$ ($$n = 118$$) of clients. For the remaining barriers, $13\%$ were unresolved due to issues outside the scope of HAs, $10\%$ were unresolved due to client loss to follow up, and $4\%$ were resolved without HA intervention (Fig. 2). ## Affordability Forty-eight percentage of clients experienced affordability barriers, which accounted for 72 of the 244 barriers experienced. Many insured clients had difficulty affording their monthly premiums and cost-sharing responsibilities, such as deductibles and co-pays. These affordability barriers were most often related to Medicare. HAs were able to resolve 49 of the 72 affordability barriers using various strategies. Premiums and cost-sharing were reduced for some Medicare enrollees through supplemental insurance programs like the Medicare Savings Program (MSP).[41] HAs also assisted residents who were ineligible for supplemental insurance. HAs often directed clients to HHC Options, which provides care on a sliding fee scale through NYC’s public hospital system or through alternative private philanthropy resources, like The New York Times (NYT) Neediest Cases Fund, as demonstrated through the case below.[42–44]A 72-year-old client with advanced stage periodontitis needed assistance from an HA to secure coverage for her procedures. The client’s Medicare only covered preventive dental services and her supplemental dental coverage only offered a $20\%$ discount on services. The client is the primary caregiver for her grandchildren and could not afford to pay the remaining costs. The HA helped her find a dentist who would treat her at a lower cost than any standalone dental insurance on the market could offer and helped her secure a NYT Neediest grant of $930 to pay for the procedure.. ## Outstanding Bills Thirty-seven percentage of clients experienced unexpected medical bills, which accounted for 56 of the 244 barriers experienced. Most bills experienced by residents could be categorized as cost-sharing bills, balance bills, hospital out-of-network emergency service bills, surprise bills (unexpected bills for out-of-network care received unbeknownst to patients), bills for non-covered services, bills for services denied on the basis of medical necessity, and bills for failure to request a prior authorization. HAs had to be well-versed in operational and legal intricacies of the health care and insurance systems and apply a multifaceted approach to solving outstanding bill issues. Because of the fragmented nature of health care coverage in the U.S., the HA’s decision on which strategy to use to achieve a positive outcome depended not only on the type of bill but also on the resident’s insurance type. An example of strategies used to resolve unexpected medical bills experienced by a client is described below. A client had over $14,000 in outstanding medical bills. The client had not worked since 2014 due to a work-related injury and had a pending workers’ compensation case. The coverage from the client’s former employer was still active, but the plan had unaffordable co-pays and a high deductible. The HA helped the client apply for Medicaid as secondary insurance through the NYS of Health Marketplace and resolved many of the bills through the retroactive approval of coverage. In Table 2, we summarized common billing issues experienced by HHAP clients, the strategies employed by HAs to address them, and broader policy recommendations to resolve underlying structural drivers of the recurring billing issues consumers encounter. Medical billing can be challenging for consumers to navigate without an understanding of the specific billing protections for the type of plan in which they are enrolled and that plan’s contract details. Robust consumer assistance programs are needed to ensure that plans adhere to billing protections and provide any mandated coverage to their members. A recent report indicated that “[e]ighteen percent of payment denials were for claims that met Medicare coverage rules and [Medicare Advantage Organization] billing rules. ”[45] Several of the issues identified in Table 2 are discussed in turn below. Table 2Common issues regarding outstanding bills, client strategies, and policy recommendationsIssueDefinitionAdvocacy strategiesState-level policy recommendationsHospital/provider cost-sharing billsBills from hospital or provider such as copayments, coinsurance, deductibles, or facility feesVerify debt or charges against client’s plan contract. If client is unable to pay:• Help client apply for Hospital Financial Assistance (HFA)• Negotiate with hospital/provider (e.g., find fair market price for service(s) performed, write financial hardship letter, offer lesser lump sum payment, and/or set up a payment plan)• Help Medicare-eligible clients enroll in the Medicare Savings ProgramIncrease price transparency to help patient avoid high-cost providers. Medicare requires members to pay $20\%$ coinsurance for most hospital/medical care. Standardize provider billing practices and issue only one bill for hospitalizations and proceduresClients with commercial insurance have higher cost-sharing than individuals enrolled into public insurance programs like Medicaid, Essential Plan and Child Health PlusProhibit providers from holding patients accountable for facility fees unrelated to medical services. Such fees should be negotiated between providers and insurersHospital/provider balance billsBalance billing is when a patient receives a bill for the difference between the amount paid by the health plan and the amount charged for servicesVerify hospital/provider has accurate insurance information. Determine if balance billing protections apply, such as:• Members of Medicaid and Qualified Medicaid Beneficiaries in a Medicare Savings program are protected against balance billing.• Hospitals that are part of an HMO network cannot balance bill commercial, fully-insured enrollees.• HAs can help negotiate balance bills if necessary (see above)Expand balance billing protections to protect patients who receive erroneous information from their provider or plan regarding in-network providersHospital out-of-network emergency services billsEmergency Room bills from out-of-network (OON) providers in an in-network or OON hospital• If client has a fully-insured commercial plan, they should be held harmless. If the plan does not comply, HA can help the client file a complaint with Department of Financial Services (DFS)• If client has a self-insured commercial plan verify if No Surprises Act protections apply. If not, help client participate in an Independent Dispute Resolution (IDR)• Help clients apply for hospital financial assistance or to negotiate down their medical billsProhibit OON billing for emergency services, defined to include all hospital, physician and ambulance charges, and any other pre-emergency services. Create an independent dispute resolution process for plans and providers, and prohibit all balance billing for emergency servicesSurprise billsA bill is a surprise bill if:• Bill is from an OON provider at an in-network hospital and either:• in-network doctor was not available;• client did not know an in-network physician provided services; or• unforeseen medical circumstance arose at time services were provided• A client is referred by an in-network provider to an OON provider and client is not aware the provider they were referred to was OON. Only applicable if plan requires referralsIf client has a fully-insured commercial plan, HAs help the client complete and submit an assignment of benefits (AOB) form to be held harmless for the billExtend balance billing protections to patients who receive false information about a provider’s inclusion in a network from either the provider or the plan. If the plan does not honor the AOB or mistakenly process the AOB as an appeal, file a complaint with DFSApply these protections in all instances when patients unknowingly receive care from an out-of-network providerIf client has a self-insured commercial plan or is uninsured, use same approach as Hospital out-of-network emergency services billsHospital/provider bills for non-covered servicesAll health insurance plans can deny coverage and bill for a service on the grounds that it is “not a covered benefit”, meaning that the service is excluded from the plan’s contractVerify that the plan is not violating any applicable federal or state laws by failing to cover the service. Expand coverage for preventative care services to include ultrasounds in lieu of mammograms for women with dense breast tissue. Review the plan contract. If the plan is not responsible to cover the service, help client apply for hospital financial assistance or negotiate the billExpand coverage of IVF and fertility services and improve dental coverage and benefitsHospital/provider bills for services denied on medical judgementBoth private and public health insurance plans can deny coverage for services deemed not meeting the plan’s clinical criteria, or if the service is from an OON provider and not materially different from the in-network serviceThe HA can help the client file an internal and/or external appeal• Fully-insured – Department of Financial Services (DFS) external appeal• Self-insured – Independent Review Organization (IRO) external appealProvide robust consumer assistance programs and include contact information for programs on all claim denialsIf the appeal is lost, help the client apply for hospital financial assistance or negotiate the billMaintain external appeals databases so that consumer and advocates can review past decisionsHospital/provider bills for services that need prior authorizationMany insurance plans require prior authorization before they will cover certain medical services or medications. Clients who fail to request prior authorization may be billed for servicesVerify the medical service requires prior authorization. A client may be held harmless when an in-network provider fails to obtain prior authorizationLimit the types of care that require pre-authorization. For example, New York State recently prohibited prior authorization for pediatric mental health hospitalizationsRequest that the provider submit prior authorization retroactively. If that is unsuccessful, help the client apply for hospital financial assistance or negotiate the billStates must provide independent consumer assistance programs to aid patients in navigating prior authorization requirements for care ## Non-Covered Benefits Twenty seven percentage of clients experienced non-covered benefits, accounting for 40 of the 244 barriers. HAs resolved $58\%$ of these barriers. The HAs educated clients about their rights when benefits were exhausted and helped them explore secondary and tertiary insurance options. For instance, HAs helped clients enroll in the NYS Elderly Pharmaceutical Insurance Coverage (EPIC) program, a supplemental insurance option for NYS residents who are 65 and older, enrolled in Medicare Part D drug coverage, and are unable to afford the cost of a necessary prescription medication or are caught in the annual Medicare “doughnut hole. ”[46]A client questioned why several of her medications were not covered by her insurance. Each prescription was either a Tier 2 or 3 drug, for a total monthly cost of $145. None of the prescriptions had cheaper generic alternatives, which the client preferred. The HA introduced the client to the EPIC program as an option. In EPIC, members may pay an annual fee ranging from $8 to $300 based on their income. After any Part D deductible is met, members then only pay the EPIC co-payment for drugs. Co-payments range from $3 to $20 based on the drug cost not covered by Part D. This client was eligible to pay $230 annually or $52.50 quarterly, allowing her to receive her prescriptions at a more affordable rate.. ## Billing Errors Billing errors were common among clients enrolled in both Medicare and Medicaid. They were experienced by $24\%$ of the clients in our sample and accounted for 36 of the 244 barriers reported. HAs resolved $94\%$ of these barriers, often by advocating with billing offices to correct errors by citing the Medicaid law that protects dual-eligible clients from balance billing and ensuring that claims are properly resubmitted. Billing errors also occurred when a client with a Medicaid spend-down was hospitalized; Medicaid limits the amount that hospitals can charge these patients. HAs advocated to ensure that clients were only held responsible for the allowable amount and negotiated that amount down further based on the client’s ability to pay. HHAP clients with Medicaid coverage often had billing errors related to out-of-state emergency medical care. Medicaid covers emergency care in all 50 states, but it can be difficult to get out-of-state providers to honor this provision. Through advocacy, HAs helped Medicaid clients resolve these out-of-state emergency bills. HAs also advocated on behalf of clients confronted with out-of-network surprise bills by using their knowledge of recently passed consumer protection laws, like NYS’s 2015 Surprise Bill Law. Trained to navigate complex and unique health insurance issues, HAs held billing offices accountable, while also educating clients about their rights, as demonstrated by the case described below. A client received a denial from his health insurance company claiming he had gone to an out-of-network provider for cataract surgery and would need to pay the out-of-network rate of $3,500. However, this provider was listed on his Explanation of Benefits as being in-network. The client worked with the HA to inquire into this discrepancy and request that the health insurance company review the determination, before filing an appeal of the decision. The insurance company resubmitted the claim, and the bill was ultimately covered as in-network. ## Service Denials Service denials for medically necessary treatments and procedures were experienced by $15\%$ of clients in our sample and accounted for 22 of the 244 barriers. These denials can significantly delay or derail medical care when a client does not have the technical expertise or advocacy support to appeal. HAs resolved $81\%$ of these denials. To resolve these types of issues, HAs identified errors in billing codes, submitted appeals in disputes over medically necessary procedures/services, and discovered administrative errors or unnecessary delays. Through appropriate channels and advocacy, HAs helped their clients obtain the care they needed, as demonstrated by the cases below. A client needed a special prosthetic for a knee surgery because of an ongoing infection. The client’s insurance refused to cover the alternative prosthetic. The HA investigated the case and determined that the provider had used an incorrect billing code. The HA helped the provider resubmit the authorization with the correct information and the client was able to get her surgery. A client’s 14-year-old son was denied braces by his Medicaid plan. The child was in a lot of pain and having trouble concentrating at school. The HA marshalled medical evidence from an orthodontist to support the client’s appeal of the insurance denial but lost. The HA subsequently helped the family secure a NYT Neediest Cases grant for $3,004, and the child was able to get his braces. ## Eligibility $12\%$ of clients experienced eligibility barriers, which accounted for 18 of the 244 barriers experienced. HAs resolved $78\%$ of these barriers. Some HHAP clients were eligible for but unable to enroll in health insurance that would cover necessary medical care. HAs helped clients overcome barriers to enrolling in coverage. Examples include clients who delayed Medicare enrollment and faced a late penalty, and clients who had Medicaid coverage with a surplus and thus were unable to enroll into a managed long-term care plan to cover homecare services. An example of an eligibility barrier and resolution that an HA provided is described further below:A client became eligible for Medicare after 24 months on disability, but she was unable to enroll into Medicare Part A because she was mistakenly being asked to pay a monthly premium of over $400. As an SSI recipient, the client’s eligibility to enroll should not have been contingent upon her ability to pay this premium. Through advocacy, her HA was able to remove this obstacle and enroll the client into Part A without monthly premiums. ## Discussion In this paper, we contribute to a limited body of literature to gain a more in-depth understanding of health care-associated financial barriers faced by insured individuals with low household incomes using qualitative and administrative data.[41747] We also describe an innovative, place-based model to ameliorate these barriers. Focus group data revealed residents to be deeply frustrated with the lack of coordination in the health care system and often unable to afford cost-sharing for health care services.[38] Our analysis of administrative data identified several categories of financial barriers faced by HHAP clients in accessing care and outlined strategies HAs used to resolve the majority of the most frequently reported financial barriers to health care. Clients whose issues could not be resolved were informed of the reasons why their barrier could not be addressed. HAs educate patients about both their rights and the limitations of those rights in the health care system, a key component of health care literacy. In the HHAP initiative, HAs employed direct navigation and advocacy methods while also addressing health care literacy, rather than using passive referral methods.[48–50] HAs must be highly trained on the complexities of the health care system and its programs, as well as resources and tools to resolve clients’ financial barriers. Their core strategies included helping residents apply for hospital financial assistance or supplemental insurance programs, enforcing consumer protection laws, and appealing service denials. For certain financial barriers, such as outstanding medical bills, HAs had to resort to a wider arsenal of advocacy tools to help clients eliminate or reduce the amount they owed. HA decisions on which advocacy tool to use and outcomes of HA intervention depended heavily on the resident’s insurance type. This was common across nearly all financial barriers cases and highlights one of the major challenges faced by broad-based health care literacy initiatives. For instance, residents enrolled in public programs like Medicaid, Child Health Plus, and the Essential Plan were less likely to face affordability barriers and cost-sharing bills because these types of insurance do not charge deductibles or high co-payments for services. In general, Medicaid enrollees were more protected from financial barriers such as balance billing than residents enrolled in Medicare or commercial health insurance products because providers are not allowed to charge a difference between the amount paid by a Medicaid plan and the amount charged by the provider.[10–22] Lack of financial consumer protections and affordable cost-sharing options were the main reasons residents enrolled in Medicare reported more financial barriers. HAs thus tried to help senior residents reduce their Medicare premiums and cost-sharing by enrolling them in Medicaid or the Medicare Saving Program. However, income limits for some of these programs are so low that even clients at $101\%$ of the Federal Poverty Level were not eligible to enroll. While this study demonstrates the valuable role that HAs can play in ameliorating financial barriers, some financial barriers remained intractable. Medicare enrollees ineligible for supplemental insurance plans and other cost-sharing reductions such as the pharmaceutical discount can have significant out-of-pocket expenses associated with their health care. Several benefits are not covered by Medicare—for example, dental and homecare—and raise additional affordability concerns. Many clients eligible for Medicaid with a surplus are often unable to pay the amount required to activate their Medicaid coverage. Facility fees are another health care cost imposed by providers, but sometimes not covered by insurance carriers, resulting in unexpected and unresolvable costs for consumers. Additionally, generations of economic injustice have created and perpetuated a cycle of financial disadvantage that underpins many poor health outcomes. These are reproduced and reinforced by interconnected discriminatory policies and systems of power. HAs advocate for their clients within the confines of the existing health care infrastructure where gaps in coverage persist in areas such as dental and vision care, homecare, and prescription drugs.[17] High deductible health insurance plans and other significant cost-sharing also prohibit many insured people from accessing care, especially when they are ineligible for affordable supplemental insurance options.[817]. This study has some limitations. The administrative data were based on clients’ self-reported financial barriers to health care. Due to the lack of a controlled comparison group, it was not feasible to compare the resolution of financial barriers for clients assisted by HAs to those who did not receive any outside assistance or those who were assisted by other means. However, during our observation period, we observed a modest percentage of resolutions achieved without HA intervention ($4\%$ of all identified financial barriers). ## Conclusion This study describes the financial barriers to health care faced by insured public housing residents in East Harlem, but these barriers are experienced more broadly by Black people and other people of color throughout the United States. We highlight the role that HAs play in ensuring that individuals not only have health insurance, but that they understand and are able to use their health insurance to get the care they need. Based on our experience, we recommend that CHW models incorporate HAs to ensure that health system navigation can be seamlessly integrated and that participants can effectively utilize all aspects of their insurance. In addition to addressing financial barriers, HAs help bridge community-clinical linkages, and reduce health disparities experienced by public housing residents, all of which have implications toward achieving health equity. Experimental or quasi-experimental models to test this partnership model against CHW-only service provision would provide valuable empirical evidence. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 14.5 kb) ## References 1. Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet (2017.0) 389 1453-1463. DOI: 10.1016/s0140-6736(17)30569-x 2. 2.Wilkinson, R., Marmot, M., & World Health OrganizationSocial determinants of health: The solid facts20032World Health Organization. Social determinants of health: The solid facts (2003.0) 3. Marmot M. Social determinants of health inequalities. Lancet (2005.0) 365 1099-1104. DOI: 10.1016/S0140-6736(05)71146-6 4. Marmot M, Friel S, Bell R, Houweling TA, Taylor S. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet (2008.0) 372 1661-1669. DOI: 10.1016/s0140-6736(08)61690-6 5. Braveman P. What are health disparities and health equity? We need to be clear. Public Health Reports (2014.0) 129 5-8. DOI: 10.1177/00333549141291s203 6. Purnell TS, Calhoun EA, Golden SH, Halladay JR, Krok-Schoen JL, Appelhans BM, Cooper LA. Achieving health equity: Closing the gaps in health care disparities, interventions, and research. Health Aff (Millwood) (2016.0) 35 1410-1415. DOI: 10.1377/hlthaff.2016.0158 7. Galbraith AA, Wong ST, Kim SE, Newacheck PW. Out-of-pocket financial burden for low-income families with children: socioeconomic disparities and effects of insurance. Health Services Research (2005.0) 40 1722-1736. DOI: 10.1111/j.1475-6773.2005.00421.x 8. Baird K. High out-of-pocket medical spending among the poor and elderly in nine developed countries. Health Services Research (2016.0) 51 1467-1488. DOI: 10.1111/1475-6773.12444 9. Magge H, Cabral HJ, Kazis LE, Sommers BD. Prevalence and predictors of underinsurance among low-income adults. Journal of General Internal Medicine (2013.0) 28 1136-1142. DOI: 10.1007/s11606-013-2354-z 10. Karpati A, Kerker B, Mostashari F. Health disparities in New York City (2004.0) 11. Myers C, Olson C, Kerker B, Thorpe L, Greene C, Farley T. Reducing health disparities in New York City: Health disparities in life expectancy and death (2010.0) 12. 12.Health Outreach Partners (2013, November 1). Understanding health concerns and barriers to accessing care among underserved populationshttps://outreach-partners.org/2013/11/01/understanding-health-concerns-and-barriers-to-accessing-care-among-underserved-populations/ 13. Kushel MB, Gupta R, Gee L, Haas JS. Housing instability and food insecurity as barriers to health care among low-income Americans. Journal of General Internal Medicine (2006.0) 21 71-77. DOI: 10.1111/j.1525-1497.2005.00278.x 14. Heisler M, Choi H, Rosen AB, Vijan S, Kabeto M, Langa KM, Piette JD. Hospitalizations and deaths among adults with cardiovascular disease who underuse medications because of cost: A longitudinal analysis. Medical Care (2010.0) 48 87-94. DOI: 10.1097/MLR.0b013e3181c12e53 15. Campbell DJ, King-Shier K, Hemmelgarn BR. Self-reported financial barriers to care among patients with cardiovascular-related chronic conditions. Health Reports (2014.0) 25 3-12. PMID: 24850391 16. 16.Health Outreach Partners (2014). Outreach across populations: 2013 national needs assessment of health outreach programs (5th Ed.). https://outreach-partners.org/wp-content/uploads/2015/09/2014_nna.pdf 17. Devoe JE, Baez A, Angier H, Krois L, Edlund C, Carney PA. Insurance + access not equal to health care: Typology of barriers to health care access for low-income families. Annals of Family Medicine (2007.0) 5 511-518. DOI: 10.1370/afm.748 18. McCanne D. Emphasis Type="Italic">What do insured patients see as financial burdens on health care? (2015.0) 19. Ahmed SM, Lemkau JP, Nealeigh N, Mann B. Barriers to healthcare access in a non-elderly urban poor American population. Health & Social Care in the Community (2001.0) 9 445-453. DOI: 10.1046/j.1365-2524.2001.00318.x 20. Karpman M, Long SK. QuickTake: Even with coverage, many adults have problems getting health care, with problems most prevalent among adults with disabilities. Health Reform Monitoring Survey (2015.0) 21. Malmgren JA, Martin ML, Nicola RM. Health care access of poverty-level older adults in subsidized public housing. Public Health Reports (1996.0) 111 260-263. PMID: 8643819 22. Beckman AL, Bucholz EM, Zhang W. Sex differences in financial barriers and the relationship to recovery after acute myocardial infarction. Journal of American Heart Association (2016.0) 5 e003923. DOI: 10.1161/jaha.116.003923 23. Lazar M, Davenport L. Barriers to health care access for low income families: A review of literature. Journal of Community Health Nursing (2018.0) 35 28-37. DOI: 10.1080/07370016.2018.1404832 24. Thorpe LE, Greene C, Freeman A. Rationale, design and respondent characteristics of the 2013–2014 New York City Health and Nutrition Examination Survey (NYC HANES 2013–2014). Preventive Medicine Reports (2015.0) 2 580-585. DOI: 10.1016/j.pmedr.2015.06.019 25. Carey G, Crammond B, De Leeuw E. Towards health equity: A framework for the application of proportionate universalism. International Journal of Equity Health (2015.0) 14 81. DOI: 10.1186/s12939-015-0207-6 26. 26.Allen, C. (2015). Addressing chronic disease through community health workers: A policy and systems-level approach, Second edition [Policy Brief]. National Center for Chronic Disease Prevention and Health Promotion. Retrieved from https://www.cdc.gov/dhdsp/docs/chw_brief.pdf 27. 27.American Public Health Association (2009). Support for community health workers to increase health access and to reduce health inequities [Policy Brief]. Retrieved from https://www.apha.org/policies-and-advocacy/public-health-policy-statements/policy-database/2014/07/09/14/19/support-for-community-health-workers-to-increase-health-access-and-to-reduce-health-inequities 28. Islam N, Nadkarni S, Peretz P. Integration of community health workers into primary care health systems: The time for New York is now! (2016.0) 29. Powell RE, Doty A, Casten RJ, Rovner BW, Rising KL. A qualitative analysis of interprofessional healthcare team members’ perceptions of patient barriers to healthcare engagement. Bmc Health Services Research (2016.0) 16 493. DOI: 10.1186/s12913-016-1751-5 30. Pieh-Holder KL, Callahan C, Young P. Qualitative needs assessment: healthcare experiences of underserved populations in Montgomery County, Virginia, USA. Rural And Remote Health (2012.0) 12 1816. PMID: 22812680 31. 31.Pollitz, K., Tolbert, J., & Diaz, M. (2018). Data note: Further reductions in navigator funding for federal marketplace states.Kaiser Family Foundation. https://files.kff.org/attachment/Data-Note-Further-Reductions-in-Navigator-Funding-for-Federal-Marketplace-States 32. Brown N, Vaughn NA, Lin AJ, Browne R, White M, Smith P. Healthy families Brooklyn: Working with health advocates to develop a health promotion program for residents living in New York City housing authority developments. Journal of Community Health (2011.0) 36 864-873. DOI: 10.1007/s10900-011-9388-0 33. Bowen DJ, Bhosrekar SG, Rorie JA, Goodman R, Thomas G, Maxwell NI, Smith E. Resident health advocates in public housing family developments. Family and Community Health (2015.0) 38 141-148. DOI: 10.1097/fch.0000000000000068 34. Willging CE, Sommerfeld DH, Jaramillo ET. Improving Native American elder access to and use of health care through effective health system navigation. BMC Health Services Research (2018.0) 18 464. DOI: 10.1186/s12913-018-3182-y 35. Beverly EA, Hamel-Lambert J, Jensen LL, Meeks S, Rubin A. A qualitative process evaluation of a diabetes navigation program embedded in an endocrine specialty center in rural Appalachian Ohio. BMC Endocrine Disorders (2018.0) 18 50. DOI: 10.1186/s12902-018-0278-7 36. Rogers C, Johnson J, Nueslein B, Edmunds D, Valdez RS. “I love fruit but I can’t afford it”: Using participatory action research to develop community-based initiatives to mitigate challenges to chronic disease management in an African American community living in public housing. Journal of Racial and Ethnic Health Disparities (2018.0). DOI: 10.1007/s40615-018-0480-3 37. Freeman HP. The history, principles, and future of patient navigation: Commentary. Seminars in Oncology Nursing (2013.0) 29 72-75. DOI: 10.1016/j.soncn.2013.02.002 38. 38.Feinberg, A. S., Levanon, L., Pinzon, S. A., J., et al. (December 2015). Launching a neighborhood-based community health worker initiative: Harlem Health Advocacy Partner (HHAP) community needs assessment. New York, NY: NYU-CUNY Prevention Research Center, New York City Department of Health and Mental Hygiene, New York City Housing Authority, and Community Service Society. 39. Shrout PE, Fleiss JL. Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin (1979.0) 86 420-428. DOI: 10.1037/0033-2909.86.2.420 40. Riley L, Mili S, Trinh-Shevrin C, Islam N. Using qualitative methods to understand physical activity and weight management among Bangladeshis in New York City, 2013. Preventing Chronic Disease (2016.0) 13 E87. DOI: 10.5888/pcd13.160077 41. Chatre C, Roubille F, Vernhet H, Jorgensen C, Pers YM. Cardiac complications attributed to chloroquine and hydroxychloroquine: A systematic review of the literature. Drug Safety (2018.0) 41 919-931. DOI: 10.1007/s40264-018-0689-4 42. 42.New York City Health and Hospitals (2005). Health and Hospitals Corporation optionshttp://www.cphsnyc.org/cphs/reports/december_2005-_hhc_options/http__cphsnyc_org_pdf_HHCoptions.pdf 43. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science (2009.0) 4 50. DOI: 10.1186/1748-5908-4-50 44. Thorpe LE, Kanchi R, Chamany S. Change in diabetes prevalence and control among New York City adults: NYC Health and Nutrition Examination Surveys 2004–2014. Journal of Urban Health: Bulletin of the New York Academy of Medicine (2018.0) 95 826-831. DOI: 10.1007/s11524-018-0285-z 45. 45.Grimm, C. A. (2022). Some Medicare Advantage Organization denials of prior authorization requests raise concerns about beneficiary access to medically necessary care [OEI-09-18-00260]. U.S. Department of Health and Human Services Office of Inspector General. https://oig.hhs.gov/oei/reports/OEI-09-18-00260.pdf. 46. 46.New York State Department of Health (2018, January). Elderly Pharmaceutical Insurance Coverage (EPIC) Programhttps://www.health.ny.gov/health_care/epic/ 47. King L, Hinterland K, Dragan KL. Community health profiles 2015, manhattan community district 11: East Harlem. Community Health Profiles (2015.0) 11 1-16 48. Grob R, Schlesinger M. Educating, enrolling, and engaging: The state of marketplace consumer assistance under the Affordable Care Act. Health Affairs (2015.0) 34 2052-2060. DOI: 10.1377/hlthaff.2015.0662 49. 49.Tracy, C., & Benjamin, E. (September 2010). Making health reform work: State consumer assistance programs. Community Service Society of New York. http://www.cssny.org/publications/entry/making-health-reform-workSept2010. 50. Glick JL, Andrinopoulos KM, Theall KP, Kendall C. Tiptoeing around the system”: Alternative healthcare navigation among gender minorities in New Orleans. Transgender Health (2018.0) 3 118-126. DOI: 10.1089/trgh.2018.0015
--- title: Modulation of the endoplasmic reticulum stress and unfolded protein response mitigates the behavioral effects of early-life stress authors: - Anna Solarz-Andrzejewska - Iwona Majcher-Maślanka - Joanna Kryst - Agnieszka Chocyk journal: Pharmacological Reports year: 2023 pmcid: PMC10060333 doi: 10.1007/s43440-023-00456-6 license: CC BY 4.0 --- # Modulation of the endoplasmic reticulum stress and unfolded protein response mitigates the behavioral effects of early-life stress ## Abstract ### Background Early-life stress (ELS) affects brain development and increases the risk of mental disorders associated with the dysfunction of the medial prefrontal cortex (mPFC). The mechanisms of ELS action are not well understood. Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) are cellular processes involved in brain maturation through the regulation of pro-survival or proapoptotic processes. We hypothesized that ER stress and the UPR in the mPFC are involved in the neurobiology of ELS. ### Methods We performed a maternal separation (MS) procedure from postnatal days 1 to 14 in rats. Before each MS, pups were injected with an inhibitor of ER stress, salubrinal or a vehicle. The mRNA and protein expression of UPR and apoptotic markers were evaluated in the mPFC using RT-qPCR and Western blot methods, respectively. We also estimated the numbers of neurons and glial cells using stereological methods. Additionally, we assessed behavioral phenotypes related to fear, anhedonia and response to psychostimulants. ### Results MS slightly enhanced the activation of the UPR in juveniles and modulated the expression of apoptotic markers in juveniles and preadolescents but not in adults. Additionally, MS did not affect the numbers of neurons and glial cells at any age. Both salubrinal and vehicle blunted the expression of UPR markers in juvenile and preadolescent MS rats, often in a treatment-specific manner. Moreover, salubrinal and vehicle generally alleviated the behavioral effects of MS in preadolescent and adult rats. ### Conclusions Modulation of ER stress and UPR processes may potentially underlie susceptibility or resilience to ELS. ### Supplementary Information The online version contains supplementary material available at 10.1007/s43440-023-00456-6. ## Introduction Clinical and epidemiological studies have clearly indicated that early-life stress (ELS) increases the risk of mental health problems, such as mood and anxiety disorders, substance use disorder and cognitive deficits. Moreover, ELS accelerates the early onset of the abovementioned mental disorders in children and adolescents [1]. Thanks to animal studies and advances in neuroimaging techniques in humans, it is evident that ELS interferes with brain development [2, 3]. Although psychobiological consequences of ELS have been extensively explored in the past decade (for review see: [3–5]), the specific mechanisms of ELS action on brain development and maturation are still poorly understood and, thus, require intensive study. One of the widely used and popular model of ELS and human psychopathology with a high construct validity is the repeated maternal separation procedure (MS) in rodents during the first two weeks of life [6, 7]. Our previous studies based on such MS procedure in rats have shown that this early-life experience affects the process of neurodevelopmental apoptosis in the midbrain and medial prefrontal cortex (mPFC) in males [8–10]. Specifically, we observed a sustained increase in the survival of midbrain neurons and a specific delay in neuronal apoptosis during adolescence in the mPFC, manifested as an increase in the number of neuronal cells, which could potentially affect proper neuronal network building and functioning [8, 10]. One of the key cellular processes affecting the decision of cells to survive or die in response to different environmental insults is endoplasmic reticulum (ER) stress and, closely related to it, the unfolded protein response (UPR) [11]. The ER governs the synthesis, folding, modification and transport of over one-third of cellular proteins and, thus, plays a central role in maintaining protein homeostasis (proteostasis). Many conditions, such as nutrient deprivation, hypoxia, loss of redox and calcium balance and increased protein load, may disturb proteostasis and lead to accumulation of unfolded or misfolded proteins and, consequently, to the induction of ER stress and the UPR [11, 12]. These processes, in general protective and adaptive, act to restore ER homeostasis by attenuating general translation, inducing chaperones and eliminating misfolded proteins. However, if cellular stress exceeds the pro-survival capability of the UPR, the ER induces cell death pathways through the proapoptotic component of the UPR [13, 14]. ER stress is sensed by three ER transmembrane proteins, i.e., inositol-requiring enzyme 1 (IRE1α), RNA-activated protein kinase (PKR)-like endoplasmic reticulum kinase (PERK) and activating transcription factor 6 (ATF6). Under resting conditions, these UPR sensors are bound to heat shock 70 kDa protein A 5 (HSPA5), also known as glucose-regulated protein 78 (GRP78). Upon ER stress and accumulation of unfolded or misfolded proteins, HSPA5 dissociates from these sensor molecules and allows their activation. During activation, IRE1α, also known as endoplasmic reticulum to nucleus signaling 1 and encoded by the Ern1 gene, undergoes autophosphorylation at serine 724 and then induces cleavage of X box-binding protein 1 (XBP1) mRNA and production of spliced XBP1 mRNA and protein. The spliced XBP1 protein is a highly active transcription factor that regulates genes involved in the UPR. Release of HSPA5 from ATF6 induces its translocation to the Golgi apparatus, where it is cleaved (activated). Active forms of ATF6 migrate into the nucleus, where they act as transcription factors that are also engaged in the regulation of UPR-related genes. The third sensor, PERK, also known as eukaryotic translation initiation factor 2 alpha kinase 3 and encoded by the Eif2ak3 gene, undergoes autophosphorylation at threonine 980 during its activation. Next, PERK phosphorylates (at serine 51), and in this way inactivates eukaryotic translation initiation factor 2α (eIF2α, encoded by the Eif2a gene), which causes an inhibition of general protein synthesis. Concurrently, some specific mRNA translation is allowed to further regulate UPR processes [11, 14, 15]. ER stress and UPR processes have been implicated in the pathophysiology of numerous diseases, such as cancer, diabetes, atherosclerosis and autoimmune and neurodegenerative diseases [15, 16]. However, in recent years, there has been an accumulation of data showing the involvement of ER stress and the UPR in the mechanisms of mental disorders [17–22]. Increased expression levels of UPR-related genes or proteins have been observed in the mPFC and temporal cortex of subjects with major depressive disorder (MDD) who died from suicide and in leukocytes from patients with MDD and posttraumatic stress disorder [20, 21, 23]. In contrast, an impaired ER stress response was observed in leukocytes from patients with bipolar disorder (BD) [24–26]. Additionally, functional polymorphisms in the promoter regions of XBP1 and HSPA5 were shown to have a possible association with BD in a Japanese population [17, 25]. Enhanced ER stress and activation of the UPR have also been observed in animal models of depression based on chronic restraint and chronic social defeat stress paradigms [27–29]. Surprisingly, although ELS is considered a relevant factor in the etiology of mental and neurodegenerative disorders, ER stress and UPR processes in the brain have not been well studied in animal models of ELS. We have recently shown that the repeated MS procedure in rats produces long-lasting upregulation of HSPA5 and another chaperone belonging to the 70-kDa heat shock protein family, HSPA1B, in the brain and blood, which suggests that ELS may influence ER stress and UPR processes throughout development [30]. Dynamic regulation of the UPR has been implicated in cell fate acquisition, cortical neurogenesis, cell maturation, apoptosis and neuritogenesis during prenatal development of the central nervous system [31]. However, the role of ER stress and UPR processes in postnatal brain maturation is poorly explored. Therefore, in the present study, we investigated whether MS enhances ER stress and the UPR and consequently affects cell fate, particularly the pro-survival or apoptotic processes during postnatal maturation of the mPFC. We focused on this specific brain region because the mPFC shows a prolonged developmental trajectory, characterized by waves of postnatal neurodevelopmental apoptosis and intensive structural and functional reorganization during preadolescence and adolescence periods, which makes it especially vulnerable to the effects of stress [32–35]. Moreover, the mPFC is highly implicated in the pathophysiology of mood and anxiety disorders [36]. Simultaneously, to further evaluate the potential role of ER stress and the UPR in the mechanisms of ELS, we also studied the short- and long-term effects of transient early-life inhibition of ER stress processes by salubrinal (SAL), a selective inhibitor of eIF2α dephosphorylation, on MS-induced biochemical changes and behavioral phenotypes related to anxiety, fear memory, anhedonia and response to psychostimulants. ## Animals All experimental procedures were approved by the Local Ethics Committees for Animal Research in Krakow, Poland (permit no. $\frac{186}{2018}$ issued $\frac{07}{06}$/2018 and $\frac{136}{2019}$ issued $\frac{27}{06}$/2019) and met the requirements of the Directive $\frac{2010}{63}$/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes. All efforts were made to minimize animal suffering. Adult male and female Wistar rats were purchased from Charles River Laboratories (Sulzfeld, Germany). All animals were housed under controlled conditions with an artificial 12-h light/dark cycle (lights on from 07:00 to 19:00), $55\%$ ± 10 humidity, and a temperature of 22 ºC ± 2. Food and tap water were freely available. The rats were mated at the Maj Institute of Pharmacology, PAS, Krakow Animal Facility. The offspring of primiparous dams were used in this study. Before delivery, the dams were housed individually in standard plastic cages (38 × 24 × 19 cm). The day of birth was designated as postnatal day (PND) 0. On PND 1, the litter size was standardized to eight pups per litter (four males and four females), and the litters were randomly assigned to one of the following early-life treatment: animal facility rearing (AFR), i.e., control condition, MS procedure, MS with salubrinal injections (SAL-MS) or MS with vehicle injections (VEH-MS). ## Repeated maternal separation and salubrinal injections The MS procedure was performed as described previously by Solarz et al. and Chocyk et al. [ 8, 9, 30, 37–41]. Briefly, on PNDs 1–14, the dams and pups were removed from the maternity cages for 3 h (09.00–12.00) daily. The mothers were placed individually in the holding cages (38 × 24 × 19 cm), while each litter was placed in a plastic container (22 × 16 × 10 cm) lined with fresh bedding material, and the containers were moved to an adjacent room and placed in an incubator that was set at a constant temperature of 34 °C mimicking the nest temperature (a basic MS group). Additionally, before each daily separation, the pups from SAL-MS group received a single injection of salubrinal (1 mg/kg/5 ml sc, PNDs 1–14, Tocris), whereas VEH-MS group was injected with respective vehicle, i.e., $2.5\%$ dimethyl sulfoxide (DMSO) in PBS (5 ml/kg, sc, PNDs 1–14, Sigma). SAL dose was chosen based on the data from literature which showed that SAL in a dose of 1 mg/kg can modulate ER stress processes in the brain and promotes neuroprotection [42, 43]. Moreover, such dose of SAL in repeated injections has been also safely administered to juvenile mice [44]. After the 3-h separation, the pups and dams were returned to the maternity cages. The AFR animals were left undisturbed with their mothers except during the weekly cage cleaning corresponding to a small amount of handling. Twenty-four hours after the last MS, i.e., on PND 15, a part of the animals was assigned to the experimental groups to investigate the effects of repeated MS and SAL in juveniles (Fig. 1). The rest of the animals were weaned on PND 22, sexed, and randomly distributed between subsequent experimental groups to investigate the long-term effects of repeated MS and SAL. These rats were housed under the controlled conditions (as described above) in standard plastic cages (57 × 33 × 20 cm) in the same-sex groups of five unrelated subjects according to the same treatment protocol until the preadolescence period (PND 26) or adulthood (PND 70) (Fig. 1). An exception was made in the case of sucrose preference test, when animals were singly housed in plastic cages (38 × 24 × 19 cm) from PND 22 to PND 26. The body weights of animals were measured daily from PND 1 to PND 15 and on PND 26 and PND 70 with a Kerm PCB electronic precision scale (Balingen, Germany).Fig. 1Scheme of the experimental paradigm applied in the study. On PNDs 1–14, the dams and pups were separated for 3 h daily. Before each daily separation, the pups from SAL-MS group received a single injection of salubrinal (SAL, 1 mg/kg sc), whereas VEH-MS group was injected with respective vehicle ($2.5\%$ DMSO in PBS, 5 ml/kg sc). On PND 26 and PND 70, behavioral phenotype of rats was assessed in the battery of tests, such as, the light/dark exploration, fear conditioning, sucrose preference and novelty- and amphetamine-induced locomotor activity. On PNDs 15, 26 and 70, the mPFC samples were also collected for biochemical analyses. Included photomicrograph shows a cresyl violet-stained rat brain section with marked subregions of the mPFC. AFR animal facility rearing, Cg1 cingulate cortex 1, ILC infralimbic cortex, MS maternal separation, mPFC medial prefrontal cortex, PLC prelimbic cortex, PND postnatal day, SAL salubrinal, VEH vehicle ## Experimental groups A total number of 291 male rats were used in the study: 80 AFR, 68 MS, 71 VEH-MS and 72 SAL-MS rats. Female offspring was used in other scientific projects. In the case of biochemical experiments, the final experimental groups included the animals that originated from different litters and were unrelated ($$n = 5$$–6). In the case of behavioral experiments, maximum two subjects from the same litter were used ($$n = 9$$–15). The separate groups of animals were analyzed for [1] gene expression (on PND 15 and PND 26), [2] protein expression (on PND 15 and PND 26), and [3] different behavioral test (part of animals from that cohort was also used for immunohistochemistry and gene expression on PND 70). ## RT-qPCR The RT-qPCR procedure was performed as described previously by Solarz et al. [ 30, 40, 41]. Briefly, on PND 15, PND 26, or PND70, the animals were sacrificed by decapitation (6 rats in each treatment and age group) and the brain was immediately removed from the skull. The mPFC (including the cingulate cortex 1 (Cg1), prelimbic cortex (PLC), and infralimbic cortex (ILC) regions) was dissected from 1 mm thick coronal sections using a rodent brain matrix (Ted Pella Inc., CA, USA). After dissection, the brain tissue was quickly frozen in liquid nitrogen and stored at − 80 ºC for later use. Total RNA from the brain tissue was extracted using the RNAeasy Mini Kit (Qiagen). The total RNA concentration was measured using an Eon absorbance microplate reader and Gen 5 software (BioTek, Winooski, VT, USA). RNA was reverse transcribed using a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, MA). Quantitative real-time PCR was performed in duplicate with TaqMan® Gene Expression Assays (Thermo Fisher Scientific, MA; Table 1) using TaqMan™ Universal Master Mix II, no UNG (Thermo Fisher Scientific, MA) and the QuantStudio 12 K Flex System (Thermo Fisher Scientific, MA). Real-time PCR was conducted under the following conditions: 50 °C for 2 min and 95 °C for 10 min followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The abundance of RNA was calculated according to the following equation: abundance = 2−(threshold cycle) [45]. The results were normalized to glyceraldehyde-3-phosphate dehydrogenase (Gapdh) expression levels. Table 1The list of TaqMan® Gene Expression Assays used in the studyGene productGene nameAssay IDHeat shock protein family A member 5alias: glucose-regulated protein GRP 78kDHspa5Rn01435769_g1Endoplasmic reticulum to nucleus signaling 1alias: inositol-requiring enzyme 1(IRE1α)Ern1Rn01471008_m1Eukaryotic translation initiation factor 2 α kinase 3alias: RNA-activated protein kinase (PRK)-likeendoplasmic reticulum kinase (PERK)Eif2ak3Rn00581002_m1Activating transcription factor 6Atf6Rn01490844_m1Eukaryotic initiation factor 2Eif2aRn01494813_m1Caspase-12Casp12Rn00590440_m1Caspase-9Casp9Rn00581212_m1Caspase-3Casp3Rn00563902_m1Apoptosis regulator Bcl2Bcl2Rn99999125_m1Apoptosis regulator BaxBaxRn01480161_g1Glyceraldehyde-3-phosphate dehydrogenaseGapdhRn99999916_s1 ## Western blot Western blot was performed as described previously by Majcher-Maślanka et al. [ 46, 47] and Solarz et al. [ 30]. Briefly, on PND 15 or PND 26, the animals were sacrificed by decapitation (6 rats in each treatment and age group), and the brain was rapidly removed from the skull. The mPFC (including Cg1, PLC and ILC regions) was dissected from 1 mm thick coronal sections using a rodent brain matrix (Ted Pella, CA, USA). After dissection, the brain tissue was quickly frozen in liquid nitrogen and stored at − 80 ºC until further use. The tissue was homogenized (TissueLyser, Retsch, Germany) in ice-cold lysis buffer (PathScan® Sandwich ELISA Lysis Buffer, Cell Signaling). The homogenates were then centrifuged for 15 min at 15,000 g at 4 ºC. The total protein concentration of the supernatants was determined using the bicinchoninic acid (BCA) method (Sigma-Aldrich, USA). Samples with equal protein concentrations were loaded into each lane, run on $10\%$ sodium dodecyl sulfate–polyacrylamide gels in a Laemmli buffer system, and transferred onto a nitrocellulose membrane (Bio-Rad, USA). The blots were probed with diluted primary antibodies listed in Table 2. Then, the blots were incubated with the appropriate (anti-mouse IgG or anti-rabbit IgG) horseradish peroxidase-conjugated secondary antibodies (Roche Diagnostics, Basel, Switzerland), and the bands were visualized by enhanced chemiluminescence (Lumi-LightPlus Western Blotting Kit, Roche Diagnostics, Switzerland). The immunoblots were evaluated using a luminescent image analyzer (LAS-4000, Fujifilm, USA). Immunoblot images were acquired using exposure time increment option. Representative, unprocessed images were presented in Online Resource ESM_11-14. The relative levels of immunoreactivity were quantified using the Image Gauge software (Fujifilm, USA). The ratio of the specific protein level to the actin level was calculated for each sample to normalize for small variations in loading and transfer. Table 2List of primary antibodies used in the studySpecificityHostDilutionSupplierProduct #GRP78/HSPA5Rabbit1:1000Cell Signaling Tech3183IRE1αRabbit1:1000Abcamab37073p-IRE1α (S724)Rabbit1:1000Abcamab48187PERKRabbit1:1000Cell Signaling Tech3192p-PERK (Thr980)Rabbit1:1000Cell Signaling Tech3179ATF6Rabbit1:1000Proteintech24169–1-APeIF2αRabbit1:2000Cell Signaling Tech5324p-eIF2α (S51)Rabbit1:1000Cell Signaling Tech3398BaxMouse1:500Santa Cruz Biotechsc-7480Bcl2Mouse1:500Santa Cruz Biotechsc-7382Caspase-3 (cleaved)Rabbit1:500Cell Signaling Tech9661Caspase-9Mouse1:500Cell Signaling Tech9508Caspase-12Rabbit1:500Abcamab62484NeuNMouse1:1000MilliporeMAB377GFAPGoat1:250Santa Cruz Biotechsc-6170IBA1Rabbit1:500Proteintech10904–1-AP ## Immunohistochemistry Immunohistochemistry was performed as previously described by Chocyk et al. [ 8, 39] and Majcher-Maślanka et al. [ 10]. Briefly, on PND 26 and PND 70, the animals (6 rats in each treatment and age group) were deeply anesthetized and transcardially perfused with saline followed by $4\%$ paraformaldehyde in 0.1 M PBS (pH 7.4). The brains were removed from the skulls, postfixed in $4\%$ paraformaldehyde in PBS for 24 h at 4 °C, and sectioned using a vibratome (VT1000S, Leica, Wetzlar, Germany) into 50-μm thick coronal slices at the level of the mPFC (Bregma = 3.70 to 2.20 mm) according to a stereotaxic atlas of the rat brain [48]. Every fourth section was preserved for further processing (10–11 sections from each subject). Free-floating sections were washed in 0.01 M PBS (pH 7.4) and incubated for 30 min in PBS containing $0.3\%$ H2O2 and $0.2\%$ Triton X-100. The sections were then rinsed and transferred to a blocking buffer ($5\%$ solution of appropriate normal serum (goat, horse or rabbit) in $0.2\%$ Triton X-100 in PBS) for 1 h. After the blocking procedure, the sections were incubated for 48 h at 4 °C with a mouse anti-NeuN antibody (1:1000; Millipore), a goat anti-glial fibrillary acidic protein (GFAP) antibody (1:250; Santa Cruz Biotechnology), or a rabbit anti-ionized calcium-binding adapter molecule (IBA1), also known as allograft inflammatory factor 1 (AIF-1), antibody (1:500; Proteintech) (Table 2). The antibodies were diluted with $3\%$ normal serum and $0.2\%$ Triton X-100 in PBS. After being washed in PBS, the sections were incubated for 1 h with an appropriate solution of biotinylated secondary antibodies (goat anti-rabbit, horse anti-mouse or rabbit anti-goat IgG, 1:500; Vector Laboratories), followed by a 1 h incubation with an avidin–biotin-peroxidase complex (1:200, 1 h; Vectastain ABC Kit, Vector Laboratories). The immunochemical reaction was developed in a diaminobenzidine (DAB)-nickel solution containing $0.02\%$ DAB, $0.01\%$ H2O2 and $0.06\%$ NiCl2 in TBS, which stained the immunoreactive material black. The sections were mounted onto gelatin-coated slides, air-dried and coverslipped using Permount (Fisher Scientific) as the mounting medium. ## Cell counting The number of immunoreactive (IR) cells was estimated by unbiased stereological methods [49], as described previously by Chocyk et al. [ 8, 39] and Majcher-Maślanka et al. [ 10]. Briefly, every fourth section, selected by systematic random sampling along the rostrocaudal axis of the mPFC, was chosen for analysis (10–11 sections per animal). Optical fractionator sampling was performed using a Leica DM 6000 B microscope equipped with a motorized stage (Ludl Electronic Products, Hawthorne, NY, USA) connected to a controller (MAC 5000, Ludl) and a digital camera (MBF C × 9000, Williston, VT, USA). Sampling was performed using Stereo Investigator 8.0 software (MBF Bioscience, Williston, VT, USA) by experimenters unaware of treatment group allocation to the specific slides. The studied regions of the mPFC, i.e., the Cg1, PLC and ILC, were outlined under low magnification (2.5×) according to a stereotaxic atlas of the rat brain [48] (Fig. 1). Sampling was performed bilaterally under high magnification (63×, oil-immersion objective) using counting frames with areas of 2500 µm2 and heights of 15 µm. The sampling parameters resulted in the mean of 1277 NeuN-IR cell counted (range 724–2161), 471 IBA1-IR cells counted (range 228–723) and 642 GFAP-IR cells counted (range 397–995) in each subregion of the mPFC. The total number of immunoreactive (IR) cells per region was estimated from the number of cells sampled within the optical dissectors and calculated by multiplying the numerical density of the cells (the number of IR cells/mm3) by the regional volume occupied by the cells within the studied region. The regional volumes of the studied mPFC areas in each animal were determined using the Cavalieri method [50]. The final results are presented as the estimated total number of cells within the specific regions. ## The light/dark exploration test On PND 26, separate groups of rats (10 rats per experimental group) were subjected to the light/dark exploration test to assess anxiety-like behaviors. The light/dark exploration test was performed as described previously by Chocyk et al. [ 8, 37] and Solarz et al. [ 30]. Briefly, each experimental cage included an arena (45 × 45 × 45 cm) with a light compartment made of clear acrylic and a dark compartment made of black acrylic. The black compartment covered $33\%$ of the total cage area, and the black dividing wall was equipped with a central tunnel gate (11 × 8.4 cm). The light compartment was brightly illuminated (100 lx), whereas the dark compartment received no light at all. The animals were kept in total darkness for 30 min prior to the testing, and the entire experiment was conducted with the room lights off. The animals were individually tested in single 10 min trials. Six weeks later, the same groups of rats were retested for anxiety-like behaviors when they approached adulthood (PND 70), together with additional 5 AFR, 2 VEH-MS and 2 SAL-MS adult rats. The behavioral responses during the test sessions were recorded using Fear Conditioning (FC) software (TSE, Bad Homburg, Germany). Specifically, the number of transitions between the compartments, time spent in each compartment, and locomotor activity (the distance traveled) were measured. ## Fear conditioning Behavioral tests were performed and analyzed using a computer-controlled FC system (TSE, Bad Homburg, Germany) as described previously by Chocyk et al. [ 38] and Bialon et al. [ 51]. Each FC unit consisted of sound-attenuating housing with a loudspeaker, camera, ventilation fan and 4 symmetrically mounted lamps in the ceiling construction and test box. The test box comprised the test arena and a base construction containing integrated infrared animal detection sensors in the X, Y (horizontal) and Z (vertical) axes. The sensor frames along the axes were equipped with 32 sensor pairs mounted 14 mm apart. All sensors were scanned at a sampling rate of 100 Hz, i.e., the position of the animal was checked 100 times per second. Several pilot experiments were run that compared automatic and manual scoring of freezing behavior; the data obtained from each method were highly correlated. During the experimental procedure, the animals were tested in two different arenas and contexts (A and B). For the first context (Context A), the arena (46 × 46 × 47 cm) was made of transparent acrylic and had a floor made up of stainless steel rods (4 mm in diameter) spaced 8.9 mm apart (center to center). The floor was connected to a shocker-scrambler unit for delivering shocks of defined duration and intensity. The arena was cleaned with $1\%$ acetic acid solution. A ventilation fan provided background noise (65 dB), and lamps provided uniform illumination of 60 lx inside the fear conditioning housing. During tests in Context A, the room lights remained on. Animals were transported to this context with transparent plastic boxes. Experimenters wore white clothes and gloves. For the second context (Context B), the arena (46 × 46 × 47 cm) was made of black acrylic (permeable to infrared light) with a gray plastic floor. The arena was cleaned with $70\%$ ethanol solution and faintly illuminated (4 lx). The tests in Context B were conducted with the room light off. Animals were transported to this context with black plastic boxes. Experimenters wore blue clothes and gloves. Fear conditioning (FC) and memory were assessed using the Pavlovian paradigm. The schedule of FC procedures is presented in Table 3. On day 1 of experiment (PND 26), the animals (14 AFR rats and 10 rats in each other treatment group) were subjected to FC procedure in Context A (acquisition/training). Animals were placed in Context A and allowed to habituate for 180 s. Next, the animals received five tone-shock pairings in which the tone (amplitude: 80 dB; frequency: 2 kHz; duration: 10 s) was co-terminated with foot shock (intensity: 0.7 mA; duration: 1 s). The intertrial interval was 60 s. Animals were removed from Context A 60 s after the last trial. Table 3A schedule of FC proceduresPNDProcedure26FC acquisition/training (Context A)27CFC expression (Context A)AFC expression (Context B)70CFC memory test (recall) in adulthood (Context A)71AFC memory test (recall) in adulthood (Context B)77FC retraining in adulthood (Context A)78CFC expression after retraining in adulthood (Context A)AFC expression after retraining in adulthood (Context B)AFC auditory fear conditioning, CFC contextual fear conditioning, FC fear conditioning, PND postnatal day On day 2, all animals were once again exposed to Context A and were left undisturbed for 6 min (expression of contextual fear conditioning, CFC), then returned to their home cages. Two hours later, the animals were placed in a new context (Context B) and, after 180 s of habituation, received five presentations of tone-alone with 61-s intertrial intervals (expression of auditory fear conditioning, AFC). Animals were removed from Context B 60 s after the last trial. Six weeks after the FC training (on PND 70), the same animals were once again tested in both Context A and B for recall of fear memories. First, they were placed and left undisturbed for 6 min in Context A, and 24 h later, they were exposed to five presentations of tone-alone in Context B (Table 3). Seven days later, all animals were subjected to a session of retraining of fear conditioning in Context A; retraining followed the same procedure as training during preadolescence (Day 1). On the following day, fear memory was tested both in Context A and B (the same sessions as on Day 2 during preadolescence) (Table 3). Behavioral responses during all sessions were recorded and automatically analyzed using FC software (TSE, Bad Homburg, Germany). Freezing (i.e., immobility) was taken as the behavioral measure of fear and was defined as the absence of all non-respiratory movements for at least 2 s. The cumulative duration of freezing was calculated for each session and expressed as percentage of entire session time, excluding habituation time. ## Sucrose preference test To assess anhedonic-like behavior, a sucrose preference was measured in a two-bottle choice paradigm as described previously by Chocyk et al. [ 8] and Solarz et al. [ 30]. Briefly, on PND 22 separate groups of rats (10 rats per experimental group) were singly housed and habituated to drink water from two bottles for 2 days. Then, the water in one of the bottles was replaced by $1\%$ sucrose solution for 2 days to avoid neophobia. The position of the bottles (sucrose left or right) was reversed every 8 h to prevent the development of place preference. After habituation, the rats were subjected to water deprivation for 16 h before performing the sucrose preference test. During the test, two bottles, one containing tap water and another containing $1\%$ sucrose solution, were presented to each rat for 4 h (from 8 a.m. to 12 p.m.). The positioning of the water and sucrose bottles (left or right) was balanced between the experimental groups. Six weeks later, the same groups of rats were retested for anhedonic-like behavior when they approached adulthood (PND 70). Sucrose preference was calculated as the percentage of sucrose intake versus total liquid intake (water + sucrose) over the 4-h test period. ## Measurement of novelty- and amphetamine-induced locomotor activity On PND 26, a separate group of animals was subjected to the locomotor activity test ($$n = 9$$–12). Locomotor activity was recorded and analyzed individually for each animal using Opto-Varimex cages (43 × 44 cm) and Auto-Track software (Columbus Instruments, OH, USA), as described previously by Majcher-Maślanka et al. [ 10, 52]. The rats were placed into the test cages without previous habituation and were free to explore the environment for 20 min (novelty-induced locomotion, session 1). Next, the rats received vehicle injections (saline, 1 ml/kg sc) and were left in test cages for 50 min (session 2). Finally, the same rats were injected with amphetamine (1 mg/kg) and left in test cages for another 50 min (session 3). Six weeks later, the same groups of rats were retested for locomotor activity when they approached adulthood (PND 70). Locomotor activity of the animals was recorded for each session separately. The data are presented as the average distance traveled over the entire session. ## Statistical analysis Statistical analysis of the data was performed using Statistica 13.3 software (TIBCO Software Inc., USA). Initially, data were tested for normal distribution and homogeneity of variances using Shapiro–Wilk test and Levene’s test, respectively. In the case of data that followed normal distribution and had equal variances among groups, they were further analyzed by one-way analysis of variance (ANOVA) with early-life treatment (AFR, MS, VEH-MS and SAL-MS) as independent variable followed by Tukey’s HSD post hoc test. Amphetamine-induced locomotor activity was specifically analyzed by mixed-design ANOVA with early-life treatment as a between-subject factor and saline and amphetamine injections as within-subject factor. In the case of data which did not show normal distribution and homogeneity of variances, statistical differences between experimental groups were analyzed by Kruskal–Wallis test followed by Dunn’s test for multiple comparisons. P values < 0.05 were considered significantly different. The data are presented as the group mean and standard deviation (SD, parametric statistics) or as the median and interquartile range (IQR, nonparametric statistics). ## The effects of MS and early-life SAL/VEH injections on body weight To examine whether specific early-life treatment, such as repeated MS and SAL/VEH injections, affected the body weight of juvenile rats, a body weight gain index between PND 1 and PND 15 was calculated. Statistical analysis revealed that early-life treatment significantly affected body weight gain in juvenile rats (F3,220 = 5.21, $$p \leq 0.002$$, one-way ANOVA followed by Tukey’s test). Specifically, VEH-MS rats showed a slightly higher body weight gain index in comparison to AFR and MS rats (Fig. 2).Fig. 2The effects of MS and early-life SAL/VEH injections on a body weight gain in juvenile rats. The data are presented as the mean ± SD ($$n = 48$$ − 64) and expressed as a body weight gain index calculated as the body weight difference between PND 15 and PND 1. Connectors indicate statistically significant differences between specific experimental groups (one-way ANOVA followed by Tukey’s HSD post hoc analysis). AFR animal facility rearing, MS maternal separation, SAL salubrinal, VEH vehicle Body weight was also assessed during the preadolescence and adulthood periods. However, Kruskal–Wallis test did not show any significant differences between the experimental groups (PND 26: H3 = 2.64, N1 = 31, N2 = 29, N3 = 26, N4 = 30, $$p \leq 0.450$$; PND 70: H3 = 1.46, N1 = 34, N2 = 25, N3 = 25, N4 = 26, $$p \leq 0.690$$) (Table 4).Table 4The effects of MS and early-life SAL/VEH treatment on body weight of preadolescent and adult ratsPNDGroupBody weight26AFR90.0 (11.5)MS92.5 (13.8)VEH-MS93.2 (15.0)SAL-MS93.0 (19.1)70AFR440.5 (63.0)MS449.0 (26.0)VEH-MS442.0 (82.0)SAL-MS449.5 (87.0)Data are presented as the median (IQR) ($$n = 25$$–34) and expressed in grams. AFR animal facility rearing, IQR interquartile range, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle ## The effects of MS and early-life SAL/VEH injections on ER stress and UPR processes in juvenile rats (PND 15) To study the early effects of repeated MS and SAL/VEH injections on ER stress and UPR processes, we examined the gene and protein expression of ER stress and UPR markers in the mPFC of rats on PND 15, which was 24 h after the last MS procedure. Statistical analysis of Hspa5 mRNA expression in the mPFC showed a significant effect of early-life treatment (Kruskal–Wallis test, H3 = 17.02, N1–4 = 6, $$p \leq 0.0007$$). Specifically, VEH-MS rats had lower Hspa5 mRNA levels than AFR and MS rats (Dunn’s test) (Fig. 3A).Fig. 3The effects of MS and early-life SAL/VEH injections on HSPA5 mRNA (A) and protein (B) expression in the mPFC of juvenile rats. The data are presented as the median and IQR (A) or mean ± SD (B) and were analyzed by Kruskal–Wallis test or one-way ANOVA, respectively ($$n = 6$$). Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Dunn’s post hoc test (A) or Tukey’s HSD post hoc test (B). AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle The analysis of HSPA5 (GRP 78) protein levels in the mPFC of juveniles also revealed a significant effect of early-life treatment (one-way ANOVA, F3,20 = 3.35, $$p \leq 0.039$$). In this case, MS rats showed enhanced expression of HSPA5 protein in comparison to AFR rats (Tukey’s test) (Fig. 3B). Next, we examined the effect of MS and SAL/VEH on the mRNA and protein expression of the ER stress sensors Eif2ak3 (PERK), Ern1 (IRE1α) and ATF6 in the mPFC. In the case of all ER stress sensors, analysis of their mRNA expression showed no significant differences between experimental groups (one-way ANOVA, Online Resource ESM_1: Table S1; ESM_2: Fig. S1A) (Fig. 4A, D). Moreover, we did not observe an effect of MS and SAL/VEH on the total protein levels of PERK, IRE1α and ATF6 (one-way ANOVA, Table S1) (Fig. 4B, E; ESM_2: Fig. S1B). However, we did find a significant effect of early-life treatment on activation by phosphorylation of PERK at residue Thr980 (F3,20 = 5.93, $$p \leq 0.005$$, one-way ANOVA followed by Tukey’s test). Specifically, VEH-MS rats showed increased levels of p-PERK (Thr980) protein in comparison to AFR rats (Fig. 4C). A similar trend was also observed in the case of the MS group, though it did not reach statistical significance ($$p \leq 0.051$$). Nevertheless, when a single comparison between AFR and MS groups was performed, the effect was observed to be statistically significant ($U = 2$, N1-2 = 6, $$p \leq 0.013$$, Mann‒Whitney U test). Analysis of the phosphorylation of IRE1α at residue S724 also showed a significant effect of early-life treatment (one-way ANOVA, F3,20 = 3.88, $$p \leq 0.024$$). In this case, MS increased the phosphorylation of IRE1α at S724 in comparison to AFR rats (Tukey’s test) (Fig. 4F).Fig. 4The effects of MS and early-life SAL/VEH injections on mRNA expression and protein expression and phosphorylation of ER stress sensors PERK (A–C) and IRE1α (D–F) in the mPFC of juvenile rats. The data are presented as the mean ± SD ($$n = 6$$) and were analyzed by one-way ANOVA. Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Tukey’s HSD post hoc test. AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle Because SAL acts as an inhibitor of eIF2α dephosphorylation at residue S51, we also investigated how repeated MS and SAL/VEH injections impacted the expression and phosphorylation of eIF2α in the mPFC of juveniles. We observed that SAL-MS rats showed lower levels of Eif2a mRNA than MS and AFR rats (H3 = 18.83, N1-4 = 6, $$p \leq 0.003$$, Kruskal–Wallis test followed by Dunn’s test) (Fig. 5A). We also observed a similar effect in total protein of eIF2α (F3,20 = 6.10, $$p \leq 0.004$$, ANOVA followed by Tukey’s test) (Fig. 5B). However, one-way ANOVA of eIF2α phosphorylation did not reveal any differences in the level of p-eIF2α (S51) between the experimental groups (F3,20 = 4.97, $$p \leq 0.050$$) (Fig. 5C).Fig. 5The effects of MS and early-life SAL/VEH injections on mRNA expression (A) and protein expression and phosphorylation (B–C) of eIF2α in the mPFC of juvenile rats. The data are presented as the median and IQR (A) or mean ± SD (B, C) and were analyzed by Kruskal–Wallis test or one-way ANOVA, respectively ($$n = 6$$). Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Dunn’s post hoc test (A) or Tukey’s HSD post hoc test (B). The same immunoblot of β-Actin was used for normalization of both p-eIF2α (C) and p-IRE1α immunoblots (Fig. 4F). After protein electrotransfer the blots were horizontally cut into two pieces to separately evaluate p-eIF2α and p-IRE1α protein levels from the same samples. Next, after membrane stripping procedure, appropriate blot was reprobed with β-Actin antibody. AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle ## The effects of MS and early-life SAL/VEH injections on apoptotic processes in the mPFC of juvenile rats (PND 15) Since ER stress and UPR processes are known to affect cell death and survival decisions, the next goal of our study was to examine the gene and protein expression of the main apoptotic markers in the mPFC. Statistical analysis showed that early-life MS and SAL/VEH treatment affected the mRNA expression of a few apoptotic markers but not their protein levels (Online Resource ESM_1: Table S1; ESM_2: Fig. S2; Fig. S3) (Fig. 6). Notably, we observed that MS rats subjected to SAL treatment had lower levels of caspase-9 mRNA in comparison to that of VEH-MS and AFR rats (F3,20 = 5.11, $$p \leq 0.009$$, one-way ANOVA followed by Tukey’s test) (Fig. 6A). A trend toward reduced expression of caspase-9 was also observed in the SAL-MS group compared to MS rats, though it did not reach statistical significance ($$p \leq 0.051$$). On the other hand, we did not observe any differences between the experimental groups in caspase-3 mRNA expression, a key effector caspase involved in a mitochondrial pathway of apoptosis (Kruskal–Wallis test, H3 = 7.13, N1–4 = 6, $$p \leq 0.068$$) (Fig. 6B).Fig. 6The effects of MS and early-life SAL/VEH injections on mRNA expression of apoptotic markers in the mPFC of juvenile rats: Casp9 (A), Casp3 (B), Casp12 (C), Bax (D), Bcl2 (E), and Bax/Bcl2 mRNA ratio (F). The data are presented as the mean ± SD (A, C–F) or median and IQR (B) and were analyzed by one-way ANOVA or Kruskal–Wallis test, respectively ($$n = 6$$). Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Tukey’s HSD post hoc test. AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle Interestingly, our study also revealed a statistically significant effect of early-life treatment on the mRNA expression of caspase-12, a specific caspase that is localized in the ER membrane and engaged in the ER stress-induced pathway of apoptosis (one-way ANOVA, F3,20 = 5.36, $$p \leq 0.007$$). The MS procedure increased caspase-12 mRNA expression compared to that in the AFR group (Fig. 6C). Moreover, VEH-MS rats had a significantly lower level of caspase-12 mRNA than MS rats (Tukey’s test). As described above, we did not observe a statistically significant effect of early-life treatment on procaspase-9 or procaspase-12 protein expression or cleaved (active) caspase-9, -12 or cleaved caspase-3 protein levels (one-way ANOVA, Table S1) (ESM_2: Fig. S2). Nevertheless, in the case of cleaved caspase-9, when a single comparison between AFR and MS groups was performed using a Student’s t-test, statistical significance was observed, and MS rats showed increased cleaved caspase-9 protein levels in the mPFC (t10 = 2.56, $$p \leq 0.023$$) (Fig. S2B). Next, we examined Bax and Bcl2 expression and the Bax/Bcl2 ratio and observed a significant effect of early-life treatment on Bax mRNA levels (F3,20 = 15.34, $p \leq 0.0001$, ANOVA) and the Bax/Bcl2 mRNA ratio (F3,20 = 11.29, $p \leq 0.0001$) (Fig. 6D–F). Specifically, both SAL- and VEH-injected MS rats showed lower levels of Bax mRNA compared to MS and AFR rats (Tukey’s test) (Fig. 6D). The Bax/Bcl2 mRNA ratio was decreased in SAL-MS and VEH-MS rats compared to AFR rats only (Tukey’s test) (Fig. 6F). There was no statistically significant effect of early-life treatment on the protein expression of Bax or Bcl2 or the Bax/Bcl2 protein ratio (one-way ANOVA, ESM_1: Table S1; ESM_2: Fig. S3). Nevertheless, in the case of Bax protein expression, when a single comparison between AFR and MS groups was performed using a Student’s t-test, statistical significance was observed, and the MS rats showed increased Bax protein levels in the mPFC (t10 = 3.08, $$p \leq 0.012$$) (Fig. S3A). ## The effects of MS and early-life SAL/VEH injections on ER stress and UPR processes in preadolescent rats (PND 26) Preadolescence and adolescence are crucial periods in mPFC postnatal maturation. During the preadolescence period, neurodevelopmental apoptosis starts to progress. The next goal of our study was to determine whether early-life MS and SAL/VEH treatment affect ER stress and UPR processes specifically in preadolescent rats (on PND 26) and in this way influence neurodevelopmental apoptosis in the mPFC. Statistical analysis showed that in preadolescent rats, early-life treatment only affected mRNA expression of Hspa5 (F3,20 = 11.23, $p \leq 0.0001$) and two ER stress sensors, Ern1 (F3,20 = 11.15, $$p \leq 0.0002$$) and Atf6 (F3,20 = 4.42, $$p \leq 0.015$$) (one-way ANOVA) (Fig. 7). We did not observe differences between experimental groups in protein expression or activation (phosphorylation) of any of the ER stress or UPR markers we examined (ESM_1: Table S1) (ESM_3: Fig S4; Fig S5). Post hoc analysis of mRNA expression showed that both SAL- and VEH-injected MS rats had lower levels of Hspa5 mRNA compared to MS and AFR rats (Tukey’s test) (Fig. 7A). A similar effect of early-life treatment was also observed in the case of Ern1 (Fig. 7C). However, analysis of Atf6 mRNA levels showed that only SAL-injected MS rats had reduced Atf6 mRNA expression compared to MS and AFR (Tukey’s test) (Fig. 7D).Fig. 7The effects of MS and early-life SAL/VEH injections on mRNA expression of Hspa5 (A) and ER stress sensors Eif2ak3 (B), Ern1 (C), Atf6 (D) in the mPFC of preadolescent rats. The data are presented as the mean ± SD ($$n = 6$$) and were analyzed by one-way ANOVA. Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Tukey’s HSD post hoc test. AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle Next, we examined Eif2a mRNA and protein expression and eIF2α phosphorylation at residue S51. Statistical analysis revealed a lack of any significant differences between experimental groups in all these parameters (Table S1) (ESM_3: Fig. S5). ## The effects of MS and early-life SAL/VEH injections on apoptotic processes in the mPFC of preadolescent rats (PND 26) Statistical analysis of the mRNA expression of the analyzed caspases showed a significant effect of early-life treatment on caspase-3 (H3 = 10.82, N1–4 = 6, $$p \leq 0.013$$, Kruskal–Wallis test) and caspase-12 (F3,20 = 5.26, $$p \leq 0.008$$, ANOVA) but not caspase-9 mRNA levels (H3 = 6.76, N1–4 = 6, $$p \leq 0.080$$) (Online Resource ESM_1: Table S1) (Fig. 8). VEH-injected MS rats showed a lower level of caspase-3 mRNA than AFR rats (Dunn’s test) (Fig. 8B). Both MS and MS-VEH rats showed reduced caspase-12 mRNA expression compared to AFR rats (Tukey’s test) (Fig. 8C). However, statistical analysis did not reveal any significant differences between experimental groups in protein levels of procaspase-12 or -9 or cleaved forms of caspase-12, -9 or -3 (Table S1) (ESM_3: Fig. S6A–E).Fig. 8The effects of MS and early-life SAL/VEH injections on mRNA expression of apoptotic markers in the mPFC of preadolescent rats: Casp9 (A), Casp3 (B), Casp12 (C), Bax (D), Bcl2 (E), and Bax/Bcl2 mRNA ratio (F). The data are presented as the median and IQR (A, B, D, F) or mean ± SD (C, E) and were analyzed by Kruskal–Wallis test or one-way ANOVA, respectively ($$n = 6$$). Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Dunn’s post hoc test (B, F) or Tukey’s HSD post hoc test (C, E). AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle Next, we investigated the effects of MS and SAL/VEH injections on the expression of Bcl-2 family members. Statistical analysis revealed that early-life treatment significantly affected Bcl2 mRNA expression (F3,20 = 12.02, $$p \leq 0.0001$$, ANOVA) and the Bax/Bcl2 mRNA ratio (H3 = 16.61, N1–4 = 6, $$p \leq 0.0009$$) but not Bax mRNA levels (H3 = 1.32, N1–4 = 6, $$p \leq 0.724$$, Kruskal–Wallis test) (Fig. 8). Specifically, MS increased Bcl2 mRNA expression compared to that in AFR rats, and this effect was prevented in MS rats by both VEH and SAL injections (Tukey’s test) (Fig. 8E). On the other hand, post hoc analysis of the Bax/Bcl2 mRNA ratio revealed that this parameter was similar in AFR and MS rat, but both VEH- and SAL-injected MS rats showed an increased Bax/Bcl2 mRNA ratio compared to MS rats (Dunn’s test) (Fig. 8F). However, analysis of the Bax/Bcl2 protein ratio revealed a different effect, namely that both MS and VEH-MS rats showed a lower level of this parameter compared to AFR rats (Kruskal–Wallis test, H3 = 11.29, N1–4 = 6, $$p \leq 0.010$$, followed by Dunn’s test) (ESM_3: Fig. S7C). No change in the protein levels of Bax and Bcl2 was observed between the treatment groups (Online Resource ESM_1: Table S1) (Fig. S7A–B). Finally, we investigated whether repeated MS and SAL/VEH treatment affected the number of neurons and glial cells in specific subregions of the mPFC in preadolescent rats. Statistical analysis revealed that early-life treatment did not have any significant impact on the number of neurons (NeuN-IR), astrocytes (GFAP-IR) or microglial cells (IBA1-IR) in the mPFC of preadolescent rats (results and statistics are presented in Table 5). Representative photomicrographs showing neuronal and glial cells in the subregions of the mPFC of preadolescent rats are presented in Online Resource ESM_5˗7.Table 5The effects of MS and early-life SAL/VEH treatment on the number of neurons astrocytes and microglial cells in the mPFC of preadolescent ratsCell markermPFC regionGroupNumber of IR cellsStatisticNeuNCg1AFR685,481.1 ± 59,206.6F3,18 = 0.20, $$p \leq 0.896$$MS698,794.9 ± 45,376.6VEH-MS681,216.5 ± 28,241.7SAL-MS684,556.8 ± 35,757.5PLCAFR1,250,600.6 ± 132,051.7F3,18 = 0.27, $$p \leq 0.844$$MS1,281,136.8 ± 57,292.6VEH-MS1,252,841.1 ± 64,980.4SAL-MS1,291,928.6 ± 103,713.6ILCAFR288,312.2 (35,984.3)H3 = 0.88, $$p \leq 0.830$$MS275,936.1 (29,923.8)VEH-MS275,092.7 (7595.9)SAL-MS281,630.6 (24,893.4)GFAPCg1AFR204,453.6 ± 29,435.2F3,18 = 0.80, $$p \leq 0.510$$MS200,720.3 ± 37,443.2VEH-MS189,177.3 ± 11,617.8SAL-MS181,914.7 ± 26,710.8PLCAFR374,166.3 ± 67,722.5F3,18 = 0.48, $$p \leq 0.700$$MS359,114.3 ± 34,219.7VEH-MS347,362.8 ± 46,345.4SAL-MS338,200.3 ± 55,135.5ILCAFR120,888.7 ± 18,295.7F3,18 = 0.47, $$p \leq 0.704$$MS118,740.8 ± 17,268.8VEH-MS114,362.4 ± 18,440.6SAL-MS108,330.1 ± 21,168.5IBA1Cg1AFR212,681.4 ± 26,186.8F3,18 = 0.61, $$p \leq 0.616$$MS202,346.7 ± 13,552.4VEH-MS197,790.9 ± 19,641.8SAL-MS200,501.6 ± 15,313.6PLCAFR319,342.3 ± 29,654.3F3,18 = 0.23, $$p \leq 0.872$$MS323,430.3 ± 31,567.8VEH-MS310,514.0 ± 46,495.7SAL-MS326,914.1 ± 30,180.7ILCAFR73,607.2 ± 9470.6F3,18 = 0.19, $$p \leq 0.902$$MS76,013.2 ± 6420.1VEH-MS73,424.5 ± 5295.1SAL-MS73,042.3 ± 6925.9Data indicate the numbers of IR cells per region estimated by stereological method (the mean ± SD or median (IQR), $$n = 5$$–6). AFR animal facility rearing, Cg1 cingulate cortex, ILC infralimbic cortex, IQR interquartile range, IR immunoreactive, mPFC medial prefrontal cortex, MS maternal separation, PLC prelimbic cortex, PND postnatal day, SAL salubrinal, VEH vehicle ## The effects of MS and early-life SAL/VEH injections on the anxiety-like behavior of preadolescent and adult rats in the light/dark box test The next goal of our study was to investigate the effects of early-life MS and SAL/VEH treatment on the behavioral phenotypes of both preadolescent and adult rats. We started by assessing anxiety-like behaviors in the light/dark box test. The experimental procedure for the light/dark exploration applied in the present study has been previously tested in adolescent (PND 35) [10] and adult rats [30]. Nevertheless, this specific procedure when performed on PND 26 in preadolescent rats induced a very high level of anxiety in the light compartment. For example, preadolescent AFR rats on average spent only $2.4\%$ of a trial time (14.5 s) in the light compartment, and the distance traveled in the light compartment represented only $5.7\%$ of the total distance traveled during the entire session (Fig. 9A–B). The average number of transitions between dark and light compartments was 2.6 for AFR rats (Fig. 9C). However, Kruskal–Wallis test followed by Dunn’s test revealed that VEH- and SAL-injected MS rats were significantly less fearful during this light/dark exploration procedure than AFR rats (Online Resource ESM_1: Table S2) (Fig. 9). Specifically, VEH-MS and SAL-MS preadolescent rats spent more time in the light compartment than AFR rats (H3 = 11.97, N1–4 = 10, $$p \leq 0.007$$) (Fig. 9A). Additionally, they traveled a longer distance in the light compartment than the AFR rats (H3 = 12.87, N1–4 = 10, $$p \leq 0.005$$) (Fig. 9B). There was also a trend toward a greater number of transitions between light and dark compartments for VEH-MS rats compared to AFR rats (H3 = 8.58, N1–4 = 10, $$p \leq 0.035$$, post hoc: $$p \leq 0.084$$) (Fig. 9C).Fig. 9The effects of MS and early-life SAL/VEH injections on anxiety-like behavior of preadolescent (A–C) and adult rats (D–F) in the light/dark box test. Intensity of anxiety-like behavior was assessed as the time spent in the light side (A, D) and distance traveled in the light side (B, E) (both expressed as the percentage of the entire session) and the number of transitions between the dark and light sides (C, F). The data are presented as the median and IQR (A–C, E, F) or mean ± SD (D) and were analyzed by Kruskal–Wallis test or one-way ANOVA, respectively ($$n = 10$$ − 15). Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Dunn’s post hoc test (A, B, E) or Tukey’s HSD post hoc test (D). AFR animal facility rearing, DT distance traveled, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle In adulthood, statistical analysis also revealed a significant effect of early-life treatment on the time spent in the light compartment (F3,45 = 4.51, $$p \leq 0.007$$, ANOVA) and the distance traveled in the light compartment (H3 = 12.83, $$p \leq 0.005$$) but not on the number of transitions between compartments (H3 = 6.0, N1 = 15, N2 = 10, N3–4 = 12, $$p \leq 0.112$$, Kruskal–Wallis test) (Fig. 9D–F). Adult MS rats showed a trend toward less fearful behavior during the light/dark exploration test than AFR rats (Fig. 9D–E), which is in agreement with our previous studies [8, 30]. However, in this experiment we did not observe statistical significance in a post hoc analysis (for the time in light: $$p \leq 0.093$$; for the distance traveled: $$p \leq 0.082$$). Nevertheless, we did observe a statistically significant effect when we performed a single comparison between the AFR and MS groups (for the time in light: t23 = 2.65, $$p \leq 0.014$$; for the distance traveled: $U = 31$, N1 = 15, N2 = 10, $$p \leq 0.016$$). Interestingly, both VEH-MS and SAL-MS adult rats showed more fearful behavior compared to MS rats but not AFR rats. Notably, they spent less time in the light compartment than MS rats (Tukey’s test) (Fig. 9D). They also traveled a shorter distance in the light compartment than the MS rats (Dunn’s test) (Fig. 9E). ## The effects of MS and early-life SAL/VEH injections on fear conditioning and memory in preadolescent and adult rats On PND 26, preadolescent rats underwent the FC procedure. Statistical analysis of freezing behavior during the acquisition/training session (Day 1, context A) revealed a lack of a significant effect of early-life treatment (one-way ANOVA, F3,40 = 1.82, $$p \leq 0.160$$) (Online Resource ESM_1: Table S2) (Fig. 10A). However, when the expression of CFC was analyzed (Day 2, context A), we observed that MS rats showed reduced freezing in response to context A compared to AFR rats (F3,40 = 4.20, $$p \leq 0.011$$, ANOVA followed by Tukey’s test) (Fig. 10B). The behavior of VEH-MS and SAL-MS rats did not differ significantly from MS rats (for VEH-MS: $$p \leq 0.162$$; for SAL-MS: $$p \leq 0.654$$) or AFR rats (for VEH-MS: $$p \leq 0.689$$; for SAL-MS: $$p \leq 0.156$$, Tukey’s test). Additionally, there was no significant effect of early-life treatment on the expression of AFC (Day 2, context B) in preadolescent rats (H3 = 2.91, N1 = 14, N2–4 = 10, $$p \leq 0.405$$, Kruskal–Wallis test) (Fig. 10C).Fig. 10The effects of MS and early-life SAL/VEH injections on fear conditioning and memory in preadolescent (A–C) and adult rats (D–F). The data are presented as the mean ± SD (A, B, D–F) or median and IQR (C) and expressed as a percentage of the session time ($$n = 10$$–14). Results were analyzed by one-way ANOVA or Kruskal–Wallis test, respectively. Circles represent individual data points. Connectors indicate statistically significant differences between specific experimental groups in Tukey’s HSD post hoc test. AFC auditory fear conditioning, AFR animal facility rearing, CFC contextual fear conditioning, FC fear conditioning, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle Six weeks after the FC training, the same animals were once again tested in both contexts A and B for the recall of fear memories in adulthood (Table 3). Statistical analysis of freezing behavior revealed a lack of a significant effect of early-life treatment on CFC (H3 = 4.14, $$p \leq 0.246$$) and AFC memory recall in adult rats (H3 = 1.63, N1 = 14, N2–4 = 10, $$p \leq 0.653$$, Kruskal–Wallis test) (Online Resource ESM_4: Fig. S8A–B). Seven days later, all animals underwent a session of retraining of FC in context A. Retraining followed the same procedure as training during preadolescence (Day 1 of experiment) (Table 3). On the following day, fear memory was tested both in contexts A and B (the same sessions as on Day 2 during preadolescence). One way ANOVA of freezing behavior during retraining of FC did not show any differences between experimental groups (F3,40 = 0.99, $$p \leq 0.406$$) (Fig. 10D). Interestingly, in our analysis of the CFC expression after retraining in adulthood we observed that MS rats showed increased freezing in response to context A compared to AFR rats (F3,40 = 3.28, $$p \leq 0.031$$, ANOVA followed by Tukey’s test) (Table S2) (Fig. 10E). Additionally, VEH injections significantly reduced freezing behavior in MS rats. The SAL-MS rats did not differ significantly from the MS rats ($$p \leq 0.115$$), VEH-MS ($$p \leq 0.979$$) or AFR rats ($$p \leq 0.996$$) in their CFC expression after retraining in adulthood (Tukey’s test) (Fig. 10E). Additionally, there was no significant effect of early-life treatment on the expression of AFC after retraining in adulthood (in context B) (F3,40 = 1.92, $$p \leq 0.141$$, ANOVA) (Fig. 10F). ## The effects of MS and early-life SAL/VEH injections on sucrose preference in preadolescent and adult rats To study anhedonic-like behaviors, we first applied the sucrose preference test in preadolescent rats, and then, six weeks later, the same animals were retested for sucrose preference when they approached adulthood (PND 70). Statistical analysis of sucrose preference during the preadolescence period revealed a significant effect of early-life treatment (one-way ANOVA, F3,36 = 3.82, $$p \leq 0.018$$) (Online Resource ESM_1: Table S2). Specifically, MS rats showed reduced sucrose preference compared to AFR rats (Fig. 11A) (Tukey’s test). The behavior of VEH-MS and SAL-MS rats did not differ significantly from MS rats (for VEH-MS: $$p \leq 0.770$$; for SAL-MS: $$p \leq 0.743$$) or AFR rats (for VEH-MS: $$p \leq 0.117$$; for SAL-MS: $$p \leq 0.129$$, Tukey’s test) (Fig. 11A). There was no difference in sucrose preference between the treatment groups in adulthood (Kruskal–Wallis test, H3 = 0.21, N1–4 = 10, $$p \leq 0.996$$) (Fig. 11B).Fig. 11The effects of MS and early-life SAL/VEH injections on sucrose preference in preadolescent (A) and adult rats (B). The data are presented as the mean ± SD (A) or median and IQR (B) and were analyzed by one-way ANOVA or Kruskal–Wallis test, respectively ($$n = 10$$). Circles represent individual data points. Connector indicates statistically significant difference between specific experimental groups in Tukey’s HSD post hoc test. AFR animal facility rearing, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle ## The effects of MS and early-life SAL/VEH injections on novelty- and amphetamine-induced locomotor activity in preadolescent and adult rats The next goal of the study was to determine whether early-life treatment affected the locomotor activity of rats in response to novelty and amphetamine injections. Analysis of novelty-induced locomotion in preadolescent rats revealed that VEH-injected MS rats traveled a greater distance than MS rats (F3,36 = 2.91, $$p \leq 0.047$$, ANOVA followed by Tukey’s test) (Online Resource ESM_1: Table S2) (Fig. 12A). A mixed-design ANOVA of amphetamine-induced locomotion during preadolescence showed the significant effects of early-life treatment (F3,36 = 3.53, $$p \leq 0.024$$) and amphetamine injection (F1,36 = 338.20, $p \leq 0.0001$) on PND 26 and a significant interaction between these factors (F3,36 = 3.68, $$p \leq 0.021$$). In all experimental groups, amphetamine injection enhanced locomotor activity compared to VEH injection (Tukey’s test) (Fig. 12B). Moreover, MS rats showed greater amphetamine-induced locomotor activity than AFR rats, and this effect was prevented by early-life VEH and SAL treatment (Tukey’s test) (Fig. 12B).Fig. 12The effects of MS and early-life SAL/VEH injections on locomotor activity: novelty-induced locomotion in preadolescents (A), amphetamine (AMP)-induced locomotor activity in preadolescents (B) and adults (C). The data are presented as the mean ± SD ($$n = 10$$–14) and expressed as distance traveled during the specific session. Results were analyzed by mixed-design ANOVA. Circles represent individual data points. * $p \leq 0.01$ vs. AMP in corresponding early-life treatment group (Tukey’s HSD post hoc test). Connectors indicate other statistically significant differences between specific experimental groups in Tukey’s test. AFR animal facility rearing, AMP amphetamine, DT distance traveled, MS maternal separation, PND postnatal day, SAL salubrinal, VEH vehicle After six weeks, the same groups of rats were retested for locomotor activity when they approached adulthood (PND 70) (Fig. 12C). A mixed-design ANOVA of amphetamine-induced locomotion during adulthood also revealed statistically significant effects of early-life treatment (F3,36 = 7.17, $$p \leq 0.0007$$) and amphetamine injection (F1,36 = 203.27, $p \leq 0.0001$) as well as a significant interaction between these factors (F3,36 = 7.96, $$p \leq 0.0003$$). In all experimental groups, amphetamine injection enhanced locomotor activity compared to VEH injection (Tukey’s test) (Fig. 12C). Additionally, MS and VEH-MS rats showed greater amphetamine-induced locomotion than AFR rats. Moreover, SAL injections reduced amphetamine-triggered locomotor activity in MS rats (Tukey’s test) (Fig. 12C). ## A search for a permanent imprint of MS and early-life SAL/VEH treatment on ER stress, the UPR and apoptosis in the mPFC To determine whether MS and early-life SAL/VEH treatment left a permanent imprint on the expression of ER stress, UPR and apoptosis markers in the mPFC, we measured expression levels of relevant mRNA in adult rats. Statistical analysis revealed that among ER stress and UPR markers, only the expression of Eif2a was significantly affected by early-life treatment (one-way ANOVA: F3,20 = 4.07, $$p \leq 0.021$$) (all results and statistics are presented in Table 6). Specifically, SAL-injected MS rats showed lower Eif2a mRNA levels than AFR rats. A similar trend was also observed in VEH-injected MS rats, though it was not statistically significant ($$p \leq 0.059$$) (Tukey’s test) (Table 6).Table 6The effects of MS and early-life SAL/VEH treatment on mRNA expression of ER stress, UPR and apoptosis markers in the mPFC of adult ratsGeneGroupRelative mRNA levelStatisticHspa5AFR0.1622 ± 0.0071F3,20 = 1.74, $$p \leq 0.190$$MS0.1618 ± 0.0072VEH-MS0.1534 ± 0.0134SAL-MS0.1512 ± 0.0127Eif2ak3AFR0.0222 ± 0.0019F3,20 = 3.00, $$p \leq 0.055$$MS0.0206 ± 0.0012VEH-MS0.0211 ± 0.0016SAL-MS0.0231 ± 0.0014Ern1AFR0.00420 (0.0005)H3 = 3.99, $$p \leq 0.262$$MS0.00440 (0.0004)VEH-MS0.00404 (0.0004)SAL-MS0.00463 (0.0008)Atf6AFR0.0312 (0.0074)H3 = 4.73, $$p \leq 0.193$$MS0.0305 (0.0015)VEH-MS0.0307 (0.0022)SAL-MS0.0324 (0.0032)Eif2aAFR0.0116 ± 0.0010F3,20 = 4.07, $$p \leq 0.021$$MS0.0106 ± 0.0009VEH-MS0.0091 ± 0.0013SAL-MS0.0088 ± 0.0025Casp9AFR0.0161 ± 0.0011F3,20 = 4.20, $$p \leq 0.019$$MS0.0175 ± 0.0009VEH-MS0.0181 ± 0.0018SAL-MS0.0182 ± 0.0005Casp3AFR0.00105 (0.0002)H3 = 3.85, $$p \leq 0.278$$MS0.00093 (0.0004)VEH-MS0.00093 (0.0001)SAL-MS0.00104 (0.0001)Casp12AFR0.00010 (0.00011)H3 = 5.93, $$p \leq 0.115$$MS0.00010 (0.00003)VEH-MS0.00008 (0.00002)SAL-MS0.00015 (0.00012)BaxAFR0.0506 (0.0028)H3 = 7.43, $$p \leq 0.069$$MS0.0474 (0.0018)VEH-MS0.0406 (0.0048)SAL-MS0.0463 (0.0040)Bcl2AFR0.0042 ± 0.0002F3,20 = 4.71, $$p \leq 0.012$$MS0.0043 ± 0.0004VEH-MS0.0039 ± 0.0004SAL-MS0.0046 ± 0.0002#The mRNA expression was determined by RT-qPCR and presented as relative values of mRNA levels in arbitrary units. The data are presented as the mean ± SD or median (IQR), $$n = 6$.$ Statistically significant effects are given in bold. $p \leq 0.05$ vs. AFR, #$p \leq 0.05$ vs. VEH-MS (Tukey’s HSD post hoc test). AFR animals facility rearing, ER endoplasmic reticulum, IQR interquartile range, mPFC medial prefrontal cortex, MS maternal separation, SAL salubrinal, UPR unfolded protein response, VEH vehicle Analysis of the effect of early-life treatment on the expression of apoptotic markers showed statistical significance only in the case of caspase-9 (F3,20 = 4.20, $$p \leq 0.019$$) and Bcl2 mRNA expression (F3,20 = 4.71, $$p \leq 0.012$$, ANOVA) (Table 6). Specifically, both SAL- and VEH-injected MS rats had greater levels of caspase-9 mRNA compared to AFR rats (Tukey’s test). Additionally, SAL-MS rats showed increased Bcl2 expression compared with VEH-MS rats (Tukey’s test) (Table 6). Finally, we analyzed the effect of early-life MS and SAL/VEH treatment on the number of neurons and glial cells in the mPFC of adult rats. Representative photomicrographs showing NeuN-IR neurons, GFAP-IR astrocytes and IBA1-IR microglial cells in the subregions of the mPFC of adult rats are presented in Online Resource ESM_8–10. Statistical analysis revealed that early-life treatment significantly affected the number of microglial cells but not the other populations of analyzed cells (all results and statistics are presented in Table 7). Notably, SAL-injected MS rats had a lower number of IBA1-IR microglial cells than MS and AFR rats in the PLC region (F3,20 = 4.01, $$p \leq 0.022$$, one-way ANOVA followed by Tukey’s test). Moreover, in the Cg1 region of the mPFC, SAL-MS rats also had a lower number of microglial cells than AFR rats (F3,20 = 3.47, $$p \leq 0.035$$, ANOVA followed by Tukey’s test) (Table 7).Table 7The effects of MS and early-life SAL/VEH treatment on the number of neurons astrocytes and microglial cells in the mPFC of adult ratsCell markermPFC regionGroupNumber of IR cellsStatisticNeuNCg1AFR610,968.1 ± 29,926.0F3,20 = 1.54, $$p \leq 0.236$$MS573,292.1 ± 50,768.4VEH-MS573,422.1 ± 28,788.9SAL-MS585,200.9 ± 24,677.5PLCAFR1,164,797.7 ± 77,242.8F3,20 = 1.06, $$p \leq 0.386$$MS1,148,378.2 ± 69,778.6VEH-MS1,123,820.5 ± 48,786.8SAL-MS1,100,882.2 ± 66,967.7ILCAFR225,567.6 ± 11,134.5F3,20 = 0.94, $$p \leq 0.442$$MS221,745.4 ± 13,552.3VEH-MS215,989.6 ± 16,123.5SAL-MS214,057.7 ± 12,151.9GFAPCg1AFR173,806.4 (22,206.7)H3 = 6.31, $$p \leq 0.097$$MS175,147.5 (57,261.2)VEH-MS208,999.4 (50,311.9)SAL-MS177,485.1 (22,710.1)PLCAFR350,950.1 (46,224.9)H3 = 3.39, $$p \leq 0.335$$MS387,533.7 (126,461.1)VEH-MS379,827.4 (77,912.9)SAL-MS373,984.3 (33,948.9)ILCAFR104,831.6 ± 10,104.6F3,20 = 2.88, $$p \leq 0.061$$MS118,909.7 ± 18,272.5VEH-MS124,738.9 ± 13,458.0SAL-MS107,309.7 ± 11,342.5IBA1Cg1AFR103,190.2 ± 5918.0F3,20 = 3.47, $$p \leq 0.035$$MS98,219.3 ± 7121.3VEH-MS94,696.7 ± 9686.7SAL-MS88,517.6 ± 9156.4PLCAFR175,265.5 ± 7211.8F3,20 = 4.01, $$p \leq 0.022$$MS173,557.7 ± 16,899.1VEH-MS165,775.0 ± 12,457.6SAL-MS154,006.0 ± 8281.9#ILCAFR40,693.6 ± 3630.7F3,20 = 3.06, $$p \leq 0.052$$MS40,061.2 ± 3470.1VEH-MS37,174.0 ± 4103.3SAL-MS35,219.0 ± 3023.6Data indicate the numbers of IR cells per region estimated by stereological method (the mean ± SD or median (IQR), $$n = 6$$). Statistically significant effects are given in bold. * $p \leq 0.05$ vs. AFR, #$p \leq 0.05$ vs. MS (Tukey’s HSD post hoc test). AFR animal facility rearing, Cg1 cingulate cortex, ILC infralimbic cortex, IQR interquartile range, IR immunoreactive, mPFC medial prefrontal cortex, MS maternal separation, PLC prelimbic cortex, PND postnatal day, SAL salubrinal, VEH vehicle ## Discussion The main goal of the present study was to investigate whether ER stress and UPR processes are affected by the MS procedure and thereby underlie the cellular and behavioral consequences of ELS. We found that MS enhanced the activation of the UPR in juveniles to a small degree and modulated the mRNA expression of a few apoptotic markers in the mPFC of juveniles and preadolescents but not in adults. However, MS did not affect the numbers of neurons or glial cells in the mPFC at any age. Both early-life SAL and VEH injections (often in a treatment-specific manner) affected the expression of UPR and apoptotic markers, especially in juvenile and preadolescent MS rats, and in some cases prevented MS-induced effects at the biochemical level. Moreover, SAL/VEH generally mitigated the behavioral effects of MS. ## The effects of MS procedure on ER stress and UPR processes and apoptosis in the mPFC Enhanced expression and activation of ER stress and UPR markers have been observed in animal models of depression based on chronic stress procedures in adults [27–29] and in different acute stress models [53, 54]. Moreover, the infusion of tunicamycin, an activator of the UPR, into the hippocampus produces a depressive-like phenotype in rats [55]. We have recently shown that MS produces long-lasting upregulation of chaperones HSPA5 and HSPA1B in the brain and blood, which suggests that ELS may influence ER stress and UPR processes throughout development [30]. To the best of our knowledge, the present study is the first to comprehensively examine the role of ER stress and UPR processes in ELS-induced effects. We once again confirmed that MS increased the protein expression of HSPA5 in the mPFC of juveniles on PND 15, which is 24 h after the last MS. MS also increased the phosphorylation (activation) of one of the ER stress sensors, IRE1α, in juveniles. These effects were temporal, and we generally did not observe any MS-induced changes in the expression of ER stress or UPR markers in the mPFC of preadolescents and adults. Concurrently, in this study, we observed subtle changes in the mRNA expression of apoptotic markers in MS juveniles and preadolescents but not in adults. Specifically, the expression of Casp12 was increased in juveniles and decreased in preadolescent MS rats. Casp12 is a specific caspase that is localized in the ER membrane and engaged in the ER stress-induced pathway of apoptosis [56]. We also observed an MS-induced increase in the mRNA expression of Bcl2 in preadolescents. These subtle changes in the expression of UPR and apoptotic markers were not accompanied by any changes in the numbers of neurons, astrocytes or microglial cells in the mPFC of preadolescent or adult MS rats. These results are in contrast to our previous observations in adolescent rats (on PND 35), in which we found a clear delay in neurodevelopmental apoptosis, manifested as increased numbers of neurons in the mPFC and antiapoptotic trends in the expression and activation of apoptotic markers [10]. Thus, our results suggest that adolescence is a developmental period that specifically unveils the effects of ELS on neurodevelopmental apoptosis of the mPFC [10, 37]. This is not surprising, because during adolescence, the mPFC undergoes intensive structural and functional reorganization [32, 34]. Nevertheless, we expected that the effects of MS would be manifested even earlier, in preadolescence period, but our biochemical results did not support that hypothesis. However, in the present study we did observe some behavioral effects of MS in the preadolescence period, such as a decrease in sucrose preference (anhedonia-like behavior) and an impairment in CFC expression. Moreover, MS-triggered enhancement of the locomotor response to psychostimulant drugs, a typical behavioral phenotype observed in ELS models [39, 57, 58], was also observed in preadolescents. This behavioral effect was the only enduring effect observed also in adult MS rats. Additionally, MS rats showed an increased CFC expression after retraining in adulthood. It is worth noting that the mPFC is highly implicated both in the expression of FC [59] and psychostimulant-induced hyperlocomotion [60]. Taken together, the results showed that, although we did not detect a strong impact of MS on ER stress, the UPR and apoptosis in the mPFC of juveniles and preadolescents, MS did produce long-lasting functional consequences observed at behavioral level even during preadolescence period. Our study implicates that MS procedure may influence ER stress and the UPR in an age-specific manner and manifest its strongest effects on the abovementioned processes in different developmental time points than that chosen for the present experiment. However, we should bear in mind that the studied behaviors are also regulated by many other cellular mechanisms and brain regions. ## The effects of early-life modulation of ER stress and the UPR by SAL/VEH treatment on biochemical and behavioral phenotype of MS rats To modulate ER stress and UPR processes in MS rats, we applied repeated early-life treatment with SAL before each MS procedure. SAL is a small-molecule inhibitor of eIF2α phosphatases that prolongs eIF2α phosphorylation at residue S51 and thereby its inactivation, which causes an inhibition of general protein synthesis. SAL has been shown to reduce cell death and have neuroprotective properties in animal models of neurodegenerative disorders [13, 61], cerebral ischemia [43] and traumatic brain injury [42, 62]. In the present study, we also used conventional solvent/vehicle injections (VEH) to adequately control experimental conditions. Interestingly, early-life VEH treatment by itself affected the studied parameters and sometimes produced similar effects to SAL treatment. This phenomenon greatly complicated understanding and interpretation of the results. Both VEH and SAL treatments prevented some MS-induced effects. For example, SAL treatment decreased Bcl2 mRNA levels in preadolescent rats and dampened amphetamine-induced hyperlocomotion in preadolescents and adults (SAL-MS vs. MS rats). VEH injections prevented the effects of MS at the level of Casp12 and Bcl2 transcription in juveniles and preadolescents, respectively. At behavioral level, VEH treatment also reduced amphetamine-triggered locomotor activity in preadolescent MS rats and CFC expression in MS adult rats (VEH-MS vs. MS rats). In many cases, when MS rats resembled AFR rats at the biochemical level, SAL or VEH significantly modulated the mRNA expression of ER stress, UPR and apoptosis markers especially in juvenile and preadolescent MS rats. For example, SAL- and/or VEH-MS rats generally showed reduced mRNA expression of many ER stress and UPR markers when compared to MS rats. The results suggest that both VEH and SAL treatment exerted inhibitory influence on ER stress and UPR processes in MS rats. At the behavioral level, in sucrose preference or CFC tests, SAL- and VEH-MS preadolescent rats exhibited intermediate behavioral phenotypes between AFR and MS rats (not significantly different from either AFR and MS rats), and those phenotypes turned out to be advantageous in the specific experimental conditions of this study. The effect of SAL/VEH on anxiety-like behaviors in the light/dark box test is also worth noting. Interestingly, preadolescent SAL-MS and VEH-MS rats were less anxious than AFR rats. On the other hand, in adulthood, SAL and VEH normalized the behavior of MS rats, which showed a statistically insignificant trend toward less fearful (impulsive-like) behavior. However, when a single comparison between the AFR and MS groups was performed, the analysis revealed that MS significantly enhanced impulsive-like behavior in adults, which is in line with our previous studies [8, 30]. Interestingly, Logstdon et al. reported that SAL treatment reduced impulsive-like behaviors in adult rats subjected to traumatic brain injury [42, 62]. Whereas, Jangra et al. showed that other ER stress inhibitor, the chemical chaperone sodium phenylbutyrate, abrogated anxiety- and depressive-like behaviors in adult mice subjected to chronic restraint stress [27]. It is worth emphasizing that, in contrast to the abovementioned studies, we injected SAL/VEH during the early-life period, and the behavioral consequences of that treatment were observed at later developmental stages, even in adulthood. Our results concerning SAL/VEH action in our experimental paradigm lead to the question of whether SAL treatment exerted a specific biological effect as an inhibitor of eIF2α dephosphorylation, promoting the inhibition of global translation. Some data evidently supported the specific action of SAL in our experiment. Namely, 24 h after the last separation in juveniles, the mRNA and protein expression of eIF2α was significantly lower in SAL-MS rats than in MS rats. These results may indicate that some kind of compensation or adaptation to repeated SAL injections and to an inhibition of eIF2α activity occurred. Interestingly, in adulthood, SAL-MS rats still showed low levels of Eif2a expression, though this effect was statistically significant only when compared to AFR rats. Although we did not observe an increase in eIF2α phosphorylation after SAL treatment in MS juveniles, this result was not surprising because MS by itself did not induce activation/phosphorylation of PERK or eIF2α. However, it is important to note that PERK is not the only kinase that phosphorylates eIF2α [63]. Additionally, although eIF2α phosphorylation attenuates general translation, it simultaneously promotes translation of specific proteins, such as activating transcription factor 4 (ATF4). ATF4 is known to activate the transcription of the regulatory subunit of protein phosphatase 1, also known as growth arrest and DNA damage-inducible protein GADD34, to generate active eIF2α phosphatase and initiate a feedback loop to dephosphorylate eIF2α and consequently restore general protein synthesis. This feedback loop in the regulation of eIF2α phosphorylation is the main concern associated with the use of SAL and other eIF2α phosphatase inhibitors in the clinic and in animal models because it limits the duration of their action [13]. Searching for another evidence for the specific action of SAL, it is worth noting a decrease in the number of microglial cells in the PLC of SAL-MS adult rats compared to MS and AFR rats. This interesting observation needs further studies. It is well known that ER stress and the UPR are key regulators of inflammation and function of immune cells in the periphery and brain [11, 14, 15]. Early-life SAL treatment could potentially influence the rate of postnatal proliferation and/or apoptosis of microglial cells. ## Enduring biological action of early-life VEH injections: a pitfall and challenge for controlling of experimental conditions It is well known that routine laboratory procedures such as animal handling and injections involve some level of mild to moderate physical and psychological stress. Acute procedures usually activate the hypothalamic–pituitary–adrenal axis and increase the levels of glucocorticoids, whereas chronic interventions lead to a desensitization of this response with time [64–66]. Therefore, in pharmacological studies, solvent/vehicle injections are commonly used to control experimental conditions. However, growing amount of data has accumulated and shown that both acute and chronic VEH injections not only modulate serum glucocorticoids levels but also affect animal behavior [64, 65, 67]. For example, single ip injections of saline produced anxiogenic effect in mice [66]. A recent study also demonstrated that repeated saline injections for 6 weeks (starting during the adolescent period) increased anxiety-like behaviors, decreased systemic inflammation, and increased corticosterone reactivity and microglial activation in the dentate gyrus of the hippocampus [67]. However, when saline treatment was combined with additional stress (social isolation), it did not worsen and even improved some effects produced by chronic stress [67]. A similar trend was observed in our study. It has been argued that exposure to moderate but not minimal or substantial amounts of stressors, especially during the perinatal period, may facilitate coping with other environmental challenges later in life. This phenomenon is known as stress inoculation [68, 69]. In the case of our study, we have a combination of two early-life stressors (MS and VEH/SAL injections) that turned out to be beneficial for MS rats and produced a more resilient phenotype in preadolescents and adults. Our study suggests that a modulation of ER stress and UPR processes may underlie the injection-triggered changes in animal behavior, especially when injections are applied during a critical period of early-life development. However, it is worth underlining that we studied the effects of VEH treatment only in stressed subjects and not in control (AFR) animals. We cannot also overlook in our discussion a potential biological action of the solvent/vehicle used in our experiments, $2.5\%$ DMSO diluted in PBS and given in a dose of 0.125 µl of DMSO per gram of body weight. We chose DMSO as a vehicle for SAL based on a large amount of previous data in the literature [42, 43, 70]. DMSO is routinely used in biological research as a solvent and a cryopreservative in bone marrow and organ transplants. However, data have accumulated showing that DMSO may produce both adverse and beneficial effects on brain tissue [71–75]. A small dose of 0.2 µl/g given for 5 days to juvenile rats has been shown to lead to global changes in the brain metabolome and increase oxidative stress and proteolysis markers. However, only higher doses of DMSO (2 and 4 µl/g) have been shown to affect rat behavior, i.e., decreased social habits [75]. Another group demonstrated that DMSO (0.3–10 µl/g) produced widespread apoptosis in the developing brain [73]. Nevertheless, in the above-cited studies, DMSO was injected in undiluted form (~ $100\%$) and at higher doses than our VEH treatment. On the other hand, DMSO has also been shown to have neuroprotective and procognitive properties in animal models of ischemia, cerebral hypoperfusion and Alzheimer’s disease [71, 72, 74]. It has been argued that the neuroprotective effects of DMSO may be linked to its anti-inflammatory and free radical scavenging activities [72, 76]. To the best of our knowledge, only one study showed that DMSO modulated (increased) the expression of UPR genes, including Hspa5, though in mouse embryos and in the context of the cytotoxic effects of DMSO [77]. Unfortunately, based on the results presented in our study, we cannot explicitly determine whether the effects produced by VEH/SAL treatments in MS rats are specifically related to the action of SAL or DMSO or the procedure of repeated injections during the early-life period. The results show how unpredictable the effects of repeated VEH injections can be in the early-life period. However, we can at least state that early-life VEH/SAL treatment modulated ER stress and UPR processes in MS rats to some extent. Although, SAL/VEH treatment did not leave a permanent imprint on the expression of ER stress and UPR markers in the mPFC of MS adults, it promoted resilience at the behavioral level in both preadolescent and adult rats. Recently, it has been argued that ER stress and the UPR, next to oxidative stress and hormonal regulation, may play a role in mediating inter- and intraspecific variations in response to different environmental conditions and, in this way, may shape susceptibility or resilience to stressors [78]. ER stress and the UPR are evolutionarily conserved and heritable cellular processes. There are also considerable individual variations in ER stress and UPR phenotypes in humans and nonhuman animals, suggesting that these phenotypes can be subjected to natural selection [78, 79]. Strong individual variations in ER stress and UPR phenotypes may be, to some degree, responsible for the relatively small changes in the expression of ER stress and UPR markers observed in our experimental paradigm, which involved Wistar outbred rats. Nevertheless, these small changes may better reflect the situation in naturally existing populations. The main limitation of the present study was that we did not include female subjects in the whole experiment and analysis and not explore sex differences in ER stress and UPR signaling. However, our pilot study of the mRNA expression of UPR and apoptotic markers showed that females were less affected by MS procedure and early-life SAL/VEH treatment than males. It is generally in line with our previous reports showing that in many aspects female rats are more resilient to MS procedure conducted in our laboratory [8, 9, 40]. ## The role of ER stress and UPR signaling in the pathophysiology of ELS-related diseases: potential clinical implications Epidemiological and clinical studies clearly show that ELS not only increases the risk of mental disorders but also physical health problems, such as metabolic syndrome that may lead to cardiovascular diseases and type 2 diabetes [80, 81]. Recently, broadscale attempts to identify causative mechanisms linking ELS to psycho-cardio-metabolic multimorbidity have been started [82]. We hypothesize that ER stress and UPR processes may potentially represent shared molecular pathways and mechanisms by which ELS affects both mental and physical health. ER stress and UPR signaling acts in most cells and tissues and has been implicated in the pathophysiology of numerous diseases, such as cancer, diabetes, atherosclerosis, neurodegenerative diseases [15, 16, 83], as well as MDD and BD [17–22]. Interestingly, all the above mentioned diseases have evident inflammatory components and ER stress and UPR processes are known to regulate inflammatory response [11, 14, 15, 84–86]. In the present study we applied a systemic SAL administration, therefore this inhibitor of ER stress could potentially affect not only the brain but also other organs and systems. Further multiorgan studies are needed to confirm the hypothesis that ER stress and UPR signaling can be implicated in the pathophysiology of ELS-related diseases or the phenomenon of resilience. Many pharmacological strategies targeting different components of UPR signaling for disease intervention have been tested in preclinical and clinical studies [12, 87]. They include chemical chaperones and small-molecule activators or inhibitors of the UPR, such as SAL [13, 87] The most promising strategies concern the treatment of cancer and cardiovascular and neurodegenerative disorders [87, 88]. However, UPR-targeting drugs are non-selective and their potential administration to the patients with a history of ELS and multimorbidity is rather unlikely. Nevertheless, the key players in ER stress and the UPR can be at least potential candidate biomarkers of ELS-related changes, measured in blood or peripheral organ biopsy samples and help to diagnose ELS-induced multimorbidity [12]. UPR biomarkers have been already used to monitor progression of cancer, kidney disease, neurodegenerative diseases [12, 89]. It is worth mentioning that we previously showed that MS procedure caused enduring upregulation of Hspa5 expression in the blood that was accompanied by impulsive- and depressive-like behavior in male adult rats [30]. ## Conclusions We found that MS did not exert a strong impact on ER stress and UPR processes or apoptosis at developmental stages under study. However, both early-life SAL and VEH treatment (often in an injection-specific manner) influenced the expression of UPR and apoptotic markers, especially in juvenile and preadolescent MS rats, and in some cases prevented MS-induced effects at the biochemical level. Moreover, SAL and/or VEH alleviated some behavioral effects of MS in both preadolescent and adult rats. These results suggest that a regulation of ER stress and UPR processes may play a potential role in the mechanisms of susceptibility or resilience to ELS and other environmental factors. Further multiorgan studies are needed to validate this interesting hypothesis in future. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (PDF 493 KB)Supplementary file2 (PDF 4195 KB)Supplementary file3 (PDF 6720 KB)Supplementary file4 (PDF 1219 KB)Supplementary file5 (PDF 12705 KB)Supplementary file6 (PDF 13772 KB)Supplementary file7 (PDF 13115 KB)Supplementary file8 (PDF 13115 KB)Supplementary file9 (PDF 13264 KB)Supplementary file10 (PDF 13173 KB)Supplementary file11 (PDF 10308 KB)Supplementary file12 (PDF 2971 KB)Supplementary file13 (PDF 6188 KB)Supplementary file14 (PDF 3250 KB) ## References 1. Green JG, McLaughlin KA, Berglund PA, Gruber MJ, Sampson NA, Zaslavsky AM. Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication I: associations with first onset of DSM-IV disorders. Arch Gen Psychiatry (2010) 67 113-123. DOI: 10.1001/archgenpsychiatry.2009.186 2. Smith KE, Pollak SD. Early life stress and development: potential mechanisms for adverse outcomes. J Neurodev Disord (2020) 12 34. DOI: 10.1186/s11689-020-09337-y 3. Teicher MH, Samson JA, Anderson CM, Ohashi K. The effects of childhood maltreatment on brain structure, function and connectivity. Nat Rev Neurosci (2016) 17 652-666. DOI: 10.1038/nrn.2016.111 4. Agorastos A, Pervanidou P, Chrousos GP, Baker DG. Developmental trajectories of early life stress and trauma: a narrative review on neurobiological aspects beyond stress system dysregulation. Front Psychiatry (2019) 10 118. DOI: 10.3389/fpsyt.2019.00118 5. Noll JG, Shalev I. The biology of early life stress, understanding child maltreatment and trauma (2018) 6. Italia M, Forastieri C, Longaretti A, Battaglioli E, Rusconi F. Rationale, relevance, and limits of stress-induced psychopathology in rodents as models for psychiatry research: an introductory overview. Int J Mol Sci. (2020) 21 7455. DOI: 10.3390/ijms21207455 7. Vetulani J. Early maternal separation: a rodent model of depression and a prevailing human condition. Pharmacol Rep (2013) 65 1451-1461. DOI: 10.1016/S1734-1140(13)71505-6 8. Chocyk A, Majcher-Maslanka I, Przyborowska A, Mackowiak M, Wedzony K. Early-life stress increases the survival of midbrain neurons during postnatal development and enhances reward-related and anxiolytic-like behaviors in a sex-dependent fashion. Int J Dev Neurosci (2015) 44 33-47. DOI: 10.1016/j.ijdevneu.2015.05.002 9. Chocyk A, Dudys D, Przyborowska A, Majcher I, Mackowiak M, Wedzony K. Maternal separation affects the number, proliferation and apoptosis of glia cells in the substantia nigra and ventral tegmental area of juvenile rats. Neuroscience (2011) 173 1-18. DOI: 10.1016/j.neuroscience.2010.11.037 10. Majcher-Maslanka I, Solarz A, Chocyk A. Maternal separation disturbs postnatal development of the medial prefrontal cortex and affects the number of neurons and glial cells in adolescent rats. Neuroscience (2019) 423 131-147. DOI: 10.1016/j.neuroscience.2019.10.033 11. Hetz C, Papa FR. The unfolded protein response and cell fate control. Mol Cell (2018) 69 169-181. DOI: 10.1016/j.molcel.2017.06.017 12. Almanza A, Carlesso A, Chintha C, Creedican S, Doultsinos D, Leuzzi B. Endoplasmic reticulum stress signaling—from basic mechanisms to clinical applications. FEBS J (2019) 286 241-278. DOI: 10.1111/febs.14608 13. Fullwood MJ, Zhou W, Shenolikar S. Targeting phosphorylation of eukaryotic initiation factor-2alpha to treat human disease. Prog Mol Biol Transl Sci (2012) 106 75-106. DOI: 10.1016/B978-0-12-396456-4.00005-5 14. Kitamura M. The unfolded protein response triggered by environmental factors. Semin Immunopathol (2013) 35 259-275. DOI: 10.1007/s00281-013-0371-y 15. Chaudhari N, Talwar P, Parimisetty A, Lefebvre d'Hellencourt C, Ravanan P. A molecular web: endoplasmic reticulum stress, inflammation, and oxidative stress. Front Cell Neurosci (2014) 8 213. DOI: 10.3389/fncel.2014.00213 16. Tabas I. The role of endoplasmic reticulum stress in the progression of atherosclerosis. Circ Res (2010) 107 839-850. DOI: 10.1161/CIRCRESAHA.110.224766 17. Kakiuchi C, Ishiwata M, Nanko S, Kunugi H, Minabe Y, Nakamura K. Functional polymorphisms of HSPA5: possible association with bipolar disorder. Biochem Biophys Res Commun (2005) 336 1136-1143. DOI: 10.1016/j.bbrc.2005.08.248 18. Le-Niculescu H, Balaraman Y, Patel SD, Ayalew M, Gupta J, Kuczenski R. Convergent functional genomics of anxiety disorders: translational identification of genes, biomarkers, pathways and mechanisms. Transl Psychiatry (2011) 1 e9. DOI: 10.1038/tp.2011.9 19. Yang R, Daigle BJ, Muhie SY, Hammamieh R, Jett M, Petzold L. Core modular blood and brain biomarkers in social defeat mouse model for post traumatic stress disorder. BMC Syst Biol (2013) 7 80. DOI: 10.1186/1752-0509-7-80 20. Nevell L, Zhang K, Aiello AE, Koenen K, Galea S, Soliven R. Elevated systemic expression of ER stress related genes is associated with stress-related mental disorders in the Detroit Neighborhood Health Study. Psychoneuroendocrinology (2014) 43 62-70. DOI: 10.1016/j.psyneuen.2014.01.013 21. Yoshino Y, Dwivedi Y. Elevated expression of unfolded protein response genes in the prefrontal cortex of depressed subjects: effect of suicide. J Affect Disord (2020) 262 229-236. DOI: 10.1016/j.jad.2019.11.001 22. Kowalczyk M, Kowalczyk E, Kwiatkowski P, Lopusiewicz L, Talarowska M, Sienkiewicz M. Cellular response to unfolded proteins in depression. Life (Basel) (2021) 11 1376. PMID: 34947907 23. Bown C, Wang JF, MacQueen G, Young LT. Increased temporal cortex ER stress proteins in depressed subjects who died by suicide. Neuropsychopharmacology (2000) 22 327-332. DOI: 10.1016/S0893-133X(99)00091-3 24. Pfaffenseller B, Wollenhaupt-Aguiar B, Fries GR, Colpo GD, Burque RK, Bristot G. Impaired endoplasmic reticulum stress response in bipolar disorder: cellular evidence of illness progression. Int J Neuropsychopharmacol (2014) 17 1453-1463. DOI: 10.1017/S1461145714000443 25. Bengesser SA, Fuchs R, Lackner N, Birner A, Reininghaus B, Meier-Allard N. Endoplasmic reticulum stress and bipolar disorder—almost forgotten therapeutic drug targets in the unfolded protein response pathway revisited. CNS Neurol Disord Drug Targets (2016) 15 403-413. DOI: 10.2174/1871527315666160321104613 26. Suliman M, Schmidtke MW, Greenberg ML. The role of the UPR pathway in the pathophysiology and treatment of bipolar disorder. Front Cell Neurosci (2021) 15 735622. DOI: 10.3389/fncel.2021.735622 27. Jangra A, Sriram CS, Dwivedi S, Gurjar SS, Hussain MI, Borah P. Sodium phenylbutyrate and edaravone abrogate chronic restraint stress-induced behavioral deficits: implication of oxido-nitrosative, endoplasmic reticulum stress cascade, and neuroinflammation. Cell Mol Neurobiol (2017) 37 65-81. DOI: 10.1007/s10571-016-0344-5 28. Tang J, Yu W, Chen S, Gao Z, Xiao B. Microglia polarization and endoplasmic reticulum stress in chronic social defeat stress induced depression mouse. Neurochem Res (2018) 43 985-994. DOI: 10.1007/s11064-018-2504-0 29. Li MX, Li Q, Sun XJ, Luo C, Li Y, Wang YN. Increased Homer1-mGluR5 mediates chronic stress-induced depressive-like behaviors and glutamatergic dysregulation via activation of PERK-eIF2alpha. Prog Neuropsychopharmacol Biol Psychiatry (2019) 95 109682. DOI: 10.1016/j.pnpbp.2019.109682 30. Solarz A, Majcher-Maslanka I, Kryst J, Chocyk A. A search for biomarkers of early-life stress-related psychopathology: focus on 70-kDa heat shock proteins. Neuroscience (2021) 463 238-253. DOI: 10.1016/j.neuroscience.2021.02.026 31. Godin JD, Creppe C, Laguesse S, Nguyen L. Emerging roles for the unfolded protein response in the developing nervous system. Trends Neurosci (2016) 39 394-404. DOI: 10.1016/j.tins.2016.04.002 32. Selemon LD. A role for synaptic plasticity in the adolescent development of executive function. Transl Psychiatry (2013) 3 e238. DOI: 10.1038/tp.2013.7 33. Willing J, Juraska JM. The timing of neuronal loss across adolescence in the medial prefrontal cortex of male and female rats. Neuroscience (2015) 301 268-275. DOI: 10.1016/j.neuroscience.2015.05.073 34. Caballero A, Granberg R, Tseng KY. Mechanisms contributing to prefrontal cortex maturation during adolescence. Neurosci Biobehav Rev (2016) 70 4-12. DOI: 10.1016/j.neubiorev.2016.05.013 35. Juraska JM, Drzewiecki CM. Cortical reorganization during adolescence: What the rat can tell us about the cellular basis. Dev Cogn Neurosci (2020) 45 100857. DOI: 10.1016/j.dcn.2020.100857 36. Myers-Schulz B, Koenigs M. Functional anatomy of ventromedial prefrontal cortex: implications for mood and anxiety disorders. Mol Psychiatry (2012) 17 132-141. DOI: 10.1038/mp.2011.88 37. Chocyk A, Bobula B, Dudys D, Przyborowska A, Majcher-Maslanka I, Hess G. Early-life stress affects the structural and functional plasticity of the medial prefrontal cortex in adolescent rats. Eur J Neurosci (2013) 38 2089-2107. DOI: 10.1111/ejn.12208 38. Chocyk A, Przyborowska A, Makuch W, Majcher-Maslanka I, Dudys D, Wedzony K. The effects of early-life adversity on fear memories in adolescent rats and their persistence into adulthood. Behav Brain Res (2014) 264 161-172. DOI: 10.1016/j.bbr.2014.01.040 39. Chocyk A, Przyborowska A, Dudys D, Majcher I, Mackowiak M, Wedzony K. The impact of maternal separation on the number of tyrosine hydroxylase-expressing midbrain neurons during different stages of ontogenesis. Neuroscience (2011) 182 43-61. DOI: 10.1016/j.neuroscience.2011.03.008 40. Solarz A, Majcher-Maslanka I, Kryst J, Chocyk A. Early-life stress affects peripheral, blood-brain barrier, and brain responses to immune challenge in juvenile and adult rats. Brain Behav Immun (2022) 108 1-15. DOI: 10.1016/j.bbi.2022.11.005 41. Solarz A, Majcher-Maslanka I, Chocyk A. Effects of early-life stress and sex on blood-brain barrier permeability and integrity in juvenile and adult rats. Dev Neurobiol (2021) 81 861-876. DOI: 10.1002/dneu.22846 42. Logsdon AF, Turner RC, Lucke-Wold BP, Robson MJ, Naser ZJ, Smith KE. Altering endoplasmic reticulum stress in a model of blast-induced traumatic brain injury controls cellular fate and ameliorates neuropsychiatric symptoms. Front Cell Neurosci (2014) 8 421. DOI: 10.3389/fncel.2014.00421 43. Nakka VP, Gusain A, Raghubir R. Endoplasmic reticulum stress plays critical role in brain damage after cerebral ischemia/reperfusion in rats. Neurotox Res (2010) 17 189-202. DOI: 10.1007/s12640-009-9110-5 44. Li B, Zani A, Lee C, Zani-Ruttenstock E, Zhang Z, Li X. Endoplasmic reticulum stress is involved in the colonic epithelium damage induced by maternal separation. J Pediatr Surg (2016) 51 1001-1004. DOI: 10.1016/j.jpedsurg.2016.02.073 45. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods (2001) 25 402-408. DOI: 10.1006/meth.2001.1262 46. Majcher-Maslanka I, Solarz A, Wedzony K, Chocyk A. Previous early-life stress modifies acute corticosterone-induced synaptic plasticity in the medial prefrontal cortex of adolescent rats. Neuroscience (2018) 379 316-333. DOI: 10.1016/j.neuroscience.2018.03.038 47. Majcher-Maslanka I, Solarz A, Chocyk A. The impact of early-life stress on corticosteroid carrier protein levels and 11beta-hydroxysteroid dehydrogenase 1 expression in adolescent rats. Pharmacol Rep (2019) 71 347-350. DOI: 10.1016/j.pharep.2018.11.003 48. Paxinos G, Watson C. The rat brain in stereotaxic coordinates (1998) 49. West MJ, Slomianka L, Gundersen HJ. Unbiased stereological estimation of the total number of neurons in thesubdivisions of the rat hippocampus using the optical fractionator. Anat Rec (1991) 231 482-497. DOI: 10.1002/ar.1092310411 50. West MJ. Estimating volume in biological structures. Cold Spring Harb Protoc (2012) 2012 1129-1139. PMID: 23118357 51. Bialon M, Chocyk A, Majcher-Maslanka I, Zarnowska M, Michalski K, Antkiewicz-Michaluk L. 1MeTIQ and olanzapine, despite their neurochemical impact, did not ameliorate performance in fear conditioning and social interaction tests in an MK-801 rat model of schizophrenia. Pharmacol Rep (2021) 73 490-505. DOI: 10.1007/s43440-020-00209-9 52. Majcher-Maslanka I, Solarz A, Wedzony K, Chocyk A. The effects of early-life stress on dopamine system function in adolescent female rats. Int J Dev Neurosci (2017) 57 24-33. DOI: 10.1016/j.ijdevneu.2017.01.001 53. Ishisaka M, Kudo T, Shimazawa M, Kakefuda K, Oyagi A, Hyakkoku K. Restraint-induced expression of endoplasmic reticulum stress-related genes in the mouse brain. Pharmacol Pharm (2011) 2 10-16. DOI: 10.4236/pp.2011.21002 54. Xie J, Han F, Shi Y. The unfolded protein response is triggered in rat neurons of the dorsal raphe nucleus after single-prolonged stress. Neurochem Res (2014) 39 741-747. DOI: 10.1007/s11064-014-1262-x 55. Timberlake Ii M, Roy B, Dwivedi Y. A novel animal model for studying depression featuring the induction of the unfolded protein response in hippocampus. Mol Neurobiol (2019) 56 8524-8536. DOI: 10.1007/s12035-019-01687-6 56. Szegezdi E, Fitzgerald U, Samali A. Caspase-12 and ER-stress-mediated apoptosis: the story so far. Ann N Y Acad Sci (2003) 1010 186-194. DOI: 10.1196/annals.1299.032 57. Rentesi G, Antoniou K, Marselos M, Syrrou M, Papadopoulou-Daifoti Z, Konstandi M. Early maternal deprivation-induced modifications in the neurobiological, neurochemical and behavioral profile of adult rats. Behav Brain Res (2013) 244 29-37. DOI: 10.1016/j.bbr.2013.01.040 58. Anier K, Malinovskaja K, Pruus K, Aonurm-Helm A, Zharkovsky A, Kalda A. Maternal separation is associated with DNA methylation and behavioural changes in adult rats. Eur Neuropsychopharmacol (2014) 24 459-468. DOI: 10.1016/j.euroneuro.2013.07.012 59. Sierra-Mercado D, Padilla-Coreano N, Quirk GJ. Dissociable roles of prelimbic and infralimbic cortices, ventral hippocampus, and basolateral amygdala in the expression and extinction of conditioned fear. Neuropsychopharmacology (2011) 36 529-538. DOI: 10.1038/npp.2010.184 60. Tzschentke TM. Pharmacology and behavioral pharmacology of the mesocortical dopamine system. Prog Neurobiol (2001) 63 241-320. DOI: 10.1016/S0301-0082(00)00033-2 61. Saxena S, Cabuy E, Caroni P. A role for motoneuron subtype-selective ER stress in disease manifestations of FALS mice. Nat Neurosci (2009) 12 627-636. DOI: 10.1038/nn.2297 62. Logsdon AF, Lucke-Wold BP, Nguyen L, Matsumoto RR, Turner RC, Rosen CL. Salubrinal reduces oxidative stress, neuroinflammation and impulsive-like behavior in a rodent model of traumatic brain injury. Brain Res (2016) 1643 140-151. DOI: 10.1016/j.brainres.2016.04.063 63. Moon SL, Sonenberg N, Parker R. Neuronal regulation of eif2alpha function in health and neurological disorders. Trends Mol Med (2018) 24 575-589. DOI: 10.1016/j.molmed.2018.04.001 64. Deutsch-Feldman M, Picetti R, Seip-Cammack K, Zhou Y, Kreek MJ. Effects of handling and vehicle injections on adrenocorticotropic and corticosterone concentrations in Sprague-Dawley compared with Lewis rats. J Am Assoc Lab Anim Sci (2015) 54 35-39. PMID: 25651089 65. Freiman SV, Onufriev MV, Stepanichev MY, Moiseeva YV, Lazareva NA, Gulyaeva NV. The stress effects of a single injection of isotonic saline solution: systemic (blood) and central (frontal cortex and dorsal and ventral hippocampus). Neurochem J (2016) 10 115-9. DOI: 10.1134/S1819712416020033 66. Lapin IP. Only controls: effect of handling, sham injection, and intraperitoneal injection of saline on behavior of mice in an elevated plus-maze. J Pharmacol Toxicol Methods (1995) 34 73-77. DOI: 10.1016/1056-8719(95)00025-D 67. Du Preez A, Law T, Onorato D, Lim YM, Eiben P, Musaelyan K. The type of stress matters: repeated injection and permanent social isolation stress in male mice have a differential effect on anxiety- and depressive-like behaviours, and associated biological alterations. Transl Psychiatry (2020) 10 325. DOI: 10.1038/s41398-020-01000-3 68. Parker KJ, Buckmaster CL, Hyde SA, Schatzberg AF, Lyons DM. Nonlinear relationship between early life stress exposure and subsequent resilience in monkeys. Sci Rep (2019) 9 16232. DOI: 10.1038/s41598-019-52810-5 69. Qin X, He Y, Wang N, Zou JX, Zhang YM, Cao JL. Moderate maternal separation mitigates the altered synaptic transmission and neuronal activation in amygdala by chronic stress in adult mice. Mol Brain (2019) 12 111. DOI: 10.1186/s13041-019-0534-4 70. Gao B, Liu X, Yan Q, Yang R, Jiang T, Zhang X. DMSO-mediated difunctionalization of electron-deficient olefins to access β-hydroxysulfides with high chemoselectivity. Synthesis (2022) 54 2258-2266. DOI: 10.1055/s-0041-1737341 71. Farkas E, Institoris A, Domoki F, Mihaly A, Luiten PG, Bari F. Diazoxide and dimethyl sulphoxide prevent cerebral hypoperfusion-related learning dysfunction and brain damage after carotid artery occlusion. Brain Res (2004) 1008 252-260. DOI: 10.1016/j.brainres.2004.02.037 72. Bardutzky J, Meng X, Bouley J, Duong TQ, Ratan R, Fisher M. Effects of intravenous dimethyl sulfoxide on ischemia evolution in a rat permanent occlusion model. J Cereb Blood Flow Metab (2005) 25 968-977. DOI: 10.1038/sj.jcbfm.9600095 73. Hanslick JL, Lau K, Noguchi KK, Olney JW, Zorumski CF, Mennerick S. Dimethyl sulfoxide (DMSO) produces widespread apoptosis in the developing central nervous system. Neurobiol Dis (2009) 34 1-10. DOI: 10.1016/j.nbd.2008.11.006 74. Penazzi L, Lorengel J, Sundermann F, Golovyashkina N, Marre S, Mathis CMB. DMSO modulates CNS function in a preclinical Alzheimer's disease model. Neuropharmacology (2017) 113 434-444. DOI: 10.1016/j.neuropharm.2016.10.020 75. Rabow Z, Morningstar T, Showalter M, Heil H, Thongphanh K, Fan S. Exposure to DMSO during infancy alters neurochemistry, social interactions, and brain morphology in long-evans rats. Brain Behav (2021) 11 e02146. DOI: 10.1002/brb3.2146 76. Elisia I, Nakamura H, Cederberg R, Lee L, Lam V, Adomat H. DMSO represses inflammatory cytokine production from human blood cells and reduces autoimmune arthritis. FASEB J (2015) 29 282. DOI: 10.1096/fasebj.29.1_supplement.lb282 77. Kang M-H, Das J, Gurunathan S, Park H-W, Song H, Park C. The cytotoxic effects of dimethyl sulfoxide in mouse preimplantation embryos: a mechanistic study. Theranostics (2017) 7 4735. DOI: 10.7150/thno.21662 78. Yap KN, Yamada K, Zikeli S, Kiaris H, Hood WR. Evaluating endoplasmic reticulum stress and unfolded protein response through the lens of ecology and evolution. Biol Rev Camb Philos Soc (2021) 96 541-556. DOI: 10.1111/brv.12667 79. Dombroski BA, Nayak RR, Ewens KG, Ankener W, Cheung VG, Spielman RS. Gene expression and genetic variation in response to endoplasmic reticulum stress in human cells. Am J Hum Genet (2010) 86 719-729. DOI: 10.1016/j.ajhg.2010.03.017 80. Murphy MO, Cohn DM, Loria AS. Developmental origins of cardiovascular disease: impact of early life stress in humans and rodents. Neurosci Biobehav Rev (2017) 74 453-465. DOI: 10.1016/j.neubiorev.2016.07.018 81. Womersley JS, Nothling J, Toikumo S, Malan-Muller S, van den Heuvel LL, McGregor NW. Childhood trauma, the stress response and metabolic syndrome: a focus on DNA methylation. Eur J Neurosci (2022) 55 2253-2296. DOI: 10.1111/ejn.15370 82. Mariani N, Borsini A, Cecil CAM, Felix JF, Sebert S, Cattaneo A. Identifying causative mechanisms linking early-life stress to psycho-cardio-metabolic multi-morbidity: the EarlyCause project. PLoS ONE (2021) 16 e0245475. DOI: 10.1371/journal.pone.0245475 83. Mohan S, Brown L, Ayyappan P. Endoplasmic reticulum stress: a master regulator of metabolic syndrome. Eur J Pharmacol (2019) 860 172553. DOI: 10.1016/j.ejphar.2019.172553 84. Miller AH. Beyond depression: the expanding role of inflammation in psychiatric disorders. World Psychiatry (2020) 19 108-109. DOI: 10.1002/wps.20723 85. Hasheminasabgorji E, Jha JC. Dyslipidemia, diabetes and atherosclerosis: role of inflammation and ros-redox-sensitive factors. Biomedicines. (2021) 9 1602. DOI: 10.3390/biomedicines9111602 86. Guzman-Martinez L, Maccioni RB, Andrade V, Navarrete LP, Pastor MG, Ramos-Escobar N. Neuroinflammation as a common feature of neurodegenerative disorders. Front Pharmacol (2019) 10 1008. DOI: 10.3389/fphar.2019.01008 87. Gonzalez-Teuber V, Albert-Gasco H, Auyeung VC, Papa FR, Mallucci GR, Hetz C. Small molecules to improve ER proteostasis in disease. Trends Pharmacol Sci (2019) 40 684-695. DOI: 10.1016/j.tips.2019.07.003 88. Zhang G, Wang X, Gillette TG, Deng Y, Wang ZV. Unfolded protein response as a therapeutic target in cardiovascular disease. Curr Top Med Chem (2019) 19 1902-1917. DOI: 10.2174/1568026619666190521093049 89. Li C, Krothapalli S, Chen YM. Targeting endoplasmic reticulum for novel therapeutics and monitoring in acute kidney injury. Nephron (2022) 147 1-4. DOI: 10.1159/000525913
--- title: Does health technology assessment compromise access to pharmaceuticals? authors: - Melanie Büssgen - Tom Stargardt journal: The European Journal of Health Economics year: 2022 pmcid: PMC10060338 doi: 10.1007/s10198-022-01484-4 license: CC BY 4.0 --- # Does health technology assessment compromise access to pharmaceuticals? ## Abstract In response to rapidly rising pharmaceutical costs, many countries have introduced health technology assessment (HTA) as a ‘fourth hurdle’. We evaluated the causal effect of HTA based regulation on access to pharmaceuticals by using the introduction of Germany’s HTA system (AMNOG) in 2011. We obtained launch data on pharmaceuticals for 30 European countries from the IQVIA (formerly IMS) database. Using difference-in-difference models, we estimated the effect of AMNOG on launch delay, the ranking order of launch delays, and the availability of pharmaceuticals. We then compared the results for Germany to Austria, Czechia, Italy, Portugal, and the UK. Across all six countries, launch delays decreased from the pre-AMNOG period (25.01 months) to the post-AMNOG period (14.34 months). However, the introduction of AMNOG consistently reduced the magnitude of the decrease in launch delay in Germany compared to the comparator countries (staggered DiD: + 4.31 months, $$p \leq 0.05$$). Our logit results indicate that the availability of pharmaceuticals in Germany increased as a result of AMNOG (staggered logit: + $5.78\%$, $$p \leq 0.009$$). We provide evidence on the trade-off between regulation and access. This can help policymakers make better-informed decisions to strike the right balance between cost savings achieved through HTA based regulation and access to pharmaceuticals. ## Introduction In response to years of rapidly rising pharmaceutical costs, many countries have introduced health technology assessment (HTA) as a ‘fourth hurdle’ to the market entry of pharmaceuticals over the past two decades [1–3]. The aim of HTA is to evaluate the relative advantages of newly introduced pharmaceuticals over existing pharmaceuticals in terms of effectiveness and costs. Because information from HTA can help decision-makers curb costs through measures such as price negotiations [4], critics of this fourth hurdle have speculated that HTA may compromise access to pharmaceuticals and therefore negatively affect patient care [5, 6]. The timing of the launch of a new drug is an important factor in getting the drug to the patient as quickly as possible. New pharmaceuticals generally imply better management and treatment of many illnesses, and it is widely recognised that access to modern medical treatments, including new pharmaceuticals, has contributed immensely to improving patient outcomes [7]. Delays in the launch of new pharmaceuticals can result in some patients receiving inadequate care, leading to the loss of life years and lower quality of life [8, 9], especially if the difference between the new treatment and the current standard of care is large, or if the standard of care is contraindicated or cannot be tolerated by a given patient. Additionally, poorer access to new pharmaceuticals shifts the volume of treatments prescribed to older molecules with a potentially lower therapeutic value [10], in turn leading to higher expenditure on other forms of medical care [11, 12]. For patients, a delay in the launch of a new pharmaceutical can be costly if they must have it imported from another country and/or pay for it out of pocket. In addition, in cases where a delay is not due to a deliberate decision, it can also be a problem for the manufacturer because it shortens the time available to refinance development costs. Despite the clear importance of delays in drug launches, their dynamics over time and possible cause-and-effect relationships with regulatory factors have received little research attention to date [13]. Moreover, there is little evidence on whether HTA itself which can be regarded as a form of strict regulation could lead to longer launch delays and poorer access to pharmaceuticals in the first place. Studies have shown that delays in the launch of new pharmaceuticals increased in the United States (US) following the introduction of the ‘third hurdle’ requirement to prove their safety, quality, and efficacy by the 1962 Amendments to the Food, Drug and Cosmetics Act [14, 15]. Additionally, and more generally, prior research suggests that countries that regulate prices more aggressively or in which the market size of a product is expected to be small have fewer launches and longer launch delays [16]. Empirical studies showing a causal relationship between HTA and access to pharmaceuticals are, however, still lacking. To address these gaps in the literature, we used the introduction of Germany’s HTA system through the Pharmaceutical Market Restructuring Act (AMNOG) in 2011 [17] to analyse whether HTA-based regulation in Germany has a causal effect on access to pharmaceuticals. HTA has already been carried out before (e.g. DIMDI, IQWIG, etc.) in Germany, but not as a mandatory requirement for every new prescription pharmaceutical. By using the AMNOG as an example for HTA-based regulation we looked in particular at the length of the launch delays -as this is often used as a proxy for patient access in research [16, 18–23], changes in the ranking order of launch delays across countries, and the availability of new pharmaceuticals in each country before and after the introduction of the German HTA system. This system takes a two-stage approach in which evidence-based assessments of the medical benefits of a new pharmaceutical are undertaken using data from prior clinical trials and serve as the basis for price negotiations that start 6 months after a pharmaceutical has been launched; in the interim, the manufacturer may set the price for the pharmaceutical and this is reimbursed in full by statutory health insurance [24]. This being said, the AMNOG is primarily a form of price regulation and only affects reimbursement indirectly. In contrast to the National Institute for Health and Care Excellence (NICE) in England and Wales, the German HTA system does not consider quality-adjusted life years (QALYs)/cost-effectiveness in its final decision, and the average duration of the process in Germany is shorter (German HTA system: 6 months, NICE: 9–12 months) [25]. ## Data The first international launch date (worldwide) and national launch dates of prescription pharmaceuticals in the following 30 European countries were extracted from the IQVIA (former IMS) MIDAS Sales Database: Austria, Belgium, Bosnia-Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, and the United Kingdom (UK). IQVIA collects these data from two major sources: wholesalers and hospital pharmacies. The IQVIA dataset has been used for related research before [26–28]. We restricted the sample to prescription pharmaceuticals that were launched between 2003 and 2017 to have a solid base covering both a pre-AMNOG period (2003–2009) and a post-AMNOG period (2011–2017). For national launch dates, our follow-up period was until the second calendar quarter of 2020. We excluded data from the year 2010 from our analyses because the announcement of the new HTA-based regulation system was published in that year, and companies may have attempted to launch products more quickly after the announcement to avoid these being subject to the new regulation starting in 2011. To avoid confounding (a) by pharmaceuticals being launched very late in some countries after already having been taken from the market elsewhere, or by (b) peculiarities in how certain substances are defined as a (prescription) drug in some countries, two researchers independently reviewed all pharmaceuticals that were launched in fewer than five countries to verify the launch dates in the database. Furthermore, we excluded vaccines from the analyses because these are not regulated via the AMNOG. We operationalized access to pharmaceuticals in three ways: (I) Launch delay, which we calculated as the length of time between the first international launch date of each pharmaceutical and its corresponding national launch date in a given country; (II) changes in the ranking order of launch delays across all 30 countries in our sample (with the country with the shortest launch delay being ranked first and the country with the longest launch delay being ranked thirtieth, etc.); ( III) differences in the availability of pharmaceuticals before and after the introduction of HTA-based regulation in Germany; we calculated these by determining the number of pharmaceuticals launched internationally during the two intervals (i.e., 2003–2009 and 2011–2017) and calculating the percentage of these pharmaceuticals that were available in a given country during each interval. Furthermore, we differentiated between potential blockbusters (defined as bestselling products) and all other products. We have defined a pharmaceutical as a bestseller if it was among the top 50 blockbusters worldwide in the last two decades. Drugs that were defined as bestseller drugs (ranked by sales in Mio. USD) can be found in appendix (Table 4). ## Empirical model To assess the impact of HTA on access to new pharmaceuticals in Germany, we calculated the values for our three measures of access for Germany for the pre-AMNOG and post-AMNOG period. We then compared these to those in five EU centralized and regulated countries that (a) had not had any major regulatory interventions related to HTA or reimbursement after 2011 and (b) had a similar pre-AMNOG trend from 2003 to 2009. These countries were Austria, Czechia, Italy, Portugal, and the UK. As of late 2021, Austria has used a reimbursement system consisting of three tiers subsequent to price setting since 2005 [29]. In Czechia, the State Institute for Drug Control has been responsible for pricing and reimbursement decisions since 2008 and manages reimbursement the same way [30]. In Italy, AIFA (Agenzia Italiana del Farmaco) has assumed the role of HTA agency and negotiated prices on behalf of the Italian NHS (National Health Service) in a decentralised system since 2003 [31]. In Portugal, INFARMED (Instituto Nacional da Farmácia e do Medicamento) and (since 2015) SINATS (Sistema Nacional de Avaliação de Tecnologias de Saúde) has conducted HTA of pharmaceuticals pertaining to their pricing and reimbursement; additionally, external reference pricing has been used since 2003 [32]. The UK introduced HTA procedures as part of the remit of the National Institute for Clinical Excellence (now the National Institute for Health and Care Excellence) in 1999 in England and Wales but has not made any major changes since then [33]. Most importantly, there has not been any major change to HTA-based regulation in Germany since the AMNOG legislation came into force in 2011 [34]. Also, there was no major intervention from 2009 on in the other countries that could have been effective from 2011 on. To analyse changes in access to pharmaceuticals, i.e., (I) launch delay, (II) changes in the ranking order of launch delays, and (III) the availability of pharmaceuticals, we estimated two difference-in-difference (DiD) models (for (I) launch delay and the (II) ranking order of launches) and one logit model (for the (III) availability):\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Access \,to \,pharmaceuticals}}_{i,t}={\beta }_{0}+ {\beta }_{1}* \mathrm{time}+ {\beta }_{2}* \mathrm{treated}+ {\beta }_{3}\mathrm{ time}\mathrm{ treated}+\varepsilon$$\end{document}Accesstopharmaceuticalsi,t=β0+β1∗time+β2∗treated+β3∗time∗treated+ε Access to pharmaceuticals refers to our outcome of interest in country i at time t (launch delay, ranking order of launches, availability). Time was coded as 0 if the first international launch of a pharmaceutical took place before the introduction of the German HTA system (2009 or earlier) and as 1 if the first international launch took place after its introduction (2011 or later). Treated was coded as 1 for Germany and 0 otherwise. Parallel pre-trends were checked using (a) graphical inspection (for graphs see appendix Fig. 1, 2, 3, 4, 5) and (b) placebo regression (see appendix Table 5). We modelled a placebo regression as if the German HTA system had been introduced in 2008, making 2003–2007 the pre-AMNOG period and 2008–2009 the post-AMNOG period. We could not detect any significant coefficients in our interaction terms when using placebo regression, which supports the validity of our original model. We further estimated a staggered DiD to aggregate the effects of introducing HTA-based regulation in Germany across countries:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Access \,to \,pharmaceuticals}}_{it}= {\mu }_{i}+ {\lambda }_{t}+ \delta {\mathrm{\,interaction \,term}}_{it}+ {\varepsilon }_{it}$$\end{document}Accesstopharmaceuticalsit=μi+λt+δinteractiontermit+εit with δ being the effect of the introduction of the HTA system in Germany. Furthermore, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mu }_{i}$$\end{document}μi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{t}$$\end{document}λt are country and time fixed effects. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varepsilon }_{it}$$\end{document}εit is an unobserved error term. For our subgroup analysis, we differentiated between bestseller drugs (ranked by sales in Mio. USD) and pharmaceuticals with lower sales. Furthermore, to differentiate between the short-, medium-, and long-term consequences of the introduction of the HTA system, we looked at three time intervals after the system came into effect: 2011–2013, 2014–2016, and 2017. All analyses were performed using Stata SE 16. Analyses are computed based on the total number of non-missing cases. Our outcomes are thus conditioned on availability. ## Results Our final sample included 492 different pharmaceuticals, 269 of which were launched in the pre-AMNOG period (2003–2009), and 223 of which were launched in the post-AMNOG period (2011–2017). In total, 37 ($13.75\%$) of the pharmaceuticals we classified as bestsellers were launched in the pre-AMNOG period and 15 were launched in the post-AMNOG period ($6.72\%$). Generally, we saw a trend towards a decrease in launch delay across Germany and all five comparator countries from the pre-AMNOG period to the post-AMNOG period. Among the six countries, the launch delay was 25.01 months on average in the pre-AMNOG period and 14.34 months on average in the post-AMNOG period. In absolute figures, however, it was the countries with a large expected market size that launched earlier than countries with the small expected market size. Across our full sample of 30 countries, Germany's ranking worsened slightly from a rank 3.79 in the pre-AMNOG period to a rank 3.93 in the post-AMNOG period. For the five comparator countries, we found that Austria went from rank 8.63 in the pre-AMNOG period to rank 4.59 in the post-AMNOG period, Czechia from rank 16.94 to rank 16.75, Italy from rank 13.28 to rank 12.60, Portugal from rank 13.69 to rank 9.59, and the UK from rank 4.67 to rank 3.64. With regard to the availability of new pharmaceuticals, in the pre-AMNOG period (2003–2009), we found that 221 ($82.16\%$) of the 269 pharmaceuticals launched internationally were also launched in Germany. In the post-AMNOG period, this percentage increased, with 201 ($90.13\%$) of the 223 products launched internationally also being launched in Germany. Among the five comparator countries, the availability of new pharmaceuticals increased from 78.07 to $83.86\%$ in Austria, from 68.77 to $69.06\%$ in Czechia, from 78.87 to $82.06\%$ in Italy, and from 79.93 to $87.00\%$ in the UK, but decreased from 75.84 to $70.40\%$ in Portugal. Table 1 reports the descriptive characteristics of the specified variables for Germany and all five comparator countries. Table 1Dataset characteristicsAustriaCzechiaGermanyItalyPortugalUKØSDNew molecules Before 2009210185221212204215207.8311.42 After 2011187154201183157194179.3317.78 Difference23312029472128.509.20Average launch delay (months) Before 200922.0734.4416.9828.2729.5418.7425.016.23 After 20119.9122.159.8418.5116.049.5914.344.89 Difference12.1612.297.149.7613.59.1510.672.18Ranking order of launch delays Before 20098.6316.943.7913.2813.694.6710.174.85 After 20114.5916.753.9312.609.593.648.524.93 Difference4.040.19− 0.140.684.11.031.651.75Availability of pharmaceuticals (%) Before 200978.0768.7782.1678.8775.8479.9377.274.25 After 201183.8669.0690.1382.0670.4087.0080.427.97 Difference5.790.297.973.19− 5.447.073.154.61 Our descriptive results were confirmed by our DiD models. In general, the launch delay in Germany and all five comparator countries decreased significantly from the pre-AMNOG period to the post-AMNOG period (-12.16 months for Austria, -12.29 months for Czechia, -7.14 months for Germany, -9.76 months for Italy, -13.50 months for Portugal, and -9.15 months for the UK). However, it appears that the introduction of AMNOG in Germany consistently reduced the magnitude of the decrease in launch delay compared to each of the five comparator countries (the coefficient for timetreat is always positive, see Table 2), although the reduction was not always statistically significant. More specifically, due to the introduction of AMNOG, the launch delay in Germany increased by + 5.02 months compared to Austria ($$p \leq 0.067$$), by + 5.15 months compared to Czechia ($$p \leq 0.081$$), by + 2.62 months compared to Italy ($$p \leq 0.316$$), by + 6.36 months compared to Portugal ($$p \leq 0.032$$) and by + 2.00 months compared to the UK ($$p \leq 0.437$$). The staggered DiD resulted in an estimate of + 4.31 months for Germany due to AMNOG. ( $$p \leq 0.050$$).Table 2DiD Results: launch delay, ranking order of launch delays, and availability of new pharmaceuticals for Germany compared with Austria, Czechia, Italy, Portugal, and the UKAccess to pharmaceuticalsDiDGermany vs. AustriaDiDGermany vs. CzechiaDiDGermany vs. ItalyDiDGermany vs. PortugalDiDGermany vs. UKStaggered DiDCoefSECoefSECoefSECoefSECoefSECoefSE(I) Launch delay _Cons22.071.3734.441.5128.271.3029.541.4718.741.2926.350.65* Time− 12.161.96*− 12.292.20− 9.761.87− 13.512.18− 9.141.83− 11.460.93* Treated− 5.081.92− 17.452.04− 11.281.82− 12.552.03− 1.751.81− 9.361.54** Time##treated5.022.735.152.942.622.616.362.962.002.574.312.19(II) Ranking order of launches _Cons8.900.71*16.811.0113.450.6413.000.764.810.4211.400.67* Time− 4.331.14*− 0.531.62− 0.881.04− 3.421.21− 1.240.67− 2.081.07 Treated− 5.091.00− 13.002.30− 9.630.91− 9.181.07− 1.000.59− 7.581.64* Time##treated4.511.61*0.712.181.061.473.611.721.420.952.262.64(III) Availability of pharmaceuticals _Cons1.260.14*0.780.131.310.141.140.141.380.151.160.06* Time0.370.230.010.190.200.22− 0.270.200.510.250.120.09 Treated0.250.210.730.200.210.210.380.210.140.220.350.17 Time##treated0.300.360.670.330.470.350.960.34**0.160.370.550.29 Margins0.020.040.070.050.040.040.130.05*0.010.040.050.03$p \leq 0.1$$p \leq 0.05$$p \leq 0.01$ Across the larger sample of 30 countries, the ranking order of launch delays in Germany and the five comparator countries changed from the pre-AMNOG period to the post-AMNOG period. While Germany’s ranking order improved only slightly over time, that of the five comparator countries generally improved more substantially. Thus, our DiD models resulted in positive but, with the exception of Austria and Portugal, non-significant changes in the ranking order of launch delays. More specifically, due to the introduction of AMNOG, the ranking order of launch delay in Germany declined by + 4.51 ranks compared to Austria ($$p \leq 0.009$$), by + 0.71 ranks compared to Czechia ($$p \leq 0.758$$), by + 1.06 ranks compared to Italy ($$p \leq 0.474$$), by + 3.61 ranks compared to Portugal ($$p \leq 0.044$$), and by + 1.42 ranks compared to the UK ($$p \leq 0.146$$). The staggered DiD resulted in an estimate of an increase of 2.26 ranks for Germany due to AMNOG ($$p \leq 0.393$$). With regard to the availability of new pharmaceuticals, our results indicate that AMNOG led to an increase in Germany compared to our comparator countries—i.e., by + $2.18\%$ compared to Austria ($$p \leq 0.639$$), + $7.69\%$ compared to Czechia ($$p \leq 0.138$$), + $4.72\%$ compared to Italy ($$p \leq 0.317$$), + $13.41\%$ compared to Portugal ($$p \leq 0.01$$), and + $0.90\%$ compared to the UK ($$p \leq 0.841$$). The staggered logit resulted in an estimate of an increase of + $5.78\%$ in the availability of new pharmaceuticals in Germany due to AMNOG ($$p \leq 0.099$$). In our subgroup analyses distinguishing between bestsellers and pharmaceuticals with lower sales, the DiD results for bestsellers indicate that launch delay and the ranking order of launch delays increased to a smaller extent compared to pharmaceuticals with lower sales and that these were no longer significant. The effect sizes for the availability of new pharmaceuticals could not be computed for bestsellers as all bestseller were available in Germany and our 5 comparator countries (outcome does not vary). The results of our subgroup analyses also indicate that the increase in launch delay seen in Germany was mostly stable or slightly larger when compared over the different post-AMNOG time intervals (see Table 3). For example, comparing Germany to Italy, we found an increase in launch delay by + 2.88 months ($$p \leq 0.398$$) in 2011–2013, by + 2.41 months ($$p \leq 0.466$$) in 2014–16 and + 4.18 months in 2017 ($$p \leq 0.444$$). The DiD results for changes in the ranking order of launch delays and logit results for the availability of new pharmaceuticals show a similar picture. The results from the staggered DiD/logit support the results from individual DiDs/logits. Table 3DiD Results for subgroup analysis (i.e. bestsellers and different time intervals after introduction of German HTA system)Subgroup analysisPost-AMNOG time intervalsBestsellerDiDGermany vs. AustriaDiDGermany vs. CzechiaDiDGermany vsItalyDiDGermany vs. PortugalDiDGermany vsUKStaggered DiDDiDGermany vs. AustriaDiDGermany vs. CzechiaDiDGermany vsItalyDiDGermany vs. PortugalDiDGermany vsUKStaggered DiDCoefSECoefSECoefSECoefSECoefSECoeffSECoefSECoefSECoefSECoefSECoefSECoeffSE(I) Launch delay _Cons22.071.3334.441.5128.271.3029.541.4618.741.2826.350.6485.7710.4999.7311.9892.789.9384.8514.6883.8310.2789.395.44* Time− 72.6510.92*− 78.2212.40− 77.1310.28− 66.6915.19− 71.3310.72− 72.945.64* 2011–2013− 10.272.52*− 8.252.75− 6.792.42− 9.372.86− 7.102.38− 8.271.21* 2014–2016− 13.172.40*− 15.062.85− 11.242.39− 15.652.81− 10.432.36− 13.181.20* 2017–2019− 14.274.00*− 19.745.30− 14.894.09− 19.854.73− 11.293.67− 16.262.00* Treated− 5.081.92*− 17.452.04− 11.281.81− 12.552.02− 1.751.80− 9.361.53− 0.9114.84− 14.8816.94− 7.9314.04*0.0020.761.0114.52− 4.5413.33 Time##treated− 0.0415.475.5217.604.4414.60− 6.0021.59− 1.3615.170.2413.90 2011–20136.373.514.353.782.883.415.463.903.193.384.362.82 2014–20164.333.346.233.772.413.316.823.781.593.294.342.77 2017–20193.565.529.026.584.185.459.146.190.575.155.554.43(II) Ranking order of launches _Cons8.900.7516.811.0413.450.6813.000.804.810.4411.400.684.220.479.810.758.780.646.710.684.270.566.900.36* Time− 0.280.71− 1.461.20− 2.850.94**0.211.07− 1.500.87− 1.290.56 2011–2013− 4.901.621.512.26− 1.121.47− 2.661.73− 0.810.95− 1.601.47 2014–2016− 4.241.62− 2.482.26− 1.121.47− 3.661.73− 1.810.95− 2.661.47 2017–2019− 2.902.60− 0.813.620.542.37− 5.002.77− 0.811.52− 1.802.37 Treated− 5.091.06− 13.001.47− 9.630.96− 9.181.13− 1.000.62− 7.581.67− 0.630.68− 6.221.13− 5.200.93− 3.121.01− 0.680.80− 3.310.95** Time##treated− 0.151.021.011.752.401.38− 0.651.551.061.220.851.42 2011–20135.422.29− 1.003.191.632.093.182.441.331.342.113.62 2014–20164.422.292.663.201.302.093.842.442.001.342.843.62 2017–20192.093.680.005.12-1.363.354.183.920.002.160.985.81(III) Availability of pharmaceuticals _Cons1.260.140.780.131.310.141.140.141.380.151.160.06* Time 2011–20130.330.310.660.29*0.650.35-0.070.280.940.400.450.14* 2014–20160.410.30− 0.180.240.090.29-0.360.250.150.30− 0.010.12 2017–20190.370.51− 0.850.38-0.410.42-0.540.400.850.62− 0.240.18 Treated0.250.210.730.200.210.210.380.210.140.220.350.17 Time##treated 2011–20130.210.48− 0.110.47− 0.100.510.620.46− 0.390.540.090.39 2014–20160.280.480.870.440.590.461.050.44*0.530.470.700.38 2017–20190.760.902.000.84*1.560.851.690.84*0.290.971.390.77 Margins0.010.020.070.02**0.040.020.080.02*0.010.020.040.02$p \leq 0.1$$p \leq 0.05$*$p \leq 0.01$ ## Discussion In this study, we analysed the impact of health technology assessment (HTA) on access to pharmaceuticals using the introduction of Germany’s HTA system through the Pharmaceutical Market Restructuring Act (AMNOG) in 2011 as an example. We used difference-in-difference models to look at three different outcomes measuring access in Germany and five comparator countries: (I) launch delay, (II) the ranking order of launch delays across Europe, and (III) the availability of new pharmaceuticals. Although we found that launch delay generally decreased from the pre- to the post-AMNOG period in all six countries, we found that the decrease was significantly smaller in Germany due to the introduction of AMNOG. We also observed (minor) changes in the ranking order of launch delays that were consistent with these results. Thus, our study provides important evidence that HTA-based regulation can negatively affect access to pharmaceuticals when this is measured in terms of a launch delay. When looking at the availability of new pharmaceuticals, however, the effect sizes for Germany increased compared to those in the five comparator countries. Although this is, to the best of our knowledge, the first study to investigate the causal link between HTA-based regulation and access to pharmaceuticals, our results are in accordance with related literature. In the US, for example, the Kefauver-Harris amendment to the Food, Drug, and Cosmetic Act in 1962 was shown to be associated with longer launch delays in that market [14, 15]. In England and Wales, HTA was introduced with the creation of NICE in 1999, which considers comparative effectiveness and cost-effectiveness data when evaluating new drugs. Unfortunately, no research has been undertaken on the impact of HTAs conducted by NICE on launch delay so far, but there are studies showing that positive guidance issued by NICE does not necessarily eradicate inequalities in access [35]. In research on drugs to treat orphan diseases, it has been found that while more than a half of the centrally approved pharmaceuticals were on the market in the investigated countries, patients’ access to orphan medical products was restricted by different national reimbursement policies, especially in the UK, Italy and Spain [36]. Furthermore, using country-specific average prices as an indirect measure for regulation, it has been shown that stricter (price) regulation is correlated with increased launch delay [37]. Also, because a low price in one market can spill over to other markets through parallel trading and external reference pricing [38], it appears reasonable for manufacturers to prefer a longer launch delay in certain countries if negotiations after HTA in one country results in a lower price. In the case of AMNOG, these negotiations must be completed within 12 months after the product has been launched. Another study that investigated the effect of price regulation on delays in the launch of new drugs found that countries with lower expected prices and smaller expected market size have fewer launches (i.e., less availability) and longer launch delays [16]. However, it should be noted that the pharma world has significantly changed in the past two decades. Several processes were optimised globally (logistics, manufacturing, economies of scale, etc.) which impacted launch delay in general and is visible in the downward time trend. However, sensitivity analysis also confirm that the effect of HTA-based regulation was consistent when judged upon in different time intervals (i.e. in the first, second and last three years after AMNOG). Also, with stricter regulation, pharmaceutical manufacturers earn less over time, so they have had an incentive to push into the market faster. The German HTA system is unique in that HTAs are undertaken while products are already on the market and being fully reimbursed by insurers. This helps ensure that patients have quick access to new pharmaceuticals and may also serve as an incentive for companies to innovate [39], while also including a mechanism to contain costs after 12 months. This being said, manufacturers must deal with a large amount of bureaucracy when submitting evidence for the benefit assessment. For example, a dossier consisting of four modules with a total of around 1000 pages per new drug has to be submitted to a body known as the Federal Joint Committee (G-BA), a process that takes anywhere from six to 12 months to prepare [40]. If manufacturers choose to start this process very early when EMA approval is still uncertain, they run the risk of wasting resources in the event that approval is not obtained. Indeed, this may be one of the reasons for the later launches we observed in our sample, although the AMNOG system itself guarantees immediate market access. In Germany, a new drug can be accessed immediately at launch and is reimbursed for all indications by statutory health insurance funds. However, this is not the case in every country. In some countries, only parts of the indication are reimbursed, the drug is not reimbursed at all or is reimbursed years later. Thus, our measure of launch delay -based on launch dates and not reimbursement dates- has certain limits when it comes to evaluating patient access. However, we concentrate on changes in access and these restrictions did also apply to the Pre-AMNOG-period. We do not consider any withdrawals from the market. The opportunity of ‘opting-out’ in Germany that allows the manufacturer to withdraw the pharmaceutical from the market might thus overestimate results for availability in favour of HTA-based regulation. This is because in our post-observation period 22 pharmaceuticals were withdrawn from the market in Germany [41] which is about $10.9\%$ of all launched products. It could be that those drugs would have not been launched in the pre-AMNOG-period anyway and are responsible for the increase in availability from 82.2 to $90.1\%$ from the pre- to the post-period. However, since our data does not allow us to observe withdrawals in any other market, this is highly speculative. HTA involves important trade-offs for individual patients and society alike: On the one hand, lower prices come at the cost of later access to pharmaceuticals, potentially leading to an increase in the number of life-years lost. On the other hand, lower prices mean that a health care system will save money in the long run, potentially allowing for spending in other areas that could lead to a gain in life years or other beneficial outcomes for society. Also, prior research has shown that a positive HTA evaluation may be seen by physicians as a positive rating of a pharmaceutical’s quality from a trusted third-party source, thereby increasing the speed with which the pharmaceutical is diffused [42]. This faster diffusion could at least partly compensate for the effects of a delayed launch. For the pharmaceutical industry, HTA comes with an important trade-off, as well: Manufacturers must either undergo the HTA process and potentially accept lower prices for a new pharmaceutical, or they must cope with a reduced market size for their product. In fact, since the introduction of the German HTA system, there have been more market withdrawals in Germany than ever before [43]. However, it is important to put this observation into perspective and consider that (a) a large share of these market withdrawals have involved drugs that had no added benefit compared to an appropriate comparator [44] and (b) in general, more pharmaceuticals were launched in the post-AMNOG period [269] than in the pre-AMNOG period [223] in Germany. A widely accepted problem described in the related literature is that pricing and reimbursement policies are frequently assessed in terms of their financial consequences, including their ability to contain costs, but less frequently in terms of their effects on access to care [45]. There is an urgent need for policymakers to take this point into account when examining the trade-offs of new regulations pertaining to HTA. For example, future researchers may wish to investigate whether there are variations among different indication groups with respect to the launch delay caused by HTA-based regulation, for example in oncology [46, 47]. We were not able to investigate this with our data set because we could not filter pharmaceuticals by ATC codes or indication. Our DiD results distinguishing between bestsellers and pharmaceuticals with fewer sales need to be interpreted with caution. Interestingly, the effect sizes across all three indicators decreased and/or were no longer statistically significant. It may therefore be the case that AMNOG does not have the same impact on all pharmaceuticals, but rather has a stronger effect on pharmaceuticals with lower sales (e.g. through the long bureaucratic process or because the bestsellers are meant to be on the market as fast as possible anyway). ## Limitations Our analyses have several limitations. First, there may have been small regulatory changes in Germany and the five comparator countries that took place during the post-AMNOG period. Although we screened the literature very carefully for large changes in HTA and reimbursement regulations, we may have overlooked small changes, such as increases in co-payments, that could have affected our results. This being said, the general trend in all six countries has been in the same direction: towards stricter regulation. Although small regulatory changes will not have happened at the same time or have been of the same magnitude in each country, the robustness of our results when these were differentiated by time intervals can be interpreted as a sign that any bias from this source is minor. Second, we cannot draw any conclusions about whether pharmaceuticals whose launch was delayed might serve an urgent need on the market—for example, whether the pharmaceuticals were first-in-class innovations or me-too drugs. However, our logit results suggest that the availability of bestseller pharmaceuticals increased in the post-AMNOG period, and this finding is worthy of further investigation. Third, the ranking order of launch delays changed only slightly for Germany in absolute terms (i.e., from rank 3.79 pre-AMNOG to rank 3.93 post-AMNOG), whereas the five comparator countries showed much bigger changes (e.g., Portugal went from rank 13.69 pre-AMNOG to rank 9.59 post-AMNOG), which are, of course, driving our empirical results. Nevertheless, compared to a country like Germany that started at rank 3.78, it should be noted that there is much more scope for improvement in countries with an initially high number in the ranking and a comparatively longer launch delay. However, our results also hold if Germany is* compared to the UK, which started at a similarly rank as Germany in terms of a launch delay. In addition, looking at the two countries with on average the earliest launch in Europe (The Netherlands and Sweden), that were not used as comparators due to lack of parallel pre-trends, we see that they reduced launch delay from 9.51 to 5.35 months (The Netherlands) and from 13.81 to 6.02 months (Sweden). Thus, the 9.84 months of average launch delay that were observed for Germany in the time period after 2011 does by no means represent the lowest launch delay possible. Moreover, Germany has never been the first country to launch ($0\%$ of our sample before AMNOG and in $0\%$ of our sample after AMNOG). However, Germany has been the second country to launch in $17.19\%$ of our sample before AMNOG, but in $0\%$ of our sample after AMNOG. Fourth, IQVIA’s methods for collecting data on launch dates differ slightly from country to country. For example, the launch dates for Estonia, Greece, and Luxembourg are calculated based on reports from the retail sector, whereas the launch dates for all other countries in our sample are calculated based on both the retail and hospital sectors. This does not present a problem for our analysis, however, given that Germany and the five comparator countries in our sample all used the latter method. However, when calculating the ranks of these six countries among the larger sample of 30 countries, there may have been a small bias. Fifth, especially for pharmaceuticals whose market launch had not yet taken place in all countries, our approach to dealing with missing values might have had two consequences: For older pharmaceuticals that may be launched in a country rather late in the future, the launch delay may have been even longer than we measured. This could mean that the increase in launch delay in Germany due to AMNOG may be smaller than our results suggest. For very new pharmaceuticals, however, missing values during a time of generally decreasing launch delays will result in the opposite bias—i.e., a slightly inflated measure of launch delay due to AMNOG. Which of the two biases dominates in our analysis or whether they balance each other out is difficult to determine. Finally, as mentioned previously in the methods section, two researchers independently reviewed all pharmaceuticals that were launched in fewer than five countries to verify the related launch dates in the database. Although carried out to the best of our abilities, a manual check is always subject to uncertainties. The fact that this review was conducted separately by the two reviewers should have reduced errors to a minimum, however. ## Conclusion The findings of our study provide important evidence on the effects of HTA based regulation on access to pharmaceuticals, especially in terms of a launch delay, changes in the ranking order of launch delays among 30 European countries, and the market availability of new pharmaceuticals. In particular, it appears that the implementation of HTA systems like that introduced by the Pharmaceutical Market Restructuring Act (AMNOG) in 2011 may lead to longer launch delays and thus later patient access in Germany, while also having a significant impact on the availability of new pharmaceuticals -compared to other countries. Policymakers may wish to consider these findings when taking decisions about the trade-off between regulation and access to care. Future research should investigate whether the delay in the launch of new pharmaceuticals we detected in our study may undermine patient care, leading to a loss of life years and quality of life and, if so, to what extent. Additionally, further research should explore whether the current trade-off between lower prices for new pharmaceuticals and longer launch delays are in line with the preferences of the population. ## Appendix See Fig. 1, 2, 3, 4, 5, 6 See Tables 4, 5Fig. 1Germany vs. Austria Fig. 2Germany vs. Czechia Fig. 3Germany vs. Italy Fig. 4Germany vs. Portugal Fig. 5Germany vs. UK Fig. 6Launch order before and after 2011 ## References 1. Lee YS. Value-based health technology assessment and health informatics. Healthc Inform Res (2017.0). DOI: 10.4258/hir.2017.23.3.139 2. Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. Eur J Health Econ (2018.0) 19 123-152. DOI: 10.1007/s10198-017-0871-0 3. Olberg B, Fuchs S, Panteli D, Perleth M, Busse R. Scientific evidence in health technology assessment reports: an in-depth analysis of european assessments on high-risk medical devices. Value Health (2017.0) 20 1420-1426. DOI: 10.1016/j.jval.2017.05.011 4. Lauenroth VD, Stargardt T. Pharmaceutical pricing in germany: how is value determined within the scope of AMNOG?. Value Health (2017.0) 20 927-935. DOI: 10.1016/j.jval.2017.04.006 5. Akehurst RL, Abadie E, Renaudin N, Sarkozy F. Variation in health technology assessment and reimbursement processes in Europe. Value Health (2017.0) 20 67-76. DOI: 10.1016/j.jval.2016.08.725 6. Wilking N, Jönsson B. A pan-European comparison regarding patient access to cancer drugs (2005.0) 7. Lichtenberg FR. The impact of new drug launches on longevity: evidence from longitudinal, disease-level data from 52 countries, 1982–2001. Int J Health Care Finance Econ (2005.0). DOI: 10.1007/s10754-005-6601-7 8. Zhu X, Liu B. Launch delay of new drugs in china and effect on patients’ health. Clin Ther (2020.0) 42 1750-1761.e7. DOI: 10.1016/j.clinthera.2020.06.023 9. Schöffski O. Diffusion of medicines in Europe (2002.0) 10. Danzon PM, Ketcham JD. Reference pricing of pharmaceuticals for Medicare: evidence from Germany, The Netherlands, and New Zealand. Front Health Policy Res (2004.0) 7 1-54. DOI: 10.2202/1558-9544.1050 11. Kessler DP. The effects of pharmaceutical price controls on the cost and quality of medical care: a review of the empirical literature (2004.0) 12. 12.Wertheimer und Santella, “[PDF] Pharmacoevolution : the advantages of incremental innovation \| Semantic Scholar“. https://www.semanticscholar.org/paper/Pharmacoevolution-%3A-the-advantages-of-incremental-Wertheimer/3c0ea81a136fc623e3c32080b3ef95f44eb0a07c (zugegriffen 25 Nov 2020). 13. Bhaduri S, Brenner T. Examining the determinants of drug launch delay in pre-TRIPS India. Eur J Health Econ (2013.0) 14 761-773. DOI: 10.1007/s10198-012-0422-7 14. Dranove D, Meltzer D. Do Important Drugs Reach the Market Sooner?. Rand J Econ (1994.0) 25 402-423. DOI: 10.2307/2555769 15. Peltzman S. An evaluation of consumer protection legislation: the 1962 drug amendments. J Polit Econ (2015.0). DOI: 10.1086/260107 16. Danzon PM, Wang YR, Wang L. The impact of price regulation on the launch delay of new drugs–evidence from twenty-five major markets in the 1990s. Health Econ (2005.0) 14 269-292. DOI: 10.1002/hec.931 17. Fischer KE, Stargardt T. Early benefit assessment of pharmaceuticals in germany: manufacturers’ expectations versus the federal joint committee’s decisions. Med Decis Making (2014.0) 34 1030-1047. DOI: 10.1177/0272989X14546377 18. Garattini L, Ghislandi S. Should we really worry about “launch delays” of new drugs in OECD countries?. Eur J Health Econ (2007.0) 8 1-3. DOI: 10.1007/s10198-006-0022-5 19. Cohen J, Faden L, Predaris S, Young B. Patient access to pharmaceuticals: an international comparison. Eur J Health Econ (2007.0) 8 253-266. DOI: 10.1007/s10198-006-0028-z 20. Degrassat-Théas A, Paubel P, Parent de Curzon O, Le Pen C, Sinègre M. Temporary authorization for use: does the french patient access programme for unlicensed medicines impact market access after formal licensing?. Pharmacoeconomics (2013.0) 31 335-343. DOI: 10.1007/s40273-013-0039-4 21. Russo P, Mennini FS, Siviero PD, Rasi G. Time to market and patient access to new oncology products in Italy: a multistep pathway from European context to regional health care providers. Ann Oncol (2010.0) 21 2081-2087. DOI: 10.1093/annonc/mdq097 22. Ferrario A. Time to entry for new cancer medicines: from European union-wide marketing authorization to patient access in Belgium, Estonia, Scotland, and Sweden. Value Health (2018.0) 21 809-821. DOI: 10.1016/j.jval.2018.01.003 23. Cohen J, Cairns C, Paquette C, Faden L. Comparing patient access to pharmaceuticals in the UK and US. Appl Health Econ Health Policy (2006.0) 5 177-187. DOI: 10.2165/00148365-200605030-00004 24. Paris V, Belloni A. Value in pharmaceutical pricing. Juli (2013.0). DOI: 10.1787/5k43jc9v6knx-en 25. Fischer KE, Heisser T, Stargardt T. Health benefit assessment of pharmaceuticals: an international comparison of decisions from Germany, England, Scotland and Australia. Health Policy (2016.0) 120 1115-1122. DOI: 10.1016/j.healthpol.2016.08.001 26. Detiček A, Locatelli I, Kos M. Patient Access to medicines for rare diseases in european countries. Value Health (2018.0) 21 553-560. DOI: 10.1016/j.jval.2018.01.007 27. Greiner W, Patel K, Crossman-Barnes C-J, Rye-Andersen TV, Hvid C, Vandebrouck T. High-expenditure disease in the EU-28: does drug spend correspond to clinical and economic burden in oncology, autoimmune disease and diabetes?. Pharmacoecon Open (2021.0). DOI: 10.1007/s41669-020-00253-4 28. Kim Y, Kwon H-Y, Godman B, Moorkens E, Simoens S, Bae S. Uptake of Biosimilar Infliximab in the UK, France, Japan, and Korea: budget savings or market expansion across countries?. Front Pharmacol (2020.0) 11 970. DOI: 10.3389/fphar.2020.00970 29. Führlinger S. Relevance of pharmacoeconomic analyses to price and reimbursement decisions in Austria. Wien Med Wochenschr (2006.0). DOI: 10.1007/s10354-006-0357-7 30. Gulácsi L. Health technology assessment in Poland, the Czech Republic, Hungary, Romania and Bulgaria. Eur J Health Econ (2014.0) 15 13-25. DOI: 10.1007/s10198-014-0590-8 31. Ciani O, Tarricone R, Torbica A. Diffusion and use of health technology assessment in policy making: what lessons for decentralised healthcare systems?. Health Policy (2012.0) 108 194-202. DOI: 10.1016/j.healthpol.2012.09.017 32. 32.Infarmed. https://www.infarmed.pt/ (zugegriffen 8 Feb 2021). 33. 33.NICE\|The National Institute for Health and Care Excellence“, NICE. https://www.nice.org.uk/ (zugegriffen 1. Feb 2021). 34. 34.Startseite-Gemeinsamer Bundesausschuss“. https://www.g-ba.de/ (zugegriffen 10 Mär 2021). 35. Summerhayes M, Catchpole P. Has NICE been nice to cancer?. Eur J Cancer (2006.0) 42 2881-2886. DOI: 10.1016/j.ejca.2006.08.013 36. Zamora B, Maignen F, O’Neill P, Mestre-Ferrandiz J, Garau M. Comparing access to orphan medicinal products in Europe. Orphanet J Rare Dis (2019.0) 14 95. DOI: 10.1186/s13023-019-1078-5 37. Danzon P, Epstein A. Effects of regulation on drug launch and pricing in interdependent markets. Adv Health Econ Health Serv Res (2012.0) 23 35-71. DOI: 10.1108/S0731-2199(2012)0000023005 38. Stargardt T, Schreyögg J. Impact of cross-reference pricing on pharmaceutical prices: manufacturers’ pricing strategies and price regulation. Appl Health Econ Health Policy (2006.0) 5 235-247. DOI: 10.2165/00148365-200605040-00005 39. 39.Arzneimittelmarktneuordnungsgesetz (AMNOG), Bundesgesundheitsministerium. https://www.bundesgesundheitsministerium.de/service/begriffe-von-a-z/a/arzneimittelmarktneuordnungsgesetz-amnog.html (zugegriffen 1 Feb 2021). 40. Sattelmeier J, Prenzler A, Frank M. Das Arzneimittelmarktneuordnungsgesetz (AMNOG) in der Praxis–erste Erfahrungen, Kritikpunkte und Weiterentwicklungsmöglichkeiten. Health Econ Qual Manag (2013.0). DOI: 10.1055/s-0033-1350528 41. Staab TR. Market withdrawals‘ of medicines in Germany after AMNOG: a comparison of HTA ratings and clinical guideline recommendations. Health Econ Rev (2018.0) 8 23. DOI: 10.1186/s13561-018-0209-3 42. Blankart KE, Stargardt T. The impact of drug quality ratings from health technology assessments on the adoption of new drugs by physicians in Germany. Health Econ (2020.0) 29 63-82. DOI: 10.1002/hec.4108 43. Staab TR. “Market withdrawals” of medicines in Germany after AMNOG: a comparison of HTA ratings and clinical guideline recommendations. Health Econ Rev (2018.0) 8 23. DOI: 10.1186/s13561-018-0209-3 44. Stern AD, Pietrulla F, Herr A, Kesselheim AS, Sarpatwari A. The impact of price regulation on the availability of new drugs in Germany. Health Aff (Millwood) (2019.0) 38 1182-1187. DOI: 10.1377/hlthaff.2018.05142 45. Vogler S. How can pricing and reimbursement policies improve affordable access to medicines? Lessons learned from European countries. Appl Health Econ Health Policy (2017.0) 15 307-321. DOI: 10.1007/s40258-016-0300-z 46. Sussell J, Vanderpuye-Orgle J, Vania D, Goertz H-P, Lakdawalla D. Understanding price growth in the market for targeted oncology therapies. Am J Manag Care (2019.0) 25 273-277. PMID: 31211554 47. Kantarjian H, Steensma D, Sanjuan J, Elshaug A, Light D. High cancer drug prices in the United States: reasons and proposed solutions. J Oncology Pract (2014.0). DOI: 10.1200/JOP.2013.001351
--- title: Multi-omic analysis of the cardiac cellulome defines a vascular contribution to cardiac diastolic dysfunction in obese female mice authors: - Malathi S. I. Dona - Ian Hsu - Alex I. Meuth - Scott M. Brown - Chastidy A. Bailey - Christian G. Aragonez - Jacob J. Russell - Crisdion Krstevski - Annayya R. Aroor - Bysani Chandrasekar - Luis A. Martinez-Lemus - Vincent G. DeMarco - Laurel A. Grisanti - Iris Z. Jaffe - Alexander R. Pinto - Shawn B. Bender journal: Basic Research in Cardiology year: 2023 pmcid: PMC10060343 doi: 10.1007/s00395-023-00983-6 license: CC BY 4.0 --- # Multi-omic analysis of the cardiac cellulome defines a vascular contribution to cardiac diastolic dysfunction in obese female mice ## Abstract Coronary microvascular dysfunction (CMD) is associated with cardiac dysfunction and predictive of cardiac mortality in obesity, especially in females. Clinical data further support that CMD associates with development of heart failure with preserved ejection fraction and that mineralocorticoid receptor (MR) antagonism may be more efficacious in obese female, versus male, HFpEF patients. Accordingly, we examined the impact of smooth muscle cell (SMC)-specific MR deletion on obesity-associated coronary and cardiac diastolic dysfunction in female mice. Obesity was induced in female mice via western diet (WD) feeding alongside littermates fed standard diet. Global MR blockade with spironolactone prevented coronary and cardiac dysfunction in obese females and specific deletion of SMC-MR was sufficient to prevent obesity-associated coronary and cardiac diastolic dysfunction. Cardiac gene expression profiling suggested reduced cardiac inflammation in WD-fed mice with SMC-MR deletion independent of blood pressure, aortic stiffening, and cardiac hypertrophy. Further mechanistic studies utilizing single-cell RNA sequencing of non-cardiomyocyte cell populations revealed novel impacts of SMC-MR deletion on the cardiac cellulome in obese mice. Specifically, WD feeding induced inflammatory gene signatures in non-myocyte populations including B/T cells, macrophages, and endothelium as well as increased coronary VCAM-1 protein expression, independent of cardiac fibrosis, that was prevented by SMC-MR deletion. Further, SMC-MR deletion induced a basal reduction in cardiac mast cells and prevented WD-induced cardiac pro-inflammatory chemokine expression and leukocyte recruitment. These data reveal a central role for SMC-MR signaling in obesity-associated coronary and cardiac dysfunction, thus supporting the emerging paradigm of a vascular origin of cardiac dysfunction in obesity. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00395-023-00983-6. ## Introduction Coronary microvascular dysfunction (CMD) is independently predictive of cardiac morbidity and mortality in people with obesity and diabetes [46]. Importantly, obesity is more common in women and females are more likely than males to develop CMD rather than obstructive coronary artery disease [57]. While premenopausal women are protected from heart disease relative to men, that protection is lost in females with obesity and metabolic dysfunction [1]. A recent report revealed an association of CMD and cardiac diastolic dysfunction in females, but not males, with obesity and diabetes [27]. This association has significant implications for outcomes since patients with both CMD and diastolic dysfunction have a > fivefold increased risk of heart failure with preserved ejection fraction (HFpEF) hospitalization versus patients with isolated CMD or diastolic dysfunction [58]. Accordingly, it has been suggested that obesity-associated CMD may be a primary mechanism of cardiac diastolic dysfunction and HFpEF [30, 47, 50], conditions for which treatments are limited. While cardiovascular disease mortality is declining in males, it is rising in middle-aged premenopausal females [59, 65]; thus, delineation of mechanisms of CMD in females with obesity and metabolic disease is a critical unmet medical need [49]. Recent evidence, from us and others, revealed that mineralocorticoid receptor (MR) blockade attenuates obesity and diabetes-associated CMD in preclinical models [4, 7] and patients [19, 33] independent of blood pressure. Further, pharmacologic MR antagonism with spironolactone (Spiro) prevented obesity-associated diastolic dysfunction in female mice [6] consistent with a recent post hoc analysis of the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial suggesting greater benefit of MR blockade in obese female HFpEF patients compared to males [43]. Mechanistically, SMC dysfunction (i.e., hypercontractility) has been found to precede endothelial dysfunction in obesity [22] suggesting a potential role for SMC-MR signaling to contribute to impaired coronary and cardiac function in obese females via crosstalk between SMC and other cells in the heart. Indeed, deletion of SMC-MR abrogates aging-associated arteriolar hypercontractility and hypertension [15, 40]. The role of SMC-MR in obesity-associated coronary and cardiac dysfunction has not been explored. To that end, we hypothesized that SMC-MR deletion would prevent both CMD and cardiac diastolic dysfunction in obese female mice thereby expanding our mechanistic understanding of these common conditions. To test this hypothesis, we induced obesity in female SMC-MR knockout (SMC-MR-KO) and MR-Intact littermate mice by feeding a high-fat/high-carbohydrate western diet (WD) for 16 weeks. Additional studies were conducted in obese mice treated with and without Spiro. We quantified cardiac diastolic function and characterized the coronary and cardiac phenotype compared to SMC-MR-KO and MR-Intact mice fed control diet. We further determined gene expression changes in the non-cardiomyocyte fraction of the mouse heart in response to WD in the presence or absence of SMC-MR utilizing single-cell RNA sequencing (scRNA-seq). We show that SMC-MR deletion protects females from obesity-induced cardiac and coronary dysfunction and scRNA-seq analysis revealed novel cell-specific gene expression changes induced by obesity that are modified by SMC-MR. These findings support the emerging concept of a vascular origin of cardiac diastolic dysfunction in obesity. ## Animals All animal protocols were approved by the Institutional Animal Care and Use Committees of the Harry S Truman Memorial Veterans Hospital and the University of Missouri in compliance with the Guide for the Care and Use of Laboratory Animals (National Institutes of Health). All animals were housed in a temperature-controlled room (12:12-h light–dark cycle) and provided ad libitum access to water and either a standard control diet (Con; LabDiet 5008) or a western diet (WD; TestDiet 58Y1 modified) consisting of $46\%$ fat and $36\%$ carbohydrate ($17.5\%$ each from sucrose and high fructose corn syrup) for 16 weeks beginning at 14–24 weeks of age. Two experimental paradigms were utilized. First, female C57BL/6J mice (Jackson Labs) were randomly divided into 3 groups: Con, WD, or WD treated with the MR antagonist spironolactone (Spiro, sc, 0.63 mg·day−1; Innovative Research of America) for 16 weeks. Con and WD-fed mice received placebo pellets for 16 weeks. Second, mice with inducible MR deletion specifically in smooth muscle cells (SMC-MR knockout [SMC-MR-KO] mice) were generated, as previously described [15, 40], by crossing floxed MR mice with mice containing a Cre-recombinase-ERT2 gene driven by the smooth muscle actin promoter (SMA-Cre-ERT2) and activated by tamoxifen. These mice are compared with floxed MR/SMA-Cre-ERT2 negative (MR-intact) littermates. Both strains were treated with tamoxifen at 6–8 weeks of age, resulting in MR deletion in the Cre positive animals as previously confirmed [15, 40]. SMR-MR-KO and MR-intact mice were fed Con and WD for 16 weeks. ## Plasma and urine parameters Twenty-four hour urine collection was performed during the final week of diet feeding as was glucose tolerance testing. Following measurement of fasting blood glucose, mice were injected with glucose (ip, 1 g/kg) and blood glucose subsequently measured at 15, 30, 60, and 120 min post-injection via glucometer (AlphaTRAK 2, Zoetis). On the day of euthanasia, mice were fasted for 5 h and anesthetized with isoflurane (2–$4\%$ in $100\%$ O2); blood was collected via the inferior vena cava, processed to plasma, and frozen at – 80 °C. Blood glucose was determined by glucometer. Plasma aldosterone was quantified by radioimmunoassay (Tecan MG13051). All other plasma and urine measures were analyzed at Comparative Clinical Pathology Services (Columbia, MO). ## Echocardiography During the final week of diet feeding/treatment, transthoracic echocardiography was performed (Vevo2100, FUJIFILMS, Visualsonics, Toronto) with an MS400 high frequency echo probe at the Small Animal Ultrasound Imaging Center at the Harry S Truman Memorial Veterans Hospital. Under anesthesia (0.75–$4\%$ isoflurane in $100\%$ O2), mice were placed on a heated platform to maintain body temperature at 37 °C and two-dimensional echocardiograms were performed in the apical four chamber view. Initially, a small sample volume was positioned in the left ventricle (LV) just proximal to the mitral leaflets to acquire early (E) and late (A) diastolic blood flow velocities in pulse wave (PW) Doppler mode. Isovolumic relaxation time (IVRT) was also determined from the PW spectra. B- and M-mode images of the LV and septum in short axis view were acquired at the level of the papillary muscles. Left ventricular posterior and septal wall thicknesses at end diastole (LV PWTd and LV SWTd), luminal diameters (LVIDs and LVIDd), and ejection fraction (EF) were determined offline in M-mode. B-mode images in modified long axis (ascending aortic) view were acquired for determination of left atrial and aortic diameters. Next, Tissue Doppler Imaging (TDI) was performed in the apical four chamber view to acquire early (E’) and late (A’) septal annular velocities. Parameters were assessed using an average of three beats from three different spectra, and calculations were made in accordance with the American Society of Echocardiography guidelines as well as specific guidelines for rodent echocardiography. All data were acquired and analyzed offline by a single blinded observer. ## Aortic vascular measurements and blood pressure During the final week of diet feeding/treatment, in vivo aortic stiffness was evaluated in isoflurane-anesthetized mice ($1.75\%$ in $100\%$ O2) by pulse wave velocity (PWV) using Doppler ultrasound (Indus Mouse Doppler System, Webster, TX), as previously described [13]. Briefly, using the transit time method, PWV was quantified as the difference in arrival times of a Doppler pulse wave at two locations along the aorta at a fixed distance [29]. The distance between the two locations along the aorta is divided by the difference in arrival times and is expressed in m/s. Velocity waveforms were acquired at the aortic arch followed immediately by measurement at the descending aorta 35 mm distal to the aortic arch. Systolic blood pressure was determined by tail-cuff plethysmography (BP-2000, Visitech) during the final week of treatment, as previously described [4]. ## Coronary vasomotor function Following blood collection under anesthesia (2–$4\%$ in $100\%$ O2), mice were perfused via the left ventricle at 100 mmHg with ice-cold physiological salt solution (PSS) comprised of (in mM): 119 NaCl, 4.7 KCl, 2.5 CaCl·2H2O, 1.17 MgSO4·7H2O, 1.18 KH2PO4, 0.027 EDTA, 25 NaHCO3, and 5.5 glucose (pH 7.4). The heart was subsequently removed and segments of the left coronary artery (~ 1 mm) dissected and mounted on 17 µm stainless steel wires in oxygenated PSS ($95\%$ O2–$5\%$ CO2) in a small vessel myograph for isometric tension recording (Danish Myo Technology, Aarhus, Denmark), as previously described [2, 3]. Vessel length was quantified after mounting with a calibrated ocular micrometer. Following equilibration and normalization using an established procedure [52], vessel viability was confirmed by exposure to 80 mM KCl PSS. Following washing, vasoconstrictor responses to the thromboxane A2 analog U46619 (10 nM–1 µM) were assessed as well as vasodilator responses to acetylcholine (ACh; 1 nM–0.1 mM) and sodium nitroprusside (SNP; 1 nM–0.1 mM) following preconstriction with U46619 (100–300 nM). A subset of vessels were pretreated with the superoxide dismutase mimetic Tempol (1 mM for 20 min) prior to assessment of ACh-induced vasodilation. Vasodilator responses are reported as percent maximal dilation from U46619 preconstriction. Vasoconstrictor responses are reported as developed tension normalized to vessel length (mN/mm). Since this vessel does not develop spontaneous myogenic tone, minimum tension (i.e., maximal dilation) was determined following the normalization procedure. ## Atomic force microscopy Aortic endothelial cortical stiffness was quantified on en face preparations, as previously described [8]. Briefly, the endothelial surface of a section of thoracic aorta (~ 2 mm) was exposed by opening the vessel longitudinally and fastening the section to a plastic cover slip with Cell-Tak. Endothelial stiffness was then assessed via a cell nanoindentation protocol with an atomic force microscope. ## RT-PCR Aortas and periovarian adipose tissue were homogenized in a Tissue-Lyser (Qiagen) and total RNA was extracted using the Qiagen RNeasy Fibrous (aorta) or Lipid (adipose) Tissue kit and quantified using a Nanodrop spectrophotometer (Thermo Scientific). First-strand cDNA was synthesized from total RNA using the Improm-II reverse transcription kit (Promega) and quantitative real-time PCR was performed using the CFX Connect Real-Time PCR Detection System (Biorad) using target specific primers (Online Resource 1). PCR reactions using iTaq Universal SYBR Green SMX (Biorad), thermal conditions, and melt curve analysis were performed as previously described [4]. GAPDH was used as an internal control gene and messenger RNA (mRNA) expression values were calculated based on cycle thresholds (CTs) via the 2ΔΔCT method, where ΔCT = GAPDH CT – gene of interest CT and are presented normalized to control mice, which were set at 1. ## Immunohistochemistry and staining Left ventricular tissue was immersion fixed in $10\%$ buffered formalin, dehydrated in ethanol, paraffin embedded, and sectioned in 5 µm slices. To evaluate fibrosis, sections were stained with picrosirius red (PR) for determination of cardiac interstitial and periarterial collagen. Images were obtained using an EVOS FL Auto Imaging System and quantified using the thresholding function in ImageJ. Periarterial fibrosis was quantified as the ratio of PR-stained periarterial area to luminal circumference. Interstitial fibrosis was quantified as the percent area of myocardial PR staining. Cardiac capillary density was quantified in FITC-conjugated CD31 (1:50, Novus)-stained cardiac sections. In additional sections, following sodium citrate buffer antigen retrieval, hearts were blocked with $10\%$ FBS in PBS and $0.3\%$ H2O2 to prevent endogenous peroxide activity. Hearts were incubated with antibodies against CD3 (1:100; Abcam #ab5690), CD68 (1:100; Abcam #ab31630), or mast cell tryptase (1:100; Abcam #ab2378). Washed slides were incubated with the appropriate HRP conjugated secondary antibody. Stained hearts were developed using a DAB Substrate Kit (Thermo Fisher Scientific) and conjugated with hematoxylin to identify nuclei. Staining was visualized on a Nikon Eclipse microscope at 20X magnification and analyzed using ImageJ from 10 fields per heart. Additional immunofluorescence studies were performed to assess VCAM-1 and Cyp1a1 protein expression in coronary vessels and coronary endothelium, respectively. Briefly, following antigen retrieval (heat and sodium citrate buffer) and blocking non-specific protein binding (Abcam #ab64226), sections were incubated with smooth muscle α-actin (1:500; Novus #nb300-978) antibody in conjunction with antibodies against either VCAM-1 (1:100; Abcam #ab134047) or Cyp1a1 (1:100; Proteintech #13241-1-ap) at 4 °C overnight. Slides were then washed and incubated with Alexa Fluor 488 (1:200; ThermoFisher #A-11055) and Alexa Fluor 647 (1:200; ThermoFisher #A-21443) conjugated secondary antibodies after which slides were washed once more and mounted with antifade reagent. Fluorescence was detected on a Leica DMI4000 B confocal microscope and analyzed using ImageJ software. Specifically, vessels were identified by smooth muscle α-actin staining with subsequent quantification of VCAM-1 staining within the α-actin stained region and endothelial Cyp1a1 staining inside the luminal border of the α-actin stained region. ## Cardiac RNA-Seq High-throughput sequencing of LV was performed at the University of Missouri DNA Core Facility. Briefly, LV tissue was homogenized in a Tissue-Lyser (Qiagen) and total RNA was extracted using the Qiagen RNeasy Lipid Tissue kit and quantified using a Nanodrop spectrophotometer (Thermo Scientific). Libraries were constructed following the manufacturer’s protocol using the Illumina TruSeq mRNA stranded sample preparation kit. The RNA input concentration was determined using the Qubit HS RNA assay kit and Qubit fluorometer (Invitrogen) and RNA quality assessed using the Fragment Analyzer automated electrophoresis system (Agilent). Briefly, the poly-A containing mRNA is purified from total RNA, fragmented, double-stranded cDNA is generated from fragmented RNA, the index containing adapters are ligated, and the amplified cDNA constructs were purified by addition of AxyPrep Mag PCR Clean-up beads. The final construct of each purified library was evaluated using the Fragment Analyzer, quantified using the Qubit HS dsDNA assay kit and fluorometer, and diluted according to Illumina’s standard sequencing protocol for sequencing on the NextSeq 500 via single end 75 base pair reads. RNA-Seq data were processed and analyzed as previously described [20]. Briefly, latent Illumina adapter sequence was identified and removed from input 100-mer RNA-Seq data using Cutadapt. Subsequently, input RNA-Seq reads were trimmed and filtered to remove low quality nucleotide calls and whole reads, respectively, using the Fastx-Toolkit. To generate the final set of quality-controlled RNA-Seq reads, foreign or undesirable sequences were removed by similarity matching to the Phi-X genome (NC_001422.1), the relevant ribosomal RNA genes as downloaded from the National Center for Biotechnology Information, or repeat elements in RepBase, using Bowtie. This final set of quality-controlled RNA-Seq reads was aligned to the Ensemble Mus musculus genome sequence, GRCm38.p5, using STAR with the default settings, which also generated the initial expression estimates for each annotated gene. The R Bioconductor package DESeq2 was used to normalize the gene expression estimates across the samples and to analyze the differential expression of genes between sample types. Potential outlier samples were identified and removed at this stage using a combination of Principle Component Analysis and the R libraries nortest and outliers. Gene expression estimates were recalculated after outliers were removed. A gene was identified as being differentially expressed between two conditions when the FDR-corrected p value of its expression ratio was less than 0.05. Subsequent data were reformatted, sorted and filtered using a variety of R commands and Bash command-line scripts, which are available upon request. Ingenuity Pathway Analysis (IPA; Qiagen) was utilized for examination of the top differentially up- and downregulated genes and the corresponding top networks, pathways, and associated biological processes. ## Cardiac non-cardiomyocyte single-cell preparation Single-cell suspensions from isolated mouse hearts were prepared, as previously described [54]. Briefly, mice were euthanized with isoflurane and the heart exposed via bilateral thoracotomy before perfusion with DPBS (0.8 mM CaCl2; 10 min). Hearts were subsequently isolated, the atria, valves, and right ventricle removed, and the LV minced to ~ 1 mm cubes. Minced LV tissue was digested in perfusion buffer containing collagenase IV (2 mg/ml; Worthington Biochemical) and Dispase II (1.2 units/ml; Sigma-Aldrich) for 45 min at 37 °C with suspension trituration every 15 min using 1000 µl micropipettes. The resulting cell suspension was filtered through a 70 µm filter, diluted in 15 ml perfusion buffer, and pelleted at 200 rcf for 20 min at 4 °C with centrifuge brakes disengaged. Cell supernatant was aspirated, the pellet resuspended in perfusion buffer, and re-pelleted as described above. The resulting cell pellets were resuspended in FACS buffer (HBSS, $2\%$ FBS; Gibco), passed through a 40 µm filter, pelleted as described above, and resuspended in FACS buffer for subsequent staining and cell sorting. Intact, nucleated non-myocyte cells were subsequently isolated via flow cytometry after staining with Vybrant DyeCycle Ruby nuclear stain (10 µM; ThermoFisher V10273) and SYTOX Green viability stain (30 nM; ThermoFisher S7020). ## Single-cell RNA library preparation and sequencing Libraries were constructed by following the manufacturer’s protocol with reagents supplied in 10x Genomics Chromium Next GEM Single Cell 3′ Kit v3.1. Briefly, cell suspension concentration and viability were measured manually and with an Invitrogen Countess II automated cell counter. Cell suspension (900 cells per microliter), reverse transcription master mix, and partitioning oil were loaded on a Chromium Next GEM G chip with a cell capture target of 5000 cells per library. Post-Chromium controller GEMs were transferred to a PCR strip tube and reverse transcription performed on an Applied Biosystems Veriti thermal cycler at 53 °C for 45 min. cDNA was amplified for 12 cycles and purified using Axygen AxyPrep MagPCR Clean-up beads. cDNA fragmentation, end-repair, A-tailing and ligation of sequencing adaptors was performed according to manufacturer specifications. The final library was quantified with the Qubit HS DNA kit and the fragment size was analyzed using an Agilent Fragment Analyzer system. Libraries were pooled and sequenced on an Illumina NovaSeq to generate 50,000 reads per cell with a sequencing configuration of 28 base pair (bp) on read1 and 98 bp on read2. Isolation of single cells was performed in three batches on separate days with samples from each treatment group included each day to mitigate batch effects. ## Analysis of single-cell RNA-Seq data The raw sequencing data were processed using Cell Ranger version 3.1.0 (10x Genomics) before the subsequent analysis. Cell Ranger pipeline used fastq files and aligned sequencing reads to the mm10 transcriptome version 3.0.0 to quantify the expression of genes in each cell. This resulted in data for 83,669 cells that passed quality control steps implemented in Cell Ranger. The filtered count data matrices obtained from cell ranger software were then used for the subsequent analysis. Analyses of scRNA-seq processed data were performed in R version 3.6 and 4.0.1 (https://www.R-project.org/) using Seurat suite versions 3.0 [56] and tidyverse [64] packages. Further quality control measures of cells with < 200 or > 8000 expressed genes and genes that were expressed in less than 3 cells were applied per sample manner. In addition, cells with more than $30\%$ of UMI mapping to mitochondrial genes were filtered out to control dead or damaged cells. These steps further removed 298 cells from the analysis. The final dataset contains 83,371 cells from 12 mice in 4 conditions and gene expression information for 19,905 genes. Dimensionality reduction was performed using principal component analysis (PCA) to explore transcriptional heterogeneity and clustering. PC loading for 40 PCs were used as input for a graph-based clustering approach to cluster cells with clustering resolution 0.8. Cells and clusters were visualized on a t-distributed stochastic neighbor embedding (t-SNE) two-dimensional plot generated using the same PC loadings used for the clustering. To optimize the t-SNE plot, 1000 iterations and 289 perplexities were used. The identified cell clusters were then annotated based on known marker genes. Figures were primarily generated using Seurat and ggplot2 R packages (https://ggplot2-book.org/). ## Differential expression analysis The differential expression (DE) analysis was performed for each cell population separately. To identify DE genes between groups, we first identified genes expressed in at least $10\%$ of cells in at least one of the groups being compared. We then used MAST R package version 1.12.0 [17] to perform DE testing method MASTcpmDetRate considering the cellular detection rate as a covariate. A threshold of uncorrected $p \leq 0.01$ was used to define statistically significant DE genes between groups. ## Gene ontology analysis Gene Ontology (GO) over-representation analysis for differentially expressed gene lists (uncorrected $p \leq 0.01$) was performed using the enrichGO function from clusterProfiler R package version 3.16.1 [67]. The R package org.Mm.eg.db: Genome wide annotation for Mouse, R package version 3.11.4 [9] was used to obtain all gene ontology mappings. The over-representation of GO Biological Process terms (GO-BP) was calculated using the entire list of genes identified in the experiment as the background gene list for Mus musculus with minimum and maximum gene set sizes 10 and 500, respectively. The similarity between enriched GO-BP terms were calculated using the simplify R function from clusterProfiler R package. GO-BP terms with semantic similarity more than 0.7 were treated as redundant terms and discarded from the analysis. The Benjamini–Hochberg adjusted p value cutoff of 0.05 was used to determine statistically significant GO-BP terms. ## Flow cytometry Immune cells were isolated from the heart by enzymatic digestion, as previously described [23]. Briefly, hearts were isolated, flushed with HBSS, and manually digested into ~ 1 mm3 pieces. Heart pieces were then enzymatically digested in collagenase II (150 U/mL; Worthington Biochemical) and trypsin (0.6 mg/mL; Worthington Biochemical) at 37 °C with agitation. Following digestion, myocyte and non-myocyte fractions were separated by centrifugation at 8 × g for 5 min. The non-myocyte containing supernatant was passed through a 70 µm cell strainer prior to flow cytometry analysis. Cells were stained in $1\%$ FBS in PBS for 30 min at 4 °C with the following antibodies: LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (1:40, Invitrogen #L34957), CD3-PE-Cy7 (1:100; Biolegend #100220), CD4-PE (1:100; BD Biosciences #5530449), CD11b-FITC (1:200; Biolegend #101206), CD68-PE (1:50; Biolegend #137014), CD45-BV480 (1:100; BD Biosciences #746682), CD80-PE-Cy7 (1:100; Biolegend #104712), CD117-APC-H7 (1:100; BD Biosciences #560185) and CD196-BV711 (1:100; BD Biosciences #740648). Positive staining was identified based on single antibody controls which were performed for all antibodies on all tissues examined and fluorescence minus one controls were performed on splenic samples to validate cell staining. Isotype controls were also performed on splenic samples using PE-Cy7 rat IgG2b (κ isotype, 1:100, Biolegend 400617), PE rat IgG2a (κ isotype, 1:100, Biolegend 400507), FITC rat IgG2b (κ isotype, 1:100, Biolegend 400633), BV480 rat IgG2b (κ isotype, 1:100, BD Biosciences #565649), BV711 rat IgG2b (κ isotype, 1:100, BD Biosciences #563045) and APC-H7 rat IgG2b (κ isotype, 1:100, BD Biosciences #560200). Following staining, cells were washed twice with PBS and analyzed by flow cytometry using a BD LSRFortessa X-20. Analysis was performed in FlowJo software. ## Cardiac cytokine analysis Quantitative proteomic analysis of cardiac cytokines was performed on whole left ventricular lysates by RayBiotech (Mouse Cytokine Array Q4000) and statistical differences between groups were assessed by Wilcoxon test. ## Data analysis and statistics Data are presented as mean ± standard error with individual data points shown, when appropriate. Statistical analysis was performed using Student t-test for planned comparisons, two-way analysis of variance (for repeated measures, when appropriate) with Fisher least significant difference post hoc analysis, as appropriate, in SigmaPlot (SyStat) or Prism (Graphpad). A p value ≤ 0.05 was considered significant ## Results Systemic MR blockade and SMC-specific MR deletion do not impact traditional cardiac risk factors in obese female mice. WD feeding-induced phenotypic changes in female mice were unchanged by MR blockade with Spiro (Online Resource 2), as we previously reported [6, 32], nor by SMC-specific MR deletion (Fig. 1A, Online Resource 3). Specifically, WD-induced increases of blood glucose, plasma insulin, plasma aldosterone, and plasma cholesterol as well as WD-induced proteinuria and increased urinary blood urea nitrogen (BUN) levels were unchanged by SMC-MR deletion (Online Resource 3). Similar to a recent report [45], SMC-MR-KO mice fed WD exhibited a modest (~ $10\%$) reduction in average body weight and reduced periovarian adipose weight, compared to MR-Intact controls fed WD (Online Resource 3), with no change in glucose intolerance (Online Resource 4).Fig. 1Smooth muscle cell mineralocorticoid receptor knockout (SMC-MR-KO) prevents cardiac diastolic and coronary vascular dysfunction in western diet (WD)-fed female mice independent of adipose inflammation. a Mouse cohorts and experimental conditions of the study. All mice were analyzed 16 weeks after Western diet (WD) or control diet (Con) feeding at 30–40 weeks of age. b Indices of cardiac diastolic function, specifically estimated left ventricular filling pressure (E/E’) and early-to-late diastolic septal annulus motion ratio (E’/A’), and cardiac weights (heart weight-to-tibia length ratio; HW/TL) in control (Con) and WD-fed mice. c Vasodilator responses of isolated coronary arteries to endothelium-dependent (acetylcholine, ACh), -independent (sodium nitroprusside, SNP) agonists as well as vasoconstrictor responses to the thromboxane A2 analog U46619. d Expression of inflammatory genes in reproductive adipose tissue. Values are mean ± SE with individual data points shown (b, d). * $p \leq 0.05$ versus Con or comparison indicated; *$p \leq 0.05$ versus all other groups Systemic MR blockade and SMC-specific MR deletion prevent WD-induced cardiac diastolic and coronary vascular dysfunction independent of blood pressure and adipose inflammation. WD feeding impaired cardiac diastolic function, indicated by increased LV filling pressure (E/E’), reduced septal wall motion in diastole (E’/A’), left atrial distension (LA/Ao), and induced cardiac hypertrophy in female mice (Fig. 1B, Online Resources 5-7). Results herein extend prior work by demonstrating concomitant impairment of coronary endothelium-dependent vasodilation with WD feeding in female mice. Furthermore, MR blockade with Spiro attenuated both the coronary endothelial and cardiac dysfunction, but not the cardiac hypertrophy, induced by WD feeding (Online Resources 5 and 6). Additional mechanistic studies revealed that SMC-specific MR deletion in female mice is sufficient to reproduce the benefits of global MR inhibition in obese females (Fig. 1). Indeed, unlike WD-fed MR-Intact mice, SMC-MR-KO mice fed a WD did not exhibit impaired diastolic function despite similar WD-induced cardiac hypertrophy (Fig. 1B, Online Resource 7). Further, impaired endothelium-dependent vasodilation and enhanced vasoconstriction to the thromboxane analog U46619 induced by WD feeding in MR-Intact mice was absent in WD-fed SMC-MR-KO mice (Fig. 1C). There were no differences in diameters of coronary vessels studied (Online Resource 8). Importantly, blood pressure and aortic pulse wave velocity (i.e., aortic stiffness) were not changed by either WD feeding or SMC-MR deletion (Online Resource 3). Lastly, WD-induced visceral adipose inflammation indicated by increased gene expression of Adgre1, Itgax, Vcam1, and Tnf, was unchanged by SMC-MR-KO mice (Fig. 1D). SMC-specific MR deletion shifts WD-induced changes in the cardiac transcriptome. Since SMC-MR deletion did not change traditional risk factors, we explored local cardiac-specific changes associated with SMC-MR signaling in the setting of WD feeding. Specifically, we performed bulk RNA sequencing of LV tissues and examined the unique cardiac transcriptomic signatures induced by WD feeding (Fig. 2A; Online Resource 9) and how the transcriptome differed with SMC-MR deletion in the setting of WD feeding (i.e., compared to WD-fed MR-Intact; Fig. 2B). Differential* gene expression analysis did not reveal any genes/pathways altered as a result of SMC-MR-KO (compared to MR-Intact) in control chow-fed mice (Online Resource 10); however, key pathways that were identified using gene ontology (GO) enrichment analysis across other group comparisons included: water homeostasis (downregulated in WD-fed versus control-fed MR-Intact mice), circadian regulation (upregulated in WD-fed SMC-MR-KO WD versus WD-fed MR-Intact mice), and ketone metabolism (downregulated in WD-fed SMC-MR-KO versus WD-fed MR-Intact mice) (Online Resource 10). Changes in water homeostasis and circadian regulation are consistent with established associations of MR in regulating these biological processes. Moreover, Ingenuity Pathway Analysis (IPA) analysis of differentially expressed genes in WD-fed versus control-fed MR-Intact mice, revealed enrichment of ‘hypertrophy’ (consistent with increased HW/TL with WD feeding) and ‘quantity of reactive oxygen species (ROS)’ biological processes in the top gene network (Online Resource 11). Accordingly, increased ROS in WD-fed MR-Intact mice was confirmed by restoration of coronary endothelium-dependent vasodilation by the superoxide dismutase mimetic Tempol (Online Resource 11). Further, in WD-fed SMC-MR-KO compared to WD-fed MR-Intact mice, the top gene network was enriched for biological processes including ‘inflammation of organ’ and ‘leukocyte migration’ (Fig. 2C). Interestingly, WD-induced cardiac transcriptomic changes were unique across genotypes with only 3 shared genes (Fig. 2D). Directional gene changes in these IPA pathways in WD-fed SMC-MR-KO mice were generally consistent with reduced inflammation and leukocyte migration. Accordingly, assessment of aortic adhesion molecule gene expression demonstrated upregulation of Vcam1 and Icam1 in WD-fed MR-Intact mice indicative of vascular inflammation that is prevented in WD-fed SMC-MR-KO mice (Fig. 2E). Assessment of coronary VCAM-1 protein expression with immunofluorescence revealed increased VCAM-1 staining in WD-fed MR-Intact mice compared to Con-fed MR-Intact mice that was prevented in WD-fed SMC-MR-KO mice (Fig. 2F and G). Lastly, atomic force microscopy revealed that WD-fed MR-Intact mice exhibited increased aortic endothelial cortical stiffness, a marker of vascular injury [37], that was prevented in WD-fed SMC-MR-KO mice (Fig. 2H).Fig. 2Smooth muscle cell mineralocorticoid receptor knockout (SMC-MR-KO) alters western diet (WD)-induced changes of the cardiac transcriptome. a Analysis of cardiac transcripts (13,598 transcripts) revealed differential expression of 30 transcripts induced by WD feeding in MR-Intact mice. Blue and red circles indicated genes down- or upregulated after WD (14 downregulated, blue dots; 16 upregulated, red dots). b Differential gene expression in WD-fed SMC-MR-KO mice versus WD-fed MR-Intact mice (16 downregulated, blue dots; 12 upregulated, red dots). Colored circles in volcano plots (panels a and b) indicate genes with log2 fold change > 0.5 and corrected $p \leq 0.05.$ c Top differentially regulated IPA network (IPA score = 42; green nodes, downregulated; red nodes, upregulated) in WD-fed SMC-MR-KO versus WD-fed MR-Intact mice with enriched relevant biological processes. d WD feeding induced unique transcriptomic signatures in WD-fed MR-Intact and SMC-MR-KO mice (3 overlapping differentially expressed [log2 fold change > 0.5, corrected $p \leq 0.05$] transcripts). e Gene expression of adhesion molecules in whole aortic tissue from each group. f Expression of VCAM-1 in coronary vessels by immunofluorescence and g representative images by group. h Aortic endothelial cortical stiffness assessed by atomic force microscopy. Values are mean ± SE with individual data points shown; $p \leq 0.05$ from all other groups or noted comparison SMC-MR deletion alters non-myocyte gene signatures in mice fed control diet. Since bulk RNA sequencing analysis is largely dominated by cardiomyocyte mRNA, we next performed high-resolution scRNA-seq analysis of the non-myocyte cell populations to elucidate cell-specific changes elicited by WD feeding and potential mechanisms underlying protection via SMC-MR deletion in obesity. Using established protocols (Fig. 3A) [54], our analysis demonstrated a wide diversity of cardiac non-myocyte cell types including multiple clusters of fibroblasts, vascular smooth muscle and endothelial cells, and diverse immune cell types identified by expression of canonical and non-canonical marker genes (Fig. 3B–D, Online Resource 12). The relative proportions of the various cell types did not differ in response to diet or to SMC-MR deletion (Online Resource 13). We confirmed the SMC-specificity of the model with a reduction of Nr3c2 (the gene for the MR) across all coronary SMC clusters from SMC-MR-KO compared to MR-Intact mice (Fig. 3E and F; Online Resource 14) with no reduction in MR expression in other non-myocyte cell populations in the heart (Online Resource 15). Our first analysis compared gene expression between MR-Intact and SMC-MR-KO mice fed control diet. In SMCs, the most differentially expressed gene in response to SMC-MR deletion was a marked upregulation of the estrogen receptor (Esr1) independent of diet feeding (Fig. 3G). We also found significant gene expression differences between genotypes in a cell-specific manner (Fig. 3H, Online Resource 16). Analysis of differentially regulated genes indicates a key feature of SMC-MR-KO is increased expression of major histocompatibility complex (MHC) class 1 genes (H2-Q7, -Q4, -Q6, H2-Eb1, and others) in multiple cell types of SMC-MR-KO animals, particularly ECs, macrophages, and fibroblasts corresponding to GO terms associated with immune modulation and T cell-mediated cell targeting (Online Resources 17 and 18). Also noted were increased levels of transcripts corresponding to “response to glucocorticoid” and “response to corticosteroid” in fibroblast and EC subsets, and “angiogenesis” in fibroblasts and SMCs from SMC-MR-KO mice (Online Resource 18). Gene programs corresponding to “angiogenesis” were also frequently downregulated in multiple cell types, in SMC-MR-KO animals, in addition to those involved in “extracellular matrix organization” (in fibroblast subsets) (Online Resources 17 and 18). However, no differences in inflammation, coronary function, or cardiac function were detected relative to control-fed MR-Intact mice (Figs. 1 and 2); hence, the relevance of these differences in gene expression and programs identified in unstressed mice is unclear. scRNA sequencing of non-myocyte cardiac cell populations reveals that WD induces inflammatory pathways in EC and inflammatory cells independent of fibrosis. We next examined the impact of WD-induced obesity on the cardiac cellulome in MR-Intact mice. This examination of disparate cardiac cell types revealed that WD feeding altered the transcriptional profile of all cell populations examined in MR-Intact mice (Fig. 4A, Online Resource 19). Genes upregulated by WD feeding in MR-Intact mice include genes previously implicated in WD-induced pathology such as Angptl4 (upregulated in ECs and fibroblasts) [11], Cyp1a1 (upregulated in ECs) [61], Plin2 (upregulated in ECs, fibroblasts and macrophages) [41], and Sgk1 (upregulated in ECs, fibroblasts, pericytes, and B cells) [38] (Fig. 4B). Also upregulated was Pparγ (ECs) which positively regulates many of these genes (Angptl4, Plin2, Sgk1) and others (Fabp4, Cd36, Tsc22d1, Hmox1, Aqp7, Ucp2, Klf4) that are upregulated in ECs after WD [16] (Online Resource 19). Conversely, genes downregulated by WD feeding in MR-Intact mice include genes involved in energy metabolism (Ckb, downregulated in EC, fibroblasts, Schwann and T cells, and SMCs), matrix regulation (Plod1, downregulated in EC and macrophages), and clearance of advanced glycosylation end products (Dcxr, downregulated in fibroblasts, Schwann cells, and SMCs) (Online Resources 19 and 20).Fig. 3Isolation and analysis of cardiac non-myocyte populations by single-cell RNA sequencing (scRNA-seq). a Schematic outlining the experimental procedure for cell isolation and analysis of adult mouse cardiac non-myocytes by scRNA-seq. b t-SNE projection of cardiac cell populations identified by scRNA-seq analysis. Each dot represents a cell that is colored based on distinct cell populations. c Heatmap showing relative expression of canonical cell type markers for major cell types identified in adult mouse heart. d Dot plot for top 5 highly and uniquely expressed genes in each major cardiac cell population identified using an unsupervised analysis. Dot color and size indicate the relative expression and percentage of cells expressing that gene within each cell population, respectively (also see Online Resource 11). e Average expression of mineralocorticoid receptor (MR; Nr3c2) in coronary smooth muscle cells (SMC) from MR-Intact and SMC-MR knockout mice fed control (Con) and western diet (WD). Dot color and size indicate the diet group and the percentage of cells expressing Nr3c2 gene within each group, respectively. f MR (Nr3c2) gene expression (red dots) in cell populations and in 3 SMC clusters identified using an unsupervised analysis. Dot color and size (right plots) indicate the diet group and the percentage of cells expressing Nr3c2 gene within each group, respectively. g Estrogen receptor (Esr1) expression (red dots) in cell populations from each treatment group with SMC1 population indicated by blue circle. h Lollipop plot summarizing number of up- and downregulated genes (uncorrected $p \leq 0.01$) in Con-fed SMC-MR-KO mouse heart cells relative to Con-fed MR-Intact cells (also see Online Resource 16)Fig. 4Gene expression changes in cardiac non-myocyte populations in response to western diet (WD) in MR-Intact mice are independent of cardiac fibrosis. a Lollipop plot summarizing the number of up- and downregulated genes (uncorrected $p \leq 0.01$) identified in WD-fed MR-Intact mice relative to control (Con) diet-fed MR-Intact mice (also see Online Resource 19). b Dot plot summarizing the relative expression of top 3 upregulated genes in response to WD, in each cardiac cell population. Dot color intensity and size are proportional to the relative gene expression in WD cells and the fold change increment in WD cells compared to the Con cells within each cell population, respectively. Black points at the centers of some dots highlight statistically significant differences in gene expression in WD relative to Con cells (uncorrected $p \leq 0.01$). c Sankey plot summarizing the top 3 statistically significant Gene Ontology (GO) terms (corrected $p \leq 0.05$) enriched by WD upregulated genes in each cell population. Lines connect GO terms associated with each cell population. Note: not all cell types have 3 statistically significant GO terms (also see Online Resource 22). d Heatmap of collagen isoform gene expression in non-myocyte populations from WD versus Con-fed MR-Intact mice (see also Online Resource 24 for all extracellular matrix-related genes). Box color and intensity indicate direction (blue, downregulation; red, upregulation) and magnitude of WD-induced expression changes, respectively. e Representative images of cardiac interstitial and perivascular fibrosis assessed by picrosirius red staining. f, g Levels of interstitial and coronary fibrosis in mice from all groups, relative to levels in hearts of Con-fed MR-Intact mice. Values are mean ± SE with individual data points shown To determine genetic programs that are up- and downregulated following WD feeding we examined GO terms corresponding to biological processes in differentially expressed gene sets (Online Resources 21 and 22). Among the top biological processes upregulated by WD feeding in MR-Intact mice, we found enrichment of terms associated with regulation/function of immune cells (i.e., antigen processing and presentation, leukocyte activation) (Fig. 4C, Online Resources 21 and 22) and that these were also the most commonly upregulated programs shared across multiple cells populations including ECs and macrophages (Fig. 4C). Further, consistent with exposure to WD, multiple cell populations also activated gene programs linked to fatty acid and lipid metabolism and transport (Online Resource 22). Angiogenesis* gene programs were both up- and downregulated by WD feeding (Online Resources 21 and 22) in line with no change in capillary density across groups (Online Resource 23). Lastly, WD feeding did not induce extracellular matrix genes (Online Resource 24), including collagens (Fig. 4D), in non-myocyte populations from MR-Intact mice and GO terms associated with fibrosis (“extracellular matrix organization” and “extracellular structure organization”) were downregulated in major fibroblast clusters (Online Resources 21 and 22). Picrosirius red staining confirmed no change in interstitial or perivascular collagen deposition (i.e., fibrosis) across all treatment groups (Fig. 4E–G; Online Resource 24). Diabetes- and obesity-associated gene programs are induced by WD feeding independent of MR genotype. Next, we examined the key common gene expression features induced by WD feeding in MR-Intact and SMC-MR-KO mouse hearts. Indeed, a majority of genes altered by WD were equivalently up- or downregulated across genotypes (Fig. 5A and B; Online Resources 25 and 26). Top downregulated genes included those previously associated with diabetes and obesity, such as Manf [66] and Creld2 [34] that facilitate protein folding (Fig. 5C), and Igfbp3 and Igfbp7, two structurally similar proteins that regulate the bioavailability of IGFs and insulin. As noted for WD-fed MR-Intact mice, WD also downregulated genes associated with extracellular matrix in SMC-MR-KO mice (Fig. 5C, Online Resources 26 and 27) and top upregulated genes also included those implicated in diabetes and obesity, such as Cd36, Txnip, Angptl4, Cyp1a1, and Fabp4 (Fig. 5D). Immunofluorescence confirmed increased protein expression of the top gene upregulated by WD feeding, Cyp1a1, in coronary endothelium independent of genotype (Fig. 5 E and F). Consistent with a change in diet, genes corresponding to programs involved in fatty acid and lipid metabolism were upregulated in both genotypes by WD feeding; however, these were primarily restricted to mural cells (Fig. 5D, Online Resources 26 and 27). Since SMC-MR-KO mice are protected from WD-induced coronary and cardiac dysfunction, these genotype-independent WD-induced gene changes likely represent pathways not involved in this protection. Fig. 5Western diet (WD) feeding induces common and distinct gene activation in MR-Intact and SMC-MR-KO mouse heart cells. a, b Lollipop plots summarizing genes similarly (white circles) and differentially downregulated (a) or upregulated (b) by WD feeding in MR-Intact (blue circles) and SMC-MR-KO (red circles) mice by cell population. c, d Top 20 genes down- or upregulated (c and d, respectively) following WD in SMC-MR-KO and MR-Intact mice (also see Online Resource 26). Bolded genes indicate those that have been associated with diabetes or obesity. Bottom panels summarize GO terms corresponding to down or upregulated genes (also see Online Resource 27). e Representative images and f summary data of Cyp1A1 protein expression in coronary endothelium by immunofluorescence. Values are mean ± SE with individual data points shown. * $p \leq 0.05$ for noted comparison SMC-MR deletion impacts WD-induced gene expression across cell populations, including MR target genes. Next, we examined genes and gene programs that were more robustly induced or repressed by WD feeding in SMC-MR-KO mice compared to MR-Intact mice. Top genes differentially induced or suppressed between genotypes after WD generally followed a cell-specific pattern (Fig. 6, Online Resource 28). Notably, genes whose expression was increased more robustly in MR-Intact mouse hearts included the anti-proliferation genes Btg3 (ECs and fibroblasts) and Cdkn1a (ECs and SMC1), as well as angiogenic genes Cyr61 (ECs) and Nrarp (ECs), and the cholesterol uptake regulator Ldlr (fibroblasts and pericytes). Further, genes more robustly expressed in MR-Intact mouse hearts after WD feeding included MR- and GR-sensitive genes such as Zbtb16, Fam46b, and Angptl4 in SMC populations [10]. Closer examination of reported MR-sensitive genes showed upregulation of a number of these genes in SMCs from MR-Intact, but not SMC-MR-KO, mouse hearts after WD (Fig. 6B). Indeed, Zbtb16, a key transcriptional repressor, and Pgf, a pro-angiogenic and pro-inflammatory MR target gene, were upregulated by WD in MR-Intact SMCs and downregulated or not changed, respectively, in SMC-MR-KO SMCs after WD.Fig. 6Smooth muscle cell mineralocorticoid receptor knockout (SMC-MR-KO) alters cardiac gene expression response to western diet (WD) feeding. a Top 50 genes that are upregulated after WD and differentially expressed between MR-Intact and SMC-MR-KO mouse heart cells. Blue and red circles indicate genes that are more highly expressed in MR-Intact or SMC-MR-KO mouse heart cells, respectively. Black dot (center of some circles) indicates statistical significance of $p \leq 0.001$ (see Online Resource 28). b Changes in expression of MR target genes in cardiac SMCs from MR-Intact and SMC-MR-KO mice fed WD versus Con-fed mice from each genotype. Black dot (center of some circles) indicates statistical significance. c Sankey plot summarizing the top 3 statistically significant Gene Ontology (GO) terms (corrected $p \leq 0.05$) enriched in up- (right) and downregulated (left) genes in WD-fed SMC-MR-KO mice versus WD-fed MR-Intact mice. Lines connect GO terms associated with each cell population. Note: not all cell types have 3 statistically significant GO terms (see Online Resource 29) SMC-MR deletion attenuates WD-induced inflammation across the cardiac cellulome. The most significant differences in gene expression programs activated by WD in the two genotypes were those related to immune activity. Direct comparison of genetic programs among genes down- and upregulated in SMC-MR-KO mice by WD, versus WD-fed MR-Intact mice, further revealed reduced WD-induced immune response in these mice (Fig. 6C; Online Resource 29). Specifically, downregulated GO terms in WD-fed SMC-MR-KO hearts were enriched for terms associated with antigen processing and presentation (ECs and Mac1) as well as positive regulation of immune responses (ECs, Mac1, Fibro5). Intriguingly, in control diet-fed mice, many of these genes and GO terms were more highly expressed in SMC-MR-KO heart ECs, compared to MR-Intact heart ECs (Online Resources 16 and 18). Further, downregulation of GO terms related specifically to T cell immunity were enriched in B cells (‘CD4-positive, alpha–beta T cell proliferation’), ECs and Mac1 (‘positive regulation of T cell mediated cytotoxicity’), and T cell2 (‘T cell activation’ and ‘differentiation’) (Fig. 6C; Online Resource 29). The enrichment of genes related to leukocyte trafficking and inflammation among the most statistically significant programs in multiple cell types (Fig. 6C) identifies a key distinction in the phenotypes of MR-Intact and SMC-MR-KO mouse hearts after WD. Next, using orthogonal approaches, we sought to validate differences in inflammatory responses in the two genotypes identified by scRNA-seq. First, a cytokine array confirmed a pro-inflammatory cytokine signature in hearts from WD-fed MR-Intact mice. Notably, WD feeding upregulated the T cell chemokine lymphotactin and the adhesion molecule L-selectin (Fig. 7A). In addition, factors implicated in promotion of local immune/cytokine signaling including M-CSF, GITR-L, and Axl were upregulated by WD feeding in MR-Intact hearts (Fig. 7A). Deletion of SMC-MR altered the cardiac cytokine profile in both control (Online Resource 30) and WD-fed SMC-MR-KO mice (Fig. 7B, Online Resource 30). In contrast to WD-fed MR-Intact mice, SMC-MR-KO mice fed a WD exhibited cytokine changes indicative of reduced cardiac inflammation compared to control-fed SMC-MR-KO mice. Specifically, the pro-inflammatory mediators TWEAK, PDGF-AA, MIP-1b, IL-22, and IL-1a were reduced in WD-fed SMC-MR-KO mice (Fig. 7B). Accordingly, the impact of SMC-MR deletion on WD-induced cardiac immunity was further explored by immunohistochemistry and flow cytometry. In MR-Intact mice, WD feeding increased cardiac CD3+ T cells but not CD68+ monocytes/macrophages (Fig. 7C–F, Online Resource 31). Single-cell transcriptomics revealed upregulation of macrophage activation markers Cd86 and Cd83 (Mac1; Online Resource 19) in WD-fed MR-Intact mice consistent with a trend for increased cardiac CD11b + CD80+ macrophages by flow cytometry ($$p \leq 0.07$$; Online Resource 31). These changes in cardiac leukocytes were prevented in WD-fed SMC-MR-KO mice. Furthermore, consistent with downregulation of the ‘Th17 cell differentiation’ GO term in the Tcell2 cluster from WD-fed SMC-MR-KO mice compared to WD-fed MR-Intact mice (Fig. 6C), SMC-MR deletion was associated with reduced cardiac Th17 cells (Online Resource 31). Lastly, SMC-MR-KO mice exhibited a reduction in cardiac mast cells, a cell population recently implicated in diabetes-associated cardiac dysfunction [25], independent of diet feeding (Fig. 7E and F, Online Resource 31). Together, these data implicate SMC-MR signaling as necessary for development of coronary and cardiac dysfunction in obesity and as a critical driver of obesity-associated cardiac inflammation, not only through changes in SMC but across the cardiac cellulome, supporting a role for SMC-MR in intercellular crosstalk underlying coronary and cardiac dysfunction in obesity. Fig. 7Smooth muscle cell mineralocorticoid receptor knockout prevents obesity-associated cardiac inflammation. Differentially expressed cardiac cytokines in a WD-fed MR-Intact mice and b WD-fed SMC-MR-KO mice versus Con-fed mice of each genotype. Red, upregulation; blue, downregulation; colored dot is mean with bar indicating spread of data in WD group; gray dots indicate individual data points in Con group. * $p \leq 0.05$; *$p \leq 0.01.$ Cardiac c CD3+, d CD68+, and e Tryptase+ cells assessed by immunohistochemistry and f representative images by group, arrows point to positively stained cells, insets include zoomed in view of positive staining. Values are mean ± SE with individual data points shown. $p \leq 0.05$ versus all other groups or noted comparison ## Discussion Our data demonstrate critical involvement of SMC-MR in obesity-associated coronary and cardiac dysfunction in female mice. Specifically, in obese female mice, SMC-MR deletion prevented the decline in coronary endothelium-dependent vasodilation and increase of vasoconstriction as well as impaired cardiac diastolic function, but not cardiac hypertrophy. Importantly, these benefits of SMC-MR deletion occurred independent of changes in blood pressure, aortic stiffening, and obesity-associated adipose inflammation, metabolic dysregulation, and kidney injury. Further, scRNA-seq revealed a distinct inflammatory gene profile in non-myocyte populations from obese mice that was independent of cardiac fibrosis and associated with cardiac leukocyte infiltration. This obesity-associated cardiac inflammatory phenotype was generally prevented by SMC-MR deletion. Together, these data provide unique insight and support the emerging paradigm of a vascular origin of cardiac dysfunction in obese females [46, 50], patients at high risk for developing HFpEF [27, 58]. Involvement of MR signaling in obesity-associated cardiovascular morbidity and mortality is supported by both clinical and preclinical work, from us [4, 6, 7, 13, 21] and others [19, 33, 53], utilizing MR antagonists. In obese rodent and swine models, MR blockade with spironolactone or eplerenone mitigated endothelial dysfunction and vascular remodeling as well as diastolic dysfunction, cardiac oxidative stress, fibrosis, and inflammation [4, 6, 7, 13, 21, 53]. Mechanistically, several prior studies have revealed an important role for EC MR signaling underlying vascular and cardiac dysfunction in obesity. Specifically, deletion of EC MR prevented obesity-associated endothelial dysfunction in aorta and mesenteric arterioles as well as cardiac diastolic dysfunction [12, 31, 32]. Improved vascular function in obesity following EC MR deletion was associated with modulation of reactive oxygen species production/degradation and NO bioavailability [12, 31], while prevention of cardiac dysfunction was associated with reduced cardiac pro-oxidant and pro-inflammatory signaling [32]. Improved cardiac function in obese female mice with EC MR deletion may also be due, in part, to attenuated obesity-associated aortic stiffening in these mice [31]. Our data expand this previous work by demonstrating, for the first time, a critical role of SMC-MR signaling underlying coronary and cardiac diastolic dysfunction in obese females. That obesity-associated endothelial dysfunction was prevented by SMC-MR deletion supports recent evidence that SMC dysfunction may precede [22], and contribute to development of, impaired endothelium-dependent vasodilation in obesity. Taken together, these studies highlight novel, independent roles of MR signaling in vascular cells in the pathogenesis of obesity-associated cardiac dysfunction in females. Importantly, the protection afforded by SMC-MR deletion occurs independent of changes in traditional risk factors (blood pressure, glucose intolerance, hypercholesterolemia, kidney injury) and aortic stiffening suggesting local SMC-MR-dependent mechanisms of dysfunction within the cardiac microenvironment. Systemic and cardiac inflammation have been implicated as requisite contributors in the etiology of obesity-associated cardiac impairments, including HFpEF [18, 50, 60]. Indeed, cardiac inflammation has been suggested to precede and contribute to other common characteristics of cardiac diastolic dysfunction, such as fibrosis and sarcomeric stiffening, in the setting of co-morbid conditions [50, 63]. Consistent with this paradigm, in our hands, WD-fed MR-Intact female mice do not exhibit cardiac fibrosis despite increased cardiac expression of pro-inflammatory cytokines (e.g., neprilysin, M-CSF). These data suggest that WD-induced diastolic impairments in this model likely involve sarcomeric stiffening (i.e., titin hypophosphorylation) [28] and/or altered cardiomyocyte calcium handling [44, 62]. Unbiased scRNA-seq data confirmed upregulation of inflammatory pathways across cardiac non-myocyte populations by WD feeding independent of a pronounced upregulation of extracellular matrix-related genes. These data further highlight a vascular contribution to cardiac inflammation in obesity via upregulation of gene programs for EC antigen presentation and processing via MHC class I molecules in conjunction with upregulation of genes associated with T cell-mediated cytotoxicity in cardiac ECs and macrophages. These gene changes correspond to cardiac infiltration of CD3+ T cells supporting recent evidence that increased endothelial MHC class I molecule expression enhances T cell transmigration [39]. Further, cytokine analysis reveals obesity-associated upregulation of the adhesion molecule L-selectin, the T cell chemokine lymphotactin, and the T cell costimulatory ligand GITR-L. Interestingly, GITR-L engagement of T cell GITR has been reported to reduce susceptibility of effector T cells to suppression by T regulatory cells [55]. Prior work has established a link between cardiac T cell infiltration and the development of systolic dysfunction in response to cardiac pressure overload [48]. Our data extend these findings and, to our knowledge, are the first to suggest a role for cardiac T cell infiltration in the inflammatory processes contributing to cardiac diastolic dysfunction in obese female mice. MR-dependent inflammation has been implicated in a variety of disease states, including obesity. Indeed, global MR blockade reduces obesity-associated adipose [4, 7, 26], cardiac [4, 6, 7, 26], and vascular [4, 13] inflammation. Our data delineate a novel role for SMC-MR signaling as a primary contributor to coronary and cardiac, but not adipose, inflammation in obesity. Specifically, obesity-associated upregulation of aortic adhesion molecules and endothelial MHC class I molecules as well as cardiac T cell infiltration were prevented in obese SMC-MR knockout mice. This protective effect of SMC-MR deletion corresponded with generally anti-inflammatory shifts in cardiac cytokines in obese, versus lean, SMC-MR knockout mice and prevention of obesity-associated upregulation of the pro-inflammatory SMC-MR target Pgf. Similar prevention of cardiac inflammation by SMC-MR deletion, including prevention of cardiac Pgf upregulation, was recently reported in male mice subjected to cardiac pressure overload [35]. Further, cardiac leukocyte infiltration in response to pressure overload consisted entirely of CD3-CD11b+ myeloid cells and was prevented by SMC-MR deletion [35]. In conjunction with the present results, these data argue for significant context-specificity of SMC-MR-dependent mechanisms of cardiac inflammation and leukocyte recruitment. Intriguingly, our data reveal a novel reduction of cardiac mast cells in SMC-MR-KO mice independent of diet treatment. Recent evidence in female db/db mice demonstrated a critical role for activation/degranulation of resident cardiac mast cells in diabetes-associated cardiac leukocyte infiltration and diastolic dysfunction [25]. Thus, reduced cardiac mast cells may be a unifying mechanism of cardioprotection accounting for reduced cardiac dysfunction in SMC-MR-KO mice following obesity, coronary ligation [24], aging [14, 36], and pressure overload [35]. Our use of inducible SMC-MR deletion suggests that any impact of SMC-MR signaling on cardiac mast cells is not of developmental origin and potential SMC-MR-dependent mechanisms of cardiac mast cell recruitment/maturation are warranted. Mechanistically, our data suggest that enhanced SMC estrogen signaling in SMC-MR-KO mice may contribute to prevention of obesity-associated cardiovascular dysfunction. Indeed, SMC-MR deletion resulted in pronounced upregulation of SMC estrogen receptor (ER; Esr1) gene expression. This finding is consistent with recent reports of ER upregulation in macrophages/Kupffer cells [69] and EC [5] following cell-specific MR deletion. In the latter study, double deletion of EC MR and ER eliminated the prevention of obesity-associated endothelial dysfunction afforded by EC MR deletion alone [5]. While potential SMC ER-dependent mechanisms of cardioprotection remain unclear in the present study, recent evidence supports paracrine SMC ER signaling as a contributor to endothelial healing/regeneration following vascular injury [51, 68]. Since this study was performed only in female mice, whether SMC ER upregulation might occur in male SMC-MR-KO mice is not known. We focused on female mice in this study in light of the high prevalence of coronary microvascular dysfunction in women with co-morbid conditions and its close association with cardiac dysfunction [27, 49, 58]. Our study also provides a useful resource for examining pathways that may be important for cardiac remodeling, but not WD-induced diastolic dysfunction. These include genes such as such Angptl4, Cd36, Cyp1a1 and Pparγ and others which are upregulated after WD in both MR-Intact and SMC-MR-KO mouse hearts. Examination of these pathways to abrogate cardiovascular remodeling have been active areas of research for many years and master regulators, such as Pparγ, may be key for WD-induced changes in EC phenotypes we have determined here by scRNAs-seq. Further, our proteomic analysis also detected neprilysin—an enzyme that degrades natriuretic peptide and angiotensin II—at higher levels after WD in both genotypes. Indeed, targeting neprilysin while inhibiting the angiotensin receptor improves morbidity and mortality associated with heart failure [42]. However, modulating the activity of these elements that are activated in both MR-Intact and SMC-MR-KO mouse hearts may have limited effect on diastolic impairment of heart function in obesity. In summary, this study reveals a central role for SMC-MR signaling in the development of obesity-associated coronary and cardiac diastolic dysfunction in female mice. This is the first report, to our knowledge, of obesity-associated transcriptomic changes across the cardiac non-myocyte cellulome. This unbiased approach revealed cardiac inflammation, associated with lymphocyte infiltration, and hypertrophy independent of fibrosis in obese female hearts. SMC-MR deletion mitigated obesity-associated cardiac and coronary inflammation and dysfunction, but not hypertrophy, potentially involving reduced cardiac mast cells and enhanced SMC estrogen signaling that warrant further investigation. These results shed new light on vascular mechanisms of obesity-associated cardiac dysfunction in premenopausal women and provide rationale for further study of MR inhibition and pathways downstream of SMC-MR as sex-specific strategies to treat cardiac and coronary dysfunction, critical contributors to development of HFpEF in obese women. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (PDF 352 KB)Supplementary file2 (XLSX 60 KB)Supplementary file3 (PDF 618 KB)Supplementary file4 (XLSX 708 KB)Supplementary file5 (PDF 465 KB)Supplementary file6 (XLSX 548 KB)Supplementary file7 (PDF 301 KB)Supplementary file8 (XLSX 106 KB)Supplementary file9 (XLSX 684 KB)Supplementary file10 (PDF 524 KB)Supplementary file11 (XLSX 137 KB)Supplementary file12 (PDF 929 KB)Supplementary file13 (XLSX 129 KB)Supplementary file14 (XLSX 38 KB)Supplementary file15 (XLSX 11446 KB)Supplementary file16 (XLSX 102 KB)Supplementary file17 (PDF 283 KB) ## References 1. Barrett-Connor E, Giardina E-GV, Gitt AK, Gudat U, Steinberg HO, Tschoepe D. Women and heart disease: the role of diabetes and Hyperglycemia. Arch Intern Med (2004.0) 164 934-942. DOI: 10.1001/archinte.164.9.934 2. Barrett Mueller K, Bender SB, Hong K, Yang Y, Aronovitz M, Jaisser F, Hill MA, Jaffe IZ. Endothelial mineralocorticoid receptors differentially contribute to coronary and mesenteric vascular function without modulating blood pressure. Hypertension (2015.0) 66 988-997. DOI: 10.1161/hypertensionaha.115.06172 3. Bender SB, Castorena-Gonzalez JA, Garro M, Reyes-Aldasoro CC, Sowers JR, DeMarco VG, Martinez-Lemus LA. Regional variation in arterial stiffening and dysfunction in Western diet-induced obesity. Am J Physiol Heart Circ Physiol (2015.0) 309 H574-H582. DOI: 10.1152/ajpheart.00155.2015 4. Bender SB, DeMarco VG, Padilla J, Jenkins NT, Habibi J, Garro M, Pulakat L, Aroor AR, Jaffe IZ, Sowers JR. Mineralocorticoid receptor antagonism treats obesity-associated cardiac diastolic dysfunction. Hypertension (2015.0) 65 1082-1088. DOI: 10.1161/hypertensionaha.114.04912 5. Biwer LA, Carvajal BV, Lu Q, Man JJ, Jaffe IZ. Mineralocorticoid and Estrogen receptors in endothelial cells coordinately regulate microvascular function in obese female mice. Hypertension (2021.0) 77 2117-2126. DOI: 10.1161/HYPERTENSIONAHA.120.16911 6. Bostick B, Habibi J, DeMarco VG, Jia G, Domeier TL, Lambert MD, Aroor AR, Nistala R, Bender SB, Garro M, Hayden MR, Ma L, Manrique C, Sowers JR. Mineralocorticoid receptor blockade prevents Western diet-induced diastolic dysfunction in female mice. Am J Physiol Heart Circ Physiol (2015.0) 308 H1126-1135. DOI: 10.1152/ajpheart.00898.2014 7. Brown SM, Meuth AI, Davis JW, Rector RS, Bender SB. Mineralocorticoid receptor antagonism reverses diabetes-related coronary vasodilator dysfunction: a unique vascular transcriptomic signature. Pharmacol Res (2018.0) 134 100-108. DOI: 10.1016/j.phrs.2018.06.002 8. Brown SM, Smith CE, Meuth AI, Khan M, Aroor AR, Cleeton HM, Meininger GA, Sowers JR, DeMarco VG, Chandrasekar B, Nistala R, Bender SB. Dipeptidyl peptidase-4 inhibition with Saxagliptin ameliorates angiotensin ii–induced cardiac diastolic dysfunction in male mice. Endocrinology (2017.0) 158 3592-3604. DOI: 10.1210/en.2017-00416 9. 9.Carlson M (2019) org.Mm.eg.db: Genome wide annotation for Mouse. R package version 3.11.4. Doi: 10.18129/B9.bioc.org.Mm.eg.db 10. Carter BS, Meng F, Thompson RC. Glucocorticoid treatment of astrocytes results in temporally dynamic transcriptome regulation and astrocyte-enriched mRNA changes in vitro. Physiol Genomics (2012.0) 44 1188-1200. DOI: 10.1152/physiolgenomics.00097.2012 11. Daniels A, Van Bilsen M, Janssen BJA, Brouns AE, Cleutjens JPM, Roemen THM, Schaart G, Van Der Velden J, Van Der Vusse GJ, Van Nieuwenhoven FA. Impaired cardiac functional reserve in type 2 diabetic db/db mice is associated with metabolic, but not structural, remodelling. Acta Physiol (2010.0) 200 11-22. DOI: 10.1111/j.1748-1716.2010.02102.x 12. Davel AP, Lu Q, Moss ME, Rao S, Anwar IJ, DuPont JJ, Jaffe IZ. Sex-specific mechanisms of resistance vessel endothelial dysfunction induced by cardiometabolic risk factors. J Am Heart Assoc (2018.0). DOI: 10.1161/jaha.117.007675 13. DeMarco VG, Habibi J, Jia G, Aroor AR, Ramirez-Perez FI, Martinez-Lemus LA, Bender SB, Garro M, Hayden MR, Sun Z, Meininger GA, Manrique C, Whaley-Connell A, Sowers JR. Low-dose mineralocorticoid receptor blockade prevents western diet-induced arterial stiffening in female mice. Hypertension (2015.0) 66 99-107. DOI: 10.1161/hypertensionaha.115.05674 14. DuPont JJ, Kim SK, Kenney RM, Jaffe IZ. Sex differences in the time course and mechanisms of vascular and cardiac aging in mice: role of the smooth muscle cell mineralocorticoid receptor. Am J Physiol Heart Circ Physiol (2021.0) 320 H169-H180. DOI: 10.1152/ajpheart.00262.2020 15. DuPont JJ, McCurley A, Davel AP, McCarthy J, Bender SB, Hong K, Yang Y, Yoo J-K, Aronovitz M, Baur WE, Christou DD, Hill MA, Jaffe IZ. Vascular mineralocorticoid receptor regulates microRNA-155 to promote vasoconstriction and rising blood pressure with aging. JCI Insight (2016.0) 1 e88942. DOI: 10.1172/jci.insight.88942 16. Fang L, Zhang M, Li Y, Liu Y, Cui Q, Wang N. PPARgene: a database of experimentally verified and computationally predicted PPAR target genes. PPAR Res (2016.0) 2016 6042162. DOI: 10.1155/2016/6042162 17. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, Slichter CK, Miller HW, McElrath MJ, Prlic M, Linsley PS, Gottardo R. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol (2015.0) 16 278. DOI: 10.1186/s13059-015-0844-5 18. Franssen C, Chen S, Unger A, Korkmaz HI, De Keulenaer GW, Tschöpe C, Leite-Moreira AF, Musters R, Niessen HWM, Linke WA, Paulus WJ, Hamdani N. Myocardial microvascular inflammatory endothelial activation in heart failure with preserved ejection fraction. JACC Heart Failure (2016.0) 4 312-324. DOI: 10.1016/j.jchf.2015.10.007 19. Garg R, Rao AD, Baimas-George M, Hurwitz S, Foster C, Shah RV, Jerosch-Herold M, Kwong RY, Di Carli MF, Adler GK. Mineralocorticoid receptor blockade improves coronary microvascular function in individuals with type 2 diabetes. Diabetes (2015.0) 64 236-242. DOI: 10.2337/db14-0670 20. Givan SA, Bottoms CA, Spollen WG. Computational analysis of RNA-seq. Methods Mol Biol (2012.0) 883 201-219. DOI: 10.1007/978-1-61779-839-9_16 21. Goodwill AG, Baker HE, Dick GM, McCallinhart PE, Bailey CA, Brown SM, Man JJ, Tharp DL, Clark HE, Blaettner BS, Jaffe IZ, Bowles DK, Trask AJ, Tune JD, Bender SB. Mineralocorticoid receptor blockade normalizes coronary resistance in obese swine independent of functional alterations in Kv channels. Basic Res Cardiol (2021.0) 116 35. DOI: 10.1007/s00395-021-00879-3 22. Greenstein AS, Kadir SZAS, Csato V, Sugden SA, Baylie RA, Eisner DA, Nelson MT. Disruption of pressure-induced Ca2+ spark vasoregulation of resistance arteries, rather than endothelial dysfunction, underlies obesity-related hypertension. Hypertension (2020.0) 75 539-548. DOI: 10.1161/HYPERTENSIONAHA.119.13540 23. Grisanti LA, de Lucia C, Thomas TP, Stark A, Strony JT, Myers VD, Beretta R, Yu D, Sardu C, Marfella R, Gao E, Houser SR, Koch WJ, Hamad EA, Tilley DG. Prior β-blocker treatment decreases leukocyte responsiveness to injury. JCI Insight (2019.0). DOI: 10.1172/jci.insight.99485 24. Gueret A, Harouki N, Favre J, Galmiche G, Nicol L, Henry J-P, Besnier M, Thuillez C, Richard V, Kolkhof P, Mulder P, Jaisser F, Ouvrard-Pascaud A. Vascular smooth muscle mineralocorticoid receptor contributes to coronary and left ventricular dysfunction after myocardial infarction. Hypertension (2016.0) 67 717-723. DOI: 10.1161/hypertensionaha.115.06709 25. Guimbal S, Cornuault L, Rouault P, Hollier P-L, Chapouly C, Bats M-L, Imbault J, Gadeau A-P, Couffinhal T, Renault M-A. Mast cells are the trigger of small vessel disease and diastolic dysfunction in diabetic obese mice. Arterioscler Thromb Vasc Biol (2021.0) 41 e193-e207. DOI: 10.1161/ATVBAHA.121.315900 26. Guo C, Ricchiuti V, Lian BQ, Yao TM, Coutinho P, Romero JR, Li J, Williams GH, Adler GK. Mineralocorticoid receptor blockade reverses obesity-related changes in expression of adiponectin, peroxisome proliferator-activated receptor-gamma, and proinflammatory adipokines. Circulation (2008.0) 117 2253-2261. DOI: 10.1161/CIRCULATIONAHA.107.748640 27. Haas AV, Rosner BA, Kwong RY, Rao AD, Garg R, Di Carli MF, Adler GK. Sex differences in coronary microvascular function in individuals with type 2 diabetes mellitus. Diabetes (2018.0) 68 631-636. DOI: 10.2337/db18-0650 28. Hamdani N, Hervent A-S, Vandekerckhove L, Matheeussen V, Demolder M, Baerts L, De Meester I, Linke WA, Paulus WJ, De Keulenaer GW. Left ventricular diastolic dysfunction and myocardial stiffness in diabetic mice is attenuated by inhibition of dipeptidyl peptidase 4. Cardiovasc Res (2014.0) 104 423-431. DOI: 10.1093/cvr/cvu223 29. Hartley CJ, Reddy AK, Madala S, Entman ML, Michael LH, Taffet GE. Doppler velocity measurements from large and small arteries of mice. Am J Physiol Heart Circ Physiol (2011.0) 301 H269-H278. DOI: 10.1152/ajpheart.00320.2011 30. Heusch G. Coronary blood flow in heart failure: cause, consequence and bystander. Basic Res Cardiol (2022.0) 117 1. DOI: 10.1007/s00395-022-00909-8 31. Jia G, Habibi J, Aroor AR, Martinez-Lemus LA, DeMarco VG, Ramirez-Perez FI, Sun Z, Hayden MR, Meininger GA, Mueller KB, Jaffe IZ, Sowers JR. Endothelial mineralocorticoid receptor mediates diet-induced aortic stiffness in females. Circ Res (2016.0) 118 935-943. DOI: 10.1161/circresaha.115.308269 32. Jia G, Habibi J, DeMarco VG, Martinez-Lemus LA, Ma L, Whaley-Connell AT, Aroor AR, Domeier TL, Zhu Y, Meininger GA, Barrett Mueller K, Jaffe IZ, Sowers JR. Endothelial mineralocorticoid receptor deletion prevents diet-induced cardiac diastolic dysfunction in females. Hypertension (2015.0) 66 1159-1167. DOI: 10.1161/hypertensionaha.115.06015 33. Joffe HV, Kwong RY, Gerhard-Herman MD, Rice C, Feldman K, Adler GK. Beneficial effects of eplerenone versus hydrochlorothiazide on coronary circulatory function in patients with diabetes mellitus. J Clin Endocrinol Metab (2007.0) 92 2552-2558. DOI: 10.1210/jc.2007-0393 34. Kern P, Balzer NR, Blank N, Cygon C, Wunderling K, Bender F, Frolov A, Sowa J-P, Bonaguro L, Ulas T, Homrich M, Kiermaier E, Thiele C, Schultze JL, Canbay A, Bauer R, Mass E. Creld2 function during unfolded protein response is essential for liver metabolism homeostasis. FASEB J (2021.0) 35 e21939. DOI: 10.1096/fj.202002713RR 35. Kim SK, Biwer LA, Moss ME, Man JJ, Aronovitz MJ, Martin GL, Carrillo-Salinas FJ, Salvador AM, Alcaide P, Jaffe IZ. Mineralocorticoid receptor in smooth muscle contributes to pressure overload-induced heart failure. Circ Heart Fail (2021.0) 14 e007279. DOI: 10.1161/circheartfailure.120.007279 36. Kim SK, McCurley AT, DuPont JJ, Aronovitz M, Moss ME, Stillman IE, Karumanchi SA, Christou DD, Jaffe IZ. Smooth muscle cell-mineralocorticoid receptor as a mediator of cardiovascular stiffness with aging. Hypertension (2018.0) 71 609-621. DOI: 10.1161/hypertensionaha.117.10437 37. Lang F. Stiff endothelial cell syndrome in vascular inflammation and mineralocorticoid excess. Hypertension (2011.0) 57 146-147. DOI: 10.1161/HYPERTENSIONAHA.110.164558 38. Lang F, Böhmer C, Palmada M, Seebohm G, Strutz-Seebohm N, Vallon V. (Patho) physiological significance of the serum - and glucocorticoid-inducible kinase isoforms. Physiol Rev (2006.0) 86 1151-1178. DOI: 10.1152/physrev.00050.2005 39. Lozanoska-Ochser B, Peakman M. Level of major histocompatibility complex class I expression on endothelium in non-obese diabetic mice influences CD8 T cell adhesion and migration. Clin Exp Immunol (2009.0) 157 119-127. DOI: 10.1111/j.1365-2249.2009.03940.x 40. McCurley A, Pires PW, Bender SB, Aronovitz M, Zhao MJ, Metzger D, Chambon P, Hill MA, Dorrance AM, Mendelsohn ME, Jaffe IZ. Direct regulation of blood pressure by smooth muscle cell mineralocorticoid receptors. Nat Med (2012.0) 18 1429-1433. DOI: 10.1038/nm.2891 41. McManaman JL, Bales ES, Orlicky DJ, Jackman M, MacLean PS, Cain S, Crunk AE, Mansur A, Graham CE, Bowman TA, Greenberg AS. Perilipin-2-null mice are protected against diet-induced obesity, adipose inflammation, and fatty liver disease. J Lipid Res (2013.0) 54 1346-1359. DOI: 10.1194/jlr.M035063 42. McMurray JJV, Packer M, Desai AS, Gong J, Lefkowitz MP, Rizkala AR, Rouleau JL, Shi VC, Solomon SD, Swedberg K, Zile MR. Angiotensin-Neprilysin inhibition versus Enalapril in heart failure. N Engl J Med (2014.0) 371 993-1004. DOI: 10.1056/NEJMoa1409077 43. Merrill M, Sweitzer NK, Lindenfeld J, Kao DP. Sex differences in outcomes and responses to spironolactone in heart failure with preserved ejection fraction: a secondary analysis of TOPCAT trial. JACC Heart Failure (2019.0) 7 228-238. DOI: 10.1016/j.jchf.2019.01.003 44. Miranda-Silva D, Wüst RCI, Conceição G, Gonçalves-Rodrigues P, Gonçalves N, Gonçalves A, Kuster DWD, Leite-Moreira AF, van der Velden J, de Sousa Beleza JM, Magalhães J, Stienen GJM, Falcão-Pires I. Disturbed cardiac mitochondrial and cytosolic calcium handling in a metabolic risk-related rat model of heart failure with preserved ejection fraction. Acta Physiol (2020.0) 228 e13378. DOI: 10.1111/apha.13378 45. Moss ME, DuPont JJ, Iyer SL, McGraw AP, Jaffe IZ. No significant role for smooth muscle cell mineralocorticoid receptors in atherosclerosis in the apolipoprotein-E knockout mouse model. Front Cardiovasc Med (2018.0). DOI: 10.3389/fcvm.2018.00081 46. Murthy VL, Naya M, Foster CR, Gaber M, Hainer J, Klein J, Dorbala S, Blankstein R, Di Carli MF. Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation (2012.0) 126 1858-1868. DOI: 10.1161/CIRCULATIONAHA.112.120402 47. Nelson MD, Wei J, Bairey Merz CN. Coronary microvascular dysfunction and heart failure with preserved ejection fraction as female-pattern cardiovascular disease: the chicken or the egg?. Eur Heart J (2018.0) 39 850-852. DOI: 10.1093/eurheartj/ehx818 48. Nevers T, Salvador AM, Grodecki-Pena A, Knapp A, Velázquez F, Aronovitz M, Kapur NK, Karas RH, Blanton RM, Alcaide P. Left ventricular T-cell recruitment contributes to the pathogenesis of heart failure. Circ Heart Fail (2015.0) 8 776-787. DOI: 10.1161/circheartfailure.115.002225 49. Packer M, Lam CSP, Lund LH, Maurer MS, Borlaug BA. Characterization of the inflammatory-metabolic phenotype of heart failure with a preserved ejection fraction: a hypothesis to explain influence of sex on the evolution and potential treatment of the disease. Eur J Heart Fail (2020.0) 22 1551-1567. DOI: 10.1002/ejhf.1902 50. Paulus WJ, Tschöpe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol (2013.0) 62 263-271. DOI: 10.1016/j.jacc.2013.02.092 51. Ren J, Zhou T, Pilli VSS, Phan N, Wang Q, Gupta K, Liu Z, Sheibani N, Liu B. Novel paracrine functions of smooth muscle cells in supporting endothelial regeneration following arterial injury. Circ Res (2019.0) 124 1253-1265. DOI: 10.1161/CIRCRESAHA.118.314567 52. Richard V, Kaeffer N, Tron C, Thuillez C. Ischemic preconditioning protects against coronary endothelial dysfunction induced by ischemia and reperfusion. Circulation (1994.0) 89 1254-1261. DOI: 10.1161/01.cir.89.3.1254 53. Schafer N, Lohmann C, Winnik S, van Tits LJ, Miranda MX, Vergopoulos A, Ruschitzka F, Nussberger J, Berger S, Luscher TF, Verrey F, Matter CM. Endothelial mineralocorticoid receptor activation mediates endothelial dysfunction in diet-induced obesity. Eur Heart J (2013.0) 34 3515-3524. DOI: 10.1093/eurheartj/eht095 54. Skelly DA, Squiers GT, McLellan MA, Bolisetty MT, Robson P, Rosenthal NA, Pinto AR. Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep (2018.0) 22 600-610. DOI: 10.1016/j.celrep.2017.12.072 55. Stephens GL, McHugh RS, Whitters MJ, Young DA, Luxenberg D, Carreno BM, Collins M, Shevach EM. Engagement of glucocorticoid-induced TNFR family-related receptor on effector t cells by its ligand mediates resistance to suppression by CD4+CD25+ T cells. The J Immunol (2004.0) 173 5008-5020. DOI: 10.4049/jimmunol.173.8.5008 56. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. Cell (2019.0) 177 1888-1902.e1821. DOI: 10.1016/j.cell.2019.05.031 57. Taqueti VR, Shaw LJ, Cook NR, Murthy VL, Shah NR, Foster CR, Hainer J, Blankstein R, Dorbala S, Carli MFD. Excess cardiovascular risk in women relative to men referred for coronary angiography is associated with severely impaired coronary flow reserve, not obstructive disease. Circulation (2017.0) 135 566-577. DOI: 10.1161/CIRCULATIONAHA.116.023266 58. Taqueti VR, Solomon SD, Shah AM, Desai AS, Groarke JD, Osborne MT, Hainer J, Bibbo CF, Dorbala S, Blankstein R, Di Carli MF. Coronary microvascular dysfunction and future risk of heart failure with preserved ejection fraction. Eur Heart J (2018.0) 39 840-849. DOI: 10.1093/eurheartj/ehx721 59. Towfighi A, Zheng L, Ovbiagele B. Sex-specific trends in midlife coronary heart disease risk and prevalence. Arch Intern Med (2009.0) 169 1762-1766. DOI: 10.1001/archinternmed.2009.318 60. Tromp J, Lim SL, Tay WT, Teng T-HK, Chandramouli C, Ouwerkerk W, Wander GS, Sawhney JPS, Yap J, MacDonald MR, Ling LH, Sattar N, McMurray JJV, Richards AM, Anand I, Lam CSP. Microvascular disease in patients with diabetes with heart failure and reduced ejection versus preserved ejection fraction. Diabetes Care (2019.0) 42 1792-1799. DOI: 10.2337/dc18-2515 61. Wang P, Shao X, Bao Y, Zhu J, Chen L, Zhang L, Ma X, Zhong X-b. Impact of obese levels on the hepatic expression of nuclear receptors and drug-metabolizing enzymes in adult and offspring mice. Acta Pharmaceutica Sinica B (2020.0) 10 171-185. DOI: 10.1016/j.apsb.2019.10.009 62. Wang S, Wang C, Turdi S, Richmond KL, Zhang Y, Ren J. ALDH2 protects against high fat diet-induced obesity cardiomyopathy and defective autophagy: role of CaM kinase II, histone H3K9 methyltransferase SUV39H, Sirt1, and PGC-1α deacetylation. Int J Obes (2018.0) 42 1073-1087. DOI: 10.1038/s41366-018-0030-4 63. Westermann D, Lindner D, Kasner M, Zietsch C, Savvatis K, Escher F, Jv S, Skurk C, Steendijk P, Riad A, Poller W, Schultheiss H-P, Tschöpe C. Cardiac inflammation contributes to changes in the extracellular matrix in patients with heart failure and normal ejection fraction. Circ Heart Fail (2011.0) 4 44-52. DOI: 10.1161/CIRCHEARTFAILURE.109.931451 64. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T, Miller E, Bache S, Müller K, Ooms J, Robinson D, Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H. Welcome to the Tidyverse. J Open Source Software (2019.0) 4 1686. DOI: 10.21105/joss.01686 65. Wilmot KA, O’Flaherty M, Capewell S, Ford ES, Vaccarino V. Coronary heart disease mortality declines in the united states from 1979 through 2011. Circulation (2015.0) 132 997-1002. DOI: 10.1161/CIRCULATIONAHA.115.015293 66. Wu T, Liu Q, Li Y, Li H, Chen L, Yang X, Tang Q, Pu S, Kuang J, Li R, Huang Y, Zhang J, Zhang Z, Zhou J, Huang C, Zhang G, Zhao Y, Zou M, Jiang W, Mo L, He J. Feeding-induced hepatokine, Manf, ameliorates diet-induced obesity by promoting adipose browning via p38 MAPK pathway. J Exp Med (2021.0). DOI: 10.1084/jem.20201203 67. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS (2012.0) 16 284-287. DOI: 10.1089/omi.2011.0118 68. Zahreddine R, Davezac M, Smirnova N, Buscato M, Lhuillier E, Lupieri A, Solinhac R, Vinel A, Vessieres E, Henrion D, Renault M-A, Gadeau A-P, Flouriot G, Lenfant F, Laffargue M, Métivier R, Arnal J-F, Fontaine C. Tamoxifen accelerates endothelial healing by targeting ERα in smooth muscle cells. Circ Res (2020.0) 127 1473-1487. DOI: 10.1161/CIRCRESAHA.120.317062 69. Zhang Y-Y, Li C, Yao G-F, Du L-J, Liu Y, Zheng X-J, Yan S, Sun J-Y, Liu Y, Liu M-Z, Zhang X, Wei G, Tong W, Chen X, Wu Y, Sun S, Liu S, Ding Q, Yu Y, Yin H, Duan S-Z. Deletion of macrophage mineralocorticoid receptor protects hepatic steatosis and insulin resistance through ERα/HGF/Met pathway. Diabetes (2017.0) 66 1535-1547. DOI: 10.2337/db16-1354
--- title: Autophagic reprogramming of bone marrow–derived macrophages authors: - Mayada Mazher - Yomna Adel Moqidem - Mona Zidan - Ahmed A. Sayed - Ahmed Abdellatif journal: Immunologic Research year: 2022 pmcid: PMC10060350 doi: 10.1007/s12026-022-09344-2 license: CC BY 4.0 --- # Autophagic reprogramming of bone marrow–derived macrophages ## Abstract Macro-autophagy is a highly conserved catabolic process among eukaryotes affecting macrophages. This work studies the genetic regulatory network involving the interplay between autophagy and macrophage polarization (activation). Autophagy-related genes (Atgs) and differentially expressed genes (DEGs) of macrophage polarization (M1–M2) were predicted, and their regulatory networks constructed. Naïve (M0) mouse bone marrow–derived monocytes were differentiated into M1 and M2a. Validation of the targets of Smad1, LC3A and LC3B, Atg16L1, Atg7, IL-6, CD68, Arg-1, and Vamp7 was performed in vitro. Immunophenotyping by flow cytometry revealed three macrophage phenotypes: M0 (IL-6 + /CD68 +), M1 (IL-6 + /CD68 + /Arg-1 +), and M2a (CD68 + /Arg-1). Confocal microscopy revealed increased autophagy in both M1 and M2a and a significant increase in the pre-autophagosomes size and number. Bafilomycin A increased the expression of CD68 and Arg-1 in all cell lineages. In conclusion, our approach predicted the protein targets mediating the interplay between autophagy and macrophage polarization. We suggest that autophagy reprograms macrophage polarization via CD68, arginase 1, Atg16L1-1, and Atg16L1-3. The current findings provide a foundation for the future use of macrophages in immunotherapy of different autoimmune disorders. ### Supplementary Information The online version contains supplementary material available at 10.1007/s12026-022-09344-2. ## Introduction Macrophages are major players in the immune system, and their phagocytic function contributes to host–pathogen defense mechanisms [1]. The activation of macrophages affects the quality of phagocytosis [2]. Autophagy is a highly conserved cellular catabolic process essential for cellular recycling that regulates phagocytosis in macrophages through modulation of the surface receptors [3–7]. Autophagy is also involved in the pathophysiology of many diseases such as neurodegenerative disorders [8], tumorigenesis [9], diabetes [10], and the immune response to infections [11]. The process starts with phagophore formation and elongation, autophagosome formation, and lysosomal fusion, followed by autolysosomal degradation [12]. Autophagy is initiated in response to starvation or amino acid depletion via inhibiting the nutrient-sensing kinase mammalian target of rapamycin complex 1 (mTORC1) and activating the AMP-activated protein kinase (AMPK) pathway, which activates mammalian Atg1/ULK1 kinase [13, 14]. Atg16L1 is a component of the phagophore elongation complex (Atg5–Atg12–Atg16L1) [15–19]. Atg16L1 and Atg9 regulate autophagosome formation by enhancing the conjugation of phosphatidylethanolamine (PE) with 1A/1B light chain 3 (LC3) (Atg8-like) to form LC3-II (MAP1LC3A, MAP1LC3B, and MAP1LC3C) [20, 21]. LC3 is critical for autophagosome–lysosomal fusion [22]. Mature autophagosomes [23] fuse with lysosomes to degrade autophagosome contents. The lysosomal protein vesicle-associated membrane protein 7 (Vamp7) is essential for the phagocytosis of opsonized particles [24–26]. Autophagy regulates the secretion of pro-inflammatory cytokines, such as IL-1β [27], IL-23 [28], IL-18, interleukin 6 (IL-6), and IL-1α [29]. Interferon gamma (INF-γ) induces autophagy via increasing the autophagosome formation and the turnover of LC3-II protein through the interferon regulatory factor 1 (IFR-1) signaling pathway [30], and it also mediates the upregulation of STAT1 and STAT2 in human peripheral blood mononuclear cells and macrophages [31]. According to their polarization state, inflammatory macrophages are classified into pro-inflammatory M1 macrophages and anti-inflammatory M2 macrophages [32–34]. Previous studies reported in vitro polarization of macrophages with IFN-γ, lipopolysaccharide, and interleukin 4 (IL-4) or IL-13 and showed high levels of IL-6 in the M2 phase [35]. The inhibition of the IL-6/STAT3 pathway with anti-IL-6 treatment caused M2 to change into M1 type. Here, we investigate how autophagy reprograms macrophage polarization, as the interplay between autophagy and macrophage polarization is poorly understood. Finding the targeted proteins that mediate the interplay between autophagy and macrophage polarization among a pool of autophagy-related proteins and hundreds of growth factors and proteins that regulate macrophage polarization is quite challenging. Therefore, we implemented a systems biology approach to narrow down the protein targets that mediate the interplay between the two processes. These target proteins were validated in vitro using bone marrow–isolated macrophages. ## Ethical disclosure All procedures were performed in compliance with the National Institutes of Health (NIH) guidelines for the Care and Use of Laboratory Animals (NIH Publications No. 8023, revised 1978), and according to Directive $\frac{2010}{63}$/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes. All methods are reported in accordance with ARRIVE guidelines. ## Isolation and characterization of bone marrow–derived monocytes Female C57B/6 J mice were euthanized by an overdose of ketamine xylazine followed by cervical dislocation. The femur and tibia were removed and rinsed in ethanol $70\%$ for 5 min, followed by 1 × phosphate-buffered saline (PBS), 6.7 mM PO4, without calcium and magnesium. The tibia and femur were rinsed in Dulbecco’s modified Eagle’s medium: F12, DMEM:F12 with HEPES (25 mM), 1:1 mixture with 3.151 g/l glucose, with l-glutamine (Lonza, Basil, Switzerland) for 10 min. The bones were flushed with 1 × PBS over a 70-µm cell strainer (Greiner, Kremsmünster, Austria). The cell suspension was lysed with 1 × ammonium–chloride–potassium lysing buffer saline (Lonza, Basil, Switzerland) for 5 min to eliminate red blood cell and thrombocyte contamination. Following the lysis, the cell suspension was centrifuged for 5 min at 500 g. The cells were resuspended in lymphocyte separation medium (Lonza Basil, Switzerland) combined with DMEM/F12 Complete Medium (DMEM/F12 + $10\%$ FBS + $1\%$ penicillin and streptomycin) and centrifuged at 500 g for 10 min. The cell suspension was collected, counted, and seeded at a density of 300,000 cells/well in 12-well plates (Greiner, Kremsmünster, Austria) and incubated for 72 h, at 37 °C and $5\%$ CO2. ## M1–M2a lineage polarization Monocytes were maintained in complete DMEM/F12 medium (DMEM/F12 + $20\%$ L929 conditioned medium + $10\%$ FBS + $1\%$ penicillin and streptomycin). Mouse skin fibroblast cell line L929 was used as a source for monocyte colony-stimulating factor (M-CSF) for alternative activation of bone marrow–derived macrophages as previously reported [50]. Five days after isolation, naive macrophage lineage (M0) was polarized to M1 using type II interferon gamma (1250 IU/ml; STEMCELL Technologies, Cambridge Research Park, UK). M2a was polarized using interleukin-4 (2500 IU/ml; Cambridge Research Park, United Kingdom) in combination with 10 ng/ml lipopolysaccharide (LPS) [51] from Escherichia coli (Thermo Fisher Scientific, Waltham, MA, USA) for 48 h as previously reported [52]. On day 7, cells were polarized to reach either M1 or M2a lineage for further experimental use. ## Cell viability and cytotoxicity assay Macrophages were seeded in 96-well plates (10,000 cells/well). MTT tetrazolium reduction assay was performed as previously reported [53]. In summary, following a 3-h incubation with MTT reagent, the media were removed, and DMSO was added to dissolve the formazan crystals. The cells were examined using an inverted microscope (Olympus 1X70, Tokyo, Japan), and absorbance was measured at 570 nm using a microplate reader (Ultrospec 3100 pro). Cell viability (%) was calculated based on the following equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Survival\; rate }(\mathrm{\%})=(\mathrm{Ab\; sample}-A\mathrm{b\; blank})/(\mathrm{Ab \;control}-\mathrm{Ab \;blank})\times 100$$\end{document}Survivalrate(%)=(Absample-Abblank)/(Abcontrol-Abblank)×100where Ab sample is the sample absorbance, Ab blank is the absorbance of blank, and Ab control is the absorbance of the control. ## Autophagy assay On day 5, naïve macrophages (M0) were seeded at 96-well plates at a seeding density of 10,000 cells/well for 48 h. Autophagy assay was performed according to the manufacturer’s instruction (MAK138 fluorometric assay kit; Sigma-Aldrich, Saint Louis, MO, USA). The media was removed, and autophagosome detection reagent was added and incubated in the dark for 1 h at 37 °C and $5\%$ CO2. Cells were washed gently by adding 100 µl of washing buffer, and the fluorescence intensity was measured (λex = 360/λem = 520 nm). ## Phagocytosis assay Naïve macrophages were seeded at day 5 into 96-well plates at a seeding density of 10,000 cells/well to contain a final volume 100 µl/well primed for 48 h to M1 and M2a lineages as previously mentioned. Cells were stained with MAK138 autophagosome detection reagent as mentioned earlier. E. coli top 10 bacteria were grown in LB broth liquid (purchased from Thermo Fisher Scientific, Waltham, MA, USA) and were added to the cells. Cells were stained with 1 µg/ml 4′,6-diamidino-2-phenylindole·2HCl (DAPI) stain (Lonza, Basil, Switzerland) and examined under fluorescent microscopy (inverted fluorescent microscope; Leica Microsystems, Germany). Phagocytic events were counted for each condition. ## Early apoptosis detection Macrophages were primed to M1 and M2a as previously described. SH-SY5Y neuroblastoma cells (ATCC CRL-2266) were cultured in conditioned media from naïve macrophages (M1 and M2a) for 24 h. Cells were fixed with $4\%$ PFA and permeabilized for 10 min with $0.3\%$ triton X-100. Cells were washed and stained with DAPI and mounted on slides. Cells were examined under the microscope (inverted fluorescent microscope; Leica Microsystems, Germany). Cells treated with 20 ng/ml cisplatin were used as a positive control. ## Immunofluorescent staining Macrophages were fixed with $4\%$ PFA for 10 min and washed with PBS. Cells were blocked and permeabilized with blocking buffer ($5\%$ BSA, $0.3\%$ Triton X-100 in 1 × PBS) for 1 h. Cells were incubated overnight at 4 °C in the dark with the following primary antibodies: rabbit Mab LC3B (1:200), rabbit Mab Atg16L1 (1:100), rabbit Mab Smad1 (1:200), rabbit Mab Atg7 (1:200), and rabbit Mab IL-6 (1:200) (Cell Signaling Technologies, Danvers, MA, USA). Cells were later incubated with anti-rabbit Mab polyclonal secondary antibody for 2 h (Alexa Flour 488, 1:500), followed by washing and DAPI counterstaining for 10 min. Cells were examined under a fluorescence microscope (fluorescent microscope; Leica Microsystems, Germany). For confocal microscopy, a Leica Microsystems laser confocal microscope was used. Images were deconvoluted using Carl Zeiss Zen Blue 12 (Carl Zeiss, USA) software, and Z-stacks were 3D reconstructed using ICY software [54]. To detect intracellular trafficking of Atg7, Atg16l1, and LC3B inside the cytoplasmic or nuclear compartment, an automated spot detector plug-in SICE was used as described by Bayle et al. [ 55]. Images were taken by a fluorescent microscope (Leica Microsystems, Germany) and imported to ImageJ® software. A minimum of 8 images was counted for each condition. ## Flow cytometry Macrophages were collected and washed with $0.5\%$ FBS in 1 × PBS and centrifuged at 350 g for 5 min. Cells were stained with mouse-specific antibody conjugate eFlour660 CD68 and Alexa Flour 488 conjugated arginase 1 (eBioscience, USA) for 30 min and washed with 1 × PBS at 500 g for 10 min. Unstained samples were used as a negative control. Samples were measured and gated on a flow cytometer (CytoFLEX, Beckman Coulter, USA, using two lasers: red laser (with an excitation wavelength of 660 nm) for allophycocyanin (APC) and blue laser (with an excitation wavelength of 488 nm) for fluorescein isothiocyanate (FITC). ## Statistical analysis Statistical analyses were carried out using GraphPad Prism® software. Data was expressed as mean ± standard deviation, or median and range were used for data expression. All tests were two-tailed. Post hoc tests and one-way ANOVA were used to compare the differences of mean values between different groups. p values that were less than 0.05 were considered statistically significant. ## In silico analysis of autophagy-related genes We used a network-based systems biology approach to model the interplay between the complex signaling pathways of autophagy and macrophage polarization. The analysis of the different databases identified common significantly enriched pathways and common regulatory transcription factors that co-regulate both transcription factors and Atgs and M1–M2–DEGS (Supplementary Data). ## In vitro isolation and polarization of bone marrow macrophages Murine bone marrow monocytes were isolated and differentiated to M0 using $20\%$ L929 conditioned media. On day 5, type II interferon-γ was used (1250 IU/ml) combined with LPS (100 ng/ml) for 48 h to activate the M0 into M1 lineage. For M2a, IL-4 was used (2500 IU/ml) in combination with LPS (100 ng/ml) for 48 h. The three lineages were characterized using flow cytometry with three markers: interleukin-6, CD68, and arginase 1 (Fig. 1, Supp. Data Fig. 2S).Fig. 1Co-expression of CD68 and arginase 1 in macrophages at day 7. Flow cytometry analysis for M1 and M2a, using M0 macrophages as a control. Samples were gated on $81\%$, CD68 expression was assessed using an APC filter, and arginase 1 was read using a FITC filter. A, E, and I show the gated cells (M0, M1, and M2a lineages), respectively, on FSC-H and SSC-H. B, F, and J are quadrant plots for M0, M1, and M2a, respectively. C, G, and K are histogram fluorescence peak signal plots for CD68 expression in M0, M1, and M2a cells. D, H, and L are fluorescence peak signal plots for arginase 1 expression in M0, M1, and M2a. Statistical analysis for the expression of arginase 1 (M) and CD68 (N) in bone marrow–derived macrophages at day 7 showed that M2a lineage significantly expressed both CD68 and arginase 1 compared to M0 and M1 ($$n = 3$$, p value = < 0.5). No expression of arginase 1 in M0 lineage was seen The phenotypes of the isolated cells were verified using CD68 and arginase 1. CD68 was expressed in all cell phenotypes, although M2a showed a significantly higher expression of $84\%$. Flow cytometry analysis showed a significant increase in total expression of arginase 1 in M2a phenotype more than M1 and was absent in the control M0 lineage. The resulting phenotypes were M0 (IL-6 + /CD68 +), M1 (IL-6 + /CD68 + /Arg-1 +), and M2a (CD68 + /Arg-1 +). Flow cytometry showed that M2a lineage significantly expressed both CD68 ($$n = 3$$, p value = < 0.5 and R2 = 0.7) and arginase 1 ($$n = 3$$, p value = < 0.05) (Fig. 1 and Supp. Data Fig. 11S). Immunostaining showed that M0, M1, and M2a expressed arginase 1 ($$n = 4$$, p value = < 0.0001, R2 = 0.9) and CD68 ($$n = 4$$, p value = < 0.05, R2 = 0.68) (Fig. 2).Fig. 2Microscopic examination of bone marrow–derived macrophages. Morphological examination for bone marrow–derived macrophages. A represents the fully differentiated M0 by using L929 conditioned medium 20 ng/ml at day 7. B represents the fully differentiated M1 activated by INF-γ (1250 IU/ml) + LPS (100 ng/ml) for (48 h) at 7-day polarization. C represents the fully differentiated M2a activated by IL-4 (2500 IU/ml) + LPS (100 ng/ml) for (48 h) at 7-day polarization. D, E, and F show the expression of both phagocytosis markers CD68 (cell surface and intracellular) and Arg-1 (intracellular). M0, M1, and M2a cells (D, E, and F, respectively) stained with arginase 1 read using a FITC filter and counterstained with DAPI. The expression of intracellular arginase 1 can be seen in green. G, H, and I are stained with CD68 red (TRITC) and counterstained with DAPI. Manual cell counting was performed for cells expressing CD68 (J) and Arg-1 (K) in M0, M1, and M2a lineages on ImageJ.® using the cell counter plugin ($$n = 4$$, p value = < 0.01, **p-value = <0.0001) ## Interferon-γ promotes IL-6 expression in M1 lineage, while IL-4 inhibits the IL-6 expression in M2a lineage Flow cytometry studies show that interferon-γ stimulated M1 lineage expressing the phagocytosis marker IL-6 significantly ($56\%$) compared to M2a lineage ($37\%$) (Fig. 3). However, IL-6 expression was also high in the control M0 lineage. Also, the fluorescence intensity for IL-6 protein showed that M1 lineage had the most significant increase in IL-6 protein expression. Surprisingly, the conditioned media of M2a 7-day macrophages showed cytotoxic activity on neuroblastoma cell line SH-SY5Y (Supp. Data Fig. 11S).Fig. 3Expression of IL-6 by the flow cytometry. IL-6 expression by flow cytometry analysis in M1 and M2a, with M0 macrophages used as control. Samples were gated on $81\%$, and IL-6 expression was read using a FITC filter. A, C, and E represent the gating for 5000 events (event = single cell). B, D, and F are fluorescence peak signals for IL-6 percentage expression in M0, M1, and M2a cells. High expression of IL-6 in M0 and M1 was seen with very low expression in M2a macrophages. H shows a violin plot showing statistical significance for IL-6 expression ($$n = 3$$, p value = < 0.05). I shows a bar plot showing the expression of IL-6 protein terms of relative fluorescence intensity using a multi-plate reader. M0 (day 7) was used as control, and M0 + Earle’s balanced salt was used as positive autophagy control. M1 lineage significantly expressed IL-6 ($$n = 3$$, p value = < 0.05, ***p value = < 0.0001, R.2 = 1) ## Increased Atg16L1 expression in M1 and M2a lineages Atg16L1 serves as a precursor for the homotypic fusion of lysosomal Vamp7/SNARE proteins for the pre-autophagosome formation and LC3 autophagosome maturation. Therefore, we examined the expression of the Vamp7* gene at M0, M1, and M2a lineages at 7-day and 14-day polarizations. Finally, M2a lineage showed the highest and the most significant Atg16L1-1, Atg16L1-3, and Vamp7 fold change in both 7-day and 14-day polarizations (Fig. 4).Fig. 4Summary of gene expression data at 7-day and 14-day polarizations. Summary of gene expression data at day 7 (A) and day 14 (B). A significance of fold increase in Atg16L1-3 and Vamp7 gene expression was seen in M2a lineage at day 7. An increase in Atg16L1-1 (but not Atg16L1-3) and Smad1 was seen in M2a lineage at day 14. These results indicate a high autophagic activity in the M2a lineage Interestingly, Atg16L1-1 alpha showed a 30-fold increase in M2a day-14 polarization than in M2a 7-day polarization. Also, the Atg16L1-3 gamma variant showed an 18-fold increase in M2a 7-day polarization than in M2a 14-day polarization. The same as for Vamp7 in M2a 7-day polarization showed a tenfold increase than in M2a 14-day polarization (***p-value = < 0.0001) (Fig. 13S). ## Atg16L1-1 and Atg16L1-3 are upregulated in M2a lineage M2a cell lineage showed upregulation for both Atg16L1-1 and Atg16L1-3 variants (Fig. 5). We were able to detect the pre-autophagosomes and their size in M0, M1, and M2a lineages. Cytoplasmic and nuclear pre-autophagosomes were stained for Atg16L1 (yellow to green spots, Fig. 5). M1 lineage showed the highest number of pre-autophagosomes in the cytoplasm ($$n = 6$$ images, at least 5 cells/image, p value = 0.0045– < 0.05) (Fig. 5). M1 was highly significant. However, M2a lineage showed a significant increase in cytoplasmic Atg16L1 spot size compared to M0 lineage control ($$n = 6$$ images, 5 cells/image, p value = 0.043– < 0.05, Fig. 4). Interestingly, the average size of Atg16L1 in M1 lineage is more than 4 µm diameter, which is above normal value for pre-autophagosome (from 500 to 1000 nm, 0.5–1 µm) diameter. Fig. 5Immune co-localization of Atg16L1 + pre-autophagosomes. A, B, D, G, and H show the M0, M1, and M2a macrophages, respectively. Cytoplasmic pre-autophagosomes appeared as yellow to green spots. C shows the count of cytoplasmic pre-autophagosomes per cell ($$n = 6$$, p value = 0.027– < 0.05). F shows the size of pre-autophagosomes in µm.2 per cell ($$n = 6$$, p value = 0.0045). I shows nuclear Atg16L1 pre-autophagosome count ($$n = 3$$, p value = 0.036). M1 lineage showed the highest number and size of cytoplasmic pre-autophagosomes in the cytoplasm. J shows relative fluorescence intensity was measured using a multi-plate reader. M2a showed the most significant increase in fluorescence intensity ($$n = 3$$, p value = <0.05, p value = <0.01, *p value = < 0.0001) Immunolocalization using Atg16L1 in M0, M1, M2a, and M0 + Earle’s balanced salt revealed that no autophagosomes were observed in the M0 control and in M1 macrophages. However, we observed pre-autophagosomes in M2a lineage and in the positive autophagy M0 cells treated with EBS. M2a lineage showed a significant increase in both nuclear pre-autophagosome numbers (p value = < 0.05) and cytoplasmic Atg16L1 size. This supports our gene expression data that showed increased fold change of Atg16L1-1 gene variation M2a at 7-day polarization (Fig. 5). ## INF-γ increased Atg7 expression in M1 cells and increased pre-autophagosome size Immune co-localization studies show Atg7 expression as pre-autophagosomes distributed in the cytoplasmic compartment in M1 and M2a lineages. Confocal images revealed a significant number of pre-autophagosomes formed in M1 and M2a lineages. However, M0 control showed the largest size of pre-autophagosomes (Fig. 6). Statistical analysis of Atg7 and the pre-autophagosome number per cell show that there was no significant difference between M1 and M2a (300 spots/cell). M1 cells showed an increased pre-autophagosome size to more than 1 µm in diameter ($$n = 6$$ images, p value = < 0.05) (Fig. 6).Fig. 6Immune co-localization of Atg7 using confocal microscopy. Three-dimensional reconstruction of Z-stack confocal microscopy images revealed a significant number of pre-autophagosomes formed at M1 and M2a lineages. M0 control showed the largest size of pre-autophagosomes. Atg7 expression is seen as green or yellow dots (pre-autophagosomes) distributed in the cytoplasmic compartment (red arrows). J shows the count of Atg7 + pre-autophagosomes per cell ($$n = 6$$, p value = < 0.05). No significant difference between M1 and M2a was seen. K shows a violin plot of Atg7 pre-autophagosome size in µm2 measured using ImageJ.®. M1 showed an increased spot size ($$n = 6$$, p value = < 0.05). L shows Atg7* gene expression was significantly upregulated in M1 cells ($$n = 4$$, p value = < 0.05, p value = <0.01, ** p value = < 0.001) Relative fold change of gene expression normalized to GAPDH as endogenous control shows that the fold changes relative to GAPDH in M0 were as follows: mean = 3.7 and 2.36 folds, ± 0.46 and ± 0.56 for M1 and M2a, respectively (Fig. 6). Therefore, INF-γ promoted the expression of the Atg7 protein and mediated upregulation of Atg7 gene expression in M1 and M2a cells. In contrast, INF-γ and lipopolysaccharide increased Atg7 protein and messenger RNA (mRNA) in M1 lineage. ## Autophagy-associated protein complex LC3A and LC3B expression increased in M1 and M2a macrophages The MAP1-LC3s or LC3A and LC3B quantification showed that the distribution of autophagosomes inside the nuclear and cytoplasmic compartments is not uniformly distributed. However, autophagosomes were not localized in the nucleus in M1 and M0 control. The average number of basal autophagosomes in M0 was 1800. In M1, it was 2436 spot and in M2a increased to 2471 (Fig. 12S). Remarkably autophagosome aggregations were also observed. Flow cytometry single-cell quantification showed a significant increase in M1 and M2a cells (p value = 0.01– < 0.05, Fig. 7).Fig. 7Flow cytometry analysis of LC3A and LC3B protein expression in bone marrow–derived macrophages. Samples were gated on $81\%$, and LCA and LCB protein expression was read using a FITC filter. A, C, and E represent the gating for 5000 events inside scatter plots for M0, M1, and M2a lineages, respectively. B, D, and F show fluorescence peak signals for LC3A and LC3B protein expression in M0, M1, and M2a cells, respectively. Higher expression of the LC3A and LC3B was seen in M1 and M2a, compared to M0. G shows a violin plot for total cells expressing LC3A and LC3B ($$n = 3$$, p value 0.01). H shows cytoplasmic LC3A and LC3B per cell ($$n = 5$$, p value = < 0.05). Manual counting of nuclear autophagosomes (I) showed that M2a was significantly higher ($$n = 5$$, p value = < 0.05, p value = < 0.01) Finally, mRNA levels of LC3B but not LC3A increased in M1 to 4 folds and in M2a to 3 folds, respectively. Collectively, INFG + LPS induced macro-autophagy inside M1 and IL-4 + LPS induced macro-autophagy in M2a cells. High autophagosome aggregates were formed in both M1 and M2a lineages compared to M0 control (Fig. 8). Relative fluorescence intensity of LC3A and LC3B showed a significant increase in M1 and M2a lineages compared to M0 control (Supp. Data Fig. 12S).Fig. 8Immune co-localization of LC3A and LC3B protein complex using laser confocal microscopy. Reconstruction for Z-stack images revealed a significant number of autophagosomes formed at M0 and M1 lineages. M2a showed a large size of pre-autophagosomes (B, E, and H). C, F, and I show immune co-localization of cytoplasmic autophagosome (yellow to green spots) in M0, M1, and M2a lineages. LC3A and LC3B nuclear autophagosomes (yellow dots inside the nuclear compartment) (J, K). The number of autophagosomes was counted (L) inside the cytoplasm and in the nuclear using ImageJ® ## Increased Smad1 gene expression in M1 and M2a lineages We report Smad1 as one of our predicted transcription factors and its downstream targets, IL-6, and MAPLC3A genes. Smad1 was downregulated in M1 and M2a at 7-day polarization compared with 14-day polarization results. Flow cytometry showed a significant difference in Smad1 protein expression at 7-day polarization in M2a compared to M0 and M1. Fold change in M2a cells at 7-day polarization was only 1.5 folds (p values = < 0.05, Fig. 9). However, there was significant overexpression of Smad1 in both M1 and M2a lineages at 14-day polarization (p value = < 0.05, Fig. 9).Fig. 9Flow cytometry analysis for Smad1 expression in bone marrow–derived macrophages. Smad1 flow cytometry analysis in M1 and M2a lineages using M0 macrophages as a control. Samples were gated on $81\%$, and Smad1 expression was read using a FITC filter. A, C, and E represent the gating for 5000 events (event = single cell) in scatter plots (SSC-A) on the X-axis and forwarded scatter plots (FSC-A) on the Y-axis. A, C, and E show M0, M1, and M2a lineages, respectively. B, D, and F show fluorescence peak signals for Smad1 expression in M0, M1, and M2a cells, indicating higher expression of Smad1 at M2a. G Violin plot shows statistical significance for Smad1 total expression ($$n = 3$$, p value = < 0.05). Bar plot shows the statistical representation of Smad1 transcription factor fold change in both M1 and M2a at 7-day polarization (H) ($$n = 4$$, p value = < 0.05) and 14-day polarization (I) ($$n = 4$$, p value = < 0.05) normalized to GAPDH. M1 and M2a at 14-day polarization showed a significant increase in Smad1 gene expression ## Autophagy inhibitor bafilomycin A significantly increased CD68 and arginase 1 expression in M0, M1, and M2a Lineages Autophagy inhibition using bafilomycin A (200 nM) in M0, M1, and M2a cell lineages showed a high expression pattern for both arginase 1 and CD68 in M0, M1, and M2a cells. Also, bafilomycin A increased the percentage of double-positive (CD68/arginase1) cells in both M1 and M2a. Surprisingly, autophagy inhibition showed a great increase in arginase 1 expression more than $50\%$ in M0 lineage at 7-day polarization compared to M0 at 7-day polarization with the normal basal autophagy activity (Figs. 10 and 14S).Fig. 10Co-expression of both CD68 and arginase 1 by flow cytometry analysis for M1 and M2a with bafilomycin A. Flow cytometry analysis for M0, M1, and M2a cells at 7-day polarization incubated with autophagy inhibitor bafilomycin A (200 nM). M0 macrophages were used as control. Samples were gated on $81\%$, and CD68 expression was assessed using an APC filter, and arginase-1 was read using a FITC filter. A, E, and I represent the gating for 5000 events for M0, M1, and M2a lineages, respectively. B, F, and J are quadrant plots for M0, M1, and M2a, respectively. C, G, and K are fluorescence peak signal plots for CD68 expression in M0, M1, and M2a cells. An increased expression of CD68 in all cell lineages was seen. D, H, and L represent the fluorescence signal peak for arginase 1 expression in M0, M1, and M2a. Autophagy inhibition (D) showed a great increase in arginase 1 expression (> $50\%$) in M0 lineage at 7-day polarization ## Autophagy induction decreased the phagocytosis activity of M2a but not M1 The average number of phagocytic events in M2a lineage showed decreased phagocytic activity. However, no significant effect on M0 and M1 lineage was observed. Immune staining studies using Mak38 autophagy detection kit showed that autophagy decreased the phagocytic activity of M2a compared to M1 and M0 (Supp. Data Fig. 15S). ## Discussion Autophagy depends on the formation of double-membrane autophagosomes that fuse with the lysosome to degrade pathogens, proteins, and organelles. Both phagocytosis and autophagy are interdependent processes. The interplay between autophagy, macrophage activation, and phagocytosis is still poorly understood [60, 61]. In this study, we dissected both the autophagy and macrophage activation process to understand the nature of this interplay. We were able to identify a list of common pathways, transcription factors, and target proteins that mediate this interplay (Supplementary Data). We further validated these targets in an in vitro study. The predicted target proteins, Atg7, and Atg16L1 serve as central proteins for several signaling pathways in autophagy, macrophage polarization, and phagocytosis. Nevertheless, more experimental validation is needed for other predicted targets. Atg16L1 mediates the pre-autophagosome formation, which is essential for interaction with the Atg5–Atg12 complex that mediates the conjugation with PE [70]. Bone marrow–derived macrophages are a heterogeneous population. To characterize the phenotypes of the isolated bone marrow–derived macrophages and the activated macrophages in vitro, we investigated the expression of phagocytic markers CD68, IL-6, and arginase 1 among various macrophage populations. CD68 is a cell surface heavily glycosylated glycoprotein localized near the endosomal/lysosomes compartment, that is commonly used as a phagocytic marker in dendritic cells and strongly expressed in total macrophages, including M1 and M2 [78, 79]. It is also a marker of tumor-associated macrophages [80]. M0 and M1 macrophages were confirmed by the high expression of CD68 (more than $60\%$). Arginase 1 is a novel marker for activated M2a cells [81]. In M1 cells, the Arg-1 + expression was $20\%$, and in M2a, Arg-1 + expression was $46\%$. However, M0 showed a rare expression for Arg-1 (less than $2\%$). Flow cytometry analysis and immunostaining studies showed strong expression of CD68 in both M1 and M2a, and the absence of arginase 1 in M0. Altogether, these data positively characterize all lineages, M0, M1, and M2a [82, 83]. IL-6 is a pro-inflammatory cytokine that we predicted to mediate the interplay between autophagy and macrophage activation. Interferon-γ and lipopolysaccharide combination promoted the expression of IL-6 in M1 lineage. Besides phagocytosis, cytotoxic activity is one of the characteristics of bone marrow–derived macrophages [84]. Flow cytometry studies on LC3A and LC3B protein expression revealed that interferon-γ and lipopolysaccharide induced macro-autophagy in IL-6 + /CD68 + M1. Also, Interleukin 4 and lipopolysaccharide combination induced macro-autophagy in Arg-1 + /CD68 + M2a macrophages. In the current study, INF-γ and IL-4 in combination with LPS significantly induced macro-autophagy in both M1 and M2a lineages at 7-day polarization. Previous reports [85, 86] showed that INF-γ induced autophagy in hepatocellular carcinoma through increased LC3A and LC3B expression. Increased autophagy activity was found to increase the phagocytosis of Mycobacterium tuberculosis by the INF-γ signaling pathway [87]. IL-4 induced macro-autophagy in antigen-presenting B cells and is linked to asthma pathophysiology [88]. Finally, it is noteworthy to mention that IL-4 boosted autophagy induction to form LC3A and LC3B aggregates. Our results show that the 14-day polarization resulted in the loss of arginase expression and increased autophagy-related gene expression Atg16L1-1. Since arginase 1 is a phagocytic marker for M1 and M2a, loss of expression of Arg-1 indicates loss of activation in M1 and M2a lineages at 14-day polarization [89]. Interestingly, Atg16L1-1 alpha and Atg16L1-3 gamma variant showed an increase in M2a 7-day poloarization than in M2a 14-day polarization. These studies suggest that high autophagy activity at 14-day polarization can attenuate arginase 1 expression. However, previous reports [90] show that autophagy is required for arginase 1 expression in alternatively activated M2a at 7-day polarization. Phagocytosis assay was performed to test the ability of M0, M1, and M2a cells to engulf heat-killed E. coli bacteria. Interestingly, M0 (IL-6 + /CD68 +) and M1 (IL-6 + /CD68 +) cells showed significant phagocytic activity. However, M2a (Arg-1 + /CD68 +) cells showed decreased phagocytic activity. Several studies reported autophagy induction altered macrophage polarization and altered M2a phagocytic function [93–95]. As mentioned earlier, Atg16L1 is the most important hub protein in macro-autophagy and macrophage polarization. Overexpression of Atg16L1-3 and Vamp7 in M2a at 7 days of polarization and the increased number of cytoplasmic pre-autophagosomes suggest that Atg16L1 is essential for IL-4-induced macro-autophagy in M2a (Arg-1 + /CD68 +) cells. Also, autophagy induction decreased the phagocytic ability of M2a (Arg-1 + /CD68 +) cells. To better understand the interplay between autophagy and phagocytosis, we blocked the autophagosome and lysosomal fusion with autophagy inhibitor bafilomycin A as previously described [96]. Our results indicate that bafilomycin increased CD68 and arginase 1 expression in M0, M1, and M2a lineages, while autophagy induction decreased phagocytosis of M2a but not M1. Other studies reported bafilomycin-induced autophagy inhibition and the knockdown of autophagy-related protein Atg5 promoted M2 polarization [97]. Other studies [98] reported that autophagy inhibition by 3-MA (autophagy inhibitor) increased the phagocytic ability of macrophages and rescued mice from methicillin-resistant Staphylococcus aureus (MRSA) bacterial infection. Also, Atg16L1 mutation increased the phagocytosis ability of monocytes isolated from Crohn’s disease patients [99]. Therefore, we suggest that Atg16L1 might serve as a therapeutic target for the treatment of altered phagocytosis-related diseases such as bacterial infection, inflammation, lupus nephritis, and cancer. Further studies for this target protein are needed. ## Conclusion Our findings suggest that autophagy induction decreased the phagocytosis activity of M2a but not M1 macrophages. We also suggest that autophagy reprograms macrophage polarization (M1 and M2a) through CD68 and arginase 1 in an Atg16L1-1 and Atg16L1-3-dependent manner. These results might be potentially beneficial for further investigation as therapeutic targets for immunotherapy in different autoimmune disorders where macrophages play an important role in disease pathophysiology. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 7208 KB) ## References 1. Richards DM, Endres RG. The mechanism of phagocytosis: two stages of engulfment. Biophys J (2014.0) 107 1542-1553. DOI: 10.1016/j.bpj.2014.07.070 2. Cassetta L, Cassol E, Poli G. Macrophage polarization in health and disease. Sci World J (2011.0) 11 2391-2402. DOI: 10.1100/2011/213962 3. Mizushima N, Levine B. Autophagy in mammalian development and differentiation. Nat Cell Biol (2010.0) 12 823-830. DOI: 10.1038/ncb0910-823 4. Bonilla DL. Autophagy regulates phagocytosis by modulating the expression of scavenger receptors. Immunity (2013.0) 39 537-547. DOI: 10.1016/j.immuni.2013.08.026 5. King JS. Autophagy across the eukaryotes: is S cerevisiae the odd one out?. Autophagy (2012.0) 8 1159-1162. DOI: 10.4161/auto.20527 6. Sharma V, Verma S, Seranova E, Sarkar S, Kumar D. Selective autophagy and xenophagy in infection and disease. Front Cell Dev Biol (2018.0) 6 147. DOI: 10.3389/fcell.2018.00147 7. Parzych KR, Klionsky DJ. An overview of autophagy: morphology, mechanism, and regulation. Antioxid Redox Signal (2014.0) 20 460-473. DOI: 10.1089/ars.2013.5371 8. Nixon RA. The role of autophagy in neurodegenerative disease. Nat Med (2013.0) 19 983-997. DOI: 10.1038/nm.3232 9. Parkhitko AA, Favorova OO, Henske EP. Autophagy: mechanisms, regulation, and its role in tumorigenesis. Biochem Biokhimiia (2013.0) 78 355-367. DOI: 10.1134/s0006297913040044 10. Quan W, Lim YM, Lee MS. Role of autophagy in diabetes and endoplasmic reticulum stress of pancreatic β-cells. Exp Mol Med (2012.0) 44 81-88. DOI: 10.3858/emm.2012.44.2.030 11. Pan H. Autophagy-associated immune responses and cancer immunotherapy. Oncotarget (2016.0) 7 21235-21246. DOI: 10.18632/oncotarget.6908 12. Dikic I, Elazar Z. Mechanism and medical implications of mammalian autophagy. Nat Rev Mol Cell Biol (2018.0) 19 349-364. DOI: 10.1038/s41580-018-0003-4 13. Shibutani ST, Saitoh T, Nowag H, Münz C, Yoshimori T. Autophagy and autophagy-related proteins in the immune system. Nat Immunol (2015.0) 16 1014-1024. DOI: 10.1038/ni.3273 14. Shang L, Wang X. AMPK and mTOR coordinate the regulation of Ulk1 and mammalian autophagy initiation. Autophagy (2011.0) 7 924-926. DOI: 10.4161/auto.7.8.15860 15. Zachari M, Ganley IG. The mammalian ULK1 complex and autophagy initiation. Essays Biochem (2017.0) 61 585-596. DOI: 10.1042/ebc20170021 16. Gammoh N, Florey O, Overholtzer M, Jiang X. Interaction between FIP200 and ATG16L1 distinguishes ULK1 complex-dependent and -independent autophagy. Nat Struct Mol Biol (2013.0) 20 144-149. DOI: 10.1038/nsmb.2475 17. Matsushita M, Suzuki NN, Fujioka Y, Ohsumi Y, Inagaki F. Expression, purification and crystallization of the Atg5-Atg16 complex essential for autophagy. Acta Crystallographica. Section F, Struct Biol Crystallization Communi (2006.0) 62 1021-1023. DOI: 10.1107/s1744309106036232 18. Mercer TJ, Gubas A, Tooze SA. A molecular perspective of mammalian autophagosome biogenesis. J Biol Chem (2018.0) 293 5386-5395. DOI: 10.1074/jbc.R117.810366 19. Zavodszky E, Vicinanza M, Rubinsztein DC. Biology and trafficking of ATG9 and ATG16L1, two proteins that regulate autophagosome formation. FEBS Lett (2013.0) 587 1988-1996. DOI: 10.1016/j.febslet.2013.04.025 20. Xie Y. Posttranslational modification of autophagy-related proteins in macroautophagy. Autophagy (2015.0) 11 28-45. DOI: 10.4161/15548627.2014.984267 21. 21.Yoshii SR, Mizushima N. Monitoring and measuring autophagy. Int J Mol Sci. 2017;18. 10.3390/ijms18091865 22. Deretic V. Autophagosome and phagosome. Methods Mol Biol (Clifton, NJ) (2008.0) 445 1-10. DOI: 10.1007/978-1-59745-157-4_1 23. Kimura S, Noda T, Yoshimori T. Dynein-dependent movement of autophagosomes mediates efficient encounters with lysosomes. Cell Struct Funct (2008.0) 33 109-122. DOI: 10.1247/csf.08005 24. Braun V. TI-VAMP/VAMP7 is required for optimal phagocytosis of opsonised particles in macrophages. EMBO J (2004.0) 23 4166-4176. DOI: 10.1038/sj.emboj.7600427 25. Huynh KK, Kay JG, Stow JL, Grinstein S. Fusion, fission, and secretion during phagocytosis. Physiology (Bethesda) (2007.0) 22 366-372. DOI: 10.1152/physiol.00028.2007 26. Chitirala P. Cytotoxic granule exocytosis from human cytotoxic T lymphocytes is mediated by VAMP7. Front Immunol (2019.0) 10 1855. DOI: 10.3389/fimmu.2019.01855 27. Iula L. Autophagy mediates interleukin-1β secretion in human neutrophils. Front Immunol (2018.0) 9 269. DOI: 10.3389/fimmu.2018.00269 28. Peral de Castro C. Autophagy regulates IL-23 secretion and innate T cell responses through effects on IL-1 secretion. J Immunol (Baltimore, MD: 1950) (2012.0) 189 4144-4153. DOI: 10.4049/jimmunol.1201946 29. Harris J. Autophagy and IL-1 family cytokines. Front Immunol (2013.0) 4 83. DOI: 10.3389/fimmu.2013.00083 30. 30.Delgobo M, et al. An evolutionary recent IFN/IL-6/CEBP axis is linked to monocyte expansion and tuberculosis severity in humans. eLife. 2019;8. 10.7554/eLife.47013. 31. Mühl H, Pfeilschifter J. Anti-inflammatory properties of pro-inflammatory interferon-gamma. Int Immunopharmacol (2003.0) 3 1247-1255. DOI: 10.1016/s1567-5769(03)00131-0 32. Shapouri-Moghaddam A. Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol (2018.0) 233 6425-6440. DOI: 10.1002/jcp.26429 33. Perdiguero EG, Geissmann F. The development and maintenance of resident macrophages. Nat Immunol (2016.0) 17 2-8. DOI: 10.1038/ni.3341 34. Italiani P, Boraschi D. From monocytes to M1/M2 macrophages: phenotypical vs. functional differentiation. Front Immunol (2014.0) 5 514. DOI: 10.3389/fimmu.2014.00514 35. Yin Z. IL-6/STAT3 pathway intermediates M1/M2 macrophage polarization during the development of hepatocellular carcinoma. J Cell Biochem (2018.0) 119 9419-9432. DOI: 10.1002/jcb.27259 36. Klionsky DJ. A unified nomenclature for yeast autophagy-related genes. Dev Cell (2003.0) 5 539-545. DOI: 10.1016/S1534-5807(03)00296-X 37. Szklarczyk D. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res (2019.0) 47 D607-d613. DOI: 10.1093/nar/gky1131 38. Shannon P. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res (2003.0) 13 2498-2504. DOI: 10.1101/gr.1239303 39. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics (2003.0) 4 2. DOI: 10.1186/1471-2105-4-2 40. Bindea G. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics (Oxford, England) (2009.0) 25 1091-1093. DOI: 10.1093/bioinformatics/btp101 41. Jiang L. Microarray and bioinformatics analyses of gene expression profiles in BALB/c murine macrophage polarization. Mol Med Rep (2017.0) 16 7382-7390. DOI: 10.3892/mmr.2017.7511 42. Jablonski KA. Novel markers to delineate murine M1 and M2 macrophages. PLoS One (2015.0) 10 e0145342. DOI: 10.1371/journal.pone.0145342 43. Ritchie ME. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res (2015.0) 43 e47. DOI: 10.1093/nar/gkv007 44. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol (2014.0) 67 850-857. DOI: 10.1016/j.jclinepi.2014.03.012 45. Rivals I, Personnaz L, Taing L, Potier MC. Enrichment or depletion of a GO category within a class of genes: which test?. Bioinformatics (Oxford, England) (2007.0) 23 401-407. DOI: 10.1093/bioinformatics/btl633 46. Janky R. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol (2014.0) 10 e1003731. DOI: 10.1371/journal.pcbi.1003731 47. 47.Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife. 2015;4. 10.7554/eLife.05005. 48. Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell (2003.0) 115 787-798. DOI: 10.1016/s0092-8674(03)01018-3 49. Zhou G. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research (2019.0) 47 W234-w241. DOI: 10.1093/nar/gkz240 50. Gordon S, Martinez FO. Alternative activation of macrophages: mechanism and functions. Immunity (2010.0) 32 593-604. DOI: 10.1016/j.immuni.2010.05.007 51. Zhang W, Zhang Y, He Y, Wang X, Fang Q. Lipopolysaccharide mediates time-dependent macrophage M1/M2 polarization through the Tim-3/galectin-9 signalling pathway. Exp Cell Res (2019.0) 376 124-132. DOI: 10.1016/j.yexcr.2019.02.007 52. 52.Zhang X, Goncalves R, Mosser DM. The isolation and characterization of murine macrophages. Curr Protoc Immunol. Chapter 14, Unit 14.11, 2008; 10.1002/0471142735.im1401s83. 53. 53.Kumar P, Nagarajan A, Uchil PD. Analysis of cell viability by the MTT assay. Cold Spring Harbor Protocols 2018;2018. 10.1101/pdb.prot095505. 54. de Chaumont F. Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods (2012.0) 9 690-696. DOI: 10.1038/nmeth.2075 55. 55.Bayle V, Platre MP, Jaillais Y. Automatic quantification of the number of intracellular compartments in Arabidopsisthaliana root cells. Bio Protocol. 2017;7. 10.21769/BioProtoc.2145. 56. Choi JS, Jeong IS, Han JH, Cheon SH, Kim SW. IL-10-secreting human MSCs generated by TALEN gene editing ameliorate liver fibrosis through enhanced anti-fibrotic activity. Biomater Sci (2019.0) 7 1078-1087. DOI: 10.1039/c8bm01347k 57. Marks-Bluth J. SMAD1 and SMAD5 expression is coordinately regulated by FLI1 and GATA2 during endothelial development. Mol Cell Biol (2015.0) 35 2165-2172. DOI: 10.1128/mcb.00239-15 58. Park SM. U2AF35(S34F) promotes transformation by directing aberrant ATG7 pre-mRNA 3' end formation. Mol Cell (2016.0) 62 479-490. DOI: 10.1016/j.molcel.2016.04.011 59. Scherz-Shouval R. p53-dependent regulation of autophagy protein LC3 supports cancer cell survival under prolonged starvation. Proc Natl Acad Sci USA (2010.0) 107 18511-18516. DOI: 10.1073/pnas.1006124107 60. Uribe-Querol E, Rosales C. Phagocytosis: our current understanding of a universal biological process. Front Immunol (2020.0) 11 1066. DOI: 10.3389/fimmu.2020.01066 61. Duan Z. Phagocytosis of Candida albicans inhibits autophagic flux in macrophages. Oxid Med Cell Longev (2018.0) 2018 4938649. DOI: 10.1155/2018/4938649 62. Lang H. MicroRNA-149 contributes to scarless wound healing by attenuating inflammatory response. Mol Med Rep (2017.0) 16 2156-2162. DOI: 10.3892/mmr.2017.6796 63. Icli B. MicroRNA-26a regulates pathological and physiological angiogenesis by targeting BMP/SMAD1 signaling. Circ Res (2013.0) 113 1231-1241. DOI: 10.1161/circresaha.113.301780 64. Icli B. Regulation of impaired angiogenesis in diabetic dermal wound healing by microRNA-26a. J Mol Cell Cardiol (2016.0) 91 151-159. DOI: 10.1016/j.yjmcc.2016.01.007 65. Banerjee J, Sen CK. microRNA and wound healing. Adv Exp Med Biol (2015.0) 888 291-305. DOI: 10.1007/978-3-319-22671-2_15 66. 66.Newton K, Dixit VM. Signaling in innate immunity and inflammation. Cold Spring Harbor Perspect Biol. 2012;4. 10.1101/cshperspect.a006049. 67. Qing G, Yan P, Xiao G. Hsp90 inhibition results in autophagy-mediated proteasome-independent degradation of IkappaB kinase (IKK). Cell Res (2006.0) 16 895-901. DOI: 10.1038/sj.cr.7310109 68. Qing G, Yan P, Qu Z, Liu H, Xiao G. Hsp90 regulates processing of NF-kappa B2 p100 involving protection of NF-kappa B-inducing kinase (NIK) from autophagy-mediated degradation. Cell Res (2007.0) 17 520-530. DOI: 10.1038/cr.2007.47 69. Wang Y. Brucella dysregulates monocytes and inhibits macrophage polarization through LC3-dependent autophagy. Front Immunol (2017.0) 8 691. DOI: 10.3389/fimmu.2017.00691 70. Zhao YG, Zhang H. Autophagosome maturation: an epic journey from the ER to lysosomes. J Cell Biol (2019.0) 218 757-770. DOI: 10.1083/jcb.201810099 71. Pinno J. Interleukin-6 influences stress-signalling by reducing the expression of the mTOR-inhibitor REDD1 in a STAT3-dependent manner. Cell Signal (2016.0) 28 907-916. DOI: 10.1016/j.cellsig.2016.04.004 72. Kim HY. Interleukin-6 upregulates Th17 response via mTOR/STAT3 pathway in acute-on-chronic hepatitis B liver failure. J Gastroenterol (2014.0) 49 1264-1273. DOI: 10.1007/s00535-013-0891-1 73. Jacquel A. Autophagy is required for CSF-1-induced macrophagic differentiation and acquisition of phagocytic functions. Blood (2012.0) 119 4527-4531. DOI: 10.1182/blood-2011-11-392167 74. Boulakirba S. IL-34 and CSF-1 display an equivalent macrophage differentiation ability but a different polarization potential. Sci Rep (2018.0) 8 256. DOI: 10.1038/s41598-017-18433-4 75. Mancino A, Lawrence T. Nuclear factor-kappaB and tumor-associated macrophages. Clin Cancer Res: An Off J Am Assoc Cancer Res (2010.0) 16 784-789. DOI: 10.1158/1078-0432.ccr-09-1015 76. Moreno L, Gatheral T. Therapeutic targeting of NOD1 receptors. Br J Pharmacol (2013.0) 170 475-485. DOI: 10.1111/bph.12300 77. Travassos LH. Nod1 and Nod2 direct autophagy by recruiting ATG16L1 to the plasma membrane at the site of bacterial entry. Nat Immunol (2010.0) 11 55-62. DOI: 10.1038/ni.1823 78. Bisgaard LS. Bone marrow-derived and peritoneal macrophages have different inflammatory response to oxLDL and M1/M2 marker expression - implications for atherosclerosis research. Sci Rep (2016.0) 6 35234. DOI: 10.1038/srep35234 79. Chistiakov DA, Killingsworth MC, Myasoedova VA, Orekhov AN, Bobryshev YV. CD68/macrosialin: not just a histochemical marker. Lab Investig; J Tech Methods Pathol (2017.0) 97 4-13. DOI: 10.1038/labinvest.2016.116 80. Troiano G. Prognostic significance of CD68. Oral Oncol (2019.0) 93 66-75. DOI: 10.1016/j.oraloncology.2019.04.019 81. Yang Z, Ming XF. Functions of arginase isoforms in macrophage inflammatory responses: impact on cardiovascular diseases and metabolic disorders. Front Immunol (2014.0) 5 533. DOI: 10.3389/fimmu.2014.00533 82. 82.Weischenfeldt J, Porse B. Bone marrow-derived macrophages (BMM): isolation and applications. CSH Protocols. 2008;2008, pdb.prot5080, 10.1101/pdb.prot5080. 83. Huang X, Li Y, Fu M, Xin HB. Polarizing macrophages in vitro. Methods Mol Biol (Clifton, N.J.) (2018.0) 1784 119-126. DOI: 10.1007/978-1-4939-7837-3_12 84. 84.Uribe-Querol E, Rosales C. Phagocytosis: our current understanding of a universal biological process. Front Immunol. 2020;11. 10.3389/fimmu.2020.01066. 85. Yang S. New insights into autophagy in hepatocellular carcinoma: mechanisms and therapeutic strategies. Am J Cancer Res (2019.0) 9 1329-1353. PMID: 31392073 86. Luo XY. Increased macroautophagy in interferon-gamma-producing T cells from patients with newly diagnosed systemic lupus erythematosus. Chin Med J (2018.0) 131 1527-1532. DOI: 10.4103/0366-6999.235110 87. Singh N. Antimycobacterial effect of IFNG (interferon gamma)-induced autophagy depends on HMOX1 (heme oxygenase 1)-mediated increase in intracellular calcium levels and modulation of PPP3/calcineurin-TFEB (transcription factor EB) axis. Autophagy (2018.0) 14 972-991. DOI: 10.1080/15548627.2018.1436936 88. Xia F. IL4 (interleukin 4) induces autophagy in B cells leading to exacerbated asthma. Autophagy (2018.0) 14 450-464. DOI: 10.1080/15548627.2017.1421884 89. Rőszer T. Understanding the mysterious M2 macrophage through activation markers and effector mechanisms. Mediators Inflamm (2015.0) 2015 816460. DOI: 10.1155/2015/816460 90. 90.Luo J, et al. The role of autophagy in M2 polarization of macrophages induced by micro/nano topography. Int J Nanomed. 15:7763–7774 (2020). http://europepmc.org/abstract/MED/33116499. 10.2147/IJN.S270100. https://europepmc.org/articles/PMC7553265. https://europepmc.org/articles/PMC7553265?pdf=render. 91. Franchi L, Warner N, Viani K, Nuñez G. Function of Nod-like receptors in microbial recognition and host defense. Immunol Rev (2009.0) 227 106-128. DOI: 10.1111/j.1600-065X.2008.00734.x 92. Creagh EM, O'Neill LA. TLRs, NLRs and RLRs: a trinity of pathogen sensors that co-operate in innate immunity. Trends Immunol (2006.0) 27 352-357. DOI: 10.1016/j.it.2006.06.003 93. Schulz D, Severin Y, Zanotelli VRT, Bodenmiller B. In-depth characterization of monocyte-derived macrophages using a mass cytometry-based phagocytosis assay. Sci Rep (2019.0) 9 1925. DOI: 10.1038/s41598-018-38127-9 94. Liu K. Impaired macrophage autophagy increases the immune response in obese mice by promoting proinflammatory macrophage polarization. Autophagy (2015.0) 11 271-284. DOI: 10.1080/15548627.2015.1009787 95. Thomson AW, Turnquist HR, Raimondi G. Immunoregulatory functions of mTOR inhibition. Nat Rev Immunol (2009.0) 9 324-337. DOI: 10.1038/nri2546 96. Sotthibundhu A. Rapamycin regulates autophagy and cell adhesion in induced pluripotent stem cells. Stem Cell Res Ther (2016.0) 7 166. DOI: 10.1186/s13287-016-0425-x 97. Lapaquette P, Guzzo J, Bretillon L, Bringer M-A. Cellular and molecular connections between autophagy and inflammation. Mediators Inflamm (2015.0) 2015 398483. DOI: 10.1155/2015/398483 98. Zhu Y, Li H, Ding S, Wang Y. Autophagy inhibition promotes phagocytosis of macrophage and protects mice from methicillin-resistant Staphylococcus aureus pneumonia. J Cell Biochem (2018.0) 119 4808-4814. DOI: 10.1002/jcb.26677 99. Wolfkamp SC. ATG16L1 and NOD2 polymorphisms enhance phagocytosis in monocytes of Crohn’s disease patients. World J Gastroenterol (2014.0) 20 2664-2672. DOI: 10.3748/wjg.v20.i10.2664
--- title: 'Sarcopenia as a potential risk factor for senile blepharoptosis: Nationwide Surveys (KNHANES 2008–2011)' authors: - Hyeong Ju Byeon - Yong Joon Kim - Jin Sook Yoon - JaeSang Ko journal: Scientific Reports year: 2023 pmcid: PMC10060385 doi: 10.1038/s41598-023-31097-7 license: CC BY 4.0 --- # Sarcopenia as a potential risk factor for senile blepharoptosis: Nationwide Surveys (KNHANES 2008–2011) ## Abstract As the world’s population is aging, sarcopenia is recognized as essential to assess people’s lifelong condition and do appropriate early intervention. Senile blepharoptosis is also a problem in old age deteriorating visual function and causing a cosmetic decline. We investigated the association between sarcopenia and the prevalence of senile blepharoptosis, using a nationwide representative survey in Korea. A total of 11,533 participants were recruited. We used the body mass index (BMI)- adjusted appendicular skeletal muscle (ASM) definition as the muscle mass index (MMI, ASM [kg] divided by BMI [kg/m2]). The association between blepharoptosis prevalence and MMI was analyzed using multivariate logistic regression. Sarcopenia, defined as the lowest MMI quintile group in both men and women, was also associated with the prevalence of blepharoptosis (ORs 1.92, $95\%$ CI 1.17–2.16; $p \leq 0.001$). These associations remained statistically significant after adjusting for various factors related to blepharoptosis using multivariate analysis (ORs 1.18, $95\%$ CI 1.04–1.34; $$p \leq 0.012$$). Moreover, MMI was found to have a proportional relationship with eyelid lifting force (levator function), which is closely related to the occurrence and severity of ptosis. Sarcopenia is related to the prevalence of senile blepharoptosis, and patients with lower MMI were more likely to have blepharoptosis. These results suggest that sarcopenia can affect visual function and aesthetics. ## Introduction Sarcopenia was first described as an age-related decline in the lean body mass. However, as the world’s population is aging and sarcopenia is recognized to start earlier in life1,2, assessing people’s lifelong condition and appropriate early intervention has become essential. In addition, many researchers have reported that untreated sarcopenia impairs daily living, lowers the quality of life, and imposes burdens on the personal and social economy3. This is reflected in the introduction of sarcopenia into the International Classification of Diseases-10 codes in 20164–6. Moreover, this concept was extended to include muscle function, represented as muscle strength and physical performance, in 2018, led by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)7. Blepharoptosis is defined as drooping of the upper eyelid, which impairs quality of life by blocking the visual axis and causing cosmetic concerns8,9. Based on etiology, it is classified as aponeurotic, myogenic, neurogenic, mechanical, or traumatic10. Acquired blepharoptosis in old age is called senile blepharoptosis. As senile blepharoptosis is characteristic of disinserted levator aponeurosis from the upper eyelid tarsal plate, which cannot effectively transfer muscle contraction, it was also considered aponeurotic blepharoptosis. However, except for the aponeurotic factor, previous studies have reported that senile blepharoptosis shows degenerative changes of muscle itself including fat infiltration11–14. Levator palpebrae superioris muscle is also a skeletal muscle, and sarcopenia, a progressive and generalized skeletal muscle disorder, is expected to be associated with blepharoptosis. However, there are no reports on the relationship between sarcopenia and blepharoptosis. In this study, we investigated the association between sarcopenia and senile blepharoptosis using a nationwide representative survey in Korea. ## Baseline characteristics Finally, 11,553 participants (5159 men and 6394 women) aged 40–79 years who completed the ophthalmological examination and DXA were included (Table 1). The mean age of the group with blepharoptosis was 62.8 ± 10.3 years in men and 65.3 ± 9.4 years in women, which is significantly older than the group without blepharoptosis (56.4 ± 10.8 years in men, 56.0 ± 10.7 years in women, $p \leq 0.001$). Mean MMI was significantly lower in individuals with blepharoptosis than those without blepharoptosis in both men and women (0.86 ± 0.10 (kg/kg/m2), 0.90 ± 0.11 (kg/kg/m2) in men, with and without blepharoptosis respectively; $p \leq 0.001$, 0.57 ± 0.08 (kg/kg/m2), 0.60 ± 0.08 (kg/kg/m2) in women, with and without blepharoptosis respectively; $p \leq 0.001$).Table 1Baseline characteristics of the study population. Men ($$n = 5159$$)pWomen ($$n = 6394$$)pBlepharoptosis ($$n = 781$$)No blepharoptosis ($$n = 4378$$)Blepharoptosis ($$n = 918$$)No blepharoptosis ($$n = 5476$$)Age (years) (range)62.8 ± 10.3 (40–79)56.4 ± 10.8 (40–79)< 0.00165.3 ± 9.4 (40–79)56.0 ± 10.7 (40–79)< 0.001Age groups< 0.001< 0.001 40–49, n (%)112 ($14.3\%$)1416 ($32.3\%$)66 ($7.2\%$)1827 ($33.4\%$) 50–59, n (%)163 ($20.9\%$)1221 ($27.9\%$)166 ($18.1\%$)1617 ($29.5\%$) 60–69, n (%)250 ($32.0\%$)1093 ($25.0\%$)321 ($35.0\%$)1248 ($22.8\%$) 70–79, n (%)256 ($32.8\%$)648 ($14.8\%$)365 ($39.8\%$)784 ($14.3\%$)Hypertension, n (%)308 ($39.4\%$)1249 ($28.5\%$)< 0.001412 ($44.9\%$)1500 ($27.4\%$)< 0.001Diabetes, n (%)148 ($18.8\%$)508 ($11.6\%$)< 0.001159 ($17.3\%$)484 ($8.8\%$)< 0.001Dyslipidemia, n (%)99 ($12.7\%$)480 ($11.0\%$)0.182141 ($15.4\%$)760 ($13.9\%$)0.253Stroke, n (%)36 ($4.6\%$)130 ($3.0\%$)0.02238 ($4.1\%$)111 ($2.0\%$)< 0.001Ischemic heart disease, n (%)40 ($5.1\%$)155 ($3.5\%$)0.04235 ($3.8\%$)163 ($3.0\%$)0.211Smoking, n (%)280 ($35.9\%$)1702 ($39.0\%$)0.1238 ($4.2\%$)249 ($4.6\%$)0.64Regular exercise, n (%)114 ($14.6\%$)830 ($19.0\%$)0.004111 ($12.1\%$)767 ($14.1\%$)0.131Height (cm)166.4 ± 5.8168.1 ± 6.1< 0.001152.3 ± 6.0155.1 ± 5.9< 0.001Body weight (kg)66.1 ± 9.867.1 ± 10.00.01456.5 ± 9.057.2 ± 8.50.029Fat mass (kg)15.5 ± 5.015.1 ± 4.90.01819.7 ± 5.619.5 ± 5.20.318Lean body mass (kg)50.6 ± 6.552.0 ± 6.7< 0.00136.8 ± 4.737.7 ± 4.7< 0.001ASM (kg)20.6 ± 3.021.5 ± 3.2< 0.00114.0 ± 2.014.4 ± 2.1< 0.001Fat percentage (%)23.1 ± 5.322.1 ± 5.1< 0.00134.3 ± 5.733.6 ± 5.30.001WC (cm)86.5 ± 9.085.0 ± 8.5< 0.00183.6 ± 9.480.7 ± 9.3< 0.001BMI (kg/m2)23.8 ± 3.023.7 ± 2.90.26624.3 ± 3.323.8 ± 3.2< 0.001FMI (kg/m2)5.6 ± 1.85.3 ± 1.7< 0.0018.5 ± 2.38.1 ± 2.1< 0.001LBMI (kg/m2)18.2 ± 1.818.4 ± 1.80.04815.9 ± 1.515.7 ± 1.60.001MMI (kg/kg/m2)0.86 ± 0.100.90 ± 0.11< 0.0010.57 ± 0.080.60 ± 0.08< 0.001MMI quintile groups< 0.001< 0.001 Q1, lowest, n (%)238 ($29.2\%$)804 ($18.4\%$)283 ($30.8\%$)996 ($18.2\%$) Q2, n (%)192 ($24.6\%$)840 ($19.2\%$)200 ($21.8\%$)1079 ($19.7\%$) Q3, n (%)147 ($18.8\%$)885 ($20.2\%$)168 ($18.3\%$)1111 ($20.3\%$) Q4, n (%)123 ($15.7\%$)909 ($20.8\%$)154 ($16.8\%$)1125 ($20.5\%$) Q5, highest, n (%)91 ($11.7\%$)940 ($21.5\%$)113 ($12.3\%$)1165 ($21.3\%$)Levator function< 0.001< 0.001 Good (≥ 12 mm), n (%)244 ($31.2\%$)2934 ($67.0\%$)245 ($26.7\%$)3509 ($64.1\%$) Fair (8–11 mm), n (%)398 ($51.0\%$)1325 ($30.3\%$)450 ($49.0\%$)1796 ($32.8\%$) Poor (≤ 7 mm), n (%)139 ($17.8\%$)119 ($2.7\%$)223 ($24.3\%$)171 ($3.1\%$)Lens< 0.001< 0.001 Cataract, n (%)427 ($55.0\%$)1586 ($36.3\%$)540 ($59.1\%$)1915 ($35.0\%$) No cataract, n (%)280 ($36.0\%$)2654 ($58.7\%$)256 ($28.0\%$)3239 ($59.2\%$) Pseudophakia, n (%)65 ($8.4\%$)218 ($5.0\%$)117 ($12.8\%$)312 ($5.7\%$) Aphakia, n (%)5 ($0.6\%$)3 ($0.1\%$)1 ($0.1\%$)3 ($0.1\%$)ASM Appendicular skeletal muscle mass, WC Waist circumference, BMI Body mass index, FMI Fat mass index, LBMI Lean body mass index, MMI Muscle mass index. Participants of both sexes with blepharoptosis had a higher prevalence of hypertension and diabetes ($p \leq 0.001$). Women with blepharoptosis had a higher BMI ($p \leq 0.001$); however, there was no difference in BMI between men with and without blepharoptosis ($$p \leq 0.266$$). ## Muscle mass index and blepharoptosis The calculated MMI ranged from 0.498 to 1.350 (kg/kg/m2) and from 0.322 to 0.917 (kg/kg/m2) in men and women, respectively (Fig. 1A). Individuals in the lowest MMI quintile group in men and women were defined as having sarcopenia, and the cutoff values were 0.809 kg/kg/m2 in men and 0.532 kg/kg/m2 in women. A negative correlation was found between MMI and blepharoptosis prevalence; the lower the MMI, the higher the prevalence of blepharoptosis (Fig. 1B, p value for linear trend < 0.001 in both men and women). This trend of negative correlation between MMI and blepharoptosis was also maintained when stratified with ages 40–59 and 60–79 (Supplementary Fig. S1).Figure 1Muscle mass index and blepharoptosis prevalence. ( A) The muscle mass index distribution is lower in the blepharoptosis group compared to the no blepharoptosis group ($p \leq 0.001$). ( B) The prevalence of blepharoptosis increased as MMI decreased (p value for linear trend < 0.001). Using multivariable logistic regression, the effects of potential confounders (age, hypertension, diabetes, obesity, smoking, and history of cataract surgery) were adjusted to evaluate the association between MMI and blepharoptosis (Table 2). The degree of MMI affecting blepharoptosis, represented as an odds ratio (OR), was greater in men than in women. In men, after adjusting for all potential confounding factors in model 3, the prevalence of blepharoptosis had a significant inverse association with MMI (OR, 0.17; $95\%$ CI 0.07–0.39; $p \leq 0.001$). In women, similar to men, a significant negative association between MMI and the prevalence of blepharoptosis was found in model 2 (OR, 0.36; $95\%$ CI 0.13–0.96; $$p \leq 0.042$$).Table 2Multivariate analysis of muscle mass index and blepharoptosis prevalence. CrudeModel 1Model 2Model 3Muscle mass index Men OR0.020.120.130.17 $95\%$ CI(0.01, 0.05)(0.05, 0.27)(0.06, 0.31)(0.07, 0.39) p-value< 0.001< 0.001< 0.001< 0.001 Women OR0.010.310.360.40 $95\%$ CI(0.00, 0.02)(0.12, 0.81)(0.13, 0.96)(0.14, 1.14) p-value< 0.0010.0170.0420.086OR Odds ratio. Model 1: adjusted for age. Model 2: adjusted for age, hypertension, diabetes, smoking, and history of cataract surgery. Model 3: adjusted for age, hypertension, diabetes, smoking, history of cataract surgery, and obesity (BMI > 25). ## Sarcopenia and blepharoptosis After identifying a linear correlation between MMI and blepharoptosis prevalence, the relationship between sarcopenia, defined as the lowest MMI quintile group, and blepharoptosis prevalence was analyzed using a multivariate analysis (Table 3). Even after adjusting for multiple confounders (age, sex, hypertension, diabetes, history of cataract surgery, and obesity), sarcopenia was significantly correlated with the prevalence of blepharoptosis (OR, 1.18; $95\%$ CI 1.04–1.34; $$p \leq 0.012$$).Table 3Multivariate analysis of sarcopenia and blepharoptosis prevalence stratified by levator function. CrudeModel 1Model 2Model 3Total population ($$n = 11$$,553) Sarcopenia* OR1.921.251.231.18 $95\%$ CI(1.71, 2.16)(1.10, 1.41)(1.08, 1.39)(1.04, 1.34) p-value< 0.001< 0.0010.0010.012Men† ($$n = 5159$$) Sarcopenia* OR1.831.301.281.22 $95\%$ CI(1.54, 2.18)(1.09, 1.56)(1.06, 1.54)(1.01, 1.47) p-value< 0.0010.0040.0090.038Women† ($$n = 6394$$) Sarcopenia* OR2.001.201.191.17 $95\%$ CI(1.72, 2.34)(1.01, 1.42)(1.00, 1.41)(0.98, 1.39) p-value< 0.0010.0340.0480.085LF ≥ 8 mm ($$n = 10$$,901) Sarcopenia* OR1.881.261.241.19 $95\%$ CI(1.66, 2.14)(1.10, 1.44)(1.08, 1.43)(1.04, 1.37) p-value< 0.001< 0.0010.0020.013LF ≤ 7 mm ($$n = 652$$) Sarcopenia* OR1.180.960.951.00 $95\%$ CI(0.80, 1.74)(0.64, 1.43)(0.63, 1.44)(0.65, 1.53) p-value0.4020.8360.8200.992LF Levator function, OR Odds ratio. Model 1: adjusted for age and sex. Model 2: adjusted for age, sex, hypertension, diabetes, smoking, and history of cataract surgery. Model 3: adjusted for age, sex, hypertension, diabetes, smoking, history of cataract surgery, and obesity (BMI > 25).Individuals in the lowest MMI quintile group in men and women were defined as having sarcopenia.†All models were adjusted for the same multivariates except sex. ## Sarcopenia, blepharoptosis, and levator function Levator function was compared according to MMI quintile groups. Participants with lower MMI showed decreased levator function, indicating a linear correlation between appendicular muscle mass and eyelid lifting force. ( Fig. 2; all p values for linear trend in each levator function classification < 0.001).Figure 2Muscle mass index and blepharoptosis stratified with levator function. As muscle mass index decreased, the distribution of participants with poor levator function increased (all p value for linear trend in each levator function groups < 0.001). Patients were stratified according to levator function, and the relationship between sarcopenia and blepharoptosis prevalence was analyzed using multivariate logistic regression (Table 3). In the fair to good levator function group (levator function ≥ 8mm), the correlation between blepharoptosis and sarcopenia, which was found in the entire population, was also identified (OR, 1.19; $95\%$ CI 1.04–1.37; $$p \leq 0.013$$). However, there was no significant relationship between sarcopenia and blepahroptosis in the poor levator function group (levator function ≤ 7mm) (OR, 1.00; $95\%$ CI 0.65–1.53; $$p \leq 0.992$$). ## BMI and blepharoptosis Since previous studies have reported obesity as a potential risk factor for blepharoptosis16,17, the relationship between BMI and the prevalence of blepharoptosis was analyzed using the same methods as in this study. Divided by BMI quintiles, the incidence of blepharoptosis had no relation with BMI in men (p value for linear trend = 0.359) but a positive relationship in women (p value for linear trend < 0.001) (Supplementary Fig. S2A). Levator function (good, ≥ 12 mm; fair, 8–11 mm; poor, ≤ 7 mm) in each BMI quintile group was analyzed (Supplementary Fig. S2B). Women with high BMI showed significantly low levator function (all p values for linear trend in each levator function group < 0.05), but the relevance was not as evident as MMI in Fig. 2. Moreover, men had no relationship between BMI and levator function (all p values for linear trend in each levator function group > 0.05). ## Discussion This study demonstrated an association between sarcopenia and blepharoptosis. The sarcopenia representative index, MMI, was significantly inversely correlated with the prevalence of blepharoptosis, and MMI was an independent variable after adjusting for age, sex, hypertension, diabetes, smoking, cataract surgery history, and obesity. The levator function of the eyelid was also significantly associated with MMI, and we could assume that sarcopenic patients with low MMI showed lower levator function, causing blepharoptosis. Sarcopenic muscles exhibit two apparent changes as part of the aging process. The first is increased intramuscular fatty infiltration6,18,19. Inter and intramuscular lipid accumulation is accelerated with aging, as fibro-adipogenic precursor cells reside in muscular tissue and differentiate into adipocytes. In addition, intramyocellular lipid droplets are mediated by impaired hormone regulation and mitochondrial dysfunction during aging20. Fat infiltration into the muscle is not only a burden for muscle activity but also a preliminary step in infiltrating macrophages, inflammatory cytokines, and fat-associated hormones21. Likewise, on general intraoperative observations, the levator palpebrae superioris muscle in involutional blepharoptosis patients frequently shows myosteatosis (Fig. 3). Previous reports have shown that the group with higher fat infiltration had lower levator muscle function, consequently resulting in blepharoptosis11,22–24. Second, the loss of motor units is accelerated in type II muscle fibers, and additional atrophy of type II muscle fibers decreases muscle mass and gradually decreases muscle power6,19. The levator palpebrae superioris muscle is also composed of type I and II muscle fibers, approximately $85\%$ of which are type II25. With a high proportion of type II muscle fibers, these sarcopenic changes can also occur in the levator palpebral superior muscle and deteriorate its function. Figure 3Intraoperative findings of levator palpebrae superioris muscle. These pictures were taken by one of the authors (J.K.) during routine blepharoplasty surgery. ( A) The white arrowhead indicates normal levator palpebrae superioris muscle showing muscle fibers. ( B) The black arrowhead indicates fat infiltration of the levator palpebrae superioris muscle. Several studies have reported that blepharoptosis is related to systemic diseases, such as hypertension, diabetes, dyslipidemia, and obesity16,17,22,26–29. In these reports, the causative mechanisms of these relationships were suggested to be end-organ damage related to metabolic syndrome. Atherogenic dyslipidemia affects microvascular complications of the eyelid28, and blepharoptosis in diabetes is attributed to chronic tissue hypoxia of the levator muscle30. Reactive oxygen species resulting from metabolic syndrome may contribute to the degeneration of the eyelid26. Moreover, obesity and its related body composition indices, such as waist circumference (WC), fat mass (FM), fat mass index (FMI), BMI, and lean body mass index (LBMI), were related to the prevalence of blepharoptosis16,17,26. Fat gain on the upper eyelid due to obesity can cause mechanical push-down of the upper eyelid, stretching of the levator aponeurosis, and fatty inflammation17. In our study, multivariate analyses showed that MMI and sarcopenia were significantly associated with blepharoptosis, independent of confounding factors related to metabolic syndrome. In addition, our additional analysis using BMI supported this result and showed that blepharoptosis in men is strongly associated with sarcopenia compared to BMI. This implies that blepharoptosis in men is caused by a degenerative change in the levator muscle rather than by obesity related fat gain. Meanwhile, in women, obesity may influence the prevalence of blepharoptosis. This sex difference may be due to differences in sex hormones30,31. Age particularly may interact with MMI and blepharoptosis among the confounding variables, further complicating the interpretation of the association between them. As getting older, MMI decreases, and the incidence of blepharoptosis increases (Table 1)27,32,33. Our stratified analyses into a middle-aged group (40–59 years) and an older-aged group (60–79 years) showed a consistent negative correlation between MMI and blepharoptosis in both groups. These results suggest the correlation between sarcopenia itself and blepharoptosis, independently from the effect of aging. Many efforts and methodologies have been made to define sarcopenia34,35. A recent trend is to focus on muscle strength; according to the new definition in 20187, low muscle strength is the primary parameter for “probable sarcopenia”, and low muscle quantity or quality can confirm the diagnosis. However, the KNEHS 2008–2011 study design did not include the assessment of muscle strength, such as the handgrip test. Therefore, in this study, we used MMI to apply ASM to quantify the muscle mass and define sarcopenia. Adjusting body size to ASM using BMI was introduced by the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project in 201436 and has been proven to be a better way to define sarcopenia in the Korean population and validated in multiple studies37–39. We defined sarcopenia as the lowest quintile of MMI not following the cut-offs of FNIH Sarcopenia Project suggested since the difference of the participants’ race and age distribution38,40. However, our cutoff values, 0.809 in men and 0.532 in women, are comparable to the cut-offs of FNIH Sarcopenia Project in 2014, which were 0.789 and 0.512 in men and women respectively. Additionally, blepharoptosis may have the potential to be used as a screening tool to suggest sarcopenia. As shown in Table 1, participants with blepharoptosis were approximately three times more likely to belong to the lowest MMI quintile group (Q1, $29.2\%$ in men, $30.8\%$ in women) than those in the highest quintile group (Q5, $11.7\%$ in men, $12.3\%$ in women). In addition, as shown in Fig. 2, as the muscle mass index decreased, levator muscle function decreased, indicating reduced levator muscle strength. As discussed above, the relationship between blepharoptosis and MMI should be due to the systemic sarcopenic process, and blepharoptosis can be a practical screening tool for sarcopenia. Since SARC-F, sarcopenia screening questionnaire, and assessment of skeletal muscle strength are limited to handgrip test, chair stand test, and walking ability, blepharoptosis will be more meaningful when assessing patients with disabilities, such as quadriplegia, or selecting patients who should assess sarcopenia even before SARC-F screening. This study has some limitations. First, it was a cross-sectional study and could not clarify causality. We could not explore the direct relationship between blepharoptosis and systemic muscle strength, such as in the handgrip test, because of limited data. However, the levator function represented the levator palpebrae superioris muscle strength, which could complement systemic muscle strength. Second, the data are restricted to the Korean population. The eyelid anatomical structures like orbital fat or insertion of levator aponeurosis may differ from other ethnicities; therefore, caution is needed when applying the results of this study to different ethnicities, and further research encompassing other ethnicities is valuable. This is the first study to investigate the association between blepharoptosis and sarcopenia. Multivariate analysis revealed that sarcopenia and MMI were independently associated with senile blepharoptosis. Our results may explain the reduced levator muscle function induced by sarcopenic processes, such as myosteatosis, resulting in senile blepharoptosis. Our study is valuable as it shows that sarcopenia may impair an individual’s life by impairing visual function and aesthetics, and it provides a new perspective on the mechanisms of senile blepharoptosis. ## Study population All analyses were conducted using data obtained from the Korea National Health and Nutrition Examination Survey (KNHANES) between 2008 and 2011. This nationwide survey is a population-based, cross-sectional health examination and survey regularly conducted by the Korean Center for Disease Control and Prevention in the Ministry of Health and Welfare to monitor the general health and nutritional status of South Koreans. It comprises standardized health examinations and questionnaires on nutrition, lifestyle, and medical information. Of the 37,753 subjects from the KNHANES 2008–2011, we initially selected 20,748 patients who completed the ophthalmological examination and dual-energy X-ray absorptiometry (DXA, QDR 4500A, Hologic Inc., Waltham, MA). Among the participants aged from 10 to 80 years old, patients aged 40–79 years ($$n = 12$$,338) were included32,33. Those with anophthalmos ($$n = 22$$), facial palsy ($$n = 35$$), and thyroid diseases ($$n = 605$$), which could affect eyelid position, were excluded. Finally, 11,533 patients (5159 men and 6394 women) were included in the final statistical analysis. The study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants before the study began, and the KNHANES was conducted following ethical approval by the Institutional Review Board of the Korea Center for Disease Control and Prevention (2008-04EXP-01-C, 2009-01CON-03-2C, 2010-02CON-21-C, 2011-02CON-06C). ## Blepharoptosis and ophthalmic measurement Trained ophthalmologists performed ophthalmic examinations. The participants underwent a comprehensive ophthalmic examination, including visual acuity measurement, automated refraction, slit-lamp biomicroscopy, intraocular pressure measurement, and fundus photography. Marginal reflex distance 1 (MRD1) was measured from the upper eyelid margin to the corneal light reflex after the participants looked straight at a distant target. We selected a subject eye with a smaller MRD1 of either eye, and defined blepharoptosis when the MRD1 of the eye was less than 2 mm. The levator function was measured by having the patient look down and with a hand on the individual’s forehead to prevent any brow action, asking the patient to look upward as far as possible without changing the head position. Elevation of the upper lid margin (in mm) is the levator muscle function. If the distance was over 8 mm, the participant was considered to have fair to good levator function. Slit-lamp examination confirmed lens status with cataract or intraocular lens. Individuals who were pseudophakic or aphakic were considered to have a history of cataract surgery. ## Sarcopenia and systemic assessment In this study, we used the BMI-ASM definition as muscle mass index (MMI, ASM [kg] divided by BMI [kg/m2]) to quantify muscle mass and assess sarcopenia. Individuals were separated by sex and classified into quintiles of the MMI; those in quintile 1 (Q1), representing $20\%$ of the patients with the lowest MMI, were considered to have sarcopenia. ASM, FM and LBM were measured using DXA15. Participants who had systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or had taken hypertension medication were included in the study. Diabetes was diagnosed in those with a fasting blood glucose level of ≥ 126 mg/dL or those taking diabetes medication or insulin. Dyslipidemia was defined as fasting blood total cholesterol > 240 mg/dL, triglyceride > 200 mg/dL, or taking lipid-lowering medication. Based on a self-administered questionnaire, a diagnostic history of stroke and ischemic heart diseases, including myocardial infarction and angina, was reported. Participants were assessed for their current smoking status. Regular exercise was considered vigorous exercise for more than 20 min, 3 days per week. ## Statistical analyses A chi-square test was used to compare the clinical characteristics between the groups with and without blepharoptosis. The Cochran-Armitage test was performed to analyze the trend in the prevalence of blepharoptosis according to the MMI quintiles. Multivariate logistic regression analysis was performed to determine the relationship between MMI, sarcopenia, and blepharoptosis. Model 1 was adjusted for age, and model 2 was adjusted for hypertension, diabetes, smoking, and previous cataract surgery in addition to age. Obesity (BMI > 25 kg/m2) was added to model 3 for adjustment. R ver. 4.0.3. for Windows (The R Foundation for Statistical Computing, Vienna, Austria) was used for data analysis, and a p-value less than 0.05 was considered statistically significant. Continuous numeric data are represented as mean ± standard deviation. ## Supplementary Information Supplementary Figures. The online version contains supplementary material available at 10.1038/s41598-023-31097-7. ## References 1. 1.United Nations Department of Economic and Social Affairs, Population Division (2022). World population prospects 2022: Summary of Results. UN DESA/POP/2022/TR/NO. 3. 2. Sayer AA. The developmental origins of sarcopenia. J. Nutr. Health Aging* (2018.0) 12 427-432. DOI: 10.1007/BF02982703 3. Mijnarends DM. Muscle, health and costs: A glance at their relationship. J. Nutr. Health Aging (2018.0) 22 766-773. DOI: 10.1007/s12603-018-1058-9 4. Rosenberg IH. Sarcopenia: Origins and clinical relevance. J. Nutr. (1997.0) 127 990-991. DOI: 10.1093/jn/127.5.990S 5. Anker SD, Morley JE, Haehling SV. Welcome to the ICD-10 code for sarcopenia. J. Cachexia Sarcopenia Muscle (2016.0) 7 512-514. DOI: 10.1002/jcsm.12147 6. Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet (2019.0) 393 2636-2646. DOI: 10.1016/S0140-6736(19)31138-9 7. Cruz-Jentoft AJ. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing (2019.0) 48 16-31. DOI: 10.1093/ageing/afy169 8. Richards HS. The psychological well-being and appearance concerns of patients presenting with ptosis. Eye (2014.0) 28 296-302. DOI: 10.1038/eye.2013.264 9. Briceño CA, Fuller ML, Bradley EA, Nelson CC. Assessment of the abbreviated National eye Institute visual function questionnaire (NEI VFQ 9) in blepharoptosis and dermatochalasis. Arq. Bras. Oftalmol. (2016.0) 79 226-228. DOI: 10.5935/0004-2749.20160065 10. Clauser L, Tieghi R, Galie M. Palpebral ptosis: Clinical classification, differential diagnosis, and surgical guidelines: An overview. Aesthetic Plast. Surg. (2003.0) 27 193-204. DOI: 10.1097/00001665-200603000-00008 11. Kim CY, Lee SY. Distinct features in Koreans with involutional blepharoptosis. Plast. Reconstr. Surg. (2015.0) 135 1693-1699. DOI: 10.1097/PRS.0000000000001280 12. De Figueiredo AR. Blepharoptosis. Semin. Ophthalmol. (2010.0) 25 39-51. DOI: 10.3109/08820538.2010.496695 13. Shore JW, McCord CD. Anatomic changes in involutional blepharoptosis. Am. J. Ophthalmol. (1984.0) 98 21-27. DOI: 10.1016/0002-9394(84)90183-1 14. Cahill KV, Buerger GF, Johnson BL. Ptosis associated with fatty infiltration of Muller’s muscle and levator muscle. Ophthal. Plast. Reconstr. Surg. (1986.0) 2 213-217. DOI: 10.1097/00002341-198601070-00008 15. Weber DR, Leonard MB, Shults J, Zemel BS. A comparison of fat and lean body mass index to BMI for the identification of metabolic syndrome in children and adolescents. J. Clin. Endocrinol. Metab. (2014.0) 99 3208-3216. DOI: 10.1210/jc.2014-1684 16. Paik JS. Obesity as a potential risk factor for blepharoptosis: The Korea National Health and Nutrition Examination Survey 2008–2010. PLoS ONE (2015.0) 10 e0131427. DOI: 10.1371/journal.pone.0131427 17. Lee CH, Jeong SM, Sa HS. Correlation between body composition and blepharoptosis in adults. Eye (2021.0) 35 1999-2007. DOI: 10.1038/s41433-020-01214-1 18. Frontera WR. Physoilogic changes of the musculoskeletal system with aging: A brief review. Phys. Med. Rehabil. Clin. N. Am. (2017.0) 28 705-711. DOI: 10.1016/j.pmr.2017.06.004 19. Lang T. Sarcopenia: Etiology, clinical consequences, intervention, and assessment. Osteoporos. Int. (2010.0) 21 543-559. DOI: 10.1007/s00198-009-1059-y 20. Li CW. Pathogenesis of sarcopenia and the relationship with fat mass: Descriptive review. J. Cachexia Sarcopenia Muscle. (2022.0) 13 781-794. DOI: 10.1002/jcsm.12901 21. Neels JG, Olefsky JM. Inflamed fat: What starts the fire?. J. Clin. Invest. (2006.0) 116 33-35. DOI: 10.1172/JCI27280 22. Kim MH. Prevalence and associated factors of blepharoptosis in Korean adult population: The Korea National Health and Nutrition Examination Survey 2008–2011. Eye (2017.0) 31 940-946. DOI: 10.1038/eye.2017.43 23. Pereira LS, Hwang TN, Kersten RC, Ray K, McCulley TJ. Levator superioris muscle function in involutional blepharoptosis. Am. J. Ophthalmol. (2008.0) 145 1095-1098. DOI: 10.1016/j.ajo.2008.02.002 24. Lai HT. Analysis of levator function and ptosis severity in involutional blepharoptosis. Ann. Plast. Surg. (2017.0) 78 S58-S60. DOI: 10.1097/SAP.0000000000001007 25. Campbell SP, Williams DA, Frueh BR, Lynch GS. Contractile activation characteristics of single permeabilized fibres from levator palpebrae superioris, orbicularis oculi and vastus lateralis muscles from humans. J. Physiol. (1999.0) 519 615-622. DOI: 10.1111/j.1469-7793.1999.0615m.x 26. Koh CH, Kim GY, Han KD, Park JW. Association between metabolic syndrome and blepharoptosis in the Korean adults: The Korea National Health and Nutrition Examination Survey 2010–2012. J. Cosmet. Med. (2018.0) 2 85-90. DOI: 10.25056/JCM.2018.2.2.85 27. Paik JS. Blepharoptosis among Korean adults: Age-related prevalence and threshold age for evaluation. BMC Ophthalmol. (2020.0) 20 99. DOI: 10.1186/s12886-020-01350-y 28. Shrado M. Dyslipidaemia and age-related involutional blepharoptosis. J. Plast. Reconstr. Aesthet. Surg. (2012.0) 65 e146-150. DOI: 10.1016/j.bjps.2012.01.009 29. Bastiaensen LA. Narrowing of the palpebral fissure in diabetes. Doc. Ophthalmol. (1983.0) 56 5-10. DOI: 10.1007/BF00154702 30. Hansen M. Female hormones: Do they influence muscle and tendon protein metabolism?. Proc. Nutr. Soc. (2018.0) 77 32-41. DOI: 10.1017/S0029665117001951 31. Lovejoy JC. The influence of sex hormones on obesity across the female life span. J. Womens. Health. (1998.0) 7 1247-1256. DOI: 10.1089/jwh.1998.7.1247 32. Metter EJ, Conwit R, Tobin J, Fozard JL. Age-associated loss of power and strength in the upper extremities in women and men. J. Gerontol. A Biol. Sci. Med. Sci. (1997.0) 52 B267-B276. DOI: 10.1093/gerona/52a.5.b267 33. Ferrucci L, de Cabo R, Knuth ND, Studenski S. Of Greek heroes, wiggling worms, mighty mice, and old body builders. J. Gerontol. A Biol. Sci. Med. Sci. (2012.0) 67 13-16. DOI: 10.1093/gerona/glr046 34. Cooper C. Tools in the assessment of sarcopenia. Calcif. Tissue. Int. (2013.0) 93 201-210. DOI: 10.1007/s00223-013-9757-z 35. Kim KM, Jang HC, Lim S. Differences among skeletal muscle mass indices derived form height-, weight-, and body mass index-adjusted models in assessing sarcopenia. Korean J. Intern. Med. (2016.0) 31 643-650. DOI: 10.3904/kjim.2016.015 36. Cawthon PM. Cutpoints for low appendicular lean mass that identify older adults with clinically significant weakness. J. Gerontol. A. Biol. Sci. Med. Sci. (2014.0) 69 567-575. DOI: 10.1093/gerona/glu023 37. Jang HC. How to diagnose sarcopenia in Korean older adults?. Ann. Geriatr. Med. Res. (2018.0) 22 73-79. DOI: 10.4235/agmr.2018.22.2.73 38. Tyrovolas S. The role of muscle mass and body fat on disability among older adults: A cross-national analysis. Exp. Gerontol. (2015.0) 69 27-35. DOI: 10.1016/j.exger.2015.06.002 39. Kim TN. Comparisons of three different methods for defining sarcopenia: An aspect of cardiometabolic risk. Sci. Rep. (2017.0) 7 6491. DOI: 10.1038/s41598-017-06831-7 40. Newman AB. Sarcopenia: Alternative definitions and associations with lower extremity function. J. Am. Geriatr. Soc. (2003.0) 51 1602-1609. DOI: 10.1046/j.1532-5415.2003.51534.x
--- title: Hypothalamic melanin-concentrating hormone neurons integrate food-motivated appetitive and consummatory processes in rats authors: - Keshav S. Subramanian - Logan Tierno Lauer - Anna M. R. Hayes - Léa Décarie-Spain - Kara McBurnett - Anna C. Nourbash - Kristen N. Donohue - Alicia E. Kao - Alexander G. Bashaw - Denis Burdakov - Emily E. Noble - Lindsey A. Schier - Scott E. Kanoski journal: Nature Communications year: 2023 pmcid: PMC10060386 doi: 10.1038/s41467-023-37344-9 license: CC BY 4.0 --- # Hypothalamic melanin-concentrating hormone neurons integrate food-motivated appetitive and consummatory processes in rats ## Abstract The lateral hypothalamic area (LHA) integrates homeostatic processes and reward-motivated behaviors. Here we show that LHA neurons that produce melanin-concentrating hormone (MCH) are dynamically responsive to both food-directed appetitive and consummatory processes in male rats. Specifically, results reveal that MCH neuron Ca2+ activity increases in response to both discrete and contextual food-predictive cues and is correlated with food-motivated responses. MCH neuron activity also increases during eating, and this response is highly predictive of caloric consumption and declines throughout a meal, thus supporting a role for MCH neurons in the positive feedback consummatory process known as appetition. These physiological MCH neural responses are functionally relevant as chemogenetic MCH neuron activation promotes appetitive behavioral responses to food-predictive cues and increases meal size. Finally, MCH neuron activation enhances preference for a noncaloric flavor paired with intragastric glucose. Collectively, these data identify a hypothalamic neural population that orchestrates both food-motivated appetitive and intake-promoting consummatory processes. Food intake is determined by learned appetitive responses and physiological “appetition” signals after eating begins. Here, authors show melanin-concentrating hormone (MCH)-producing neurons integrate these processes to promote caloric intake. ## Introduction Caloric regulation is determined by the integration of appetitive food-motivated responses and consummatory physiological processes that govern meal size1–3. Once eating is initiated, the amount of calories consumed during a meal is regulated by two opposing processes: an early meal positive feedback process known as appetition that promotes further consumption3,4, and a later meal negative feedback process known as satiation that leads to meal termination1. While the neurobiological systems that regulate premeal appetitive responses and meal-termination satiation signaling have been widely investigated, the neural substrates mediating appetition remain elusive. Here we seek to identify how the brain integrates appetitive and consummatory signals, specifically those driving appetition, to promote caloric consumption. The hypothalamus regulates both appetitive and consummatory behaviors. Regarding appetitive food-motivated behaviors, Agouti-related Protein (AgRP)-expressing neurons in the arcuate nucleus of the hypothalamus (ARH) have extensively been shown to potently and reliably trigger food-seeking responses5–8. However, given that fasting-induced AgRP neuron activity is inhibited upon access to food consumption9 or exposure to food-associated cues7, these neurons are unlikely to serve as key integrators of appetitive and consummatory processes. Orexin (aka, hypercretin)-producing neurons in the lateral hypothalamic area (LHA) are also associated with foraging and other premeal appetitive responses10–12. However, like AgRP neurons, orexin neuron activity ceases immediately upon eating13,14, and therefore, similar to AgRP neurons, orexin neurons are also unlikely candidates to integrate appetitive processes with prandial appetition. Melanin-concentrating hormone (MCH)-producing neurons, also located in the LHA but distinct from orexin neurons, are glucose-responsive15,16 and pharmacological MCH administration increases food intake via an increase in meal size17, thus making them a feasible candidate population of neurons for such integration. Furthermore, the MCH receptor, MCH-1R, is required for palatable food-associated cues to promote overeating in mice18, thus supporting a role for MCH signaling in linking conditioned appetitive behaviors with consummatory intake-promoting processes. Here we aim to understand the role of MCH neurons in integrating food-motivated appetitive and intake-promoting consummatory processes. Our findings reveal that physiological MCH neuron Ca2+ activity increases upon exposure to both discrete and contextual-based food-predictive cues and that these MCH neural responses are strongly associated with appetitive cue-induced behavioral actions. In addition to contributing to appetitive processes, MCH neuron Ca2+ activity dynamically increases during eating, and this response is positively correlated with calories consumed during a meal. This eating-induced elevation of MCH neuron activity also dampens throughout the course of a meal, which supports the involvement of these neurons in promoting early-phase eating (appetition). Lastly, chemogenetic results functionally validate the physiological MCH neural responses, as MCH neuron activation increased appetitive responses to food-predictive cues, elevated meal size under the same conditions as the Ca2+ imaging consummatory analyses, and enhanced flavor-nutrient learning. Taken together, these findings support a role for MCH neurons in integrating appetite and appetition. ## MCH neuron Ca2+ activity increases during discrete sucrose-predictive Pavlovian cues and is associated with appetitive responses To determine if MCH neurons are responsive to discrete exteroceptive food-predictive cues, rats were injected with an AAV9.pMCH.GCaMP6s.hGH (MCH promoter-driven GCaMP6s) followed by an optic fiber implanted into the LHA (Fig. 1a). To confirm the selectivity of the MCH promoter, immunofluorescence colocalization analyses reveal that the GCaMP6s fluorescence signal was exclusive to MCH immunoreactive neurons (Fig. 1b). Results from MCH GCAMP6s neuroanatomical quantification show that using this approach approximately 70.9 ± $3.5\%$ (standard error of the mean) of LHA MCH neurons and 18.9 ± $1.1\%$ of perifornical area MCH neurons were colocalized with the MCH GCAMP6s. Post viral transduction, the animals were trained to associate one auditory cue with access to sucrose (conditioned-stimulus positive, CS+) and a different auditory cue with no sugar access (CS−). Then, fiber photometry was used to measure MCH neuron Ca2+ activity in response to the CS+ and CS− (Fig. 1c). The animals readily learned the Pavlovian discrimination, exhibited a significantly increased number of licks per trial, reduced latency to lick, and increased number of CS+ trials with a consummatory response at the end (Day 7) vs. beginning (Day 1) of training (Fig. 1d–f). During the test recording session on Day 7 of training, physiological MCH neuron Ca2+ activity reflected the learned CS discrimination, such that there was increased activity during the CS+ presentation in comparison to the CS− (Fig. 1g–i). Moreover, this effect was specific to the CS period, as there were no differences 5s-pre-CS and 5s-post-CS between the CS+ and CS− (Fig. 1j). Additionally, MCH neuron Ca2+ activity was negatively correlated with latency to lick, such that the CS+ induced MCH neuron Ca2+ response was predictive of a faster appetitive response to initiate sucrose consumption upon cue offset (Fig. 1k). Finally, that food cue-induced MCH neuron responses reflect learning is further supported by findings that elevated CS+ induced Ca2+ responses are observed on Day 5 of training, corresponding with statistical evidence of learning in behavioral measures, but not on Day 2 of training where behavioral analyses are subthreshold to support learning (Supplementary Fig. 6). These data indicate that physiological MCH neuron Ca2+ activity increases to and is associated with behavioral appetitive responsivity to discrete food-predictive cues. Fig. 1Physiological MCH neuron Ca2+ activity increases in response to discrete food-predictive cues and is associated with behavioral appetitive responsivity.a Schematic diagram depicting a viral approach to record physiological MCH neuron Ca2+ activity with fiber photometry in rats ($$n = 6$$). An adeno-associated virus containing an MCH promoter-driven GCaMP6s (AAV9.pMCH.GCaMP6s.hGH) is injected into the LHA and an optic fiber was implanted above the injection site. b Representative images of fluorescent reporter in MCH GCaMP6s colocalizing with MCH immunoreactive neurons (repeated and verified independently in $$n = 6$$ rats). c Schematic cartoon depicting fiber photometry recording of MCH neuron Ca2+ activity during the Pavlovian discrimination task ($$n = 8$$ CS+, $$n = 8$$ CS− cues). d–f Training data for Pavlovian discrimination task (data were analyzed using a one-way ANOVA with repeated measures and multiple comparisons, $$n = 6$$ rats): d Average number of licks for sucrose solution per CS+ trial, e Average latency to lick from sucrose solution per CS+ trial, and f Average number of CS+ trials with a response via licking sucrose solution. g–k Fiber photometry recording of MCH neuron Ca2+ activity during the test phase (data analyzed using Student’s two-tailed paired t-test, $$n = 6$$ rats, with g MCH neuron Ca2+ activity (z-score) time-locked to cue onset (CS+ in red and CS− in green; −15 to 25 s relative to the start of the 5 s cue [gray box]). h Heatmap of the MCH neuron Ca2+ activity (z-score) for each animal during each of the 5 s of CS presentation. i MCH neuron Ca2+ activity during cue period [gray box] (max activity during CS − activity during cue onset), $$P \leq 0.0042$$, j MCH neuron Ca2+ activity for 5s-pre-CS, 5 s CS period and 5s-post-CS, compared between CS, $$P \leq 0.001$$ and k Simple linear regression of MCH neuron Ca2+ activity (max activity during CS − activity during cue onset) during each CS+ trial relative to latency to lick from sucrose solution. The solid line is linear fit to data and dashed lines are $95\%$ confidence interval error bars, R2 = 0.5039, $$P \leq 1.08$$E-10. Data shown as mean ± SEM; Scale = 100 μm; $P \leq 0.01$, *$P \leq 0.0001.$ Source data* are provided as a Source Data file. Created with Biorender.com. ## MCH neuron Ca2+ activity increases in response to contextual-based food-predictive cues and is associated with food-seeking behavior To assess whether physiological MCH neuron Ca2+ activity is engaged by contextual cues associated with food, rats with the MCH promoter-driven GCaMP6s in the LHA were trained and tested in a palatable food-reinforced conditioned place preference (CPP) procedure in which one context is associated with access to highly palatable food and another is not (Fig. 2a, left)19–21. To evaluate MCH neuron activity based on learned food-associated contextual cues independent of food consumption, on the test day, the animals had access to both food-paired and unpaired contexts while food was not present, and MCH neuron Ca2+ activity was measured via a fiber photometry system (Fig. 2a, right). Results reveal that animals successfully learned to prefer the food-paired context as exhibited by an increased percentage of time spent on the paired context during testing and the preference shift from baseline (difference in percentage time spent on side pre- and post-training) relative to the unpaired context (Fig. 2b–d). During testing, MCH neuron Ca2+ activity was elevated when the animals were on the food-paired context compared to the unpaired context throughout the test session (Fig. 2e). However, the magnitude of this response was not correlated with contextual cue-based food-seeking behavior (assessed as preference shift from baseline for the paired context; Fig. 2f). MCH neuron Ca2+ activity was also increased upon the first 2-s entry period into the paired compared to the unpaired context (Fig. 2g). In this case, the magnitude of the MCH neuron Ca2+ response upon entry to the paired context was positively associated with contextual-based food-seeking behavior (Fig. 2h). These data indicate that physiological MCH neuron Ca2+ activity increases upon entry into a context associated with palatable food access, and that this response is linked to food-seeking memory based on contextual cues under test conditions where food was not available. Fig. 2Physiological MCH neuron Ca2+ activity increases in response to contextual-based food-predictive cues and is associated with food-seeking memory behavior.a Schematic diagram depicting fiber photometry recording of MCH neuron Ca2+ activity during conditioned place preference (CPP) in rats ($$n = 6$$). b Representative heat maps (single animal) depicting the time spent on the food-paired or unpaired side during habituation and test day. The less preferred side during habituation is assigned as the food-paired side during training. c, d CPP behavior data during the test phase (data analyzed using Student’s two-tailed paired t-test, $$n = 6$$ rats), with c percentage time spent on a side, $$P \leq 0.0027$$ and d shift from baseline (difference in percentage time spent on the side between pre- and post-training), $$P \leq 0.0016.$$ e–h Fiber photometry recording of MCH neuron Ca2+ activity during CPP test phase (data analyzed using Student’s two-tailed paired t-test, $$n = 6$$ rats) with e MCH neuron Ca2+ activity on a side, $$P \leq 0.0133$$, f simple linear regression of MCH neuron Ca2+ activity on food-paired side relative to shift from baseline R2 = 0.2711, $$P \leq 0.2895$$, g MCH neuron Ca2+ activity upon entry into a side, *$$P \leq 0.0001$$ and h simple linear regression of MCH neuron Ca2+ activity upon entry into food-paired side relative to shift from baseline, R2 = 0.7448, $$P \leq 0.0269.$$ For all linear regression analysis, the solid line is linear fit to data and dashed lines are $95\%$ confidence interval error bars. Data shown as mean ± SEM; $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001.$ Source data* are provided as a Source Data file. Created with Biorender.com. ## MCH neuron Ca2+ activity increases during eating and is highly predictive of eating bout duration and caloric intake during a meal To evaluate whether physiological MCH neuron Ca2+ activity is altered during consummatory processes, rats with the MCH promoter-driven GCaMP6s in the LHA were allowed to refeed on chow following an overnight fast and MCH neuron Ca2+ activity was recorded and time-stamped to active eating behavior and interbout intervals when animals were not eating (Fig. 3a, b). Results reveal that MCH neuron Ca2+ activity dynamically increased during eating in comparison to interbout periods and that this effect is greatest during the early part of the meal (Fig. 3c–e; representative trace from a single animal in 3c). In addition, MCH neuron Ca2+ activity was significantly elevated 5-min post voluntary meal termination as compared to the 5-min pre-food access period (Fig. 3e). Combined, these data indicate that physiological MCH neuron Ca2+ activity dynamically increases during active eating behavior and that MCH neuron Ca2+ activity tone is higher in the satiated vs. fasted state. Additional analyses show that average ∆MCH neuron Ca2+ activity within an eating bout and overall meal ∆AUC (AUC 5-min post-last bout − AUC 5-min pre-food access) were both strongly correlated with cumulative chow intake, indicating that physiological MCH neuron Ca2+ activity is highly predictive of caloric intake within a meal (Fig. 3f, g). The ∆MCH neuron Ca2+ activity within an eating bout negatively correlated with the temporal phase of the meal [bouts occurring in the first (early), second (mid), or third (late) tertile of the meal] such that MCH neuron Ca2+ activity was greatest during the first part of the meal, and waned as the rats approached meal termination (Fig. 3h). The ∆MCH neuron Ca2+ activity was also positively correlated with eating bout duration (Fig. 3i). These latter two correlations suggest that MCH neuron responses are tightly coupled to ingestive behaviors subserving appetition (e.g., rapid invigoration of consummatory actions and sustained periods of eating). Overall these data indicate that physiological MCH neuron Ca2+ activity is tightly responsive to early meal consummatory behaviors and predictive of total caloric intake within a meal. Fig. 3Physiological MCH neuron Ca2+ activity increases during active feeding and is highly predictive of cumulative caloric intake and eating bout duration within a meal.a Schematic diagram depicting fiber photometry recording of MCH neuron Ca2+ activity during refeeding after an overnight fast in rats ($$n = 7$$). b Schematic cartoon depicting different feeding phases of refeeding after the fast. c Representative trace of MCH neuron Ca2+ activity during refeeding after a fast time locked to food access (−10 to 30 min relative to the start food access [dotted vertical line]). Premeal [gray box], active eating [pink boxes; quantified as the difference in activity between start and end of eating bout], interbout intervals [white boxes; quantified as the difference in activity between after ending eating bout and before starting next eating bout] and voluntary satiation [dark pink box] are represented. d Heatmap representing each animal’s MCH neuron Ca2+ signal during active eating bouts and interbout intervals e–j Fiber photometry recording of MCH neuron Ca2+ activity during refeeding after a fast (data analyzed using Student’s two-tailed paired t-test, $$n = 7$$ rats) with e MCH neuron Ca2+ activity within eating bouts [pink box] and interbout intervals [white box], $$P \leq 0.003$$, f Area under curve (AUC) MCH neuron Ca2+ activity during 5 min pre-food access [gray box] and 5 min post-last bout [dark pink box], $$P \leq 0.0061$$, g Simple linear regression of MCH neuron Ca2+ activity within eating bouts relative to cumulative chow intake, R2 = 0.9299, *$$P \leq 0.0005$$, and h Simple linear regression of ∆AUC (AUC post-last-bout − AUC pre-food access) MCH neuron Ca2+ activity relative to cumulative chow intake, R2 = 0.8594, $$P \leq 0.0027$$, i Simple linear regression of MCH neuron Ca2+ activity within eating bouts relative to different time points in meal period (early: first tertile, mid: second tertile, late: last tertile), R2 = 0.2005, $$P \leq 0.0029$$ and j Simple linear regression of MCH neuron Ca2+ activity within eating bouts relative to bout duration, R2 = 0.2085, $$P \leq 0.0024.$$ For all linear regression analyses, the solid line is linear fit to data, and the dashed lines are $95\%$ confidence interval error bars. Data are shown as mean ± SEM; *$P \leq 0.001.$ Source data* are provided as a Source Data file. Created with Biorender.com. ## Activation of MCH neurons increases appetitive responses to discrete sucrose-predictive Pavlovian cues To confirm that the elevated MCH neuron Ca2+ activity in response to food-predictive cues is functionally relevant to cue-induced appetitive behavior, we utilized a virogenetic approach to chemogenetically activate MCH neurons. Rats were injected with an AAV2-MCH DREADDs-hM3D(Gq)-mCherry (MCH DREADDs) targeting the LHA and zona incerta (ZI) with excitatory DREADDs (designer receptors exclusively activated by designer drugs) under the control of an MCH promoter (Fig. 4a). Previous work from our lab has shown that this approach transfects approximately ~$80\%$ of all MCH neurons and is highly selective to MCH neurons22, which is consistent with the immunofluorescence histological chemistry results in the present study (Fig. 4b). Animals were trained in the Pavlovian-Instrumental-Transfer (PIT) task, which measures instrumental appetitive actions in response to discrete food-predictive cues under conditions where food is not available23,24. PIT consists of four phases: [1] Pavlovian discrimination conditioning, [2] instrumental conditioning, [3] instrumental extinction, and [4] PIT test day. On test day, animals received intraperitoneal (IP) injections of Deschloroclozapine (DCZ; DREADDs ligand) or vehicle ($1\%$ DMSO in $99\%$ saline) to selectively increase MCH neuron activity (Fig. 4c). Results reveal that animals successfully learned the Pavlovian discrimination as exhibited by increased licking, reduced latency to lick, and increased percentage of CS+ trials with an appetitive response across training (Fig. 4d–f). Further, DCZ-induced activation of MCH neurons during the Pavlovian discrimination phase led to an increased number of licks and reduced latency to lick per CS+ trial (Fig. 4g, h), thus revealing that the physiological MCH neuron Ca2+ responses to the CS+ during Pavlovian conditioning are functionally relevant to cue-induced appetitive behavior. Importantly, IP DCZ had no impact on appetitive responsivity in the Pavlovian discrimination task without the MCH DREADDs expressed in MCH neurons (control MCH promoter AAV; Supplementary Fig. 1). Throughout phase 2 (instrumental conditioning), animals showed increased licking, reduced latency to lick, and increased the number of lever presses for the sucrose solution across training (Fig. 4j, k). In phase 3 (instrumental extinction), animals increased their latency to lever press and overall reduced the number of lever presses per session, suggesting that they learned that lever pressing no longer provides reinforcement (Fig. 4j, k). In phase 4 (PIT test day), chemogenetic activation of MCH neurons reduced latency to lever press when the CS+ was presented, yet had no effect on latency following CS− presentation (Fig. 4l, m). Furthermore, MCH neuron activation increased the total number of lever presses following the CS+ presentation, but decreased the number of presses following the CS− presentation (Fig. 4n, o). Overall, these data show that chemogenetic activation of MCH neurons increases appetitive behavioral responses to food-predictive cues. Fig. 4MCH neuron activation increases appetitive responses to discrete food-predictive cues.a Schematic diagram depicting a viral approach to chemogenetically activate MCH neurons. An adeno-associated-virus containing excitatory MCH DREADDs-mCherry transgene (AAV2-rMCHp-hM3D(Gq)-mCherry) is injected into the LHA/ZI. b Representative images of fluorescent reporter in MCH DREADDs colocalizing with MCH immunoreactive neurons (repeated and verified independently in $$n = 8$$ rats) c Schematic cartoon depicting chemogenetic activation of MCH neurons during Pavlovian-Instrumental-Transfer (PIT). d–f Training data for Pavlovian discrimination task (data were analyzed using a one-way ANOVA with repeated measures and multiple comparisons, $$n = 8$$ rats): d Average number of licks for sucrose solution per CS+ trial, e Average latency to lick from sucrose solution per CS+ trial and f average number of CS+ trials with a response via licking sucrose solution. g, h Effects of chemogenetic activation of MCH neurons during the test phase of Pavlovian discrimination task in rats (data analyzed using Student’s two-tailed paired t-test, $$n = 8$$ rats) with g average number of licks for sucrose solution per CS+ trial, *$$P \leq 0.0022$$ and h average latency to lick from sucrose solution per CS+ trial, $$P \leq 0.0287.$$ i–k Training data for instrumental conditioning/extinction (data were analyzed using a one-way ANOVA with repeated measures and multiple comparisons, $$n = 12$$ rats): i Average number of licks for sucrose solution per lever press during conditioning, j Percentage number of lever presses represented for conditioning and extinction and k Latency to lever press for conditioning and extinction. l–o Effects of chemogenetic activation of MCH neurons during test phase for PIT in rats (data analyzed using Student’s two-tailed paired t-test, $$n = 12$$ rats) with l latency to lever press after the CS+, $$P \leq 0.0498$$, m and CS− cue, $$P \leq 0.6894$$, n Number of lever presses after the CS+, $$P \leq 0.0323$$ o and CS− cue, $$P \leq 0.0388.$$ Data shown as mean ± SEM; Scale = 100 μm; $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$, **$P \leq 0.0001.$ Source data* are provided as a Source Data file. Created with Biorender.com. ## MCH neuron activation increases food seeking based on contextual food cues To determine whether MCH neuron Ca2+ responses reflect a functional role for MCH neurons in driving food seeking based on contextual cues, animals with the MCH promoter-driven excitatory DREADDs underwent CPP training and testing (Fig. 5a). Results reveal that chemogenetic activation of MCH neurons during CPP testing increased the percentage of time spent on the food-paired context and increased the shift from baseline for the paired context (difference in time spent on side pre- and post-training), indicating that activation of MCH neurons increases appetitive responses to contextual-based food-predictive cues (Fig. 5b–d). There were no differences in total distance traveled on CPP test day (Fig. 5e), suggesting that MCH neuron activation did not influence locomotor activity. IP DCZ had no impact on appetitive responsivity in CPP in animals without the MCH DREADDs expressed in MCH neurons (control MCH promoter AAV; Supplementary Fig. 3).Fig. 5MCH neuron activation increases appetitive responses to contextual-based food-predictive cues and meal size after an overnight fast.a Schematic diagram of chemogenetic activation of MCH neurons during CPP in rats ($$n = 12$$). b Representative heat maps (single animal) depicting the time spent on the food-paired or unpaired side between vehicle and DCZ treatments. c–e Effects of chemogenetic activation of MCH neurons during CPP test phase (data analyzed using Student’s two-tailed paired t-test, $$n = 12$$ rats) with c percentage time spent on the food-paired side,$$P \leq 0.0028$$ and d shift from baseline (difference in percentage time spent on the side between pre- and post-training), $$P \leq 0.0028.$$ e Total distance traveled during CPP test day ($$P \leq 0.9829$$). f, g Effects of chemogenetic activation of MCH neurons during refeeding after a fast in rats (data analyzed using Students two-tailed test, $$n = 12$$ rats) with f schematic diagram of chemogenetic activation of MCH neurons during refeeding after a fast and g cumulative chow intake after refeeding after a fast, $$P \leq 0.0277.$$ Data shown as mean ± SEM; $P \leq 0.05$, *$P \leq 0.01.$ Source data* are provided as a Source Data file. Created with Biorender.com. ## MCH neuron activation increases meal size after a fast To examine whether MCH neuron Ca2+ responses during eating are functionally relevant to cumulative caloric consumption, animals with excitatory MCH promoter DREADDS were tested under the same eating conditions as the previous Ca2+ imaging experiment, where ad libitum chow was offered following a 24-h fast and animals consumed food until voluntary meal termination. Results reveal that chemogenetic activation of MCH neurons increased meal size under these conditions (Fig. 5f). In addition, chemogenetic activation of MCH neurons via IP DCZ increased home cage chow intake under ad libitum free-feeding conditions, which is consistent with our previous work using a different DREADDs ligand, Clozapine-N-oxide22. IP DCZ had no impact, however, on home cage intake in animals without the MCH DREADDS expressed in MCH neurons (control MCH promoter AAV; Supplementary Fig. 4). ## MCH neuron activation promotes a preference for noncaloric flavor paired with IG glucose In addition to driving appetition within a meal, positive interoceptive events (e.g., nutrition) condition lasting preferences for the associated flavor3,4. Thus, here we evaluate whether MCH neuron activation bolsters this type of flavor-nutrient learning. Animals that were surgically implanted with a gastric catheter and had excitatory MCH promoter DREADDs went through training to associate two noncaloric saccharin-sweetened flavors (CS+) with intragastric glucose infusion. One flavor was paired with DCZ injection-induced MCH neuron activation (CS+) and the other with vehicle injections (CS−). Preference for the two flavors, where animals were given access to both flavors without drug treatments or infusions, was measured both before and after training, (Fig. 6a). Results reveal increased consumption of the CS+ post-training relative to pre-training, whereas there were no pre- vs. post-training differences for the CS− (Fig. 6b, c). In addition, the difference in the number of licks pre- and post-training was increased for the CS+ relative to the CS− (Fig. 6d). These findings indicate that MCH neuron activation enhanced flavor-nutrient conditioning. IP DCZ did not, however, induce flavor-nutrient preference conditioning without the MCH DREADDs expressed in MCH neurons (control MCH promoter AAV; Supplementary Fig. 5).Fig. 6Activation of MCH neurons enhances preference for a noncaloric flavor paired with intragastric (IG) glucose.a Schematic diagram depicting the paradigm for chemogenetic activation of MCH neurons during flavor preference conditioning paired with IG glucose in rats ($$n = 12$$). b–d Effects of chemogenetic activation of MCH neurons paired with IG glucose (data analyzed using Student’s two-tailed paired t-test, $$n = 12$$ rats) with b number of licks for the vehicle-paired CS−, c and DCZ-paired CS+ flavors during pre- and post-tests, *$$P \leq 0.0028$$ and d difference in the number of licks for CS between pre- and post-tests, $$P \leq 0.0353.$$ Data shown as mean ± SEM; $P \leq 0.05$, $P \leq 0.01.$ Source data* are provided as a Source Data file. Created with Biorender.com. ## Discussion Once eating is initiated, hedonic orosensory gustatory processes are thought to be the main driving factors in promoting further food consumption, whereas post-oral gut-mediated processes are functionally linked with satiating processes leading to meal termination. However, work from Sclafani and colleagues using flavor-nutrient conditioning procedures elucidated an early meal positive feedback process known as appetition, which acts through post-oral processes to sustain ongoing consummatory behaviors4. The neural substrates underlying appetition, however, remain elusive. Here we show evidence that LHA MCH-producing neurons participate in the appetition process. Specifically, MCH neurons dynamically respond to both appetitive and consummatory signals. That these physiological neural responses are involved in mediating appetition is supported by our results revealing that MCH neuron activity increases during eating and is strongly predictive of cumulative caloric intake during a meal, eating bout duration, and, importantly, is augmented during eating bouts that occur earlier vs. later in the course of a meal. These latter findings suggest that MCH neurons function to prolong eating bout duration, particularly during the early prandial stage, and that these responses contribute to overall larger meal consumption. The involvement of MCH neurons in promoting appetition has been previously proposed25,26 and is also supported by a recent study revealing that optogenetic activation of MCH neurons during consumption increases food intake, and this effect was specific to time-locking MCH neuron activation to active eating periods27. Additionally, optogenetic activation of MCH neurons in mice reversed the preference for sucrose over noncaloric sucralose28, indicating that MCH neurons may provide a nutritive signal even in the absence of calories. Here we reasoned that if MCH neurons are involved in appetition, then their activity should be associated with enhanced flavor-nutrient preference conditioning, potentially by augmenting the reinforcing properties of post-oral nutrient processing. Indeed, MCH neuron activation promoted a preference for a noncaloric saccharin-sweetened flavored solution paired with intragastric (IG) glucose infusion. However, it is important to note that these results may be mediated by MCH neuron activity enhancing hedonic taste-mediated processes, post-oral nutritive processes (i.e., appetition), or both. Indeed, pharmacological MCH enhances positive orofacial responses to sucrose during a taste reactivity test via downstream opioid signaling29, which suggests that MCH is interacting with the opioid system to enhance the hedonic flavor processes. An interesting follow-up direction would therefore be to record physiological MCH neuron Ca2+ activity while animals have IG glucose and fructose infusions, as such an experiment bypasses orosensory processes, and post-oral glucose has been shown to promote flavor-nutrient preference conditioning whereas fructose does not30. It is also important to note that MCH receptor knockout mice have intact post-ingestive glucose-mediated flavor-nutrient conditioning31. While this might suggest that MCH is not involved with appetition processing, this could be the result of putative compensatory mechanisms based on genetic developmental MCH receptor defects. Further, MCH neurons express transcripts for multiple neuropeptides and neurotransmitter markers32, and thus it is possible that activation of MCH receptors is not sufficient to drive MCH neuron-mediated effects on appetition. In addition to associations with consummatory processes, our results reveal that physiological MCH neuron Ca2+ activity increases in response to discrete and contextual-based food-predictive cues, and that chemogenetic MCH neuron activation increases appetitive food-seeking behaviors. These results cannot be secondary to consumption as MCH Ca2+ responses during CPP testing were evaluated without food present during the test days, and further, MCH neuron-induced operant responses during PIT testing were not reinforced with food. It is also unlikely that MCH neuron-associated appetitive responses were secondary to effects on general locomotor activity, as chemogenetic activation of MCH neurons during PIT resulted in fewer lever presses following CS− presentations and did not influence locomotor activity during CPP testing, and moreover, previous work has shown that MCH neuron activation does not impact short-term locomotor activity27. Altogether, these findings suggest that MCH neuron-associated appetitive responses are not secondary to food consumption or altered physical activity, but, more likely, are neural and behavioral responses induced by food-predictive cues. It is unclear how MCH neurons are signaling to integrate appetitive and consummatory behaviors. A possible mechanism is that increased MCH neuron Ca2+ activity during appetitive behaviors enhances the mental imagery of food in response to food-associated cues, resulting in the animals showing stronger appetitive drive. In regard to consummatory behaviors, MCH signaling may be augmenting either the oral sensory component of eating (hedonic) and/or the nutritive content of the food. Regarding the former, lateral ventricle or direct ACBsh MCH administration enhances the positive hedonic orosensory response29. For the latter, future work is needed to determine if MCH signaling enhances different nutritive and absorptive aspects of eating to drive consummatory behaviors early in the course of a meal. More specifically, it would be interesting to evaluate the extent that nutrient modulation of MCH neuron activity is correlated with levels of peripheral metabolites. While recording of physiological MCH neuron Ca2+ activity and selectively activated MCH neurons during appetitive and consummatory behaviors provide complementary approaches that together inform about the endogenous role of these neurons, we note that there are two evident limitations of this study. First, this study does not evaluate the loss of MCH neuron function during these behaviors. Second, only male rats were used in this study, and given that the MCH system has previously been shown to have sex-specific effects on feeding17,33, such future evaluation of potential sex differences in MCH neuron mediation of food cues is warranted. Central MCH signaling promotes caloric consumption and overall positive energy balance in rodents34–37 and, therefore, has been increasingly targeted as a potential pharmaceutical target for obesity treatment. Our collective results extend knowledge of the MCH system by suggesting that MCH neurons increase food consumption by enhancing the reinforcing effects of both preprandial appetitive and early prandial appetition processes. Future work is needed to decipher the prandial ingestive stage(s) (e.g., orosensory flavor, post-oral gustatory, post-oral nutritive) through which MCH neurons enhance food consumption. ## Animals Based on studies that have shown that the endogenous estrous stage is a critical determinant of MCH effects on eating in females17,33, we have chosen to use male rats for this manuscript. For all experiments, male Sprague-Dawley rats (Envigo, Indianapolis, IN) weighing 300–400 g were used and individually housed in shoebox cages. Except where noted, rats were given ad libitum access to chow (Rodent Diet 5001, LabDiet, St. Louis, MO) and water. Rats were housed in a 12 h:12 h reverse light/dark cycle (lights off at 10:00 a.m.) or a 12 h:12 h light/dark cycle (lights off at 6:00 p.m.). All experiments were performed in accordance with NIH Guidelines for the Care and Use of Laboratory Animals, and all procedures were approved by the Institutional Animal Care and Use Committee of the University of Southern California. ## Stereotaxic optic fiber implantation Surgeries were adapted from procedures described previously38. Rats were first anesthetized and sedated via intramuscular injection of a ketamine (90 mg/kg), xylazine (2.8 mg/kg), and acepromazine (0.72 mg/kg) cocktail, prepped for surgery, and placed in stereotaxic apparatus. They were given subcutaneous injections of buprenorphine SR (0.65 mg/kg) during surgery as an analgesic. A fiber-optic cannula (Doric Lenses Inc, Quebec, Canada; flat 400-μm core, 0.48 numerical aperture (NA) was implanted in the LHA at the following coordinates:39 −2.9 mm anterior/posterior (AP), +1.6 mm medial/lateral (ML), −8.6 mm dorsal/ventral (DV) (0 reference point at bregma for ML, AP, and DV). The optic fiber was then affixed to the skull with jeweler’s screws, instant adhesive glue, and dental cement. All subjects were given 1 week to recover from surgery prior to experiments. ## Intragastric (IG) catheter implantation Gastric catheter surgeries were performed and adapted from procedures described previously40,41. Following an overnight fast, rats were anesthetized with a ketamine (90 mg/kg), xylazine (2.8 mg/kg), and acepromazine (0.72 mg/kg) cocktail and then laparotomized while under isoflurane (~$5\%$ induction rate; ~1.5–$3\%$ maintenance rate). A gastric catheter made of silastic tubing (inside diameter = 0.64 mm, outside diameter = 1.19 mm; Dow Corning, Midland, MI) was inserted through a puncture wound in the greater curvature of the forestomach. Importantly, the tip of the tubing was fitted with a small silastic collar (inside diameter = 0.76 mm, outside diameter = 1.65 mm; Dow Corning, Midland, MI) that served as an anchor to keep the tube in the stomach and secured via a purse-string suture. The catheter was then fixed against the stomach with a single stay suture and a small piece of square Marlex mesh (Davol, Cranston, RI). A purse-string suture and concentric serosal tunnel were used to close the wound in the stomach. The other end of the catheter was then passed through an incision through the abdominal muscle and was tunneled subcutaneously to an interscapular exit site, where it was attached using a single stay suture and a larger square piece of Marlex mesh. The tube was then connected to a Luer lock adapter, as part of a backpack harness worn by the rat around-the-clock (Quick Connect Harness, Strategic Applications, Lake Villa, IL). Rats were treated postoperatively with gentamicin (8 mg/kg sc) and ketoprofen (1 mg/kg sc). Rats were given increasing increments of chow (1–3 pellets) after surgery and then ad libitum access to chow. The gastric catheter was routinely flushed with 0.5 ml of isotonic saline beginning 48 h after surgery to maintain its patency. Harness bands were adjusted daily to accommodate changes in body mass. ## Immunohistochemistry Rats were first anesthetized and sedated via intramuscular injections of ketamine (90 mg/kg), xylazine (2.8 mg/kg), and acepromazine (0.72 mg/kg) cocktail and then perfused using $0.9\%$ sterile saline (pH 7.4), followed by $4\%$ paraformaldehyde (PFA) in 0.1 M borate buffer (pH 9.5; PFA). Brains were extracted and post-fixed with $12\%$ sucrose in PFA overnight, and then flash-frozen in methyl-butane cooled by dry ice. Brains were sectioned into 30-μm sections on a microtome cooled with dry ice and collected in an antifreeze solution, and stored in a −20 °C freezer. The following IHC fluorescence labeling procedures were adapted from previous work22,42. Rabbit anti-MCH (1:5000; PhoenixPharmaceuticals, Burlingame, CA, USA; Catalog #: H-070-47; Clonality: Polyclonal; Lot #: 46317) and rabbit anti-RFP (1:2000, Rockland Inc., Limerick, PA, USA; Catalog #:600-401-379; Clonality: Polyclonal) were the two antibodies used. Antibodies were prepared in 0.02 M potassium phosphate-buffered saline (KPBS) solution containing $0.2\%$ sodium azide and $2.0\%$ normal donkey serum and stored at 4 °C overnight. After many series of washing with 0.02 M KPBS, brain sections were incubated in a secondary antibody solution. The two secondary antibodies used, donkey anti-rabbit AF647 (Catalog #: 711-606-152; Lot #: 160172) and donkey anti-rabbit AF488 (Catalog #: 711-546-152; Lot #: 126798), had a 1:500 dilution and were stored overnight at 4 °C (Jackson Immunoresearch; West Grove, PA, USA). Sections were then mounted and coverslipped using $50\%$ glycerol in 0.02 M KPBS and clear nail polish was used to seal the coverslip onto the slide. Antibody tagging of MCH first involved washing the brain sections on a motorized rotating platform in the following order (overnight incubations on a motorized rotating platform at 4 °C): [1] 0.02 M KPBS (change KPBS every 5 min for 30 min), [2] $0.3\%$ Triton X-100 in KPBS (30 min), [3] KPBS (change KPBS every 5 min for 15 min), [4] $2\%$ donkey serum in KPBS (10 min), [5] $2\%$ normal donkey serum, $0.2\%$ sodium azide, and rabbit anti-MCH antibodies [1:2000; rabbit anti-MCH] in KPBS (~24 h)43 [6] KPBS (change KPBS every 10 min for 1 h), [7] $2\%$ normal donkey serum, $0.2\%$ sodium azide, and secondary antibodies (1:1000; donkey anti-rabbit AF647, Jackson Immunoresearch; overnight) in KPBS (~30 h), [8] KPBS (change KPBS every 2 min for 4 min). Sections were then mounted, air-dried, and coverslipped with $50\%$ glycerol in a 0.02 M KPBS mounting medium. Photomicrographs were acquired using a Nikon 80i (Nikon DS-QI1,1280 × 1024 resolution, 1.45 megapixel) microscope under epifluorescence. ## Intracranial virus injection Rats were first anesthetized and sedated via intramuscular injections of ketamine (90 mg/kg), xylazine (2.8 mg/kg), and acepromazine (0.72 mg/kg) cocktail, prepped for surgery, and placed in stereotaxic apparatus. Stereotaxic injections of viruses were delivered using a micro-infusion pump (Harvard Apparatus, Cambridge, MA, USA) connected to a 33-gauge microsyringe injector attached to a PE20 catheter and Hamilton syringe. The flow rate was calibrated and set to 5 μl/min. Injectors were left in place for 2 min post-injection. Following viral injections, animals were either implanted with an optic fiber or surgically closed with sutures or skin glue. Experiments occurred either 21 days after virus injection to allow for virus transduction and expression (MCH DREADDS) or when animals showed viable fluorescence signals (GCAMP6s with MCH promoter). Successful virally mediated transduction was confirmed postmortem in all animals via IHC staining using immunofluorescence-based antibody amplification to enhance the fluorescence transgene signal, followed by manual quantification under epifluorescence illumination using a Nikon 80i (Nikon DS-QI1,1280 × 1024 resolution, 1.45 megapixel). The MCH DREADDS virus is now commercially available from Vector Biolabs (Malvern, PA, USA) upon request, and MCH GCAMP6s is available from the authors upon reasonable request. For the recording of MCH neuron Ca2+ activity, 1 μl of an AAV9.pMCH.GCaMP6s.hGH (MCH promoter-driven GCaMP6s) was unilaterally injected at the following coordinates:39 −2.9 mm AP, +1.6 mm ML, −8.8 mm DC (0 reference point for AP, ML, and DV at bregma). An optic fiber was implanted (−2.9 mm AP, +1.6 mm ML, −8.6 mm DV) above the injection site as described before. For chemogenetic activation of MCH neurons via DREADDS, an AAV2-rMCHp-hM3D(Gq)-mCherry (MCH DREADDs) was bilaterally injected at the following coordinates:39 injection [1] −2.6 mm AP, ±1.8 mm ML, −8.0 mm DV; [2] −2.6 mm AP, ±1.0 mm ML, −8.0 mm DV; [3] −2.9 mm AP, ±1.1 mm ML, −8.8 mm DV; [4] −2.9 mm AP, ±1.6 mm ML, −8.8 mm DV (0 reference point for AP, ML, and DV at bregma) to target MCH neurons in the LHA and ZI. The injection volume was 200 nl/site. ## Characterization of MCH GCAMPS and DREADDs expression Immunofluorescence colocalization of MCH and fluorescence reporter in MCH GCaMPs virus was conducted in sections from Swanson Atlas levels 28–3239, based on IHC staining for MCH (as described above). All animals showed selective immunofluorescence colocalization, such that the fluorescence reporter was exclusive to neurons with the MCH tag. All animals were included in experimental analyses. For MCH DREADDs experiments, staining for RFP to amplify the mCherry signal was conducted as described above. Counts were performed in sections from Swanson Brain Atlas level 28–3239, which encompasses all MCH-containing neurons in the LHA and ZI. For MCH DREADD experiments, animals were excluded from all experimental analyses if fewer than $\frac{2}{3}$ of the total number of MCH neurons were transduced with RFP (based on IHC staining for MCH). All animals met these criteria and were included for experimental analyses. For MCH GCAMPs quantification, GCAMPs6 expression was quantified in one out of five series of brain tissue sections from the perfused brains cut at 30 µm on a freezing microtome based on counts for the fluorescence reporter GFP. Counts were performed in sections from Swanson Brain Atlas level 27–3239, which encompasses all MCH-containing neurons. Sections were stained for MCH+ neurons and cell counts were performed in four GCAMP6s virus-injected animals. Researchers who performed the counting were kept consistent between cohorts and blind to experimental assignments. ## Drug preparation For chemogenetic activation of MCH neurons, 1 ml/kg DCZ (100 µg/kg) or vehicle ($1\%$ DMSO in $99\%$ saline) is administered intraperitoneally through a syringe. Doses and concentration of DCZ and vehicle were based on previous work44. Prior to drug administrations, animals were handled and prepared for injections. Reagent-grade glucose (8 and $12\%$) was prepared fresh with dH2O as needed. Saccharin-sweetened Kool-Aid solutions were made by mixing either $0.05\%$ unsweetened cherry or grape Kool-Aid powder with $0.01\%$ sodium saccharin solution. Each solution was made fresh for each training or test day. ## In vivo fiber photometry In vivo fiber photometry was performed according to previous work45. Photometry signal was acquired using the Neurophotometrics fiber photometry system (Neurophotometrics, San Diego, CA) at a sampling frequency of 40 Hz and administering alternating wavelengths of 470 nm (Ca2+ dependent) or 415 nm (Ca2+ independent). The fluorescence light is transmitted through an optical patch cord (Doric Lenses) and converges onto the implanted optic fiber, which in turn sends back neural fluorescence through the same optic fiber/patch cord and is focused onto a photoreceiver. All behaviors (cues, entries, eating bouts, etc.) were time-stamped using the data acquisition software (Bonsai). The resulting signals were then corrected by subtracting the Ca2+ independent signal from the Ca2+ dependent signal to calculate fluorescence fluctuations due to Ca2+ (corrected signal) and not due to baseline neural activity or motion artifacts and fitted to a biexponential curve. The corrected fluorescence signal was then normalized within each rat by calculating the ΔF/F using the average fluorescence signal for the entire recording and converting the signal to z-scores. The normalized signal was then aligned to behavioral events of interest (cues, entries, eating bouts, etc.), and data extraction was done using the original MatLab code. ## Pavlovian Discrimination Task Testing and training were adapted from previous procedures46. For this experiment, animals were housed in a reverse light/dark cycle (lights off at 10:00 a.m.). Animals were provided with overnight access to sucrose solution ($11\%$ weight/volume) in the home cage and were required to consume 50 ml of the solution to move onward with training. Animals were then chronically restricted to 15 g of chow daily (Rodent Diet 5001, LabDiet, St. Louis, MO, USA) and given chow after each session. Animals were placed in identical operant chambers (Med Associates), which contained an accessible lickometer filled with sucrose solution when activated. Each session consisted of eight conditioned-stimulus-positive (CS+) and eight conditioned-stimulus-negative (CS−) audio cues, where immediately after the CS+, the sucrose solution became accessible for 20 s, and after the CS−, no sucrose reward was provided. The order of the cues and the time between each cue was random, with the average time between cues being around 110 s. The cues consisted of either a clicking noise or tone-like frequency and were counterbalanced across animals, for which was the CS+ or CS−. Each session was 45 min long and animals had seven total training sessions. For the effects of physiological MCH neuron Ca2+ activity during the Pavlovian Discrimination Task, using in vivo fiber photometry, a patch cord was connected to the implanted optical fiber, and LEDs were delivered, alternating between 470 nm (Ca2+ dependent) and 415 nm (Ca2+ independent) during the second, fifth, and seventh CS training sessions to assess the speed of cue response development. For the effects of chemogenetic activation of MCH neurons on performance in the Pavlovian Discrimination Task, animals were randomized to receive either IP DCZ or vehicle using a counterbalanced (based on performance during training), within-subjects design with 72 h between treatments. IP injections of DCZ or vehicle occurred 5 min before the behavioral test. ## Conditioned place preference (CPP) CPP training and testing procedures were conducted as described previously19–21. For this experiment, animals were housed in a reverse light/dark cycle (lights off at 10:00 a.m.). Briefly, the CPP apparatus consisted of two conjoined plexiglass compartments with a guillotine door separating the two sides (Med Associates, Fairfax, VT, USA). The two sides (contexts) were distinguished by wall color and floor texture. Rats were given a 15-min habituation session with the guillotine door open and video recording software (Anymaze) to measure time spent in each context. For each rat, the least preferred context during habituation was designated as the food-paired context for subsequent training. Training occurred in the early phase of the dark cycle and home cage chow was pulled 1 h prior to each training session. CPP training consisted of 12 (20 min, 5 days/week) sessions: six sessions isolated in the food-paired context and six sessions isolated in the non-food-paired context. Context training order was randomized. During the food-paired sessions, 5 g of $45\%$ kcal high fat/sucrose diet (D12451, Research Diets, New Brunswick, NJ, USA) was placed on the chamber floor, and no food was presented during non-food-paired sessions. All rats consumed the entire 5 g of food during each food-paired session. CPP testing occurred 2 days after the last training session and 1 h prior to the test session, home cage chow was removed. During testing, the guillotine door remained open and rats were allowed to freely explore both contexts for 15 min. No food was present during testing. Time spent in each context during the test was calculated from video recording software (Anymaze). For the effects of physiological MCH neuron Ca2+ activity during the CPP, using in vivo fiber photometry, a patch cord was connected to the implanted optical fiber, and LEDs were delivered, alternating between 470 nm (Ca2+ dependent) and 415 nm (Ca2+ independent) during the task. In addition, a customized door (Viterbi/Dornsife USC machine shop) separating both contexts was made to ensure the optic fiber/patch cord connection could safely maneuver between contexts without interfering with the integrity of the experiment. For the effects of chemogenetic activation of MCH neurons on performance in CPP, animals were randomized to receive either IP DCZ or vehicle using a counterbalanced (based on performance during training), within-subjects design with 72 h between treatments. IP injections of DCZ or vehicle occurred 5 min before the behavioral test. ## Refeeding after an overnight fast Animals were housed in reverse light/dark cycle (lights off at 10:00 a.m.). Prior to test day, animals were overnight fasted but had access to water. On test days, animals were exposed to a neutral context, where they had 15 min access to the context with no food, followed by a 30-min chow (Laboratory Rodent Diet 5001, St. Louis, MO, USA) access period and then a 10-min post-food access period, where the food is removed. For the effects of physiological MCH neuron Ca2+ activity during the refeeding after an overnight fast, using in vivo fiber photometry, a patch cord was connected to the implanted optical fiber, and LEDs were delivered, alternating between 470 nm (Ca2+ dependent) and 415 nm (Ca2+ independent) during the task. For the effects of chemogenetic activation of MCH neurons on performance on refeeding after an overnight fast, animals were randomized to receive either IP DCZ or vehicle using a counterbalanced (based on performance during training), within-subjects design with 72 h between treatments. IP injections of DCZ or vehicle occurred 5 min before the behavioral test. ## Pavlovian-instrumental transfer (PIT) The paradigm was adapted from previous procedures23,24. For this experiment, animals were housed in a reverse light/dark cycle (lights off at 10:00 a.m.). This behavioral procedure consists of four phases: Pavlovian discrimination conditioning, instrumental conditioning, instrumental extinction, and PIT test day. Each stage occurred in identical operant chambers (Med Associates), where each chamber was equipped with a retractable lickometer, retractable levers on the opposite sides of the chamber, and speakers to emit audio cues. The procedure for Pavlovian training is the same as the Pavlovian Discrimination Task described above. For instrumental conditioning, animals were placed in identical operant chambers (Med Associates) containing a retractable lickometer filled with sucrose solution and a retractable lever on the opposite side of the chamber. When the lever is pressed, the lickometer becomes accessible for 20 s. Animals received five sessions where the lever remained retracted unless pressed. After eight presses of the lever or 20 min, the session was over. For the next 12 sessions, the lever became accessible at random intertrial intervals (average 110 s between trials), consisting of eight opportunities to press the lever. If the animals did not press the lever within 30 s of it being retracted, the lever detracted and the next trial began. Each of these sessions was 45 min. For instrumental extinction, animals were placed in identical operant chambers (Med Associates) and received similar instrumental conditioning, except that when the lever became accessible at random intertrial intervals (average 110 s), pressing the lever did not result in access to a lickometer filled with sucrose solution. Each session consisted of eight opportunities to press the lever, lasting 45 min, and they received 12 such sessions. For PIT test day, the effect of the Pavlovian stimuli on instrumental behavior (lever pressing) was evaluated during the transfer test, where animals were placed in identical operant chambers (Med Associates) for 45 min. The test session consisted of eight CS+ and eight CS− audio cues, the same ones used during the Pavlovian discrimination training, followed immediately by the lever being accessible for each audio cue. Pressing the lever did not result in a sucrose reward, regardless of which audio cue was played prior. For the effects of chemogenetic activation of MCH neurons on performance during PIT, animals were randomized to receive either IP DCZ or vehicle using a counterbalanced (based on performance during training), within-subjects design with 72 h between treatments. IP injections of DCZ or vehicle occurred 5 min before the behavioral test. ## Flavor preference conditioning Animals were housed in a light/dark cycle (lights off at 6:00 p.m.). After rats successfully recovered from surgery (IG catheter implantation and intracranial injection of excitatory MCH DREADDS), they were food-restricted to maintain $85\%$ of their current body weight (fed rations daily after lights out). To acclimate the rats to the lickometers, they were overnight water-deprived and given a 1-h session to drink $8\%$ glucose from the lickometer, while receiving IG $8\%$ glucose infusions. To acclimate the rats to the saccharin-sweetened solution, rats were given two 30-min sessions on separate days with unflavored $0.1\%$ saccharin-sweetened solution with no IG infusions. Following this acclimation period, rats were trained to lick two different saccharin-sweetened Kool-Aid flavors, being exposed to each flavor for six sessions. Animals are only exposed to one flavor per training session and each flavor is paired with IG glucose. Prior to each training session, using a counterbalanced, within-subjects design, animals were injected with IP DCZ (to activate MCH neurons) or vehicle, dependent on the flavor they were exposed to that day. The flavor paired with DCZ is the conditioned-stimulus positive (CS+), while the vehicle-paired flavor is the conditioned-stimulus negative (CS−). Animals were water-deprived overnight and given 1-h access to lickometer each session, and IG glucose was infused each time they licked. In addition, for each training session, animals were capped at 1499 licks, to ensure that the same amount of glucose was infused for the CS− and CS+. Animals were given two test days (pre- and post-training) where they had access to both flavors at once. During test days, there were no treatments, infusion of nutrients, or overnight water deprivation. The lesser preferred flavor during the first test day (pre-test) was paired with DCZ during the training sessions. The number of licks per flavor was recorded for each training and test session and used as a measure to indicate preference. ## Food intake studies Animals were housed in a reverse light/dark cycle (lights off at 10:00 a.m.). Home cage chow (Rodent Diet 5001, LabDiet, St. Louis, MO, USA) was removed 2 h prior to the light onset (10am). For chemogenetic activation of MCH neurons, animals were counterbalanced, using a within-subjects design to receive IP DCZ or vehicle 5 min prior to light onset, and pre-weighed amounts of the test chow diet were deposited in the home cage immediately after the light onset. The same procedure for evaluating the effects of IP DCZ without MCH DREADDs. Spill papers were placed underneath the cages to collect food crumbs. Food spillage was weighed and added to the difference between the initial hopper weight and the hopper weight at each measurement time point. A total of 72 h was allotted between treatments ## Statistical analyses Statistical analyses were performed using GraphPad Prism 9.0 software (GraphPad Software Inc., San Diego, CA, USA) and Microsoft Excel V16.66.1. Data are expressed as mean ± SEM. Statistical details can be found in the figure legends and n’s refer to the number of animals for each condition. Differences were considered statistically significant at $P \leq 0.05.$ A two-tailed paired Student’s t-test was used to compare MCH neuron Ca2+ activity for the CS− and CS+ during the Pavlovian Discrimination Task, for the unpaired and paired contexts during CPP and for within eating bout and interbout intervals and 5 min pre-food and post-meal periods during refeeding. Simple linear regression analysis assessed a correlation between MCH Ca2+ activity and latency to lick for sucrose solution in the Pavlovian Discrimination Task, entrance into the paired side of the CPP, and chow intake, meal period and bout duration during refeeding. Two-tailed paired Student’s t-tests were also used to compare vehicle to DCZ conditions in the Pavlovian Discrimination Task, PIT, and CPP, as well as to compare CS− versus CS+ in the flavor preference conditioning. A two-tailed unpaired Student’s t-test was used to compare vehicle to DCZ conditions during refeeding. Two-way ANOVA with repeated measures and multiple comparisons were used to compare home cage chow intake under vehicle vs. DCZ conditions. Outliers were identified using the Grubb’s test for outliers post-hoc at signficane level of alpha = 0.05. For all experiments, assumptions of normality, homogeneity of variance (HOV), and independence were met where required. One-way ANOVAs with repeated measures were used to assess differences in training data for the Pavlovian Discrimination Task and PIT. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37344-9. ## Source data Source Data ## Peer review information Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. ## References 1. Grill HJ, Hayes MR. Hindbrain neurons as an essential hub in the neuroanatomically distributed control of energy balance. Cell Metab. (2012.0) 16 296-309. DOI: 10.1016/j.cmet.2012.06.015 2. Alcantara IC, Tapia APM, Aponte Y, Krashes MJ. Acts of appetite: neural circuits governing the appetitive, consummatory, and terminating phases of feeding. Nat. Metab. (2022.0) 4 836-847. DOI: 10.1038/s42255-022-00611-y 3. Watts AG, Kanoski SE, Sanchez-Watts G, Langhans W. The physiological control of eating: signals, neurons, and networks. Physiol. Rev. (2022.0) 102 689-813. DOI: 10.1152/physrev.00028.2020 4. Sclafani A. Gut-brain nutrient signaling. Appetition vs. satiation. Appetite (2013.0) 71 454-458. DOI: 10.1016/j.appet.2012.05.024 5. Mezei C, Palmer FB. Hydrolytic enzyme activities in the developing chick central and peripheral nervous systems. J. Neurochem. (1974.0) 23 1087-1089. DOI: 10.1111/j.1471-4159.1974.tb10764.x 6. Palmiter RD. Is dopamine a physiologically relevant mediator of feeding behavior. Trends Neurosci. (2007.0) 30 375-381. DOI: 10.1016/j.tins.2007.06.004 7. Betley JN. Neurons for hunger and thirst transmit a negative-valence teaching signal. Nature (2015.0) 521 180-185. DOI: 10.1038/nature14416 8. Denis RG. Palatability can drive feeding independent of AgRP neurons. Cell Metab. (2015.0) 22 646-657. DOI: 10.1016/j.cmet.2015.07.011 9. Chen Y, Lin YC, Kuo TW, Knight ZA. Sensory detection of food rapidly modulates arcuate feeding circuits. Cell (2015.0) 160 829-841. DOI: 10.1016/j.cell.2015.01.033 10. Barson JR. Orexin/hypocretin and dysregulated eating: promotion of foraging behavior. Brain Res. (2020.0) 1731 145915. DOI: 10.1016/j.brainres.2018.08.018 11. Burdakov D. Electrical signaling in central orexin/hypocretin circuits: tuning arousal and appetite to fit the environment. Neuroscientist (2004.0) 10 286-291. DOI: 10.1177/1073858404263597 12. Cason AM. Role of orexin/hypocretin in reward-seeking and addiction: implications for obesity. Physiol. Behav. (2010.0) 100 419-428. DOI: 10.1016/j.physbeh.2010.03.009 13. Gonzalez JA. Inhibitory interplay between orexin neurons and eating. Curr. Biol. (2016.0) 26 2486-2491. DOI: 10.1016/j.cub.2016.07.013 14. Mileykovskiy BY, Kiyashchenko LI, Siegel JM. Behavioral correlates of activity in identified hypocretin/orexin neurons. Neuron (2005.0) 46 787-798. DOI: 10.1016/j.neuron.2005.04.035 15. Burdakov D, Gerasimenko O, Verkhratsky A. Physiological changes in glucose differentially modulate the excitability of hypothalamic melanin-concentrating hormone and orexin neurons in situ. J. Neurosci. (2005.0) 25 2429-2433. DOI: 10.1523/JNEUROSCI.4925-04.2005 16. Kong D. Glucose stimulation of hypothalamic MCH neurons involves K(ATP) channels, is modulated by UCP. Cell Metab. (2010.0) 12 545-552. DOI: 10.1016/j.cmet.2010.09.013 17. Santollo J, Eckel LA. The orexigenic effect of melanin-concentrating hormone (MCH) is influenced by sex and stage of the estrous cycle. Physiol. Behav. (2008.0) 93 842-850. DOI: 10.1016/j.physbeh.2007.11.050 18. Sherwood A, Holland PC, Adamantidis A, Johnson AW. Deletion of Melanin Concentrating Hormone Receptor-1 disrupts overeating in the presence of food cues. Physiol. Behav. (2015.0) 152 402-407. DOI: 10.1016/j.physbeh.2015.05.037 19. Hsu TM, Hahn JD, Konanur VR, Lam A, Kanoski SE. Hippocampal GLP-1 receptors influence food intake, meal size, and effort-based responding for food through volume transmission. Neuropsychopharmacology (2015.0) 40 327-337. DOI: 10.1038/npp.2014.175 20. Kanoski SE, Alhadeff AL, Fortin SM, Gilbert JR, Grill HJ. Leptin signaling in the medial nucleus tractus solitarius reduces food seeking and willingness to work for food. Neuropsychopharmacology (2014.0) 39 605-613. DOI: 10.1038/npp.2013.235 21. Kanoski SE. Hippocampal leptin signaling reduces food intake and modulates food-related memory processing. Neuropsychopharmacology (2011.0) 36 1859-1870. DOI: 10.1038/npp.2011.70 22. Noble EE. Control of feeding behavior by cerebral ventricular volume transmission of melanin-concentrating hormone. Cell Metab. (2018.0) 28 55-68.e57. DOI: 10.1016/j.cmet.2018.05.001 23. Cartoni E, Balleine B, Baldassarre G. Appetitive Pavlovian-instrumental transfer: a review. Neurosci. Biobehav. Rev. (2016.0) 71 829-848. DOI: 10.1016/j.neubiorev.2016.09.020 24. Holmes NM, Marchand AR, Coutureau E. Pavlovian to instrumental transfer: a neurobehavioural perspective. Neurosci. Biobehav. Rev. (2010.0) 34 1277-1295. DOI: 10.1016/j.neubiorev.2010.03.007 25. Barson JR, Morganstern I, Leibowitz SF. Complementary roles of orexin and melanin-concentrating hormone in feeding behavior. Int. J. Endocrinol. (2013.0) 2013 983964. DOI: 10.1155/2013/983964 26. Lord MN, Subramanian K, Kanoski SE, Noble EE. Melanin-concentrating hormone and food intake control: sites of action, peptide interactions, and appetition. Peptides (2021.0) 137 170476. DOI: 10.1016/j.peptides.2020.170476 27. Dilsiz P. MCH neuron activity is sufficient for reward and reinforces feeding. Neuroendocrinology (2020.0) 110 258-270. DOI: 10.1159/000501234 28. Domingos AI. Hypothalamic melanin concentrating hormone neurons communicate the nutrient value of sugar. Elife (2013.0) 2 e01462. DOI: 10.7554/eLife.01462 29. Lopez CA. Involvement of the opioid system in the orexigenic and hedonic effects of melanin-concentrating hormone. Am. J. Physiol. Regul. Integr. Comp. Physiol. (2011.0) 301 R1105-R1111. DOI: 10.1152/ajpregu.00076.2011 30. Sclafani A, Cardieri C, Tucker K, Blusk D, Ackroff K. Intragastric glucose but not fructose conditions robust flavor preferences in rats. Am. J. Physiol. (1993.0) 265 R320-R325. PMID: 8368385 31. Sclafani A, Adamantidis A, Ackroff K. MCH receptor deletion does not impair glucose-conditioned flavor preferences in mice. Physiol. Behav. (2016.0) 163 239-244. DOI: 10.1016/j.physbeh.2016.05.024 32. Mickelsen LE. Neurochemical heterogeneity among lateral hypothalamic hypocretin/orexin and melanin-concentrating hormone neurons identified through single-cell gene expression analysis. eNeuro (2017.0) 4 ENEURO.0013-17. DOI: 10.1523/ENEURO.0013-17.2017 33. 33.Terrill, S. J. et al. Nucleus accumbens melanin-concentrating hormone neurons promotes feeding in a sex-specific manner. Neuropharmacology178, 108270 (2020). 34. Chambers J. Melanin-concentrating hormone is the cognate ligand for the orphan G-protein-coupled receptor SLC-1. Nature (1999.0) 400 261-265. DOI: 10.1038/22313 35. Kowalski TJ. Effects of a selective melanin-concentrating hormone 1 receptor antagonist on food intake and energy homeostasis in diet-induced obese mice. Eur. J. Pharm. (2006.0) 535 182-191. DOI: 10.1016/j.ejphar.2006.01.062 36. Lembo PM. The receptor for the orexigenic peptide melanin-concentrating hormone is a G-protein-coupled receptor. Nat. Cell Biol. (1999.0) 1 267-271. DOI: 10.1038/12978 37. Shearman LP. Chronic MCH-1 receptor modulation alters appetite, body weight and adiposity in rats. Eur. J. Pharm. (2003.0) 475 37-47. DOI: 10.1016/S0014-2999(03)02146-0 38. Anastasiou CA, Karfopoulou E, Yannakoulia M. Weight regaining: from statistics and behaviors to physiology and metabolism. Metabolism (2015.0) 64 1395-1407. DOI: 10.1016/j.metabol.2015.08.006 39. Swanson LW. Brain maps 4.0—structure of the rat brain: an open access atlas with global nervous system nomenclature ontology and flatmaps. J. Comp. Neurol. (2018.0) 526 935-943. DOI: 10.1002/cne.24381 40. Schier LA, Spector AC. Post-oral sugar detection rapidly and chemospecifically modulates taste-guided behavior. Am. J. Physiol. Regul. Integr. Comp. Physiol. (2016.0) 311 R742-R755. DOI: 10.1152/ajpregu.00155.2016 41. Sclafani A, Glendinning JI. Sugar and fat conditioned flavor preferences in C57BL/6J and 129 mice: oral and postoral interactions. Am. J. Physiol. Regul. Integr. Comp. Physiol. (2005.0) 289 R712-R720. DOI: 10.1152/ajpregu.00176.2005 42. Noble EE. Hypothalamus-hippocampus circuitry regulates impulsivity via melanin-concentrating hormone. Nat. Commun. (2019.0) 10 4923. DOI: 10.1038/s41467-019-12895-y 43. Bittencourt JC. The melanin-concentrating hormone system of the rat brain: an immuno- and hybridization histochemical characterization. J. Comp. Neurol. (1992.0) 319 218-245. DOI: 10.1002/cne.903190204 44. Nagai Y. Deschloroclozapine, a potent and selective chemogenetic actuator enables rapid neuronal and behavioral modulations in mice and monkeys. Nat. Neurosci. (2020.0) 23 1157-1167. DOI: 10.1038/s41593-020-0661-3 45. 45.Martianova, E., Aronson, S. & Proulx, C. D. Multi-fiber photometry to record neural activity in freely-moving animals. J. Vis. Exp.10.3791/60278 (2019). 46. Liu CM. Central oxytocin signaling inhibits food reward-motivated behaviors and VTA dopamine responses to food-predictive cues in male rats. Horm. Behav. (2020.0) 126 104855. DOI: 10.1016/j.yhbeh.2020.104855
--- title: Activation of the CA2-ventral CA1 pathway reverses social discrimination dysfunction in Shank3B knockout mice authors: - Elise C. Cope - Samantha H. Wang - Renée C. Waters - Isha R. Gore - Betsy Vasquez - Blake J. Laham - Elizabeth Gould journal: Nature Communications year: 2023 pmcid: PMC10060401 doi: 10.1038/s41467-023-37248-8 license: CC BY 4.0 --- # Activation of the CA2-ventral CA1 pathway reverses social discrimination dysfunction in Shank3B knockout mice ## Abstract Mutation or deletion of the SHANK3 gene, which encodes a synaptic scaffolding protein, is linked to autism spectrum disorder and Phelan-McDermid syndrome, conditions associated with social memory impairments. Shank3B knockout mice also exhibit social memory deficits. The CA2 region of the hippocampus integrates numerous inputs and sends a major output to the ventral CA1 (vCA1). Despite finding few differences in excitatory afferents to the CA2 in Shank3B knockout mice, we found that activation of CA2 neurons as well as the CA2-vCA1 pathway restored social recognition function to wildtype levels. vCA1 neuronal oscillations have been linked to social memory, but we observed no differences in these measures between wildtype and Shank3B knockout mice. However, activation of the CA2 enhanced vCA1 theta power in Shank3B knockout mice, concurrent with behavioral improvements. These findings suggest that stimulating adult circuitry in a mouse model with neurodevelopmental impairments can invoke latent social memory function. The SHANK3 gene is linked to autism spectrum disorder and Phelan McDermid syndrome, which have been associated with social memory deficits. Here, authors show activation of the hippocampal CA2-ventral CA1 circuit in adult Shank3B knockout mice restores social memory. ## Introduction Social memory is an important ability that gives rise to adaptive social interactions. Social memory dysfunction has been reported in several neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and major depressive disorder1–5. Deficits in the ability to recognize individuals and to make associations between individuals and specific traits, emotions, events, and settings can significantly impair the formation and maintenance of social relationships6,7. These findings raise the importance of identifying the mechanisms of social memory as potential targets for therapeutic intervention. Evidence from human studies indicates hippocampal involvement in social memory8,9, including reports of hippocampal abnormalities in individuals with conditions associated with social memory dysfunction10–13. A growing number of studies aim to investigate mechanisms of social memory using experimental animals, with many reporting that the hippocampus plays an important role in this function14–16. Circuitry supporting social memory has been identified in the rodent hippocampus, with studies describing the CA2 as a social memory “hub” that integrates signals from a variety of afferents17. Afferents to the CA2 are both extrahippocampal, from the supramammillary nucleus and paraventricular nucleus of the hypothalamus, the cholinergic basal forebrain, and the entorhinal cortex, and intrahippocampal, from the dentate gyrus and CA3 region18–22. Studies have shown that afferents from the lateral entorhinal cortex, hypothalamus, and cholinergic basal forebrain to the CA2 play important roles in social novelty recognition and social discrimination19–23. Several types of network oscillatory patterns in the hippocampus have been associated with social behavior. Recent studies have shown that social stimuli result in changes to sharp wave ripples (SWRs), high frequency oscillatory events known to be associated with nonsocial memory consolidation and retrieval24,25, in both the CA226 and ventral CA127,28 (vCA1). The CA2 communicates with the vCA129,30, which serves as the main hippocampal output carrying social memory information31,32. Social stimuli also increase CA1 oscillations in the gamma and theta ranges30,33, and several animal models of social dysfunction exhibit abnormal hippocampal gamma and theta power34–36. Taken together, these findings suggest that atypical SWRs, as well as gamma and theta rhythms, in the CA2-vCA1 pathway may contribute to social memory impairment. ASD represents a range of conditions with defining core symptoms (social impairments and restrictive interests/repetitive behaviors) of differing severity, as well as a wide range of potential comorbidities (intellectual disability, anxiety disorders, epilepsy)37. Perhaps not surprisingly, given the wide range of symptom presentation, the etiology of ASD appears to be multifactorial38,39. It is generally accepted that ASD arises through a complex interaction between genes and the environment38. Genome-wide association studies have identified over a hundred genes linked to ASD, with a significant number playing a role in neuronal communication40. Due to this multifactorial etiology with clustered risk genes, understanding the mechanisms of social memory and associated dysfunctional neuronal circuitry may reveal translatable discoveries beyond genetic approaches. Among the genes shown to be linked to ASD is SHANK3, whose mutation or deletion also causes Phelan-McDermid syndrome, a condition that often presents with similar symptoms to ASD41,42. The SHANK3 gene encodes the SH3 and multiple ankyrin repeat domains 3 protein, a synaptic scaffolding protein that functions to anchor receptors and ion channels to the postsynaptic site43. Shank3 protein also plays a role in excitatory synapse and dendritic spine formation44. While SHANK3 mutations are only strongly linked to a subset of ASD cases, the broader way that altered synaptic transmission and neuronal communication produce ASD symptomology may also provide translatable insight. Several Shank3 models have been created in nonhuman primates and rodents using transgenic and CRISPR technologies. These models, which involve mutations or knockouts of parts or all of the Shank3 gene, exhibit behavioral phenotypes analogous to the core symptoms of ASD, including social communication deficits and excessive repetitive behaviors45–49. Shank3 knockout (KO) mice have also been shown to exhibit social recognition deficits28,50, which may be analogous to deficits in recognizing familiar faces and familiar voices reported in some people with ASD and Phelan-McDermid syndrome1,4,51–55. Taken together, these findings suggest that studies using Shank3 animal models have the potential to provide translationally relevant information about circuits impacted in ASD and, importantly, to suggest points of intervention for improving function. In the majority of cases, ASD symptoms persist throughout life56. Studies have shown that adults with ASD experience lower life satisfaction57–60 and are more likely to be unemployed and socially isolated than peers without ASD, with social impairments and socially disruptive behaviors being significant predictors57,58,61–63. These findings emphasize the need to identify interventions for adults with ASD. Along these lines, recent studies have shown that deep brain stimulation of the striatum diminishes excessive repetitive behavior and improves social communication in adults with ASD64,65. These promising results raise the possibility that activating brain regions involved in social memory might restore this ability as well. To investigate this in a mouse model of social dysfunction, we first confirmed that Shank3B KO mice have deficient social discrimination abilities, in that they respond similarly to novel and familiar mice, despite the fact that most excitatory afferents to the CA2 appear to be similar to their wildtype (WT) littermates, including those that have been directly linked to social novelty recognition and social discrimination. We next used chemogenetics to activate excitatory neurons in the CA2 region, and also more directly the CA2-vCA1 pathway, both of which restored social discrimination abilities in adult Shank3B KO mice. We found that Shank3B KO mice have typical oscillatory rhythms in the SWR, gamma, and theta ranges in the vCA1, yet DREADD-induced improvement of social discrimination was accompanied by a boost in theta power. ## Shank3B KO mice have impaired social discrimination, but intact object location memory We first confirmed previous reports that Shank3B KO mice have low social investigation times as well as impaired social discrimination memory28,50. We utilized a three-trial direct social interaction test, with each trial separated by 24 h, in which mice were exposed to a novel mouse (Novel 1) in trial 1, re-exposed to the same mouse (Familiar) in trial 2, and exposed to a second novel mouse (Novel 2) in trial 3 (Fig. 1a). Adult WT mice typically prefer novelty and thus investigate novel mice more than familiar mice. To assess whether there was a sex difference related to genotype, we carried out a three-way ANOVA (sex × genotype × trial) and found no significant interaction between sex and genotype (F [1, 21] = 0.1275; $$p \leq 0.7246$$), so male and female data were collapsed for all subsequent analyses. We found the WT mice to have significantly higher interaction times for the novel 1 and novel 2 than for the familiar mouse (Fig. 1b, Table S1). Shank3B KO mice did not have significant differences in interaction times across trials (Fig. 1b, Table S1) and had difference scores of significantly lower magnitudes that were approaching zero (Fig. 1c, Table S1), indicating lower preference or discrimination of novelty. Shank3B KO mice had low novel interaction times as compared to WT, perhaps indicating reduced novelty detection (Fig. 1b, Table S1). It should be noted that Shank3B KO mice also exhibit a hypomobility phenotype48 that may contribute to the reduced novel investigation effect. We then examined whether this difference in investigation time generalized to other hippocampal-dependent cognitive tests, particularly without social components. Using the object location memory test (Fig. 1d), which requires the hippocampus66, we found that Shank3B KO mice were similarly capable as WT mice at distinguishing between objects in a novel versus familiar location (Fig. 1e, Table S1). There was also no difference between genotypes in their total investigation times of the objects (Fig. 1f, Table S1) or time to reach the criterion for familiarization (Fig. 1g, Table S1). Taken together, these data show that Shank3B KO mice have impairments in social discrimination abilities, yet have other intact hippocampal processes, including non-social novelty detection. Fig. 1Shank3B KO mice have impaired social, but not object location, discrimination and mostly typical CA2 afferents.a Schematic of social discrimination test. b WT mice have lower interaction times for familiar (F) than first novel mice (N1) ($$p \leq 0.0001$$) and higher interaction times for second novel mice (N2) than F ($$p \leq 0.0112$$). KO mice do not show different interaction times for F compared to N1 or N2 ($$n = 13$$ for WT and $$n = 12$$ for KO). c Compared to WT, KO mice showed significantly different difference scores (N1 minus F: $$p \leq 0.0001$$; F minus N2: $$p \leq 0.000491$$; $$n = 13$$ for WT and $$n = 12$$ for KO). d Schematic of object location test. e WT and KO mice showed no difference in discrimination ratios in the object location test, f no difference in time interacting with the objects, and g no difference in time to reach criterion (E–G, $$n = 9$$ for each genotype). h Top: Confocal image from the CA2 immunolabeled with PCP4 (green) and abGC afferent marker 3R-Tau (magenta). Bottom: Compared to WT, KO mice have lower intensity of 3R-Tau+ afferents in the CA2 ($$p \leq 0.002$$) ($$n = 10$$ for WT, $$n = 9$$ for KO). i Top: Confocal image of the CA2 immunolabeled with RGS14 (green) and SUM afferent marker VGLUT2 (magenta). Bottom: WT and KO mice have similar intensity of VGLUT2 + afferents in the CA2 ($$n = 9$$ for each genotype). j Top: Confocal image from the CA2 immunolabeled with RGS14 (green) and cholinergic afferent marker VAChT (magenta). Bottom: WT and KO mice have similar intensity of VAChT+ afferents in the CA2 ($$n = 9$$ for each genotype). Scale bars = 50 µm. See Table S1 for complete statistics. Data presented as mean ± SEM. * $p \leq 0.05$, two-sided unpaired t tests (c,e,f,g,h,j), Mann Whitney U test (i), or two-way repeated measures ANOVA with Bonferroni tests (b). DR = discrimination ratio; N = novel; F = familiar; KO = Shank3B knockout; WT = wildtype; 3R-Tau=3-repeat tau isoform; PCP4 = Purkinje cell protein 4; VGLUT2 = vesicular glutamate transporter 2; VAChT=vesicular acetylcholine transporter; RGS14 = regulator of G protein signaling. Images in a, d were created using BioRender.com. Source data are provided as a Source Data file. ## Shank3B KO mice exhibit similar avoidance behavior to WT mice To consider the possibility that reduced social interaction of the novel stimulus mouse in Shank3B KO mice is the result of high levels of general avoidance behavior, we examined behavior on the elevated plus maze (EPM) (Fig. S1A, Table S2). Indicators of avoidance behavior are lower time and fewer entries onto open arms. Shank3B KO mice showed no differences on these measures (Fig. S1B, C, Table S2), which suggests that Shank3B KO mice do not display more avoidance behavior on the EPM relative to WT mice, which is consistent with some48 but not all67 previous findings. Shank3B KO mice did show significant differences with lower percentage of time spent in the center (Fig. S1B, Table S2) and fewer entries into the closed arms, as compared to WT mice (Fig. S1C, Table S2). These differences might be better explained by activity or decision-making differences rather than avoidance behavior. ## Most afferents to CA2 appear similar in WT and Shank3B KO mice Shank3 is a synaptic scaffolding protein that participates in the formation of excitatory synapses throughout the brain, and its disruption has been shown to reduce synapses and overall connectivity68,69. To investigate whether Shank3B KO mice exhibit differences in CA2 inputs, we examined afferent populations that have been linked to social discrimination or novelty recognition. First, we examined adult-born granule cells (abGCs), which are known to project to the CA270 and have been linked to social memory71. We analyzed the intensity of 3R-Tau, a microtubule-associated protein that labels abGC cell bodies and their mossy fibers72, in the CA2. Compared to WT mice, we found that Shank3B KO mice have lower 3R-Tau+ mossy fiber intensity in the CA2 (Figs. 1h, S3B, S4A, Table S1). Since fewer afferents from abGCs to the CA2 might result from lower numbers of abGCs in the dentate gyrus of Shank3B KO mice, we then examined the number of 3R-Tau+ cell bodies in the dorsal dentate gyrus and found a slight but significantly lower density in the suprapyramidal, but not infrapyramidal, blade of Shank3B KO mice (Fig. S5, Table S2). Next, we considered excitatory projections from extrahippocampal structures to the CA2 region, including glutamatergic inputs from the hypothalamic supramammillary nucleus (SUM) and cholinergic inputs from the medial septum, both of which have been implicated in either novelty preference or social discrimination21,23,73. We examined the intensity of vesicular glutamate transporter 2 (VGLUT2), which is known to label SUM afferents to the CA274, and vesicular acetylcholine transporter (VAChT), which is known to label cholinergic afferents75, and unexpectedly found no differences between WT and Shank3B KO mice in the CA2; the intensity of both markers was similar between groups (Fig. 1i, j, Fig. S4, Table S1). Within the hippocampus, Shank3 protein is present in excitatory synapses that co-label with the vesicular glutamate transporter 1 (VGLUT1)74. VGLUT1 labels afferents from CA3 and entorhinal cortex pyramidal cells74,76,77 as well as from mature dentate gyrus granule cells74. The lateral entorhinal inputs to the CA2 have been causally linked to social discrimination22 although those from the CA3 and dentate gyrus have not. It remains possible that the developmental disruption of these projections, either alone or in some combination, in this transgenic model may impact the ability of the CA2 to participate in these functions. Since the CA3, entorhinal cortex, and dentate gyrus each directly project to the CA278,79, we analyzed intensity of VGLUT1 labeling in the CA2 and found no difference between WT and Shank3B KO mice (Figs. S2A, S3A, Table S2). Because Shank3 protein is especially concentrated in mossy fibers74, we examined intensity of zinc transporter 3 (ZnT3), which labels mossy fibers and their synaptic vesicles80, in the CA2 and also found no difference between WT and Shank3B KO mice (Fig. S2B, Table S2). Our results suggest that social discrimination dysfunction in Shank3B KO mice is not the result of obvious abnormalities in several developmentally generated inputs to this brain region, although an adult-generated afferent population is diminished. The extent to which diminished abGC numbers and afferents arise directly from Shank3B KO and contribute to social memory dysfunction remains unknown. ## Increasing CA2 activity improves social discrimination in Shank3B KO mice Using chemogenetics, we tested whether activating excitatory neurons in CA2 would improve social discrimination in Shank3B KO mice. Excitatory DREADD virus (AAV-CaMKIIa-hM3D(Gq)-mCherry) or control virus (AAV-CaMKIIa-GFP) was bilaterally injected into the CA2 of WT and Shank3B KO mice (Fig. 2a, b). Histological analyses of virus infection revealed robust expression that was largely confined to the CA2 region (Fig. S6) with very little labeling in the adjacent CA1 or CA3 regions. We analyzed mCherry expression in PCP4 + CA2 cells of a subset of mice and found that ~$91\%$ of mCherry+ cells were PCP4+, and ~$50\%$ of PCP4 + cells were mCherry+. This suggests that the majority of infected cells were pyramidal neurons and about half of the CA2 pyramidal cell population was infected. Fig. 2Chemogenetic activation of excitatory neurons in CA2 improved social discrimination in Shank3B KO mice.a Timeline for experiment. b Confocal image from the CA2 immunolabeled with PCP4 (green) and mCherry (magenta), showing localized virus infection in CA2 neurons. Scale bar = 50 µm. c Following VEH injections, virus-infected WT mice had greater interaction times for novel mice (N) than familiar mice (F) (WT + control virus, $$p \leq 0.0001$$; WT + DREADD virus, $$p \leq 0.0001$$), while virus-infected KO mice showed no difference in interaction times of N compared to F. d Following VEH injections, WT virus groups had higher difference scores (N minus F) than KO virus groups (WT + control virus vs KO + control virus: $$p \leq 0.0110$$; WT + DREADD virus vs KO + DREADD virus: $$p \leq 0.0011$$). ( c, d, $$n = 14$$ for WT + control virus, WT + DREADD virus, $$n = 15$$ for KO + control virus, and $$n = 16$$ for KO + DREADD virus. e Following CNO injections, virus-infected WT mice had greater interaction times for N than F (WT + control virus, $$p \leq 0.0002$$; WT + DREADD virus, $$p \leq 0.0001$$). Control virus-infected KO mice had no interaction time differences between N and F, while DREADD virus-infected KO mice had greater interaction times for N than F ($$p \leq 0.0001$$). f Following CNO injections, WT virus groups had higher difference scores than KO + control virus-infected mice (WT + control virus vs KO + control virus: $$p \leq 0.0809$$; WT + DREADD virus vs KO + control virus: $$p \leq 0.0042$$) but not KO + DREADD virus-infected mice, WT + DREADD virus or KO + DREADD virus mice. KO + control virus-infected mice had lower difference scores than KO + DREADD virus-injected mice (KO + control virus vs KO + DREADD virus: $$p \leq 0.0002$$) (e, f, $$n = 14$$ for WT + control virus, WT + DREADD virus, $$n = 15$$ for KO + control virus, and $$n = 16$$ for KO + DREADD virus). See Table S1 for complete statistics. Data are presented as mean ± SEM. * $p \leq 0.05$; three-way repeated measures ANOVA with Bonferroni tests (c, e); two-way ANOVA with Bonferroni tests (d, f). N = novel; F = familiar; KO = Shank3B knockout; WT = wildtype; VEH = vehicle; CNO = clozapine-N-oxide; DREADD = designer receptors exclusively activated by designer drugs; GFP = green fluorescent protein; PCP4 = purkinje cell protein 4. Images in a were created using BioRender.com. Source data are provided as a Source Data file. Two weeks after CA2 virus infection, mice were tested for social discrimination abilities using a direct social interaction test. Each virus-injected mouse was tested with both vehicle (VEH) and CNO in separate behavior testing. For each behavioral testing bout, VEH or CNO was injected 30 min prior to each novel and familiar mouse exposures. Testing with VEH and CNO were counterbalanced to control for any order effects with a minimum of two days between each test to allow for wash out of CNO (Fig. 2a). Consistent with our previous results from unoperated mice (Fig. 1), in the VEH trials, WT mice showed typical social discrimination, whereas Shank3B KO mice showed impaired social discrimination. WT mice virus groups (control and DREADD virus) injected with VEH showed lower interaction times for familiar mice than novel mice; whereas, KO mice virus groups (control and DREADD virus) injected with VEH showed no such change across trials (Fig. 2c, Table S1). In addition, the difference in time investigating novel and familiar mice was relatively unchanged for Shank3B KO mice virus groups and was significantly different than WT mice virus groups (Fig. 2d, Table S1). As expected, WT mice injected with either control or DREADD virus decreased their interaction times for familiar mice when administered CNO with no difference noted between control and DREADD virus groups (Fig. 2e, Table S1). CNO had no effects on social discrimination in Shank3B KO mice injected with control virus, while Shank3B KO mice injected with DREADD virus showed significantly greater investigation times with novel mice compared to familiar mice. The CNO-induced increase in difference between investigation times with novel mice compared to familiar mice was observed in Shank3B KO mice regardless of whether they were tested with CNO or VEH first, strongly suggesting that the results are not dependent on order effects (Fig. S7, Table S2). Shank3B KO + DREADD virus mice treated with CNO had social discrimination abilities that did not differ from WT mice (Fig. 2e, Table S1). Moreover, following CNO injections, the difference in time investigating novel and familiar mice was not significantly different between the WT + control and DREADD virus groups and the Shank3B KO + DREADD virus group, while the Shank3B KO mice control virus group was significantly lower (Fig. 2f, Table S1). In addition, the difference score (difference in time investigating novel and familiar mice) changed within subject in response to drug treatment. We found that KO + DREADD mice have higher difference scores following CNO treatment compared to VEH treatment, while all other groups showed no change in difference scores between VEH and CNO treatment (Fig. S8; Table S2). ## Increasing activity in the CA2 to vCA1 pathway improves social discrimination in Shank3B KO mice Silencing the connection between the CA2 and ventral CA1 has been shown to produce social memory deficits in controls29, including reducing the difference in interaction times with novel and familiar mice. We next explored whether activating CA2 afferents to vCA1 would improve social discrimination in Shank3B KO mice. WT and Shank3B KO mice received bilateral injections of excitatory DREADD virus (AAV-CaMKIIa-hM3D(Gq)-mCherry) or control virus (AAV-CaMKIIa-GFP) in the CA2 and were then implanted with bilateral cannula into vCA1 to allow for local and selective excitation of CA2 projecting neurons (Fig. 3a, b). Two weeks after surgery, mice were tested for social discrimination abilities using a direct social interaction test. Each virus-injected mouse was tested with both VEH and CNO in separate behavior testing. For each behavioral testing bout, VEH or CNO was infused into the vCA1 bilaterally 30 min prior to each novel and familiar mouse exposures. Testing with VEH and CNO was counterbalanced to control for any order effects with a minimum of two days between each test to allow for wash out of CNO (Fig. 3a).Fig. 3Chemogenetic activation of CA2 excitatory neurons projecting to the vCA1 improved social discrimination in Shank3B KO mice.a Timeline for experiment. b Schematic of the cannula placement sites in vCA1. c Following VEH infusions into the vCA1, virus-infected KO mice showed no significant difference in interaction times between novel mice (N) and familiar mice (F), while control virus- or DREADD virus-infected WT mice showed a decrease in interaction times between N and F, although the difference in WT + DREADD virus was not statistically significant (WT + control virus, $$p \leq 0.0270$$; WT + DREADD virus, $$p \leq 0.3529$$). There was no effect of virus group (see Table S1). d Following VEH infusions into the vCA1, both WT virus groups had greater difference scores (N minus F) than KO virus groups, although these comparisons were not statistically significant (c, d, $$n = 14$$ for WT + control virus, $$n = 13$$ for WT + DREADD virus, $$n = 17$$ for KO + control virus, $$n = 17$$ for KO + DREADD virus). See Table S1 for complete statistics. e Following CNO infusions into vCA1, WT virus groups and DREADD virus-injected KO mice had lower interaction times of F than N (KO + DREADD virus, $$p \leq 0.0005$$; WT + control virus, $$p \leq 0.0024$$; WT + DREADD virus, $$p \leq 0.0079$$), while control virus-injected KO mice did not. f Following CNO infusions into vCA1, DREADD virus-injected KO mice had higher difference scores (N minus F) than control virus-injected KO mice ($$p \leq 0.0302$$) and no difference from WT mice injected with control virus or DREADD virus mice (e, f, $$n = 14$$ for WT + control, $$n = 13$$ for WT + DREADD, $$n = 17$$ for KO + control virus, $$n = 17$$ for KO + DREADD virus). See Table S1 for complete statistics. * $p \leq 0.05$; three-way repeated measures ANOVA with Bonferroni comparisons (c, e); two-way ANOVA with Bonferroni comparisons (d, f). Data are presented as mean ± SEM. N = novel; F = familiar; KO = Shank3B knockout; WT = wildtype; VEH = vehicle; CNO = clozapine-N-oxide; DREADD = designer receptors exclusively activated by designer drugs; GFP = green fluorescent protein. Images in a, b were created using BioRender.com. Source data are provided as a Source Data file. Following VEH infusions into vCA1, compared to their interaction times with novel mice, WT + control virus mice showed typical social discrimination as evidenced by their lower interaction for familiar mice, whereas Shank3B KO + control virus mice showed no difference in their interaction times for familiar mice (Fig. 3c, Table S1). It should be noted that although the majority of WT + DREADD virus mice treated with VEH in this study showed typical social discrimination, the overall effect was not statistically significant in this group since a few mice showed a reversed social preference (Fig. 3c,d). The reasons for this remain unknown although it should be noted that we and others have occasionally observed control mice that investigate familiar stimulus mice more than novel mice in other studies22,81–83. This does not reflect an inability of social recognition, but instead, a preference for social familiarity, rather than social novelty. We included these mice in the analysis although they are biological outliers because they reflect the range of typical mouse behavior. As a result, in this study, while the difference in time investigating novel and familiar was on average lower for Shank3B KO than WT mice, the difference between WT and Shank3B KO mice did not reach significance using a stringent post hoc comparison that controls for multiple comparisons (Fig. 3d, Table S1). CNO infusions into the vCA1 had no effect on WT + control virus or WT + DREADD virus mice as demonstrated by their typical social memory (Fig. 3d, Table S1). As expected, Shank3B KO + control virus mice infused with CNO into the vCA1 had impaired social discrimination abilities (Fig. 3d, Table S1). However, Shank3B KO + DREADD virus mice injected with CNO into the vCA1 displayed typical social discrimination (Fig. 3d). After vCA1 CNO infusion, Shank3B KO + control virus mice showed no change in time spent investigating novel and familiar mice with difference times that were significantly lower than WT + control virus and Shank3B KO + DREADD virus mice (Fig. 3e, Table S1). WT mice virus groups (control and DREADD virus) had lower interaction times for familiar mice than novel mice when infused with CNO, showing no difference between control and DREADD virus groups. Shank3B KO + control virus group retained their lack of difference across trials even when infused with CNO, while the Shank3B KO mice DREADD virus group infused with CNO showed significantly greater investigation times with novel mice compared to familiar mice. The Shank3B KO + DREADD virus group treated with CNO had social discrimination abilities that did not differ from WT virus groups treated with CNO (control and DREADD virus) (Fig. 3e, Table S1). Moreover, for the CNO infusions, the difference in time investigating novel and familiar mice was not significantly different between WT virus groups and the Shank3B KO + DREADD virus group, while the Shank3B KO + control virus group was significantly lower (Fig. 3f, Table S1). ## vCA1 theta power does not differ between WT and Shank3B KO mice, but increases in Shank3B KO mice after chemogenetic activation of the CA2 vCA1 theta (4–12 Hz) power is associated with higher avoidance behavior84–86 and because compared to WT mice, Shank3B KO mice spend considerably less time investigating novel mice, we explored whether Shank3B KO mice had higher vCA1 theta power during exposure to novel mice. WT and Shank3B KO mice received bilateral injections of excitatory DREADD virus (AAV-CaMKIIa-hM3D(Gq)-mCherry) or control virus (AAV-CaMKIIa-GFP) in the CA2 and were then implanted with recording electrodes into vCA1 (Fig. 4a). We found no difference in vCA1 theta power between WT and Shank3B KO mice (Fig. 4c, Table S1), nor did we find a correlation between vCA1 theta power and the time spent investigating novel mice in WT or Shank3B KO mice (Pearson’s rank correlation coefficient test, WT: r = −0.2965, $$p \leq 0.4384$$; KO: $r = 0.1291$, $$p \leq 0.7405$$). Taken together with our EPM results, these findings suggest that compared to WT mice, low investigation of novel social stimuli observed in Shank3B KO mice is not directly related to general avoidance, or to avoidance-associated vCA1 theta power. Fig. 4Chemogenetic activation of the CA2 increased vCA1 theta power in Shank3B KO mice, but did not affect gamma power nor SWRs.a Timeline for experiment. b vCA1 power spectra during exposure to a novel mouse (0-25 Hz) of VEH- and CNO-treated KO + DREADD virus-injected mice. Shaded area represents SEM. c vCA1 theta power (4–12 Hz) was not different between WT and KO virus groups treated with VEH (see Table S1). KO + DREADD virus mice treated with CNO had higher theta power than KO + DREADD virus-injected mice treated with VEH ($$p \leq 0.0009$$), while no other groups had significant differences (n = number of mice with 3 electrodes per mouse; $$n = 10$$ WT + control virus, KO + DREADD virus; $$n = 9$$ WT + DREADD virus, KO + control virus). d vCA1 power spectra during exposure to a novel mouse (25–100 Hz) of VEH- and CNO-treated KO + DREADD virus-injected mice. Shaded area represents SEM. e vCA1 low gamma power (30–55 Hz) was not different between WT and KO virus groups injected with VEH or CNO during novel mouse exposure (see Table S1; n = number of mice with three electrodes per mouse; $$n = 10$$ WT + control virus, KO + DREADD virus; $$n = 9$$ WT + DREADD virus, KO + control virus). f Representative example of SWRs (Top: Raw LFP trace. Bottom: Filtered SWR trace) recorded in the vCA1. g There were no differences in normalized SWR amplitude after exposure to a familiar mouse between WT and KO control virus- or DREADD virus-infected groups treated with VEH, nor was this changed by CNO-induced activation of the CA2 ($p \leq 0.05$, see Table S1) (n = number of mice with three electrodes per mouse; $$n = 10$$ WT + control virus, KO + DREADD virus; $$n = 9$$ WT + DREADD virus, KO + control virus). See Table S1 for complete statistics. Data are presented as mean ± SEM for error bars and bands. * $p \leq 0.05$; linear mixed effects ANOVA (c, e, g) with Tukey comparisons (c, g). Arb.units=arbitrary units; KO = Shank3B knockout; WT = wildtype; VEH = vehicle; CNO = clozapine-N-oxide; DREADD = designer receptors exclusively activated by designer drugs; GFP = green fluorescent protein; SWR = sharp wave ripples; Hz=hertz. Images in a were created using BioRender.com. Source data are provided as a Source Data file. Because social stimuli have been shown to increase hippocampal theta power33, and because chemogenetic activation of CA2 and the CA2-vCA1 pathway increased investigation of novel, but not familiar, mice (Figs. 2e, 3e), we examined whether Shank3B KO CA2 DREADD-infected mice treated with CNO showed changes in vCA1 theta power during novel mouse exposure. We found that chemogenetic activation of the CA2 increased vCA1 theta power in the Shank3B KO + DREADD virus group, but not in the WT groups (Fig. 4b, c, Table S1). Because dorsal hippocampal theta power has been linked to indices of locomotion87–90, we examined whether mice with increased vCA1 theta power (i.e., Shank3B KO + DREADD virus + CNO) showed increased locomotion. We found a genotype difference in mobility (Shank3B KO mice moved less than WT mice), but no difference with CNO treatment in either control virus or DREADD virus mice of either genotype (Fig. S9, Table S2). Taken together, these findings suggest that increases in vCA1 theta power in Shank3B KO + DREADD virus mice are not related to increased locomotion. ## vCA1 low gamma power does not differ between WT and Shank3B KO mice, and remains unchanged after chemogenetic activation of the CA2 Previous studies have shown that low gamma (30–55 Hz) is altered by chemogenetic activation of CA2 neurons in the CA2/proximal dCA1, especially during periods of mobility91. Thus, we investigated whether DREADD activation of CA2 neurons altered low gamma power in WT and Shank3B KO mice during exposure to novel mice and found no differences across any groups when the data were analyzed overall or specifically during periods of mobility (Figs. 4d, e, S10, Tables S1, S2). The difference between our findings and the previous report may be due to recording from different areas of the CA1 (dorsal in ref. 19 versus ventral in the present study) or differences in the proportion of CA2 pyramidal cells infected in each study (the percentage of PCP4 + cells that expressed DREADD virus in ref. 19 was greater than $90\%$ versus $51\%$ in the present study). ## SWRs are similar between WT and Shank3B KO mice, and not changed with chemogenetic activation of CA2 SWRs have been linked to consolidation and retrieval of spatial memories25. The vCA1 has been shown to have increased SWR events in the presence of a social stimulus27 and CA2-generated SWRs, which are known to influence vCA1 SWRs, are necessary for social memory26. Given that our behavioral findings suggest diminished function in the CA2-vCA1 pathway in Shank3B KO mice, we explored the possibility of alterations in SWRs in the vCA1 that may be rescued by chemogenetic activation of the CA2. We detected SWR events in the vCA1 during baseline or exposure to familiar mice (Fig. 4f). Since SWRs typically only occur during sleep and behavioral immobility25, we first measured SWR frequency, with SWR number as a function of immobility time. However, Shank3B KO mice spend significantly more time immobile than WT mice (WT: 100.8 ± 10.18 s, KO: 184.8 ± 10.29 s, unpaired t test, t17 = 5.785, $$p \leq 0.0001$$), and had reduced SWR frequency (WT: 0.6113 ± 0.05623 event frequency, KO: 0.3492 ± 0.01797 event frequency, LME, t17 = 2.770, $$p \leq 0.0131$$), an effect that clearly confounds the SWR frequency analysis. To eliminate this confound, we focused our analysis on SWR number. We first examined SWR measures normalized to baseline during the first minute of familiar conspecific exposure, a time when retrieval of social memories is most likely. We analyzed normalized SWR number, amplitude, duration and time interval between SWRs in this data set. We found no significant differences between Shank3B KO and WT mice with VEH or CNO treatment on any of these measures (Figs. 4g, S11, Tables S1, S2). We then examined SWR number, amplitude, duration, and time interval between SWRs during each minute of baseline (3 min) and exposure to a familiar mouse (5 min). Again, we found no difference in SWR number, amplitude, duration, or time interval between SWRs in WT compared to Shank3B KO mice during any minute of testing, overall, or following chemogenetic activation of the CA2 (Figs. S12–S15, Table S2). It should be noted that we detected significant differences between WT DREADD VEH vs CNO (SWR numbers at minute 2 of the familiar conspecific trial: CNO was greater than VEH, Table S2), but these effects were not seen during any other time of the trial, nor did they correspond to any of our behavioral findings. These findings suggest that although WT and Shank3B KO mice differ in their ability to discriminate between novel and familiar mice, there were no differences in SWR measures in response to a familiar mouse, nor is there any notable change in response to CA2 activation. Because we applied a standard cut-off for amplitude in our definition of a SWR, it remains possible that some excluded “SWR-like” events were lower in Shank3B KOs than WTs. However, this seems unlikely given the similarities in so many SWR measures between genotypes, which suggest that social discrimination differences are not related to our SWR measures. ## Discussion Our findings suggest that Shank3B KO mice have impaired social discrimination, with a major deficit in time spent investigating novel social stimuli, compared to WT littermates. We found no difference between groups in a simple object location task or in avoidance behavior, suggesting that Shank3B KO mice have some unimpaired non-social memory and that differences in novel social investigation seem unlikely due to general avoidance. Because Shank3 is a synaptic scaffolding protein, we next investigated whether multiple developmentally-generated afferents to the CA2 are different in Shank3B KO mice compared to WT mice. Unexpectedly, we found that markers of CA2 afferents known to be involved in social discrimination and social novelty detection, including VGLUT2 and VAChT, were not different from WT in Shank3B KO mice. In contrast, we found that 3R-Tau, a marker of abGC afferents, was diminished in the CA2 of Shank3B KO compared to WT mice. Because the CA2 has been associated with social memory, we next attempted to improve social discrimination by chemogenetically activating excitatory neurons in the CA2 of Shank3B KO mice. We found that activating the CA2 with DREADD virus + CNO restored social discrimination in Shank3B KO mice to WT levels. We next investigated whether specific activation of projections from the CA2 to the vCA1 would also produce this effect and found restored social discrimination in Shank3B KO mice to WT levels. These findings suggest that Shank3B KO social discrimination ability can be improved by activating the CA2-vCA1 pathway in adulthood. We next tested whether vCA1 SWRs, which have been linked to memory consolidation and retrieval, differ between WT and Shank3B KO mice and found similarities in amplitude, number, duration, and time interval, as well as no change in vCA1 SWRs during chemogenetic manipulation. Although we also detected no differences in vCA1 theta or low gamma power in Shank3B KO compared to WT mice for control virus or vehicle treatments, theta power was increased in Shank3B KO mice infected with DREADD virus and treated with CNO beyond WT levels. Increased vCA1 theta power was independent of mobility and was observed during exposure to the novel mouse, which is the experience that elicited behavioral change after CA2 activation in Shank3B KO mice. Our behavioral data raise the possibility that low social investigation of novel mice by Shank3B KO mice may be the result of greater social avoidance compared to WT mice. However, we observed no evidence of high avoidance behavior of Shank3B KO mice on a non-social task, the elevated plus maze, findings that are consistent with some48, but not all67, published reports. We also found no evidence of increased vCA1 theta power, an electrophysiological correlate of non-social avoidance behavior86, in Shank3B KO mice displaying low levels of novel social investigation (Shank3B KO + control virus, Shank3B KO + DREADD virus + VEH), and no correlation between investigation times of novel mice and vCA1 theta power. In fact, the one experimental group that showed elevated vCA1 theta power (Shank3B KO + DREADD virus + CNO) had been subjected to a manipulation that increased investigation of novel mice compared to familiar mice. Taken together, these results suggest that low investigation times are not the result of global behavioral inhibition. Low investigation of a novel social stimulus may represent a number of specific deficits, including faulty recognition of a novel stimulus as familiar, inattention to social stimuli, reduced motivation/reward associated with social stimuli, and/or inability to recognize social novelty, all of which have been reported in humans with ASD1,51,92–94. More specifically related to the Shank3B mouse model, individuals diagnosed with both Phelan-McDermid syndrome and ASD exhibit reduced social attention and reduced social novelty recognition under certain conditions51. Social novelty recognition, along with broader social memory, has been linked to the CA2 region in mice79. Electrophysiological studies have shown increased firing of CA2 pyramidal cells in response to a novel social stimulus95. Additional studies have identified afferents to the CA2 as being involved in these functions. In particular, projections from the SUM and the cholinergic basal forebrain have been linked to novelty recognition21,23,83, whereas those from the vasopressinergic paraventricular nucleus and adult-generated neurons from the dentate gyrus seem more likely associated with memory of familiar social stimuli71,96. Despite the fact that Shank3, a synaptic scaffolding protein, is expressed in the hippocampus, we found no differences between WT and Shank3B KO mice in markers of several of these afferents to the CA2. These results were unexpected and suggest that compensation for the lack of Shank3 might occur during development, particularly at synapses with high concentrations of this molecule, including those made by mossy fibers, as well as those expressing VGLUT174. In this regard, it may be relevant that Shank3 colocalizes with other synaptic scaffolding proteins (Shank1 and 2)74, which may enable the development of functional synapses in the absence of Shank3. It is also possible that these pathways are impacted by Shank3B KO, but that the level of resolution of our analyses is not sufficient to detect differences. Despite the lack of global abnormalities in CA2 afferents of Shank3B KO mice, we did observe a decrease in the afferents from 3R-Tau labeled abGCs in the dentate gyrus. abGCs, like mature granule cells, are known to project to the CA270,78 and have been shown to participate in social memory71. Although it is possible that the lower numbers of abGCs and their afferents to CA2 contribute to social discrimination deficits in Shank3B KO mice, it seems unlikely to be the main contributor, since transgenic reduction of abGCs reduces social memory function without affecting investigation times of novel social stimuli71. It remains unknown how Shank3B KO affects abGC projections to the CA2 without exerting a measurable influence on the overall mossy fiber projection (stained with ZnT3). Despite the lack of obvious neuroanatomical differences in afferent projections which may be involved in the recognition of novel social stimuli between WT and Shank3B KO mice, it remains possible that synapses between these afferents and the CA2 are affected. Since inputs from the SUM release Substance P, a neuromodulator known to enhance NMDA responses in the CA296, it is possible that this connection is atypical in Shank3B KO mice due to differences in Substance P release or NMDA receptor trafficking at the synapse. With regard to the latter possibility, however, a study has shown that social deficits in Shank3B KO mice are not resolved by treatment with a partial agonist of the NMDA receptor97. Future studies investigating the effects of additional NMDA receptor manipulations as well as whether postsynaptic elements of relevant connections are different in Shank3B KO mice will be necessary to answer these questions. Regardless of the lack of global abnormality of several excitatory afferent labels in CA2 of Shank3B KO mice, chemogenetic activation of CA2 pyramidal cells in general or the CA2-vCA1 pathway directly in these mice restored social discrimination to resemble that of WT mice. Analysis of social investigation times suggests that the main effect of CA2 excitatory neurons or CA2-vCA1 activation is to increase investigation times of the novel mouse. This effect was only seen in the Shank3B KO mice, with no changes observed in the WT mice following identical treatment. These findings suggest that an, as yet, unidentified dysfunction upstream or within the vCA1, potentially of the CA2 and/or CA2-vCA1 pathway, exists in Shank3B KO mice, but that stimulating existing circuitry is sufficient to invoke latent function. Thus, although our experiments did not uncover a mechanism underlying the social deficit in Shank3B KO mice, we have identified a manipulation that can restore this behavior to WT levels. Oscillatory activity in the CA2 and vCA1 regions has been linked to social memories. CA2 SWRs have been causally associated with social memory consolidation26, raising the possibility that abnormalities in their number, magnitude, or duration might contribute to social memory dysfunction in Shank3B KO mice. Because previous studies have shown that SWRs are also involved in retrieval of nonsocial memories25, we hypothesized that vCA1 SWRs might differ between WT and Shank3B KO mice during familiar mouse presentation, a time when social memory retrieval is most likely. However, we found no differences in several SWR measures between WT and Shank3B KO mice, as well as no changes in these measures after chemogenetic activation of CA2 excitatory neurons during baseline trials or throughout the familiar mouse exposure trial. It remains possible that vCA1 SWRs might differ between WT and Shank3B KO mice but that the parameters of our recordings did not capture this effect. Along these lines, it is relevant to note that a recent study found diminished SWR amplitude in Shank3B KO compared to WT mice during the 2 h rest period after social interaction, a time likely important for social memory consolidation28. However, since the main difference we noted in social interaction between WT and Shank3B KO mice was with investigation of novel social stimuli, which does not involve memory consolidation or retrieval, it seems likely that this social novelty deficit is not dependent on differences in SWRs. Hippocampal theta and gamma rhythms have been associated with novelty recognition30,98–103. Since social stimuli have been shown to increase hippocampal theta and gamma oscillations30,33,104 and mouse models of social dysfunction have been associated with alterations in hippocampal oscillations at both frequencies34,35,105, we examined theta and low gamma power in the vCA1 of WT and Shank3B KO mice in the presence of a novel mouse. Unexpectedly, we found no differences in either theta or low gamma power in the absence of CA2 activation. Chemogenetic activation of CA2, however, produced an increase in vCA1 theta but not low gamma power in Shank3B KO mice. This effect was not observed in WT mice, paralleling the results of our behavioral studies. Taken together, these findings suggest that although theta power does not appear to be atypical in Shank3B KO mice in response to novel mice, it is increased under conditions associated with a restoration of novel mouse investigation times to WT levels. Thus, although our studies did not uncover an underlying abnormality of Shank3B KO mice in social memory circuitry, elevated vCA1 theta power corresponds with enhanced investigation of novel mice by Shank3B KO mice. The possibility that chemogenetic activation of CA2-induced vCA1 theta power in Shank3B KO mice is responsible for increased investigation of novel social stimuli may seem contradictory with reports of causal links between vCA1 theta power and avoidance behavior86, however, that association has only been observed with nonsocial avoidance, and we found no correlation between theta power and social investigation times in WT or Shank3B mice without DREADD virus and CNO. Studies have shown that the vCA1 is heterogeneous anatomically and functionally, with subsets of neurons connected to the hypothalamus/medial prefrontal cortex, amygdala, and nucleus accumbens, each serving different functions, including avoidance, learning, and social behavior respectively106. Indeed, theta coherence between hippocampus and different target regions has been reported to differ after exposure to social stimuli versus nonsocial stimuli, with the latter linked to defensive/avoidance behavior104. Thus, it seems likely that chemogenetic activation of the CA2 may activate theta oscillations among subsets of vCA1 neurons that have downstream connections with regions associated specifically to social behavior. CA2 pyramidal cells are known to synapse onto vCA1 pyramidal cells31 and likely also influence parvalbumin (PV) + interneurons, which are known to participate in the generation of theta oscillations107. Since vCA1 PV + cells have been shown to increase their firing in response to novel social stimuli108, increased CA2 excitatory input may drive rhythmic firing of PV + interneurons in the theta range, producing activity that is sufficient to enhance investigation of novel mice and facilitate discrimination between them and familiar mice. Whether PV + interneurons in the vCA1 are impacted in Shank3B KO mice remains to be determined but if they are diminished in some way, increased excitatory drive from the CA2 might be sufficient to compensate and restore social discrimination abilities. However, since we detected no differences in vCA1 theta power between WT and Shank3B KO mice without CA2 activation, any abnormality in vCA1 PV + cell function is likely beyond their ability to generate theta oscillations. Several studies have shown that Shank3B KO mice exhibit lower basal synaptic transmission as well as deficient LTP in the CA1109–112. While these effects have only been investigated in the dorsal, not the ventral, CA1, studies in wildtype rodents suggest that vCA1 exhibits LTP, albeit less robustly, than dorsal CA1113,114. This raises the possibility that chemogenetically-induced increases in theta oscillations may be sufficient to induce vCA1 plasticity. However, it should be noted that no previous studies have linked vCA1 LTP to social novelty detection or social discrimination. Collectively, our findings suggest that Shank3B KO mice have deficits in social discrimination as a result of reduced investigation of novel social stimuli. Despite the lack of gross morphological abnormality in the CA2 of Shank3B KO mice, we found that chemogenetic stimulation of the CA2 and the CA2-vCA1 circuit was sufficient to restore social investigation of novel stimuli. Behavioral restoration in Shank3B KO mice with CA2 activation was associated with increased vCA1 theta power, but not low gamma power or alterations in SWRs. These findings suggest that activation of a hippocampal social memory circuit in adulthood is sufficient to restore a behavioral deficit arising from a neurodevelopmental genetic anomaly. The extent to which our results are relevant to humans with ASD remains to be determined. ## Animals All animal procedures were approved by the Princeton University Institutional Animal Care and Use Committee and were in accordance with the National Research Council Guide for the Care and Use of Laboratory Animals. All mice were group housed by genotype and sex in Optimice cages on a reverse $\frac{12}{12}$ h light/dark cycle and tested in the dark. Humidity of the room was ~$50\%$. The mice were provided ad lib access to food and water. To generate WT and Shank3B KO mice, adult male and female Shank3B ± (JAX Stock no. 17688) mice were obtained from The Jackson Laboratory and bred at Princeton University using a heterozygous X heterozygous strategy. At postnatal day 15, mice were genotyped by Transnetyx using real-time PCR. Mixed groups of male and female WT and male and female Shank3B-/- null mutant mice were used as test mice. Shank3B +/- heterozygous mice that were the same sex and age as the test mice were used as stimulus animals. All mice were group housed by genotype and sex in Optimice cages on a reverse $\frac{12}{12}$ h light/dark cycle. For social memory and object location memory behavioral studies, as well as histological studies, 6- to 8-week old mice were used ($$n = 9$$–13/group). For elevated plus maze studies, 2–5 month old mice were used ($$n = 20$$/group). For behavioral experiments involving chemogenetic activation of the CA2 and CA2-vCA1 pathway, 2–5 month old mice were used ($$n = 13$$–18/group). For electrophysiological experiments, 2–5 month old mice were used ($$n = 9$$–10/group). ## Surgical procedures Mice were deeply anesthetized with isoflurane (2–$3\%$) and placed in a stereotaxic apparatus (Kopf) under a temperature controlled thermal blanket. The head was levelled using bregma, lambda, and medial-lateral reference points before craniotomy was performed. Each mouse received bilateral injections (15 nl/hemisphere at a rate of 15 nl/min) of either excitatory DREADD virus AAV-CaMKIIa-hM3D(Gq)-mCherry (Addgene viral prep # 50476-AAV5, titer: 1.7 × 1013) or control virus AAV-CaMKIIa-EGFP (Addgene viral prep # 50469-AAV5, titer: 4.3 × 1012) into the CA2 using the following coordinates from Bregma: −1.82 AP, ±2.15 ML, and −1.67 DV. Both viruses were serotype AAV5. The virus was delivered using a 10 µl syringe with a 33-gauge beveled needle (NanoFil) controlled by a microinjection pump (WPI). The needle remained in place for an additional 5 min after the injection was completed to prevent backflow of the virus upon removal. For activating the CA2-vCA1 projections, mice injected with DREADD or control virus into the CA2 were implanted bilaterally with a cannula guide extending 4 mm (Plastics One, Cat# C315GS-5/SP) into the vCA1 (−3.5 AP, ±3.45 ML). Dummy cannula (Plastics One, Cat# C315DCS-5/SPC) were inserted into the guides and the guide was lowered to −3.8 mm. Cannula guides were kept in place using metabond and dental cement (Bosworth Trim). For vCA1 recordings, mice injected with control or DREADD virus into the CA2 were implanted unilaterally with a custom-made 3 wire electrode array (Microprobes) into the right hemisphere of the vCA1 (electrode 1, AP: −3.3, ML: 3.45, DV: −3.8 with each electrode separated by 200 nm). Four bone screws were implanted on the skull and one screw was used as a ground. A ground wire was wrapped around the ground screw and covered with metallic paint. Electrode implants were kept in place using Metabond and dental cement (Bosworth Trim). Two to four weeks after surgeries, mice were i.p. injected or cannula infused with either clozapine-N-oxide (CNO) or VEH (see CNO administration) before being tested on behavioral tasks (see Behavioral assays) and/or underwent electrophysiological recordings (see Electrophysiology recordings). ## CNO administration Each CA2 virus-injected mouse underwent social discrimination testing (with novel and familiar stimulus mice) twice, once after CNO i.p. injection or vCA1 cannula infusion and once after VEH i.p. injection or vCA1 cannula infusion. The order of drug administration (CNO or vehicle) was counterbalanced across groups. Because previous studies have shown that DREADD manipulations of neurons are transient and return to baseline by 10–24 h post-CNO injections91,115,116, mice were given a minimum 2-day rest period between CNO and VEH tests. 30 min prior to both the novel (trial 1) and familiar (trial 2) stimulus mouse exposure, test mice received CNO or VEH i.p. injections or vCA1 cannula infusions. For systemic administration of CNO, CA2 virus injected mice received i.p. injections of 1 mg/kg of CNO (dissolved in DMSO and then suspended in saline) or VEH. To activate the CA2-vCA1 pathway, CA2 virus injected mice with implanted cannula received cannula infusions of CNO into the vCA1 under light isoflurane anesthesia ($2\%$). After the dummy cannula was removed, an internal cannula projecting 0.8 mm (Plastics One, Cat# C315IS-5/SPC) from the tip of the guide cannula was inserted. 1 µl of CNO (2 μg/μl of CNO dissolved in DMSO and then suspended in saline)117 or VEH was infused per hemisphere over 1 min into the vCA1 using a syringe pump (Harvard apparatus) mounted with a 1 µl syringe (Hamilton). The internal cannula remained in place for 1 additional minute after the infusion was completed to allow for diffusion of the drug. Mice were returned to their home cages and resumed typical ambulatory activity from the light anesthesia within 5 min. ## Behavioral assays All behavioral testing occurred during the active cycle for mice (dark cycle). The testing arena was an open field box or an elevated plus maze (see below for details). All behavior was analyzed manually from videotapes by researchers blind to the treatment condition. Since WT and KO mice look virtually identical to one another and all mice in a given experiment had similar manipulations (e.g., bilateral CA2 infection with or without bilateral vCA1 cannula or unilateral vCA1 electrode), a numerical ear tag code, which was not decoded until the behavioral analyses were complete, ensured unbiased scoring. Video recordings were made using a digital HD video camera recorder (Sony Hanycam HDR-XR150) with standard definition high-quality settings (30 frames per second). ## Social discrimination memory testing To assess social discrimination, two versions of the direct social interaction test were adapted from previously established protocols17,71,82,118. Each version of this test consisted of two or three social stimulus trials, each separated by 24 h. For behavioral characterization studies, each mouse was tested once with a three social stimulus trials paradigm. For virus manipulation studies, each mouse underwent social discrimination testing two times, each time with either VEH or CNO treatment (order of injections or infusions was counterbalanced across groups) with VEH and CNO treatment separated by at least a 2-day wash-out period. All habituation and testing was done under dim light (10–15 lux). Prior to the test beginning, mice were acclimated to the behavior testing room for at least 30 min and then also habituated to the testing box for 5 min prior to the first social stimulus trial. The testing was conducted in low lighting in an open-field box (23 × 25 × 25 cm). For the three-social stimulus trial paradigm, the test mouse and a never-before-encountered mouse (Novel 1, trial 1) were placed together in the testing box and allowed to interact for 5 min. After this interaction period, the test mouse was returned to their home cage for 24 h and then placed back into the testing box the previously encountered mouse (Familiar, trial 2). 24 hours after the second trial, the test mouse was introduced to a new, novel mouse (Novel 2, trial 3). For experiments involving virus manipulations, mice underwent two social stimulus trials during which the test mouse was introduced to a novel mouse (Novel 1) in trial 1, followed by the same novel mouse (Familiar) in trial 2. Sex-matched non-littermate heterozygous mice were used as stimulus mice for social discrimination testing. For each trial, the interaction time of the test mouse with the stimulus mouse was measured from video recordings. Social investigation was defined as the test mouse directing its snout toward the stimulus mouse’s anogenital region or body < 1 cm away, following, or allogrooming that was initiated by the test mouse. ## Object location memory To assess a form of non-social memory, the object location test was used66. The testing was conducted in low lighting (10–15 lux) in an open-field box (23 × 25 × 25 cm). For 5 min, twice per day, for 3 days, mice were habituated to the testing arena as previously described82,119,120. Mice were habituated to the objects on the third day of habituations. Two objects, each <8 cm in height or width, with varying surfaces for the mice to explore, such as LEGO toys and plastic clips, were used. Different, but similar size and shape, objects were used for habituation and testing. In the familiarization trial, two identical objects were positioned on the same side of the testing box (6 cm away from the walls and 10 cm between each other). Mice were free to explore the objects until they reached 30 s of cumulative exploration time with both objects or up to a maximum of 10 min elapsed. The criterion for object exploration was directing their nose at 2 cm or less distance from the object. After the familiarization trial, mice were placed in their home cage for 5 min. Between trials, one object (moved object) was rotated 180° and moved to the opposite wall of the chamber so that it was diagonal to the first object, while the other object was not moved (familiar object). The moved object was counterbalanced throughout testing. For the test trial, mice were returned to the testing arena and were free to explore the objects for 2 min. The object exploration times were scored manually from video recordings. A discrimination ratio (DR) was calculated for each mouse as follows: (Time exploring moved object – time exploring familiar object)/total time exploring objects). ## Elevated plus maze Mice were placed on an elevated plus maze that consisted of an elevated (50 cm) plus-shaped track with two arms that were enclosed with high walls (30 cm) and two open arms that had no walls and illuminated to 200 lux. All arms were 50 cm in length. During testing, the mouse was placed in the center of the maze and allowed to explore for 5 min. The number of entries into the open and closed arms and time spent in the open arms, closed arms, and center was measured for each mouse from video recordings. ## Electrophysiology recordings Local field potentials (LFPs) were recorded using a wireless head stage (TBSI, Harvard Biosciences). Mice were habituated to the weight of recording headstage using a custom headstage with equivalent weight in the home cage for 5 min the day before the first test and in the testing box for 5 min on day of testing. The two social stimulus trial paradigm was used. For each trial, 30 min after drug administration, the test mouse was placed with the stimulus mouse and LFPs recorded continuously for 5 min. To get a baseline measurement, LFPs were also recorded for 3 min in the testing box prior to exposure to the social stimulus each day. The data were transmitted to a wireless receiver (Triangle Biosystems) and recorded using NeuroWare software version 3.0 (Triangle Biosystems). ## Electrophysiological analyses All recordings were preprocessed using Neuroexplorer software version 5.21 (Nex Technologies) and analyzed using an in-house script (Python). For theta and gamma analyses, continuous LFP data were notched at 60 Hz and band-pass filtered from 0 to 100 Hz. To normalize theta and gamma oscillations, the sum of power spectra values from 0 to 100 Hz were set to equal 1. To obtain power estimates within theta (4–12 Hz) and low gamma (30–55 Hz) bands, the summed power across time for the entire session within each frequency was taken. For SWR analyses, continuous LFP data were notched at 60 Hz and band-pass filtered from 150 and 250 Hz. Signals were then Hilbert transformed and z-scored. SWR events were detected using a custom python script121. SWR events were defined in the analysis as instances where the signal exceeded three standard deviations across a rolling-average amplitude threshold for at least 15 ms. The total number of events for each recording was then quantified and exported to an excel sheet for statistical analysis. To determine SWR event frequency, the number of SWRs detected were normalized to immobility time (defined as quiet wakefulness where mice do not move except to groom). The mean amplitude and duration of the detected SWRs were calculated across the recording session. Theta and gamma power were analyzed across the 5 min of trial 1, during exposure to the novel conspecific, and normalized by dividing by the baseline trial. SWR numbers, amplitude, and duration were analyzed across the first 1 min of trial 2, during exposure to the familiar conspecific, and normalized by dividing by the baseline trial. Minute-by-minute analyses of SWRs were also performed during the 3 min of baseline and 5 min of exposure to the familiar conspecific for SWR number, amplitude, duration, and interval of time between SWRs. ## Histology Mice were deeply anesthetized with Euthasol (Virbac) and were transcardially perfused with cold $4\%$ paraformaldehyde (PFA). Extracted brains were post-fixed for 48 h in $4\%$ PFA at 4 °C followed by an additional 48 h in $30\%$ sucrose at 4 °C for cryoprotection before being frozen in cryostat embedding medium at −80 °C. Hippocampal coronal sections (40 µm) were collected using a cryostat (Leica). Sections were blocked for 1½ h at room temperature in a PBS solution that contained $0.3\%$ Triton X-100 and $3\%$ normal donkey serum. Sections were then incubated overnight while shaking at 4 °C in the blocking solution that contained combinations of the following primary antibodies: mouse anti-three microtubule-binding domain tau protein (3R-Tau, 1:500, Millipore, Cat# 05-803), rabbit anti-Purkinje cell protein 4 (PCP4, 1:500, Sigma-Aldrich, Cat# HPA005792), rat anti-mCherry (1:1000, Invitrogen, Cat# M11217), mouse anti-regulator of G protein signaling 14 (RGS14, 1:500, UC Davis/NIH NeuroMab, Cat# 75–170), rabbit anti-zinc transporter 3 (ZnT3, 1:500, Alomone labs, Cat# AZT-013), rabbit anti-vesicular glutamate transporter 2 (VGLUT2, 1:500, Synaptic Systems, Cat# 135 403), rabbit anti-vesicular glutamate transporter 1 (VGLUT1, 1:250, Invitrogen, Cat# 48-2400), rabbit anti-vesicular acetylcholine transporter (VAChT, 1:500, Synaptic Systems, Cat# 139 103). For 3R-Tau immunohistochemistry, sections were subjected to an antigen retrieval protocol that involved incubation in sodium citrate and citric acid buffer for 30 min at 80 °C prior to blocking solution incubation. For all primary antibody reactions, washed sections were then incubated for 1½ h at room temperature in secondary antibody solutions that contained combinations of the following secondaries: donkey anti-rat Alexa Fluor 568 (1:500, Abcam), donkey anti-mouse Alexa Fluor 568 or 647 (1:500, Invitrogen), or donkey anti-rabbit Alexa Fluor 488 (1:500, Invitrogen). Washed sections were then counterstained with Hoechst 33342 for 10 min (1:5,000 in PBS, Molecular Probes), mounted onto slides, and coverslipped with Vectashield (Vector labs). Slides were coded until completion of the data analysis. Sections through the ventral hippocampus from cannula and electrophysiology studies were stained for Hoechst 33342 in order to verify accurate cannula and electrode placement. ## Verification of CA2 infection, vCA1 cannula, and vCA1 electrode placement Only mice with evidence of CA2 infection (control or DREADD virus) were included in behavioral and electrophysiological analyses. Virus infection in CA2 was largely confined to this region with minimal expression noted in neighboring CA1 or CA3 (Fig. S6). All mice in the systemic CNO study (Fig. 2) had CA2 virus expression. In the cannula study (Fig. 3), one control virus KO female was excluded from the analysis and in the electrophysiology study (Fig. 4), one DREADD virus WT female was excluded from the analysis because there was no evidence of CA2 infection. In the cannula study, all mice with CA2 virus infection had histological evidence of cannula placement in the vCA1. In the electrophysiology study, all mice showed histological evidence of electrode implantation in the vCA1. ## Optical intensity measurements Z-stack images of the CA2 and corpus callosum were taken on a Leica SP8 confocal using LAS X software version 35.6 and a 40x oil objective. The CA2 was defined by PCP4 or RGS14 labeling. Collected z-stack images were analyzed for optical intensity in Image J (version 2.9.0). A background subtraction using a rolling ball radius (50 pixels) was applied to the image stacks. A region of interest (ROI) was drawn and the mean gray value was collected throughout the image stack. In the CA2, the ROI was confined to the stratum lucidum for 3R-Tau and ZnT3, the stratum radiatum and lacunosum moleculare for VGLUT1, and the pyramidal layer and stratum oriens for VGLUT2 and VAChT. The mean gray value of the ROI was calculated for each z-slice and the maximum mean gray value for each z-stack was taken. That maximum of the CA2 ROI was divided by the maximum of the corpus callosum ROI for each section. Each brain’s normalized intensity was the average of 3 sections. ## Cell density and percentage measurements The number of 3R-Tau+ cells were counted in the dorsal dentate gyrus of the hippocampus on 4 neuroanatomically matched sections using an Olympus BX-60 microscope with a 100× oil objective. The counts for the suprapyramidal blade and infrapyramidal blade of the dentate gyrus were analyzed separately and area measurements were collected using Stereo Investigator software (version 11.03). The density of 3R-Tau was determined by dividing the total number of positively labeled cells by the volume of the subregion (ROI area multiplied by 40 µm section thickness). To determine the extent and cell types of CA2 DREADD infection, we counted the numbers of mCherry+ cells, PCP4+ cells, and mCherry+/PCP4+ cells in a subset of DREADD virus infected mice ($$n = 5$$, 4 sections per mouse). The percentage of PCP4+ cells that express mCherry were determined, as well the percentage of mCherry+ cells that express PCP4. ## Statistical analyses and reproducibility For histological analyses, data sets were analyzed using an unpaired two-tailed Student’s t test or Mann Whitney U tests. For behavioral analyses involving two group comparisons, data sets were analyzed using either unpaired two-tailed Student’s t tests or a repeated measures two-way ANOVA, as appropriate. For behavioral analyses involving virus manipulations, data sets were analyzed using either a two-way ANOVA or a repeated measures three-way ANOVA, as appropriate. Bonferroni post hoc comparisons were used to follow up any significant main effects or interactions of the ANOVAs. Pearson’s correlation coefficient test was used to analyze the association between theta power and social investigation times. Because electrophysiological measurements were taken from multiple electrodes within each mouse, these data were analyzed with linear mixed-effects ANOVAs using the lme4 R package122. The level of the measurement was explained by drug, virus, genotype, the three two-way interactions, the three-way interaction, and a random effect of mouse. Tukey post hoc comparisons were used to follow up any significant main effects or interactions using the emmeans R package123. All data sets are expressed as the mean ± SEM on the graphs and statistical significance was set at $p \leq 0.05.$ GraphPad Prism 9.2.0 (GraphPad Software), Excel 16.38 (Microsoft), or R studio were used for statistical analyses. All graphs were prepared using GraphPad Prism 9.2.0 (GraphPad Software). Statistical values (n sizes, p values, and statistical test) are reported in the figure legends or supplementary tables. There were no planned attempts to reproduce findings of the paper, but there were internal replications of Shank3B KO social discrimination deficits in three experiments (Figs. 1, 2, 3), and in reversal of this deficit with DREADD virus and CNO (systemic and cannula infused, Figs. 2,3). Staining patterns for each afferent marker were similar across all mice included in the study (Figs. S3a, b, 4a–c), and viral infection/expression of reporter genes (Figs. 2b, S6) was similar in all mice included in experiments shown in Figs. 2–4. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37248-8. ## Peer review information Nature Communications thanks Teruhiro Okuyama and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. ## References 1. Weigelt S, Koldewyn K, Kanwisher N. Face identity recognition in autism spectrum disorders: a review of behavioral studies. Neurosci. Biobehav. Rev. (2012.0) 36 1060-1084. DOI: 10.1016/j.neubiorev.2011.12.008 2. Williams DL, Goldstein G, Minshew NJ. Impaired memory for faces and social scenes in autism: clinical implications of memory dysfunction. Arch. Clin. Neuropsychol. (2005.0) 20 1-15. DOI: 10.1016/j.acn.2002.08.001 3. Schafer M, Schiller D. The hippocampus and social impairment in psychiatric disorders. Cold Spring Harb. Symp. Quant. Biol. (2018.0) 83 105-118. DOI: 10.1101/sqb.2018.83.037614 4. Schelinski S, Roswandowitz C, von Kriegstein K. Voice identity processing in autism spectrum disorder. Autism Res. (2017.0) 10 155-168. DOI: 10.1002/aur.1639 5. Porcelli S. Social brain, social dysfunction and social withdrawal. Neurosci. Biobehav. Rev. (2019.0) 97 10-33. DOI: 10.1016/j.neubiorev.2018.09.012 6. Kurtz MM, Bronfeld M, Rose J. Cognitive and social cognitive predictors of change in objective versus subjective quality-of-life in rehabilitation for schizophrenia. Psychiatry Res. (2012.0) 200 102-107. DOI: 10.1016/j.psychres.2012.06.025 7. García-Casal JA. The efficacy of emotion recognition rehabilitation for people with Alzheimer’s Disease. J. Alzheimer Dis. (2017.0) 57 937-951. DOI: 10.3233/JAD-160940 8. Quian Quiroga R. How are memories stored in the human hippocampus?. Trends Cogn. Sci. (2021.0) 25 425-426. DOI: 10.1016/j.tics.2021.03.006 9. Montagrin A, Saiote C, Schiller D. The social hippocampus. Hippocampus (2018.0) 28 672-679. DOI: 10.1002/hipo.22797 10. Laakso MP, Lehtovirta M, Partanen K, Riekkinen PJ, Soininen H. Hippocampus in Alzheimer’s disease: a 3-year follow-up MRI study. Biol. Psychiatry (2000.0) 47 557-561. DOI: 10.1016/S0006-3223(99)00167-5 11. Sheline YI, Mittler BL, Mintun MA. The hippocampus and depression. Eur. Psychiatry (2002.0) 3 300-305. DOI: 10.1016/S0924-9338(02)00655-7 12. Brambilla P. Brain anatomy and development in autism: review of structural MRI studies. Brain Res. Bull. (2003.0) 61 557-569. DOI: 10.1016/j.brainresbull.2003.06.001 13. Kalmady SV. Clinical correlates of hippocampus volume and shape in antipsychotic-naïve schizophrenia. Psychiatry Res. Neuroimaging (2017.0) 263 93-102. DOI: 10.1016/j.pscychresns.2017.03.014 14. Kogan JH, Frankland PW, Silva AJ. Long-term memory underlying hippocampus-dependent social recognition in mice. Hippocampus (2000.0) 10 47-56. DOI: 10.1002/(SICI)1098-1063(2000)10:1<47::AID-HIPO5>3.0.CO;2-6 15. Stevenson EL, Caldwell HK. Lesions to the CA2 region of the hippocampus impair social memory in mice. Eur. J. Neurosci. (2014.0) 40 3294-3301. DOI: 10.1111/ejn.12689 16. Okuyama T. Social memory engram in the hippocampus. Neurosci. Res. (2018.0) 129 17-23. DOI: 10.1016/j.neures.2017.05.007 17. Hitti FL, Siegelbaum SA. The hippocampal CA2 region is essential for social memory. Nature (2014.0) 508 88-92. DOI: 10.1038/nature13028 18. Dudek SM, Alexander GM, Farris S. Rediscovering area CA2: unique properties and functions. Nat. Rev. Neurosci. (2016.0) 17 89-102. DOI: 10.1038/nrn.2015.22 19. Raam T, McAvoy KM, Besnard A, Veenema AH, Sahay A. Hippocampal oxytocin receptors are necessary for discrimination of social stimuli. Nat. Commun. (2017.0) 8 2001. DOI: 10.1038/s41467-017-02173-0 20. Piskorowski RA, Chevaleyre V. Memory circuits: CA2. Curr. Opin. Neurobiol. (2018.0) 52 54-59. DOI: 10.1016/j.conb.2018.04.015 21. Chen S. A hypothalamic novelty signal modulates hippocampal memory. Nature (2020.0) 586 270-274. DOI: 10.1038/s41586-020-2771-1 22. Lopez-Rojas J, de Solis CA, Leroy F, Kandel ER, Siegelbaum SA. A direct lateral entorhinal cortex to hippocampal CA2 circuit conveys social information required for social memory. Neuron (2022.0) 110 1559-1572.e4. DOI: 10.1016/j.neuron.2022.01.028 23. Pimpinella D. Septal cholinergic input to CA2 hippocampal region controls social novelty discrimination via nicotinic receptor-mediated disinhibition. Elife (2021.0) 10 e65580. DOI: 10.7554/eLife.65580 24. Buzsáki G. Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus (2015.0) 25 1073-1088. DOI: 10.1002/hipo.22488 25. Joo HR, Frank LM. The hippocampal sharp wave-ripple in memory retrieval for immediate use and consolidation. Nat. Rev. Neurosci. (2018.0) 19 744-757. DOI: 10.1038/s41583-018-0077-1 26. Oliva A, Fernández-Ruiz A, Leroy F, Siegelbaum SA. Hippocampal CA2 sharp-wave ripples reactivate and promote social memory. Nature (2020.0) 587 264-269. DOI: 10.1038/s41586-020-2758-y 27. Rao RP, von Heimendahl M, Bahr V, Brecht M. Neuronal responses to conspecifics in the ventral CA1. Cell Rep. (2019.0) 27 3460-3472. DOI: 10.1016/j.celrep.2019.05.081 28. Tao K. Disrupted social memory ensembles in the ventral hippocampus underlie social amnesia in autism-associated Shank3 mutant mice. Mol. Psychiatry (2022.0) 27 2095-2105. DOI: 10.1038/s41380-021-01430-5 29. Meira T. A hippocampal circuit linking dorsal CA2 to ventral CA1 critical for social memory dynamics. Nat. Commun. (2018.0) 9 4163. DOI: 10.1038/s41467-018-06501-w 30. Brown LY, Alexander GM, Cushman J, Dudek SM. Hippocampal CA2 organizes CA1 slow and fast γ oscillations during novel social and object interaction. eNeuro (2020.0) 7 0084-20.2020. DOI: 10.1523/ENEURO.0084-20.2020 31. Okuyama T, Kitamura T, Roy DS, Itohara S, Tonegawa S. Ventral CA1 neurons store social memory. Science (2016.0) 353 1536-1541. DOI: 10.1126/science.aaf7003 32. Watarai A, Tao K, Wang MY, Okuyama T. Distinct functions of ventral CA1 and dorsal CA2 in social memory. Curr. Opin. Neurobiol. (2021.0) 68 29-35. DOI: 10.1016/j.conb.2020.12.008 33. Villafranca-Faus M. Integrating pheromonal and spatial information in the amygdalo-hippocampal network. Nat. Commun. (2021.0) 12 5286. DOI: 10.1038/s41467-021-25442-5 34. Aoki Y. Selective attenuation of electrophysiological activity of the dentate gyrus in a social defeat mouse model. J. Physiol. Sci. (2017.0) 67 507-513. DOI: 10.1007/s12576-016-0481-0 35. Arbab T, Battaglia FP, Pennartz CMA, Bosman CA. Abnormal hippocampal theta and gamma hypersynchrony produces network and spike timing disturbances in the Fmr1-KO mouse model of Fragile X syndrome. Neurobiol. Dis. (2018.0) 114 65-73. DOI: 10.1016/j.nbd.2018.02.011 36. Modi B. Possible implication of the CA2 hippocampal circuit in social cognition deficits observed in the neuroligin 3 knock-out mouse, a non-syndromic animal model of autism. Front. Psychiatry (2019.0) 10 513. DOI: 10.3389/fpsyt.2019.00513 37. 37.NIMH (2022) U.S. Department of Health and Human Services. Autism spectrum Disorder. National Institute of Mental Health. https://www.nimh.nih.gov/health/topics/autism-spectrum-disorders-asd. 38. Chaste P, Leboyer M. Autism risk factors: genes, environment, and gene-environment interactions. Dialogues Clin. Neurosci. (2012.0) 14 281-292. DOI: 10.31887/DCNS.2012.14.3/pchaste 39. Lai MC, Lombardo MV, Baron-Cohen S. Autism. Lancet (2014.0) 383 896-910. DOI: 10.1016/S0140-6736(13)61539-1 40. Satterstrom FK. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell (2020.0) 180 568-584. DOI: 10.1016/j.cell.2019.12.036 41. Costales JL, Kolevzon A. Phelan-McDermid syndrome and SHANK3: implications for treatment. Neurotherapeutics (2015.0) 12 620-630. DOI: 10.1007/s13311-015-0352-z 42. De Rubeis S. Delineation of the genetic and clinical spectrum of Phelan-McDermid syndrome caused by SHANK3 point mutations. Mol. Autism (2018.0) 9 31. DOI: 10.1186/s13229-018-0205-9 43. Monteiro P, Feng G. SHANK proteins: roles at the synapse and in autism spectrum disorder. Nat. Rev. Neurosci. (2017.0) 18 147-157. DOI: 10.1038/nrn.2016.183 44. Roussignol G. Shank expression is sufficient to induce functional dendritic spine synapses in aspiny neurons. J. Neurosci. (2005.0) 25 3560-3570. DOI: 10.1523/JNEUROSCI.4354-04.2005 45. Zhou Y. Atypical behaviour and connectivity in SHANK3-mutant macaques. Nature (2019.0) 570 326-331. DOI: 10.1038/s41586-019-1278-0 46. Guo B. Anterior cingulate cortex dysfunction underlies social deficits in Shank3 mutant mice. Nat. Neurosci. (2019.0) 22 1223-1234. DOI: 10.1038/s41593-019-0445-9 47. Mei Y. Adult restoration of Shank3 expression rescues selective autistic-like phenotypes. Nature (2016.0) 530 481-484. DOI: 10.1038/nature16971 48. Dhamne SC. Replicable in vivo physiological and behavioral phenotypes of the Shank3B null mutant mouse model of autism. Mol. Autism (2017.0) 8 26. DOI: 10.1186/s13229-017-0142-z 49. Qin L. Social deficits in Shank3-deficient mouse models of autism are rescued by histone deacetylase (HDAC) inhibition. Nat. Neurosci. (2018.0) 21 564-575. DOI: 10.1038/s41593-018-0110-8 50. Peça J. Shank3 mutant mice display autistic-like behaviours and striatal dysfunction. Nature (2011.0) 472 437-442. DOI: 10.1038/nature09965 51. Guillory SB. Social visual attentional engagement and memory in Phelan-McDermid syndrome and autism spectrum disorder: a pilot eye tracking study. J. Neurodev. Disord. (2021.0) 13 58. DOI: 10.1186/s11689-021-09400-2 52. Boucher J, Lewis V. Unfamiliar face recognition in relatively able autistic children. J. Child Psychol. Psychiatry (1992.0) 33 843-859. DOI: 10.1111/j.1469-7610.1992.tb01960.x 53. Boucher J, Lewis V, Collis G. Familiar face and voice matching and recognition in children with autism. J. Child Psychol. Psychiatry (1998.0) 39 171-181. DOI: 10.1111/1469-7610.00311 54. Hauck M, Fein D, Nicholas Maltby N, Waterhouse L, Feinstein C. Memory for faces in children with autism. Child Neuropsychol. (1998.0) 4 187-198. DOI: 10.1076/chin.4.3.187.3174 55. McPartland JC, Webb SJ, Keehn B, Dawson G. Patterns of visual attention to faces and objects in autism spectrum disorder. J. Autism Dev. Disord. (2011.0) 41 148-157. DOI: 10.1007/s10803-010-1033-8 56. Blumberg SJ. Diagnosis lost: Differences between children who had and who currently have an autism spectrum disorder diagnosis. Autism (2016.0) 20 783-795. DOI: 10.1177/1362361315607724 57. Griffiths S. The Vulnerability Experiences Quotient (VEQ): a study of vulnerability, mental health and life satisfaction in autistic adults. Autism Res. (2019.0) 12 1516-1528. DOI: 10.1002/aur.2162 58. Moss P, Mandy W, Howlin P. Child and adult factors related to quality of life in adults with autism. J. Autism Dev. Disord. (2017.0) 47 1830-1837. DOI: 10.1007/s10803-017-3105-5 59. van Heijst BF, Geurts HM. Quality of life in autism across the lifespan: a meta-analysis. Autism (2015.0) 19 158-167. DOI: 10.1177/1362361313517053 60. Ayres M. A systematic review of quality of life of adults on the autism spectrum. Autism (2018.0) 22 774-783. DOI: 10.1177/1362361317714988 61. Roux AM. Postsecondary employment experiences among young adults with an autism spectrum disorder. J. Am. Acad. Child Adolesc. Psychiatry (2013.0) 52 931-939. DOI: 10.1016/j.jaac.2013.05.019 62. Orsmond GI, Shattuck PT, Cooper BP, Sterzing PR, Anderson KA. Social participation among young adults with an autism spectrum disorder. J. Autism Dev. Disord. (2013.0) 43 2710-2719. DOI: 10.1007/s10803-013-1833-8 63. Solomon C. Autism and employment: implications for employers and adults with ASD. J. Autism Dev. Disord. (2020.0) 50 4209-4217. DOI: 10.1007/s10803-020-04537-w 64. Doshi PK, Hegde A, Desai A. Nucleus accumbens deep brain stimulation for obsessive-compulsive disorder and aggression in an autistic patient: a case report and hypothesis of the role of nucleus accumbens in autism and comorbid symptoms. World Neurosurg. (2019.0) 125 387-391. DOI: 10.1016/j.wneu.2019.02.021 65. Davis RA. Deep brain stimulation for OCD in a patient with comorbidities: epilepsy, tics, autism, and major depressive disorder. J. Neuropsychiatry Clin. Neurosci. (2021.0) 33 167-171. DOI: 10.1176/appi.neuropsych.20060153 66. Barker GR, Warburton EC. When is the hippocampus involved in recognition memory?. J. Neurosci. (2011.0) 31 10721-10731. DOI: 10.1523/JNEUROSCI.6413-10.2011 67. Liu H. Dissection of the relationship between anxiety and stereotyped self-grooming using the Shank3B mutant autistic model, acute stress model and chronic pain model. Neurobiol. Stress. (2021.0) 15 100417. DOI: 10.1016/j.ynstr.2021.100417 68. Wang W. Striatopallidal dysfunction underlies repetitive behavior in Shank3-deficient model of autism. J. Clin. Investig. (2017.0) 127 1978-1990. DOI: 10.1172/JCI87997 69. Pagani M. Deletion of autism risk gene Shank3 disrupts prefrontal connectivity. J. Neurosci. (2019.0) 39 5299-5310. DOI: 10.1523/JNEUROSCI.2529-18.2019 70. Llorens-Martín M, Jurado-Arjona J, Avila J, Hernández F. Novel connection between newborn granule neurons and the hippocampal CA2 field. Exp. Neurol. (2015.0) 263 285-292. DOI: 10.1016/j.expneurol.2014.10.021 71. Cope EC. Adult-born neurons in the hippocampus are essential for social memory maintenance. eNeuro (2020.0) 7 ENEURO.0182-20.2020. DOI: 10.1523/ENEURO.0182-20.2020 72. Llorens-Martin M. Tau isoform with three microtubule binding domains is a marker of new axons generated from the subgranular zone in the hippocampal dentate gyrus: implications for Alzheimer’s disease. J. Alzheimers Dis. (2012.0) 29 921-930. DOI: 10.3233/JAD-2012-112057 73. Robert V. Local circuit allowing hypothalamic control of hippocampal area CA2 activity and consequences for CA1. Elife (2021.0) 10 e63352. DOI: 10.7554/eLife.63352 74. Heise C. Selective localization of shanks to vGLUT1-positive excitatory synapses in the mouse hippocampus. Front. Cell Neurosci. (2016.0) 10 106. DOI: 10.3389/fncel.2016.00106 75. Al-Onaizi MA. Regulation of cognitive processing by hippocampal cholinergic tone. Cereb. Cortex (2017.0) 27 1615-1628. PMID: 26803167 76. Balschun D. Vesicular glutamate transporter VGLUT1 has a role in hippocampal long-term potentiation and spatial reversal learning. Cereb. Cortex (2010.0) 20 684-693. DOI: 10.1093/cercor/bhp133 77. Zander JF. Synaptic and vesicular coexistence of VGLUT and VGAT in selected excitatory and inhibitory synapses. J. Neurosci. (2010.0) 30 7634-7645. DOI: 10.1523/JNEUROSCI.0141-10.2010 78. Kohara K. Cell type-specific genetic and optogenetic tools reveal hippocampal CA2 circuits. Nat. Neurosci. (2014.0) 17 269-279. DOI: 10.1038/nn.3614 79. Middleton SJ, McHugh TJ. CA2: a highly connected intrahippocampal relay. Annu. Rev. Neurosci. (2020.0) 43 55-72. DOI: 10.1146/annurev-neuro-080719-100343 80. Wenzel HJ, Cole TB, Born DE, Schwartzkroin PA, Palmiter RD. Ultrastructural localization of zinc transporter-3 (ZnT-3) to synaptic vesicle membranes within mossy fiber boutons in the hippocampus of mouse and monkey. Proc. Natl Acad. Sci. USA (1997.0) 94 12676-12681. DOI: 10.1073/pnas.94.23.12676 81. Laham BJ, Diethorn EJ, Gould E. Newborn mice form lasting CA2-dependent memories of their mothers. Cell Rep. (2021.0) 34 108668. DOI: 10.1016/j.celrep.2020.108668 82. Cope EC. Atypical perineuronal nets in the CA2 region interfere with social memory in a mouse model of social dysfunction. Mol. Psychiatry (2022.0) 27 3520-3531. DOI: 10.1038/s41380-021-01174-2 83. Wu X, Morishita W, Beier KT, Heifets BD, Malenka RC. 5-HT modulation of a medial septal circuit tunes social memory stability. Nature (2021.0) 599 96-101. DOI: 10.1038/s41586-021-03956-8 84. Adhikari A, Topiwala MA, Gordon JA. Synchronized activity between the ventral hippocampus and the medial prefrontal cortex during anxiety. Neuron (2010.0) 65 257-269. DOI: 10.1016/j.neuron.2009.12.002 85. Murthy S. Perineuronal nets, inhibitory interneurons, and anxiety-related ventral hippocampal neuronal oscillations are altered by early life adversity. Biol. Psychiatry (2019.0) 85 1011-1020. DOI: 10.1016/j.biopsych.2019.02.021 86. Padilla-Coreano N. Hippocampal-prefrontal theta transmission regulates avoidance behavior. Neuron (2019.0) 104 601-610. DOI: 10.1016/j.neuron.2019.08.006 87. McNaughton BL, Barnes CA, O’Keefe J. The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats. Exp. Brain Res. (1983.0) 52 41-49. DOI: 10.1007/BF00237147 88. Buzsáki G. Theta rhythm of navigation: Link between path integration and landmark navigation, episodic and semantic memory. Hippocampus (2005.0) 15 827-840. DOI: 10.1002/hipo.20113 89. Hinman JR, Penley SC, Long LL, Escabí MA, Chrobak JJ. Septotemporal variation in dynamics of theta: speed and habituation. J. Neurophysiol. (2011.0) 105 2675-2686. DOI: 10.1152/jn.00837.2010 90. Schultheiss NW. Awake delta and theta-rhythmic hippocampal network modes during intermittent locomotor behaviors in the rat. Behav. Neurosci. (2020.0) 134 529-546. DOI: 10.1037/bne0000409 91. Alexander GM. CA2 neuronal activity controls hippocampal low gamma and ripple oscillations. Elife (2018.0) 7 e38052. DOI: 10.7554/eLife.38052 92. Hedger N, Chakrabarti B. Autistic differences in the temporal dynamics of social attention. Autism (2021.0) 25 1615-1626. DOI: 10.1177/1362361321998573 93. Aldridge-Waddon L, Vanova M, Munneke J, Puzzo I, Kumari V. Atypical social reward anticipation as a transdiagnostic characteristic of psychopathology: a meta-analytic review and critical evaluation of current evidence. Clin. Psychol. Rev. (2020.0) 82 101942. DOI: 10.1016/j.cpr.2020.101942 94. Chevallier C, Kohls G, Troiani V, Brodkin ES, Schultz RT. The social motivation theory of autism. Trends Cogn. Sci. (2012.0) 16 231-239. DOI: 10.1016/j.tics.2012.02.007 95. Donegan ML. Coding of social novelty in the hippocampal CA2 region and its disruption and rescue in a 22q11.2 microdeletion mouse model. Nat. Neurosci. (2020.0) 23 1365-1375. DOI: 10.1038/s41593-020-00720-5 96. Dasgupta A. Substance P induces plasticity and synaptic tagging/capture in rat hippocampal area CA2. Proc. Natl Acad. Sci. USA (2017.0) 114 E8741-E8749. DOI: 10.1073/pnas.1711267114 97. Rhine MA, Parrott JM, Schultz MN, Kazdoba TM, Crawley JN. Hypothesis-driven investigations of diverse pharmacological targets in two mouse models of autism. Autism Res. (2019.0) 12 401-421. DOI: 10.1002/aur.2066 98. Penley SC. Novel space alters theta and gamma synchrony across the longitudinal axis of the hippocampus. Front. Syst. Neurosci. (2013.0) 7 20. DOI: 10.3389/fnsys.2013.00020 99. Kitanishi T. Novelty-induced phase-locked firing to slow gamma oscillations in the hippocampus: requirement of synaptic plasticity. Neuron (2015.0) 86 1265-1276. DOI: 10.1016/j.neuron.2015.05.012 100. Zheng C, Bieri KW, Hwaun E, Colgin LL. Fast gamma rhythms in the hippocampus promote encoding of novel object-place pairings.. eNeuro (2016.0) 3 0001-16.2016. DOI: 10.1523/ENEURO.0001-16.2016 101. Kragel JE. Hippocampal theta coordinates memory processing during visual exploration. Elife (2020.0) 9 e52108. DOI: 10.7554/eLife.52108 102. Park AJ. Reset of hippocampal-prefrontal circuitry facilitates learning. Nature (2021.0) 591 615-619. DOI: 10.1038/s41586-021-03272-1 103. Wang C. Hippocampus-prefrontal coupling regulates recognition memory for novelty discrimination. J. Neurosci. (2021.0) 41 9617-9632. DOI: 10.1523/JNEUROSCI.1202-21.2021 104. Tendler A, Wagner S. Different types of theta rhythmicity are induced by social and fearful stimuli in a network associated with social memory. Elife (2015.0) 4 e03614. DOI: 10.7554/eLife.03614 105. Cheaha D, Kumarnsit E. Alteration of spontaneous spectral powers and coherences of local field potential in prenatal valproic acid mouse model of autism. Acta Neurobiol. Exp. (2015.0) 75 351-363 106. Gergues MM. Circuit and molecular architecture of a ventral hippocampal network. Nat. Neurosci. (2020.0) 23 1444-1452. DOI: 10.1038/s41593-020-0705-8 107. Amilhon B. Parvalbumin interneurons of hippocampus tune population activity at theta frequency. Neuron (2015.0) 86 1277-1289. DOI: 10.1016/j.neuron.2015.05.027 108. Deng X, Gu L, Sui N, Guo J, Liang J. Parvalbumin interneuron in the ventral hippocampus functions as a discriminator in social memory. Proc. Natl Acad. Sci. USA (2019.0) 116 16583-16592. DOI: 10.1073/pnas.1819133116 109. Kouser M. Loss of predominant Shank3 isoforms results in hippocampus-dependent impairments in behavior and synaptic transmission. J. Neurosci. (2013.0) 33 18448-18468. DOI: 10.1523/JNEUROSCI.3017-13.2013 110. Wang X. Synaptic dysfunction and abnormal behaviors in mice lacking major isoforms of Shank3. Hum. Mol. Genet. (2011.0) 20 3093-3108. DOI: 10.1093/hmg/ddr212 111. Frank Y. The neurological manifestations of Phelan-McDermid Syndrome. Pediatr. Neurol. (2021.0) 122 59-64. DOI: 10.1016/j.pediatrneurol.2021.06.002 112. Jaramillo TC. Novel Shank3 mutant exhibits behaviors with face validity for autism and altered striatal and hippocampal function. Autism Res. (2017.0) 10 42-65. DOI: 10.1002/aur.1664 113. Maggio N, Segal M. Unique regulation of long term potentiation in the rat ventral hippocampus. Hippocampus (2007.0) 17 10-25. DOI: 10.1002/hipo.20237 114. Kouvaros S, Papatheodoropoulos C. Theta burst stimulation-induced LTP: Differences and similarities between the dorsal and ventral CA1 hippocampal synapses. Hippocampus (2016.0) 26 1542-1559. DOI: 10.1002/hipo.22655 115. Alexander GM. Remote control of neuronal activity in transgenic mice expressing evolved G protein-coupled receptors. Neuron (2009.0) 63 39. DOI: 10.1016/j.neuron.2009.06.014 116. Ray RS. Impaired respiratory and body temperature control upon acute serotonergic neuron inhibition. Science (2011.0) 333 642. DOI: 10.1126/science.1205295 117. Chang CH, Gean PW. The ventral hippocampus controls stress-provoked impulsive aggression through the ventromedial hypothalamus in post-weaning social isolation mice. Cell Rep. (2019.0) 28 1195-1205.e3. DOI: 10.1016/j.celrep.2019.07.005 118. Smith AS, Williams Avram SK, Cymerblit-Sabba A, Song J, Young WS. Targeted activation of the hippocampal CA2 area strongly enhances social memory. Mol. Psychiatry (2016.0) 21 1137-1144. DOI: 10.1038/mp.2015.189 119. Cope EC. Microglia play an active role in obesity-associated cognitive decline. J. Neurosci. (2018.0) 38 8889-8904. DOI: 10.1523/JNEUROSCI.0789-18.2018 120. Cope EC. The effects of living in an outdoor enclosure on hippocampal plasticity and anxiety-like behavior in response to nematode infection. Hippocampus (2019.0) 29 366-377. DOI: 10.1002/hipo.23033 121. 121.Laham, B. J. & Zahn, E. einaraz/SharpWaveRipple: V0.0.2 (v0.0.2). Zenodo. 10.5281/zenodo.7592606 (2023). 122. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. (2015.0) 67 1-48. DOI: 10.18637/jss.v067.i01 123. 123.Lenth, R. _emmeans: Estimated Marginal Means, aka Least-Squares Means_. R package version 1.7.5, https://CRAN.R-project.org/package=emmeans (2022).
--- title: Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model authors: - Kevin Knoernschild - Hans J. Johnson - Kimberly E. Schroeder - Vicki J. Swier - Katherine A. White - Takashi S. Sato - Christopher S. Rogers - Jill M. Weimer - Jessica C. Sieren journal: Scientific Reports year: 2023 pmcid: PMC10060411 doi: 10.1038/s41598-023-32071-z license: CC BY 4.0 --- # Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model ## Abstract Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (− 9.06 cm3), gray matter (− $4.37\%$ 95 CI − 7.41; − 1.83), caudate (− $0.16\%$, 95 CI − 0.24; − 0.08) and putamen (− $0.11\%$ 95 CI − 0.23; − 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ $3.42\%$, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (− $8.27\%$, 95 CI − 10.1; − 5.56) and cerebrospinal fluid (+ $6.88\%$, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation. ## Introduction Batten disease, or neuronal ceroid lipofuscinoses, is a group of neurodegenerative diseases in which mutations in 13–14 different genes can cause issues with a lysosome’s capability to recycle or process molecules. All forms of Batten disease show similar symptoms but are caused by different gene mutations1,2. The late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease is rare, and usually not diagnosed until the patient is already symptomatic. This disease occurs in children around 2–4 years of age causing early brain degeneration and cognitive decline, in addition to the loss of visual and motor functions, ultimately leading to death2–4. These regressions are caused by an enzyme deficiency of tripeptidyl peptidase 1, which causes an inability to remove waste that would normally be metabolized by lysosomes1. No cure exists for CLN2 disease, but enzyme replacement therapy protocols have been approved as a method to slow disease progression5. For these replacement therapies to be effective, they must be administered as soon as possible. Early disease recognition and initiation of therapy are essential in treating patients with CLN2 disease. Disease frequency within CLN2 is not well reported across the world population, with sources varying from 0.15 to 9 in 100,000 births6–8. This low frequency in the population hinders single-center study recruitment and data collection, making consistent longitudinal data acquisition of humans affected by CLN2 disease difficult. It is particularly challenging to obtain data at a pre-symptomatic stage of the disease. MRI studies have been completed with human subjects with CLN2 disease, but the imaging studies used are at variable points in the disease course due to differences in disease onset, diagnosis, and patient availability9. Therefore, there is a need to model CLN2 disease progression from pre- to post-symptomatic stages that is standardized. One way to analyze disease progression in neurodegenerative diseases is through volumetrics from non-invasive medical imaging, such as MRI, as a measure of cerebral atrophy. Cerebral atrophy is the loss of neurons in the brain, which results in overall brain tissue shrinkage. Batten disease studies in humans have used volumetric analysis to show noteworthy gray matter (GM) and white matter (WM) atrophy over time9,10. Additionally, CLN2 disease studies that include volumetric analysis show ventricular expansion and higher overall volumes of cerebrospinal fluid (CSF) compared to a normal brain10 An increase in total CSF volume is one potential sign of neurodegenerative progression when observed in combination with intracranial volume (ICV), GM, and WM changes11,12. The purpose of our study is to test our hypothesis that brain volumetry at 12- and 17-months for CLN2R208X/R208X miniswine differs from that of wild type controls. We hypothesize that degeneration due to CLN2R208X/R208X will mirror reported brain tissue degeneration in humans with CLN2 disease. Volumetric analysis of specific regional brain degeneration in miniswine affected by CLN2R208X/R208X can then be used as early biomarkers for disease progression severity in patients and for monitoring pre-clinical therapy response. ## Animals All procedures were approved by the Institutional Animal Care and Use Committees (IACUC) of the University of Iowa and Precigen Exemplar, all methods were performed in accordance with relevant regulations and guidelines, and reported in accordance with ARRIVE guidelines. 23 unique Yucatan miniswine underwent MR imaging over a two-year period. 18 female miniswine were CLN2R208X/R208Xconfirmed, with processes further described in a characterization paper including behavioral, pathological, and phenotypical analysis13. Of these 18 CLN2R208X/R208X miniswine, 10 animals completed longitudinal imaging procedures at 12-month and 17-month time points. Of the remaining 8 CLN2R208X/R208X confirmed miniswine, 4 completed imaging at the 12-month time point only, and 4 completed imaging at the 17-month time point only. An additional comparator group of 5 wild type (WT) Yucatan miniswine completed MR imaging at both 12 and 17-month time points. At 12-months of age, CLN2R208X/R208X miniswine did not have overt symptom development (early disease stage)13. By 17-months of age CLN2R208X/R208X miniswine exhibited late-stage disease symptoms (such as blindness, motor deterioration and/or seizures), mirroring symptoms in CLN2R208X/R208Xpatients13. Animals were pre- anesthetized with either a combination of telazol (2.2–4.4 mg/kg), ketamine (1.1–2.2 mg/kg) and xylazine (1.1–2.2 mg/kg) or ketamine (22-33 mg/kg) and acepromazine (1.1 mg/kg). Anesthesia was maintained with inhaled isoflurane (1–$5\%$). Animals were intubated with a balloon-cuffed endotracheal tube to maintain the airway and underwent imaging with free-breathing oxygen and isoflurane (~ $2\%$). If additional respiratory support was needed, mechanical ventilation was administered at a tidal volume of approximately 10 mL/kg and respiratory rate of 18–22 breaths per minute using a Premier SP MRI-compatible veterinary anesthesia ventilator (DRE Veterinary). ## Imaging MRI data were acquired using a 3 T SIGNA Premier MRI scanner (GE Healthcare) with a medium 16-channel flexible coil (GE Healthcare). Animals were positioned right-side feet first in the MRI scanner. The full miniswine imaging protocol included T1-weighted, T2-weighted, diffusion-weighted imaging, and field map acquisitions. T1-weighted images utilized the BRAVO pulse sequence (TR/TE/TI/flip angle: $\frac{7.6}{3.3}$/450 ms/12°; voxel size: 0.7 × 0.7 × 0.7 mm3). T2-weighted images utilized the CUBE pulse sequence (TR/TE: $\frac{3000}{51}$; voxel size: 0.7 × 0.7 × 0.7 mm3). To improve image quality and reduce potential motion artifacts discovered during initial miniswine test scans using longer acquisition times, multiple shorter acquisitions were acquired and then averaged (1–4 acquisitions approximately 6 min each). ## Image pre-processing Each animals’s T1-weighted and T2-weighted MRI data were visually inspected for motion artifacts before pre-processing. A single reference T1-weighted image was selected for each scanning session and the anterior commissure/posterior commissure aligned. The remaining T1-weighted images for that individual session were rigidly registered to the selected T1-weighted image using the Brain Research: Analysis of Images, Networks and Systems toolkit (BRAINStools)14,15. T2-weighted images were registered using rigid and affine transforms to the reference T1w image utilizing the Advanced Normalization Tools toolkit16. Registered images were then averaged together based off image type (T1- or T2-weighted respectively) resulting in a single T1-weighted/T2-weighted image pair for each miniswine. These averaged, aligned image pairs then underwent Rician denoising using BRAINStools’ DenoiseImage function. ## Segmentations Three-dimensional manual segmentations were created for each animal using 3D Slicer’s segmentation editing tool (https://www.slicer.org) and each MRI scan sessions averaged T1w/T2w pairs for boundary reference17. Regions of interest included caudate, putamen, lateral ventricles, and the total intracranial volume (ICV) of the skull10,18. For consistency, an adapted miniswine version of McRae’s line was used as a reference cutoff point for the intracranial segmentation19. Additionally, the Advanced Normalization Tools ATROPOS script was used to create an automated segmentation of the CSF, WM and GM using the default suggested parameters for 3-class tissue segmentation20. ## Immunohistochemistry Brains were histologically examined for classic Batten disease pathology in the somatosensory cortex as this is one of the more commonly examined cortical regions that displays Batten disease pathology21,22. Female animals were sacrificed with pentobarbital at 17 months of age, and one hemisphere of the brain was placed into $10\%$ neutral buffered formalin for approximately 3 weeks. The brain was sub-dissected into somatosensory cortex blocks and equilibrated in cryoprotectant solution ($30\%$ sucrose in TBSA) at 4 °C. Blocks were serial sectioned (50 µm) on a freezing microtome (Leica) and free-floating sections were used for standard immunohistochemistry13,23–27. The following primary antibodies were used: anti-mitochondrial ATP synthase subunit C (Abcam, ab181243; 1:2000). Immunolabeled sections were scanned using an Aperio Versa slide scanner (Leica Biosystems, IL, USA) and at least 3 images were extracted from each region of interest and processed as previously published for total percent area of mitochondrial ATP synthase subunit c (SubC)13,23,24. At 17-months-of-age, SubC significantly accumulates in the somatosensory cortex of CLN2R208X/R208X animals compared to age matched, WT counterparts (Fig. 7). While correlation between SubC and MRI brain volumetry did not reach statistical significance for this small sub-cohort, there was a weak trend of lower ICV and GM volumes at higher SubC values for CLN2R208X/R208X miniswine at 17-months-of-age (Supplementary Fig. 1).Figure 7Mitochondrial ATP synthase subunit c accumulates in the somatosensory cortex of CLN2R208X/R208X animals. Subunit c accumulation shown in the somatosensory cortex at 17-months-of-age. Mean ± SEM, Nested t-test. ** $p \leq 0.0001.$ Scale bar = 200 µm. ## Data analysis Brain region segmentations were used to calculate regional volumes. These volume measurements were expressed as percent of total ICV and used for cohort and longitudinal comparisons between 12- and 17-month CLN2R208X/R208X and WT pigs. Estimation plots are used in this study to evaluate the differences between groups and time points. A Gardner–Altman plot is an estimation plot that allows transparent visualization of the all the data as a swarm plot, the effect size (difference in the means) and the precision ($95\%$ confidence interval)28,29. For estimation plots, if the average difference and $95\%$ confidence interval do not cross the horizontal line at zero it indicates a reliable measurement difference between the cohorts. If the $95\%$ confidence interval crosses the horizontal line at zero, as an effect size equal to zero is possible and the measurement difference is unlikely to be reliable. Gardner-Altman plots were created using the DABEST open-source library for R (R, version 4.0.3)29. Plots and statistical testing of the SubC data were analyzed in Graphpad Prism 9.0 as specified in the figure legend ($p \leq 0.0001$). ## Results Thirty-eight scans were analyzed, comprised of fourteen 12-month-old CLN2R208X/R208X miniswine scan sessions, fourteen 17-month-old miniswine scan sessions, five WT 12-month-old miniswine scan sessions, and five WT 17-month-old miniswine scans scan sessions. Ten of the CLN2R208X/R208X miniswine had scan data available for both 12- and 17-month time points. Representative images of the MRI data for WT and CLN2R208X/R208X are shown in Fig. 1. All five WT animals had 12- and 17-month time point scans acquired. A single CLN2R208X/R208X 12-month scan session was left out due to scan quality causing segmentation failure for automated extraction of the GM, WM and CSF. Diffusion-weighted imaging data were collected but included pronounced artifact, caused primarily due to the large, complex sinus structure of the miniswine, hence these data were not quantitatively analyzed in this study. Figure 1Exemplary T1-weighted (T1w) and T2-weighted (T2w) MRI data from a CLN2R208X/R208X miniswine at 12-months-of-age (top left) and 17-months (top right), and a wild type (WT) comparator (bottom). Images were aligned using anterior commissure, posterior commissure, and basal pons, so that equivalent axial slices were taken from each time point. ## Intracranial volume (ICV) At 12-months, CLN2R208X/R208X miniswine had a smaller average ICV compared to WT miniswines by 9.06 cm3. At 17-months, this ICV volume difference increased, with the CLN2R208X/R208X cohort having a smaller average ICV by 19.95 cm3 compared to 17-month-old WT cohort. The average ICV volume for CLN2R208X/R208X animals was 100.46 (± 5.53) cm3 at 12-months, and 96.22 (± 6.62) cm3 at 17-months old (average decrease over time of 4.24 cm3). The average ICV volume for 12-month WT animals was 109.52 (± 5.65) cm3, and 116.17 (± 4.51) cm3 at 17-months (average increase over time of 6.65 cm3). A summary of all volume changes is available in Table 1.Table 1Mean and standard deviation of the volumes for the brain volumetry regions. Tissue measurementCLN2 12-monthCLN2 17-monthWT 12-monthWT 17-monthICV (cm3)100.46 ± 5.5396.22 ± 6.62109.52 ± 5.65116.17 ± 4.51Caudate (% ICV)0.72 ± 0.080.67 ± 0.080.88 ± 0.080.80 ± 0.07Putamen (% ICV)0.65 ± 0.090.59 ± 0.070.76 ± 0.110.65 ± 0.04Ventricles (% ICV)1.72 ± 0.482.45 ± 0.371.98 ± 1.062.24 ± 1.24Gray Matter (% ICV)34.71 ± 2.0229.29 ± 0.9639.08 ± 2.9137.56 ± 2.38White Matter (% ICV)39.18 ± 3.1040.16 ± 2.7138.23 ± 2.9838.77 ± 1.26CSF (% ICV)26.11 ± 2.5030.55 ± 2.2522.68 ± 2.4723.67 ± 2.41 We evaluated the relationship between ICV change over time and miniswine weight (Fig. 2). The reduction in ICV between 12- and 17-months-of-age was consistent in CLN2R208X/R208X miniswine, despite a large diversity in the degree of weight change between the time points (ranging from 0 to 22 kg), with no negative correlation. Figure 2The relationship between weight change (17–12 months) and change in intracranial volume (ICV) (17–12 months) in the CLN2R208X/R208X miniswine (blue, R2 = 0.0011) and WT miniswine (red, R2 = 0.13), showing reduction in ICV (volume change range: − 1 to − 8 cm3) across CLN2R208X/R208X miniswine unrelated to the large variation in weight change across the cohort (0 to 22 kg). ## Gray matter (GM) The largest proportional change in ICV volume between cohorts occurred in the GM ICV proportion measurements. Compared to the WT controls, CLN2R208X/R208X GM proportion of ICV was smaller by $4.37\%$ at 12 months, and $8.27\%$ at 17 months. The precision of the differences between CLN2R208X/R208X and WT at both 12- and 17-months are shown in the confidence intervals of Fig. 3A (12-month comparison 95 CI − 7.41; − 1.83) and Fig. 4A (17-month comparison 95 CI − 10.1; − 5.56). As the confidence intervals do not cross zero, there is $95\%$ confidence that the difference between the cohorts is not zero. The CLN2R208X/R208X cohort showed a longitudinal decrease in GM over time of $5.43\%$ (Fig. 5A, 95 CI − 6.7; − 4.1), while there was no support for a meaningful decrease in the WT (Fig. 6A, 95 CI − 5.15; 1.52).Figure 3Early disease stage (at 12-months-of-age) ICV proportion comparison of brain regions between WT and CLN2R208X/R208X cohorts. The average difference and $95\%$ confidence interval (black circle with error bar in CLN2 minus WT column) do not cross the horizontal line at zero and indicate reliable measurement difference between the cohorts in gray matter (A), CSF (B), caudate (C), and putamen (D). When the average difference and $95\%$ confidence intervals (black circle with error bar in CLN2 minus WT column) cross the horizontal line at zero as it does for white matter (E) and ventricles (F), an effect size equal to zero is possible, and reliable measured difference is unlikely. Figure 4Late disease stage (at 17-months of age) ICV proportion comparison of brain regions between WT and CLN2R208X/R208X cohorts. The average difference and $95\%$ confidence interval (black circle with error bar in CLN2 minus WT column) do not cross the horizontal line at zero and indicate reliable measurement difference between the cohorts in gray matter (A), CSF (B), caudate (C), and putamen (D). The average difference and $95\%$ confidence intervals (black circle with error bar in CLN2 minus WT column) cross the horizontal line at zero for white matter (E) and ventricles (F), indicating reliable measured difference is unlikely. Figure 5ICV proportion comparison of brain regions between 12- and 17-month-old CLN2R208X/R208X minipigs. The average difference and $95\%$ confidence interval (black circle with error bar in 17 mos minus 12 mos column) do not cross the horizontal line at zero and indicates reliable measurement difference between the cohorts in gray matter (A), CSF (B), putamen (D), and ventricles (F). When the average difference and $95\%$ confidence intervals (black circle with error bar in 17 mos minus 12 mos column) cross the horizontal line at zero as it does for caudate (C) and white matter (E), an effect size equal to zero is possible, and reliable measured difference is unlikely. Figure 6ICV proportion comparison of brain regions between 12- and 17-month-old WT miniswine. The average difference and $95\%$ confidence interval (black circle with error bar in 17 mos minus 12 mos column) do not cross the horizontal line at zero and indicates reliable measurement difference between the cohorts in the putamen (D). When the average difference and $95\%$ confidence intervals (black circle with error bar in 17 mos minus 12 mos column) cross the horizontal line at zero as it does for gray matter (A), CSF (B), caudate (C), white matter (E) and ventricles (F), an effect size equal to zero is possible, and reliable measured difference is unlikely. ## Cerebrospinal fluid (CSF) CSF also showed a large longitudinal ICV proportion change for the CLN2R208X/R208X animals. CSF proportion of ICV was larger at both 12- and 17-month measurements of CLN2R208X/R208X affected miniswines compared to WT (12-month: + $3.42\%$, 17-month: $6.88\%$). Figure 3B visualizes the confidence interval of a shift in the longitudinal CLN2R208X/R208X population (95 CI 2.54; 6.18) and Fig. 4B supports the difference between their WT counterparts at 17-months old (95 CI 4.31; 8.51). On average, longitudinal CSF proportion of ICV increased by $4.44\%$ for CLN2R208X/R208X animals (Fig. 5B, 95 CI 2.54; 6.18). By comparison, the WT had a small increase of $0.98\%$ which was not supported by the confidence interval (Fig. 6B, 95 CI − 1.89; 3.93). ## Caudate and putamen Caudate and putamen are very small interior structures compared to the ICV, and hence the numerical change longitudinally for the CLN2R208X/R208X and WT cohorts is also extremely small. CLN2R208X/R208X caudate ICV proportion compared to the average WT counterpart was smaller at both 12-months (Fig. 3C, − $0.16\%$, 95 CI − 0.24; − 0.08) and at 17-months (Fig. 4C, − $0.13\%$, 95 CI − 0.21; − 0.07). CLN2R208X/R208X putamen ICV proportion at 12 and 17-months was also smaller, than the corresponding WT measurements, at − $0.11\%$ (Fig. 3D, 95 CI − 0.23; − 0.02) and $0.06\%$ (Fig. 4D, 95 CI − 0.12; − 0.01). Over time in the CLN2R208X/R208X cohort, the average change in ICV proportion was decreased for the putamen (Fig. 5D, − $0.06\%$, 95 CI − 0.13; − 0.002). This longitudinal decrease similarly occurred in the WT putamen (Fig. 6D, − $0.11\%$, 95 CI − 0.25; − 0.03). However, for the longitudinal change in caudate ICV proportion there was not a reliable difference in either cohort (Figs. 5C, 6C). ## White matter (WM) WM proportional ICV volumes were relatively stable between the CLN2R208X/R208X and WT cohorts. Compared with the WT cohort, the CLN2R208X/R208X miniswine had non-significant, slightly higher percentage of ICV occupied by WM at 12-months (Fig. 3E: + $0.95\%$, 95 CI − 2.48; 3.58) and at 17-months (Fig. 4E: + $1.39\%$, 95 CI − 0.55; 3.27). Longitudinal changes between 12- and 17-months were not supported by the confidence interval to have a reliable difference for either cohort: the CLN2R208X/R208X difference of + $0.98\%$ (95 CI − 1.34; 3.06) (Fig. 5E) and WT difference of + $0.54\%$ (95 CI − 3.28; 2.48) (Fig. 6E). ## Ventricles Average proportion of ICV for lateral ventricles increased significantly over time for CLN2R208X/R208X animals by $0.73\%$ (Fig. 5F, 95 CI 0.38; 1.03) compared to no meaningful change in WT animals (Fig. 6F, $0.26\%$ 95 CI − 1.11; 1.71). At 12-months, CLN2R208X/R208X miniswine had a lateral ventricle average ICV proportion of 1.72 (± 0.48)% compared to the WT measure of 1.98 (± 1.06)%. Figures 3F and 4F illustrate that comparing the CLN2R208X/R208X cohort to the WT cohort, there was little difference between the two in the proportion of ICV for lateral ventricles. ## Discussion This study identified MRI biomarkers of early disease and longitudinal change in a cohort of CLN2R208X/R208X versus WT miniswines. At an early stage of disease progression (12-months-of-age in the miniswine model), we found meaningful differences in the ICV proportional volumes of GM (reduced in CLN2R208X/R208X), caudate (reduced in CLN2R208X/R208X), putamen (reduced in CLN2R208X/R208X) and CSF (elevated in CLN2R208X/R208X) between CLN2R208X/R208X and WT cohorts. As the diseased progressed to a late stage (17-months-of-age in the miniswine model), the difference in the ICV proportional volumes of GM and CSF became more pronounced, while the other measurement differences remained relatively consistent to the 12-month relationship. This indicates that evaluation of MRI derived brain volumes could be utilized to monitor treatment response in preclinical miniswine studies. Characterization of the CLN2R208X/R208X miniswine model used for this study has recently been reported by Swier et al.13 incorporating behavioral, pathological, and visual testing regularly from 3-months to 18-months-of-age. This study demonstrated the age of onset of phenotypes for female CLN2R208X/R208X miniswine including overt vision deficits at a mean age of 15-months, motor coordination loss at a mean age of 16-months, and early death at a mean age of 17.5-months. At 12 months-of-age, reversal learning deficits were observed in CLN2R208X/R208X females during t-maze testing13, similar to that observed in other Batten disease large animal models (TPP1−/− dog and CLN5 sheep models)30–32, as animals approached end-stage, reversal deficits increased. Accumulation of SubC in the somatosensory cortex, consistent with the findings from Swier et al.13, are presented for a subset of the miniswine that underwent MRI analysis as a connection to these characterization studies. Correlation between SubC measures and MRI brain volumetry did not reach statistical significance in this study, potentially due to the small sample size for which ex-vivo tissue samples were available. Miniswine present a valuable translational research tool for exploring and optimizing medical image acquisition and analysis, understanding disease etiology, and exploring innovative treatment options. Genetically modified miniswine have been used for advancing a broad range of diseases including cystic fibrosis, cancer, neurofibromatosis, and others33–47. For neurological studies, the brain morphology development and gray to white matter ratios in miniswine are similar to humans. Porcine models have been used to study neurodegenerative diseases, including Alzheimer’s and traumatic brain injury48,49. Genetically modified miniswine can have standardized brain image acquisition times at pre-determined longitudinal timepoints, creating a more consistent representation of CLN2R208X/R208X disease progression which is challenging to achieve in humans for rare pediatric diseases. Beyond the miniswine, Batten disease imaging studies have been completed in other large animal models such as sheep (CLN5 and CLN6)50, and non-human primates (CLN7), as well as CLN2 dachshund studies that focused on multifocal retinopathy and extraneuronal pathology39,51,52. Expression of brain volumetry as a proportion of total ICV is commonly used in neurological disease studies to compensate between-subject variation in head size at both juvenile and elderly stage disease progressions53,54. In this miniswine model, ICV volumes showed a longitudinal decrease across the CLN2R208X/R208X cohort, as opposed to ICV longitudinal volume increase for the WT cohort. The average ICV volumetric difference at the 17-month time point was larger between the CLN2R208X/R208X and WT cohorts than at 12-months. This serves as a baseline identifier that is consistent with a recent longitudinal NCL sheep study, which also showed a longitudinal decrease in ICV for CLN5 and CLN6 affected sheep as compared to a control cohort that showed an ICV increase over time50,55. Russell et al. hypothesized this ICV reduction could be caused by ventricular shunting50. Ventricular shunting, or a widening in the CSF fluid draining pathways in the brain, can cause decreased pressure of the brain and CSF against the skull. This may result in a thickening of the skull to balance out the pressure differential of the affected subjects and a decreased ICV. Our results indicate that while some CLN2R208X/R208X miniswine failed to thrive as indicated by low weight gain over a five-month period, there was no correlation between change in ICV volume and weight change (Fig. 2). GM ICV proportional measurements at both 12- and 17-months showed the largest longitudinal proportion change, which is in line with other neurodegenerative diseases effecting motor control such as Alzheimer’s and Huntington’s disease56,57. The longitudinal progression trend for GM ICV proportion follows that of a human study of CLN2 disease that showed a decrease in GM is a significant marker for disease progression, with the highest decrease found in the supratentorial region of the brain9,10,58. The longitudinal increase in CSF associated with CLN2 disease progression, as was found in our CLN2R208X/R208X miniswine and human patients10,59, supports this miniswine model as a translatable model. In our study, caudate and putamen proportion of ICV in CLN2R208X/R208X miniswine were smaller than WT counterparts. This finding is supported by similar findings by Löbel et al. in a human patient study that examined segmentation of basal ganglia and thalami in CLN210. The increase in ventricle proportion in the CLN2R208X/R208X miniswine mimics that reported in a previous human study of CLN2 disease10. The slight change in WT ventricular volume is similar to patient studies assessing lateral ventricle volume trajectories in non-diseased subjects60. This study includes some limitations. The research cost associated with large animal studies is higher than that of small animal studies or human studies utilizing clinically acquired medical imaging data, due to the animal purchase cost, housing per-diems, and expense of time on research-dedicated MRI systems. Due to cost-related restrictions the cohort selection for CLN2R208X/R208X and WT miniswine was limited to female animals only as uncastrated male miniswine present additional challenges for longitudinal imaging and housing. Additionally, to maximize the number of CLN2R208X/R208X miniswine in the study, the WT cohort was smaller. There was more variability in the WT raw and ICV proportional ventricle volume compared to the CLN2R208X/R208X cohort, and two WT miniswine had much larger ventricular volumes compared to the other three. The WT cohort pedigree was examined and no parental/genealogical pattern for the phenotype was found. We found no meaningful measurement difference in the ICV proportional ventricle volumes between the WT and CLN2R208X/R208X cohort but due to the diversity in WT volumes, further study of the ventricles in a larger, future cohort will be required. This dataset provides a valuable resource for future analysis. In this study diffusion weighted image data were collected but not analyzed due to significant artifact, however, future efforts in post-acquisition artifact correction could permit investigation of this additional data. Recent work by Norris et al., has generated an MRI brain template for male Yucatan miniswine61. The dataset and segmentations produced in our study could be used in the future to optimize parameterization and validate precision of the atlas for segmentation. In addition, post validation the atlas may be used to expand the brain segmentation regions for assessment in this female CLN2/WT cohort. ## Conclusion The purpose of our study was to examine brain volumetry at early (12-months) and late (17-months) stages of disease development in CLN2R208X/R208X miniswine and illustrate notable differences compared to that of wild type miniswine. We found valuable early disease MRI biomarkers in the ICV, GM, caudate and putamen (all reduced), along with the CSF (increased) in CLN2R208X/R208X compared to WT. For tracking progressive disease, GM and CSF differences continued to become more pronounced over time. We found supporting evidence from the literature that the miniswine findings recapitulated observations in patients with CLN2 disease, thus supporting the utilization of MRI brain volumetry in CLN2R208X/R208X miniswine as a valuable pre-clinical tool set for exploring disease etiology and treatment development. ## Supplementary Information Supplementary Figure 1. The online version contains supplementary material available at 10.1038/s41598-023-32071-z. ## References 1. Mole SE, Cotman SL. Genetics of the neuronal ceroid lipofuscinoses (batten disease)*. Biochim. Biophys. Acta* (2015.0) 1852 2237-2241. DOI: 10.1016/j.bbadis.2015.05.011 2. Mole S, Williams R, Goebel H. The Neuronal Ceroid Lipofuscinoses (Batten Disease) (2012.0) 3. Nickel M, Simonati A, Jacoby D, Lezius S, Kilian D, Van de Graaf B, Pagovich OE, Kosofsky B, Yohay K, Downs M. Disease characteristics and progression in patients with late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease: An observational cohort study. Lancet Child Adolesc. Health (2018.0) 2 582-590. DOI: 10.1016/S2352-4642(18)30179-2 4. Kovacs KD, Patel S, Orlin A, Kim K, Van Everen S, Conner T, Sondhi D, Kaminsky SM, D'Amico DJ, Crystal RG. Symmetric age association of retinal degeneration in patients with CLN2-associated batten disease. Ophthalmol. Retina (2020.0) 4 728-736. DOI: 10.1016/j.oret.2020.01.011 5. Specchio N, Pietrafusa N, Trivisano M. Changing times for CLN2 disease: The era of enzyme replacement therapy. Ther. Clin. Risk Manag. (2020.0) 16 213-222. DOI: 10.2147/TCRM.S241048 6. Claussen M, Heim P, Knispel J, Goebel HH, Kohlschütter A. Incidence of neuronal ceroid-lipofuscinoses in West Germany: Variation of a method for studying autosomal recessive disorders. Am. J. Med. Genet. (1992.0) 42 536-538. DOI: 10.1002/ajmg.1320420422 7. Moore SJ, Buckley DJ, MacMillan A, Marshall HD, Steele L, Ray PN, Nawaz Z, Baskin B, Frecker M, Carr SM. The clinical and genetic epidemiology of neuronal ceroid lipofuscinosis in Newfoundland. Clin. Genet. (2008.0) 74 213-222. DOI: 10.1111/j.1399-0004.2008.01054.x 8. Uvebrant P, Hagberg B. Neuronal ceroid lipofuscinoses in scandinavia: Epidemiology and clinical pictures. Neuropediatrics (1997.0) 28 6-8. DOI: 10.1055/s-2007-973654 9. Dyke JP, Sondhi D, Voss HU, Yohay K, Hollmann C, Mancenido D, Kaminsky SM, Heier LA, Rudser KD, Kosofsky B. Brain region-specific degeneration with disease progression in late infantile neuronal ceroid lipofuscinosis (CLN2 disease). Am. J. Neuroradiol. (2016.0) 37 1160-1169. DOI: 10.3174/ajnr.A4669 10. Löbel U, Sedlacik J, Nickel M, Lezius S, Fiehler J, Nestrasil I, Kohlschütter A, Schulz A. Volumetric description of brain atrophy in neuronal ceroid lipofuscinosis 2: Supratentorial gray matter shows uniform disease progression. Am. J. Neuroradiol. (2016.0) 37 1938-1943. DOI: 10.3174/ajnr.A4816 11. De Vis JB, Zwanenburg JJ, van der Kleij LA, Spijkerman JM, Biessels GJ, Hendrikse J, Petersen ET. Cerebrospinal fluid volumetric MRI mapping as a simple measurement for evaluating brain atrophy. Eur. Radiol. (2016.0) 26 1254-1262. DOI: 10.1007/s00330-015-3932-8 12. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, Lindley RI, O'Brien JT, Barkhof F, Benavente OR. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. (2013.0) 12 822-838. DOI: 10.1016/S1474-4422(13)70124-8 13. Swier VJ, White KA, Johnson TB, Sieren JC, Johnson HJ, Knoernschild K, Wang X, Rohret FA, Rogers CS, Pearce DA. A novel porcine model of CLN2 batten disease that recapitulates patient phenotypes. Neurotherapeutics (2022.0) 1832 1807 14. 14.BRAINSFit: Mutual Information Registrations of Whole-Brain 3D Images, Using the Insight Toolkit (2007). 15. Kim REY, Nopoulos P, Paulsen J, Johnson H, Kim REY. Efficient and extensible workflow: Reliable whole brain segmentation for large-scale, multi-center longitudinal human MRI analysis using high performance/throughput computing resources. Clinical Image-Based Procedures Translational Research in Medical Imaging: 2016 (2016.0) 54-61 16. Avants BB, Tustison N, Song G. Advanced normalization tools (ANTS). Insight J. (2009.0) 2 1-35 17. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M. 3D Slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging (2012.0) 30 1323-1341. DOI: 10.1016/j.mri.2012.05.001 18. Schubert R, Frank F, Nagelmann N, Liebsch L, Schuldenzucker V, Schramke S, Wirsig M, Johnson H, Kim EY, Ott S. Neuroimaging of a minipig model of Huntington's disease: Feasibility of volumetric, diffusion-weighted and spectroscopic assessments. J. Neurosci. Methods (2016.0) 265 46-55. DOI: 10.1016/j.jneumeth.2015.11.017 19. McRae DL, Barnum AS. Occipitalization of the atlas. Am. J. Roentgenol. (1953.0) 70 23-46 20. Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics (2011.0) 9 381-400. DOI: 10.1007/s12021-011-9109-y 21. Sleat DE, Wiseman JA, El-Banna M, Kim KH, Mao Q, Price S, Macauley SL, Sidman RL, Shen MM, Zhao Q. A mouse model of classical late-infantile neuronal ceroid lipofuscinosis based on targeted disruption of the CLN2 gene results in a loss of tripeptidyl-peptidase I activity and progressive neurodegeneration. J. Neurosci. (2004.0) 24 9117-9126. DOI: 10.1523/JNEUROSCI.2729-04.2004 22. Kielar C, Maddox L, Bible E, Pontikis CC, Macauley SL, Griffey MA, Wong M, Sands MS, Cooper JD. Successive neuron loss in the thalamus and cortex in a mouse model of infantile neuronal ceroid lipofuscinosis. Neurobiol. Dis. (2007.0) 25 150-162. DOI: 10.1016/j.nbd.2006.09.001 23. Johnson TB, Langin LM, Zhao J, Weimer JM, Pearce DA, Kovács AD. Changes in motor behavior, neuropathology, and gut microbiota of a Batten disease mouse model following administration of acidified drinking water. Sci. Rep. (2019.0) 9 14962. DOI: 10.1038/s41598-019-51488-z 24. Langin L, Johnson TB, Kovács AD, Pearce DA, Weimer JM. A tailored Cln 3(Q352X) mouse model for testing therapeutic interventions in CLN3 Batten disease. Sci. Rep. (2020.0) 10 10591. DOI: 10.1038/s41598-020-67478-5 25. Poppens MJ, Cain JT, Johnson TB, White KA, Davis SS, Laufmann R, Kloth AD, Weimer JM. Tracking sex-dependent differences in a mouse model of CLN6-Batten disease. Orphanet J. Rare Dis. (2019.0) 14 19. DOI: 10.1186/s13023-019-0994-8 26. Swier VJ, White KA, Meyerholz DK, Chefdeville A, Khanna R, Sieren JC, Quelle DE, Weimer JM. Validating indicators of CNS disorders in a swine model of neurological disease. PLoS ONE (2020.0) 15 e0228222. DOI: 10.1371/journal.pone.0228222 27. Johnson TB, White KA, Brudvig JJ, Cain JT, Langin L, Pratt MA, Booth CD, Timm DJ, Davis SS, Meyerink B. AAV9 gene therapy increases lifespan and treats pathological and behavioral abnormalities in a mouse model of CLN8-batten disease. Mol. Ther. (2021.0) 29 162-175. DOI: 10.1016/j.ymthe.2020.09.033 28. Gardner MJ, Altman DG. Confidence intervals rather than P values: Estimation rather than hypothesis testing. Br. Med. J. (1986.0) 292 746-750. DOI: 10.1136/bmj.292.6522.746 29. Ho J, Tumkaya T, Aryal S, Choi H, Claridge-Chang A. Moving beyond P values: Data analysis with estimation graphics. Nat. Methods (2019.0) 16 565-566. DOI: 10.1038/s41592-019-0470-3 30. Katz ML, Coates JR, Sibigtroth CM, Taylor JD, Carpentier M, Young WM, Wininger FA, Kennedy D, Vuillemenot BR, O'Neill CA. Enzyme replacement therapy attenuates disease progression in a canine model of late-infantile neuronal ceroid lipofuscinosis (CLN2 disease). J. Neurosci. Res. (2014.0) 92 1591-1598. DOI: 10.1002/jnr.23423 31. Katz ML, Tecedor L, Chen Y, Williamson BG, Lysenko E, Wininger FA, Young WM, Johnson GC, Whiting RE, Coates JR. AAV gene transfer delays disease onset in a TPP1-deficient canine model of the late infantile form of Batten disease. Sci. Transl. Med. (2015.0) 7 313. DOI: 10.1126/scitranslmed.aac6191 32. Mitchell NL, Russell KN, Wellby MP, Wicky HE, Schoderboeck L, Barrell GK, Melzer TR, Gray SJ, Hughes SM, Palmer DN. Longitudinal in vivo monitoring of the CNS demonstrates the efficacy of gene therapy in a sheep model of CLN5 batten disease. Mol. Ther. (2018.0) 26 2366-2378. DOI: 10.1016/j.ymthe.2018.07.015 33. Cooney AL, Thornell IM, Singh BK, Shah VS, Stoltz DA, McCray PB, Zabner J, Sinn PL. A novel AAV-mediated gene delivery system corrects CFTR function in pigs. Am. J. Respir. Cell Mol. Biol. (2019.0) 61 747-754. DOI: 10.1165/rcmb.2019-0006OC 34. Pino-Argumedo MI, Fischer AJ, Hilkin BM, Gansemer ND, Allen PD, Hoffman EA, Stoltz DA, Welsh MJ, Abou Alaiwa MH. Elastic mucus strands impair mucociliary clearance in cystic fibrosis pigs. Proc. Natl. Acad. Sci. U.S.A. (2022.0) 119 e2121731119. DOI: 10.1073/pnas.2121731119 35. Welsh MJ, Rogers CS, Stoltz DA, Meyerholz DK, Prather RS. Development of a porcine model of cystic fibrosis. Trans. Am. Clin. Climatol. Assoc. (2009.0) 120 149-162. PMID: 19768173 36. Hryhorowicz M, Lipiński D, Hryhorowicz S, Nowak-Terpiłowska A, Ryczek N, Zeyland J. Application of genetically engineered pigs in biomedical research. Genes (2020.0) 11 670. DOI: 10.3390/genes11060670 37. Zettler S, Renner S, Kemter E, Hinrichs A, Klymiuk N, Backman M, Riedel EO, Mueller C, Streckel E, Braun-Reichhart C. A decade of experience with genetically tailored pig models for diabetes and metabolic research. Anim. Reprod. (2020.0) 17 e20200064. DOI: 10.1590/1984-3143-ar2020-0064 38. Zhang J, Khazalwa EM, Abkallo HM, Zhou Y, Nie X, Ruan J, Zhao C, Wang J, Xu J, Li X. The advancements, challenges, and future implications of the CRISPR/Cas9 system in swine research. J. Genet. Genom. (2021.0) 48 347-360. DOI: 10.1016/j.jgg.2021.03.015 39. McBride JL, Neuringer M, Ferguson B, Kohama SG, Tagge IJ, Zweig RC, Renner LM, McGill TJ, Stoddard J, Peterson S. Discovery of a CLN7 model of Batten disease in non-human primates. Neurobiol. Dis. (2018.0) 119 65-78. DOI: 10.1016/j.nbd.2018.07.013 40. Sieren JC, Meyerholz DK, Wang XJ, Davis BT, Newell JD, Hammond E, Rohret JA, Rohret FA, Struzynski JT, Goeken JA. Development and translational imaging of a TP53 porcine tumorigenesis model. J. Clin. Investig. (2014.0) 124 4052-4066. DOI: 10.1172/JCI75447 41. Hendricks-Wenger A, Nagai-Singer MA, Uh K, Vlaisavljevich E, Lee K, Allen IC. Employing novel porcine models of subcutaneous pancreatic cancer to evaluate oncological therapies. Methods Mol. Biol. (2022.0) 2394 883-895. DOI: 10.1007/978-1-0716-1811-0_47 42. Boas FE, Nurili F, Bendet A, Cheleuitte-Nieves C, Basturk O, Askan G, Michel AO, Monette S, Ziv E, Sofocleous CT. Induction and characterization of pancreatic cancer in a transgenic pig model. PLoS ONE (2020.0) 15 e0239391. DOI: 10.1371/journal.pone.0239391 43. Ehrenfeld M, Schrade A, Flisikowska T, Perl M, Hirsch ND, Sichler A, Geyer L, Flisikowski K, Wilhelm D, Schober SJ. Tumor targeting with bacterial shiga toxin B subunit in genetic porcine models for colorectal cancer and osteosarcoma. Mol. Cancer Ther. (2022.0) 21 686-699. DOI: 10.1158/1535-7163.MCT-21-0445 44. Rubinstein CD, McLean DT, Lehman BP, Meudt JJ, Schomberg DT, Krentz KJ, Reichert JL, Meyer MB, Adams M, Konsitzke CM. Assessment of mosaicism and detection of cryptic alleles in CRISPR/Cas9-engineered neurofibromatosis type 1 and TP53 mutant porcine models reveals overlooked challenges in precision modeling of human diseases. Front. Genet. (2021.0) 12 721045. DOI: 10.3389/fgene.2021.721045 45. White KA, Swier VJ, Cain JT, Kohlmeyer JL, Meyerholz DK, Tanas MR, Uthoff J, Hammond E, Li H, Rohret FA. A porcine model of neurofibromatosis type 1 that mimics the human disease. JCI Insight (2018.0) 3 0402. DOI: 10.1172/jci.insight.120402 46. Uthoff J, Larson J, Sato TS, Hammond E, Schroeder KE, Rohret F, Rogers CS, Quelle DE, Darbro BW, Khanna R. Longitudinal phenotype development in a minipig model of neurofibromatosis type 1. Sci. Rep. (2020.0) 10 5046. DOI: 10.1038/s41598-020-61251-4 47. Osum SH, Watson AL, Largaespada DA. Spontaneous and engineered large animal models of neurofibromatosis type 1. Int. J. Mol. Sci. (2021.0) 22 1954. DOI: 10.3390/ijms22041954 48. Hoffe B, Holahan MR. The use of pigs as a translational model for studying neurodegenerative diseases. Front. Physiol. (2019.0) 10 838. DOI: 10.3389/fphys.2019.00838 49. Søndergaard LV, Herskin MS, Ladewig J, Holm IE, Dagnæs-Hansen F. Effect of genetic homogeneity on behavioural variability in an object recognition test in cloned Göttingen minipigs. Appl. Anim. Behav. Sci. (2012.0) 141 20-24. DOI: 10.1016/j.applanim.2012.07.006 50. Russell KN, Mitchell NL, Anderson NG, Bunt CR, Wellby MP, Melzer TR, Barrell GK, Palmer DN. Computed tomography provides enhanced techniques for longitudinal monitoring of progressive intracranial volume loss associated with regional neurodegeneration in ovine neuronal ceroid lipofuscinoses. Brain Behav. (2018.0) 8 e01096. DOI: 10.1002/brb3.1096 51. Katz ML, Johnson GC, Leach SB, Williamson BG, Coates JR, Whiting REH, Vansteenkiste DP, Whitney MS. Extraneuronal pathology in a canine model of CLN2 neuronal ceroid lipofuscinosis after intracerebroventricular gene therapy that delays neurological disease progression. Gene Therapy (2017.0) 24 215. DOI: 10.1038/gt.2017.4 52. Whiting RE, Pearce JW, Castaner LJ, Jensen CA, Katz RJ, Gilliam DH, Katz ML. Multifocal retinopathy in Dachshunds with CLN2 neuronal ceroid lipofuscinosis. Exp. Eye Res. (2015.0) 134 123-132. DOI: 10.1016/j.exer.2015.02.012 53. Nordenskjöld R, Malmberg F, Larsson E-M, Simmons A, Brooks SJ, Lind L, Ahlström H, Johansson L, Kullberg J. Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements. Neuroimage (2013.0) 83 355-360. DOI: 10.1016/j.neuroimage.2013.06.068 54. Tereshchenko A, Magnotta V, Epping E, Mathews K, Espe-Pfeifer P, Martin E, Dawson J, Duan W, Nopoulos P. Brain structure in juvenile-onset Huntington disease. Neurology (2019.0) 92 e1939. DOI: 10.1212/WNL.0000000000007355 55. Caspi Y, Brouwer RM, Schnack HG, van de Nieuwenhuijzen ME, Cahn W, Kahn RS, Niessen WJ, van der Lugt A, Pol HH. Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study. Neuroimage (2020.0) 220 116842. DOI: 10.1016/j.neuroimage.2020.116842 56. Anderson VM, Schott JM, Bartlett JW, Leung KK, Miller DH, Fox NC. Gray matter atrophy rate as a marker of disease progression in AD. Neurobiol. Aging (2012.0) 33 1194-1202. DOI: 10.1016/j.neurobiolaging.2010.11.001 57. Coppen EM, Jacobs M, van den Berg-Huysmans AA, van der Grond J, Roos RAC. Grey matter volume loss is associated with specific clinical motor signs in Huntington's disease. Parkinsonism Relat. Disord. (2018.0) 46 56-61. DOI: 10.1016/j.parkreldis.2017.11.001 58. Schulz A, Ajayi T, Specchio N, de Los RE, Gissen P, Ballon D, Dyke JP, Cahan H, Slasor P, Jacoby D. Study of intraventricular cerliponase alfa for CLN2 disease. N. Engl. J. Med. (2018.0) 378 1898-1907. DOI: 10.1056/NEJMoa1712649 59. Dyke JP, Sondhi D, Voss HU, Shungu DC, Mao X, Yohay K, Worgall S, Hackett NR, Hollmann C, Yeotsas ME. Assessment of disease severity in late infantile neuronal ceroid lipofuscinosis using multiparametric MR imaging. Am. J. Neuroradiol. (2013.0) 34 884-889. DOI: 10.3174/ajnr.A3297 60. Lundervold AJ, Vik A, Lundervold A. Lateral ventricle volume trajectories predict response inhibition in older age—A longitudinal brain imaging and machine learning approach. PLoS ONE (2019.0) 14 e0207967. DOI: 10.1371/journal.pone.0207967 61. Norris C, Lisinski J, McNeil E, VanMeter JW, VandeVord P, LaConte SM. MRI brain templates of the male Yucatan minipig. Neuroimage (2021.0) 235 118015. DOI: 10.1016/j.neuroimage.2021.118015
--- title: Retinal vessel geometry in patients with idiopathic epiretinal membrane authors: - Eun Kyoung Lee - Hye Jee Kim - Sang-Yoon Lee - Su Jeong Song - Hyeong Gon Yu journal: Scientific Reports year: 2023 pmcid: PMC10060414 doi: 10.1038/s41598-023-32025-5 license: CC BY 4.0 --- # Retinal vessel geometry in patients with idiopathic epiretinal membrane ## Abstract We investigated the associations between retinal vascular geometric measurements and idiopathic epiretinal membrane (ERM). Whether changes in retinal vascular geometry are independent of systemic cardiovascular risk factors was also evaluated. This retrospective, cross sectional study included 98 patients with idiopathic ERM, and 99 healthy age-matched controls. Quantitative retinal vascular parameters were measured from digital retinal fundus photographs using a semi-automated computer-assisted program. Multivariate logistic regression analyses were performed to evaluate associations between retinal vascular geometric parameters and the presence of idiopathic ERM after adjusting for systemic cardiovascular risk factors. There was no significant difference in the baseline characteristics of the two groups, except that the ERM group had a higher proportion of females than the control group. In multivariate regression analyses, female sex (odds ratio [OR] 0.402; $95\%$ CI 0.196–0.802; $$P \leq 0.011$$), wider retinal venular caliber (OR 16.852; $95\%$ CI 5.384–58.997; $P \leq 0.001$) and decreased total fractal dimension (OR 0.156; $95\%$ CI 0.052–0.440; $$P \leq 0.001$$) were associated with idiopathic ERM. Idiopathic ERM was associated with alterations in global retinal microvascular geometric parameters, wider retinal venules, and less complex vascular branching patterns, independent of cardiovascular risk factors. ## Introduction Idiopathic epiretinal membrane (ERM) is defined as a fibrocellular proliferation on the inner retinal surface of the macular area without any associated ocular abnormalities1. ERM may cause visual impairments and metamorphopsia, and may cause retinal vessels to stretch or contract along with neural tissue. Movement or shift of retinal vessels in patients with ERM has been described in several reports2,3. Morphological changes in the foveal capillary architecture that occur due to traction caused by ERM have been studied recently using optical coherence tomography angiography4. Furthermore, epidemiologic studies of the prevalence of ERM have described hypercholesterolemia5,6 and narrower retinal arteriolar diameter7 as risk factors for ERM. A thorough evaluation of the retinal vascular geometry may provide clues to the association between retinal microvascular abnormalities and ERM. Newer retinal parameters such as fractal dimension, tortuosity, bifurcation/branching angle, and retinal vascular caliber using Singapore I Vessel Assessment (SIVA, cloud-based version, National University of Singapore, Singapore), a semi-automated software are now available to assess the retinal vascular geometry, and are a reflection of the efficiency and optimal functioning of microcirculation in the retinal network8. Previous studies have shown that these geometric retinal vascular parameters are associated with hypertension9, and Alzheimer’s disease10. Nevertheless, the association between retinal vascular parameters using SIVA software and idiopathic ERM has not been investigated. In this study, we quantitatively measured retinal vascular geometric parameters in eyes with idiopathic ERM and analyzed which retinal vascular geometric parameters are associated with idiopathic ERM. We also sought to determine whether these changes in retinal vessel geometry was independent of systemic cardiovascular risk factors. ## Results Ninety-eight eyes were included in the idiopathic ERM group and 99 eyes were included in the control group. Table 1 outlines the summary of the patients’ demographics and baseline clinical characteristics. There were no statistically significant differences in the prevalence of hypertension, diabetes, and dyslipidemia, or in body mass index (BMI) values of the two groups. The idiopathic ERM group had a higher proportion of female patients than the control group ($$P \leq 0.015$$).Table 1Baseline characteristics of patients with epiretinal membrane and normal controls. VariableIdiopathic ERM (98 eyes)Controls (99 eyes)P valueAge (yrs)63.3 ± 7.564.4 ± 10.60.397Sex (male:female)29:6947:520.015†RE:LE42:5650:490.351†HTN, n (%)32 ($32.7\%$)26 ($26.3\%$)0.408†DM, n (%)14 ($14.3\%$)20 ($20.2\%$)0.363†Hypertriglyceridemia10 ($10.2\%$)12 ($12.1\%$)0.841†Hypercholesterolemia27 ($27.6\%$)18 ($18.2\%$)0.163†BMI24.18 ± 2.9524.21 ± 2.940.950ERM epiretinal membrane, yrs years, RE right eye, LE left eye, HTN hypertension, DM diabetes mellitus, BMI body mass index. Continuous variables are reported as mean ± standard deviation (range). All other data are numbers. Significant factors appear in boldface.Student t test.†Chi-square test. Table 2 shows the comparison of retinal parameters between the idiopathic ERM and control groups. Compared with the controls, the idiopathic ERM group had wider arteriolar (163.56 ± 14.15 vs. 158.61 ± 10.42 µm, $$P \leq 0.006$$) and venular calibers (211.64 ± 15.87 vs. 199.79 ± 15.71 µm, $P \leq 0.001$). The idiopathic ERM group had smaller total (1.333 ± 0.059 vs. 1.355 ± 0.075, $$P \leq 0.022$$) and arteriolar fractal dimensions (1.146 ± 0.072 vs. 1.181 ± 0.092, $$P \leq 0.003$$) than the control group. Venular fractal dimensions were not different between the groups ($$P \leq 0.638$$). Furthermore, the idiopathic ERM group had more tortuous venules (0.593 ± 0.137 vs. 0.544 ± 0.119 [× 10–4], $$P \leq 0.008$$) and larger venular branching angles (81.30 ± 14.15 vs. 76.55 ± 12.81°, $$P \leq 0.014$$) than the control group. Arteriolar tortuosity ($$P \leq 0.156$$) and arteriolar branching angles ($$P \leq 0.821$$) were not significantly different between the groups. Table 2Comparison of retinal vascular parameters between eyes with idiopathic epiretinal membrane and normal controls. Retinal vascular parameterIdiopathic ERM (98 eyes)Controls (99 eyes)P valueCaliber CRAE (µm)163.56 ± 14.15158.61 ± 10.420.006 CRVE (µm)211.64 ± 15.87199.79 ± 15.71< 0.001Fractals Total fractal dimension1.333 ± 0.0591.355 ± 0.0750.022 Arteriolar fractal dimension1.146 ± 0.0721.181 ± 0.0920.003 Venular fractal dimension1.107 ± 0.0631.111 ± 0.0720.638Tortuosity Arteriolar tortuosity (× 10–4)0.672 ± 0.1670.641 ± 0.1410.156 Venular tortuosity (× 10–4)0.593 ± 0.1370.544 ± 0.1190.008Bifurcation Arteriolar branching angle (°)77.72 ± 17.3277.18 ± 15.880.821 Venular branching angle (°)81.30 ± 14.1576.55 ± 12.810.014CRAE central retinal arteriolar equivalent, CRVE central retinal venular equivalent, ERM epiretinal membrane. Continuous variables are reported as mean ± standard deviation. Significant factors appear in boldface.Student t test. Table 3 shows the associations between idiopathic ERM and retinal vascular geometric measurements. Multivariate regression analysis using variables selected by backward stepwise logistic regression showed that female sex (odds ratio [OR] 0.402; $95\%$ CI 0.196–0.802; $$P \leq 0.011$$), wider retinal venular caliber (OR 16.852; $95\%$ CI 5.384–58.997; $P \leq 0.001$) and decreased total fractal dimension (OR 0.156; $95\%$ CI 0.052–0.440; $$P \leq 0.001$$) were more likely to have idiopathic ERM.Table 3Associations between idiopathic epiretinal membrane and retinal vascular parameters. Retinal vascular parameterUnivariate analysisMultivariate analysisOR$95\%$ CIP valueOR$95\%$ CIP valueAge0.9870.956, 1.0170.396Sex0.4650.257, 0.8310.0100.4020.196, 0.8020.011Hypertension1.3610.737, 2.5330.326Diabetes mellitus0.6580.306, 1.3830.274Hypertriglyceridemia0.8240.332, 2.0080.670Hypercholesterolemia1.7110.876, 3.4080.119Body mass index0.9970.895, 1.1100.950Caliber CRAE, per 50 increase5.1121.61, 17.3620.0074.0600.987, 17.8810.057 CRVE, per 50 increase10.8664.151, 31.0550.00016.8525.384, 58.997< 0.001Fractals Total fractal dimension, per 0.2 increase0.3730.155, 0.8630.0240.1560.052, 0.4400.001 Arteriolar fractal dimension, per 0.2 increase0.3590.175, 0.7130.004 Venular fractal dimension, per 0.2 increase0.8180.353, 1.8800.636Tortuosity Arteriolar tortuosity, per 0.1 × 10–3 increase3.8420.614, 26.7800.159 Venular tortuosity, per 0.1 × 10–3 increase21.7452.245, 248.0080.01011.3930.770, 200.7220.085Bifurcation Arteriolar branching angle, per 100 increase1.2170.223, 6.7190.820 Venular branching angle, per 100 increase14.8081.747, 144.5530.0169.7380.780, 140.6140.084Significant values are in bold. CRAE central retinal arteriolar equivalent, CRVE central retinal venular equivalent, OR odds ratio, CI confidence interval. Figure 1 demonstrates retinal microvasculature analyses performed using the Singapore I Vessel Assessment (SIVA) program, showing wider retinal vascular caliber, smaller retinal arteriolar fractal dimension, and higher retinal venular tortuosity in idiopathic ERM patients. Figure 1The geometric measurement of the retinal vasculature assessed by the Singapore I Vessel Assessment (SIVA) software of an eye with idiopathic epiretinal membrane (ERM) (A) and a normal control (B). ( A) The retinal calibers of the arterioles and venules are 175.20 and 230.05 µm, respectively, the fractal dimensions of the arterioles and venules are 1.244 and 1.087, respectively, the tortuosity values of the arterioles and venules are 0.663 × 10–4 and 0.569 × 10–4, respectively, and the branching angles of the arterioles and venules are 65.14 and 71.86°, respectively. B. The retinal calibers of the arterioles and venules are 168.16 and 199.17 µm, respectively, the fractal dimensions of the arterioles and venules are 1.309 and 1.098, respectively, the tortuosity values of the arterioles and venules are 0.556 × 10–4 and 0.356 × 10–4, respectively, and the branching angles of the arterioles and venules are 62.79 and 76.76°, respectively. ## Discussion In the present study, we measured retinal vascular geometric parameters in eyes with idiopathic ERM and analyzed which retinal vessel geometric parameters are associated with idiopathic ERM. Our results demonstrate that compared with age-matched controls, and while adjusting for concomitant risk factors, eyes with idiopathic ERM are more likely to manifest structural changes in retinal microvascular network; wider retinal venules and smaller total fractal dimensions. To the best of our knowledge, our study is the first to comprehensively examine the direct association between idiopathic ERM and retinal vascular geometric parameters. Previous epidemiological studies have reported inconsistent associations between idiopathic ERM and potential systemic cardiovascular risk factors. Miyazaki et al.6 demonstrated that serum cholesterol is significantly associated with ERM and Ng et al.5 showed that hypercholesterolemia and narrower arteriolar diameter are significantly associated with ERM. Furthermore, in the Singapore Malay Eye Study7, narrower retinal arteriolar diameter was found to be associated with ERM. However, in the Beaver Dam study, neither narrowing of the retinal arterioles nor a history of cardiovascular disease was associated with the presence of ERM11. In the present study, we observed that wider retinal venules are significantly associated with idiopathic ERM, independent of vascular risk factors. We assume that the wider retinal vascular caliber may be a result of disturbances in the hemodynamics of the retinal blood flow of eyes with idiopathic ERM. It has earlier been speculated that hypoxia may have a role in the formation of idiopathic ERMs12–14. Armstrong et al.12 have reported on the presence of vascular endothelial growth factor (VEGF) and tumor necrosis factor-α (TNF-α) not only in proliferative diabetic membranes, but in idiopathic ERMs as well. Lim et al.14 have described the presence of hypoxia-inducible factor-1α (HIF-1α), a transcription factor that plays an essential role in the systemic homeostasis response to hypoxia, in nondiabetic ERMs. The HIF-1 triggers the activation of several genes that result in the production of VEGF and other angiogenic factors13. It is possible that the presence of ERM—that is attributable to traction and/or shear stress applied to the retina—and associated hypoxic conditions affect retinal caliber. Moreover, we speculate that wider retinal venules may be related to the macular edema, which is often observed in eyes with idiopathic ERM. Nevertheless, the result of this study does not allow us to conclude that wider retinal venules are due to the ischemia and disturbances in the hemodynamics of retinal blood flow caused by traction in eyes with ERM. Fractal dimension and branching angle reflect the status of the circulatory function of blood vessels. An optimal branching angle is associated with greater efficiency in blood flow with lower energy expenditure15. Given that the normal human retinal circulation is a “self-similar” and fractal pattern, fractal analysis may provide an objective, quantitative technique to evaluate retinal vessel geometry16. In previous studies, smaller retinal fractal dimension was found to be associated with systemic disease, including proliferative diabetic retinopathy17 and hypertension9. However, the relationship between fractal dimension and branching angle and idiopathic ERM has not been well studied. In the present study, smaller total and arteriolar fractal dimension and larger venular branching angle were significantly associated with idiopathic ERM. However, this significance was lost (except for total fractal dimension) after multivariate adjustment for confounding variables. A smaller fractal dimension represents less branching density, reflecting potential changes in blood flow or endothelial dysfunction in eyes with idiopathic ERM. Tortuosity or curvature of the retinal vessels is also a crucial parameter that describes the geometric pattern of retinal vasculature, which may represent the state of the retinal microcirculation. Vascular tortuosity may be linked with tissue perfusion impairment as a complex response mechanism mediated by secretions from vascular endothelial cells. These vascular endothelial cells play an essential role in autoregulation of blood flow by producing vasoactive endothelial factors such as nitric oxide and endothelin18. These mediators stimulate angiogenesis and thus increase tortuosity, which subsequently promotes better tissue perfusion. In the present study, the finding that increased venular tortuosity is associated with idiopathic ERM was not significant after multivariate adjustment. Despite being insignificant, this trend may demonstrate a potential role for alterations in blood flow and changes in the geometric pattern of the retinal vasculature in the pathogenesis of idiopathic ERM. There are recent studies that investigated foveal microvasculature using optical coherence tomography angiography (OCTA) in eyes with ERMs. Okawa et al.19 have reported that the foveal avascular zones of eyes with ERM were significantly smaller than those of the control eyes. Kim et al.4 have also described that compared with the fellow eyes, eyes with ERMs had a lower parafoveal vascular densities and smaller FAZ areas even after surgery. While OCTA allows visualization of retinal microvasculature, including capillaries, and can reveal vessel area density, vessel length density, and foveal avascular zone metrics, such as circularity index and size, it has limitations in quantitatively showing the geometry of retinal arterioles and venules, including retinal vascular caliber, fractal dimension, tortuosity, and bifurcation/branching angle. Moreover, the flow images of OCTA can be affected by overlying ERM and shown as dark shadow artifacts20. This interferes with reliable vascular analysis, in particular quantitative flow assessment as vessel density. Therefore, we believe that even in the era of OCTA, this study still has the advantage of confirming the vessel geometry with SIVA software. Nevertheless, it would be helpful to understand the pathogenesis of ERM and changes in vascular metrics if further studies could correlate functional changes in retinal blood vessels using OCTA, with vessel geometry by SIVA software. Previous studies that involved the use of fluorescein angiography to image the macular regions of eyes with ERMs revealed a reduced mean capillary flow velocity in these eyes21. Kadonosono et al.22,23 have also evaluated the retinal capillary blood flow velocity in patients with ERM and reported that the mean capillary blood flow in the perifoveal area was reduced as well. Afterward, Shinoda et al.24 measured the tissue blood flow in the macular area using scanning laser Doppler flowmetry and reported that the mean blood flow was significantly lower in eyes with ERMs than in control eyes. Furthermore, they suggested that the pathological reduction of retinal capillary blood flow velocity reported by Kadonosono et al.22,23 may not be compensated for, even by the blood vessel dilation found in eyes with ERMs, to decrease the mean blood flow. These findings are consistent with our results, showing the changes that occur in the geometric pattern of the retinal vasculature of eyes with idiopathic ERM. It is assumed that there is a mutually exacerbating relationship between retinal vascular insufficiency and ERM. Coppe et al.25,26 reported a reduction in perifoveal blood flow in the unaffected fellow eye of unilateral ERM, suggesting that relative tissue hypoxia probably precedes ERM formation and contraction and stimulates activation of Müller cells, and thus suggests that retinal vascular insufficiency may contribute to ERM formation. In addition, in the pathogenesis of ERM, glial tissues are affected by various growth factors and cytokines as they gradually proliferate and contract, which are known to further disrupts macular structures and retinal vasculature27. It may be possible that Müller cells in the foveal center, which are stressed by the traction, release growth factors like fibroblast growth factor-2 (FGF2) and platelet-derived growth factor (PDGF) that stimulate astrocyte migration and proliferation and thus facilitate ERM formation28,29. Deformities in the retinal tissues and neurovascular components caused by ERM-induced tractional forces may further accelerate relative tissue hypoxia and lead to the retinal vascular geometry changes identified in the current study. Alterations in the retinal vasculature, wider retinal venules and smaller fractal dimensions, may further induce the hemodynamic disturbances in the microcirculation of eyes with ERM, even though the pathophysiologic mechanisms and a causality relationship between retinal vascular geometry and ERM remains unclear. There are some limitations to our study. Firstly, the design of the study was retrospective and cross-sectional. Therefore, whether retinal vascular geometric changes are antecedent or consequent to idiopathic ERM cannot be determined from these data. Further longitudinal studies are needed to assess causality. Secondly, we only included eyes with idiopathic ERM, therefore, our results cannot be extended to eyes with secondary ERMs. The different causes and pathogenic mechanisms of idiopathic and secondary ERMs may result in not only differences in membrane characteristics but also in differences in retinal vascular geometry. Thirdly, differences in vascular geometry analysis based on the ERM stage were not included in this study. Govetto et al.30 categorized OCT-based ERM into stages 1 through 4. In the future, analyzing vascular parameters to reflect disease severity according to ERM stage may lead to a deeper understanding of ERM pathogenesis. Fourthly, although the structural pathology in patients with ERM predominantly involve the macula, the SIVA program analyzed retinal microvasculature taken centered on the optic disc. Lastly, fine vessels beneath the ERM with diameters smaller than 25 µm were not outlined, which may have affected the results of this study. In future study, further analysis with OCTA, which can analyze the retinal microvasculature, including the fine capillaries, may improve the clinical significance. In conclusion, alterations in retinal vascular geometric parameters, particularly wider retinal venules and smaller fractal dimensions, were found to be associated with the presence of idiopathic ERM. This association was independent of cardiovascular risk factors. The results of our study suggest that there may be microvascular network changes in the retinas of patients with idiopathic ERM, which could potentially be related to hemodynamic disturbances in their microcirculation. ## Study participants This was a retrospective, cross-sectional study that included 98 eyes of 98 consecutive patients who presented at the Seoul National University Hospital with a diagnosis of idiopathic unilateral ERM from January 2015 to December 2018; 99 eyes of healthy age-matched individuals who were examined at the Seoul National University Hospital clinic were also included as controls. The study protocol was approved by the Institutional Review Board and the study conduct adhered to the tenets of the Declaration of Helsinki. Institutional Review Board of the Seoul National University Hospital waived the need for written informed consent from the participants, because of the study’s retrospective design. ERM diagnosis was made by a physician via indirect fundus examination and confirmed with spectral-domain optical coherence tomography (SD-OCT). Subjects were excluded if any of the following were present: [1] secondary ERM associated with a history of uveitis, retinal vein occlusion, retinal detachment, proliferative diabetic retinopathy, retinal dystrophy, ocular trauma, or other underlying maculopathy; [2] high hyperopia (≥ + 5.0 diopters) or high myopia (≤ − 6.0 diopters); [3] macular edema due to retinal vascular diseases; [4] optical media opacity that could significantly interfere with image acquisition in fundus photography (e.g., cataract of more than grade III in the Emery–Little classification). ## Laboratory tests and examination All participants underwent comprehensive ophthalmologic and systemic examinations, including collection of blood samples. Hemoglobin A1c (HbA1c), total serum cholesterol, triglyceride, low density lipoprotein cholesterol (LDL-cholesterol) and high-density lipoprotein cholesterol (HDL-cholesterol) levels were all measured. BMI was calculated as weight (in kilograms) divided by height (in meters) squared. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the current use of antihypertensive medication. Diabetes mellitus was defined as a random or post-load glucose level ≥ 11.1 mmol/l, use of antidiabetic medication, or self-reported history of diabetes. Hypertriglyceridemia was defined as triglyceride level ≥ 150 mg/dl (1.7 mmol/l) and hypercholesterolemia was defined as a total cholesterol level ≥ 220 mg/dl (5.69 mmol/l)31. Ophthalmologic examinations included slit-lamp biomicroscopy, indirect fundus examination, fundus photography (Vx-10; Kowa Optimed, Tokyo, Japan), and SD-OCT (Cirrus; Carl Zeiss Meditec, Dublin, CA). ## Retinal vessel geometry Digital fundus photographs were obtained using a digital retinal camera (Vx-10; Kowa Optimed, Tokyo, Japan) in 45-degree mode centered on the optic disc. A semi-automated software (SIVA, cloud-based version, National University of Singapore, Singapore) was used to quantitatively measure the following retinal vascular geometric parameters from the digital photographs: retinal vascular caliber, retinal vascular fractal dimension, retinal vascular tortuosity, and retinal vascular branching pattern. Briefly, the program automatically detected and traced the optic disc and set the grading grid on the fundus photograph. It then outlined all retinal vessels (arterioles and venules) greater than 25 µm in diameter and generated a skeleton image of the retinal microvasculature. Two graders, masked to the identities and characteristics of the participants, assessed the fundus photographs for retinal vessel geometric measures. The graders were responsible for evaluation of the measurements acquired with SIVA according to a standardized grading protocol32. The measured area was the region between 0.5 and 2.0 disc diameters away from the disc margin. Retinal vascular caliber was measured using the SIVA program, which followed the standardized protocol used in the Atherosclerosis Risk in Communities study33. The revised Knudtson–Parr–Hubbard formula* was used to calculate average retinal arteriolar and venular calibers, which were presented as central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE), respectively34. Total, arteriolar, and venular fractal dimensions were calculated from a skeletonized line tracing of the retinal vessels using the box-counting method, in which each photograph is divided into a series of squares of various side lengths35. These represent a “global” measure that summarizes the whole branching pattern of the retinal vascular tree9. A fractal can be defined as a geometric pattern that is “self-similar” and summarized by the fractal “dimension,” which measures the complexity of the branching pattern9. The fractal dimension is usually a ratio and has no units. Larger values indicate a more complex branching pattern. Retinal vascular tortuosity was derived from the integral of the curvature square along the path of the vessel, normalized by the total path length8. The straighter the vessel, the lower the tortuosity value. The estimates were summarized as retinal arteriolar tortuosity and retinal venular tortuosity, representing the average tortuosity of the arterioles and venules of the eye, respectively. Retinal vascular branching angle was defined as the first angle subtended between two daughter vessels at each vascular bifurcation15. Retinal arteriolar branching angle and retinal venular branching angle quantify the average branching angles of the arterioles and venules of the eye, respectively. ## Statistical analysis Statistical analyses were performed using R software (version 2.13.0; Vienna, Austria). The Student’s t test and the chi-square test were used to compare the characteristics of the eyes with idiopathic ERM with those of the healthy controls. Factors related to the presence of idiopathic ERM were identified using univariate and multivariate analyses for those factors selected by the stepwise logistic regression. ## References 1. Mitchell P, Smith W, Chey T, Wang JJ, Chang A. Prevalence and associations of epiretinal membranes. The blue mountains eye study, Australia. Ophthalmology (1997.0) 104 1033-1040. DOI: 10.1016/S0161-6420(97)30190-0 2. Kofod M, la Cour M. Quantification of retinal tangential movement in epiretinal membranes. Ophthalmology (2012.0) 119 1886-1891. DOI: 10.1016/j.ophtha.2012.03.022 3. Weinberger D. Digital red-free photography for the evaluation of retinal blood vessel displacement in epiretinal membrane. Ophthalmology (1999.0) 106 1380-1383. DOI: 10.1016/S0161-6420(99)10164-7 4. Kim YJ, Kim S, Lee JY, Kim JG, Yoon YH. Macular capillary plexuses after epiretinal membrane surgery: An optical coherence tomography angiography study. Br. J. Ophthalmol. (2018.0) 102 1086-1091. DOI: 10.1136/bjophthalmol-2017-311188 5. Ng CH. Prevalence and risk factors for epiretinal membranes in a multi-ethnic United States population. Ophthalmology (2011.0) 118 694-699. DOI: 10.1016/j.ophtha.2010.08.009 6. Miyazaki M. Prevalence and risk factors for epiretinal membranes in a Japanese population: The Hisayama study. Graefes Arch. Clin. Exp. Ophthalmol. (2003.0) 241 642-646. DOI: 10.1007/s00417-003-0723-8 7. Kawasaki R. Racial difference in the prevalence of epiretinal membrane between Caucasians and Asians. Br. J. Ophthalmol. (2008.0) 92 1320-1324. DOI: 10.1136/bjo.2008.144626 8. Hart WE, Goldbaum M, Cote B, Kube P, Nelson MR. Measurement and classification of retinal vascular tortuosity. Int. J. Med. Inform. (1999.0) 53 239-252. DOI: 10.1016/S1386-5056(98)00163-4 9. Liew G. The retinal vasculature as a fractal: Methodology, reliability, and relationship to blood pressure. Ophthalmology (2008.0) 115 1951-1956. DOI: 10.1016/j.ophtha.2008.05.029 10. Cheung CY-L. Microvascular network alterations in the retina of patients with Alzheimer's disease. Alzheimers Dement. (2014.0) 10 135-142. DOI: 10.1016/j.jalz.2013.06.009 11. Klein R, Klein BE, Wang Q, Moss SE. The epidemiology of epiretinal membranes. Trans. Am. Ophthalmol. Soc. (1994.0) 92 403-425. PMID: 7886875 12. Armstrong D. Detection of vascular endothelial growth factor and tumor necrosis factor alpha in epiretinal membranes of proliferative diabetic retinopathy, proliferative vitreoretinopathy and macular pucker. Ophthalmologica (1998.0) 212 410-414. DOI: 10.1159/000027378 13. Dery MA, Michaud MD, Richard DE. Hypoxia-inducible factor 1: Regulation by hypoxic and non-hypoxic activators. Int. J. Biochem. Cell Biol. (2005.0) 37 535-540. DOI: 10.1016/j.biocel.2004.08.012 14. Lim JI, Spee C, Hinton DR. A comparison of hypoxia-inducible factor-alpha in surgically excised neovascular membranes of patients with diabetes compared with idiopathic epiretinal membranes in nondiabetic patients. Retina (2010.0) 30 1472-1478. DOI: 10.1097/IAE.0b013e3181d6df09 15. Zamir M, Medeiros JA, Cunningham TK. Arterial bifurcations in the human retina. J. Gen. Physiol. (1979.0) 74 537-548. DOI: 10.1085/jgp.74.4.537 16. Masters BR. Fractal analysis of the vascular tree in the human retina. Annu. Rev. Biomed. Eng. (2004.0) 6 427-452. DOI: 10.1146/annurev.bioeng.6.040803.140100 17. Grauslund J. Retinal vascular fractals and microvascular and macrovascular complications in type 1 diabetes. Ophthalmology (2010.0) 117 1400-1405. DOI: 10.1016/j.ophtha.2009.10.047 18. Gidday JM, Zhu Y. Endothelium-dependent changes in retinal blood flow following ischemia. Curr. Eye Res. (1998.0) 17 798-807. DOI: 10.1080/02713689808951261 19. Okawa Y. Foveal structure and vasculature in eyes with idiopathic epiretinal membrane. PLoS ONE (2019.0) 14 e0214881. DOI: 10.1371/journal.pone.0214881 20. Kwon HJ, Park SW, Lee JE, Byon I. microvascular leakage is a poor prognostic factor for idiopathic epiretinal membrane: A fluorescein angiography study. Retina (2021.0) 41 2515-2522. DOI: 10.1097/IAE.0000000000003223 21. Yagi T, Sakata K, Funatsu H, Hori S. Evaluation of perifoveal capillary blood flow velocity before and after vitreous surgery for epiretinal membrane. Graefes Arch. Clin. Exp. Ophthalmol. (2012.0) 250 459-460. DOI: 10.1007/s00417-011-1618-8 22. Kadonosono K, Itoh N, Nomura E, Ohno S. Capillary blood flow velocity in patients with idiopathic epiretinal membranes. Retina (1999.0) 19 536-539. DOI: 10.1097/00006982-199911000-00010 23. Kadonosono K, Itoh N, Nomura E, Ohno S. Perifoveal microcirculation in eyes with epiretinal membranes. Br. J. Ophthalmol. (1999.0) 83 1329-1331. DOI: 10.1136/bjo.83.12.1329 24. Shinoda K. Microcirculation in the macular area of eyes with an idiopathic epiretinal membrane. Graefes Arch. Clin. Exp. Ophthalmol. (2001.0) 239 941-945. DOI: 10.1007/s00417-001-0394-2 25. Coppe AM, Lapucci G, Gilardi M, Petruzzella F, Ripandelli G. Alterations of macular blood flow in superficial and deep capillary plexuses in the fellow and affected eyes of patients with unilateral idiopathic epiretinal membrane. Retina (2020.0) 40 1540-1548. DOI: 10.1097/IAE.0000000000002617 26. Coppe AM. Inner macular changes in fellow eye of patients with unilateral idiopathic epiretinal membrane. Investig. Ophthalmol. Vis. Sci. (2021.0) 62 29. DOI: 10.1167/iovs.62.10.29 27. Harada C, Mitamura Y, Harada T. The role of cytokines and trophic factors in epiretinal membranes: Involvement of signal transduction in glial cells. Prog. Retin. Eye Res. (2006.0) 25 149-164. DOI: 10.1016/j.preteyeres.2005.09.001 28. Bringmann A. Foveal configurations with disappearance of the foveal pit in eyes with macular pucker: Presumed role of Muller cells in the formation of foveal herniation. Exp. Eye Res. (2021.0) 207 108604. DOI: 10.1016/j.exer.2021.108604 29. Bringmann A. Muller cells and astrocytes in tractional macular disorders. Prog. Retin Eye Res. (2022.0) 86 100977. DOI: 10.1016/j.preteyeres.2021.100977 30. Govetto A, Lalane RA, Sarraf D, Figueroa MS, Hubschman JP. Insights into epiretinal membranes: Presence of ectopic inner foveal layers and a new optical coherence tomography staging scheme. Am. J. Ophthalmol. (2017.0) 175 99-113. DOI: 10.1016/j.ajo.2016.12.006 31. 31.Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA285, 2486–2497 (2001). 32. Cheung CY. Quantitative and qualitative retinal microvascular characteristics and blood pressure. J. Hypertens. (2011.0) 29 1380-1391. DOI: 10.1097/HJH.0b013e328347266c 33. Hubbard LD. Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology (1999.0) 106 2269-2280. DOI: 10.1016/S0161-6420(99)90525-0 34. Knudtson MD. Revised formulas for summarizing retinal vessel diameters. Curr. Eye Res. (2003.0) 27 143-149. DOI: 10.1076/ceyr.27.3.143.16049 35. Cheung CY. Retinal vascular fractal dimension and its relationship with cardiovascular and ocular risk factors. Am. J. Ophthalmol. (2012.0) 154 663-674 e661. DOI: 10.1016/j.ajo.2012.04.016
--- title: RIP3-mediated microglial necroptosis promotes neuroinflammation and neurodegeneration in the early stages of diabetic retinopathy authors: - Zijing Huang - Jiajian Liang - Shaolang Chen - Tsz Kin Ng - Marten E. Brelén - Qingping Liu - Rucui Yang - Biyao Xie - Shuping Ke - Weiqi Chen - Dingguo Huang journal: Cell Death & Disease year: 2023 pmcid: PMC10060420 doi: 10.1038/s41419-023-05660-z license: CC BY 4.0 --- # RIP3-mediated microglial necroptosis promotes neuroinflammation and neurodegeneration in the early stages of diabetic retinopathy ## Abstract Diabetic retinopathy (DR) is a leading cause of blindness that poses significant public health concerns worldwide. Increasing evidence suggests that neuroinflammation plays a key role in the early stages of DR. Microglia, long-lived immune cells in the central nervous system, can become activated in response to pathological insults and contribute to retinal neuroinflammation. However, the molecular mechanisms of microglial activation during the early stages of DR are not fully understood. In this study, we used in vivo and in vitro assays to investigate the role of microglial activation in the early pathogenesis of DR. We found that activated microglia triggered an inflammatory cascade through a process called necroptosis, a newly discovered pathway of regulated cell death. In the diabetic retina, key components of the necroptotic machinery, including RIP1, RIP3, and MLKL, were highly expressed and mainly localized in activated microglia. Knockdown of RIP3 in DR mice reduced microglial necroptosis and decreased pro-inflammatory cytokines. Additionally, blocking necroptosis with the specific inhibitor GSK-872 improved retinal neuroinflammation and neurodegeneration, as well as visual function in diabetic mice. RIP3-mediated necroptosis was activated and contributed to inflammation in BV2 microglia under hyperglycaemic conditions. Our data demonstrate the importance of microglial necroptosis in retinal neuroinflammation related to diabetes and suggest that targeting necroptosis in microglia may be a promising therapeutic strategy for the early stages of DR. ## Introduction Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and remains the leading cause of vision loss in working-age adults, resulting in a significant burden on both households and societies [1]. Anti-vascular endothelial growth factor (VEGF) agents and pars plana vitrectomy (PPV) have shown great clinical success in managing the late stages of DR [2, 3]. However, the precise pathogenesis and cellular and molecular events in the early stages of DR, including neuronal degeneration, microvascular abnormalities, and glial cell dysfunction, are not fully understood. Acute inflammation is a protective mechanism against injuries, while persistent inflammation can worsen the damage and accelerate the progression of the disease. In addition to microvascular changes, laboratory and clinical evidence suggests that inflammation plays a key role and is a therapeutic target in the early stages of DR [4, 5]. Neuroinflammation in the central nervous system is characterized by the activation of glial cells (mainly microglia) and the release of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), IL-6, and chemokines such as monocyte chemoattractant protein-1 (MCP-1) [6]. There is a strong correlation between the levels of these pro-inflammatory molecules and the severity of DR [7, 8]. High blood sugar levels can also lead to an influx of leukocytes, called leukostasis, which has been linked to endothelial cell death and disruption of the blood-retinal barrier [9]. However, the precise molecular mechanism of neuroinflammation in the early stages of DR, particularly how neuroinflammation is triggered by glial cells, is not fully understood. In recent years, a novel form of programmed cell death called necroptosis has gained significant attention due to its role in various pathologies, particularly inflammatory, autoimmune, and neurodegenerative disorders [10–13]. Necroptosis is different from apoptosis, in which cell fragments are engulfed by macrophages without inducing inflammatory responses, and is distinct from necrosis, which is uncontrolled and highly inflammatory [11]. Classic necroptosis is triggered by death receptors such as TNFRs and TLRs, mediated by the activation of the receptor interacting protein kinase 1 (RIP1)/RIP3 complex, and executed by the phosphorylation and oligomerization of mixed lineage kinase domain-like (MLKL) [10, 14]. Targeting cell necroptosis using specific RIP inhibitors has been shown to reduce local inflammation and improve the outcome of various diseases, including inflammatory bowel disease [15], interface dermatitis [16], and donor kidney inflammatory injury after allograft transplantation [17]. Necroptosis has been previously described in retinal detachment [18] and ischemia-reperfusion injury [19], but its role in ischemic retinopathy, specifically the early stages of DR, is not yet clear. In the present study, using in vivo and in vitro assays, we explore the involvement of necroptosis in microglia and its contribution to neuroinflammation, with the aim of furthering our understanding of DR pathogenesis and potentially providing new therapeutic targets for early DR intervention. ## Ethics statement All animal studies adhered to guidelines for the care and use of laboratory animals issued by the National Institutes of Health (NIH) and were approved by the local Animal Ethic Committee of Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong. Mice were anesthetized with intramuscular injection of zoletil and xylazine. Body temperature was maintained at 37 ± 0.5 °C through a heating pad during surgery. At the end of the experiments, mice were euthanized through carbon dioxide asphyxiation of air. ## Establishment and treatment of streptozotocin (STZ)-induced diabetes model Streptozotocin (STZ) is an antibiotic that causes pancreatic islet β-cell destruction and is widely used for inducing type 1 diabetes mellitus on experimental models, which are used for evaluating the pathological consequences of diabetes and its complications, and for screening potential therapies [20]. DR phenotypes observed in STZ-induced mice include thinning of retinal layers, loss of retinal cells, vascular hyperpermeability, and increased inflammation and neuroinflammation [21]. C57BL/6 J mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. C57BL/6-background RIP3-targeted mutant (rip3−/−) mice were purchased from The Jackson Laboratory. Animals were kept in a specific pathogen-free facility. The streptozotocin (STZ)-induced diabetic retinopathy model was established as previously described [22]. Mice at 8 weeks of age were fasted overnight and then received intraperitoneal injections of STZ (50 mg/kg, Sigma-Aldrich) diluted in 10 mM sodium citrate buffer for 5 consecutive days. Blood glucose levels were measured using the kit with an automatic analyzer (Accu-Chek @Active, Roche) in blood samples from the tail vein. Animals with blood glucose levels maintained at over 16.6 mmol/L (300 mg/dl) were considered as diabetic. Control mice were injected intraperitoneally with sodium citrate buffer. For treatment, the specific RIP3 inhibitor GSK-872 [1 μM resolved in $10\%$ (v/v) DMSO in PBS; Abcam, Cambridge, MA, USA] or vehicle (PBS containing equivalent DMSO) were injected intravitreally on the first day of the 4th week and the first day of the 8th week after STZ induction, respectively. Intravitreal injections were performed using a 5-µL Hamilton syringe with a 33-gauge needle. Twelve weeks after diabetes induction, mice were euthanized and the retinas were collected for further analysis (Supplementary Information 1). ## Immunofluorescence assay Eyes were removed and fixed in $4\%$ paraformaldehyde (PFA) for 30 min in room temperature. For retinal flat-mounts, retinas were separated carefully from the eyeballs, incubated with primary and secondary antibodies, and then washed in PBST and flat-mounted. For retinal sections, retinas were cryoprotected in Tissue-Tek (Sakura Finetek USA Inc.) at −20 °C overnight and 8 μm cryosections were prepared. Sections were incubated with primary, secondary antibodies, and 4’, 6-diamidino-2-phenylindole (DAPI). The flat mounts and sections were observed using a confocal microscope (Carl Zeiss LSM700, German) or a Leica DM4000 B LED automated upright microscope system. Primary antibodies include anti-iba-1 (Wako Chemicals, 1:100), anti-phospho-MLKL (p-MLKL, 1:100), anti-RIP3, anti-GFAP, anti-TUJ1, anti-TNF-α, anti-Tmem119, anti-brn3a, and anti-CD31 (Abcam, 1:100). The secondary antibodies include donkey anti-rabbit IgG H&L (Alexa Fluor 488, 555), donkey anti-rat IgG H&L (Alexa Fluor 555), and donkey anti-goat IgG H&L (Alexa Fluor 488, 555) antibodies (Abcam, 1:1000). For double staining of p-MLKL and terminal deoxynucleotidyl transferase-mediated dUTP-fluorescein nick end labeling (TUNEL) assay, cryosections were first stained with rabbit anti-p-MLKL and then incubated with donkey anti-rabbit IgG H&L (Alexa Fluor 555) secondary antibody before TUNEL staining (In Situ Cell Death Detection Kit, Fluorescein; Roche, IN, USA) was performed according to the manufacturer’s instructions. For section analysis, 6 retinas from 6 mice in each group were used. Three sections from each retina were randomly chosen and a mean value was determined. For flat-mount assays, 3 images were obtained in different locations of the retina (central, mid-peripheral and peripheral areas) and 6 retinas from 6 mice in each group were used for analysis. ## Quantitative reverse transcription polymerase chain reaction (qRT-PCR) The mRNA levels of IL-1β, IL-6, TNF-α, and MCP-1 were detected by qRT-PCR. The total RNA of the BV2 cells and retinas were extracted with TRIzol (Invitrogen) and converted into first-strand cDNA using random hexamer primers and the Reverse Transcriptase Superscript II Kit (Invitrogen) according to the manufacturer’s instructions. qRT-PCR was performed in a total volume of 20 μL containing 2 μL of cDNA, 10 μL of 2×SYBR Premix Ex Taq, and 10 μmol/L of the primer pairs. The PCR amplification protocols consisted of 95 °C for 30 s and up to 40 cycles of 95 °C for 5 s and 60 °C for 34 s according to the manufacturer’s instructions. The primers were il-1β fwd 5′ TCA GGC AGA TGG TGT CTG TC-3′ and rev 5′- GGT CTA TAT CCT CCA GCT GC-3′; il-6 fwd 5′-ACT CAC CTC TTC AGA ACG AAT TG-3′ and rev 5′-CCA TCT TTG GAA GGT TCA GGT TG -3′; tnf-α fwd 5′-GAG GCC AAG CCC TGG TAT G-3′ and rev 5′-CGG GCC GAT TGA TCT CAG C-3′; mcp-1 fwd 5′-TAA AAA CCT GGA TCG GAA CCA AA-3′ and rev 5′-GCA TTA GCT TCA GAT TTA CGG GT-3′. Gapdh was used as a reference gene. ## Western blotting and enzyme-linked immunosorbent assay (ELISA) Retina tissue and cells were harvested and lysed in RIPA buffer containing 10 mM Tris-HCl (pH 7.5), 150 mM NaCl, $0.1\%$ sodium dodecyl sulfate (SDS), $1\%$ Nonidet P-40, and $1\%$ sodium deoxycholate, and was supplemented with protease and phosphatase inhibitor mini tablets (Thermo Fisher Scientific, Waltham, MA). The protein concentration was determined with bicinchoninic acid protein assay. Western blotting was performed as previously described [23]. Briefly, 30 μg of protein was loaded per lane on a $10\%$ SDS-PAGE. The membrane blots were saturated with $5\%$ BSA in PBST for 1 h at room temperature and then incubated overnight at 4 °C with antibodies. Primary antibodies used included anti-RIP1 (BD Bio-Sciences, 1:1000), anti-RIP3 (Abcam, 1:1000), anti-phospho-RIP1 (Invitrogen, 1:1000), anti-phospho-RIP3 (Abcam, 1:1000), anti-MLKL (Abcam, 1:1000), anti-phospho-MLKL (Abcam, 1:1000), and anti-β-actin (Abcam, 1:2000) antibodies. Band intensities were measured using the Image J software (US National Institutes of Health). Co-immunoprecipitation assays were performed using the Thermo Scientific Pierce co-IP kit according to the manufacturer’s instruction. The levels of TNF-α and MCP-1 in cultured microglia was detected with ELISA kits (R&D Systems, Minneapolis, MN) according to manufacturer’s instruction. ## Hematoxylin-Eosin (H&E) staining Eyes were embedded in paraffin and cut into 5μm vertical slices. Retinal sections were washed and stained with hematoxylin buffer for 10 min at room temperature. The sections were rinsed in deionized water and then dipped in $1\%$ Eosin solution for 15 s. After rehydrated in alcohol gradient, slices were washed again and mounted. Histological analyses of retinal tissues were observed under a microscope (Leica DM4000, Germany). Six eyes from 6 mice in each group were analyzed. For each eye, three horizontal sections were randomly chosen and a mean retinal thickness per image was determined. For each section, 4 images were obtained in different locations of the retina (central, mid-central, mid-peripheral and peripheral). Retinal thickness was calculated using the Image-Pro Plus software with a masked protocol. ## Retinal leukostasis The retinal leukostasis assay was performed according to a previous report [24]. Mice were anesthetized before their chest cavity was opened. A 20-gauge perfusion cannula was inserted into the left heart ventricle. The right atrium was cut with microscissors to create an outflow pathway. Then phosphate buffer saline was perfused to remove erythrocytes and nonadherent leukocytes. Fluorescein-conjugated concanavalin A (Con A, 50 μg/ml dissolved in PBS; Vector Laboratories, CA) was injected to label the adherent leukocytes for 5 min. The unbound lectin was then removed with PBS perfusion. The eyes were enucleated and fixed in $4\%$ paraformaldehyde before the retinas were dissociated and flat-mounted. Leukostasis were observed using a Leica microscope (DM4000, Germany) and total adherent leukocytes per retina were calculated. Six eyes from 6 mice in each group were analyzed. ## Electroretinogram (ERG) Following 12-h dark adaptation, mice were anesthetized and the pupil was dilated with $0.5\%$ tropicamide eye drops. Two gold plated loop electrodes contacting the corneal surface were used as the active electrodes. A reference electrode was attached to the skin near the eye and a ground electrode was clipped into the skin of the tail. ERG recordings were collected using RETI scan system (Roland Consult, Wiesbaden, Germany) at a sampling rate of 2 kHz. The amplitudes of b-wave were measured from the average of three responses by a set of three flashes of stimulation, using two flash intensities, 3.0 and 10.0 cd s/m2. ERG recording was performed on 10 eyes in each group. ## Open field test The Open field test was carried out using a method similar to a previous report [25]. The apparatus consisted of a cage (45 × 30 × 30 cm) divided into a white open field (300 lux light intensity, 30 × 30 × 30 cm) and a dark zone (no illumination, 15 × 30 × 30 cm) with a door (5 × 5 cm) between them. To test rats in the open field, the mice were placed in the dark chamber for 2 min for adaptation. Thereafter, the door is opened to allow the mice to move freely between the two chambers for 5 min. The time spent in the dark chamber and white open field was video-recorded. Twelve mice in each group were used for statistical analysis. ## Measurement of pupil light reflection The visual function of experimental animals can be evaluated by measuring pupil constriction in response to a pupillary light reflex [26, 27]. The mice were dark-adapted for 30 min and maintained under anesthesia during examination. The stimulus was provided by a halogen light source (100 W, 12 V) from a surgical microscope (Topcon, Japan). The light source was maintained to focus with 8x objective and aligned perpendicular to the center of the pupil. The stimulus time was 20 s, followed by 60 s of darkness to restore the pupil diameter to baseline level. The pupil status was recorded with the WinFast PVR photography system. The constricted pupil area was measured using the ImageJ software. Twelve eyes from 12 mice in each group were used for statistical analysis. ## Cell culture and treatments The BV2 murine microglial cell lines were purchased from Kunming Institute of Zoology, Chinese Academy of Sciences, China. The cells were seeded into a 24-well plate with 1 × 105 cells/well, and maintained with Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS), streptomycin (50 mg/ml, Invitrogen), and penicillin (50 U/L, Invitrogen). For high glucose stimulus, cells were grown in the 50 mM glucose medium (Sigma-Aldrich) for 48 h. For treatments, cells were pre-stimulated with GSK-872 (5 μM) before cultured in high glucose condition for 12 h. D-Mannitol was used as an osmotic control. ## Statistics All experiments, including western blotting, qRT-PCR, and immunofluorescence assays, were independently repeated 3 times. The number of eyes and animals used in each experiment and the number of images used for analysis were described in specific sections. Data are shown as mean ± standard deviation (S.D). Two-tailed Student’s t test was used to detect the differences between two groups while one-way analysis of variance (ANOVA) was carried out to compare multiple groups with post hoc analysis. $P \leq 0.05$ was considered as statistically significant. ## Microglial activation and increased production of pro-inflammatory cytokines in early stages of diabetic retinopathy Neuroinflammation has been shown to play a critical role in the initiation and development of DR. The inflammatory response in the early stages of DR using the STZ-induced diabetes murine model were evaluated by detecting the levels of key pro-inflammatory cytokines, including IL-1β, IL-6, TNF-α, and MCP-1, which are well-known to facilitate neuroinflammation and exacerbate neural injury in the CNS. The mRNA expression of these cytokines was elevated in the diabetic retinas (Fig. 1A–D). Meanwhile, increased number of activated microglia, characterized by swollen bodies with short processes, was observed in both superficial and deep layers of the diabetic retina, as compared with the resting ones with highly ramified processes in the non-diabetic control (Fig. 1E). In addition, these ameboid-like microglia expressed TNF-α abundantly, suggesting that activated microglia were the main source of pro-inflammatory cytokines (Fig. 1F). The iba1+ microglia were distinguished from resident or infiltrating macrophages by immunolabeling of Tmem119, a novel specific microglial marker (Fig. 1G). These results showed that microglia were activated and adopted proinflammatory phenotypes with higher expression levels of TNF-α at early stages of experimental DR.Fig. 1Microglial activation and neuroinflammation cascade in the early stages of diabetic retinopathy. Streptozotocin-induced diabetes model was established and retina samples were collected 12 weeks after diabetes induction. Age-matched non-diabetes mice were used as control. A–D qRT-PCR array was carried out for several inflammation-related genes, including IL-1β (A), IL-6 (B), TNF-α (C), and MCP-1 (D). Data were shown as mean ± S.D. The experiments were repeated 3 times for statistical analysis. E Representative images of iba-1 immunostaining on retinal flat-mounts. Arrows indicated activated microglia characterized by swollen cell bodies and short branched processes. Scale bar: 50 μm. Three images were obtained in different locations of the retina (central, mid-peripheral and peripheral areas) and 6 retinas from 6 mice in each group were used for analysis. F Immunostaining of iba1 and TNF-α on retinal flat-mounts. Arrows indicated cells co-stained with both iba1 and TNF-α. Scale bar: 50 μm. G Immunostaining of iba1 and Tmem119 on retinal flat-mounts. Scale bar: 50 μm. * $P \leq 0.05$, *$P \leq 0.01.$ ## Microglia underwent necroptosis in the diabetic retina Since increasing evidence has implicated necroptosis as an active mediator of inflammation, we next investigated whether activated microglia underwent necroptosis in the diabetic retina. The expression of key necroptotic machineries, including RIP1, RIP3, were significantly upregulated (Fig. 2A). Since RIP1 and RIP3 mediate cell death in a kinase activity-dependent manner, we measured phosphorylated RIP1 and RIP3 to support the activation of necroptotic pathway. It was found that both RIP1 and RIP3 were significantly phosphorylated in STZ retina (Fig. 2B). In addition, since RIP3 interacted with RIP1 to form the necrosome, we performed immunoprecipitation assays, which further confirm the presence of necroptosis process (Fig. 2C). Moreover, the level of MLKL and its phosphorylation (p-MLKL), a key executioner of necroptosis, was up to five times higher in the diabetic retinas than non-diabetic controls (Fig. 2D). In addition, we carried out combined p-MLKL immunostaining and TUNEL assays to label retinal cells undergoing necroptosis. The TUNEL+ p-MLKL+ necroptotic cells were observed in the inner layers of the diabetic retinas instead of non-diabetic controls (Fig. 2E). To further identify the involvement specific cell types in necroptosis, retinal sections were prepared and co-staining of p-MLKL and markers for cells in the inner layer, including microglia, astrocytes, retinal ganglion cells, and vascular endothelial cells, were performed respectively. As shown in Fig. 2F, the key necroptotic machinery p-MLKL was mainly located in iba1+ microglia, little expressed in GFAP astrocytes, and was almost negative in other cell types. Co-staining of p-MLKL and iba1+ microglia using retinal flat-mounts further confirmed that diabetes-induced necroptosis occurs specifically in microglia (Fig. 2G).Fig. 2Activation of microglial necroptosis in the diabetic retina. Streptozotocin-induced diabetes model was established and retina samples were collected 12 weeks after diabetes induction. Age-matched non-diabetes mice were used as control. A, B Protein levels of RIP1 and RIP3 (A), and phospho-RIP1 (p-RIP1) and p-RIP3 (B), were detected by western blotting. C Co-immunoprecipitation assays of RIP1 and RIP3 revealed that RIP1 coimmunoprecipitated with RIP3 in the diabetic retina. D Protein levels of MLKL and p-MLKL were detected by western blotting. E Combined p-MLKL staining and TUNEL assay on retinal cryosections. Arrows indicated necroptotic cells that were both p-MLKL and TUNEL positive. Scale bar: 50 μm. F Double staining of p-MLKL with different cell markers on cryosections of the diabetic retinas. Iba1, a marker for microglia. GFAP, a marker for astrocytes and Müller cells. Brn3a, a marker for retinal ganglion cells. CD31, a marker for vascular endothelial cells. Arrows indicated cells co-stained with p-MLKL and iba1 and partially GFAP. Scale bar: 50 μm. G Double staining of p-MLKL and iba1 on flat-mounts of the diabetic retina and non-diabetic control. Arrows indicated cells co-stained with p-MLKL and iba1. Scale bar: 50 μm. Western blotting assays were repeated 3 times. Data were shown as mean ± S.D. $P \leq 0.05.$ ** $P \leq 0.01.$ ## RIP3 deficiency impaired the activation of microglial necroptosis RIP3 is an essential upstream protein kinase of MLKL and a crucial player in the RIP1-RIP3-MLKL necroptotic signaling pathway [28]. We introduced the RIP3−/− mice to investigate the role of RIP3 in diabetes-induced necroptosis. As expected, knockdown of RIP3 silenced the expression of RIP3 in the diabetic retina while significantly reduced the level of RIP1, MLKL, and p-MLKL, in the diabetic retina (Fig. 3A–C). Consistently, the diabetic retinas of RIP3−/− mice exhibited almost no iba1+RIP3+ and less iba1+p-MLKL+ cells as compared to that of normal mice (Fig. 3D, E). In addition, both RIP3 gene knockdown and RIP3 inhibition using a specific inhibitor GSK872 led to a decrease in microglia cell death (Fig. 3F), further indicating that RIP3 deficiency could block the RIP1/RIP3/MLKL necroptotic pathway in DR.Fig. 3RIP3 deficiency blocked the process of necroptosis in microglia. Wild-type C57BL/6 J and RIP3−/− mice were used to create the streptozotocin-induced diabetes model and retinal samples were collected 12 weeks after diabetes induction. A, B The protein level of RIP1 and RIP3 was detected by western blotting. C The protein level of MLKL and p-MLKL were detected by western blotting. D Double immunofluorescence staining of iba1 and RIP3 on retinal flat-mounts. Arrows indicated cells that were both iba1 and RIP3 positive. Scale bar: 50 μm. E Double immunofluorescence staining of iba1 and p-MLKL on retinal flat-mounts. Arrows indicated cells that were both iba1 and p-MLKL positive. Scale bar: 100 μm. Three images were obtained in different locations of the retina (central, mid-peripheral and peripheral areas) and 6 retinas from 6 mice in each group were used for analysis. F Double immunofluorescence staining of iba1 and p-TUNEL assay on retinal cryosections. Arrows indicated iba1+ microglia that were undergoing cell death. Scale bar: 100 μm. Three horizontal sections were randomly chosen and 6 eyes from 6 mice in each group were analyzed. GSK, the specific RIP3 inhibitor GSK-872. Western blotting assays were repeated 3 times. Data were mean ± S.D. $P \leq 0.05$, $P \leq 0.01$, *$P \leq 0.001.$ ## Blockade of microglial necroptosis rescued diabetes-mediated retinal neuroinflammation and neurodegeneration We next explored whether blocking necroptosis in microglia using a specific RIP3 inhibitor could attenuate retinal neuroinflammation and neurodegeneration in the diabetic retina. We first assessed the binding of several RIP3 inhibitors by molecular docking assays, through which GSK-872 was considered an optimal candidate for RIP3 inhibitors (Supplementary information 2). As shown in Fig. 4A–C, inhibition of RIP3 and the necroptotic signaling pathway using GSK-872 significantly down-regulated the mRNA levels of several key pro-inflammatory cytokines, including IL-1β, IL-6, and TNF-α. Leukostasis and gliosis, two major events in neuroinflammation and early pathologic changes in DR, was evaluated. The leukostasis assay revealed severe leukocytes adhesion in the diabetic micro-vessels, which was largely ablated by GSK-872 treatment (Fig. 4D). Gliosis was evaluated using GFAP immunostaining. In the normal retina, GFAP was mainly localized to astrocytes in the superficial layer. In the diabetic retina, intensive GFAP labeling in astrocytes and Müller cells in the inner layer was detected, whereas RIP3 inhibition using GSK-872 significantly reduced GFAP immunoreactivity as compared to the controls (Fig. 4E). Retinal neurodegeneration is an early pathology of DR that can precede visible vasculopathy [29]. Of note, diabetes induced a significant decrease in both neuron density and neuro-retinal thickness, which can be partially rescued by GSK-872 treatment (Fig. 4F, G). It was also noted that necroptosis inhibition by GSK-872 administration had no visible impact on non-diabetic mice. Fig. 4RIP3 inhibition attenuated retinal neuroinflammation and neurodegeneration. Streptozotocin-induced diabetes model was established. Age-matched non-diabetes mice were used as control. GSK-872 or vehicle were injected intravitreally on the first day of the 4th week and 8th week after STZ induction. Twelve weeks after diabetes induction, retina samples were collected and RNA was isolated using TRIzol Reagent. A–C qRT-PCR array was carried out for inflammation-related genes, including IL-1β, IL-6, and TNF-α. D Retinal leukostasis* was performed using fluorescein-conjugated concanavalin A to label the adherent leukocytes. The number of adherent leukocytes were observed using a Leica microscope. Total adherent leukocytes per retina and six retinas from 6 mice in each group were used for analysis. Arrows indicated adherent leukocytes. Scale bar: 100 μm. E Immunostaining of GFAP, a marker for astrocytes and Müller cells, on retinal cryosections. Scale bar: 100 μm. F Immunostaining of TUJ1, a marker for neurons, on retinal flat-mounts. Scale bar: 50 μm. Three images were obtained in different locations of the retina (central, mid-peripheral and peripheral areas) and 6 retinas from 6 mice in each group were used for analysis. G H&E staining was carried out to evaluate retinal thickness in each group. Scale bar: 100 μm. Retinal thickness at different locations, including central (c), mid-central (mc), mid-peripheral (mp), and peripheral (p), were analyzed. Three horizontal sections were randomly chosen and 6 eyes from 6 mice in each group were analyzed. * DR + PBS vs DR + GSK, $P \leq 0.05.$ GSK, the specific RIP3 inhibitor GSK-872. ONL outer nuclear layer, INL inner nuclear layer, GCL ganglion cell layer. Western blotting assays were repeated 3 times for statistical analysis. Data were shown as mean ± S.D. $P \leq 0.05$, $P \leq 0.01.$ ** $P \leq 0.001$, ns no significance. ## Necroptosis inhibition with GSK-872 improved visual function of the diabetic mice To explore whether blocking of microglial necroptosis and neuroinflammation could enhance the light sensitivity and visual function of diabetic mice, ERG was first carried out to measure the electrical potential change of the entire retina in response to light stimulation. The results showed significant improvements of b-wave amplitudes at different light intensities (3.0 and 10.0 cd·s/m2) after dark-adapted condition, which represented the neural signals derived from inner-layer neurons, in the GSK-872-treated diabetic mice (Fig. 5A). In addition, an open field test was introduced to assess the light sensitivity and visual functions of the mice based on the theory that normal mice avoid open and bright spaces while those with retinal diseases spend a decreased amount of time in the dark area [30, 31]. This innate tendency can thus be utilized as the basis of a simple test of their ability to light detection. The test revealed that diabetic mice spent less time in the dark chamber whereas GSK-872 treatment partially abolished this effect, implicating increased light sensitivity of the GSK-872-treated mice (Fig. 5B). We also evaluated the pupillary light reflex by measuring the pupil constriction in the diabetic mice with and without treatment. As a result, diabetic mice presented a larger pupil area with light stimulation and longer pupil contraction time than non-diabetic ones. GSK-872 treatment greatly improved the pupillary light reflex by enhancing the contraction amplitude while decreasing contraction time (Fig. 5C). Collectively, these data demonstrated that blockade of microglial necroptosis with GSK-872 improved the light sensitivity and visual function of diabetic mice. Fig. 5RIP3 inhibition improved visual functions and light sensitivity of the diabetic mice. Streptozotocin-induced diabetes model was established. Age-matched non-diabetes mice were used as control. GSK-872 or vehicle were injected intravitreally on the first day of the 4th week and 8th week after STZ induction. The experiments were carried out 12 weeks after diabetes induction. A Electroretinogram (ERG) was performed using two flash intensities, 3.0 cd·s/m2 and 10.0 cd·s/m2. The amplitudes of b-wave were measured from the average of three responses by a set of three flashes of stimulation. $$n = 10$$ eyes in each group. B The open field test was carried out to test the light sensitivity and visual functions of the mice. The device consisted of a cage (45 × 30 × 30 cm) divided into a white open field and a dark zone with a door (5 × 5 cm) between them. The time spent in the dark chamber was recorded. $$n = 12$$ mice in each group. C Pupillary light reflex measured by pupil constriction was conducted to evaluate RGC function. $$n = 10$$ eyes in each group. GSK, the specific RIP3 inhibitor GSK872. Data were presented as mean ± S.D. $P \leq 0.05$, $P \leq 0.01$, *$P \leq 0.001.$ ## RIP3-mediated necroptosis was activated in BV2 microglia under glycemic condition We finally investigated whether microglia underwent necroptosis and contributed to the inflammatory cascade in vitro. BV2 microglial cell lines were treated with high glucose or isosmotic mannitol controls. GSK-872 was added before high glucose stimulation. Consistent with the in vivo studies, short-term high glucose exposure induced overexpression of necroptotic machineries, including RIP1, RIP3, MLKL, and p-MLKL, in cultured microglial cells, which could be ablated by GSK-872 treatment (Fig. 6A, B). Co-immunostaining assays revealed the presence of p-MLKL+TUNEL+ cells under glycemic condition, indicating that microglia cells were undergoing necroptosis. Similarly, GSK-872 supplement rescued microglial cells from necroptotic death (Fig. 6C, D). In addition, RIP3 inhibition by GSK-872 significantly down-regulated the levels of TNF-α and MCP-1 in high glucose-stimulated microglial cells (Fig. 6E, F), suggesting that blocking the necroptotic signaling could orchestrate high glucose-mediated inflammation cascade in vitro. Fig. 6High glucose triggered necroptosis in BV2 microglia. The BV2 murine microglial cell lines were treated with high glucose (50 mM) for 48 h. For treatments, cells were pre-stimulated with GSK-872 (5 μM) before high glucose stimulation for 12 h. D-Mannitol was used as an osmotic control. A The protein levels of RIP1 and RIP3 were detected by western blotting. B The protein levels of MLKL and p-MLKL were detected by western blotting. C, D Combined p-MLKL immunostaining and TUNEL assays on BV2 microglial cells under high glucose and normal condition. Ten images in each group were randomly chosen for analysis. E, F The levels of TNF-α and MCP-1 in cultured microglia was detected using TNF-α and MCP-1 ELISA kit respectively. HG high glucose. GSK, the specific RIP3 inhibitor GSK-872. Western blotting and ELISA assays were repeated 3 times. Data were mean ± S.D. $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$, ns no significance. ## Discussion Although diabetic retinopathy (DR) is primarily a retinal microvascular disease, increasing evidence now shows that various molecular abnormalities characteristic of inflammation are present in the retinas of diabetic patients and animals, as well as in retinal cells exposed to high blood sugar levels, linking inflammatory processes to the development of DR [4, 32]. Diabetes can cause various inflammatory changes in the retina, including upregulation of NF-κB signaling and other pro-inflammatory transcription factors [33], glial activation with elevated levels of pro-inflammatory cytokines and chemokines [34, 35], leukostasis and platelet aggregation [36], increased vascular permeability [37], and others [38]. Microglial activation, a hallmark of neuroinflammation, has been identified as playing an emerging role in DR pathology. Microglia are resident immune cells in the brain and retina that serve as immune surveillance and provide host defense. When they encounter danger signals, microglia become activated to reduce nociceptive stimulus and maintain tissue homeostasis. However, persistent stress can cause microglia to become over-activated, resulting in the production of large amounts of pro-inflammatory cytokines and chemokines, which can worsen neuronal damage [39, 40]. The precise molecular mechanism of microglia-based neuroinflammation is not fully understood. In recent years, the concept of microglia polarization has been proposed, in which activated microglia are classified into pro-inflammatory “M1” and anti-inflammatory “M2” phenotypes based on specific cytokines and surface antigens [41]. Targeting microglia polarization has been suggested as an alternative strategy for preventing and treating retinal neurodegeneration [42] and ischemic retinopathy [43]. However, the M1/M2 terminology may limit research on the characterization of microglia activation as microglia can assume a diversity of phenotypes and shift functions under different circumstances [44, 45]. It is important to explore novel and specific mechanisms of microglial activation and neuroinflammation. Necroptosis, a novel form of regulated cell death, has been observed in different cell types in diseased retinas, including photoreceptors in retinal detachment [18], neuronal cells in retinal ischemia-reperfusion injury [19], and retinal pigment epithelium in age-related macular degeneration [46]. We have previously demonstrated the role of microglial necroptosis in retinal degeneration and neuroinflammation, focusing on TLR signaling and the specific inhibition of RIP1 by necrostatin-1 [47]. In this study, we identified the role of microglial necroptosis in the early stages of DR. Importantly, the necroptotic microglia mainly assumed a classically activated “ameboid” shape, indicating that activated microglia are more prone to undergo necroptosis. We also found that necroptosis in microglia contributed to the neuroinflammatory pathogenesis in the diabetic retina, as evidenced by increased production of key pro-inflammatory cytokines such as TNF-α, IL-1β, and MCP-1. Blocking necroptosis with a specific RIP3 receptor successfully downregulated the levels of these inflammatory mediators, further supporting the hypothesis that microglial necroptosis has pro-inflammatory and detrimental effects. Our findings may broaden the scope for microglia-mediated neuroinflammation and necroptosis-targeting therapies for the early stages of DR. Retinal neurodegeneration has been increasingly recognized as an early event in the pathogenesis of DR that precedes and contributes to microvasculopathy [48]. In this study, blocking microglial necroptosis significantly prevented the reduction of neuronal cell density and neuroretinal thickness in the diabetic retina, which are histological markers of retinal neurodegeneration [49], suggesting that necroptotic microglia may be a therapeutic target for neuroprotection. In addition, an open field test was used to evaluate the visual function following anti-necroptosis treatment, based on the natural aversion of normal mice to open and bright areas [50] and the observation that mice with retinal neurodegeneration spend less time in dark spaces [25]. In this study, mice treated with GSK-872 spent more time in the dark area compared to untreated diabetic mice, indicating that blocking necroptosis was able to attenuate retinal neurodegeneration or restore neuron function. Finally, GSK-872 treatment partially rescued the electrophysiological response to flashes of light and suppressed the impairment of the pupillary light reflex, further supporting the neuroprotective effect of inhibiting microglial necroptosis in diabetic mice. Necrostatin-1 (Nec-1), a specific RIP1 kinase inhibitor, has been used to block the necroptotic RIP1/RIP3/MLKL pathway and has been shown to be effective in several neurological diseases [51, 52]. However, recent studies have revealed non-death-related biological functions of RIP1 beyond necroptosis, such as directly activating NF-κB signaling and promoting cell survival [53, 54]. In this study, we used GSK-872 to inhibit the activity of RIP3, which is a core and specific molecular machinery for necroptosis [28]. Intravitreal injection of GSK-872 significantly downregulated the level of MLKL, a major necroptosis effector, and suppressed TUNEL staining on microglia in the diabetic retina, indicating that GSK-872 is a promising candidate compound for anti-necroptotic strategies. In this study, we noticed a modest activation of p-MLKL even in the absence of RIP3. Indeed, it has been described that RIP3-independent modifications on the MLKL kinase facilitate necroptosis execution, including the activation of membrane-associated TAM kinases [55] and other unknown kinases [56]. In addition, phosphorylation and translocation of MLKL to the autophagosome membrane also contribute to cell autophagy via a RIP3-independent pathway [57]. These findings may explain the RIP3-independent partial activation of p-MLKL in our study. Further study using MLKL gene knockout strategies might help to verify this hypothesis. There are several limitations to this study. First, we evaluated the role of necroptosis using a STZ-induced murine model, which mimics the early stages of DR pathology. However, patients with DR often suffer from severe visual loss when diabetic edema or neovascular complications occur, and VEGF plays a well-recognized role in increased permeability and retinal neovascularization [58]. The effect of necroptosis inhibition on neovascularization, as well as the neuroprotective potential of necroptosis-targeting therapy on late stages of DR, requires further investigation. In addition, the necroptotic machinery p-MLKL in the diabetic retina was slightly expressed on GFAP + astroglia (astrocytes and Müller cells), which also participate in the process of neuroinflammation. Tmem119 was used to differentiate resident microglia from circulating myeloid cells, but the contribution of peripheral myeloid cells to the current disease process cannot be completely ruled out. Further study is needed to confirm the potential role of necroptosis in these cell types. Although the primary insult in DR may not be inflammation, the immune cell-mediated neuroinflammatory cascade can exacerbate the progression of microvascular injuries and neurodegeneration. Our data demonstrate the presence of microglial necroptosis in the early pathogenesis of DR, which amplifies neuroinflammatory processes and exacerbates neuronal damage in the diabetic retina. Targeting the necroptosis process in microglia may provide a promising therapeutic alternative for early stages of DR. ## Supplementary information Co-author approves for adding a new author aj-checklist Supplementary information 1. Establishment and treatment of STZ-induced diabetic retinopathy mice. Supplementary information 2. Molecular docking assays. Supplementary information 3. Original western blot gels The online version contains supplementary material available at 10.1038/s41419-023-05660-z. ## References 1. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet* (2010.0) 376 124-36. DOI: 10.1016/S0140-6736(09)62124-3 2. Antonetti DA, Klein R, Gardner TW. Diabetic retinopathy. N. Engl J Med (2012.0) 366 1227-39. DOI: 10.1056/NEJMra1005073 3. Wells JA, Glassman AR, Ayala AR, Jampol LM, Bressler NM, Bressler SB. Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema: two-year results from a comparative effectiveness randomized clinical trial. Ophthalmology (2016.0) 123 1351-9. DOI: 10.1016/j.ophtha.2016.02.022 4. Forrester JV, Kuffova L, Delibegovic M. The role of inflammation in diabetic retinopathy. Front Immunol (2020.0) 11 583687. DOI: 10.3389/fimmu.2020.583687 5. Semeraro F, Morescalchi F, Cancarini A, Russo A, Rezzola S, Costagliola C. Diabetic retinopathy, a vascular and inflammatory disease: therapeutic implications. Diabetes Metab (2019.0) 45 517-27. DOI: 10.1016/j.diabet.2019.04.002 6. Gilhus NE, Deuschl G. Neuroinflammation - a common thread in neurological disorders. Nat Rev Neurol (2019.0) 15 429-30. DOI: 10.1038/s41582-019-0227-8 7. Cvitkovic K, Sesar A, Sesar I, Pusic-Sesar A, Pejic R, Kelava T. Concentrations of selected cytokines and vascular endothelial growth factor in aqueous humor and serum of diabetic patients. Semin Ophthalmol (2020.0) 35 126-33. DOI: 10.1080/08820538.2020.1755320 8. Taghavi Y, Hassanshahi G, Kounis NG, Koniari I, Khorramdelazad H. Monocyte chemoattractant protein-1 (MCP-1/CCL2) in diabetic retinopathy: latest evidence and clinical considerations. J Cell Commun Signal (2019.0) 13 451-62. DOI: 10.1007/s12079-018-00500-8 9. Herdade AS, Silva IM, Calado A, Saldanha C, Nguyen NH, Hou I. Effects of diabetes on microcirculation and leukostasis in retinal and non-ocular tissues: implications for diabetic retinopathy. Biomolecules (2020.0) 10 1583. DOI: 10.3390/biom10111583 10. Pasparakis M, Vandenabeele P. Necroptosis and its role in inflammation. Nature (2015.0) 517 311-20. DOI: 10.1038/nature14191 11. Linkermann A, Green DR. Necroptosis. N. Engl J Med (2014.0) 370 455-65. DOI: 10.1056/NEJMra1310050 12. Choi ME, Price DR, Ryter SW, Choi AMK. Necroptosis: a crucial pathogenic mediator of human disease. JCI Insight (2019.0) 4 e128834. DOI: 10.1172/jci.insight.128834 13. Zhang S, Tang MB, Luo HY, Shi CH, Xu YM. Necroptosis in neurodegenerative diseases: a potential therapeutic target. Cell Death Dis (2017.0) 8 e2905. DOI: 10.1038/cddis.2017.286 14. Najjar M, Saleh D, Zelic M, Nogusa S, Shah S, Tai A. RIPK1 and RIPK3 kinases promote cell-death-independent inflammation by toll-like receptor 4. Immunity (2016.0) 45 46-59. DOI: 10.1016/j.immuni.2016.06.007 15. Pierdomenico M, Negroni A, Stronati L, Vitali R, Prete E, Bertin J. Necroptosis is active in children with inflammatory bowel disease and contributes to heighten intestinal inflammation. Am J Gastroenterol (2014.0) 109 279-87. DOI: 10.1038/ajg.2013.403 16. Lauffer F, Jargosch M, Krause L, Garzorz-Stark N, Franz R, Roenneberg S. Type I immune response induces keratinocyte necroptosis and is associated with interface dermatitis. J Invest Dermatol (2018.0) 138 1785-94. DOI: 10.1016/j.jid.2018.02.034 17. Lau A, Wang S, Jiang J, Haig A, Pavlosky A, Linkermann A. RIPK3-mediated necroptosis promotes donor kidney inflammatory injury and reduces allograft survival. Am J Transpl (2013.0) 13 2805-18. DOI: 10.1111/ajt.12447 18. Trichonas G, Murakami Y, Thanos A, Morizane Y, Kayama M, Debouck CM. Receptor interacting protein kinases mediate retinal detachment-induced photoreceptor necrosis and compensate for inhibition of apoptosis. Proc Natl Acad Sci USA (2010.0) 107 21695-700. DOI: 10.1073/pnas.1009179107 19. Rosenbaum DM, Degterev A, David J, Rosenbaum PS, Roth S, Grotta JC. Necroptosis, a novel form of caspase-independent cell death, contributes to neuronal damage in a retinal ischemia-reperfusion injury model. J Neurosci Res (2010.0) 88 1569-76. PMID: 20025059 20. Furman BL. Streptozotocin-induced diabetic models in mice and rats. Curr Protoc Pharmacol (2015.0) 70 5.47.1-5.47.20. DOI: 10.1002/0471141755.ph0547s70 21. Lelyte I, Ahmed Z, Kaja S, Kalesnykas G. Structure-function relationships in the Rodent streptozotocin-induced model for diabetic retinopathy: a systematic review. J Ocul Pharm Ther (2022.0) 38 271-86. DOI: 10.1089/jop.2021.0128 22. Liu Y, Yang Z, Lai P, Huang Z, Sun X, Zhou T. Bcl-6-directed follicular helper T cells promote vascular inflammatory injury in diabetic retinopathy. Theranostics (2020.0) 10 4250-64. DOI: 10.7150/thno.43731 23. Zhang Y, Zhang J, Wang Q, Lei X, Chu Q, Xu GT. Intravitreal injection of exendin-4 analogue protects retinal cells in early diabetic rats. Invest Ophthalmol Vis Sci (2011.0) 52 278-85. DOI: 10.1167/iovs.09-4727 24. Miyamoto K, Khosrof S, Bursell SE, Rohan R, Murata T, Clermont AC. Prevention of leukostasis and vascular leakage in streptozotocin-induced diabetic retinopathy via intercellular adhesion molecule-1 inhibition. Proc Natl Acad Sci USA (1999.0) 96 10836-41. DOI: 10.1073/pnas.96.19.10836 25. Liu M, Dai J, Liu W, Zhao C, Yin ZQ. Overexpression of melanopsin in the retina restores visual function in Royal College of Surgeons rats. Mol Med Rep (2016.0) 13 321-6. DOI: 10.3892/mmr.2015.4549 26. Whiteley SJ, Young MJ, Litchfield TM, Coffey PJ, Lund RD. Changes in the pupillary light reflex of pigmented royal college of surgeons rats with Age. Exp Eye Res (1998.0) 66 719-30. DOI: 10.1006/exer.1997.0479 27. Yang Y, Xu C, Chen Y, Liang JJ, Xu Y, Chen SL. Green tea extract ameliorates ischemia-induced retinal ganglion cell degeneration in rats. Oxid Med Cell Longev (2019.0) 2019 8407206. DOI: 10.1155/2019/8407206 28. Moriwaki K, Chan FK. RIP3: a molecular switch for necrosis and inflammation. Genes Dev (2013.0) 27 1640-9. DOI: 10.1101/gad.223321.113 29. Simo R, Hernandez C. Neurodegeneration is an early event in diabetic retinopathy: therapeutic implications. Br J Ophthalmol (2012.0) 96 1285-90. DOI: 10.1136/bjophthalmol-2012-302005 30. Lin B, Koizumi A, Tanaka N, Panda S, Masland RH. Restoration of visual function in retinal degeneration mice by ectopic expression of melanopsin. Proc Natl Acad Sci USA (2008.0) 105 16009-14. DOI: 10.1073/pnas.0806114105 31. Padilla E, Shumake J, Barrett DW, Holmes G, Sheridan EC, Gonzalez-Lima F. Novelty-evoked activity in open field predicts susceptibility to helpless behavior. Physiol Behav (2010.0) 101 746-54. DOI: 10.1016/j.physbeh.2010.08.017 32. Joussen AM, Poulaki V, Le ML, Koizumi K, Esser C, Janicki H. A central role for inflammation in the pathogenesis of diabetic retinopathy. FASEB J (2004.0) 18 1450-2. DOI: 10.1096/fj.03-1476fje 33. Patel S, Santani D. Role of NF-kappa B in the pathogenesis of diabetes and its associated complications. Pharm Rep (2009.0) 61 595-603. DOI: 10.1016/S1734-1140(09)70111-2 34. Kinuthia UM, Wolf A, Langmann T. Microglia and Inflammatory Responses in Diabetic Retinopathy. Front Immunol (2020.0) 11 564077. DOI: 10.3389/fimmu.2020.564077 35. Altmann C, Schmidt MHH. The role of microglia in diabetic retinopathy: inflammation, microvasculature defects and neurodegeneration. Int J Mol Sci (2018.0) 19 110. DOI: 10.3390/ijms19010110 36. Joy SS, Siddiqui K. Molecular and pathophysiological mechanisms of diabetic retinopathy in relation to adhesion molecules. Curr Diabetes Rev (2019.0) 15 363-71. DOI: 10.2174/1573399814666181017103844 37. Sigurdardottir S, Zapadka TE, Lindstrom SI, Liu H, Taylor BE, Lee CA. Diabetes-mediated IL-17A enhances retinal inflammation, oxidative stress, and vascular permeability. Cell Immunol (2019.0) 341 103921. DOI: 10.1016/j.cellimm.2019.04.009 38. Youngblood H, Robinson R, Sharma A, Sharma S. Proteomic biomarkers of retinal inflammation in diabetic retinopathy. Int J Mol Sci (2019.0) 20 4755. DOI: 10.3390/ijms20194755 39. Woodburn SC, Bollinger JL, Wohleb ES. The semantics of microglia activation: neuroinflammation, homeostasis, and stress. J Neuroinflammation (2021.0) 18 258. DOI: 10.1186/s12974-021-02309-6 40. Subhramanyam CS, Wang C, Hu Q, Dheen ST. Microglia-mediated neuroinflammation in neurodegenerative diseases. Semin Cell Dev Biol (2019.0) 94 112-20. DOI: 10.1016/j.semcdb.2019.05.004 41. Martinez FO, Helming L, Gordon S. Alternative activation of macrophages: an immunologic functional perspective. Annu Rev Immunol (2009.0) 27 451-83. DOI: 10.1146/annurev.immunol.021908.132532 42. Zhou T, Huang Z, Sun X, Zhu X, Zhou L, Li M. Microglia polarization with M1/M2 phenotype changes in rd1 mouse model of retinal degeneration. Front Neuroanat (2017.0) 11 77. DOI: 10.3389/fnana.2017.00077 43. Li J, Yu S, Lu X, Cui K, Tang X, Xu Y. The phase changes of M1/M2 phenotype of microglia/macrophage following oxygen-induced retinopathy in mice. Inflamm Res (2021.0) 70 183-92. DOI: 10.1007/s00011-020-01427-w 44. Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK. A unique microglia type associated with restricting development of Alzheimer’s Disease. Cell (2017.0) 169 1276-90.e1217. DOI: 10.1016/j.cell.2017.05.018 45. Ransohoff RM. A polarizing question: do M1 and M2 microglia exist?. Nat Neurosci (2016.0) 19 987-91. DOI: 10.1038/nn.4338 46. Hanus J, Anderson C, Wang S. RPE necroptosis in response to oxidative stress and in AMD. Ageing Res Rev (2015.0) 24 286-98. DOI: 10.1016/j.arr.2015.09.002 47. Huang Z, Zhou T, Sun X, Zheng Y, Cheng B, Li M. Necroptosis in microglia contributes to neuroinflammation and retinal degeneration through TLR4 activation. Cell Death Differ (2018.0) 25 180-9. DOI: 10.1038/cdd.2017.141 48. Sohn EH, van Dijk HW, Jiao C, Kok PH, Jeong W, Demirkaya N. Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proc Natl Acad Sci USA (2016.0) 113 E2655-64. DOI: 10.1073/pnas.1522014113 49. Bogdanov P, Corraliza L, Villena JA, Carvalho AR, Garcia-Arumi J, Ramos D. The db/db mouse: a useful model for the study of diabetic retinal neurodegeneration. PLoS One (2014.0) 9 e97302. DOI: 10.1371/journal.pone.0097302 50. 50.Takao K, Miyakawa T. Light/dark transition test for mice. J Vis Exp. 2006:104. 51. Cao L, Mu W. Necrostatin-1 and necroptosis inhibition: Pathophysiology and therapeutic implications. Pharm Res (2021.0) 163 105297. DOI: 10.1016/j.phrs.2020.105297 52. Deng XX, Li SS, Sun FY. Necrostatin-1 prevents necroptosis in brains after ischemic stroke via inhibition of RIPK1-mediated RIPK3/MLKL signaling. Aging Dis (2019.0) 10 807-17. DOI: 10.14336/AD.2018.0728 53. Moriwaki K, Chan FK. Necroptosis-independent signaling by the RIP kinases in inflammation. Cell Mol Life Sci (2016.0) 73 2325-34. DOI: 10.1007/s00018-016-2203-4 54. Festjens N, Vanden Berghe T, Cornelis S, Vandenabeele P. RIP1, a kinase on the crossroads of a cell’s decision to live or die. Cell Death Differ (2007.0) 14 400-10. DOI: 10.1038/sj.cdd.4402085 55. Najafov A, Mookhtiar AK, Luu HS, Ordureau A, Pan H, Amin PP. TAM kinases promote necroptosis by regulating oligomerization of MLKL. Mol Cell (2019.0) 75 457-68.e454. DOI: 10.1016/j.molcel.2019.05.022 56. Ying Z, Pan C, Shao T, Liu L, Li L, Guo D. Mixed lineage kinase domain-like protein MLKL breaks down myelin following nerve injury. Mol Cell (2018.0) 72 457-68.e455. DOI: 10.1016/j.molcel.2018.09.011 57. Wu X, Nagy LE. MLKL contributes to Western diet-induced liver injury through inhibiting autophagy. Autophagy (2020.0) 16 1351-2. DOI: 10.1080/15548627.2020.1760624 58. Ferrara N. Vascular endothelial growth factor: basic science and clinical progress. Endocr Rev (2004.0) 25 581-611. DOI: 10.1210/er.2003-0027
--- title: A birefringent spectral demultiplexer enables fast hyper-spectral imaging of protoporphyrin IX during neurosurgery authors: - Mikael Marois - Jonathan D. Olson - Dennis J. Wirth - Jonathan T. Elliott - Xiaoyao Fan - Scott C. Davis - Keith D. Paulsen - David W. Roberts journal: Communications Biology year: 2023 pmcid: PMC10060426 doi: 10.1038/s42003-023-04701-9 license: CC BY 4.0 --- # A birefringent spectral demultiplexer enables fast hyper-spectral imaging of protoporphyrin IX during neurosurgery ## Abstract Hyperspectral imaging and spectral analysis quantifies fluorophore concentration during fluorescence-guided surgery1–6. However, acquisition of the multiple wavelengths required to implement these methods can be time-consuming and hinder surgical workflow. To this end, a snapshot hyperspectral imaging system capable of acquiring 64 channels of spectral data simultaneously was developed for rapid hyperspectral imaging during neurosurgery. The system uses a birefringent spectral demultiplexer to split incoming light and redirect wavelengths to different sections of a large format microscope sensor. Its configuration achieves high optical throughput, accepts unpolarized input light and exceeds channel count of prior image-replicating imaging spectrometers by 4-fold. Tissue-simulating phantoms consisting of serial dilutions of the fluorescent agent characterize system linearity and sensitivity, and comparisons to performance of a liquid crystal tunable filter based hyperspectral imaging device are favorable. The new instrument showed comparable, if not improved, sensitivity at low fluorophore concentrations; yet, acquired wide-field images at more than 70-fold increase in frame rate. Image data acquired in the operating room during human brain tumor resection confirm these findings. The new device is an important advance in achieving real-time quantitative imaging of fluorophore concentration for guiding surgery. A wide-field imaging system uses birefringence to efficiently permit quantitative fluorophore concentration mapping, with clear relevance for neurosurgery. ## Introduction Image-guidance methods relying on pre-operative assessment of tumor location are limited by brain shift and other changes that occur during surgery7. Fluorescence-guided surgery (FGS), which does not depend on pre-operative image co-registration, is free of these limitations and is an effective way of identifying exposed tumor during a surgical procedure4,6,8–11. Fluorescence guided neurosurgery using 5-aminoluvelinic acid (ALA)-induced protoporphyrin IX (PpIX) is an FDA-approved procedure that leverages differential accumulation of the endogenous fluorophore PpIX in tumor cells after oral administration of ALA. The approach has been shown to improve surgical outcomes in the removal of high-grade gliomas12,13, as well as meningiomas and metastatic brain cancers11,14–16, and most commercial neurosurgical microscopes can be adapted with an ALA-PpIX module for surgical guidance. Previous research has shown that quantification of PpIX concentration increases both sensitivity and specificity of tumor detection1,3,5,14,17–22, and is particularly valuable when visible fluorescence is not observed3,5. This functionality is critical, as post-operative patient survival is linked to completeness of tumor removal23. Optical fiber probes have been used to quantify PpIX fluorescence with high accuracy3,14,17–20. While results from probe measurements determine fluorescence intensity at a specific location, and quantify PpIX concentration in some cases, spatially locating the extent of disease and its associated boundaries with a probe is inefficient and impractical for neurosurgeons in the operating room (OR). To enable clinical adoption of quantitative fluorescence imaging in neurosurgery, widefield systems that rely on liquid crystal tunable filters (LCTF) to acquire images at multiple wavelengths have been developed to generate quantitative fluorophore concentration maps corresponding to the surgeon’s field-of-view1,5,21,22. Although these images provide valuable information on tumor location and extent of disease intraoperatively, acquisition times can be lengthy (>10 s) depending on the number of wavelengths being collected. To reduce time delays and their impact on surgical workflow and to facilitate adoption of quantitative fluorescence imaging during neurosurgical procedures, an image replicating imaging spectrometer (IRIS) relying on birefringent spatial demultiplexing has been designed and developed to acquire full hyperspectral stacks of 64 channels of different wavelengths and polarizations simultaneously, leading to reduced acquisition times. In this paper, channel responses of IRIS were characterized, and the system was calibrated to generate quantitative PpIX concentration maps. A set of PpIX dilutions was measured with both IRIS and an earlier LCTF imaging system to compare accuracy of quantitative estimates generated over a wide range of PpIX concentrations and associated phantom optical properties. Both these systems were also deployed during a high-grade glioma resection procedure to image tumor and assess relative performances of the two instruments in the OR. To the best of our knowledge, this works presents the highest number of image replication channels ever to be realized, at least to date (by 4-fold), and more importantly, the first application of the technology intraoperatively to achieve near-video rate (4–6 Hz) acquisition of wide-field images of the absolute concentration of PpIX – a fluorophore used widely in molecularly-guided neurosurgery. Here, we show head-to-head comparisons, including in the OR, of the new imaging system relative to our earlier (and much slower) LCTF technology. The IRIS system demonstrated improved linearity of response to PpIX concentration compared to the LCTF instrument and a factor of 70 (or more) increase in frame rate. While system-use described in this paper centers on acquisition of quantitative PpIX fluorescence images for surgical guidance, its broad wavelength range (~425 nm to ~825 nm) makes the technology suitable for a wide range of fluorescent agents and/or wideband hyperspectral reflectance applications. ## Optical channel response Spectral characteristics of the IRIS channels are shown in Fig. 1. Data are split into two sets of 32 channels with orthogonal polarizations. Channel to channel transmission responses vary in intensity and bandwidths as expected. Wavelengths ranging from ~425 to ~825 nm are detected with peak transmission occurring for channels situated in the 575nm–650nm band. Because the transfer function of each channel was recovered using a tunable filter to scan through wavelengths, the transmission curves are coupled with the wavelength response of the LCTF which explains noise in the data in the low 400 nm (LCTF has poor efficiency there).Fig. 1Spectral characteristics of IRIS channels.a Measured spectral response of IRIS in two representative channels with and without the mosaic filter. Side lobes from the BSD alone are present in the spectrum. Interference filter mosaic removes side lobes in each channel. b Simulated spectral response of all channels overlaid on one another. ## IRIS Specific PpIX spectra Normalized PpIX spectra acquired with IRIS, averaged over many optical properties and concentrations, are shown in Fig. 2. Here, combinations of blood volume fraction (BVL) and intralipid concentration (IL) in the phantoms were [$1\%$IL, $2\%$BVF], [$1.5\%$IL, $2\%$BVF], [$2\%$IL, $2\%$BVF], [$1.5\%$IL, $1\%$BV,], and [$1.5\%$IL, $3\%$BVF] for a fixed PpIX concentration (1 µg/ml) similar to past experiments5,24. Differences in the spectral shape between polarizations can be attributed to the mosaic interference filter attenuating different passbands and sidelobes in each case. Fig. 2PpIX spectra acquired with IRIS.IRIS specific PpIX spectra for the two orthogonal polarizations acquired. Each marker represents data from one optical channel. ## Sensitivity curves IRIS PpIX concentration estimates obtained after spectral fitting at various PpIX concentrations are shown in Fig. 3 as a function of actual PpIX concentration. These sensitivity curves include data collected with IRIS at exposure times of 0.167 s (6 FPS), 0.25 s (4 FPS), and 1 s (1 FPS), as well as with our existing LCTF system5,20 at 0.25 s per wavelength over 42 wavelengths. The LCTF instrument loses linearity at the lower PpIX concentrations while the IRIS system produced a more linear response at these levels (down to 0.025 µg/ml, the lowest PpIX concentration evaluated here). Both systems deviated from the unity line at high concentrations. Using these data, a scaling factor was computed to relate imaged concentration estimates to actual phantom concentration values, allowing concentration maps computed from IRIS data to be quantitative. Fig. 3Imaged PpIX concentrations compared to actual PpIX concentrations in phantoms. Correlation between concentration estimates computed with LCTF and IRIS systems relative to actual phantom PpIX concentrations for both IRIS polarizations. ## Feasibility study in human glioma surgery PpIX concentration maps obtained in human brain tumor during resection with IRIS and LCTF systems are shown in Fig. 4. To display concentrations in areas of high importance preferentially, an opacity map proportional to concentration value was applied to the overlays. Differences in acquisition time between the two systems are substantial; the LCTF consumed about 12 s to scan through 42 wavelengths whereas the IRIS acquired 64 hyperspectral channels simultaneously in 167 ms, a factor of 71.9 faster. Examination of overlaid PpIX concentration maps suggests that values and their distribution recovered with IRIS match counterparts generated with LCTF. Concentration values from both systems are in the same range, with regions of highest fluorescence registering about 5 µg/mL, which is a common value for high-grade tumor fluorescence that is easily visible to the surgeon. The correlation coefficient between the LCTF and IRIS images, a measure of image similarity, was found to be 0.90, indicating a high degree of similarity. Finally, no difference was found between concentration maps generated by each IRIS polarization as expected since PpIX fluorescence emission from tissue is not known to be anisotropic. Fig. 4Comparison of visual PpIX fluorescence and quantitative concentration maps imaged during neurosurgery.a Visible fluorescence of PpIX illuminated under blue light. b PpIX concentration map generated from hyperspectral data acquired with the LCTF. c PpIX concentration map generated with IRIS using channels from the 1st polarization. d PpIX concentration map generated with IRIS using channels from the 2nd polarization. Concentration maps are overlaid on RGB images of the surgical field of view acquired with the Pentero operating microscope. Green box represents the field of view of the hyperspectral imaging system used to detect fluorescence. Color scale bars show imaged PpIX concentrations in micrograms per milliliter (μg/mL). Spatial scale bar (1 cm) appears in a). In a clinical case, IRIS and LCTF images of the same surgical field were compared. The subject was enrolled as part of a clinical research study (NCT02191488) approved by the Committee for Protection of Human Subjects at Dartmouth which serves as its Institutional Review Board, and provided informed consent to participate. Surgery began with IRIS mounted on the auxiliary port of the neurosurgical microscope. Once tumor was exposed, IRIS acquired 3 hyperspectral images of the visual field under white and blue light illumination. Exposure time was 167 ms (6 FPS) for these acquisitions, and all wavelengths were acquired simultaneously. RGB captures of the surgical field of view being displayed to the surgeon were taken under both blue and white illumination. Once complete, the surgical microscope was removed from the sterile field, undraped, the LCTF/PCO CMOS camera system was mounted to the same port, and the microscope was re-draped. The microscope was then moved back to the patient position, and a full set of hyperspectral images was acquired using the newly configured instrument under the same reflectance and fluorescence lighting conditions. In this configuration, we acquired 42 wavebands at 250 ms per waveband for a total acquisition time of 12 s. For all IRIS and LCTF recordings, RGB captures of the surgeon’s field of view were acquired for coregistration of the computed PpIX concentration maps. To generate quantitative concentration maps, spectral fitting was performed at each pixel of the hyperspectral stacks, using each system’s corresponding PpIX spectra and offsets as bases. The hyperspectral images acquired with both systems were also converted to RGB format. In order to compare IRIS and LCTF acquisitions, their respective images were coregistered by manually identifying 8 homologous feature point pairs evenly distributed within the cortical surface region to produce a similarity transformation which accounts for rotational, translational, and scaling differences between the two imaging systems. To refine the registration, Matlab’s OnePlusOneEvolutionary optimizer was applied using default settings. Once registered, the correlation coefficient between the two images was computed using the Matlab (corrcoef function). The respective concentration maps were also overlaid on images of the surgical field of view, which were presented to the surgeon. Notably, this registration process will not be necessary once the instrument is integrated onto the main port of the surgical microscope. ## Discussion The IRIS hyperspectral fluorescence imaging system described in this paper acquires 64 channels of varying wavelength and polarization responses simultaneously to form absolute PpIX concentration images coregistered with the surgeon’s field of view through an operating microscope at rates up to 6 frames per second (FPS). The new instrument exceeds channel counts of prior image replicating imaging spectrometers by four-fold25, and also differs from previous realizations by accepting unpolarized incoming light, which increases overall optical efficiency and provides additional information on polarization state of incoming light. Hyperspectral data acquired for both polarizations are treated independently, and concurrent fluorophore concentration maps are generated. While polarization sensitive images have not provided additional information in fluorescence-guided neurosurgery to date, other studies have shown that polarization sensitivity has applications in molecular interaction quantification26–28 and skin analyses29–31. Liquid phantom experiments showed that our sister LCTF system underestimated concentrations lower than 0.1 µg/mL whereas IRIS estimates remained accurate for the full range of PpIX phantoms evaluated here (as low as 0.025 µg/ml). Thus, the threshold for accurate sensing of PpIX concentration appears to be 4 times lower with IRIS relative to its LCTF counterpart. The improvement is likely attributed to IRIS camera specifications being optimized for low light fluorescence applications, combined with the minimal amount of light rejection inherent to image replication techniques. Moreover, collecting light in a single image acquisition, rather than acquiring contributions in a series of filtered images, minimizes noise amplification and yields higher signal-to-noise ratio. Concentration estimates from both systems were underestimated for phantoms with values above 1.6 µg/mL, which could be due to PpIX aggregation inhibiting fluorescence at higher concentrations32,33. Images acquired with IRIS during a surgical procedure confirmed its rapid frame rate which did not degrade resulting concentration maps relative to ones generated from multi-spectral data collected with a conventional LCTF. To the best of our knowledge, the results presented here represent the first time an image replicating system has been used in the fluorescence guided surgery context. The speed with which the system captures hyperspectral data (up to 6 FPS) provides rapid feedback in terms of concentration maps overlaid on the surgeon’s view. Time required to produce overlays is limited by image registration computations and other data transfer overhead arising from lack of direct access to camera data. The latency caused by both of these steps is readily remedied through software optimization, which is now underway, and will allow concentration maps to be updated multiple times per second. Spectral fitting capabilities of IRIS are currently focused on PpIX as the fluorophore for which IRIS specific spectra were generated. However, we intend to produce a spectral conversion matrix, allowing IRIS specific responses to be generated for any fluorophore with emissions in its spectral range. This accomplishment would enable more complex processing, such as white light correction from reflectance measurements1,20,34 to account for blood and tissue absorption when computing concentration estimates. While ALA-PpIX guidance is approved for high grade glioma (HGG) in the US at this time, and the example shown in Fig. 4 was confirmed pathologically to be HGG, accurate fluorescence guidance for low grade glioma (LGG) surgery could have a major impact on this patient population. The literature on ALA-PpIX in LGG suggests heterogeneity in fluorescence emissions from these tumors11–13,34,35. However, data acquired with imaging systems similar to the IRIS device reported here (e.g., with its companion LCTF and related probe systems) in LGG under IND-regulated protocols indicate fluorescence detection is possible even though the emissions are not always visible to the surgeon2,5,36–38. In summary, a wide-field imaging system for quantitative fluorophore concentration mapping using birefringence was designed, fabricated and tested to accelerate hyperspectral acquisition times during fluorescence-guided neurosurgery. The instrument is able to acquire the spectral information needed to compute quantitative concentration maps in a fraction of a second. PpIX concentration maps were acquired in human glioma surgery using both IRIS and LCTF systems, and the two units showed robust agreement. Near-video-rate hyperspectral image analysis and visualization in the microscope oculars should be achievable with improvements in image co-registration speed. Other applications taking advantages of the inherent separation of polarization of this system are under investigation as well. ## IRIS Instrument Central to the new imaging instrument is an Image Replicating Imaging Spectrometer (IRIS) conceptualized by Harvey et al. 39, which exploits birefringence to split incoming wavelengths onto different sections of a camera sensor. Having wavelengths separated spatially, rather than temporally scanning narrow transmission bands, increases acquisition throughput substantially and records a full hyperspectral cube in a single acquisition. Consequently, the approach is much faster than sequential wavelength filtering and mitigates the impact of temporal artifacts inherent to sequential data acquisition. The primary trade-off compared to systems that acquire spectral bands sequentially is a loss of spatial resolution which arises from the fact that IRIS spatially divides the light into spectral components, sending different wavelength bands to different regions of the image sensor. The impact of this compromise is specific to the application and depends on a number of factors, including: 1) the resolution of the optical system, 2) the required spatial resolution for the application, and 3) the importance of temporal resolution. This trade-off is discussed further below. Spatial multiplexing is achieved through a birefringent spectral demultiplexer (BSD)—the optical component responsible for efficient redirection of wavelength bands to different sections of the camera sensor. BSD exploits birefringence filtering implemented through a succession of alternating waveplates and Wollaston prisms. Each waveplate-prism pair splits the incoming signal into two separate polarizations having transfer functions:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{\parallel }\left(k\right)={\cos }^{2}$$\end{document}T∥k=cos22\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{\perp }\left(k\right)={\sin }^{2}\left(\frac{{btk}}{2}\right)$$\end{document}T⊥k=sin2btk2where b is the birefringence of the waveplate, t is its thickness, and k is the wavenumber. The working principle of a 4-way birefringent spectral demultiplexer is illustrated in Fig. 5.Fig. 5Birefringent spectral demultiplexing and IRIS birefringent spectral demultiplexer design.a Diagram of a four-way birefringent spectral demultiplexer. Incoming signal is split into four different regions which can be imaged with a camera sensor. b Design used for IRIS which produces two sets of 32 channels with orthogonal polarizations. This example uses two waveplate and Wollaston prisms; however, by combining N waveplate-prism pairs, an image is split into 2N hyperspectral planes; the global transfer function for each plane is the product of the transfer functions that acted on the light rays reaching that plane. Previous implementations have been reported with up to sixteen channels39–42. Herein, we designed a six-prism system (Fig. 1b) which splits the incoming signal into 64 channels of different wavelengths and polarizations. A BSD typically polarizes incoming light linearly and ensures that the signal is separated into orthogonally polarized waves of equal amplitude. However, we left out the input polarizing filter to detect incoming polarization information, and to increase overall light sensitivity. With this arrangement, the first Wollaston prism in the system divides the incoming signal in two orthogonal polarizations that are split into two sets of 2N-1 channels with identical channel responses but different polarizations. To demonstrate the splitting capabilities of the system, a picture of the view through the demultiplexer pointing at a white target (the word “IRIS” displayed in white letters on a computer monitor) is shown in Fig. 6.Fig. 6Birefringent spectral demultiplexer output and mosaic interference filter.a BSD view of the word “IRIS” displayed in white letters on a computer monitor. Channels that appear dark have their peak transmission wavelength falling outside of the emission range of the computer monitor. b Hand crafted mosaic interference filter mounted on the camera sensor. Optical design of IRIS required consideration of several parameters to ensure high light throughput, adequate spectral separation, and localization on the image sensor. Specifically, an optimal combination of waveplates were identified by varying waveplate thickness (t) and wavenumber (k) in the polarization rotation functions provided above over a wide range using MATLAB. Light transmission through the Wollaston prisms was simulated using the ZEMAX optical modeling software package. Additionally, filter transfer functions from the interference filter supplier were incorporated in the computations, which enabled selection of bandpass filters for the mosaic that maximized transmission in the bandpass while removing contaminating side lobes. The mosaic filter was crafted from commercial glass interference filters that were cut, glued, and manually fitted directly onto the image sensor as shown in Fig. 6b. Since interference filters were only used to remove side lobes, and most of the light was redirected rather than filtered, high optical efficiency was achieved in the system design. The remaining optical components, e.g. imaging lenses, aperture, collimating lens and mirror, were designed using manual lens calculations followed by confirmation using ZEMAX, which provides ray tracing for any optical system. These simulations were used to choose and position optical components that [1] maintain high light throughput, and [2] ensure that the light in each channel from the BSD was properly positioned on the image sensor and had appropriate scale. The imaging sensor chosen was a 16.25 megapixels DS-Qi2 microscope camera (Nikon Instruments Inc, Melville, NY), which was selected for its low read noise, high dynamic range, good linearity, and most importantly, the large 36 × 24 mm surface area of its CMOS sensor. Resulting images have a resolution of 4908 × 3264 pixels, with each channel dedicated to a 600 × 400 pixel region. A diagram of the internal schematics, as well as the final dimensions of the system, can be seen in Fig. 7.Fig. 7IRIS component diagram and system dimensions.a Internal components and optics modeled for the IRIS. b Final dimensions of the system. c IRIS mounted on the Pentero surgical microscope. ## Basic optical characterization: spatial resolution and field of view As described above, IRIS divided the imaging array into wavelength channels, and the number of pixels available per channel was limited compared to systems which sample wavelengths sequentially on a full sensor array. In the current implementation, each IRIS channel consists of 400 × 600 pixels, whereas the LCTF system makes use of a full 2048 × 2048 imaging sensor for each wavelength. However, in practice, the spatial resolution was limited by the optics of the surgical microscope on which both units were mounted when in use. Using a standard USAF resolution test chart, image resolution was found to be identical (0.63 mm) for IRIS and LCTF systems, and accordingly, they operated with similar spatial resolutions as implemented here. The IRIS system’s field of view at the typical 25 cm working distance for the surgical microscope was approximately 6 × 9 cm. Images from which spatial resolution was determined for the IRIS and LCTF systems scaled (windowed and leveled) for visualization are provided as Supplementary Figs. 1–2 (raw 16 bit image data can be found in Supplementary Data 3; also see Data Availability). ## Spectral responses of the channels To corroborate channel responses that were simulated optically, channel bandwidth was recorded by imaging a $99\%$ Spectralon® diffuse reflectance target (SRT-99; LabSphere) with a calibrated white light source (SL1 Tungsten-Halogen, StellarNet Inc., Tampa, FL) and placing an LCTF (Varispec, PerkinElmer, Waltham, MA) at the input of the optical system. In this configuration, the full spectrum white light source is incident upon the LCTF, which allows only a selected waveband to pass into the IRIS. By sequentially selecting wavebands over the wavelength range of interest (400–900 nm) in steps of 1 nm, and acquiring images with the IRIS at each waveband, the spectral response of the system was recorded. Because LCTF’s have a limited spectral operating range, we used a visible-spectrum LCTF to cover 400–700 nm and a near-infrared LCTF for 650–900 nm. These data were combined, and average values in a 50 pixel radius circle in the center of each channel image were analyzed to generate spectral sensitivity curves for all 64 channels. ## Spectral fitting and IRIS-specific PpIX basis spectra The hyperspectral images enable the application of spectral fitting for each image pixel. The linear least squares spectral fit process used herein, which has been reported on extensively5,20,22 with our LCTF-based imaging system, separates a normalized fluorescence spectrum into a weighted sum of its assumed constituent components (e.g., PpIX, its photoproducts, tissue autofluorescence, etc,), each of which is pre-defined by its known spectral shape that serves as a basis function during the fit. This process decouples the PpIX-specific signal from other fluorescence sources in the tissue. The spectral fitting algorithms used here run at 60 frames-per-second on a standard laptop, and thus are not a bottleneck for imaging time. IRIS-specific PpIX basis spectra were established with experimental data collected from liquid PpIX phantoms with varying optical properties. Here, phosphate buffer saline (PBS) phantoms with blood volume fractions varying in the range of 1 to 3 [%], intralipid volume fractions in the range of 1 to 2 [%], and PpIX concentration in the range of 1 to 5 [µg/mL] were imaged. PBS used as the background medium also included $0.1\%$ Tween 20, since the latter prevents PpIX aggregation which yields better fluorescence emission24,43. IRIS was mounted on the Zeiss Pentero operating microscope, and a box was fabricated to block ambient light, and ensure the only source of illumination came from the top (of the box), where an opening let the microscope illuminate the phantoms from a distance of approximately 25 cm. Liquid phantoms were poured into falcon tube (Corning, USA) caps 30 mm in diameter and 10 mm in depth, which were then placed into the box where they were illuminated with the Pentero’s blue light module (400 nm). An exposure time of 1 s was used to acquire the images, and data were averaged over the area of the phantom (disregarding pixels affected by specular reflection). These spectra were then averaged to account for the full range of phantom optical properties illuminated during an experiment. ## System sensitivity and comparison to sequential hyperspectral imaging using an LCTF An extensive phantom study was completed to establish the linearity of response to PpIX and compare this performance to the LCTF system. Using averaged basis spectra, spectral fitting was performed on a series of liquid phantoms over a wide range of known PpIX concentrations. Liquid phantoms with 2[%] BVF, 1.5 [%] IVF, and PpIX concentrations of {0, 0.025, 0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4} [µg/mL] were measured, and data were acquired with exposure times of 1 s (1 FPS), 0.25 s (4 FPS), and 0.167 s (6 FPS). To compare IRIS sensitivity to a more common temporal filtering system, the same set of phantoms was also imaged with a CMOS camera (PCO Edge) connected to an LCTF mounted to the microscope. Acquisition time of about 12 s, scanning 42 wavelengths, was necessary with the LCTF. For hyperspectral images acquired with IRIS, concentration estimates were generated using the averaged IRIS-specific PpIX spectra generated in Fig. 1. The averaged signal received from the 0 µg/mL phantom from this set served as an offset during the fitting process. For images acquired with the LCTF, fitting was performed using PpIX spectra from the literature, and a constant value of 1 as an offset. Once concentration estimates were computed, linear regression extracted the scaling factors needed to produce PpIX estimates that matched actual concentrations. ## Statistics and reproducibility Mean values were used to convert spatially and spectrally distributed data into the graphical forms presented in Figs. 1, 2 and 3. For Fig. 1, an average of spatially distributed image pixel groups were formed and averaged for each spectrally distinct IRIS channel. In the case of Fig. 2, image pixel values in a central region of interest were averaged to generate the spectral response curve shown for all 64 IRIS channels which separated into the two 32-channel polarizations. Similarly, to generate source data in Fig. 3, pixels from images acquired from liquid phantoms with IRIS and LCTF instruments were averaged for each acquisition for different PpIX concentrations in phantoms of $2\%$ blood volume fraction and $1.5\%$ intralipid to replicate standard brain tissue optical properties. The process was repeated for different IRIS data acquisition rates and for both channel polarizations. Images in Fig. 4 were compared through correlation coefficient image processing. Reproducibility was maintained by using standardized reference targets to estimate spatial resolution of IRIS and LCTF systems observed independently by 2 users. Similarly, commercial software and associated intrinsic functions (in Matlab) were used to compare image similarities between IRIS and LCTF results. Phantoms with known and repeatable optical properties served as gold standards for evaluation of quantitative PpIX concentrations obtained from IRIS and LCTF instruments. Use of multiple IRIS data acquisition rates and spectral data from both system polarizations which are recorded through distinct instrument data acquisition channels support reproducibility of results in Fig. 3, and again in Fig. 4. Comparisons between completely different imaging systems support reproducibility of results in Fig. 4, and their similarity with visually recorded fluorescence patterns also lend credibility to the findings. Data processed from two distinct polarization paths with IRIS demonstrate system reproducibility. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Supplementary information Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04701-9. ## Peer review information Communications Biology thanks Marcus Axer, Lia Talozzi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Michel Thiebaut de Schotten, Anam Akhtar and George Inglis. ## References 1. Valdés PA. Quantitative, spectrally-resolved intraoperative fluorescence imaging. Sci. Rep. (2012.0) 2 798. DOI: 10.1038/srep00798 2. Valdés PA. Quantitative fluorescence in intracranial tumor: implications for ALA-induced PpIX as an intraoperative biomarker. J. Neurosurg. (2011.0) 115 11-17. DOI: 10.3171/2011.2.JNS101451 3. Valdés PA. Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery. J. Biomed. Opt. (2011.0) 16 116007. DOI: 10.1117/1.3646916 4. Pogue BW. Review of neurosurgical fluorescence imaging methodologies. IEEE J. Sel. Top. Quantum Electron. (2010.0) 16 493-505. DOI: 10.1109/JSTQE.2009.2034541 5. Bravo JJ. Hyperspectral data processing improves PpIX contrast during fluorescence guided surgery of human brain tumors. Sci. Rep. (2017.0) 7 9455. DOI: 10.1038/s41598-017-09727-8 6. Roberts DW. Coregistered fluorescence-enhanced tumor resection of malignant glioma: relationships between δ-aminolevulinic acid–induced protoporphyrin IX fluorescence, magnetic resonance imaging enhancement, and neuropathological parameters. J. Neurosurg. (2011.0) 114 595-603. DOI: 10.3171/2010.2.JNS091322 7. Roberts DW, Hartov A, Kennedy FE, Miga MI, Paulsen KD. Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery (1998.0) 43 749-758. DOI: 10.1097/00006123-199810000-00010 8. Johansson A. 5-aminolevulinic acid-induced protoporphyrin IX levels in tissue of human malignant brain tumors. Photochem. Photobio. (2010.0) 86 1373-1378. DOI: 10.1111/j.1751-1097.2010.00799.x 9. Stummer W. Extent of resection and survival in glioblastoma multiforme: identification of and adjustment for bias. Neurosurgery (2008.0) 62 564-576. DOI: 10.1227/01.neu.0000317304.31579.17 10. Valdés PA. Estimation of brain deformation for volumetric image updating in protoporphyrin IX fluorescence-guided resection. Stereotact. Funct. Neurosurg. (2010.0) 88 1-10. DOI: 10.1159/000258143 11. Widhalm G. 5-Aminolevulinic acid is a promising marker for detection of anaplastic foci in diffusely infiltrating gliomas with nonsignificant contrast enhancement. Cancer (2010.0) 116 1545-1552. DOI: 10.1002/cncr.24903 12. Stummer W. Fluorescence-guided resection of glioblastoma multiforme utilizing 5-ALA-induced porphyrins: a prospective study in 52 consecutive patients. J. Neurosurg. (2000.0) 93 1003-1013. DOI: 10.3171/jns.2000.93.6.1003 13. Stummer W. Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial. Lancet Oncol. (2006.0) 7 392-401. DOI: 10.1016/S1470-2045(06)70665-9 14. Kim A. Quantitative fluorescence in intracranial tumor: implications for ALA-induced PpIX as an intraoperative biomarker. J. Neurosurg. (2011.0) 115 11-17. DOI: 10.3171/2011.2.JNS101451 15. Kajimoto Y. Use of 5-aminolevulinic acid in fluorescence-guided resection of meningioma with high risk of recurrence. J. Neurosurg. (2007.0) 106 1070-1074. DOI: 10.3171/jns.2007.106.6.1070 16. Bekelis K. Quantitative and qualitative 5-aminolevulinic acid–induced protoporphyrin IX fluorescence in skull base meningiomas. Neurosurg. Focus (2011.0) 30 E8. DOI: 10.3171/2011.2.FOCUS1112 17. Ishihara R. Quantitative spectroscopic analysis of 5-aminolevulinic acid-induced protoporphyrin IX fluorescence intensity in diffusely infiltrating astrocytomas. Neurol. Med. Chir. (2007.0) 47 53-57. DOI: 10.2176/nmc.47.53 18. Stummer W. Technical principles for protoporphyrin-IX-fluorescence guided microsurgical resection of malignant glioma tissue. Acta Neurochir. (1998.0) 140 995-1000. DOI: 10.1007/s007010050206 19. Kim A, Khurana M, Moriyama Y, Wilson BC. Quantification of in vivo fluorescence decoupled from the effects of tissue optical properties using fiber-optic spectroscopy measurements. J. Biomed. Opt. (2010.0) 15 067006. DOI: 10.1117/1.3523616 20. Valdés PA. A spectrally constrained dual-band normalization technique for protoporphyrin IX quantification in fluorescence-guided surgery. Opt. Lett. (2012.0) 37 1817-1819. DOI: 10.1364/OL.37.001817 21. Gebhart SC, Thompson RC, Mahadevan-Jansen A. Liquid-crystal tunable filter spectral imaging for brain tumor demarcation. Appl. Opt. (2007.0) 46 1896-1910. DOI: 10.1364/AO.46.001896 22. Valdes PA. System and methods for wide-field quantitative fluorescence imaging during neurosurgery. Opt. Lett. (2013.0) 38 2786-2788. DOI: 10.1364/OL.38.002786 23. Stummer W. Intraoperative detection of malignant gliomas by 5-aminolevulinic acid-induced porphyrin fluorescence. Neurosurgery (1998.0) 42 518-525. DOI: 10.1097/00006123-199803000-00017 24. Marois M, Bravo J, Davis SC, Kanick SC. Characterization and standardization of tissue-simulating protoporphyrin IX optical phantoms. J. Biomed. Opt. (2016.0) 21 35003. DOI: 10.1117/1.JBO.21.3.035003 25. Hagen N, Kudenov MW. Review of snapshot spectral imaging technologies. Opt. Eng. (2013.0) 52 090901. DOI: 10.1117/1.OE.52.9.090901 26. 26.Gijsbers, A., Nishigaki, T. & Sánchez-Puig, N. Fluorescence anisotropy as a tool to study protein-protein interactions. J. Vis. Exp. 54640 10.3791/54640 (2016). 27. Zhao Q. Aptamer binding assays and molecular interaction studies using fluorescence anisotropy—a review. Analytica Chim. Acta (2020.0) 1125 267-278. DOI: 10.1016/j.aca.2020.05.061 28. Suhling K. Time-resolved fluorescence anisotropy imaging applied to live cells. Opt. Lett. (2004.0) 29 584-586. DOI: 10.1364/OL.29.000584 29. Dremin V. Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning. IEEE Trans. Med. Imaging (2021.0) 40 1207-1216. DOI: 10.1109/TMI.2021.3049591 30. Nkengne A. SpectraCam®: A new polarized hyperspectral imaging system for repeatable and reproducible in vivo skin quantification of melanin, total hemoglobin, and oxygen saturation. Ski. Res. Technol. (2018.0) 24 99-107. DOI: 10.1111/srt.12396 31. Vasefi F. Polarization-sensitive hyperspectral imaging in vivo: a multimode dermoscope for skin analysis. Sci. Rep. (2014.0) 4 4924. DOI: 10.1038/srep04924 32. Alston L, Rousseau D, Hebert M, Mahieu-Williame L. Nonlinear relation between concentration and fluorescence emission of protoporphyrin IX in calibrated phantoms. J. Biomed. Opt. (2018.0) 23 1-7. DOI: 10.1117/1.JBO.23.9.097002 33. 33.Monsù Scolaro, L. et al. Aggregation behavior of protoporphyrin IX in aqueous solutions: clear evidence of vesicle formation. J. Phys. Chem. B106 2453–2459 (2002). 34. Ewelt C. Finding the anaplastic focus in diffuse gliomas: the value of Gd-DTPA enhanced MRI, FET-PET, and intraoperative, ALA-derived tissue fluorescence. Clin. Neurol. Neurosurg. (2011.0) 113 541-547. DOI: 10.1016/j.clineuro.2011.03.008 35. 35.Widhalm G. et. al. The value of visible 5-ALA fluorescence and quantitative protoporphyrin IX analysis for improved surgery of suspected low-grade gliomas. J. Neurosurg. 10 1–10 (2019). 36. Valdes PA. 5-aminolevulinic acid-induced protoporphyrin IX fluorescence in low-grade gliomas. Neurosurg (2014.0) 61 227-228. DOI: 10.1227/01.neu.0000452476.51442.18 37. Valdes PA. Quantitative fluorescence using 5-aminolevulinic acid-induced protoporphyrin IX biomarker as a surgical adjunct in low-grade glioma surgery. J. Neurosurg. (2015.0) 123 771-780. DOI: 10.3171/2014.12.JNS14391 38. Roberts DW. Red-light excitation of protoporphyrin IX fluorescence for subsurface tumor detection. J. Neurosurg. (2018.0) 128 1690-1697. DOI: 10.3171/2017.1.JNS162061 39. 39.Harvey, A. R. et al. SPIE Proceedings, Spectral imaging in a snapshot Spectral Imaging in a Snapshot. 10.1117/12.604609 (2019). 40. Gorman A, Fletcher-Holmes DW, Harvey AR. Generalization of the Lyot filter and its application to snapshot spectral imaging. Opt. Express (2010.0) 18 5602-5608. DOI: 10.1364/OE.18.005602 41. 41.Wong, G. Snapshot Hyperspectral Imaging: Near-Infrared Image Replicating Imaging Spectrometer and Achromatisation of Wollaston Prisms. PhD Thesis, Heriot-Watt University, School of Engineering & Physical Sciences, Edinburgh (2012). 42. 42.Fernandez Ramos, J., Brewer, L. R., Gorman, A. & Harvey, A. R. Video-Rate Multispectral Imaging: Application to Microscopy and Macroscopy. in Classical Optics 2014. 10.1364/COSI.2014.CW1C.3 (OSA, 2014). 43. Lu H, Floris F, Rensing M, Andersson-Engels S. Fluorescence spectroscopy study of protoporphyrin IX in optical tissue simulating liquid phantoms. Materials (2020.0) 13 2105. DOI: 10.3390/ma13092105
--- title: Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients authors: - Arsen Batagov - Rinkoo Dalan - Andrew Wu - Wenbin Lai - Colin S. Tan - Frank Eisenhaber journal: Health Information Science and Systems year: 2023 pmcid: PMC10060506 doi: 10.1007/s13755-023-00218-x license: CC BY 4.0 --- # Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients ## Abstract Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients’ health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system’s coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES [517] from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79–0.95 (sensitivity 80–$92\%$, specificity 62–$94\%$) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice. ### Supplementary Information The online version contains supplementary material available at 10.1007/s13755-023-00218-x. ## Introduction In the current understanding, a metabolic disorder can be traced to a particular set of biochemical reactions and metabolites, whose abnormal changes lead to syndrome manifestation and progression. These altered metabolic states typically have complex causes and develop due to individual genetic variations, changes in the environment, behavioral factors, lifestyle, or as a side effect of disease treatment [1, 2]. As these alterations are associated with chronic disease and aging, understanding the trend of their long-term development is vital. Despite advances in large-scale and high-precision techniques for metabolite measurements and their successful applications in identification of certain inborn metabolic disorders, quantifying the progress of chronic diseases remains challenging [3–5]. To enable broad adoption of large-scale metabolic analysis in the clinical setting, we need to bridge several major gaps. First, the high-throughput analytical techniques are frequently not as robustly established as clinical assays, which include reference ranges, standards of reproducibility, and validated metrics of sensitivity and specificity [4, 6–8]. Second, the lack of robust and flexible bioinformatics frameworks and computational approaches hinders contextualizing metabolic models with rich clinical meta-data and physiological readings [7, 9–11]. Third, the issue of time scale is pertinent in a clinical application with processes equilibrating within hours and days, whilst subtle drifts along health state trajectories could occur in the time frame of years and decades. Mathematical methods developed within the metabolic modeling research area, such as enzymatic kinetic models, metabolic flux analysis, or stochastic models, were proposed to analyze metabolic systems. Traditional enzymatic kinetics models using the Michaelis–Menten and the Briggs–Haldane equations [12], have met a great success in describing the dynamics of isolated biochemical reactions in vitro. Since these models essentially describe dynamics of intermediate states of any bound substrate, they are general enough to also describe elementary behavior of more complex systems, ranging from bacterial growth [13] to physiological processes [14–16] and epidemiology [17, 18]. The following three examples of kinetic models application with increasing complexity (an enzyme study, an in vivo metabolite dynamics, a particle absorption study) illustrate opportunities and limitations of such an approach. Standard enzymatic kinetics analysis is applied to quantify the effects of several genetic variants of the arachidonate 15-lipoxygenase type II, an enzyme with a potential role in the development of coronary artery disease [16]. The authors determined mutation-specific kinetic parameters. However, the link between the kinetic model and the process of atherosclerotic plaque formation remains opaque. In a clinical study of glucose disposal with 88 healthy volunteers receiving four types of insulin infusions, kinetic parameters of glucose concentration for an M–M model of glucose metabolism were determined [14]. The authors found the kinetic constants insulin concentration-dependent, probably as a result of their model representing the entire metabolic mechanism only in parts. Study [15] applied the M–M-like model to describe pulmonary absorption kinetics of particles in the lungs of rats. The authors found their generalized kinetic model correctly reproducing the clearance rate of all four studied dust types at the phenomenological level but without a biomolecular mechanistic explanation. At the same time, kinetic models have been proven of limited value for clinical relevant applications, because the models require a large number of biochemical reaction parameters, the physiological values of which are difficult to determine in a clinical setting. In contrast, MFA allows a more coarse-grain representation of a complex biochemical system of reactions and binding processes as a composition of metabolic fluxes connected with stoichiometric and mass balance relations [19–23]. This methodology has been applied to prokaryotic and eukaryotic organisms on the level from individual pathways [19, 21, 23] to whole genome scale [24]. Most importantly, unlike enzymatic kinetic models, MFA explicitly relates the dynamics of metabolites to complex phenotypes [21, 25, 26]. This methodology enables the discovery of control mechanisms emerging on the level of entire biochemical pathways and networks [20, 21, 27]. Typically, MFA computational models in clinical application studies include the basics of the carbon metabolism network (pyruvate metabolism, tri-carbon cycle, glyoxylate shunt, etc.) complemented with selected pathways from the amino acid, lipid or other types metabolic or signaling network elements if necessary. With detail up to individual reactions, the models are just focused of specific aspects of the networks behavior and do not look into potentially more global metabolism changes. The limited opportunities provided by such type of modelling are illustrated by the study from Gregory et al. [ 28] who explored the impact of various FTL3 inhibitors on glutamine utilization in patients with acute myeloid leukemia. Yet, the model was sufficient to assess flux changes caused by combined inhibitor application and to delineate glutathione depletion as major mechanism of leukemic cell elimination. The work of Karlstädt et al. [ 29] is an example where multiple state-of-the-art methods of computational modeling complement each other to address the complexity of the studied system. The authors analyzed cardiomyocyte glycolysis kinetics to reveal and elucidate the underlying control mechanisms at the metabolic and protein levels and experimentally validated predictions made in their in silico studies. They applied MFA to obtain the steady-state flux distribution, which maximized ATP production in the muscle tissue [23]. The MFA analysis allowed them to quantify systems-wide contributions of individual reaction rates and perturbations in individual metabolic parameters. To overcome the inherent limitation of MFA not addressing changes in metabolite concentrations, the simulated system was expanded with M–M-like equations. This allowed the authors to simulate metabolite concentrations and flux rates as a function of time and to predict steady-state fluxes and metabolite concentrations [29]. The human health state, being a specific case of a phenotype at a given point of a disease development trajectory, can thus be analyzed using MFA in principle. Yet, at present, the formalism is not ready for this purpose. There are several issues. First, the number of parameters in MFA models grows linearly with the number of reactions. Although this makes MFA a more scalable approach to build mechanistic models of complex systems, the number of parameters in biochemical reaction-oriented, detailed models is still enormous and finding proper values for them is problematic. Second, the metabolite concentrations typically used in MFA calculations are not commonly available from clinical laboratories. Third, at the same time, many biochemical and physiological parameters, such as urine albumin or blood pressure, currently measured in clinics, are not incorporated into MFA models. The GMFA approach introduced in this work implements a number of methodical innovations that enhance the suitability of MFA for clinical applications. To reduce the demand on the input data scale, we apply GMFA to a reduced, coarse-grain metabolic network, wherein individual metabolic entities/fluxes are grouped in accordance with biological processes. We map non-metabolic clinical modalities and measurements onto the metabolic network. To make health states and their progression comparable across patients, we replace the time variable with the extent variable quantifying the degree of progression between two extreme health states—the healthy state and the fully manifested disease state, assuming quasi-linearity of metabolite concentration changes along the trajectory of states. We applied our methodology to the analysis of observational data of two cross-sectional cohorts of diabetes type 2 patients. We constructed a simplified metabolic map including key components of glucose and lipid metabolism, onto which physiological characteristics, for example, the pulse wave velocity and carotid intima-media thickness were mapped. With statistical rigor, we show that fluxes calculated with the data from patients are predictive for the development of individual ophthalmic complications in type 2 diabetes. ## Study design and population data To evaluate the applicability of the proposed methods to clinical data, we studied the EVAS multi-ethnic cohort of 289 patients with type 2 diabetes visiting a tertiary medical center in Singapore [30] (data collected in 2015–2020) and the NHANES multi-ethnic multi-centre general population cohort obtained in the National Health and Nutrition Examination Survey carried out in the United States (data collected in 1999–2018) [31]. From the pool of 6652 available NHANES patients with type 2 diabetes history, we selected only 517, whose measured data modalities sufficiently overlap with those of EVAS (see Table 1 for the list of respective NHANES variables).Table 1Baseline clinical and biochemical characteristics of the observational cohort studyEVAS VariableNHANES variableEVAS summaryNHANES summaryN total–289517Males (%)riagender144 [50]257 [50]Ethnicity: n (%) Chinese–174 [60]NA Malays–48 [17]NA Indians–67 [23]NA Otherridreth10517 [100]Age years, Mean (SD)ridageyr54.3 (11.14)60 (14.68)T2DM Duration, years, Median (IQR)did04011 [5-17]11 [5-18]Hypertension duration years, Median (IQR)NA6.5 [0-13]NAHyperlipidemia duration years, Median (IQR)NA7 [2-13]NABMI kg/cm2, Mean (SD)bmxbmi27.7 (4.99)32.2 (7.33)Systolic BP, mm Hg, Mean (SD)bpxsy133.2 (14.78)129.6 (20.12)Fasting glucose mmol/L, mean (SD)lbxglu, lbxglusi8.89 (3.18)9.14 (3.80)HbA1c %, mean (SD)diq2808.60 (1.84)7.47 (2.39)Total Cholesterol mmol/L, Mean (SD)lbxtc, lbxtcsi4.39 (1.09)4.53 (1.09)HDL-Cholesterol mmol/L, Mean (SD)lbdhdd, lbdhddsi1.12 (0.30)1.29 (0.36)LDL-Cholesterol mmol/L, Mean (SD)lbdldl, lbdldlsi2.51 (0.84)2.53 (0.94)Triglycerides mmol/L, Mean (SD)lbxtr, lbxtrsi1.85 (2.17)1.54 (0.82)Creatinine mmol/L, Mean (SD)NA74.2 (26.85)NAFerritin mmol/L, Mean (SD)NA109.48 (123.43)NAThe combined patient cohort represents a group of 804 Type 2 diabetes mellitus patients. The EVAS data was originally published in [30]. The NHANES data is available in the official web portal of the National Health and Nutrition Examination Survey [31] In the EVAS patient cohort, we examined data with regards to diagnosis of ophthalmic complications of diabetes at baseline, as well as their development over a period of up to 3 years after enrolment into the cohort study (Table 2). In both the EVAS and the NHANES cohorts male and female participants were represented in equal proportion (Table 1). In the EVAS cohort the mean age was 54, the median duration of type 2 diabetes, hyperlipidemia and hypertension was 10, 7 and 6.5 years, respectively. In the NHANES cohort the mean age of the participants was 60, and the data on the hyperlipidemia and hypertension was unavailable. In the EVAS cohort, at the baseline time point, cataract and retinopathy were diagnosed in 118 ($41.0\%$) and 88 (30.4 %) patients, respectively. In the NHANES cohort, diabetic retinopathy was declared in 106 participants ($20.5\%$), while the data on other ophthalmic complications was not collected. In this cohort, retinopathy declaration was done by answering “yes” to the question “Has a doctor ever told you that diabetes has affected your eyes or that you had retinopathy?” ( dataset variable diq080).Table 2Summary statistics of ophthalmic complications in the EVAS observational cohort studyVariableSummaryCataracts, n (%)118 (41.0)Diabetic retinopathy, n (%)88 (30.4)The data was originally published in [30] The summary biochemical characteristics of the patients are listed in Table 1. Similar to the earlier study [30], vascular functions of the EVAS patients were assessed. Table 3 provides the summary. The patients were characterized by measuring the concentrations of C-reactive protein (CRP), reactive oxygen molecules (ROM) and oxidized LDL (ox-LDL). Arterial stiffness was quantified by the pulse wave velocity (PWV), and the endothelial function was assessed with reactive hyperemia index (RHI).Table 3Vascular function measurements in the observational cohort studyVariableSummaryCRP, mg/L Median (IQR)1.45(0.7–3.8)ROM, Median(IQR)271(241–313)BAP, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}μM Median(IQR)2221(2061–2388)Ox-LDL, IU/L Mean(Sd)55.92(21.65)CIMT, mm Mean(SD)0.65(0.13)LnRHI Mean(SD)0.67(0.25)Pulse Wave Velocity m/cm8.35(1.74)The data was originally published in [30] ## Generalized metabolic flux analysis (GMFA) Following the MFA theory, a metabolic network system described by a set of M metabolite species (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$i = 1...$M$$\end{document}$i = 1...$M) and N reactions/connections (enumerated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$j = 1...$N$$\end{document}$j = 1...$N) between the species, the stoichiometry matrix S (size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M \times N$$\end{document}M×N) inherently characterizes the metabolic system and is assumed to be independent of time. A metabolic state is defined as the set of concentration \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X_i$$\end{document}Xi of all M metabolites as well as of fluxes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v_j$$\end{document}vj for all N reactions. The concentrations and fluxes changing in time (t) are related via the linear system of N flux balance equations:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \frac{d[X_i]}{dt} = \sum _j{S_{i,j}v_j(t)} \end{aligned}$$\end{document}d[Xi]dt=∑jSi,jvj(t)Under a long-term smooth change along a trajectory between two metabolic states A and B is commonly quantified by the variable expressing the number of molecular transformations of one particular type that has to occur while the system transforms from state A to state B. By analogy with a single biochemical reaction progress, where this number is represented by a scalar variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ, termed the reaction extent, in a metabolic network it is represented by a reaction extent vector. Similar to Eq. 1 describing changes in time t, fluxes and metabolite concentration changes can then be expressed with respect to changes expressed in the units of the extent \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{d[X_i]}{d\xi } = \sum _j{S_{i,j}v_j(\xi)}$$\end{document}d[Xi]dξ=∑jSi,jvj(ξ)A connection between the extent \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ, time t and metabolite concentrations \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X^A$$\end{document}XA and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X^B$$\end{document}XB (at states A and B, respectively) can be described as follows:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X^B = X^A + \int _A^B \xi d\xi = X^A + \int _{t_A}^{t_B} \xi \frac{\partial \xi }{\partial t} dt = X^A + \int _A^B S v(\xi) d\xi$$\end{document}XB=XA+∫ABξdξ=XA+∫tAtBξ∂ξ∂tdt=XA+∫ABSv(ξ)dξUnder these assumptions, changes of metabolite concentrations and metabolic fluxes are fully defined as functions of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ. A single, continuous evolution path on the extent scale, includes all intermediate states of the system, including the starting state A and the final state B. If we observe an ensemble of distinct metabolic systems (e.g., organisms) observed at states A and B, their evolution on the extent scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ would reflect their evolution in time. Thus the average metabolic state of the ensemble at a point on the scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ would reflect the average time it took the systems of the ensemble to reach that state. Such process can be described as ergodic. If in the system, whose metabolic state evolution is linear (as defined in Eq. 2) and there is a non-metabolic variable in that system, which is also linear in the selected extent coordinate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ, the system of Eq. 2 can be extended with the given non-metabolic component without violations of the initial assumptions. Then, in such cases, the extent vector, the vector of metabolic fluxes and the stoichiometry matrix can be termed the generalized extent, the generalized fluxes and the generalized stoichiometry, respectively. The quantities of generalized fluxes obtained as the estimates to the observations of the individual’s measurements are termed here the GMFA digital twins. For further details, please refer to Online Appendix A. ## Digital twin construction Digital twins were created using the GMFA methodology introduced in this study (see Online Appendix A for details). The construction and primary analysis of digital twins was implemented in Python (v.3.8) programming language. The metabolic map and the generalized stoichiometry matrix were designed to include the metabolites measured in the study, to quantify the fluxes through the major biochemical and physiological pathways implicated in diabetes (Fig. 1, Supplementary Table 1).Fig. 1Metabolic flux map used in the simulation of diabetes health states. The map is a graphical representation of the extended stoichiometry matrix in Supplementary Table 1. Nodes represent metabolites and physiological parameters. Edges represent generalized fluxes. The fluxes statistically associated ($P \leq 0.01$ by the Wilcoxon–Mann–Whitney test) with proliferative retinopathy and cataract are highlighted with red and brown, respectively We extended the stoichiometry matrix by integrating in it the measured physiological parameters (see Appendix A and Supplementary Table 1). We illustrate this approach by finding a stoichiometric coefficient connecting the metabolic variable oxidized low-density lipoprotein (ox-LDL) with the physiological variable pulse wave velocity (Online Appendix B). For each patient, a personalized digital model was constructed and initiated with all available metabolite and physiological readings measured from the patient. The missing data were imputed as population averages. Then, the best fit generalized flux vectors were obtained by the quadratic optimization procedure as the solution of the system of stoichiometric equations that minimizes the squared deviations between the vector of patient’s readings and the vector of the metabolite concentrations and physiological values in the model. Hereby, the patient’s initial data were used as soft constraints. Their respective values in the model were permitted to deviate within ± $20\%$. The constraints limiting the permitted flux values were introduced based on reference literature (see Online Appendix B for more details). ## Distance metrics The state of a given digital twin is defined by the vector of all generalized fluxes in the network at a given generalized extent. The proximity of two states can be assessed by applying a distance metric. We tested the Euclidean distance as well as the health state distance, a manifold-based metric (see Online Appendix A for the formulas). ## The diabetes evolution trajectory The initial point of diabetes type 2 progression (state A) was selected to represent a healthy individual with demographic characteristics similar to the studied population. The end-point state (state B) represented advanced type 2 diabetes, where common complications, such as hypertension and nephropathy are fully manifested. Each state was characterized by measured values of key metabolite concentrations and quantitative physiological parameters of vascular health (Tables 1, 3.). On the progress scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ stretching between points A and B, we considered an individual patient’s health history as a smooth change of metabolite concentrations and physiological characteristics marked, in some cases, by developing diabetic complications. ## Data pre-processing The EVAS dataset was collected as part of the earlier clinical study [30] as doctor’s notes in the electronic spreadsheets. The input variables, patient characteristics and the diagnoses, were compiled into a single comma-separated tabular file (CSV), using the R programming environment (R v.3.4.4). The variables were classified into numerical (measurements) and categorical (classification and diagnoses). The NHANES dataset was obtained from the official website as a set of XPT (SAS export format) table files containing cross-sectional data arranged by year and by the variable group. The files were converted to CSV tables, using the R programming environment. The data were aggregated across all the years and the available variables and saved in a single CSV file. The variables were interpreted according to the official description of the tables as categorical or numerical. NA values were assigned to missing data. ## Statistical methods All the statistical methods were implemented using the R programming environment (R v.3.4.4). ## Wilcoxon–Mann–Whitney tests Each component of the metabolite and flux vectors was then evaluated as an independent predictor of the patient classification by one of the following phenotypes representing diabetic complications detected by the sample collections time point: diabetic retinopathy or cataract. Statistical associations between each vector component and each phenotype were assessed using the Wilcoxon–Mann–Whitney non-parametric test with the null hypothesis of the vector components being equal between two groups patients corresponding to two phenotypic (disease) states. Multiple hypothesis testing bias was controlled via the false discovery rate assessment. The false discovery rate was calculated according to the Benjamini–Yekutieli procedure [32], and the P-values with FDR not exceeding $15\%$ were reported. ## Logistic regression models To identify at the baseline and to predict the development of the ophthalmic complications at the follow-up time points, we employed the binomial logistic regression model, implemented in the R v.3.4.4 standard library as a Generalized Linear Model (the glm.fit function) [33]. The magnitudes and signs of individual’s generalized fluxes, obtained at the baseline time point were used as the inputs of the model. The diagnosis at the follow-up time was taken as the expected output to obtain the best logistic regression coefficients parametrizing the model. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) and the c-index (area under the curve, AUC), as implemented in the R package pROC [34, 35]. To further increase the specificity of the models, samples with the intermediate values of the calculated logistic function (the “twilight zone” exclusion) were removed from the analysis. We tested the results obtained from removing the samples from the following percentile ranges: 45 to 55, 40 to 60, 35 to 65, 33 to 67, and 25 to 75. At each range, we obtained ROC estimates as described above. ## Resampling procedures To balance the test design to ensure robustness, we sampled $50\%$ of positive cases and an equal number of negative cases to train the logistic model We used the other $50\%$ of the cases to test it. We repeated the sampling 50 times to obtain median estimates of the ROC curves. ## Linear regression between the flux distance metric and the flux-based diagnosis estimate To quantify the correlations between the health state distance metric and diagnosis of the patients, we applied a binomial logistic regression model in R v.3.4.4 [33]. We used the values of metabolic fluxes as the input variables of the model. The binary output variable was the diagnosis of the patient at the baseline time point. The parameters of the logistic function were fitted as the optimal weights of the extended metabolic fluxes minimizing deviation between the observed and expected outputs across the patient cohort. The diagnosis variable received the value of 1 if the patient was diagnosed with a particular syndrome (here, retinopathy and cataract) and 0 otherwise. Since the patient cohort contained unequal number of patients with and without diagnosis, we applied statistical resampling to obtain balanced design to train the logistic regression model. Mean values of the regression coefficients obtained after 10 resampling steps were used as a basis for subsequent evaluation of the patient’s diagnosis. Evaluating the parameterized logistic function on a particular patient resulted in the value between 0 and 1, estimating the expected likelihood that the patient’s diagnosis was positive. Across the patients, correlation between the value of the logistic function with the distance metric was evaluated with the Kendall’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ coefficient and test statistic. ## Application of the GMFA framework to diabetes patients’ data reveals metabolic and physiological mechanisms associated with diabetic retinopathy and cataract progression We considered ophthalmic complications of type 2 diabetes of our patient cohort [30] to explore metabolic and physiological pathways, using the proposed methodology. The results are presented in Tables 4 and 5 (see also Fig. 1).Table 4Statistically significant associations between individual metabolites and physiological variables and ophthalmic complications in type 2 diabetes patientsMetabolitePhenotypePFDRPulse wave velocityProliferative retinopathy1.3E−30.076RHI1.8E−30.099CIMT-avg-RCataract2.3E−52.0E−3CIMT-avg-L1.1E−48.7E−3HbA1c2.3E−30.11The P-values were obtained by using the Wilcoxon–Mann–Whitney statistical test with null hypothesis that the median flux values are identical in two groups of patients: with the phenotype being present or absent. The FDR was calculated using the Benjamini–Yekutieli P-value adjustment method for multiple hypothesis testing [32]Table 5Statistically significant associations between the fluxes connecting metabolic and physiological variables and ophthalmic complications in type 2 diabetes patientsFluxPhenotypePFDR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Food \rightarrow Protein$$\end{document}Food→ProteinProliferative retinopathy2.4E−52.0E−3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Protein \rightarrow Urine-pH$$\end{document}Protein→Urine-pH2.4E−52.0E−3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$HDL + VLDL \rightarrow LDL$$\end{document}HDL+VLDL→LDL9.7E−61.0E−3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Inflammation \rightarrow Ferritin$$\end{document}Inflammation→Ferritin1.9E−40.01\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RHI \rightarrow CIMT-avg-L$$\end{document}RHI→CIMT-avg-L4.9E−40.027\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Ox-LDL \rightarrow Pulse-wave-velocity$$\end{document}Ox-LDL→Pulse-wave-velocity2.8E−30.096\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RHI \rightarrow CIMT-avg-R$$\end{document}RHI→CIMT-avg-RCataract1.1E−47.1E−3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Bilirubin \rightarrow Bile$$\end{document}Bilirubin→Bile2.3E−30.089\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Inflammation \rightarrow Ferritin$$\end{document}Inflammation→Ferritin3.5E−30.096The P-values were obtained by using the Wilcoxon–Mann–Whitney statistical test with null hypothesis that the median flux values are identical in two groups of patients: with the phenotype being present or absent. The FDR was calculated using the Benjamini–Yekutieli P-value adjustment method for multiple hypothesis testing [32] Analysis of metabolite concentrations and physiological measurements (the results are shown in Table 4) revealed that pulse wave velocity (PWV, $$P \leq 1.3$$e−3) and reactive hyperemia index (RHI; $$P \leq 1.8$$e−3) were significantly increased in diabetes patients with proliferative diabetic retinopathy. Similar observations were reported earlier [36–38]. Cataract presence was significantly associated with carotid intima-media thickness (CIMT; $$P \leq 2.3$$e−5 in the right carotid artery and $$P \leq 1.1$$e−3 in the left one) and glycated haemoglobin (HbA1c; $$P \leq 2.3$$e−3). Analysis of statistical associations with metabolic fluxes provided more information than metabolites and physiological parameters alone (results shown in Table 5). In the case of diabetic retinopathy, we found that the rate of protein consumption and protein-dependent decrease of urine pH are the parameter significantly associated with the diagnosis ($$P \leq 2.4$$e−5). Studies report equivocal effects of protein consumption on diabetic retinopathy development [39]. At the same time, there is a consensus with respect to the role of high protein consumption in diabetic microvascular changes [40], which can also be observed in associations with diabetic nephropathy and has been reflected in clinical recommendations [41, 42]. Urine pH is considered an independent negative prognostic and progression indicator of type 2 diabetes [43]. At the same time, the ammonium ions concentration is considered a factor significantly affecting urine pH of diabetes patients [44]. Another flux of significance was conversion of high-density lipoprotein cholesterol (HDL) into low-density lipoproteins (LDL) localized in the liver ($$P \leq 9.7$$e−6). Unlike many other tissues producing cholesterol locally, for e.g., the retina, the cholesterol produced by the liver is transported via the bloodstream [45]. LDL and the HDL/LDL ratios are known as significant factors of diabetic retinopathy progression [45–47]. The flux quantifying the effect of oxidized LDL (ox-LDL) on PWV was also significant ($$P \leq 2.8$$e−3). LDL oxidation and lipid oxidation in general are important mediators implicated in retinopathy [46]. Iron and ferritin play an important role in oxidation reactions affecting diabetic retinopathy progression [48], in particular, by producing ox-LDL [49], and our results support that ($$P \leq 1.9$$e−4). Development of cataract was significantly associated with the extended flux leading from the reactive hyperemia index (RHI) characterizing the vascular health state, to carotid intima-media thickness ($$P \leq 1.1$$e−4). Notably, we also found that the flux leading to induction of ferritin and the flux converting bilirubin to bile, were also associated with cataract development ($$P \leq 3.5$$e−3 and $$P \leq 2.3$$e−3, respectively). Both associations were not detected on the level of individual metabolites. Recently, evidence was found that blood bilirubin might be a compound protecting retina from degradation in diabetes patients [50–52]. Thus, we observed that with respect to specific clinical phenotypes, statistical results obtained with metabolic and physiological flux models are not contradicting the results obtained with metabolic and physiological variables alone. Moreover, flux models have the potential to provide more biomarkers characterizing the disease and to improve statistical power. The ability of flux models to describe additional details of long-term dynamics is complementary to the descriptive power of metabolites alone. These conclusions confirm the applicability of the computational framework provided by GMFA to address the practical needs of integrative biochemical, physiological and clinical data analysis for holistic assessment. ## The distance between metabolic health states quantifies the progression of vascular diabetic complications We used the extent variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ to measure the state evolution and disease progression. Since the states are defined in terms of generalized fluxes changing along the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ scale, the distance metric on that scale should be expressed as a function of generalized fluxes and needs to reflect both qualitative changes and quantitative differences observed upon transitions between any two states. Thus, irrespective of individual variations in time scales of disease progression, health state and the extent variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi$$\end{document}ξ are expected to reflect disease-associated physiological changes observed in the generalized flux profiles across the patient cohort. We analyzed the influence of choosing any of four definitions of the distance between health states:Diabetes durationHbA1c value evolutionThe Euclidean distance between the patient flux profilesThe health state distance, the manifold-based metricThe results of this analysis are presented in Tables 6, 7 and 8.Table 6Correlation analysis of physiological parameters of diabetes complications with the health state progression extent metric and diabetes duration in diabetes patientsParameterDiabetes durationSerum HbA1cEuclidean distanceHealth state distance\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)HbA1c0.201.3E−061.003.3E−137− 0.093.0E−020.146.5E−04Reactive hyperemia index0.009.6E−01− 0.083.9E−020.092.2E−020.137.9E−04Systolic blood pressure0.122.0E−030.018.2E−010.083.9E−020.123.0E−03Left-side CIMT0.154.8E−040.051.9E−01− 0.115.1E−030.077.0E−02Average CIMT0.147.1E−040.034.0E−01−0.092.7E−02−0.085.9E−02Right CIMT0.117.5E−03− 0.026.5E−010.084.5E−02− 0.119.3E−03Patient’s age0.261.2E−10− 0.079.7E−02− 0.101.7E−02− 0.116.2E−03Body mass index0.018.3E−010.083.5E−020.092.9E−02− 0.122.7E−03Urine albumin/creatinine ratio0.129.4E−030.251.9E−08− 0.079.4E−02− 0.127.5E−03Pulse wave velocity0.255.9E−100.109.6E−030.092.2E−02− 0.131.3E−03The analysis was done using the Kendall’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ correlationTable 7Correlation analysis of physiological parameters of diabetes complications with the health state progression extent metric and diabetes duration in patients with diabetic retinopathyParameterDiabetes durationSerum HbA1cEuclidean distanceHealth state distance\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)Systolic blood pressure0.073.1E−01− 0.019.1E−01− 0.145.1E−020.231.7E−03Average CIMT0.101.9E−010.154.7E−02− 0.249.9E−040.222.7E−03Left-side CIMT0.083.1E−010.121.2E−01− 0.154.4E−020.223.7E−03HbA1c0.154.6E−021.003.5E−42− 0.181.5E−020.213.5E−03Right CIMT0.154.5E−020.083.0E−01− 0.207.5E−03− 0.172.6E−02Patient’s age0.232.5E−03− 0.073.7E−01− 0.172.0E−02− 0.181.2E−02Body mass index− 0.083.0E−01− 0.018.6E−01− 0.181.1E−02− 0.214.5E−03Pulse wave velocity0.101.6E−010.063.8E−010.163.2E−02− 0.214.1E−03Reactive hyperemia index0.028.2E−01− 0.054.8E−010.154.6E−02− 0.231.7E−03Urine albumin/creatinine ratio− 0.037.2E−010.158.3E−02− 0.211.3E−02− 0.243.5E−03The analysis was done using the Kendall’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ correlation When considering all diabetes patients (Table 6), we find that all four distance definitions deliver qualitatively similar results. Regardless along which descriptor the disease progression is measured, it strongly correlates with patient age, followed by PWV, HbA1c, CIMT values, and the urine albumin/creatinine ratio. Notably, the two flux-derived distance metrics showed significant, but consistently lower correlation coefficient values for these variables compared with diabetes duration and serum HbA1c. Yet more importantly, we observe additional significant correlations between the health distance metric and the disease progression hits; e.g., hits for RHI (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.13$$\end{document}τ=0.13, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 7.9$$e-4$$\end{document}$$P \leq 7.9$$e-4) and BMI (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = -0.12$$\end{document}τ=-0.12, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 2.7$$e-3$$\end{document}$$P \leq 2.7$$e-3) are not seen for the other metrics. Table 8Correlation analysis of physiological parameters of diabetes complications with the health state progression extent metric and diabetes duration in diabetes patients with cataractParameterDiabetes durationSerum HbA1cEuclidean distanceHealth state distance\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(\tau)$$\end{document}P(τ)Systolic blood pressure0.183.9E−030.054.6E−01− 0.124.6E−020.201.7E−03HbA1c0.193.1E−031.002.3E−560.134.2E−020.175.6E−03Left-side CIMT− 0.027.3E−010.036.1E−01− 0.125.5E−020.143.2E−02Average CIMT0.001.0E+000.027.4E−01− 0.101.3E−01− 0.142.3E−02Patient’s age0.161.1E−02− 0.125.5E−020.124.9E−02− 0.151.5E−02Urine albumin/creatinine ratio0.145.0E−020.231.2E−03− 0.145.1E−02− 0.171.7E−02Body mass index0.072.9E−010.072.7E−01− 0.169.1E−03− 0.184.1E−03Right CIMT0.063.8E−01− 0.054.8E−010.161.5E−02− 0.185.5E−03Pulse wave velocity0.272.2E−050.126.5E−020.175.7E−03− 0.192.1E−03Reactive hyperemia index− 0.028.0E−01− 0.072.7E−010.175.7E−03− 0.201.8E−03The analysis was done using the Kendall’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ correlation However, qualitatively different performances of the four distance metrics are observed if we explore the development of diabetic complications. In Table 7 (correlation with diabetic retinopathy), as a trend, the correlation coefficients and their significance measured for the Euclidean distance and our flux distance metric are consistently better than those for diabetes duration and HbA1c value evolution. Strikingly, the flux distance metric outperforms the Euclidean distance in both absolute correlation and significance in all but three cases (average and right-side CIMT, patient age). The flux distance metric significantly correlates with all the parameters, while all other distance measures are not significantly associated with some of them. Diabetes duration correlated only with patient age (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.23$$\end{document}τ=0.23, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 2.5$$e-3$$\end{document}$$P \leq 2.5$$e-3), CIMT (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.15$$\end{document}τ=0.15, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 4.5$$e-2$$\end{document}$$P \leq 4.5$$e-2 for the right carotid artery) and HbA1c (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.15$$\end{document}τ=0.15, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 4.6$$e-2$$\end{document}$$P \leq 4.6$$e-2), while the health distance metric correlated with all the parameters (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\|\tau \| \ge 0.17$$\end{document}\|τ\|≥0.17, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P \le 2.6e$$\end{document}P≤2.6e-2). Results for the diabetic patients with cataracts (see Table 6) reveal the same pattern. We find that diabetes duration and HbA1c do not significantly correlate with several listed physiological parameters, whereas the two flux-defined distances do. Hereby, for patients with complications, the metric from Equation 6 performs better than the Euclidean distance in terms of absolute correlation and significance in all but one case (left-side CIMT).Fig. 2Correlation of the health state distance metric with diagnosis. For the diabetes patients diagnosed with retinopathy (A) and cataract (B) logistic regression model was parameterized to discriminate the present patient’s diagnosis, based on the computed values of the extended fluxes. This value is plotted along the vertical axis as the model-based diagnosis prediction. For each patient, we also calculated the distance metric of proximity of each individual patient to the health state without diabetic complications. The larger is the distance metric, the further is the estimated extent of patient’s progression towards a particular complication. The distance metric is plotted along the horizontal axis. We tested the hypothesis that distance of the patient’s health state progression and the predicted diagnosis, using the Kendall’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ rank correlation coefficient. The results indicate significant statistical association between the two for both retinopathy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.39$$\end{document}τ=0.39, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 8.0$$e-12$$\end{document}$$P \leq 8.0$$e-12) and cataract (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.43$$\end{document}τ=0.43,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 6.6$$e-12$$\end{document}$$P \leq 6.6$$e-12). The dashed lines shown the $25\%$ and the $75\%$ percentiles of the variables plotted along each of the axes. The patients with complications under the lower percentile boundaries in the lower axis could be interpreted as the ones whose generalized flux profiles are close to non-complicated diabetes. The patients without complications above the upper percentile boundaries in the lower axis could be interpreted as having generalized flux profiles are close to the profiles of patients with manifested respective diabetic complications We tested whether the patients’ health state (defined by the generalized flux vector) can correlate with the trend towards an ophthalmic complication. As the output of the model on each patient, the value of the logistic function, ranging between 0 and 1, quantified the probability of the present patient’s diagnosis. This value is plotted against the flux distance metric in Fig. 2. We observe that the distance metric strongly correlates with the logistic function output for retinopathy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.39$$\end{document}τ=0.39, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 8.0$$e-12$$\end{document}$$P \leq 8.0$$e-12; see Fig. 2A) and cataract (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau = 0.43$$\end{document}τ=0.43, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 6.6$$e-12$$\end{document}$$P \leq 6.6$$e-12; see Fig. 2B). Across all patient groups, with and without diabetic complications, the positive correlations indicate the trend of increasingly poor identification of the diagnosis for the patients, whose flux profiles are further away from the complication-free status. Using the distance metric of the flux profiles and the generalized flux-driven syndrome identification, patients with high and low risk of complications can be classified into the respective high- and low-risk groups. Here, we assigned the patients to the high-risk group by their proximity to the diagnosed syndrome state if they fall into the upper quartile simultaneously by (i) their logistic function value and (ii) the distance (see the distance metric above) between their current health states (flux profiles) and the complication-free state. This group corresponds to the top-right quadrant in Fig. 2A and B. The patients of the low-risk group were defined by the values of their logistic function and the distance metric corresponding to the lower quartile (the bottom-left) quadrant in Fig. 2A, B), corresponding to the health states with the highest rates of diabetes complications. Confirming the results of the correlation analysis, the low-risk and the high-risk patients demonstrated a strong statistical association with their actual diagnoses of retinopathy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 5.1$$e-5$$\end{document}$$P \leq 5.1$$e-5) and cataract (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 1.3$$e-5$$\end{document}$$P \leq 1.3$$e-5), as shown in Table 9.Table 9Association of the patient risk group with diagnosed diabetic retinopathy and cataractSyndromeRisk groupPatients without diagnosisPatients with diagnosisRetinopathyLow risk272High risk1720Fisher’s exact test \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 5.1$$e-5$$\end{document}$$P \leq 5.1$$e-5CataractLow risk222High risk919Fisher’s exact test \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$P \leq 1.3$$e-5$$\end{document}$$P \leq 1.3$$e-5Patients were considered low risk when their flux profiles and their flux-driven syndrome prediction are within the lower quartiles (the bottom left quadrant shown in Fig. 2). The patients, whose flux profiles and their flux-driven syndrome prediction are within the higher quartiles (the top right quadrant shown in Fig. 2), were considered high risk. Both diagnoses demonstrate a highly significant co-incidence with the risk groups (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P \le 5.1e-5$$\end{document}P≤5.1e-5, Fisher’s exact test) To characterize the high- and low-risk groups, we calculated their median flux profiles and displayed them as graphs with edge weights (representing the fluxes magnitudes) proportional to the normalized deviation from their median values in the entire patient cohort. These values are displayed in a graphical form in Fig. 3. We observe that the flux rates immediately upstream and downstream ox-LDL are commonly decreased in the low-risk group, relative to both high-risk groups. High-risk retinopathy patients could be differentiated from the high-risk cataract patients by increased fluxes upstream creatinine, increased liver cholesterol metabolism, and increased haemoglobin-related fluxes. High-risk cataract patients are specifically characterized with increases in haemoglobin glycation fluxes leading from ROM to HbA1c and with hs-CRP induction. Fig. 3Graphical representation of the flux states of the diabetes patients with low risk of ocular complications (A), high-risk retinopathy (B) and high-risk cataract (C). The flux states were obtained by calculating the median flux magnitudes within the risk groups. The magnitudes are displayed normalized relative to the median across the entire studied population of diabetes patients. The blue and red edge colors in the graph represent the median flux values lower or higher than the population median, respectively. Edge thickness represents the relative magnitude of difference between the absolute values of the group median and the population median value of a particular flux. The groups are defined according to Table 9 and Fig. 2 as follows: A low-risk group for retinopathy and cataract (the lower quartile in Fig. 2A and 2B); B high risk retinopathy group (the upper quartile in Fig. 2A); C high risk cataract group (the upper quartile in Fig. 2B) Thus, we can conclude that the distance between a patient’s flux profile and a typical flux profile of a complication-free diabetes patient is indicative of the degree to which a particular diabetic complication is (or potentially will be) manifested in a given patient. Together these results show that, for a given diabetic complication, it is possible to find such a disease progression metric that would better correlate with the evolution of key progression characteristics than direct clinical parameters, such as diabetes duration or HbA1c values. Further analysis of the flux profiles may uncover the mechanisms underlying syndrome development. ## Digital twins can indicate the presence of ophthalmic complications of diabetes at the baseline and predict their development 3 years in the future To explore the potential of using the GMFA-based digital twins as diagnostic and predictive tools, we used them as inputs of logistic regression models for detection existing and predicting future occurrence of ophthalmic complications of type 2 diabetes in the EVAS dataset (see Table 2 for summary statistics). The analysis schema is shown in Fig. 4.Fig. 4GMFA Digital Twins construction and evaluation. Metabolic and non-metabolic physiological variables were obtained from T2DM patients. The GMFA methodology was applied to construct digital twins representing individual patients’ health states at the baseline time point [1]. The distance between the health state of the patient to the advanced disease state correlates with the risk of developing T2DM complications [2]. By combining the GMFA digital twin profiles with the demographic data (age, diabetes duration) we constructed logistic regression models, which can identify patients with T2DM complications [3] and the patients who will develop them in the future [4] The logistic regression models could identify in the population patient having retinopathy (AUC 0.84, SN = $80\%$, SP = $71\%$) and cataract (AUC 0.79, SN = $80\%$, SP = $62\%$). The results are reported in Fig. 5 (A and B, respectively). Sub-classifying all the retinopathy cases into proliferative and non-proliferative subtypes resulted in an increased performance for each of subtype: AUC 0.95 (SN = $92\%$, SP = $94\%$) for proliferative retinopathy and AUC 0.84 (SN = $80\%$, SP = $70\%$) for non-proliferative (Fig. 5C, D). To ensure the robustness of our results, we carried out the analysis by: [1] statistical resampling (50 iterations), [2] balancing the training and the testing set design to provide equal number of positive and negative cases. The results are shown in Fig. 5E and F.Fig. 5Performance of GMFA-based logistic regression models in identifying T2DM patients with present ophthalmic complications in the EVAS patient cohort and their validation in the NHANES cohort. Logistic regression models were built using the generalized fluxes (GMFA), patient’s age and diabetes duration as the input variables. The cross-sectional data from the EVAS patient cohort was used to evaluate the performance of the algorithm in detection of ophthalmic complications based on biochemical and physiological inputs. The regression models output the probability of the patient having diabetic retinopathy (A) or cataract (B). The retinopathy cases were further sub-classified into proliferative (C) and non-proliferative (D) subtypes. The models predicted diagnoses made at the baseline time point. The performance of the models is assessed with the area under the ROC curve (AUC). Variation in the AUC values for retinopathy (E) and cataract (F) was assessed via resampled training and testing datasets ($50\%$ positive rate in each). Median AUC values and the IQR-based confidence intervals are reported for 50 resampling iterations per each AUC estimate. The tolerance to poorly discriminated cases was tested by filtering the patients of the intermediate phenotypes by the health state distance and risk prediction logistic function (see Fig. 2 and the Results section for details). Percentile-based exclusion of patients with the intermediate phenotypes resulted in a slightly improved performance of the predictive models: from AUC 0.72 (retinopathy) and AUC 0.69 (cataract) with no filtering (Quantile 50* cutoff) to AUC 0.79 (retinopathy) and AUC 0.76 (cataract) when retaining $60\%$ of patients (Quantile 30 cutoff) and filtering out the remaining $40\%$ of patients with intermediate phenotypes. Retinopathy detection performance was validated by applying the GMFA models to the cross-section data from the NHANES patient cohort (G, H). For the subgroup of the NHANES patients with diabetes history duration within the range similar (AUC 0.78) to that of the EVAS cohort (G), the AUC value was similar to that of the EVAS cohort (A). For the NHANES patients with diabetes history spanning longer than 25 years, the AUC dropped to 0.66 (H) Our analysis of relationship between the patient’s health state distance and the ability of the logistic model to identify the patient’s risk group (Fig. 2) suggests that there are patients with intermediate metabolic phenotype, whose classification into the risk groups is difficult. We took into account the potential impact of this intermediate sub-population on the performance metrics. We varied the fraction of patients with intermediate phenotypes in the training and the testing datasets by iteratively selecting only the patients whose distance metric and logistic function value (see Fig. 2) were either higher or lower than a given quantile value. This cutoff quantile value was iterated in the range from the 30th/70th percentile ($40\%$ of the patients with intermediate phenotypes excluded) to the 50th (no patients were excluded). At each iteration we quantified the observed AUC values. The results shown in Figs. 5E and 5F demonstrate that the reported median AUC values across all the tested scenarios are within the confidence range (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm 1 IQR$$\end{document}±1IQR).Fig. 6Performance of GMFA-based logistic regression models in predicting development of ophthalmic complications in T2DM patients within 3 years. Logistic regression models were built using the generalized fluxes (GMFA), patient’s age and diabetes duration as the input variables, at the baseline. The regression models output the probability of the patient to develop any subtype of retinopathy (A), cataract (B), or a non-proliferative retinopathy. The retinopathy cases were further sub-classified into proliferative (not shown) and non-proliferative (C) subtypes within 3 years after the baseline time point To validate our findings in an independent patient cohort, we analyzed the National Health and Nutrition Examination Survey (NHANES) dataset, which included patient survey and measurements data collected across the United States [31]. From the NHANES data study, we selected a total of 517 subjects diagnosed with T2DM diabetes and characterized with the set of parameters matching those of the Singapore EVAS dataset (Appendix C, Supplementary File). Summary statistics indicated that the two populations were comparable with respect to most of the parameters (Table 1). The differences were observed in ethnicity, age distribution and incomplete information on the hypertension and hyperlipidemia in the NHANES cohort. Moreover, the rate of retinopathy in the NHANES cohort was twice lower than the EVAS cohort ($20.5\%$ vs $41\%$, respectively). When we reproduced identification of patients diabetic retinopathy in the NHANES dataset, using the logistic regression model similar to that of the EVAS dataset, we observed a markedly lower performance in NHANES (AUC 0.67). To test if the drop in performance was due to a lower homogeneity of the NHANES population, compared to EVAS, we separated the NHANES patients into two subgroups: one tightly matching EVAS patients by age (237 patients aged up to 60 y.o) the remaining subgroup containing more dissimilar patients (280 patients aged above 60). The logistic regression model in the first group demonstrated the performance close to that in the EVAS cohort (AUC 0.78, Fig. 5G). At the same time, the performance in the second group remained relatively low (AUC 0.66, Fig. 5H). These results indicated that, despite the disproportion in the retinopathy cases across the multi-ethnic populations, a good reproducibility of our methods can be achieved when key patients characteristics, such as age, are matched across the cohorts. Having demonstrated the evidence of GMFA-based digital twin models indicating the presence of T2DM ophthalmic complications, we tested if the models can be used to predict development of these complications in the future, within 3 years from the baseline time point. The results are presented in Fig. 6. We found that the GMFA models can predict all retinopathy (AUC 0.81, SN = $78\%$, SP = $70\%$, Fig. 6A) and cataract (AUC 0.93, SN = $87\%$, SP = $95\%$, Fig. 6B) cases. For non-proliferative retinopathy alone, we observed a slightly better performance (AUC 0.81, SN = $79\%$, SP = $70\%$, Fig. 6C). For proliferative retinopathy the analysis could not be performed due to the insufficient number of patients (4 patients) in this group. ## Discussion Over the past 30 years, numerous systems biology based tools have been developed in the academia and been used in the pharmaceutical and biotechnology industry, for example, for optimization of fermentation processes. Metabolic flux analysis (MFA) is a flexible method of systems biology that have been tested in applications that span from bacterial models to higher eukaryotes [19–23]. However, these methods, which were based on unicellular biological models, were not easily scalable to tissues and organs. In particular, modeling human physiology in a clinical context proved to be extra challenging. Precision medicine and digital health initiatives are driving the adoption of advanced computational tools for holistic analysis and interpretation of individual patients’ physiological and health states. At the same time, clinical science is trending towards a focus on an integrative picture of personalized health. Today, a holistic assessment of a person’s health state implies integration of detailed profiles of multiple physiologically inter-connected subsystems: metabolism, cellular signalling, immune responses, nervous system, body structure and microbiome. At present, there is no methodological framework to unify quantitative modelling of all these components. In the present study, we describe the Generalized Metabolic Flux Analysis (GMFA). The combined impact of several technical innovations and novel concepts of GMFA enables the computational simulation of complex, clinically relevant networks. The critical points are:By pooling metabolites and fluxes along the biological processes and mechanisms, we both keep the biological logic of the systems and, at the same time, greatly simplify the network, making it much more coarse-grained. We further enhance the information content of the model network by mapping non-metabolic clinical modalities and measured clinical laboratory parameters onto the network. We analyze the system’s trajectories along the disease extent progression coordinate, rather than the time scale. In this way, the progression of various patients along the path from health to the manifested disease state becomes comparable. We calculate the system fluxes by quadratic optimization using the clinical readings as soft constraints. The optimality of the system’s profile in the space of generalized fluxes is formulated as the best fit ensuring the minimal squared difference between the observed measured variables and their values predicted from the given flux solution under constraints. By applying GMFA to accessible clinical data we create descriptive and predictive personalized mathematical models of an individual patient’s metabolic state. A digital twin can be defined as a mechanistic numerical model of a particular patient calibrated to the individual’s phenotypic and clinical data at a particular time point. Thus, GMFA is used as a novel approach for creating digital twins based on evaluating observed metabolic and physiological data. Within the GMFA framework, we analyzed two cohorts of diabetes type 2 patients, EVAS [30] and NHANES [31]. We built a coarse-grained metabolic map (Fig. 1) that includes non-metabolic edges related to PWV, CIMT, RHI and other clinical characteristics. We quantified the generalized metabolic fluxes in the system individually for each patient. Our correlation analysis demonstrates that application of GMFA reveals critical mechanisms associated with diabetic retinopathy and cataract progression (Tables 4, 5; Fig. 1). For example, we found that, in the course of retinopathy, chronic changes in the PWV are mediated via LDL oxidation stimulated by ferritin or that fluxes involving RHI, CIMT, ferritin and bilirubin are associated with cataract development. Further we found that distances between the metabolic health states of patients quantify the progression of diabetic vascular complications (Tables 6, 8; Fig. 2) and that the digital twins can be used for the prediction of their outcomes (Fig. 5). The observed associations correlate with recent clinical and experimental studies reporting similar conclusions [45, 46, 48, 49]. In addition, our analysis supported earlier studies on the mechanisms relating nitric oxide production, reactive hyperemia index and atherosclerosis [53–56]. Thus, the GMFA method provides mechanistic insights into disease progression along a path in the health states space and allows us to delineate subgroups of patients that can be predicted to develop diabetic eye complications 2. This allowed us to build predictive model that can infer the present phenotypic state of the patient (the diagnosis made by the ophthalmologist) from the information on the patient’s metabolic dynamics provided by GMFA (Fig. 5). We also demonstrated that our models can predict development of ophthalmic complications in T2DM patients within 3 years from the baseline (Fig. 6). We processed the NHANES patient data with the same computational model developed for the EVAS data analysis without any further adaptation and we obtained very comparable results despite the great differences between the two cohorts (geographic location, ethnicity, age, etc.). Our GMFA based method for creating digital twins as representations of health states that vary on the scale of the progress extent, has the advantage of relating the information obtained in a cross-sectional study of the population with evolution of health state in time. In the future, this approach may be further developed to evaluate different health state transitions with respect to reversibility. This would bring a novel perspective on options for chronic disease management. In the last decade, there has been an upsurge in the use of data-driven machine learning models for the prediction of clinical outcomes. Machine learning provides prediction on outcomes of complex biological processes by ploughing through databases of inputs (exposures) and outputs (outcomes) for a given problem. These models bypass the need to understand complex mechanisms. In contrast, mechanistic modeling involves the generation of novel hypotheses for causal mechanisms that are generated through clinical observations in the datasets. A mechanistic model obtained by fitting its parameters to the available observations, would complement data-driven analyses by reducing the requirements for the data set volumes and compensating for occasional incompleteness of observations. The GMFA methodology described here, can be a candidate for this role. The advantages of similar systems biology methods can be found in integrating multi-level biological systems information, from genomics to proteomics [21, 29]. This provides future opportunities to use the GMFA as a framework for clinical systems medicine. Our present study revealed several limitations. Despite the GMFA methodology is able to model medium- and large-scale metabolic networks, in the clinical setting only small-scale models can be practically applied, since a typical biochemical analysis includes only a small number of common biochemical tests. The available data sets included only up to two longitudinal data points per patient, often collected at an interval of a few years. Having a larger number of longitudinal data points would allow us test the linearity of the disease progression extent, one of the key assumptions of the analysis. Moreover, such design would allow us to test the ergodicity of the generalized fluxes. If the generalized fluxes are indeed ergodic, as predicted from the GMFA equations, this methodology could in the future be used to utilize large volumes of cross-sectional data as sources of information on longitudinal changes in the metabolism of patients in homogeneous populations. Despite many of diabetes patients receive medications, we were unable to effectively use this information, since the variation in the treatment regimes was great, while our patient cohorts were relatively small. This did not allow us to stratify our patients by treatment type into smaller subgroups, while having enough patients in each subgroup to achieve statistically significant conclusions. Overall, our work demonstrates an example of using metabolic and physiological data to construct predictive digital twin models of patients from routinely accessible clinical data. We provide a novel analytical framework, which opens up possibilities for the elucidation of disease mechanisms in personalized health assessments. The GMFA approach was applied to modeling health states in diabetes patients and showed potential to predict the development of ophthalmic complications in patients with diabetes. With further development and validation, the GMFA approach could be applied in the clinical setting for patient risk assessment. In the future, we plan to use our methods across a broad range of patient cohorts, phenotypes and diseases. A potential area of application of this methodology is in the stratification of patients in the process of population screening. ## Conclusion The Generalized Metabolic Flux Analysis method described here aims to apply systems biology analysis principles to small- and medium-scale metabolic profiles, such as those obtained in clinical settings. The key novelty making the approach suitable for future clinical practice is building best-fit personalized constraint-based computational models (digital twins), which quantify the expected rates of inter-connected metabolic processes, based on a single time point data input. We validated this approach by generating the GMF digital twins from the biochemical and physiological data of type 2 diabetes patients. This allowed us to characterize the present metabolic states of individuals and to predict the health state progression into development of ophthalmic complications. The predictive performance in multiple patient cohorts, measured as ROC-AUC was in the range 0.79–0.95. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (PDF 192 kb)Supplementary file2 (PDF 94 kb) ## References 1. Saudubray JM, Garcia-Cazorla A. Inborn errors of metabolism overview: pathophysiology, manifestations, evaluation, and management. Pediatr Clin N Am (2018.0) 65 179. DOI: 10.1016/j.pcl.2017.11.002 2. Heindel JJ, Blumberg B, Cave M, Machtinger R, Mantovani A, Mendez MA, Nadal A, Palanza P, Panzica G, Sargis R, Vandenberg LN, Vom-Saal F. Metabolism disrupting chemicals and metabolic disorders. Reprod Toxicol (Elmsford, N.Y.) (2017.0) 68 3-33. DOI: 10.1016/j.reprotox.2016.10.001 3. Coene KLM, Kluijtmans LAJ, van der Heeft E, Engelke UFH, de Boer S, Hoegen B, Kwast HJT, van de Vorst M, Huigen MCDG, Keularts IMLW, Schreuder MF, van Karnebeek CDM, Wortmann SB, de Vries MC, Janssen MCH, Gilissen C, Engel J, Wevers RA. Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients.. J Inherit Metab Dis (2018.0) 41 337. DOI: 10.1007/s10545-017-0131-6 4. Pinu FR, Goldansaz SA, Jaine J. Translational metabolomics: current challenges and future opportunities. Metabolites (2019.0) 9 108. DOI: 10.3390/metabo9060108 5. Mordaunt D, Cox D, Fuller M. Metabolomics to improve the diagnostic efficiency of inborn errors of metabolism. Int J Mol Sci (2020.0) 21 1195. DOI: 10.3390/ijms21041195 6. Kohler I, Verhoeven A, Derks R, Giera M. Analytical pitfalls and challenges in clinical metabolomics. Bioanalysis (2016.0) 8 1509-1532. DOI: 10.4155/bio-2016-0090 7. Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: the future of metabolomics in a personalized world. New Horizons Transl Med (2017.0) 3 294 8. Gertsman I, Barshop BA. Promises and pitfalls of untargeted metabolomics.. J Inherit Metab Dis (2018.0) 41 355. DOI: 10.1007/s10545-017-0130-7 9. Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform (2019.0) 20 1957. DOI: 10.1093/bib/bbx170 10. Angione C. Human systems biology and metabolic modelling: a review–from disease metabolism to precision medicine. Biomed Res Int (2019.0) 2019 8304260. DOI: 10.1155/2019/8304260 11. Gu C, Kim GB, Kim WJ, Kim HU, Lee SY. Current status and applications of genome-scale metabolic models. Genome Biol (2019.0) 20 121. DOI: 10.1186/s13059-019-1730-3 12. Briggs GE, Haldane JB. A note on the kinetics of enzyme action. Biochem J (1925.0) 19 338. DOI: 10.1042/bj0190338 13. Monod J. The growth of bacterial cultures. Annu Rev Microbiol (1949.0) 3 371. DOI: 10.1146/annurev.mi.03.100149.002103 14. Yki-Järvinen H, Young AA, Lamkin C, Foley JE. Kinetics of glucose disposal in whole body and across the forearm in man. J Clin Investig (1987.0) 79 1713. DOI: 10.1172/JCI113011 15. Yu RC, Rappaport SM. Relation between pulmonary clearance and particle burden: a Michaelis-Menten-like kinetic model.. Occup Environ Med (1996.0) 53 567. DOI: 10.1136/oem.53.8.567 16. Wuest SJA, Horn T, Marti-Jaun J, Kühn H, Hersberger M. Association of polymorphisms in the ALOX15B gene with coronary artery disease. Clin Biochem (2014.0) 47 349. DOI: 10.1016/j.clinbiochem.2013.12.013 17. Mickens RE. An exact discretization of Michaelis-Menten type population equations. J Biol Dyn (2011.0) 5 391. DOI: 10.1080/17513758.2010.515690 18. Silverstein TP. CoViD-19 epidemic follows the Kinetics of enzymes with cooperative substrate binding. Biochem Mol Biol Educ (2020.0) 48 452. DOI: 10.1002/bmb.21397 19. Fell DA, Small JR. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J (1986.0) 238 781. DOI: 10.1042/bj2380781 20. Schuster S, Heinrich R. Time hierarchy in enzymatic reaction chains resulting from optimality principles. J Theoret Biol (1987.0) 129 189. DOI: 10.1016/S0022-5193(87)80012-7 21. Schilling CH, Schuster S, Palsson BO, Heinrich R. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog (1999.0) 15 296. DOI: 10.1021/bp990048k 22. Schneider P, Klamt S. Characterizing and ranking computed metabolic engineering strategies. Bioinformatics (2019.0) 35 3063. DOI: 10.1093/bioinformatics/bty1065 23. Karlstädt A, Fliegner D, Kararigas G, Ruderisch HS, Regitz-Zagrosek V, Holzhütter HG. CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst Biol (2012.0) 6 114. DOI: 10.1186/1752-0509-6-114 24. Edwards J, Palsson B. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci (2000.0) 97 5528. DOI: 10.1073/pnas.97.10.5528 25. Rezola A, Pey J, de Figueiredo LF, Podhorski A, Schuster S, Rubio A, Planes FJ. Selection of human tissue-specific elementary flux modes using gene expression data. Bioinformatics (2013.0) 29 2009. DOI: 10.1093/bioinformatics/btt328 26. Klamt S, Regensburger G, Gerstl MP, Jungreuthmayer C, Schuster S, Mahadevan R, Zanghellini J, Müller S. From elementary flux modes to elementary flux vectors: metabolic pathway analysis with arbitrary linear flux constraints. PLoS Comput Biol (2017.0) 13 e1005409. DOI: 10.1371/journal.pcbi.1005409 27. Kacser H, Burns JA. The control of flux. Biochem Soc Trans (1995.0) 23 341. DOI: 10.1042/bst0230341 28. Gregory MA, Nemkov T, Reisz JA, Zaberezhnyy V, Hansen KC, D’Alessandro A, DeGregori J. Glutaminase inhibition improves FLT3 inhibitor therapy for acute myeloid leukemia. Exp Hematol (2018.0) 58 52. DOI: 10.1016/j.exphem.2017.09.007 29. Karlstaedt A, Khanna R, Thangam M, Taegtmeyer H. Glucose 6-phosphate accumulates via phosphoglucose isomerase inhibition in heart muscle. Circ Res (2020.0) 126 60. DOI: 10.1161/CIRCRESAHA.119.315180 30. Dalan R, Goh LL, Lim CJ, Seneviratna A, Liew H, Seow CJ, Xia L, Chew DEK, Leow MKS, Boehm BO. Impact of Vitamin E supplementation on vascular function in haptoglobin genotype stratified diabetes patients (EVAS Trial): a randomised controlled trial. Nutr Diabetes (2020.0) 10 13. DOI: 10.1038/s41387-020-0116-7 31. 31.for Disease Control C. About the national health and nutrition examination survey. Prevention. 2022; https://www.cdc.gov/nchs/nhanes/about_nhanes.htm. 32. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat (2001.0) 29 1165. DOI: 10.1214/aos/1013699998 33. 33.Team RC. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018; https://www.R-project.org/. 34. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett (2006.0) 27 861. DOI: 10.1016/j.patrec.2005.10.010 35. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform (2011.0). DOI: 10.1186/1471-2105-12-77 36. Tanaka K, Kawai T, Saisho Y, Meguro S, Harada K, Satoh Y, Kobayashi K, Mizushima K, Abe T, Itoh H. Relationship between stage of diabetic retinopathy and pulse wave velocity in Japanese patients with type 2 diabetes. J Diabetes Res (2013.0) 2013 193514. DOI: 10.1155/2013/193514 37. Liu SC, Chuang SM, Shih HM, Wang CH, Tsai MC, Lee CC. High pulse wave velocity is associated with the severity of diabetic retinopathy in patients with type 2 diabetes. J Investig Med (2020.0) 68 1159. DOI: 10.1136/jim-2019-001240 38. Lim LS, Ling LH, Cheung CMG, Ong PG, Gong L, Tai ES, Mathur R, Wong D, Foulds W, Wong TY. Relationship of systemic endothelial function and peripheral arterial stiffness with diabetic retinopathy. Br J Ophthalmol (2015.0) 99 837. DOI: 10.1136/bjophthalmol-2014-306075 39. Wong MYZ, Man REK, Fenwick EK, Gupta P, Li LJ, van Dam RM, Chong MF, Lamoureux EL. Dietary intake and diabetic retinopathy: a systematic review. Dietary intake and diabetic retinopathy: a systematic review. PLoS ONE (2018.0) 13 e0186582. DOI: 10.1371/journal.pone.0186582 40. Tricò D, Nesti L, Frascerra S, Baldi S, Mengozzi A, Natali A. A protein/lipid preload attenuates glucose-induced endothelial dysfunction in individuals with abnormal glucose tolerance. Nutrients (2020.0) 12 2053. DOI: 10.3390/nu12072053 41. Li XF, Xu J, Liu LJ, Wang F, He SL, Su Y, Dong CP. Efficacy of low-protein diet in diabetic nephropathy: a meta-analysis of randomized controlled trials. Lipids Health Dis (2019.0) 18 82. DOI: 10.1186/s12944-019-1007-6 42. Yamauchi T, Kamiya H, Utsunomiya K, Watada H, Kawanami D, Sato J, Kitada M, Koya D, Harada N, Shide K, Joo E, Suzuki R, Bouchi R, Ohta Y, Kondo T. Medical nutrition therapy and dietary counseling for patients with diabetes-energy, carbohydrates, protein intake and dietary counseling. Diabetol Int (2020.0) 11 224. DOI: 10.1007/s13340-020-00437-7 43. Ogawa S, Nako K, Okamura M, Ito S. Lower urinary pH is useful for predicting renovascular disorder onset in patients with diabetes. BMJ Open Diabetes Res Care (2015.0) 3 e000097. DOI: 10.1136/bmjdrc-2015-000097 44. Maalouf NM, Cameron MA, Moe OW, Sakhaee K. Metabolic basis for low urine pH in type 2 diabetes. Clin J Am Soc Nephrol (2010.0) 5 1277. DOI: 10.2215/CJN.08331109 45. Hammer SS, Busik JV. The role of dyslipidemia in diabetic retinopathy. Vis Res (2017.0) 139 228. DOI: 10.1016/j.visres.2017.04.010 46. Busik JV, Esselman WJ, Reid GE. Examining the role of lipid mediators in diabetic retinopathy. Clin Lipidol (2012.0) 7 661. DOI: 10.2217/clp.12.68 47. Chou Y, Ma J, Su X, Zhong Y. Emerging insights into the relationship between hyperlipidemia and the risk of diabetic retinopathy. Lipids Health Dis (2020.0) 19 241. DOI: 10.1186/s12944-020-01415-3 48. Chen YJ, Chen JT, Tai MC, Liang CM, Chen YY, Chen WL. Serum iron and risk of diabetic retinopathy. Nutrients (2020.0) 12 2297. DOI: 10.3390/nu12082297 49. Ikeda Y, Suehiro T, Yamanaka S, Kumon Y, Takata H, Inada S, Ogami N, Osaki F, Inoue M, Arii K, Hashimoto K. Association between serum ferritin and circulating oxidized low-density lipoprotein levels in patients with type 2 diabetes. Endocr J (2006.0) 53 665. DOI: 10.1507/endocrj.K06-010 50. Zhu B, Wu X, Bi Y, Yang Y. Protective factors for diabetic retinopathy in Type 2 diabetes mellitus patients: long duration of no less than 10 years. Sci Rep (2017.0) 7 41681. DOI: 10.1038/srep41681 51. Liu Y, Duan C, Fang D, Liu Y, Xu H, Zheng Y, Xuan Y, Wang L, Ye L, Su R, An M. Protective factors for diabetic retinopathy in Type 2 diabetes mellitus patients: long duration of no less than 10 years. J Diabetes Complic (2019.0) 33 107383. DOI: 10.1016/j.jdiacomp.2019.05.014 52. Ding Y, Zhao J, Liu G, Li Y, Jiang J, Meng Y, Xu T, Wu K. Total bilirubin predicts severe progression of diabetic retinopathy and the possible causal mechanism. J Diabetes Res (2020.0) 2020 7219852. DOI: 10.1155/2020/7219852 53. Nader D, Syed H, David M, Andrews Neil P, Panza Julio A, Myron W, William S, Quyyumi Arshed A. Contribution of nitric oxide to reactive hyperemia. Hypertension (1998.0) 32 9. DOI: 10.1161/01.HYP.32.1.9 54. Bonetti PO, Pumper GM, Higano ST, Holmes DR, Kuvin JT, Lerman A. Noninvasive identification of patients with early coronary atherosclerosis by assessment of digital reactive hyperemia. J Am Coll Cardiol (2004.0) 44 2137. DOI: 10.1016/j.jacc.2004.08.062 55. Ulrich F, Ning X, Huige L. Roles of vascular oxidative stress and nitric oxide in the pathogenesis of atherosclerosis. Circ Res (2017.0) 120 713. DOI: 10.1161/CIRCRESAHA.116.309326 56. Motozato K, Suematsu Y, Norimatsu K, Kusumoto T, Miura SI. Reactive Hyperemia index associated with atherosclerotic cardiovascular disease under treatment for lifestyle diseases. J Clin Med Res (2020.0) 12 293. DOI: 10.14740/jocmr4100
--- title: A community-based study of dental fluorosis in rural children (6–12 years) from an aspirational district in Karnataka, India authors: - U. Venkateswara Prasad - Phaniraj Vastrad - Chandan N. - Manish J. Barvaliya - Rahul Kirte - Sabarinath R. - Suman K. Ray - Ravichandran B. - Tapas Chakma - Manoj V. Murhekar - Subarna Roy journal: Frontiers in Public Health year: 2023 pmcid: PMC10060513 doi: 10.3389/fpubh.2023.1110777 license: CC BY 4.0 --- # A community-based study of dental fluorosis in rural children (6–12 years) from an aspirational district in Karnataka, India ## Abstract ### Objectives The present study was planned to estimate the prevalence of dental fluorosis in 6–12 years of children and its association with various drinking water sources, water, and urine fluoride levels among the subset of children under the umbrella of a larger study to address iodine deficiency disorders and iron deficiency anemia in 17 villages of Manvi and Devadurga talukas of Raichur district of Karnataka. ### Methods Analysis of subset of data and urine samples of children under the umbrella of a larger cross-sectional community-based study was conducted in 17 villages of Manvi and Devadurga taluks of Raichur district. House to house survey was carried out to collect data using a semi-structured questionnaire in ODK software. Demographic details, source of drinking water, clinical assessment of dental fluorosis, and height and weight measurements were performed by trained staff. Urine and water samples were collected for fluoride level estimation. The overall prevalence of dental fluorosis and its severity-wise prevalence were estimated. Association between dental fluorosis and age, gender, type of diet, source of drinking water, height for age, BMI for age, water fluoride level, and urine fluoride level were carried out using logistic regression analysis. ### Results The prevalence of dental fluorosis was $46.0\%$. Mild, moderate, and severe dental fluorosis was found in 37.9, 7.8, and $0.3\%$ of children. With the increasing age of participants, the odds of dental fluorosis were found to increase by 2–4 folds. The odds of having dental fluorosis were significantly increased with increasing water fluoride levels of 3 to 5 ppm [AOR = 3.147 (1.585–6.248); $$P \leq 0.001$$] in comparison with water fluoride levels of < 1 ppm. The similar trend was found with urine fluoride level > 4 ppm [AOR = 3.607 (1.861–6.990); $P \leq 0.001$]. As compared to river water, other sources of drinking water were significantly associated with higher odds of dental fluorosis. ### Conclusions Prevalence of dental fluorosis was high in 6 to 12 years due to overexposure of fluoride from drinking water. High water and urine fluoride levels in children indicate the chronic exposure to fluoride and suggest that the population is at high risk of developing chronic fluorosis. ## Introduction Dental fluorosis is a disorder of dental enamel due to repeated exposure to high fluoride concentrations during tooth development, resulting in enamel with lower mineral content and more porosity and it is common in pediatric individuals [1, 2]. Fluorosis ranges from inconspicuous white spots or streaks to heavy brown stains in middle of the teeth away from the gum margin and pitted enamel. Fluoride is considered to be double edged weapon and overexposure to fluoride is always dangerous. Fluorosis is prevalent in many regions of the world and is brought on mainly by excessive fluoride in drinking water [3]. The consumed fluoride remains in the human body for a long time, although about $80\%$ of it is eliminated in urine [4, 5]. Biomarkers of fluoride help to detect insufficient or excessive ingestion of fluoride. Urine fluoride concentration among the biomarkers of fluoride exposure is generally regarded as the best indicator as it can be collected non-invasively and also, the concentration of fluoride in urine reflects the burden of fluoride exposure from drinking water [6]. According to WHO recommendations from 1984, the ideal level of fluoride (F−) in drinking water should be kept below 1.5 ppm (1.5 mg/L) in tropical climates [7]. Similarly, according to the Bureau of Indian Standards (BIS), 1 ppm (1.0 mg/L) of fluoride is the maximum desired level in drinking water [8], however lesser the better. Dental fluorosis is a problem in 15 states of India with high fluoride levels in drinking water [9]. During the 11th five-year plan, the Government of India started the “National Programme for Prevention and Control of Fluorosis” (NPPCF) as a new health effort to deal with the fluorosis problem in the country [10]. Karnataka is one of the fluoride-endemic states in India, and several of its districts are said to have significant levels of fluoride in their groundwater [11]. According to NPPCF data, areas with a high prevalence of dental fluorosis include Mysore, Bellary, Chikkaballapur, Koppal, Davangere, Tumkur, Bagalkot, Bangalore (U), Bijapur, Raichur, Chitradurga, Gadag, Gulbarga, Hassan, Kolar, Mandya, Ramanagaram, and Shimoga [10]. A high prevalence of dental fluorosis is associated with high fluoride concentration in drinking water and urine. A higher level of urine fluoride is associated with high exposure to fluoride, and it may increase the severity of dental fluorosis in exposed persons [12]. Because the damage and changes to the bones and teeth caused by long-term exposure to high fluoride levels are permanent, fluorosis care puts a lot of focus on prevention, and health promotion. Raichur is located in the northeastern region of Karnataka State of India. In this area, groundwater is a significant source of drinking water. The prevalence of dental fluorosis in six villages of Raichur taluk and district was reported at $32.6\%$ in school children (5 to 10 years) a decade ago in 2011 [13]. The data on dental fluorosis and its association with sources of drinking water, and fluoride levels in water and urine are scarce in Karnataka, especially in Raichur. Identifying the magnitude of problem and for further guidance of taking remedial measures, this study was planned to estimate the prevalence of dental fluorosis in 6–12 years of children and to find out its association with various drinking water sources, water, and urine fluoride levels amongst the subset of children from a larger study of addressing the issues of iodine deficiency disorders and iron deficiency anemia in 17 villages of Manvi and Devadurga talukas of Raichur district of Karnataka. ## Study design and setting This study was conducted under the umbrella of a larger study to address iodine deficiency disorders and iron deficiency anemia at the Model Rural Health Research Unit (MRHRU), Raichur. The present community-based cross-sectional study was carried out in two of the most backward taluks of the aspirational district (Raichur) i.e., Manvi and Devadurga (Figure 1). There are a total of 17 Primary health centers (PHCs) in these two taluks: 9 in Manvi and 8 in Devadurga. The village under each PHC location was included in the present study. Children aged 6 to 12 years residing in these villages whose parents consented to participate were included after taking their verbal assent. Children in whom dental fluorosis assessment was not possible due to extrinsic stains on the teeth were excluded. Figure 1: Geographice details of the study area. ## Sample size The clinical examination of fluorosis was carried out for all the children ($$n = 1$$,614) whereas, urine fluoride estimation was performed in 649 eligible (≥ 30 ml) samples after performing urine iodine estimation in 1,614 urine samples. ## Data collection and sampling method House to house survey was conducted and the households were visited by trained project staff along with Anganwadi/ ASHA workers of specific villages to conduct the study. The household was included in the study if it had children in the age group of 6–12 years and parents consented for their child to participate. In the case of a closed household, a household without a targeted study group and parents not consenting, the next household was contacted for participation in the study. If there were more than one child in the 6–12 years age group, the younger child was involved in the study and others were not. The data was collected in 2 months period (June and July 2021). Before starting the data collection, the project staff was trained for conducting the interview, height and weight measurement, clinical examination for dental fluorosis and its grading, sample collection process, and sample transportation by the experts. Interviews were conducted with the parent/guardian, and data on the age, gender, diet, and source of drinking water were collected. Height and weight were measured by project staff using a SECA® 213 portable stadiometer and SECA® 813 digital flat scale. Children were asked to rinse their mouths with water before the examination of teeth for dental fluorosis. Clinical examination for dental fluorosis was done by trained project staff, and dental fluorosis was categorized into four categories according to the classification described by Haimanot et al. [ 14]. The teeth were considered “normal” if there was no mottling and they were with a glazed white porcelain-like surface; teeth with white chalky opacities or patches on enamel with or without faint yellow lines were considered under the “mild fluorosis” category; distinct brown coloring of teeth was categorized as “moderate fluorosis”, and the presence of pitting or chipping of teeth was considered as “severe fluorosis”. The study flow has been depicted in Figure 2. Figure 2: Flow of the study. During the initial survey period, the supervisor randomly cross-checked $10\%$ of the examinations done by project staff to ensure the correct categorization of dental fluorosis. The spot urine sample was collected in a non-reactive plastic sterile container with two drops of toluene, and the container was tightly closed by the project staff. The sample was labeled and put in an ice box and later transported to the MRHRU, Sirwar. The samples were stored in a refrigerator under 2 to 8°C. After urine iodine estimation, the samples with ≥ 30 ml volumes were subjected to fluoride estimation. The samples were coded and the person who decided the eligibility of urine samples for fluoride estimation was unaware of clinical data on fluorosis. On completing the required interviews in a particular village, the main community water sources were identified from the analyzed responses, and 30 ml water samples were collected in a non-reactive plastic sterile container from all listed sources. The water samples were labeled and transported to MRHRU, Sirwar. Both urine and water samples were analyzed for fluoride levels. From MRHRU, urine and water samples were transported in a cold chain to ICMR-National Institute of Research in Tribal Health (NIRTH) Jabalpur, and ICMR-Regional Occupational Health Center (ROHC) Bengaluru, respectively, for fluoride analysis. ## Fluoride level estimation Fluoride ions in urine and water samples were measured using the procedures mentioned in the Orion instrument manual methods [15, 16]. The analysis method is based on the Ion selective method using the ORION fluoride electrode (Thermo Scientific Orion 5-star Benchtop multi-parameter). Before running the test samples, a calibration curve was obtained from lower to higher concentrations (i.e., 0.1 ppm, 1 ppm, and 10 ppm) using the standard solutions for standardization. After standardization of the ORION Ion selective electrode and Ion meter, collected urine and water samples were run for fluoride analysis. Each urine sample was added with a 9:1 ml ratio of TISAB III (Total Ionic Strength Adjustment Buffer III, Thermo Scientific, India) into a 50 ml Plastic beaker and mixed well. After immersing the electrode into the prepared mixed solution and stabilization, the fluoride concentration was measured by an ORION ion meter. The electrode was washed with distilled water and wiped with dry tissue paper in each sample to avoid cross-contamination. In a similar way, each water sample was added with TISAB buffer II used for water fluoride analysis. We followed the same procedure for the 649 urine and 62 water samples and noted down the results of fluoride levels. Internal quality control was carried out for every 10th sample run. The measurement precision was assessed with a known addition method in urine samples and the mean fluoride recovery was calculated. Water fluoride levels were categorized as < 1 ppm, 1 to 3 ppm, 3 to 5 ppm, and >5 ppm (NPPCF 2014). Urine fluoride levels were categorized as < 1 ppm, 1–2 ppm, 2–3 ppm, 3–4 ppm, and >4 ppm [12]. ## Ethical consideration Study documents were submitted for review and approval was taken from the Institutional Ethics Committee, Raichur Institute of Medical Science, Raichur for main large study and also, for subset analysis conducted in this manuscript (RIMS/IEC/Approval/Date 26-07-2021 and RIMS/IEC/Expedited Approval/ Date 23-11-2022 for No. $\frac{5}{7}$/1656/CH/Adhoc/2019-RBMCH). Informed consent was obtained from the parents of the children and verbal assent was taken from study participants. The permission to conduct the study was obtained from District and State Administration. ## Statistical analysis Data was collected using Open Data Kit (ODK) software using tablets and exported into a spreadsheet, and then analyzed using IBM Statistical Package for the Social Sciences (SPSS) V25. The prevalence of dental fluorosis was evaluated for 1,614 children whereas, the statistical analysis for an association was performed for 649 children. Descriptive statistics like frequency, percentages, mean, and standard deviation were used to describe the demographic data, water, and urine fluoride levels, prevalence of dental fluorosis, and grading of dental fluorosis. Height, weight, and BMI were converted into WHO Z scores by using WHO anthro plus software. The Chi-square test was used to find an association between age, gender, source of drinking water, type of diet, height for age, BMI for age, and urine fluoride levels with dental fluorosis grading. Bivariate and multivariate logistic regression was used to find an association between dental fluorosis with associated factors. The level of significance was kept at 0.05. ## Results The clinical evaluation was performed for 1,614 participants; while 649 urine samples and 62 water samples were analyzed. Amongst 1,614 children, 17.7, 14.4, 13.9, 14.1, 15.0, 11.5, and $13.4\%$ were aged 6, 7, 8, 9, 10, 11, and 12 years, respectively. Females ($53.3\%$) were more in comparison with males ($46.7\%$). The prevalence of dental fluorosis was $46\%$ in the study population (Table 1). The majority of cases of dental fluorosis were categorized into mild fluorosis ($37.9\%$), with moderate $7.8\%$ and severe fluorosis as $0.3\%$ (Figure 3). Images of study participants with different clinical grading have been shown in Figure 4. Village-wise prevalence of dental fluorosis is shown in Table 2. Amongst 649 children whose urine samples were subjected to fluoride estimation, the majority were 6 ($16.8\%$), 7 ($16.2\%$), and 10 years ($16.0\%$) old with a male-female ratio of 0.98. Of these, 546 ($84.1\%$) children's diet type was predominantly non-vegetarian. Tap water ($66.9\%$) was the most common source of drinking water, followed by community filters ($21.7\%$), river or pond water ($6.3\%$), and bore well ($5.1\%$). Dental fluorosis based on clinical examination was present in 372 ($57.3\%$) children out of 649 children. Urine fluoride levels of < 1, 1–2, 2–3, 3–4, and > 4 PPM were present in 121 ($18.6\%$), 224 ($34.7\%$), 139 ($21.4\%$), 70 ($10.8\%$), and 95 ($14.6\%$) study participants, respectively. 441 ($68.4\%$) children were having normal height, whereas 140 ($21.6\%$), 56 ($8.6\%$), and 08 ($1.2\%$) children were stunted, severely stunted, and tall, respectively. On BMI for age WHO Z score assessment, 387 ($59.6\%$) children had normal BMI; 159 ($24.5\%$), 86 ($13.3\%$), 07 ($1.1\%$), and 06 ($0.9\%$) children were thin, severely thin, overweight, and obese, respectively (Table 3). As shown in Table 3, age (χ2 = 40.678, df = 18, $$P \leq 0.002$$), source of drinking water (χ2 = 30.170, df = 9, $p \leq 0.001$) and urine fluoride levels (χ2= 37.181, df = 12, $p \leq 0.001$) were statistically significantly associated with dental fluorosis and its severity. Table 3 \| Variables \| Dental fluorosis \| Dental fluorosis.1 \| Dental fluorosis.2 \| Dental fluorosis.3 \| Dental fluorosis.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Absent [n (%)] \| Mild [n (%)] \| Moderate [n (%)] \| Severe [n (%)] \| Measure of association \| \| Age (n = 649) \| Age (n = 649) \| Age (n = 649) \| Age (n = 649) \| Age (n = 649) \| Age (n = 649) \| \| 6 (n = 109) \| 85 (78.0) \| 21 (19.3) \| 03 (2.7) \| 0 \| χ2= 40.678; df = 18; P = 0.002 \| \| 7 (n = 105) \| 68 (64.8) \| 32 (30.5) \| 05 (4.7) \| 0 \| χ2= 40.678; df = 18; P = 0.002 \| \| 8 (n = 87) \| 49 (56.3) \| 29 (33.3) \| 08 (9.3) \| 01 (1.1) \| χ2= 40.678; df = 18; P = 0.002 \| \| 9 (n = 87) \| 44 (50.6) \| 36 (41.4) \| 07 (8.0) \| 0 \| χ2= 40.678; df = 18; P = 0.002 \| \| 10 (n = 104) \| 49 (47.1) \| 48 (46.2) \| 07 (6.7) \| 0 \| χ2= 40.678; df = 18; P = 0.002 \| \| 11 (n = 70) \| 34 (48.6) \| 32 (45.7) \| 04 (5.7) \| 0 \| χ2= 40.678; df = 18; P = 0.002 \| \| 12 (n = 87) \| 43 (49.5) \| 37 (42.5) \| 07 (8.0) \| 0 \| χ2= 40.678; df = 18; P = 0.002 \| \| Gender (n = 649) \| Gender (n = 649) \| Gender (n = 649) \| Gender (n = 649) \| Gender (n = 649) \| Gender (n = 649) \| \| Male (n = 328) \| 179 (54.6) \| 128 (39.0) \| 20 (6.1) \| 01 (0.3) \| χ2= 3.353; df = 3; P = 0.340 \| \| Female (n = 321) \| 193 (60.2) \| 107 (33.3) \| 21 (6.5) \| 0 \| χ2= 3.353; df = 3; P = 0.340 \| \| Type of diet (n = 649) \| Type of diet (n = 649) \| Type of diet (n = 649) \| Type of diet (n = 649) \| Type of diet (n = 649) \| Type of diet (n = 649) \| \| Vegetarian (n = 103) \| 56 (54.4) \| 39 (37.8) \| 08 (7.8) \| 0 \| χ2= 0.874; df = 3; P = 0.832 \| \| Mixed (n = 546) \| 316 (57.9) \| 196 (35.9) \| 33 (6.0) \| 01 (0.2) \| χ2= 0.874; df = 3; P = 0.832 \| \| Source of drinking water [as per participants' response] (n = 649) \| Source of drinking water [as per participants' response] (n = 649) \| Source of drinking water [as per participants' response] (n = 649) \| Source of drinking water [as per participants' response] (n = 649) \| Source of drinking water [as per participants' response] (n = 649) \| Source of drinking water [as per participants' response] (n = 649) \| \| Borewell water (n = 33) \| 20 (60.6) \| 12 (36.4) \| 01 (3.0) \| 0 \| χ2= 30.170; df = 9; P < 0.001 \| \| River water (n = 43) \| 39 (90.7) \| 04 (9.3) \| 0 \| 0 \| χ2= 30.170; df = 9; P < 0.001 \| \| Reverse Osmosis (RO) filtered water (n = 139) \| 87 (62.6) \| 48 (34.5) \| 04 (2.9) \| 0 \| χ2= 30.170; df = 9; P < 0.001 \| \| Tap water (n = 434) \| 226 (52.1) \| 171 (39.4) \| 36 (8.3) \| 01 (0.02) \| χ2= 30.170; df = 9; P < 0.001 \| \| Height for age (WHO Z score) (n = 645) \| Height for age (WHO Z score) (n = 645) \| Height for age (WHO Z score) (n = 645) \| Height for age (WHO Z score) (n = 645) \| Height for age (WHO Z score) (n = 645) \| Height for age (WHO Z score) (n = 645) \| \| Severely stunted (< -3SD) n = 56 \| 32 (57.1) \| 21 (37.5) \| 03 (5.4) \| 0 \| χ2= 9.822; df = 9; P = 0.365 \| \| Stunted (−2SD to −3SD) n = 140 \| 78 (55.7) \| 48 (34.3) \| 14 (10.0) \| 0 \| χ2= 9.822; df = 9; P = 0.365 \| \| Normal (-2SD to +2SD) n = 441 \| 259 (58.8) \| 161 (36.5) \| 20 (4.5) \| 01 (0.2) \| χ2= 9.822; df = 9; P = 0.365 \| \| Tall (+2SD to +3SD) n = 8 \| 02 (25.0) \| 05 (62.5) \| 01 (12.5) \| 0 \| χ2= 9.822; df = 9; P = 0.365 \| \| BMI for age (WHO Z scores) (n = 645) \| BMI for age (WHO Z scores) (n = 645) \| BMI for age (WHO Z scores) (n = 645) \| BMI for age (WHO Z scores) (n = 645) \| BMI for age (WHO Z scores) (n = 645) \| BMI for age (WHO Z scores) (n = 645) \| \| Severe thinness (< -3SD) n = 86 \| 43 (50.0) \| 33 (38.4) \| 10 (11.6) \| 0 \| χ2= 14.04; df = 12; P = 0.298 \| \| Thinness (−2SD to −3SD) n = 159 \| 88 (55.4) \| 63 (39.6) \| 07 (4.4) \| 01 (0.06) \| χ2= 14.04; df = 12; P = 0.298 \| \| Normal (−2SD to +1SD) n = 387 \| 232 (59.9) \| 134 (34.7) \| 21 (5.4) \| 0 \| χ2= 14.04; df = 12; P = 0.298 \| \| Overweight (+1SD to +2SD) n = 7 \| 03 (42.9) \| 04 (57.1) \| 0 \| 0 \| χ2= 14.04; df = 12; P = 0.298 \| \| Obese (+2SD to +3SD) n = 6 \| 05 (83.3) \| 01 (16.7) \| 0 \| 0 \| χ2= 14.04; df = 12; P = 0.298 \| \| Urine fluoride level (n = 649) \| Urine fluoride level (n = 649) \| Urine fluoride level (n = 649) \| Urine fluoride level (n = 649) \| Urine fluoride level (n = 649) \| Urine fluoride level (n = 649) \| \| < 1 PPM (n = 121) \| 79 (65.3) \| 39 (32.2) \| 03 (2.5) \| 0 \| χ2 = 37.181; df = 12; P < 0.001 \| \| 1 to 2 PPM (n = 224) \| 141 (62.9) \| 73 (32.6) \| 10 (4.5) \| 0 \| χ2 = 37.181; df = 12; P < 0.001 \| \| 2 to 3 PPM (n = 139) \| 82 (59.0) \| 44 (31.6) \| 13 (9.4) \| 0 \| χ2 = 37.181; df = 12; P < 0.001 \| \| 3 to 4 PPM (n = 70) \| 37 (52.9) \| 29 (41.4) \| 04 (5.7) \| 0 \| χ2 = 37.181; df = 12; P < 0.001 \| \| > 4 PPM (n = 95) \| 33 (34.7) \| 50 (52.6) \| 11 (11.6) \| 1 (1.1) \| χ2 = 37.181; df = 12; P < 0.001 \| As shown in Table 4, with the increasing age of participants, the odds of dental fluorosis were found to increase by 2–4 folds. The odds of having dental fluorosis were significantly increased with increasing water fluoride levels of 3 to 5 PPM [AOR = 3.147 (1.585–6.248); $$P \leq 0.001$$] in comparison with water fluoride levels of < 1 PPM. The similar trend was found with urine fluoride level>4 PPM [AOR = 3.607 (1.861–6.990); $P \leq 0.001$]. Gender, type of diet, height for age and BMI for the age of study participants were not found to be associated ($p \leq 0.05$) with dental fluorosis. Tap water as a source of drinking water was found most frequently associated with dental fluorosis. Mean and standard deviations of water fluoride levels in samples collected from bore well water, river/pond water, RO filter water and tap water from each village is depicted in Table 5. ## Discussion The present study estimated the prevalence of dental fluorosis to be $46\%$ among 6 to 12 years of children in villages of two taluks of Raichur district of Karnataka. The prevalence of dental fluorosis was found $41.73\%$ in Mysuru among 10 to 12 years old children [17], $64.9\%$ in 9 to 15 years children of Bagalkot [18], $64.3\%$ in 12 to 17 years children of Kolar [11], and $73\%$ in 3 to 17 years children of Vijaypura [19] districts of Karnataka in earlier studies. The prevalence of dental fluorosis has increased in school children of the Raichur district from 32.6 to $46\%$ in the last decade. The difference in the prevalence of various regions of Karnataka may be attributed more to the wide range of age groups included in these studies and less to the variation in fluoride levels in drinking water. The increasing prevalence in the Raichur district requires immediate attention for improving the dental health of the children. Amongst all children affected by fluorosis, most of them were having mild ($37.9\%$) and moderate fluorosis ($7.8\%$); only $0.3\%$ of children had severe fluorosis in the present study. The prevalence of severe fluorosis was higher (8.1 to $18.6\%$) in other studies [9, 18, 20] which may be due to variation between age groups included in the study. More advanced age group children were included in these studies as compared to 6 to 12 years children in the present study. In the present study, the prevalence of fluorosis was found to increase with the increase in the age of the study participants which may be due to the irreversibility of developed effects of dental fluorosis [21]. Fluoride exposure during the first 2 years of life is an important risk factor for the development of fluorosis in permanent central incisors [22] however, late erupting teeth have a higher risk of developing fluorosis up to the age of 8 years [23]. Most of the children who are at risk of developing dental fluorosis may develop it by 6–8 years and once developed fluorosis is irreversible. Hence, its prevalence increases with increasing age. During the developmental period, the quantity and duration of fluoride ingestion determine the severity of dental fluorosis [9]. Fluoride exposure in children mainly occurs due to drinking water with high fluoride levels, certain foods, and fluoridated toothpaste if ingested frequently by them. Drinking water is the main cause of fluoride exposure. Fluoride is primarily absorbed from drinking water, through the digestive tract and enters the body, and persists as hydrofluoric acid. Also, fluoride may enter the human body through other dietary products as well which have different fluoride ingestion patterns [24]. Fluoride accumulates mainly in mineralized tissues like teeth and bones [24]. Due to excess fluoride in water dental mottling occurs mostly in permanent teeth and is clearly visible in children above 5 years of age [11]. In the present study, the odds of fluorosis events were found significantly higher with increasing water and urine fluoride levels (Table 4). Moreover, water fluoride levels of >1 ppm were found in 22 water samples from bore wells, 4 samples from tap water, and 2 samples from community RO-filtered water. Excessive fluoride ingestion leads to higher urinary fluoride excretion. The findings of the present study with water and urinary fluoride levels confirm that the exposure to excessive fluoride in the study participants was from drinking water. There was more frequency of dental fluorosis in children whose major source of drinking water was tap water, community filters, and borewell water whereas, it was less frequent with children who had river/pond water as a drinking water source. In this study, fluoride level was found less in river/pond water than in all other listed sources. Moreover, there was a significant association between water fluoride levels and dental fluorosis in the study area that confirms high fluoride levels in the water as a cause of dental fluorosis in our study population. It is further strengthened by the significant association of urine fluoride excretion with dental fluorosis. The type of diet (Veg vs. Non-veg) taken by children did not affect the occurrence of dental fluorosis. Increasing age was associated with increased odds of having dental fluorosis in this study. Similar findings were found in studies conducted by Bhagavatula et al. [ 23], Dong et al. [ 25], Shruthi et al. [ 26], and Saldarriaga et al. [ 27]. In study area, the community filter plants have been installed recently and many households also started using them as the source of drinking water that might have resulted in less fluoride exposure to younger children as compared to older children. In this study, according to height for age (WHO Z scores), $30.2\%$ of children were stunted, and on BMI for age WHO Z score assessment, $37.8\%$ were underweight and $2.8\%$ were overweight/obese. However, an association of dental fluorosis with height and BMI was found non-significant, whereas Mahantesha et al. [ 18] found the effect of nutritional status on the severity of fluorosis, with malnourished children being more affected by severe fluorosis. Fluoride exposure can affect the nutritional status and cognitive development of children. In China, low to moderate fluoride exposure was found to be associated with overweight and obesity among 7 to 13 years of children [28]. Excess fluoride exposure may negatively affect the BMI [29]. The results of a systematic review conducted by Choi et al. [ 30] supported the possibility of adverse neurodevelopment in children due to excessive fluoride exposure. In the present study, we did not evaluate the intelligence quotient/performance of children. As the effect of excess fluoride exposure are not limited to dental fluorosis and the effects once developed are irreversible, more focus should be given to preventive strategies. Educating the communities about the ill effects of excessive fluoride in water, empowering them to measure fluoride easily in drinking water, and de-fluoridation of drinking water or diluting the high fluoride water by mixing it with the rainwater are the important steps that may be helpful in prevention. Further, parents of children should be made aware of correct brushing habits with fluorinated toothpaste, which makes sure that children < 6 years of age do not swallow toothpaste while brushing or brushing with non-fluoridated toothpaste. In a study conducted by Gupta et al. [ 21], administration of ascorbic acid, calcium, and vitamin D3 was found to improve dental, clinical, and skeletal fluorosis in early-grade disease. This treatment strategy may be useful in children during the early stage of fluorosis but, it should be monitored with their serum levels to prevent toxicities of ascorbic acid, calcium, and vitamin D3. The present study measured the prevalence of clinical dental fluorosis and confirmed it due to fluoride overexposure from drinking water through urine and water fluoride level estimations in an aspirational district of Karnataka. It also focused on association of malnutrition with dental fluorosis. There were a few limitations of the study. It was conducted as a sub-study of a large study where we could not evaluate the urinary fluoride level amongst all the children in whom fluorosis was clinically evaluated due to inadequate urine samples left for fluoride analysis after the completion of a primary analysis on those samples. Thus, the sample size of the study was therefore compromised. It was a cross-sectional study, so temporal association between duration and extent of fluoride exposure to development of dental fluorosis could not be established. In conclusion, the prevalence of dental fluorosis was high in 6 to 12 years children in Manvi and Devadurga taluks of Raichur district due to overexposure of fluoride from drinking water. High water and urine fluoride levels in children indicate the chronic exposure to fluoride and suggest that the population is at high risk of developing chronic fluorosis. The measures to provide safe drinking water with permitted fluoride limit should be taken along with creating awareness amongst community about high fluoride level in water and its harmful effects. Community based intervention of ascorbic acid, calcium and vitamin D3 can be planned and evaluated for long term effect of fluorosis. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Institutional Ethics Committee, Raichur Institute of Medical Science, Raichur. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. ## Author contributions UP and PV: project implementation, management, data collection, fluoride analysis, data entry, and first draft of manuscript. PV, CN, and MB: data review, analysis, and drafting and finalizing the manuscript. RK: project implementation, fieldwork management, and finalization of draft. SR: data acquisition through ODK, data management, and finalization of the manuscript. SRa: drafting and finalization of the manuscript. RB and TC: fluoride analysis, revision, and finalization of the manuscript. MM: fieldwork, administrative support, revision, and finalization of the manuscript. SRo: conceptualization, overall project management, field work monitoring, and drafting and finalization of manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Den Besten P, Li W. Chronic fluoride toxicity: dental fluorosis. Monogr Oral Sci. (2011) 22 81-96. DOI: 10.1159/000327028 2. Gupta A, Dhingra R, Chaudhuri P, Gupta A. A comparison of various minimally invasive techniques for the removal of dental fluorosis stains in children. J Indian Soc PedodPrev Dent. (2017) 35 260-8. DOI: 10.4103/JISPPD.JISPPD_138_16 3. Fawell J, Bailey K, Chilton J, Dahi E, Fewtrell L, Magara Y. Human Health Effects: Fluoride in Drinking Water WHO Drinking Water Quality Series (2006) 29-35 4. 4.World Health Organization. Trace Elements in Human Nutrition and Health. Geneva: World Health Organization (1996).. Trace Elements in Human Nutrition and Health (1996) 5. Majumdar KK. Dental fluorosis and fluoride in urine in school students living in a rural area of West Bengal, India. Int J Res Rev. (2019) 6 59-66 6. Del Carmen AF, Javier FH, Aline CC. Dental fluorosis, fluoride in urine, and nutritional status in adolescent students living in the rural areas of Guanajuato, Mexico. J Int Soc Prev Community Dent. (2016) 6 517-22. DOI: 10.4103/2231-0762.195510 7. 7.World Health Organization. Guidelines for Drinking Water Quality: Health Criteria and Supporting Information. Geneva: World Health Organization (2011).. Guidelines for Drinking Water Quality: Health Criteria and Supporting Information (2011) 8. 8.Indian Standard Drinking Water — Specification,. (2022). Available online at: http://cgwb.gov.in/Documents/WQ-standards.pdf (accessed October 12, 2022).. Indian Standard Drinking Water — Specification,. (2022) 9. Shyam R, Manjunath B C, Kumar A, Narang R, Rani G, Singh S. Prevalence of dental fluorosis and treatment needs among 11-14 years old school children in endemic fluoride areas of Haryana, India. Indian J Dent Res. (2021) 32 110-4. DOI: 10.4103/ijdr.IJDR_835_18 10. 10.National Programme for Prevention Control of Fluorosis (NPPCF). (2022). Available online at: http://cghealth.nic.in/ehealth/2017/Instructions/NPPCFnewguidelinebyGOI.pdf (accessed October12, 2022).. (2022) 11. Verma A, Shetty BK, Guddattu V, Chourasia MK. Pundir P. High prevalence of dental fluorosis among adolescents is a growing concern: a school based cross-sectional study from Southern India. Environ Health Prev Med. (2017) 22 17. DOI: 10.1186/s12199-017-0624-9 12. Jarquín-Yañez L, de Jesús Mejía-Saavedra J, Molina-Frechero N, Gaona E, Rocha-Amador DO, López-Guzmán OD. Association between urine fluoride and dental fluorosis as a toxicity factor in a rural community in the state of San Luis Potosi. Sci World J. (2015) 2015 647184. DOI: 10.1155/2015/647184 13. Mane AB, Revathi S, Savale PG, Paul CN, Hiremath SG. Study of dental fluorosis among primary school children residing in rural area of Raichur District, Karnataka. Int J Biol Med Res. (2011) 2 716-20 14. Haimanot RT, Fekadu A, Bushra B. Endemic fluorosis in the Ethiopian Rift Valley. Trop Geogr Med. (1987) 39 209-17. PMID: 3433336 15. 15.NIOSH. Manual of Analytical Methods (NMAM) Fourth Edition. Fluoride in Urine: METHOD 8308, Issue 2. (1994). Available online at: https://www.cdc.gov/niosh/docs/2003-154/pdfs/8308.pdf (accessed November 29, 2022).. (1994) 16. 16.NIOSH. Manual of Analytical Methods (NMAM), Fourth Edition. FLUORIDES, Aerosol and Gas by ISE: METHOD 7902, Issue 2. (1994). Available online at: https://www.cdc.gov/niosh/docs/2003-154/pdfs/7902.pdf (accessed November 29, 2022).. (1994) 17. Sebastian ST, Soman RR, Sunitha S. Prevalence of dental fluorosis among primary school children in association with different water fluoride levels in Mysore district, Karnataka. Indian J Dent Res. (2016) 27 151-4. DOI: 10.4103/0970-9290.183126 18. Mahantesha T, Dixit UB, Nayakar RP, Ashwin D, Ramagoni NK, Kamavaram Ellore VP. Prevalence of dental fluorosis and associated risk factors in Bagalkot District, Karnataka, India. Int J Clin Pediatr Dent. (2016) 9 256-63. DOI: 10.5005/jp-journals-10005-1373 19. Ugran V, Desai NN, Chakraborti D, Masali KA, Mantur P, Kulkarni S. Groundwater fluoride contamination and its possible health implications in Indi taluk of Vijayapura District (Karnataka State), India. Environ Geochem Health. (2017) 39 1017-29. DOI: 10.1007/s10653-016-9869-2 20. Chaudhry M, Prabhakar I, Gupta B, Anand R, Sehrawat P, Thakar SS. Prevalence of dental fluorosis among adolescents in schools of greater Noida, Uttar Pradesh. J Indian Assoc Public Health Dent. (2017) 15 36-41. DOI: 10.4103/jiaphd.jiaphd_144_16 21. Gupta SK, Gupta RC, Seth AK, Gupta A. Reversal of fluorosis in children. Acta Paediatr Jpn. (1996) 38 513-39. DOI: 10.1111/j.1442-200X.1996.tb03536.x 22. Hong L, Levy SM, Broffitt B, Warren JJ, Kanellis MJ, Wefel JS. Timing of fluoride intake in relation to development of fluorosis on maxillary central incisors. Commun Dent Oral Epidemiol. (2006) 34 299-309. DOI: 10.1111/j.1600-0528.2006.00281.x 23. Bhagavatula P, Levy SM, Broffitt B, Weber-Gasparoni K, Warren JJ. Timing of fluoride intake and dental fluorosis on late-erupting permanent teeth. Commun Dent Oral Epidemiol. (2016) 44 32-45. DOI: 10.1111/cdoe.12187 24. Singh B, Gaur S, Garg VK. Fluoride in drinking water and human urine in Southern Haryana, India. J Hazard Mater. (2007) 144 147-51. DOI: 10.1016/j.jhazmat.2006.10.010 25. Dong H, Yang X, Zhang S, Wang X, Guo C, Zhang X. Associations of low level of fluoride exposure with dental fluorosis among U.S. children and adolescents, NHANES 2015–2016.. Ecotoxicol Environ Safety (2021) 221 112439. DOI: 10.1016/j.ecoenv.2021.112439 26. Narayanamurthy S, Santhuram AN. Prevalence of dental fluorosis in school children of Bangarpet taluk, Kolar district. J Orofac Sci. (2013) 5 105-8. DOI: 10.4103/0975-8844.124253 27. Saldarriaga A, Rojas-Gualdrón DF, Restrepo M, Bussaneli DG, Fragelli C, de Cássia Loiola Cordeiro R. Clinical changes in the severity of dental fluorosis: a longitudinal evaluation. BMC Oral Health. (2021) 21 366. DOI: 10.1186/s12903-021-01729-3 28. Liu L, Wang M, Li Y, Liu H, Hou C, Zeng Q. Low-to-moderate fluoride exposure in relation to overweight and obesity among school-age children in China. Ecotoxicol Environ Saf. (2019) 183 109558. DOI: 10.1016/j.ecoenv.2019.109558 29. Das K. Mondal NK. Dental fluorosis and urinary fluoride concentration as a reflection of fluoride exposure and its impact on IQ level and BMI of children of Laxmisagar, Simlapal Block of Bankura District, WB, India. Environ Monit Assess. (2016) 188 218. DOI: 10.1007/s10661-016-5219-1 30. Choi AL, Sun G, Zhang Y, Grandjean P. Developmental fluoride neurotoxicity: a systematic review and meta-analysis. Environ Health Perspect. (2012) 120 1362-8. DOI: 10.1289/ehp.1104912
--- title: 'The effect of systemic iron status on osteoarthritis: A mendelian randomization study' authors: - Guangfeng Ruan - Yi Ying - Shilong Lu - Zhaohua Zhu - Shibo Chen - Muhui Zeng - Ming Lu - Song Xue - Jianwei Zhu - Peihua Cao - Tianyu Chen - Xiaoshuai Wang - Shengfa Li - Jia Li - Yu Liu - Yanqi Liu - Yan Zhang - Changhai Ding journal: Frontiers in Genetics year: 2023 pmcid: PMC10060517 doi: 10.3389/fgene.2023.1122955 license: CC BY 4.0 --- # The effect of systemic iron status on osteoarthritis: A mendelian randomization study ## Abstract Objective: To assess the causal effect of systemic iron status by using four biomarkers (serum iron; transferrin saturation; ferritin; total iron-binding capacity) on knee osteoarthritis (OA), hip OA, total knee replacement, and total hip replacement using 2-sample Mendelian randomization (MR) design. Methods: Three instrument sets were used to construct the genetic instruments for the iron status: Liberal instruments (variants associated with one of the iron biomarkers), sensitivity instruments (liberal instruments exclude variants associated with potential confounders), and conservative instruments (variants associated with all four iron biomarkers). Summary-level data for four OA phenotypes, including knee OA, hip OA, total knee replacement, and total hip replacement were obtained from the largest genome-wide meta-analysis with 826,690 individuals. Inverse-variance weighted based on the random-effect model as the main approach was conducted. Weighted median, MR-Egger, and Mendelian randomization pleiotropy residual sum and outlier methods were used as sensitivity MR approaches. Results: Based on liberal instruments, genetically predicted serum iron and transferrin saturation were significantly associated with hip OA and total hip replacement, but not with knee OA and total knee replacement. Statistical evidence of heterogeneity across the MR estimates indicated that mutation rs1800562 was the SNP significantly associated with hip OA in serum iron (odds ratio, OR = 1.48), transferrin saturation (OR = 1.57), ferritin (OR = 2.24), and total-iron binding capacity (OR = 0.79), and hip replacement in serum iron (OR = 1.45), transferrin saturation (OR = 1.25), ferritin (OR = 1.37), and total-iron binding capacity (OR = 0.80). Conclusion: Our study suggests that high iron status might be a causal factor of hip OA and total hip replacement where rs1800562 is the main contributor. ## Introduction Osteoarthritis (OA) is a common joint disease characterized by loss of articular cartilage, remodeling of the synovitis, and alterations of periarticular structures (Castañeda and Vicente, 2017). Any joint can develop OA, but symptoms linked to OA most commonly affect the knees, hips, hands, and feet (Katz et al., 2021). It is reported that persons with knee or hip OA have excess mortality compared with age-matched controls (Nüesch et al., 2011; Katz et al., 2021). Currently, there is no curative drug for OA. According to the estimation of the World Health Organization, there are about 300 million OA patients worldwide, and the prevalence of OA can reach up to $10\%$–$20\%$ due to the increasing obese and longevous population (GBD, 2017 Disease and Injury Incidence and Prevalence Collaborators, 2018). Given the high health economic burden, a better understanding of the risk factors associated with the occurrence of OA, especially modifiable factors, is needed. Iron is an essential mineral to various biochemical processes, including DNA synthesis, ATP generation, and oxygen transport, where its absence or depletion can cause abnormal metabolization (Abbaspour et al., 2014). However, excess iron can also be toxic as it would be deposited into organs forming free radicals (Abbaspour et al., 2014). Therefore, disorders of iron homeostasis are involved in a wide scope of diseases (Bogdan et al., 2016). In humans, systemic iron status can be measured by clinical biomarkers: Serum iron, transferrin saturation, ferritin, and total iron-binding capacity (Bell et al., 2021). High serum iron, transferrin saturation, and ferritin signify high iron, while high total iron-binding capacity signifies low iron (Winter et al., 2014). Increasing evidence has found that systemic iron status is associated with OA. For example, high synovial iron was associated with a faster progression of OA in a murine model (Camacho et al., 2016). In vitro, iron overload was found to have detrimental effects on various joint components, such as synovium, cartilage, and subchondral bone, leading to synovial hyperplasia and inflammation, abnormal osteoblast function, and chondrocyte apoptosis (van Vulpen et al., 2015). Besides, some epidemiological evidence has shown the relationship between iron and OA risk. In a 2-year longitudinal study with 127 OA patients, radiographic findings showed that higher ferritin was significantly associated with narrower baseline joint space width and higher risks in the prediction of Kellgren-Lawrence grade severity (Kennish et al., 2014). Among 40 subjects with symptomatic knee OA, elevated serum ferritin levels were found to be associated with faster progression of cartilage damage assessed by arthroscopy (Nugzar et al., 2018). However, whether high iron status is a causal factor of OA is unclear because of the inherent defects of observational studies, such as residual confounding and reverse causality. Mendelian randomization (MR) is a study design that can strengthen the causal inference on exposure-outcome relationships by minimizing the effect of confounding and excluding the potential reverse causality based on the use of genetic variants as instruments (Sheehan et al., 2008). The rationale of the causality assessment in MR is that genetic variants solely associated with the exposure are randomly assorted so that genetic effects on the outcome cannot be affected by potential confounders. Also, genetic variants are not modified by the occurrence or development of any diseases where reverse causality is impossible. For these reasons, MR represents robust indirect evidence of a causal relationship between exposure and outcome if any effect of the selected instruments on diseases is entirely mediated through the exposure (Sheehan et al., 2008). Therefore, we conducted a 2-sample MR (Lawlor, 2016) study to examine the causal effect of four clinical iron biomarkers on four OA phenotypes, including knee OA, hip OA, total knee replacement, and total hip replacement. ## Materials and methods A 2-sample MR study was conducted to investigate the potential causal relationship between systemic iron status and knee OA, hip OA, total knee replacement, and total hip replacement by using summary-level data. Systemic iron status was comprehensively represented by serum iron, transferrin saturation, ferritin, and total iron-binding capacity. Figure 1 showed the overview of the exposures, the outcomes, and the three assumptions of the genetic instruments for the MR design. Since this study is based on existing publications and public databases, both ethical approval and participant consent have been received by each relevant institutional review committee. FIGURE 1: An overview of the Mendelian randomization design. Assumption 1: The instrumental variables must be strongly associated with the exposures; Assumption 2: The instrumental variables must be independent of the potential confounders of the association between the exposure and outcome; Assumption 3: The instrumental variables should not be associated with the outcomes directly. ## Genetic instruments for four clinical iron biomarkers We constructed three sets of genetic instruments for the clinical iron biomarkers: Liberal instruments, sensitivity instruments, and conservative instruments. From the recent meta-analysis of three genome-wide association studies for iron homeostasis biomarkers: serum iron ($$n = 163$$,511), transferrin saturation ($$n = 131$$,471), ferritin ($$n = 246$$,139), and total iron-binding capacity ($$n = 135$$,430) (Bell et al., 2021), we selected single nucleotide polymorphisms (SNPs) at the genome-wide significance threshold ($p \leq 5$ × 10−8) and in low linkage disequilibrium (r2 < 0.01) as the potential instruments. After excluding five SNPs that are not in our outcome database because their minor allele frequency within the 1000G dataset is 0 (Supplementary Tables S1–S4), 14 SNPs for serum iron, 10 SNPs for transferrin saturation, 37 SNPs for ferritin, and 15 SNPs for total iron-binding capacity were left as the liberal instruments. To avoid the violation of assumption two of the instruments (Figure 1), we searched the Phenoscanner database (http://www.phenoscanner.medschl.cam.ac.uk/) to identify whether these liberal SNPs were associated with potential confounding factors. As interleukin 6, C-reactive protein, glycosylated hemoglobin, cholesterol, and body mass index are potential risk factors of OA (Livshits et al., 2009; Louati et al., 2015; Zheng and Chen, 2015; Farnaghi et al., 2017; Kozijn et al., 2019), we further excluded SNPs that were strongly associated with these phenotypes ($p \leq 5$ × 10−8) leaving 12 SNPs for serum iron, nine SNPs for transferrin saturation, 27 SNPs for ferritin, and 14 SNPs for total iron-binding capacity as the sensitivity instruments (Supplementary Tables S1–S4). We did not exclude rs855791 from TMPRSS6, rs1799945 from HFE, and rs1800562 from HFE here since they were missense mutations of TMPRSS6 or HFE. While HFE and TMPRSS6 involve the signaling cascade of iron homeostasis directly (Bell et al., 2021), these three SNPs may have a vertical effect from iron to the potential confounding factors, namely glycosylated hemoglobin, low-density lipoprotein, and total cholesterol mentioned in the Supplementary Tables. Thus, these three SNPs may have vertical pleiotropy with the possible confounding factors which does not violate assumption two of MR (Davies et al., 2018) (Figure 1). Similar instrument selection criteria are applied to evade excluding vertical SNPs (Chong et al., 2022). Conservative instruments were constructed where only SNPs associated with all four clinical iron biomarkers were eligible; therefore, rs1800562, rs1799945, rs855791, and rs57659670 were used as conservative instruments. Because the MR estimates were for each iron biomarker, we set the liberal instruments that have greater power as the main instrument. The strength of the instruments was evaluated by the F statistic using the formula F = R2(N-2)/(1-R2) (Palmer et al., 2012), where R2 is the proportion of the variance of the four clinical iron biomarkers explained by the genetic variant and N is the sample size of the gene-each of the iron biomarker association. Thus, the computed F statistics range from 5136 to 6388 for serum iron, from 7012 to 8489 for transferrin saturation, from 1644 to 5844 for ferritin, and from 4206 to 9724 for total iron-binding capacity which was much greater than 10 demonstrating sufficient statistical strength (Burgess et al., 2013). ## Genetic associations with four OA phenotypes To obtain the associations of the genetic instruments with OA, summary-level data were extracted from the largest genome-wide meta-analysis to date across 826,690 individuals with 177,517 cases and 649,173 controls. The mean age (standard deviation) is 62.4 (11.9) for the cases and 52.4 (17.4) for the controls. The percentage of females is $62\%$ for cases and $52\%$ for controls. The meta-analysis was a combination of data from 13 cohorts with a population of >$97\%$ European descent (Boer et al., 2021). In this study, data from knee OA ($$n = 62$$,497), hip OA ($$n = 36$$,445), total knee replacement ($$n = 18$$,200), total hip replacement ($$n = 23$$,021), and a max of healthy controls ($$n = 333$$,557) were used (Boer et al., 2021). Knee (or hip) OA was defined as OA or joint replacement at the knee joint (or hip joint). Total knee (or hip) replacement was defined as having undergone total knee (or hip) replacement due to OA in the knee joint (or hip joint). All the definitions of the cases were self-reported, clinically diagnosed, ICD10, codes, or radiographic depending on the data available in the cohort. Controls were osteoarthritis-free or population-based with or without ICD code exclusions. Detailed association estimates for the genetic instruments with the four OA phenotypes were provided in Supplementary Tables S1–S4. ## MR estimates To derive MR estimates, the inverse-variance weighted method (IVW) was used as the main approach. This method first assesses the effect of each SNP on the outcome by calculating the Wald ratio and then uses the inverse variance of SNPs as weights to obtain a combined causal effect (Lee et al., 2016). Random-effects model was selected for this method because the fixed-effects models may be at risk of yielding artificially precise estimates in the presence of heterogeneity (Burgess et al., 2017). Furthermore, heterogeneity across the instrumental SNPs was evaluated using I2 statistics and Cochran’s Q test (Higgins et al., 2003). For each SNP, the MR estimates were obtained from the Wald ratio method with standard error derived using the Delta method (Thompson et al., 2016). The MR estimates are presented using odds ratios which are scaled to one standard deviation increment of genetically predicted clinical iron biomarkers. To investigate the robustness of the findings and assess the possible horizontal pleiotropy, we further performed the weighted median, MR-Egger, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) methods as sensitivity MR approaches. The weighted median method first orders the ratio estimate of each instrument SNP on the outcome by its magnitude of weight and then produces an overall MR estimate based on the median value with standard error derived using the parametric bootstrap method (Bowden et al., 2016). The MR-Egger method can detect the overall horizontal pleiotropy of genetic instruments by examining whether the intercept of the association between exposure and outcome differs from zero (Burgess and Thompson, 2017). However, the estimates of MR-Egger are generally small especially when the number of instruments is small (Burgess and Thompson, 2017), and thus, we did not use MR-Egger when using conservative instruments. MR-PRESSO identifies possible pleiotropic outliers and generates estimates after the outliers are removed (Verbanck et al., 2018). Moreover, a leave-one-out analysis was computed where we excluded one SNP at a time and conducted IVW on the rest SNPs to test whether any single variant was driving the causal association between exposure and outcome (Burgess et al., 2017). Because four OA outcomes and four biomarkers were involved in the analyses, we set a Bonferroni corrected p-value of 0.003 ($\frac{0.05}{16}$) as the statistical significance level. All statistical data analyses were conducted with R software. The MendelianRandomization and the forestplot packages were used to facilitate the MR analyses and display the results (Yavorska and Burgess, 2017). ## Repeated analysis in the second-largest meta-analysis of OA GWAS We also repeated our analyses in the second-largest meta-analysis of OA GWAS (Tachmazidou et al., 2019). This meta-analysis was a combination of data from UK Biobank and Arthritis Research UK Osteoarthritis Genetics (arcOGEN) with 455,221 individuals of European descent only (Tachmazidou et al., 2019). Due to the large number of OA participants in these two cohorts, there are some overlapping participants between the largest and the second-largest meta-analysis of OA GWAS. As the summary data of total knee replacement and total hip replacement were not available in this meta-analysis, we only repeated our analyses in the knee OA ($$n = 24$$,955) and hip OA ($$n = 15$$,704) with healthy controls ($$n = 378$$,169) (Tachmazidou et al., 2019). The OA definitions were from self-report and hospital records in UK Biobank and were from total joint replacement records or radiographic evidence of disease in arcOGEN. ## Results Tables 1–4 show the associations of the four systemic iron status biomarkers and knee OA, hip OA, knee replacement, and hip replacement using the three instrument sets. In the IVW analyses using liberal instruments, genetically predicted serum iron and transferrin saturation were found to be significantly associated with hip OA and hip replacement, and the other associations were not evident (Tables 1–4; Figure 2). The odds ratios of hip OA were 1.18 ($95\%$ CI: 1.07–1.30) per one standard deviation increment in serum iron and 1.15 ($95\%$ CI: 1.07–1.24) in transferrin saturation (Tables 1–4; Figure 2). Similar results were seen in analyses using sensitivity instruments. In the analyses using conservative instruments, genetically predicted total iron-binding capacity was negatively associated with hip OA and hip replacement with ORs ranging from 0.80 to 0.82 (Tables 2, 4). However, statistical evidence of heterogeneity across the MR estimates was found especially for hip OA and hip replacement (Tables 2, 4; Figure 2). For example, in the MR estimates of ferritin on hip OA and hip replacement, the I2 of the heterogeneity was $64.8\%$–$91.1\%$. When we used the Wald ratio method to assess the MR estimates of four individual SNPs (rs57659670, rs855791, rs1799945, and rs1800562) with hip OA and hip replacement, rs1800562 was the SNP that was significantly associated with hip OA in serum iron (OR = 1.48; $95\%$ CI: 1.29-1.68), transferrin saturation (OR = 1.57; $95\%$ CI: 1.17-1.37), ferritin (OR = 2.24; $95\%$ CI: 1.71-2.94), and total-iron binding capacity (OR = 0.79; $95\%$ CI: 0.73-0.86), and hip replacement in serum iron (OR = 1.45; $95\%$ CI: 1.25-1.69), transferrin saturation (OR = 1.25; $95\%$ CI: 1.14-1.37), ferritin (OR = 1.37; $95\%$ CI: 1.58-2.99), and total-iron binding capacity (OR = 0.80; $95\%$ CI: 0.73-0.88). The other three SNPs did not show evident associations with hip OA or hip replacement (data not shown). Also, the leave-one-out analyses demonstrated that the causal effect of systemic iron status on hip OA and total hip replacement was mainly resulted from rs1800562 (Supplementary Table S5). We repeated the MR estimates of the four systemic iron status biomarkers on knee OA and hip OA in the second-largest meta-analysis of OA GWAS, and similar results were shown in Supplementary Table S6. That is, serum iron, transferrin saturation, and total iron-binding capacity were associated with hip OA, and the other associations were not evident. ## Discussion The present 2-sample MR study used the largest public dataset to date to comprehensively evaluate the causal role of systemic iron status for knee OA, hip OA, total knee replacement, and total hip replacement. In a pattern concordant with an effect on systemic iron status, we found that genetically predicted clinical iron biomarkers were significantly associated with hip OA and total hip replacement. Given the heterogeneity across the genetic instrumental variables, these associations were mainly resulted from rs1800562, a missense mutation of the HFE gene. Our findings are in line with previous conclusions from observational and experimental studies that iron is involved in the development of OA. In an observational study, synovial fluid iron concentrations determined by the colorimetric method were significantly higher in OA patients compared to the controls (Yazar et al., 2005). Another 2-year follow-up study reported that higher iron status was associated with more severe radiographic progression in knee OA patients (Kennish et al., 2014). In murine models, cellular iron accumulation in the knee joint resulted from systemic iron overload could induce the early onset of OA and accelerate OA progression via compromising chondrocyte metabolism and over-expressing local inflammatory mediators (Camacho et al., 2016; Simão et al., 2019; Burton et al., 2020). Several studies using the MR method also assessed the association between iron and OA, though the results were inconsistent. Using HFE genotype (rs1800562 & rs1799945) instrumented transferrin saturation as the iron load, Pilling et al. [ 2019] found that homozygotes of rs1800562 or compound heterozygotes of rs1800562 & rs1799945 were positively associated with incident OA in both men and women. Using rs800562, rs1799945, and rs855791 as the instruments of serum iron, no significant association was observed between iron levels and overall OA in UK Biobank (Zhou et al., 2020; Zhou et al., 2021), but per standard deviation increment in iron was associated with increased risk of OA in males (Zhou et al., 2021). Using rs800562, rs1799945, rs855791, and rs8177240 as the instruments of nutritional iron and summary data from the UK Biobank and arcOGEN cohorts, no significant association was observed between the iron and knee OA, hip OA, or overall OA, but the positive causal effect of iron levels on overall OA was observed in females (Qu et al., 2020). Using the instruments from the Genetics of Iron Status Consortium and the summary-level data of outcomes from the UK Biobank and arcOGEN cohorts, Xu et al. [ 2022] found that transferrin saturation was positively associated with both knee OA and hip OA, transferrin was negatively associated with hip OA, but serum iron and ferritin did not show a prominent effect on OA outcomes. In our study, we assessed the iron status using four biomarkers with three instrument sets while the previous studies used one biomarker with three or four instruments. Our study used the largest meta-analysis data for OA while the others included the relatively smaller size of OA patients. The inconsistencies between the previous studies with the present study can be partly explained by the different biomarkers with non-specific instruments and underpowered samples. Thus, the conclusion from our study may be more representative of the association between iron status and OA. Our findings only suggest causal associations among hip OA and total hip replacement, but not knee OA or total knee replacement. This could be explained by the fact that genetic heritability contributes more to hip OA than knee OA [$14.7\%$ for knee OA and $51.9\%$ for hip OA (Tachmazidou et al., 2019)], and the exposures in the MR study are genetically predicted. In the main analyses and the repeated analyses, we only found causal associations between hip OA & total hip replacement and serum iron, transferrin saturation, & total iron-binding capacity, but not ferritin. This can be partly explained by the heterogeneity among the variants in the analyses (I2 = $91\%$ in hip OA and I2 = $83\%$ in the total hip replacement). Another possible explanation for this is that ferritin is a representative biomarker of body iron stores in a non-inflammatory state (Adams, 2015; Bell et al., 2021), while OA is regarded as an inflammatory disease (Dainese et al., 2022), and thus, there might be a weak relationship between ferritin and OA. A potential concern for MR conclusions relates to the horizontal pleiotropy of the instruments. Our MR study does not have a such concern, because the findings were robust to analyses using sensitivity instruments that exclude variants associated with potential confounders. Though rs1800562 from the HFE gene is associated with potential confounders glycosylated hemoglobin, low-density lipoprotein, and total cholesterol, we did not exclude this variant in our analyses. This is because the HFE protein encoded by the HFE gene is the central regulator of systemic iron homeostasis and has no other well-established roles (Barton et al., 2015), any effect of the second phenotype may probably be acting downstream of iron status, rather than independent of it. Therefore, rs1800562 may have a vertical pleiotropy with the outcome which does not violate the assumption of MR (Davies et al., 2018). However, there is still a possibility that rs1800562 has other horizontal effects on the outcomes independent of iron. Besides the causal evidence of the MR relationships, the biological plausibility adds further support for the possibility of causality. Hereditary hemochromatosis is a common disease in Europeans characterized by iron overload in multiple organs where the mutation of rs1800562 is the main contributor (Husar-Memmer et al., 2014). It is reported that people with hemochromatosis are at increased risk of OA and OA is one of the most frequent complications in people with hemochromatosis (Elmberg et al., 2013; Husar-Memmer et al., 2014). The missense mutation rs1800562 from the HFE gene affects the synthesis of HFE protein which is associated with the regulation of the circulating iron by regulating the interaction of the transferrin receptor with transferrin. Studies showed that excess iron could act as a catalyst to produce a large number of reactive oxygen species which are deleterious agents involved in OA cartilage degradation (Henrotin et al., 2003; Yang et al., 2018). Also, evidence showed that iron homeostasis dysregulation could lead to the secretion of pro-inflammatory cytokines which promote chondrocytes apoptosis and subsequent OA cartilage degeneration (Simão et al., 2019; Jing et al., 2021). Since high iron status is a treatable condition, our findings have important clinical and public health implications. It is reported that phlebotomy, oral chelation, and dietary changes are options for people with iron overload (Palmer et al., 2018). There may be a benefit to the careful reduction of iron indices for reducing OA risk in people with high iron status, especially among hemochromatosis patients or people with the mutation rs1800562. However, the interpretation of the MR finding that iron decrement might be a benefit for OA patients requires justification, because MRs are the effects of lifelong exposures while interventions consider short-term pharmacological treatments. Taken together, iron decrement could be an attractive new target for hip OA treatment which needs to be validated in randomized controlled trials in the future. The major strength of the present study is the 2-sample MR design, which minimizes the confounding and reverse causality seen in observational studies. We comprehensively investigated the causation of the systemic iron status in four representative biomarkers to knee OA, hip OA, total knee replacement, and total hip replacement using the largest summary-level data. Also, we used three instrument sets to obtain robust conclusions. Furthermore, we repeated our analyses in the second largest OA GWAS dataset with European descent, thereby diminishing bias from population stratification. However, we must acknowledge some potential limitations. First, rs1800562 was the only SNP associated with OA which needs to be verified in another dataset independent from the UK Biobank study and the arcOGEN consortium. Second, the findings of our study relate to patterns of iron status largely within the normal range, and thus, cannot be used to make inferences on the effect of abnormally high or low serum iron levels. Third, our study does not offer insight into whether the estimates are equally applicable to both men and women. Despite these limitations, the results of this work show consistent and biologically plausible effects. In summary, the present MR findings suggest that high iron status might be a causal factor of hip OA and total hip replacement where rs1800562 is the main contributor. Given the modifiable nature of the iron status, further clinical trials are warranted to validate the therapeutic role of iron decrement in people with hip OA, especially among those with the mutation rs1800562. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/yzhang666666/IronMR_23022023. ## Ethics statement Since this study is based on existing publications and public databases, both ethical approval and participant consent have been received by each relevant institutional review committee. ## Author contributions Supervision and Writing (review & editing): CD; Supervision, Writing (review & editing), and Funding acquisition: YZ; Conceptualization, Writing (original draft), and Funding acquisition: GR; Writing (original draft): SL and YY; Investigation, Visualization, and Validation: ZZ, SC, MZ, and ML; Formal analysis and Methodology: SX, JZ, and PC; Resources and Writing (review & editing): TC, XW, SL, JL, YuL, and YaL. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1122955/full#supplementary-material ## References 1. Abbaspour N., Hurrell R., Kelishadi R.. Review on iron and its importance for human health. J. Res. Med. Sci. (2014) 19 164-174. PMID: 24778671 2. Adams P. C.. Epidemiology and diagnostic testing for hemochromatosis and iron overload. Int. J. Lab. Hematol. (2015) 37 25-30. DOI: 10.1111/ijlh.12347 3. Barton J. C., Edwards C. Q., Acton R. T.. HFE gene: Structure, function, mutations, and associated iron abnormalities. Gene (2015) 574 179-192. DOI: 10.1016/j.gene.2015.10.009 4. Bell S., Rigas A. S., Magnusson M. K., Ferkingstad E., Allara E., Bjornsdottir G.. A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis. Commun. Biol. (2021) 4 156. DOI: 10.1038/s42003-020-01575-z 5. Boer C. G., Hatzikotoulas K., Southam L., Stefansdottir L., Zhang Y., Coutinho de Almeida R.. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. (2021) 184 6003-6005. DOI: 10.1016/j.cell.2021.11.003 6. Bogdan A. R., Miyazawa M., Hashimoto K., Tsuji Y.. Regulators of iron homeostasis: New players in metabolism, cell death, and disease. Trends Biochem. Sci. (2016) 41 274-286. DOI: 10.1016/j.tibs.2015.11.012 7. Bowden J., Davey Smith G., Haycock P. C., Burgess S.. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. (2016) 40 304-314. DOI: 10.1002/gepi.21965 8. Burgess S., Bowden J., Fall T., Ingelsson E., Thompson S. G.. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiol. Camb. Mass) (2017) 28 30-42. DOI: 10.1097/ede.0000000000000559 9. Burgess S., Butterworth A., Thompson S. G.. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. (2013) 37 658-665. DOI: 10.1002/gepi.21758 10. Burgess S., Thompson S. G.. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. (2017) 32 377-389. DOI: 10.1007/s10654-017-0255-x 11. Burton L. H., Radakovich L. B., Marolf A. J., Santangelo K. S.. Systemic iron overload exacerbates osteoarthritis in the strain 13 Guinea pig. Osteoarthr. Cartil. (2020) 28 1265-1275. DOI: 10.1016/j.joca.2020.06.005 12. Camacho A., Simão M., Ea H. K., Cohen-Solal M., Richette P., Branco J.. Iron overload in a murine model of hereditary hemochromatosis is associated with accelerated progression of osteoarthritis under mechanical stress. Osteoarthr. Cartil. (2016) 24 494-502. DOI: 10.1016/j.joca.2015.09.007 13. Castañeda S., Vicente E. F.. Osteoarthritis: More than cartilage degeneration. Clin. Rev. Bone Mineral Metabolism (2017) 15 69-81. DOI: 10.1007/s12018-017-9228-6 14. Chong M., Mohammadi-Shemirani P., Perrot N., Nelson W., Morton R., Narula S.. GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia. eLife (2022) 11 e70382. DOI: 10.7554/eLife.70382 15. Dainese P., Wyngaert K. V., De Mits S., Wittoek R., Van Ginckel A., Calders P.. Association between knee inflammation and knee pain in patients with knee osteoarthritis: A systematic review. Osteoarthr. Cartil. (2022) 30 516-534. DOI: 10.1016/j.joca.2021.12.003 16. Davies N. M., Holmes M. V., Davey Smith G.. Reading mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ (2018) 362 k601. DOI: 10.1136/bmj.k601 17. Elmberg M., Hultcrantz R., Simard J. F., Carlsson A., Askling J.. Increased risk of arthropathies and joint replacement surgery in patients with genetic hemochromatosis: A study of 3,531 patients and their 11,794 first-degree relatives. Arthritis Care Res. Hob. (2013) 65 678-685. DOI: 10.1002/acr.21883 18. Farnaghi S., Crawford R., Xiao Y., Prasadam I.. Cholesterol metabolism in pathogenesis of osteoarthritis disease. Int. J. Rheum. Dis. (2017) 20 131-140. DOI: 10.1111/1756-185x.13061 19. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: A systematic analysis for the global burden of disease study 2017. Lancet (2018) 392 1789-1858. DOI: 10.1016/s0140-6736(18)32279-7 20. Henrotin Y. E., Bruckner P., Pujol J. P.. The role of reactive oxygen species in homeostasis and degradation of cartilage. Osteoarthr. Cartil. (2003) 11 747-755. DOI: 10.1016/s1063-4584(03)00150-x 21. Higgins J. P., Thompson S. G., Deeks J. J., Altman D. G.. Measuring inconsistency in meta-analyses. BMJ (2003) 327 557-560. DOI: 10.1136/bmj.327.7414.557 22. Husar-Memmer E., Stadlmayr A., Datz C., Zwerina J.. HFE-Related hemochromatosis: An update for the rheumatologist. Curr. Rheumatol. Rep. (2014) 16 393. DOI: 10.1007/s11926-013-0393-4 23. Jing X., Lin J., Du T., Jiang Z., Li T., Wang G.. Iron overload is associated with accelerated progression of osteoarthritis: The role of DMT1 mediated iron homeostasis. Front. Cell. Dev. Biol. (2021) 8 594509. DOI: 10.3389/fcell.2020.594509 24. Katz J. N., Arant K. R., Loeser R. F.. Diagnosis and treatment of hip and knee osteoarthritis: A review. Jama (2021) 325 568-578. DOI: 10.1001/jama.2020.22171 25. Kennish L., Attur M., Oh C., Krasnokutsky S., Samuels J., Greenberg J. D.. Age-dependent ferritin elevations and HFE C282Y mutation as risk factors for symptomatic knee osteoarthritis in males: A longitudinal cohort study. BMC Musculoskelet. Disord. (2014) 15 8. DOI: 10.1186/1471-2474-15-8 26. Kozijn A. E., Tartjiono M. T., Ravipati S., van der Ham F., Barrett D. A., Mastbergen S. C.. Human C-reactive protein aggravates osteoarthritis development in mice on a high-fat diet. Osteoarthr. Cartil. (2019) 27 118-128. DOI: 10.1016/j.joca.2018.09.007 27. Lawlor D. A.. Commentary: Two-sample mendelian randomization: Opportunities and challenges. Int. J. Epidemiol. (2016) 45 908-915. DOI: 10.1093/ije/dyw127 28. Lee C. H., Cook S., Lee J. S., Han B.. Comparison of two meta-analysis methods: Inverse-Variance-Weighted average and weighted sum of Z-scores. Genomics Inf. (2016) 14 173-180. DOI: 10.5808/gi.2016.14.4.173 29. Livshits G., Zhai G., Hart D. J., Kato B. S., Wang H., Williams F. M. K.. Interleukin-6 is a significant predictor of radiographic knee osteoarthritis: The Chingford Study. Arthritis Rheum. (2009) 60 2037-2045. DOI: 10.1002/art.24598 30. Louati K., Vidal C., Berenbaum F., Sellam J.. Association between diabetes mellitus and osteoarthritis: Systematic literature review and meta-analysis. RMD Open (2015) 1 e000077. DOI: 10.1136/rmdopen-2015-000077 31. Nüesch E., Dieppe P., Reichenbach S., Williams S., Iff S., Juni P.. All cause and disease specific mortality in patients with knee or hip osteoarthritis: Population based cohort study. BMJ (2011) 342 d1165. DOI: 10.1136/bmj.d1165 32. Nugzar O., Zandman-Goddard G., Oz H., Lakstein D., Feldbrin Z., Shargorodsky M.. The role of ferritin and adiponectin as predictors of cartilage damage assessed by arthroscopy in patients with symptomatic knee osteoarthritis. Best. Pract. Res. Clin. Rheumatol. (2018) 32 662-668. DOI: 10.1016/j.berh.2019.04.004 33. Palmer T. M., Lawlor D. A., Harbord R. M., Sheehan N. A., Tobias J. H., Timpson N. J.. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. methods Med. Res. (2012) 21 223-242. DOI: 10.1177/0962280210394459 34. Palmer W. C., Vishnu P., Sanchez W., Aqel B., Riegert-Johnson D., Seaman L. A. K.. Diagnosis and management of genetic iron overload disorders. J. Gen. Intern Med. (2018) 33 2230-2236. DOI: 10.1007/s11606-018-4669-2 35. Pilling L. C., Tamosauskaite J., Jones G., Wood A. R., Jones L., Kuo C. L.. Common conditions associated with hereditary haemochromatosis genetic variants: Cohort study in UK Biobank. Bmj (2019) 364 k5222. DOI: 10.1136/bmj.k5222 36. Qu Z., Yang F., Hong J., Wang W., Li S., Jiang G.. Causal relationship of serum nutritional factors with osteoarthritis: A mendelian randomization study. Rheumatol. Oxf. (2020) 60 2383-2390. DOI: 10.1093/rheumatology/keaa622 37. Sheehan N. A., Didelez V., Burton P. R., Tobin M. D.. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med. (2008) 5 e177. DOI: 10.1371/journal.pmed.0050177 38. Simão M., Gavaia P. J., Camacho A., Porto G., Pinto I. J., Ea H. K.. Intracellular iron uptake is favored in Hfe-KO mouse primary chondrocytes mimicking an osteoarthritis-related phenotype. Biofactors (2019) 45 583-597. DOI: 10.1002/biof.1520 39. Tachmazidou I., Hatzikotoulas K., Southam L., Esparza-Gordillo J., Haberland V., Zheng J.. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat. Genet. (2019) 51 230-236. DOI: 10.1038/s41588-018-0327-1 40. Thompson J. R., Minelli C., Del Greco M. F.. Mendelian randomization using public data from genetic consortia. Int. J. Biostat. (2016) 12. DOI: 10.1515/ijb-2015-0074 41. van Vulpen L. F., Roosendaal G., van Asbeck B. S., Mastbergen S. C., Lafeber F. P. J. G., Schutgens R. E. G.. The detrimental effects of iron on the joint: A comparison between haemochromatosis and haemophilia. J. Clin. Pathol. (2015) 68 592-600. DOI: 10.1136/jclinpath-2015-202967 42. Verbanck M., Chen C. Y., Neale B., Do R.. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. (2018) 50 693-698. DOI: 10.1038/s41588-018-0099-7 43. Winter W. E., Bazydlo L. A., Harris N. S.. The molecular biology of human iron metabolism. Lab. Med. (2014) 45 92-102. DOI: 10.1309/lmf28s2gimxnwhmm 44. Xu J., Zhang S., Tian Y., Si H., Zeng Y., Wu Y.. Genetic causal association between iron status and osteoarthritis: A two-sample mendelian randomization. Nutrients (2022) 14 3683. DOI: 10.3390/nu14183683 45. Yang J., Zhang G., Dong D., Shang P.. Effects of iron overload and oxidative damage on the musculoskeletal system in the space environment: Data from spaceflights and ground-based simulation models. Int. J. Mol. Sci. (2018) 19 2608. DOI: 10.3390/ijms19092608 46. Yavorska O. O., Burgess S.. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. (2017) 46 1734-1739. DOI: 10.1093/ije/dyx034 47. Yazar M., Sarban S., Kocyigit A., Isikan U. E.. Synovial fluid and plasma selenium, copper, zinc, and iron concentrations in patients with rheumatoid arthritis and osteoarthritis. Biol. Trace Elem. Res. (2005) 106 123-132. DOI: 10.1385/bter:106:2:123 48. Zheng H., Chen C.. Body mass index and risk of knee osteoarthritis: Systematic review and meta-analysis of prospective studies. BMJ Open (2015) 5 e007568. DOI: 10.1136/bmjopen-2014-007568 49. Zhou J., Liu C., Francis M., Sun Y., Ryu M. S., Grider A.. The causal effects of blood iron and copper on lipid metabolism diseases: Evidence from phenome-wide mendelian randomization study. Nutrients (2020) 12 3174. DOI: 10.3390/nu12103174 50. Zhou J., Liu C., Sun Y., Francis M., Ryu M. S., Grider A.. Genetically predicted circulating levels of copper and zinc are associated with osteoarthritis but not with rheumatoid arthritis. Osteoarthr. Cartil. (2021) 29 1029-1035. DOI: 10.1016/j.joca.2021.02.564
--- title: Sex difference in the associations among risk factors with depression in a large Taiwanese population study authors: - Hsin Tseng - Jia-In Lee - Jiun-Hung Geng - Szu-Chia Chen journal: Frontiers in Public Health year: 2023 pmcid: PMC10060520 doi: 10.3389/fpubh.2023.1070827 license: CC BY 4.0 --- # Sex difference in the associations among risk factors with depression in a large Taiwanese population study ## Abstract ### Background Depression is a common psychiatric health issue affecting an estimated $5\%$ of adults worldwide, and it can lead to disability and increased economic burden. Consequently, identifying the factors associated with depression as early as possible is a vital issue. The aim of this study was to explore these associations in a large cohort of 121,601 Taiwanese participants in the Taiwan Biobank, and also to identify sex differences in the associations. ### Methods The study cohort included 77,902 women and 43,699 men (mean age, 49.9 ± 11.0 years), who were further classified into those with depression ($$n = 4$$,362; $3.6\%$) and those without depression ($$n = 117$$,239; $96.4\%$). ### Results The results of multivariable analysis showed that female sex (vs. male sex; odds ratio = 2.578; $95\%$ confidence interval = 2.319–2.866; $p \leq 0.001$) was significantly associated with depression. Older age, diabetes mellitus (DM), hypertension, low systolic blood pressure (SBP), smoking history, living alone, low glycated hemoglobin (HbA1c), high triglycerides, and low uric acid were significantly associated with depression in the men. In the women, older age, DM, hypertension, low SBP, smoking history, alcohol history, education level of middle and high school (vs. lower than elementary school), living alone, high body mass index (BMI), menopause, low HbA1c, high triglycerides, high total cholesterol, low estimated glomerular filtration rate (eGFR), and low uric acid were significantly associated with depression. Further, there were significant interactions between sex and DM ($$p \leq 0.047$$), smoking history ($p \leq 0.001$), alcohol use ($p \leq 0.001$), BMI ($$p \leq 0.022$$), triglyceride ($$p \leq 0.033$$), eGFR ($$p \leq 0.001$$), and uric acid ($$p \leq 0.004$$) on depression. ### Conclusion In conclusion, our results showed sex differences in depression, and the women were significantly associated with depression compared to men. Furthermore, we also found sex differences among the risk factors associated with depression. ## Introduction Depression is a common psychiatric health issue affecting an estimated $5\%$ of adults worldwide, and it can lead to disability and increased economic burden [1]. Although the reported lifetime prevalence in Taiwan is about $1.2\%$, this rate may be underestimated due to the culture of low help-seeking behavior [2]. Demographic risk factors including younger age, female gender, lower household income, marital status of widowed, separated, or divorced, and comorbid psychiatric disorders are associated with an increased risk of depression [3]. Depression has also been associated with multiple chronic medical diseases, and it is considered to be a significant contributor to suicide [1, 4, 5]. Compared with the general population, people in Taiwan with depression have been found to have a shorter life expectancy and higher mortality rate [6]. Therefore, identifying the potential risk factors for depression is a vital issue. Sex differences have been reported in many diseases, including liver disease, cancer, and cardiovascular disease, and these differences manifest in clinical presentation, disease progression, and response to management [7]. Associations between the disease and risk factors may also differ by sex. For example, the incidence of myocardial infarction has been reported to be three times higher in men than in women, whereas hypertension, smoking, and diabetes mellitus (DM) are associated with a greater relative risk in women than in men [8]. Epidemiological studies have also revealed that the lifetime prevalence of depression is two times higher in women compared to men [3]. These differences may be due to genetic factors, hormone modulation, stress response, or structural sex inequality [9]. For depression, the relationship with risk factors is considered to be bidirectional. On one hand, depression may lead to unhealthy behaviors such as smoking and alcohol consumption, which then elevate the possibility of developing chronic diseases. On the other hand, poor physical health conditions may cause depression due to common pathogenesis or the increased need for psychological support [4, 5]. Although previous studies have established bidirectional relationships between depression and comorbid conditions, whether sex differences also influence the risk factors is unclear. Therefore, we conducted this study to explore sex differences in the associations among risk factors with depression in a large cohort of Taiwanese participants. ## Taiwan Biobank Due to societal aging in Taiwan, the Ministry of Health and Welfare announced a policy to counter chronic diseases through health promotions, and consequently launched the TWB. Volunteers are enrolled in the TWB, with the inclusion criteria of an age between 30 and 70 years and no prior diagnosis of cancer. In this study we used data from 121,601 participants in the TWB, including lifestyle habits, medical and genetic information as detailed in the following section [10, 11]. ## Medical data, demographics, lifestyle habits, and laboratory data All enrollees in the TWB are interviewed to obtain personal information on their age, sex, lifestyle factors (i.e., exercise), educational status, living alone status, and medical history (i.e., DM and hypertension). In this study, we defined regular exercise as exercising for at least 30 min three times or more in 1 week. Body height and weight were also recorded for each enrollee, along with the body mass index (BMI) (kg/m2). Blood samples were also drawn from each enrollee, from which glycated hemoglobin (HbA1c), hemoglobin, triglycerides, total cholesterol, and uric acid were measured. Estimated glomerular filtration rate (eGFR) was also recorded using the MDRD Study equation [186 × serum creatinine−1.154 × Age−0.203 × 0.742 (if female) × 1.212 (if black patient)] [12]. DM was defined as self-reported, fasting glucose level ≥126 mg/dL or HbA1c ≥ $6.5\%$. Participants had past history of hypertension (self-reported), and whose systolic blood pressure was >140 mmHg and diastolic blood pressure was >90 mmHg were defined to have hypertension. ## Depression groups The participants were classified into two groups according to whether or not they had ever had depression. Those who answered “Yes” to the question “Have you ever had depression?” were classified into the depression group, and those who answered “No” were classified into the without depression group. ## Ethical considerations The Institutional Review Board of Kaohsiung Medical University Hospital approved this study (KMUHIRB-E(I)-20210058). Ethical approval for the TWB was granted by the IRB on Biomedical Science Research, Academia Sinica, Taiwan and the Ethics and Governance Council of the TWB. In addition, the study was conducted in accordance with the Declaration of Helsinki, and all of the participants gave written informed consent. ## Statistical analysis Continuous variables are presented as mean (±SD), with differences analyzed using the independent t-test. Categorical variables are presented as percentage, with differences analyzed using the chi-square test. Correlations among risk factors with depression were analyzed with multivariable logistic regression analyses. An interaction p in logistic analysis was identified using the following formula: Model disease (y) = x1 + x2 + x1x2 + covariates x1x2, where y = depression; x1 = sex; x2 = each risk factor; covariates = age, sex, DM, hypertension, systolic and diastolic blood pressures, smoking and alcohol history, regular exercise habit, education status, living alone, BMI, HbA1c, hemoglobin, triglycerides, total cholesterol, eGFR and uric acid. Results were considered significant at $p \leq 0.05.$ Statistical analysis was performed using SPSS for Windows (v26, SPSS Inc. Armonk, NY, USA). ## Results The enrolled participants ($$n = 121$$,601; mean age 49.9 ± 11.0 years; 77,902 females; 43,699 males) were divided into two groups according to those with depression ($$n = 4$$,362; $3.6\%$) and without depression ($$n = 117$$,239; $96.4\%$). ## Comparisons of clinical characteristics between the two depression groups Compared to the without depression group, the with depression group were older, had a higher proportion of females, higher prevalence of DM, hypertension, living alone, menopause status, and smoking, regularly exercised more. In addition, the with depression group had lower systolic and diastolic blood pressures, lower prevalence of educational status higher than college, higher levels of HbA1c, triglycerides and total cholesterol, and lower levels of hemoglobin, eGFR, and uric acid (Table 1). Table 1 \| Characteristics \| Depression (–) (n = 117,239) \| Depression (+) (n = 4,362) \| p \| \| --- \| --- \| --- \| --- \| \| Age (year) \| 49.8 ± 11.0 \| 51.3 ± 10.4 \| < 0.001 \| \| Female (%) \| 63.6 \| 76.1 \| < 0.001 \| \| DM (%) \| 5.1 \| 7.8 \| < 0.001 \| \| Hypertension (%) \| 12.1 \| 16.0 \| < 0.001 \| \| Systolic BP (mmHg) \| 120.5 ± 18.7 \| 119.2 ± 18.2 \| < 0.001 \| \| Diastolic BP (mmHg) \| 73.8 ± 11.4 \| 73.0 ± 11.2 \| < 0.001 \| \| Smoking history (%) \| 27.1 \| 31.6 \| < 0.001 \| \| Alcohol history (%) \| 8.5 \| 9.0 \| 0.235 \| \| Regular exercise habits (%) \| 40.5 \| 42.0 \| 0.046 \| \| Education status \| Education status \| Education status \| < 0.001 \| \| Lower than elementary school (%) \| 5.3 \| 5.8 \| \| \| Middle and high school (%) \| 36.5 \| 43.9 \| \| \| Higher than college (%) \| 58.2 \| 50.3 \| \| \| Living alone (%) \| 7.9 \| 13.9 \| < 0.001 \| \| BMI (kg/m2) \| 24.2 ± 3.8 \| 24.2 ± 4.1 \| 0.437 \| \| Menopause in female (%) \| 45.5 \| 52.3 \| < 0.001 \| \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| \| HbA1c (%) \| 5.76 ± 0.80 \| 5.79 ± 0.82 \| 0.034 \| \| Hemoglobin (g/dL) \| 13.8 ± 1.6 \| 13.5 ± 1.5 \| < 0.001 \| \| Triglyceride (mg/dL) \| 115.5 ± 94.3 \| 119.9 ± 85.5 \| 0.002 \| \| Total cholesterol (mg/dL) \| 195.5 ± 35.8 \| 198.4 ± 35.7 \| < 0.001 \| \| eGFR (mL/min/1.73 m2) \| 103.3 ± 23.9 \| 102.5 ± 24.2 \| 0.029 \| \| Uric acid (mg/dL) \| 5.4 ± 1.4 \| 5.2 ± 1.4 \| < 0.001 \| ## Determinants of depression The factors associated with depression in multivariable logistic regression analysis for the whole study cohort ($$n = 121$$,601) are shown in Table 2. This model adjusted age, sex, DM, hypertension, systolic and diastolic blood pressures, smoking and alcohol history, regular exercise, educational status, living alone, BMI, HbA1c, hemoglobin, triglycerides, total cholesterol, eGFR and uric acid. After analysis, older age, female (vs. male; odds ratio [OR] = 2.578; $95\%$ confidence interval [CI] = 2.319–2.866; $p \leq 0.001$), DM, hypertension, low systolic blood pressure, high diastolic blood pressure, smoking history, educational level of middle and high school (vs. lower than elementary school), living alone, high BMI, low HbA1c, high triglycerides, high total cholesterol, low eGFR, and low uric acid were significantly associated with depression. Table 2 \| Parameters \| Depression \| Depression.1 \| Depression.2 \| \| --- \| --- \| --- \| --- \| \| \| Multivariable \| Multivariable \| Multivariable \| \| \| OR \| 95% CI \| p \| \| Age (per 1 year) \| 1.012 \| 1.008–1.016 \| < 0.001 \| \| Female (vs. male) \| 2.578 \| 2.319–2.866 \| < 0.001 \| \| DM \| 1.669 \| 1.447–1.925 \| < 0.001 \| \| Hypertension \| 1.399 \| 1.272–1.539 \| < 0.001 \| \| Systolic BP (per 1 mmHg) \| 0.991 \| 0.989–0.994 \| < 0.001 \| \| Diastolic BP (per 1 mmHg) \| 1.005 \| 1.001–1.010 \| 0.022 \| \| Smoking history \| 1.978 \| 1.826–2.142 \| < 0.001 \| \| Alcohol history \| 1.106 \| 0.985–1.241 \| 0.087 \| \| Regular exercise habits \| 1.013 \| 0.949–1.082 \| 0.703 \| \| Education status \| Education status \| Education status \| Education status \| \| Lower than elementary school \| Reference \| \| \| \| Middle and high school \| 1.236 \| 1.076–1.419 \| 0.003 \| \| Higher than college \| 1.063 \| 0.922–1.225 \| 0.403 \| \| Living alone \| 1.734 \| 1.586–1.897 \| < 0.001 \| \| BMI (per 1 kg/m2) \| 1.010 \| 1.000–1.019 \| 0.041 \| \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| \| HbA1c (per 1%) \| 0.917 \| 0.874–0.963 \| 0.001 \| \| Hemoglobin (per 1 g/dL) \| 1.002 \| 0.977–1.027 \| 0.899 \| \| Triglyceride (per 10 mg/dL) \| 1.005 \| 1.003–1.008 \| < 0.001 \| \| Total cholesterol (per 1 mg/dL) \| 1.001 \| 1.000–1.002 \| 0.006 \| \| eGFR (per 1 mL/min/1.73 m2) \| 0.996 \| 0.995–0.998 \| < 0.001 \| \| Uric acid (per 1 mg/dL) \| 0.944 \| 0.917–0.972 \| < 0.001 \| ## Determinants of depression by sex The factors associated with depression by sex in multivariable logistic regression analysis are shown in Table 3. In the male participants ($$n = 43$$,699), older age, DM (OR = 1.524; $95\%$ CI = 1.179–1.971; $$p \leq 0.001$$), hypertension, low systolic blood pressure, smoking history (OR = 1.395; $95\%$ CI = 1.218–1.597; $p \leq 0.001$), living alone, low HbA1c, high triglycerides, and low uric acid (per 1 mg/dL; OR = 0.918; $95\%$ CI = 0.873–0.965; $$p \leq 0.001$$) were significantly associated with depression. In the female participants ($$n = 77$$,902), older age, DM (OR = 1.753; $95\%$ CI = 1.476–2.082; $$p \leq 0.047$$), hypertension, low systolic blood pressure, smoking history (OR = 2.323; $95\%$ CI = 2.115–2.551; $p \leq 0.001$), alcohol history (OR = 1.348; $95\%$ CI = 1.141–1.598; $p \leq 0.001$), educational level of middle and high school (vs. lower than elementary school; OR = 1.231; $95\%$ CI = 1.060–1.428; $$p \leq 0.006$$), living alone, high BMI, menopause, low HbA1c, high triglycerides, high total cholesterol, low eGFR (per 1 ml/min /1.73 m2; OR = 0.996; $95\%$ CI = 0.994–0.997; $p \leq 0.001$), and low uric acid (per 1 mg/dL; OR = 0.951; $95\%$ CI = 0.917–0.986; $$p \leq 0.006$$) were significantly associated with depression. Table 3 \| Parameters \| Male (n = 43,699) \| Male (n = 43,699).1 \| Male (n = 43,699).2 \| Female (n = 77,902) \| Female (n = 77,902).1 \| Female (n = 77,902).2 \| Interaction p \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Multivariable * \| Multivariable * \| Multivariable * \| Multivariable # \| Multivariable # \| Multivariable # \| \| \| \| OR \| 95% CI \| p \| OR \| 95% CI \| p \| \| \| Age (per 1 year) \| 1.015 \| 1.008–1.023 \| < 0.001 \| 1.006 \| 1.001–1.012 \| 0.031 \| 0.265 \| \| DM \| 1.524 \| 1.179–1.971 \| 0.001 \| 1.753 \| 1.476–2.082 \| < 0.001 \| 0.047 \| \| Hypertension \| 1.403 \| 1.187–1.659 \| < 0.001 \| 1.420 \| 1.263–1.595 \| < 0.001 \| 0.084 \| \| Systolic BP (per 1 mmHg) \| 0.989 \| 0.983–0.995 \| < 0.001 \| 0.992 \| 0.989–0.995 \| < 0.001 \| 0.082 \| \| Diastolic BP (per 1 mmHg) \| 1.008 \| 0.999–1.016 \| 0.091 \| 1.004 \| 0.999–1.010 \| 0.089 \| 0.295 \| \| Smoking history \| 1.395 \| 1.218–1.597 \| < 0.001 \| 2.323 \| 2.115–2.551 \| < 0.001 \| < 0.001 \| \| Alcohol history \| 1.006 \| 0.859–1.178 \| 0.941 \| 1.348 \| 1.141–1.598 \| < 0.001 \| < 0.001 \| \| Regular exercise habits \| 0.997 \| 0.874–1.138 \| 0.963 \| 1.013 \| 0.939–1.093 \| 0.735 \| 0.859 \| \| Education status \| Education status \| Education status \| Education status \| Education status \| Education status \| Education status \| Education status \| \| Lower than elementary school \| Reference \| \| \| Reference \| \| \| \| \| Middle and high school \| 1.384 \| 0.939–2.039 \| 0.101 \| 1.231 \| 1.060–1.428 \| 0.006 \| 0.524 \| \| Higher than college \| 1.283 \| 0.873–1.888 \| 0.205 \| 1.045 \| 0.895–1.221 \| 0.576 \| 0.123 \| \| Living alone \| 1.876 \| 1.541–2.283 \| < 0.001 \| 1.661 \| 1.501–1.838 \| < 0.001 \| 0.374 \| \| BMI (per 1 kg/m2) \| 1.003 \| 0.983–1.023 \| 0.781 \| 1.011 \| 1.000–1.020 \| 0.044 \| 0.022 \| \| Menopause in female \| – \| – \| \| 1.189 \| 1.070–1.321 \| 0.001 \| \| \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| Laboratory parameters \| \| HbA1c (per 1%) \| 0.899 \| 0.824–0.980 \| 0.016 \| 0.921 \| 0.868–0.978 \| 0.007 \| 0.063 \| \| Hemoglobin (per 1 g/dL) \| 1.020 \| 0.967–1.077 \| 0.462 \| 0.986 \| 0.958–1.015 \| 0.350 \| 0.688 \| \| Triglyceride (per 10 mg/dL) \| 1.005 \| 1.001–1.009 \| 0.021 \| 1.007 \| 1.003–1.011 \| 0.001 \| 0.033 \| \| Total cholesterol (per 1 mg/dL) \| 1.001 \| 0.999–1.003 \| 0.317 \| 1.001 \| 1.000–1.002 \| 0.024 \| 0.422 \| \| eGFR (per 1 mL/min/1.73 m2) \| 1.001 \| 0.997–1.004 \| 0.738 \| 0.996 \| 0.994–0.997 \| < 0.001 \| 0.001 \| \| Uric acid (per 1 mg/dL) \| 0.918 \| 0.873–0.965 \| 0.001 \| 0.951 \| 0.917–0.986 \| 0.006 \| 0.004 \| ## Interactions among risk factors and sex on depression There were significant interactions between sex and DM ($$p \leq 0.047$$), smoking history ($p \leq 0.001$), alcohol use ($p \leq 0.001$), BMI ($$p \leq 0.022$$), eGFR ($$p \leq 0.001$$), and uric acid ($$p \leq 0.004$$) on depression (Table 3). ## Discussion The results of this large-scale study showed that the female participants were significantly associated with depression compared to the male participants. Furthermore, we found sex differences in the associations among the risk factors. There were significant interactions between sex and DM, smoking history, alcohol use, BMI, eGFR, and uric acid on depression. A main finding of this study is that the female participants had a higher rate of depression compared to the male participants. The prevalence of depression has been shown to increase significantly during puberty, with a greater increase in girls [13]. This difference remains relatively stable into adulthood, and even after menopause in women [14]. The reason for the higher prevalence of depression in women cannot be explained by a single mechanism, and is likely to be due to risk factors including genetic factors, sex hormones, and stress [9, 15]. Considering the effect of genetic factors, although depression is a familial disorder with heritability ranging around 30~$40\%$ [16], genetic influences specifically in females have yet to be definitively concluded. Some studies have reported that genetic factors have a considerable impact on females [17], whereas other studies have reported that the impact is greater in males [18]. Thus, more research is required not only on direct genetic factors, but also on the influence of confounding factors such as environmental factors, demographic characteristics, study measurements, and so on [19]. Sex hormones are also considered to be an important risk factor for depression in women [20]. Women pass through different phases during their lifetime, including puberty, premenstrual dysphoric disorder and mood swings before menstruation, postpartum depression, and depression during perimenopause and menopause, all of which are related to fluctuations in sex hormones [21, 22]. In addition, stress is associated with the risk of the first onset, recurrence, and exacerbation of depression [23]. Previous studies have reported that adolescent girls and women may encounter sexual abuse and domestic violence, experience greater interpersonal stress, housing problems, and the burden of taking care of others [23, 24]. Thus, the effects of sex hormones, stress, and genetic factors may explain the higher risk of depression in women than in men. Another main finding of the present study is the significant interaction between sex and DM on depression, and DM was more strongly associated with depression in the female participants than in the male participants. Previous studies have shown that patients with DM have an increased risk of developing depression, while people suffering from depression also have a higher chance of developing DM [25]. Women with DM have been reported to have a more than twofold higher risk of being diagnosed with depression compared to women without DM, while the effect is significantly smaller in men [26]. The mechanism for this disparity is still unclear, but it may be due to differences in risk, glucose tolerance, and insulin sensitivity between sexes. With regards to the risk of DM, men have been reported to have a lower age and BMI at the time of diagnosis, whereas women have been more related to obesity [27]. In addition, obesity has been reported to have a stronger link with depression in women than in men, which is due to behavioral and psychosocial impairment and hypothalamic–pituitary–adrenal axis dysfunction, a stress regulation problem related to psychiatric disorders [27, 28]. We also found that the female participants with high BMI had an increased risk of depression, whereas the risk was not significant in the male participants. In response to oral glucose tolerance tests, men often have impaired fasting glucose while women usually have impaired glucose tolerance [29]. These differences may be associated with impairment in first- or second-phase insulin secretion, respectively, stimulated by glucose [30]. Although insulin sensitivity and insulin secretion status are similar in men and women diagnosed with DM, the reduction in insulin sensitivity is greater in women than in men when the metabolic condition declines from normal to illness [27]. Confounders of menopause including lower skeletal muscle mass, body fat distribution, and higher androgen activity and testosterone level, especially estrogen deficiency, have also been linked to elevated insulin resistance and the risk of DM in middle-aged women [31, 32]. Taken together, these mechanisms may partly explain the relationship between DM and depression in women. Another important finding of this study is the significant interaction between sex and alcohol and smoking history on depression, and the association was stronger in the female participants than in the male participants. Many studies have postulated a positive association between substance use and depressive symptoms in young adolescents, and that this association is more pronounced in girls [33, 34]. In middle-aged and older adults, smoking has been associated with a $20\%$ higher risk of developing depression, while those with depression have been reported to have 41 and $18\%$ higher risks of starting to smoke and heavy drinking, respectively [35]. In addition, any amount of alcohol consumption has been associated with a greater increase in depressive episodes in women than in men, especially with the synergistic effects of smoking [36]. Possible mechanisms for the relationship between smoking and depression include the self-medication hypothesis, alternative hypothesis, bidirectional relationship, or there may be no relationship at all [37]. Smoking may be a way to alleviate the symptoms of depression or keep individuals in a vulnerable state to environmental stress by regulating the hypothalamic–pituitary–adrenal axis [38, 39]. It could also be a bidirectional relationship, or just a consequence of sharing common risk factors so that there is actually no direct relationship. With regards to the influence of alcohol use on depression, previous studies have suggested that a bidirectional and mutually reinforcing relationship may explain the correlation between alcohol use disorders and major depression [40]. The association may be due to psychosocial impairment resulting from chronic heavy drinking that eventually elicits depressive symptoms or affects the release of neurotransmitters such as dopamine and gamma-aminobutyric acid receptors [41]. Taken together, an increased risk of depression related to smoking and alcohol consumption has been reported in females of all ages due to the influence of psychosocial, biological, and environmental factors. Another interesting finding of the present study is the negative correlation between eGFR and depression found in the female participants but not in the male participants. Depression has been associated with poor clinical outcomes of chronic kidney disease (CKD), including faster eGFR decline, early dialysis therapy initiation, death, or hospitalization [42, 43]. In adults with normal kidney function, the presence of depressive symptoms has also been associated with a higher risk of rapid kidney function decline [44]. The mechanism for this relationship is unclear, but it may be related to inflammation or stress-related physiological changes. Previous studies have reported that patients with kidney disease have higher levels of interleukin-6 (IL-6) and C-reactive protein, which are both considered to be related to the severity of depression due to increased production through various pro-inflammatory pathways and decreased clearance [45]. On the other hand, higher levels of psychological stress have been shown to increase the progression of kidney function decline, which can also result in depression by affecting the immune and endocrine systems [46, 47]. Sex differences have also been observed in the epidemiology of CKD, with a higher rate in women than in men. Women with CKD often suffer from a higher burden and stress, have a greater severity of symptoms, and handle the disease in a more emotional way, which can then predispose to the development of depression [48]. However, further investigations are required to elucidate the underlying mechanism and the influence of depression on kidney function in people with normal kidney function. We also noted that compared to lower than elementary school, educational level of middle and high school, not higher than college, were associated with depression. Studies have been revealed that education plays an important role in protection against depression with individuals receiving lower levels of education may have higher rates of depression. It was also indicated that adults with depression have lower educational aspirations and expectations and often their parents were also less educated [49]. Education is often considered to influence health, not only depression, through mechanisms of economy, health behavior, social-psychology, and access to health care. Higher levels of educations provide better socioeconomic conditions, less unhealthy lifestyle, better coping skills of stressors and daily hassles, and better management of health problems [50]. Interestingly, it was found that education has stronger health effects on women than men, especially on self-rated health [51]. Taken together, education have an important relationship with depression; people with lower education level would have higher rate of experiencing depression. The last important finding of this study is the significant interaction between sex and uric acid on depression, with low uric acid being more strongly associated with depression in the male participants than in the female participants. Previous studies have suggested an association between depression and lower levels of serum uric acid [52, 53]. Meng et al. [ 53] reported that although the differences were small compared to normal controls, the change in serum uric acid was consistent between subtypes of depression. In addition, Black et al. [ 52] proposed that the lower levels may be related to a greater severity and longer duration of depressive symptoms. Uric acid is the end product of purine metabolism and also considered a strong peroxynitrite scavenger, and it plays a role in dealing with oxidative stress [54]. Biological changes in purine metabolism and oxidative stress may be involved in the relationship with depression. The purinergic system has been linked to mood disorders through dysfunction driven by adenosine triphosphate and P2Y receptors, which regulate drive, cognition, appetite, sleep and mood [55]. The excessive oxidative stress associated with depression can lead to increased consumption of uric acid as an antioxidant [56]. Our results suggest that hyperactive purine degeneration with lower serum levels of inosine and guanosine and higher serum levels of xanthine may be associated with low uric acid in patients with depression [57]. In addition, we found that low uric acid was more strongly associated with depression in the male participants than in the female participants. This disparity may be due to sex differences in oxidative stress, as women under chronic stress have been shown to have better antioxidative capacity, lower reactive oxygen species-induced damage, and estrogen-driven protection, resulting in lower consumption of uric acid [58, 59]. Further studies on the sex-specific association between serum uric acid and depression are needed to elucidate the mechanism. The key strengths of this research are that we included a large cohort of healthy community-dwelling participants, and the comprehensive control of confounding factors. However, several limitations should also be noted. As this study was cross-sectional it was not possible to determine how long each participant had depression, and consequently we could not conclude causal relationships between the risk factors and depression. Longitudinal studies are needed to investigate sex differences and incident depression. Second, the prevalence of depression in TWB is $3.6\%$, higher than previous reported $1.2\%$ [2]. However, data on the presence of depression were obtained from self-reported questionnaires, and depression was not verified by psychiatrist diagnosis, which may not be rigorous enough. In addition, it is possible that some participants took medications for hypertension, glucose, hyperuricemia and lipid control. However, data on such medications are not provided by the TWB. Therefore, we could not evaluate the effects of these medications on the association between laboratory data and depression. Another limitation is that we could not ascertain the severity of depression. Fifth, the enrolled participants were all ethnically Chinese, and thus caution should be taken when extending our results to other ethnicities. Finally, because the average age in the depression group is higher than non-depression group, cohort effect could not be excluded. In conclusion, our results showed sex differences in the incidence of depression, and the female participants were significantly associated with depression compared to the male participants. Furthermore, we found sex differences in the associations among the risk factors with depression in this a large study of Taiwanese participants. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Institutional Review Board of Kaohsiung Medical University Hospital approved this study (KMUHIRB-E(I)-20210058). The patients/participants provided their written informed consent to participate in this study. ## Author contributions Conceptualization, methodology, validation, formal analysis, writing—review and editing, and supervision: HT, J-IL, J-HG, and S-CC. Software and investigation, resources, project administration, funding acquisition, and visualization: S-CC. Data curation: J-IL, J-HG, and S-CC. Writing—original draft preparation: HT, J-IL, and S-CC. All authors have read and agreed to the published version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Depression and Other Common Mental Disorders: Global Health Estimates. (2017) 2. Liao SC, Chen WJ, Lee MB, Lung FW, Lai TJ, Liu CY. Low prevalence of major depressive disorder in Taiwanese adults: possible explanations and implications. Psychol Med. (2012) 42 1227-37. DOI: 10.1017/S0033291711002364 3. Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry. (2018) 75 336-46. DOI: 10.1001/jamapsychiatry.2017.4602 4. Birk JL, Kronish IM, Moise N, Falzon L, Yoon S, Davidson KW. Depression and multimorbidity: considering temporal characteristics of the associations between depression and multiple chronic diseases. Health Psychol. (2019) 38 802-11. DOI: 10.1037/hea0000737 5. Remes O, Mendes JF, Templeton P. Biological, psychological, and social determinants of depression: a review of recent literature. Brain Sci. (2021) 11 1633. DOI: 10.3390/brainsci11121633 6. Pan YJ, Yeh LL, Chan HY, Chang CK. Excess mortality and shortened life expectancy in people with major mental illnesses in Taiwan. Epidemiol Psychiatr Sci. (2020) 29 e156. DOI: 10.1017/S2045796020000694 7. Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, Brinton RD, Carrero JJ, DeMeo DL. Sex and gender: modifiers of health, disease, and medicine. Lancet. (2020) 396 565-82. DOI: 10.1016/S0140-6736(20)31561-0 8. Millett ERC, Peters SAE, Woodward M. Sex differences in risk factors for myocardial infarction: cohort study of UK Biobank participants. BMJ. (2018) 363 k4247. DOI: 10.1136/bmj.k4247 9. Kuehner C. Why is depression more common among women than among men?. Lancet Psychiatry. (2017) 4 146-58. DOI: 10.1016/S2215-0366(16)30263-2 10. Chen CH, Yang JH, Chiang CWK, Hsiung CN, Wu PE, Chang LC. Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Hum Mol Genet. (2016) 25 5321-31. DOI: 10.1093/hmg/ddw346 11. Fan CT, Hung TH, Yeh CK. Taiwan regulation of biobanks. J Law Med Ethics. (2015) 43 816-26. DOI: 10.1111/jlme.12322 12. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med. (1999) 130 461-70. DOI: 10.7326/0003-4819-130-6-199903160-00002 13. Avenevoli S, Swendsen J, He JP, Burstein M, Merikangas KR. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry (2015) 54 37-44.e2. DOI: 10.1016/j.jaac.2014.10.010 14. Salk RH, Hyde JS, Abramson LY. Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol Bull. (2017) 143 783-822. DOI: 10.1037/bul0000102 15. Hammen C. Risk factors for depression: an autobiographical review. Annu Rev Clin Psychol. (2018) 14 1-28. DOI: 10.1146/annurev-clinpsy-050817-084811 16. Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. (2000) 157 1552-62. DOI: 10.1176/appi.ajp.157.10.1552 17. Kendler KS, Gatz M, Gardner CO, Pedersen NL. A Swedish national twin study of lifetime major depression. Am J Psychiatry. (2006) 163 109-14. DOI: 10.1176/appi.ajp.163.1.109 18. Rice F, Harold GT, Thapar A. Assessing the effects of age, sex and shared environment on the genetic aetiology of depression in childhood and adolescence. J Child Psychol Psychiatry. (2002) 43 1039-51. DOI: 10.1111/1469-7610.00231 19. Zhao L, Han G, Zhao Y, Jin Y, Ge T, Yang W. Gender differences in depression: evidence from genetics. Front Genet. (2020) 11 562316. DOI: 10.3389/fgene.2020.562316 20. Martel MM. Sexual selection and sex differences in the prevalence of childhood externalizing and adolescent internalizing disorders. Psychol Bull. (2013) 139 1221-59. DOI: 10.1037/a0032247 21. Gordon JL, Girdler SS, Meltzer-Brody SE, Stika CS, Thurston RC, Clark CT. Ovarian hormone fluctuation, neurosteroids, and HPA axis dysregulation in perimenopausal depression: a novel heuristic model. Am J Psychiatry. (2015) 172 227-36. DOI: 10.1176/appi.ajp.2014.14070918 22. Hwang WJ, Lee TY, Kim NS, Kwon JS. The role of estrogen receptors and their signaling across psychiatric disorders. Int J Mol Sci. (2020) 22 373. DOI: 10.3390/ijms22010373 23. Liu RT, Alloy LB. Stress generation in depression: a systematic review of the empirical literature and recommendations for future study. Clin Psychol Rev. (2010) 30 582-93. DOI: 10.1016/j.cpr.2010.04.010 24. Kendler KS, Thornton LM, Prescott CA. Gender differences in the rates of exposure to stressful life events and sensitivity to their depressogenic effects. Am J Psychiatry. (2001) 158 587-93. DOI: 10.1176/appi.ajp.158.4.587 25. Pan A, Lucas M, Sun Q, van Dam RM, Franco OH, Manson JE. Bidirectional association between depression and type 2 diabetes mellitus in women. Arch Intern Med. (2010) 170 1884-91. DOI: 10.1001/archinternmed.2010.356 26. Deischinger C, Dervic E, Leutner M, Kosi-Trebotic L, Klimek P, Kautzky A. Diabetes mellitus is associated with a higher risk for major depressive disorder in women than in men. BMJ Open Diabetes Res Care. (2020) 8 e001430. DOI: 10.1136/bmjdrc-2020-001430 27. Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocr Rev. (2016) 37 278-316. DOI: 10.1210/er.2015-1137 28. Vittengl JR. Mediation of the bidirectional relations between obesity and depression among women. Psychiatry Res. (2018) 264 254-9. DOI: 10.1016/j.psychres.2018.03.023 29. Unwin N, Shaw J, Zimmet P, Alberti KG. Impaired glucose tolerance and impaired fasting glycaemia: the current status on definition and intervention. Diabet Med. (2002) 19 708-23. DOI: 10.1046/j.1464-5491.2002.00835.x 30. Meyer C, Pimenta W, Woerle HJ, Van Haeften T, Szoke E, Mitrakou A. Different mechanisms for impaired fasting glucose and impaired postprandial glucose tolerance in humans. Diabetes Care. (2006) 29 1909-14. DOI: 10.2337/dc06-0438 31. Karvonen-Gutierrez CA, Park SK, Kim C. Diabetes and menopause. Curr Diab Rep. (2016) 16 20. DOI: 10.1007/s11892-016-0714-x 32. Mauvais-Jarvis F. Gender differences in glucose homeostasis and diabetes. Physiol Behav. (2018) 187 20-3. DOI: 10.1016/j.physbeh.2017.08.016 33. Fleming CB, Mason WA, Mazza JJ, Abbott RD, Catalano RF. Latent growth modeling of the relationship between depressive symptoms and substance use during adolescence. Psychol Addict Behav. (2008) 22 186-97. DOI: 10.1037/0893-164X.22.2.186 34. Needham BL. Gender differences in trajectories of depressive symptomatology and substance use during the transition from adolescence to young adulthood. Soc Sci Med. (2007) 65 1166-79. DOI: 10.1016/j.socscimed.2007.04.037 35. An R, Xiang X. Smoking, heavy drinking, and depression among U.S. middle-aged and older adults.. Prev Med. (2015) 81 295-302. DOI: 10.1016/j.ypmed.2015.09.026 36. Keyes KM, Allel K, Staudinger UM, Ornstein KA, Calvo E. Alcohol consumption predicts incidence of depressive episodes across 10 years among older adults in 19 countries. Int Rev Neurobiol. (2019) 148 1-38. DOI: 10.1016/bs.irn.2019.09.001 37. Fluharty M, Taylor AE, Grabski M, Munafò MR. The association of cigarette smoking with depression and anxiety: a systematic review. Nicotine Tob Res. (2017) 19 3-13. DOI: 10.1093/ntr/ntw140 38. McEwen A, West R, McRobbie H. Motives for smoking and their correlates in clients attending Stop Smoking treatment services. Nicotine Tob Res. (2008) 10 843-50. DOI: 10.1080/14622200802027248 39. Al'Absi M. Hypothalamic-pituitary-adrenocortical responses to psychological stress and risk for smoking relapse. Int J Psychophysiol. (2006) 59 218-27. DOI: 10.1016/j.ijpsycho.2005.10.010 40. Boden JM, Fergusson DM. Alcohol and depression. Addiction. (2011) 106 906-14. DOI: 10.1111/j.1360-0443.2010.03351.x 41. Chastain G. Alcohol, neurotransmitter systems, and behavior. J Gen Psychol. (2006) 133 329-35. DOI: 10.3200/GENP.133.4.329-335 42. Chiang HH, Guo HR, Livneh H, Lu MC, Yen ML, Tsai TY. Increased risk of progression to dialysis or death in CKD patients with depressive symptoms: a prospective 3-year follow-up cohort study. J Psychosom Res. (2015) 79 228-32. DOI: 10.1016/j.jpsychores.2015.01.009 43. Tsai YC, Chiu YW, Hung CC, Hwang SJ, Tsai JC, Wang SL. Association of symptoms of depression with progression of CKD. Am J Kidney Dis. (2012) 60 54-61. DOI: 10.1053/j.ajkd.2012.02.325 44. Zhang Z, He P, Liu M, Zhou C, Liu C, Li H. Association of depressive symptoms with rapid kidney function decline in adults with normal kidney function. Clin J Am Soc Nephrol (2021) 16 889-97. DOI: 10.2215/CJN.18441120 45. Gregg LP, Carmody T, Le D, Martins G, Trivedi M, Hedayati SS. A systematic review and meta-analysis of depression and protein-energy wasting in kidney disease. Kidney Int Rep. (2020) 5 318-30. DOI: 10.1016/j.ekir.2019.12.009 46. Yang L, Zhao Y, Wang Y, Liu L, Zhang X, Li B. The effects of psychological stress on depression. Curr Neuropharmacol. (2015) 13 494-504. DOI: 10.2174/1570159X1304150831150507 47. Kim JY, Joo YS, Jhee JH, Han SH, Yoo TH, Kang SW. Effect of psychosocial distress on the rate of kidney function decline. J Gen Intern Med. (2021) 36 2966-74. DOI: 10.1007/s11606-020-06573-9 48. Carrero JJ, Hecking M, Chesnaye NC, Jager KJ. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat Rev Nephrol. (2018) 14 151-64. DOI: 10.1038/nrneph.2017.181 49. Cohen AK, Nussbaum J, Weintraub MLR, Nichols CR, Yen IH. Association of adult depression with educational attainment, aspirations, and expectations. Prev Chronic Dis. (2020) 17 E94. DOI: 10.5888/pcd17.200098 50. Zajacova A, Lawrence EM. The relationship between education and health: reducing disparities through a contextual approach. Annu Rev Public Health. (2018) 39 273-89. DOI: 10.1146/annurev-publhealth-031816-044628 51. Ross CE, Masters RK, Hummer RA. Education and the gender gaps in health and mortality. Demography. (2012) 49 1157-83. DOI: 10.1007/s13524-012-0130-z 52. Black CN, Bot M, Scheffer PG, Snieder H, Penninx B. Uric acid in major depressive and anxiety disorders. J Affect Disord. (2018) 225 684-90. DOI: 10.1016/j.jad.2017.09.003 53. Meng X, Huang X, Deng W, Li J, Li T. Serum uric acid a depression biomarker. PLoS ONE. (2020) 15 e0229626. DOI: 10.1371/journal.pone.0229626 54. Hooper DC, Spitsin S, Kean RB, Champion JM, Dickson GM, Chaudhry I, Koprowski H. Uric acid, a natural scavenger of peroxynitrite, in experimental allergic encephalomyelitis and multiple sclerosis. Proc Natl Acad Sci U S A (1998) 95 675-80. DOI: 10.1073/pnas.95.2.675 55. Ortiz R, Ulrich H, Zarate CA, Machado-Vieira R. Purinergic system dysfunction in mood disorders: a key target for developing improved therapeutics. Prog Neuropsychopharmacol Biol Psychiatry (2015) 57 117-31. DOI: 10.1016/j.pnpbp.2014.10.016 56. Black CN, Bot M, Scheffer PG, Cuijpers P, Penninx BW. Is depression associated with increased oxidative stress? A systematic review and meta-analysis. Psychoneuroendocrinology. (2015) 51 164-75. DOI: 10.1016/j.psyneuen.2014.09.025 57. Ali-Sisto T, Tolmunen T, Toffol E, Viinamäki H, Mäntyselkä P, Valkonen-Korhonen M. Purine metabolism is dysregulated in patients with major depressive disorder. Psychoneuroendocrinology (2016) 70 25-32. DOI: 10.1016/j.psyneuen.2016.04.017 58. Kamper EF, Chatzigeorgiou A, Tsimpoukidi O, Kamper M, Dalla C, Pitychoutis PM, Papadopoulou-Daifoti Z. Sex differences in oxidant/antioxidant balance under a chronic mild stress regime. Physiol Behav (2009) 98 215-22. DOI: 10.1016/j.physbeh.2009.05.011 59. Sumien N, Cunningham JT, Davis DL, Engelland R, Fadeyibi O, Farmer GE. Neurodegenerative disease: roles for sex, hormones, and oxidative stress. Endocrinology. (2021) 162 11. DOI: 10.1210/endocr/bqab185
--- title: 'Untreated substance use disorder affects glycemic control: Results in patients with type 2 diabetes served within a network of community-based healthcare centers in Florida' authors: - Viviana E. Horigian - Renae D. Schmidt - Rui Duan - Daniel Parras - Katherine Chung-Bridges - Jacob N. Batycki - Kevin Espinoza - Peyman Taghioff - Sophia Gonzalez - Carly Davis - Daniel J. Feaster journal: Frontiers in Public Health year: 2023 pmcid: PMC10060525 doi: 10.3389/fpubh.2023.1122455 license: CC BY 4.0 --- # Untreated substance use disorder affects glycemic control: Results in patients with type 2 diabetes served within a network of community-based healthcare centers in Florida ## Abstract ### Introduction Patients with diabetes and comorbid substance use disorders (SUD) experience poor diabetes management, increased medical complications and mortality. However, research has documented that patients engaged in substance abuse treatment have better management of their comorbid conditions. The current study examines diabetes management among patients with type 2 diabetes, with and without comorbid SUD, receiving care at Florida-based Federally Qualified Health Centers (FQHC) of Health Choice Network (HCN). ### Methods A retrospective analysis was conducted using deidentified electronic health records of 37,452 patients with type 2 diabetes who received care at a HCN site in Florida between 2016 and 2019. A longitudinal logistic regression analysis examined the impact of SUD diagnosis on achievement of diabetes management [HbA1c < $7.0\%$ (53 mmol/mol)] over time. A secondary analysis evaluated, within those with an SUD diagnosis, the likelihood of HbA1c control between those with and without SUD treatment. ### Results The longitudinal assessment of the relationship between SUD status and HbA1c control revealed that those with SUD ($$n = 6$$,878, $18.4\%$) were less likely to control HbA1c over time (OR = 0.56; $95\%$ CI = 0.49–0.63). Among those with SUD, patients engaged in SUD treatment were more likely to control HbA1c (OR = 5.91; $95\%$ CI = 5.05–6.91). ### Discussion Findings highlight that untreated SUD could adversely affect diabetes control and sheds light on the opportunity to enhance care delivery for patients with diabetes and co-occurring SUD. ## Introduction The number of individuals with diabetes continues to rise in the US, with ~37 million Americans having either diagnosed or undiagnosed diabetes. An estimated 90–$95\%$ of these cases are type 2 diabetes [1]. The increasing prevalence of type 2 diabetes in the US is responsible for premature mortality, lost productivity, and elevated healthcare costs [2]. To enhance the quality of healthcare delivery, optimize diabetes management, and improve outcomes, several sets of indicators have been developed to assess diabetes care quality (3–5). Despite these efforts, challenges persist in closing the diabetes management and care gap. Disparities in diabetes care and management are prominent in individuals with lower socioeconomic status, that are of racial or ethnic minority groups. These inequities are driven by challenges in health care access, access to medications, neighborhood resources and the social determinants of health [6, 7]. To address unmet health care needs, community based Federally Qualified Health Centers (FQHCs) provide primary and preventive care to the underserved and uninsured, regardless of their ability to pay [8]. Patients seeking treatment in any of the 1,375 FQHC facilities across the US constitute many of the nation's working poor, unemployed and undocumented [9]. FQHC patients in Florida represent a particularly vulnerable group, as Florida is one of 12 states which has not expanded Medicaid coverage [10]. This has left under- and uninsured individuals in Florida with few healthcare options, making FQHCs the safety net for high-risk, low socioecomomic status individuals who have diabetes. The diabetes management gap is also notable in patients with diabetes and other comorbid conditions. Research shows that most people with type 2 diabetes have a comorbid condition [11, 12] that can complicate achieving desired glycemic control. Specifically, patients with comorbid diabetes and substance use disorders (SUD) experience poor diabetes management that increases the risk of lower-limb amputations, preventable diabetes-related hospitalizations, medical complications, and mortality (13–17). These unfortunate outcomes result from lack of adherence to medication treatment [18], laboratory testing [19], and other self-management behaviors such as diet [20]. Despite these documented outcomes, screening for SUD in primary care continues to be a barrier driven by provider and patient stigma [21]. To address SUD and combat associated adverse outcomes, the effectiveness of substance abuse treatment has been extensively demonstrated (22–30). In fact, research has documented that patients engaged in substance abuse treatment have better management of their comorbid conditions, and better adherence to medical treatments [31]. However, integration of SUD treatment into mainstream of care and adoption of evidence-based interventions such as medications for Opioid use disorder is still lagging (32–34). Approximately one in seven Americans report experiencing an SUD. Given the high prevalence of type 2 diabetes and SUD, the intersection of these two conditions is an important comorbidity to understand and manage [35]. While previous studies have documented adverse outcomes in patients with type 2 diabetes and SUD with the use of electronic health care records, these studies have been conducted in large healthcare systems and academic settings [13, 16, 18, 20, 36]. To our knowledge no studies have previously examined glycemic control in the type 2 diabetes and SUD population served in FQHCs, with the use electronic health records (EHR). Previous studies within this population have not examined the role of substance abuse treatment engagement in outcomes. The current study examines diabetes management among patients with type 2 diabetes, with and without comorbid SUD, receiving care at Florida centers of Health Choice Network, Inc. (HCN), a network of FQHCs. We hypothesized that patients with comorbid type 2 diabetes and SUD would be more likely to demonstrate worse diabetes management than those without an SUD; and that among a subsample of patients with an SUD, those who are not engaged in treatment for their SUD would be more likely to demonstrate worse diabetes management than those who are engaged in treatment. ## Data sources A retrospective analysis was conducted using a limited dataset of patients with type 2 diabetes, 18–75 years old, who received care within the HCN network of FQHCs in Florida between January 1, 2016 and December 31, 2019. Data were housed and analyzed within a secure server at the University of Miami Clinical and Translational Sciences Institute. The study was approved by the University of Miami Institutional Review Board on July 22, 2022. ## Study population The study assessed demographic information recorded in EHR, including patient age at the beginning of data collection, gender, and self-identified race and ethnicity. Patient diagnoses were determined using a standard clinical diagnostic approach. The diagnostic status for type 2 diabetes was defined using relevant International Classification of Disease Ninth Edition (ICD-9) and Tenth Edition (ICD-10) diagnosis codes. Type 2 diabetes diagnosis, based on these ICD-9 and/or ICD-10 codes, was determined over a baseline period of two years, including all patients with a diagnosis between January 1, 2016 and December 31, 2017. Specific SUD status (alcohol, chemical substances, or tobacco) was defined by [1] ICD-9 and ICD-10 codes, [2] Current Procedural Terminology (CPT) codes related to SUD-specific treatment, and/or [3] key medication terms for SUD-specific medications. Similar to type 2 diabetes, SUD status was determined during the 2-year baseline (2016–2017), and only patients without any SUD diagnosis during the entire study period from 2016 to 2019 were counted in the non-SUD group. Patients with comorbid diabetes and SUD had diagnostic codes for both Type 2 diabetes and SUD as described above. A table of codes and key words are included in Supplementary Table 1. ## Engagement in treatment Patient engagement in diabetes treatment was characterized by pharmacological treatment, using medication key terms to identify the percentage of days covered by any type 2 diabetes medication prescription during the measurement period. A table of key words are included in Supplementary Table 2. Patient engagement in SUD treatment was characterized by both behavioral treatment and pharmacological treatment, for those with an SUD diagnosis at baseline. Behavioral treatment was identified by relevant CPT codes, while pharmacological treatment was identified by key medication terms. Both behavioral and pharmacological treatments were specific to the type of SUD diagnosis (alcohol, chemical, or tobacco). Patients were classified as “engaged” in SUD treatment if their records indicated at least two visits (behavioral treatment) AND/OR if they had at least one SUD-specific prescription (pharmacological treatment) during the measurement period (either the 2-year baseline, 2018, or 2019). Participants who met the criteria for both behavioral and pharmacological were also categorized as “engaged” in SUD treatment. This flexible and overinclusive approach was to mimic what occurs in practice. While most patients are prescribed behavioral treatment, it is possible that some patients are only prescribed pharmacological treatment, such as the case in individuals with OUD, or are prescribed both. Engagement in the pharmacological treatment component for those with tobacco use disorder (TUD) was characterized by medication keywords for BOTH nicotine replacement AND tobacco anti-craving medications. Patients without at least two visits and without specific medication key terms were classified as “not engaged” in SUD treatment during the measurement period. ## Outcome As a clinically important indicator of diabetes management, the American Diabetes Association (ADA) threshold of HbA1c at $7.0\%$ (53 mmol/mol) was used to assess diabetes management. Patients were considered to have “uncontrolled” HbA1c if they had any lab value ≥$7.0\%$ during the measurement period or zero HbA1c labs reported in the given year of observation. All numeric HbA1c lab values were categorized as “controlled” [HbA1c <$7.0\%$ (53 mmol/mol)] or “uncontrolled” [HbA1c ≥ $7.0\%$ (53 mmol/mol)]. HbA1c results coded in the EHR as free text (e.g., “missing,” “null,” “error,” “too high,” etc.) or as numerical ranges above $7.0\%$ (53 mmol/mol) (e.g., “>14,” “$14\%$−$16\%$,” etc.) were coded classified as “uncontrolled”. Similarly, HbA1c results coded in free text as numerical ranges <$7.0\%$ (53 mmol/mol) were coded only categorically and classified as “controlled.” ## Analytic plan The HCN EHR dataset contains several patient-level variables in distinct datafiles, including demographics, encounters, problem lists, procedures, medications, and laboratory data. Each datafile with the fields necessary for this analysis were merged via unique patient IDs. Summary statistics were computed for each of the demographic and medical characteristics of the total sample and by SUD/non-SUD subgroups. Categorical variables were summarized using frequencies and percentages, while means and standard deviations were calculated for continuous variables. First, a generalized linear mixed model (GLMM) with a binary distribution and a logit link function was used to examine the impact of SUD diagnosis on achievement of diabetes management [HbA1c control; <$7.0\%$ (53 mmol/mol)] over time, adjusting for age, race, ethnicity, gender, and engagement in treatment for diabetes and SUD. Next, we conducted a secondary GLMM to evaluate the impact of engagement in SUD treatment (behavioral and/or pharmacological) among those with an SUD diagnosis at baseline on achievement of diabetes management over time, controlling for age, race, ethnicity, gender, and diabetes treatment (participant sub-groups shown in Figure 1). Adjusted odds ratios and $95\%$ confidence intervals were calculated. For all analyses, two-tailed $p \leq 0.05$ were considered statistically significant. Both models included a random intercept for individuals to account for the repeated measures across patients and a fixed effect for clinic to account for variation among clinics. All analyses were conducted with SAS statistical software version 9.4 [37]. Figure 1: Participant sub-groups among patients with type 2 diabetes (left) and among patients with substance use disorder (SUD) (right). ## Results Among 530,588 Florida FQHC network patients, ages 18–75, served between 2016 and 2017 as identified by EHR encounters in the Problem or Procedure lists, 38,947 ($7.3\%$) had a diagnosis of type 2 diabetes during 2016–2017 and available demographic information. Among these, 37,352 met criteria of binary SUD status of either having a baseline SUD diagnosis (6,878; $18.4\%$) vs. never having an SUD (30,574, $81.6\%$) over the study period and were included in the study. Table 1 shows the baseline characteristics of the patients overall ($56.7\%$ female, $40.5\%$ Hispanic, $32.5\%$ Black/African American, mean ± standard deviation age 52.9 ± 10.7 years), and among those with and without SUD. Table 1 \| Characteristics \| Characteristics.1 \| Total \| Non-SUD \| SUD \| \| --- \| --- \| --- \| --- \| --- \| \| \| \| (n = 37,452) \| (n = 30,574) \| (n = 6,878) \| \| Age \| Age \| 52.9 (10.7) \| 53.2 (10.9) \| 51.6 (10.0) \| \| Gender \| Male \| 16,339 (43.6) \| 12,981 (42.5) \| 3,358 (48.8) \| \| Gender \| Female \| 21,113 (56.4) \| 17,593 (57.5) \| 3,520 (51.2) \| \| Race/Ethnicity \| Hispanic/Latinx \| 15,181 (40.5) \| 13,083 (42.8) \| 2,098 (30.5) \| \| Race/Ethnicity \| Black/African American \| 12,185 (32.5) \| 10,108 (33.1) \| 2,077 (30.2) \| \| Race/Ethnicity \| White \| 7,991 (21.3) \| 5,679 (18.6) \| 2,312 (33.6) \| \| Race/Ethnicity \| Other \| 1,022 (2.7) \| 890 (2.9) \| 132 (1.9) \| \| Race/Ethnicity \| Unknown \| 1,073 (2.9) \| 814 (2.7) \| 259 (3.8) \| \| % Diabetes treatment covered days* \| % Diabetes treatment covered days* \| 36.78 (31.9) \| 36.51 (31.9) \| 37.97 (31.6) \| \| HbA1c controlled \| HbA1c controlled \| 11,029 (29.5) \| 8,826 (28.9) \| 2,203 (32.0) \| Longitudinal assessment of the relationship between baseline SUD status and HbA1c control reveals that those with SUD were less likely to control HbA1c over time as compared to those without an SUD (OR = 0.56; $95\%$ CI = 0.49–0.63). Patients who engaged in diabetes treatment were more likely to control HbA1c compared to those not engaged in treatment (OR = 5.39; $95\%$ CI = 4.97–5.84). Likelihood of control increased with age (OR = 1.02; $95\%$ CI = 1.02–1.03); for every year increase in age, there was an increased odds ($2.0\%$) of having HbA1c controlled. Finally, females as compared to males (OR = 1.33; $95\%$ CI = 1.25–1.41) and black individuals as compared to white individuals (OR = 1.21; $95\%$ CI = 1.10–1.33) were also more likely to have controlled HbA1c (Table 2). Table 2 \| Unnamed: 0 \| Unnamed: 1 \| Adj. OR \| 95% CI \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| Age \| Years \| 1.02 \| 1.02, 1.03 \| <0.0001 \| \| Gender \| Female \| 1.33 \| 1.25, 1.41 \| <0.0001 \| \| \| Male \| – \| – \| – \| \| Race/ethnicity \| Hispanic \| 1.06 \| 0.97, 1.16 \| 0.2260 \| \| \| Black \| 1.21 \| 1.10, 1.33 \| <0.0001 \| \| \| Other \| 0.97 \| 0.79, 1.18 \| 0.7440 \| \| \| Unknown \| 0.94 \| 0.77, 1.16 \| 0.5826 \| \| \| White \| – \| – \| – \| \| SUD treatment \| Engaged \| 3.65 \| 3.20, 4.17 \| <0.0001 \| \| \| Not engaged \| – \| – \| – \| \| Diabetes treatment \| %Covered days \| 5.39 \| 4.97, 5.84 \| <0.0001 \| \| SUD status \| Baseline SUD \| 0.56 \| 0.49, 0.63 \| <0.0001 \| \| \| Never SUD \| – \| – \| – \| \| Visit \| 2018 \| 0.26 \| 0.25, 0.27 \| <0.0001 \| \| \| 2019 \| 0.13 \| 0.12, 0.13 \| <0.0001 \| \| \| Baseline \| – \| – \| – \| Among the 6,878 individuals with type 2 diabetes and a baseline SUD, 770 ($11.2\%$) were not engaged in any SUD treatment. Among 6,108 individuals categorized as “engaged” in SUD treatment, 62 ($1.0\%$) met criteria for only pharmacological treatment, 5,001 ($81.9\%$) met criteria for only behavioral treatment, and 1,045 ($17.1\%$) met criteria for both behavioral and pharmacological. Longitudinal assessment of the relationship between engagement in SUD treatment and HbA1c control reveals that those engaged in treatment were more likely to manage HbA1c over time as compared to those not engaged in SUD treatment (OR = 5.91; $95\%$ CI = 5.05–6.91) (Table 3). Similar to results from the first model, we find among our subsample of those with an SUD, that individuals who are older (OR = 1.03; $95\%$ CI = 1.02–1.04), females as compared to males (OR = 1.24; $95\%$ CI = 1.08–1.43), and those engaged in treatment for their diabetes (OR = 1.85; $95\%$ CI = 1.53–2.24), are more likely to have controlled HbA1c. Table 3 \| Unnamed: 0 \| Unnamed: 1 \| Adj. OR \| 95% CI \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| Age \| Years \| 1.03 \| 1.02, 1.04 \| <0.0001 \| \| Gender \| Female \| 1.24 \| 1.08, 1.43 \| 0.0029 \| \| \| Male \| – \| – \| – \| \| Race/ethnicity \| Black \| 1.14 \| 0.94, 1.38 \| 0.1773 \| \| \| Hispanic \| 0.86 \| 0.71, 1.05 \| 0.1328 \| \| \| Other \| 1.01 \| 0.61, 1.67 \| 0.9753 \| \| \| Unknown \| 1.39 \| 0.95, 2.03 \| 0.0919 \| \| \| White \| – \| – \| – \| \| SUD treatment \| Engaged \| 5.91 \| 5.05, 6.91 \| <0.0001 \| \| \| Not engaged \| – \| – \| – \| \| Diabetes treatment \| %Covered days \| 1.85 \| 1.53, 2.24 \| <0.0001 \| \| Visit \| 2018 \| 0.28 \| 0.24, 0.33 \| <0.0001 \| \| \| 2019 \| 0.17 \| 0.15, 0.21 \| <0.0001 \| \| \| Baseline \| – \| – \| – \| It is important to note that the interaction of engagement and time was significant in the model assessing the relationship between engagement in SUD treatment and HbA1c control. As observed in the Least Squares means results in Table 4, among those engaged and not engaged in SUD treatment, the likelihood of HbA1c control decreases over time. However, the likelihood of control decreases at a faster rate over time in the group which was not engaged in SUD treatment. Furthermore, within each time point, the group engaged in treatment always has a higher likelihood of HbA1c control (at baseline OR = 2.96; $95\%$ CI 2.27–3.85; at 2018 OR = 9.01; $95\%$ CI 7.20–11.27; at 2019 OR = 7.74; $95\%$ CI 6.09–9.84). Table 4 \| Engagement status \| Time point \| Proportion (%) with HbA1c controlled \| Odds ratio of HbA1c control as compared to baseline \| Odds ratio of HbA1c control as compared to baseline.1 \| Odds ratio of HbA1c control as compared to baseline.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| Adj. OR \| 95% CI \| P -value \| \| Engaged (yes) \| 2019 \| 10.5 \| 0.28 \| 0.24, 0.34 \| <0.0001 \| \| \| 2018 \| 16.9 \| 0.49 \| 0.43, 0.56 \| <0.0001 \| \| \| Baseline \| 29.5 \| – \| – \| – \| \| Not engaged (no) \| 2019 \| 1.5 \| 0.11 \| 0.08, 0.15 \| <0.0001 \| \| \| 2018 \| 2.2 \| 0.16 \| 0.12, 0.22 \| <0.0001 \| \| \| Baseline \| 12.4 \| – \| – \| – \| ## Discussion Results of this study demonstrate that patients with type 2 diabetes and any SUD were less likely to achieve glycemic control and that engagement in SUD treatment was associated with higher likelihood of diabetes control. These results add to other studies in the literature that documented worse diabetes management, medical complications, and mortality for patients with comorbid diabetes and SUD [13, 20, 38]. Results also demonstrate that older age and female gender are associated with higher likelihood of diabetes control [39, 40]. Remarkably, the prevalence of substance use disorder was low in the patient population. Several studies have documented lack of adequate screening of SUD in primary care, causing inadequate identification [21, 41]. Some of these practices have been documented as resulting from provider stigma. Additionally, it is possible that even when identified, physicians are less likely to code an SUD diagnosis in the medical records, given the implications for patients [42, 43]. Not surprisingly, patients with type 2 diabetes and SUD that were engaged in SUD treatment were more likely to achieve glycemic control than those not engaged [31]. Engagement and retention in substance abuse treatment has been associated with improved adherence to medical treatment, reduced mortality [44] and improved quality of life [45]. It is possible that the results seen in this study are driven by adherence to treatment and other behavioral changes described in the literature consequent to SUD treatment. This study did not pursue independent examinations of the effect of pharmacological or behavioral SUD treatment on glycemic control. Data on the impact of psychosocial interventions and pharmacotherapies for SUD in patients with type 2 diabetes are limited [19] and future studies could explore independent effects of type of SUD treatment on glycemic control and explore glycemic control by type of SUD. Results of this study also highlight the decrease of the effect of engagement in treatment in glycemic control over time. It is possible that this decrease is driven by patients moving away or attending a new clinic after the baseline period; patients were categorized as “uncontrolled” due to missing HbA1c values during follow up years. This work has several strengths. First, to our knowledge this is the first examination of type 2 diabetes and SUD in FQHCs in a Medicaid non-expanded state and provides the chance of further understanding diabetes care quality for patients that are vulnerable to the inequities driven by social determinants of health. Second, results highlight the disparate likelihood of management experienced by patients with diabetes who also have comorbid SUD. Third, findings underscore the importance of SUD treatment and highlight that untreated SUD could affect glycemic control. This is relevant as it can inform treatment resource allocation for SUD within health care centers. Fourth, this study relied and capitalized on the use of EHR and point of care data drawn from FQHCs, offering population level understanding of those most challenged by adverse health outcomes. This low-cost research approach can critically inform systems-level strategies to improve patient outcomes and could lower overall healthcare costs. The current study has several limitations. First, the design of this study supported findings that are correlational in nature; therefore, causation cannot be concluded. Second, the eligibility timeframe was established by convenience and data availability. The University of Miami, through an agreement with the Miami Clinical and Translational Science Institute and HCN, started receiving data for research purposes in 2016, hence the start date of the eligibility period. Follow up data on HbA1C control were established through 2019, to avoid with confounding effects of COVID-19, starting in 2020. Third, it is possible that additional confounders could explain the associations observed. Diet and exercise, two important factors in glycemic control, are inconsistently documented in the records, and when so, only as unstructured data. This study relied only in a limited data set containing only structured data. Fourth, relying on EHR data is prone to inherent limitations, such as missing data and misclassification. Fifth, while the network of FQHCs represented in this study offers substance abuse treatment in both primary care delivery locations and satellite locations, it is possible that patients received substance abuse treatment outside of the FQHC network therefore precluding those data from being documented in the EHR. Finally, the results of this study might not be generalizable to populations not served in Florida or not served in FQHCs. In conclusion, this analysis demonstrated that patients with comorbid type 2 diabetes and SUD have worse glycemic control and that untreated SUD could adversely affect diabetes control, thereby shedding light on opportunities to enhance care delivery for patients with diabetes and co-occurring SUD. Understanding diabetes management in the context of comorbidities helps identify opportunities for improving diabetes management outcomes and reducing the risk of diabetic consequences and premature death [46]. Revealing the compounding effects of comorbid SUD can aid FQHCs focus their efforts on furthering opportunities for screening of SUDs as well as integrating evidence-based treatments into the mainstream of primary care or delivering brief interventions and linking patients to substance abuse treatment. Additionally, the findings of the current study may have wide-reaching beneficial implications for comorbid diabetes quality-of-care improvement at other FQHC networks across the US. Finally, further research is needed to advance our understanding of the mechanisms by which SUD affects glycemic control and the mechanisms underlying the effects of SUD treatment on diabetes outcomes. ## Data availability statement The data analyzed in this study is subject to the following licenses/restrictions: electronic health record data analyzed in this study were accessed via data use agreement to limit the transfer of identifiable information, and are not available to the public. ## Ethics statement The studies involving human participants were reviewed and approved by University of Miami Institutional Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions VH formulated the research question and drafted the first version of the manuscript. PT routed the data feed via University of Miami UHealth IT and provided guidance on datafile and variable management. VH, RS, RD, DP, KC-B, JB, KE, and DF reviewed and decided on operationalization of all study variables. DF provided methodological guidance and approach for the models created for data analyses. RD performed the analyses. RS and RD drafted the methods and results sections. KC-B directs research and DP serves as a data analyst for Health Choice Network, and as such, both provided guidance on understanding data structure and interpreting results. SG and CD conducted literature searches and background review and contributed to the referencing system. All authors provided revisions and approved the submission of the manuscript for publication. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1122455/full#supplementary-material ## References 1. 1.CDC. Type 2 Diabetes Centers for Disease Control and Prevention. (2023). Available online at: https://www.cdc.gov/diabetes/basics/type2.html (accessed February 20, 2023).. (2023) 2. Economic costs of diabetes in the US in 2017. Diab Care (2018) 41 917-28. DOI: 10.2337/dci18-0007 3. Calsbeek H, Ketelaar NA, Faber MJ, Wensing M, Braspenning J. Performance measurements in diabetes care: the complex task of selecting quality indicators. Int J Qual Health Care. (2013) 25 704-9. DOI: 10.1093/intqhc/mzt073 4. 4.HSRA. Uniform Data Reporting Instructions for 2019 Health Center Data. (2019). Rockville, MD: HSRA.. Uniform Data Reporting Instructions for 2019 Health Center Data (2019) 5. 5.HEDIS. Comprehensive Diabetes Care. Centers for Medicare and Medicaid Services. (2022). Available online at: https://www.ncqa.org/hedis/measures/comprehensive-diabetes-care/ (accessed October 2, 2022).. (2022) 6. Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, Navas-Acien A. Social determinants of health and diabetes: a scientific review. Diabetes Care (2020) 44 258-79. DOI: 10.2337/dci20-0053 7. Hill-Briggs F, Ephraim PL, Vrany EA, Davidson KW, Pekmezaris R, Salas-Lopez D. Social determinants of health, race, and diabetes population health improvement: Black/African Americans as a population exemplar. Curr Diab Rep. (2022) 22 117-28. DOI: 10.1007/s11892-022-01454-3 8. 8.HRSA. About the Health Center Program. (2021). U.S. Health Resources and Services Administration. Available online at: https://bphc.hrsa.gov/about/index.html (accessed December 28, 2021).. U.S. Health Resources and Services Administration (2021) 9. Beck TL, Le TK, Henry-Okafor Q, Shah MK. Medical care for undocumented immigrants: national and international issues. Prim Care. (2017) 44 e1-e13. DOI: 10.1016/j.pop.2016.09.005 10. 10.CMS. Medicaid and CHIP in Florida Centers for Medicare and Medicaid Services. (2022). Available online at: https://www.medicaid.gov/state-overviews/stateprofile.html?state=florida (accessed October 2, 2022).. (2022) 11. Nowakowska M, Zghebi SS, Ashcroft DM, Buchan I, Chew-Graham C, Holt T. The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort. BMC Med. (2019) 17 145. DOI: 10.1186/s12916-019-1373-y 12. Iglay K, Hannachi H, Joseph Howie P, Xu J, Li X, Engel SS. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Curr Med Res Opin. (2016) 32 1243-52. DOI: 10.1185/03007995.2016.1168291 13. Winhusen T, Theobald J, Kaelber DC, Lewis D. Medical complications associated with substance use disorders in patients with type 2 diabetes and hypertension: electronic health record findings. Addiction. (2019) 114 1462-70. DOI: 10.1111/add.14607 14. Gautam S, Franzini L, Mikhail OI, Chan W, Turner BJ. Novel measure of opioid dose and costs of care for diabetes mellitus: opioid dose and health care costs. J Pain. (2016) 17 319-27. DOI: 10.1016/j.jpain.2015.11.001 15. Stockbridge EL, Chhetri S, Polcar LE, Loethen AD, Carney CP. Behavioral health conditions and potentially preventable diabetes-related hospitalizations in the United States: findings from a national sample of commercial claims data. PLoS ONE. (2019) 14 e0212955. DOI: 10.1371/journal.pone.0212955 16. Wu LT, Ghitza UE, Batch BC, Pencina MJ, Rojas LF, Goldstein BA. Substance use and mental diagnoses among adults with and without type 2 diabetes: results from electronic health records data. Drug Alcohol Depend. (2015) 156 162-9. DOI: 10.1016/j.drugalcdep.2015.09.003 17. Diabetes care for clients in behavioral health treatment. Fall. (2013) 12 1-11 18. Wu LT, Ghitza UE, Zhu H, Spratt S, Swartz M, Mannelli P. Substance use disorders and medical comorbidities among high-need, high-risk patients with diabetes. Drug Alcohol Depend. (2018) 186 86-93. DOI: 10.1016/j.drugalcdep.2018.01.008 19. Walter KN, Wagner JA, Cengiz E, Tamborlane WV, Petry NM. Substance use disorders among patients with type 2 diabetes: a dangerous but understudied combination. Curr Diab Rep. (2017) 17 2. DOI: 10.1007/s11892-017-0832-0 20. DeYoung OA, Boehmer K, Hung D, McAdam-Marx C. A historical cohort study of glycemic control in patients with concurrent type 2 diabetes and substance use disorder treated in a primary care setting. Fam Pract. (2021) 38 562-8. DOI: 10.1093/fampra/cmab008 21. McNeely J, Kumar PC, Rieckmann T, Sedlander E, Farkas S, Chollak C. Barriers and facilitators affecting the implementation of substance use screening in primary care clinics: a qualitative study of patients, providers, and staff. Addict Sci Clin Pract. (2018) 13 8. DOI: 10.1186/s13722-018-0110-8 22. Stitzer ML, Petry NM, Peirce J. Motivational incentives research in the National Drug Abuse Treatment Clinical Trials Network. J Subst Abuse Treat. (2010) 1 S61-9. DOI: 10.1016/j.jsat.2009.12.010 23. Ling W, Amass L, Shoptaw S, Annon JJ, Hillhouse M, Babcock D. A multi-center randomized trial of buprenorphine-naloxone versus clonidine for opioid detoxification: findings from the National Institute on Drug Abuse Clinical Trials Network. Addiction. (2005) 100 1090-100. DOI: 10.1111/j.1360-0443.2005.01154.x 24. Ling W, Hillhouse M, Domier C, Doraimani G, Hunter J, Thomas C. Buprenorphine tapering schedule and illicit opioid use. Addiction. (2009) 104 256-65. DOI: 10.1111/j.1360-0443.2008.02455.x 25. Lee JD, Jr EVN, Novo P, Bachrach K, Bailey GL, Bhatt S. Comparative effectiveness of extended-release naltrexone versus buprenorphine-naloxone for opioid relapse prevention (X:BOT): a multicentre, open-label, randomised controlled trial. Lancet. (2018) 391 309-18. DOI: 10.1016/S0140-6736(17)32812-X 26. Trivedi MH, Walker R, Ling W, Cruz AD, Sharma G, Carmody T. Bupropion and naltrexone in methamphetamine use disorder. N Engl J Med. (2021) 384 140-53. DOI: 10.1056/NEJMoa2020214 27. Kranzler HR, Soyka M. Diagnosis and pharmacotherapy of alcohol use disorder: a review. JAMA. (2018) 320 815-24. DOI: 10.1001/jama.2018.11406 28. Ziedonis D, Das S, Larkin C. Tobacco use disorder and treatment: new challenges and opportunities. Dialogues Clin Neurosci. (2017) 19 271-80. DOI: 10.31887/DCNS.2017.19.3/dziedonis 29. Miller WR, Sorensen JL, Selzer JA, Brigham GS. Disseminating evidence-based practices in substance abuse treatment: a review with suggestions. J Subst Abuse Treat. (2006) 31 25-39. DOI: 10.1016/j.jsat.2006.03.005 30. Patnode CD, Henderson JT, Thompson JH, Senger CA, Fortmann SP, Whitlock EP. Behavioral counseling and pharmacotherapy interventions for tobacco cessation in adults, including pregnant women: a review of reviews for the U. S. Preventive Services Task Force. Ann Intern Med. (2015) 163 608-21. DOI: 10.7326/M15-0171 31. Tilbrook D, Jacob J, Parsons P, Edwards C, Loewen K, Kelly L. Opioid use disorder and type 2 diabetes mellitus: effect of participation in buprenorphine-naloxone substitution programs on glycemic control. Can Fam Physician. (2017) 63 e350-4. PMID: 28701461 32. Tai B, Volkow ND. Treatment for substance use disorder: opportunities and challenges under the affordable care act. Soc Work Public Health. (2013) 28 165-74. DOI: 10.1080/19371918.2013.758975 33. Abraham AJ, Andrews CM, Harris SJ, Friedmann PD. Availability of medications for the treatment of alcohol and opioid use disorder in the USA. Neurotherapeutics. (2020) 17 55-69. DOI: 10.1007/s13311-019-00814-4 34. Knudsen HK, Abraham AJ, Roman PM. Adoption and implementation of medications in addiction treatment programs. J Addict Med. (2011) 5 21-7. DOI: 10.1097/ADM.0b013e3181d41ddb 35. 35.SAMHSA. Key Substance Use and Mental Health Indicators in the United States: Results from the 2020 National Survey of Drug Use and Health. (2021). Available online at: https://www.samhsa.gov/data/ (accessed February 19, 2023).. (2021) 36. Schmidt EM, Barnes J, Chen C, Trafton J, Frayne S, Harris AHS. Patient and health care factors associated with long-term diabetes complications among adults with and without mental health and substance use disorders. JAMA Netw Open. (2019) 2 e1912060. DOI: 10.1001/jamanetworkopen.2019.12060 37. 37.SAS. SAS/IML® 14.1 User's Guide. SAS Institute Inc., (2015).. SAS/IML (2015) 38. Forthal S, Choi S, Yerneni R, Zhang Z, Siscovick D, Egorova N. Substance use disorders and diabetes care: lessons from new york health homes. Med Care. (2021) 59 881-7. DOI: 10.1097/MLR.0000000000001602 39. Williams JS, Bishu KG, St Germain A, Egede LE. Trends in sex differences in the receipt of quality of care indicators among adults with diabetes: United States 2002–2011. BMC Endocr Disord. (2017) 17 31. DOI: 10.1186/s12902-017-0183-5 40. Shamshirgaran SM, Mamaghanian A, Aliasgarzadeh A, Aiminisani N, Iranparvar-Alamdari M, Ataie J. Age differences in diabetes-related complications and glycemic control. BMC Endocr Disord. (2017) 17 25. DOI: 10.1186/s12902-017-0175-5 41. Bone C, Gelberg L, Vahidi M, Leake B, Yacenda-Murphy J, Andersen RM. Under-reporting of risky drug use among primary care patients in federally qualified health centers. J Addict Med. (2016) 10 387-94. DOI: 10.1097/ADM.0000000000000246 42. Adams JM, Volkow ND. Ethical imperatives to overcome stigma against people with substance use disorders. AMA J Ethics (2020) 22 E702-708. DOI: 10.1001/amajethics.2020.702 43. Hu LL, Sparenborg S, Tai B. Privacy protection for patients with substance use problems. Subst Abuse Rehabil (2011) 2 227-33. DOI: 10.2147/SAR.S27237 44. Evans E, Li L, Min J, Huang D, Urada D, Liu L. Mortality among individuals accessing pharmacological treatment for opioid dependence in California, 2006–10. Addiction. (2015) 110 996-1005. DOI: 10.1111/add.12863 45. Manning V, Garfield JBB, Lam T, Allsop S, Berends L, Best D. Improved quality of life following addiction treatment is associated with reductions in substance use. J Clin Med. (2019) 8 1407. DOI: 10.3390/jcm8091407 46. 46.NIDDK. Diabetes Health Information National Institute of Diabetes and Digestive and Kidney Diseases. (2021). Available online at: https://www.niddk.nih.gov/health-information/diabetes (accessed December 30, 2021).. (2021)
--- title: Development and validation of nomogram for unplanned ICU admission in patients with dilated cardiomyopathy authors: - Xiao-Lei Li - Dilare Adi - Qian Zhao - Aibibanmu Aizezi - Munawaer Keremu - Yan-Peng Li - Fen Liu - Xiang Ma - Xiao-Mei Li - Adila Azhati - Yi-Tong Ma journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC10060526 doi: 10.3389/fcvm.2023.1043274 license: CC BY 4.0 --- # Development and validation of nomogram for unplanned ICU admission in patients with dilated cardiomyopathy ## Abstract ### Objective Unplanned admission to the intensive care unit (ICU) is the major in-hospital adverse event for patients with dilated cardiomyopathy (DCM). We aimed to establish a nomogram of individualized risk prediction for unplanned ICU admission in DCM patients. ### Methods A total of 2,214 patients diagnosed with DCM from the First Affiliated Hospital of Xinjiang Medical University from January 01, 2010, to December 31, 2020, were retrospectively analyzed. Patients were randomly divided into training and validation groups at a 7:3 ratio. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used for nomogram model development. The area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. The primary outcome was defined as unplanned ICU admission. ### Results A total of 209 ($9.44\%$) patients experienced unplanned ICU admission. The variables in our final nomogram included emergency admission, previous stroke, New York Heart Association Class, heart rate, neutrophil count, and levels of N-terminal pro b-type natriuretic peptide. In the training group, the nomogram showed good calibration (Hosmer–Lemeshow χ2 = 14.40, $$P \leq 0.07$$) and good discrimination, with an optimal-corrected C-index of 0.76 ($95\%$ confidence interval: 0.72–0.80). DCA confirmed the clinical net benefit of the nomogram model, and the nomogram maintained excellent performances in the validation group. ### Conclusion This is the first risk prediction model for predicting unplanned ICU admission in patients with DCM by simply collecting clinical information. This model may assist physicians in identifying individuals at a high risk of unplanned ICU admission for DCM inpatients. ## Introduction Dilated cardiomyopathy (DCM) is characterized by dilatation and impaired function of ventricles [1]. Studies have shown that 5–8.34 cases of DCM occur per 100,000 people per year, with the 5-year survival rate of only $50\%$ [2, 3]. The incidence of adverse events is an important factor that affects the prognosis of patients with DCM [4, 5]. Unplanned intensive care unit (ICU) admission is the major in-hospital adverse event for DCM inpatients. Compared to direct ICU admission, unplanned ICU admission is associated with poorer in-hospital prognosis and substantially mortality rates [6, 7]. Additionally, unplanned ICU admission can significantly magnify the psychological stress of patients and their families [8]. Therefore, assessing risks from unplanned ICU admission is not only just for managing individualization prognosis but also for improving healthcare quality. According to the reports, approximately $36\%$ of unplanned ICU admission is preventable; therefore, early identification can effectively improve patient survival and rationalize the use of healthcare resources (9–11). Several scoring systems, such as Early Warning Scores (EWS) and National Early Warning Score (NEWS), have been widely developed and used for identifying patients at risk of early disease progression [12, 13]; however, most risk prediction models are established based on general emergency patients, and the generic prediction model fails to fit the featured population [14]. In DCM inpatients, to the best of our knowledge, no prediction models have been developed for assessing unplanned ICU admission. In this study, we retrospectively analyzed 2,214 inpatients with DCM and without planned ICU admission at baseline and aimed to develop a nomogram for individualized prediction of unplanned ICU admission incidents in DCM patients by simply collecting clinical information. ## Study design and population This study included 2,735 DCM patients from the retrospective cohort study, which was designed to evaluate the clinical outcomes and risk factors of cardiomyopathy, and the detailed protocol has been registered on www.chictr.org.cn (registration number: ChiCTR2200058051). This registration trial included 5,937 patients with primary cardiomyopathy, namely, DCM, hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and unclassified cardiomyopathy, and the diagnostic criteria refer to the JCS/JHFS 2018 Guideline on the Diagnosis and Treatment of Cardiomyopathies [3]. In addition, this registration trial excluded patients who had malignant tumors, hematological malignancy, autoimmune diseases, serious dysfunction of the kidney or liver, pregnant or lactating women, and patients younger than 18 years old. All patients were admitted to the First Affiliated Hospital of Xinjiang Medical University from January 01, 2010, to December 31, 2020, and the data were obtained from electronic medical records and follow-up. To investigate the individualized risk of unplanned ICU admission in DCM inpatients, a total of 2,735 patients were initially evaluated and 521 were excluded, leading to ultimately 2,214 patients in this study, of which 209 experienced unplanned ICU admission (ICU+) and 2005 were not admitted to the ICU (ICU−). Patient selection and study flow are shown in Figure 1. The inclusion criteria for the present study were as follows [15]: [1] left ventricular end-diastolic dimension (LVEDD) >5.0 cm in females and >5.5 cm in males; and [2] left ventricular ejection fraction (LVEF) <$45\%$. We excluded patients who [1] had ischemic heart disease, hypertensive heart disease, valvular heart disease, or congenital heart disease; [2] had direct admission to ICU; [3] had severe hepatic and renal failure; [4] were admitted for surgical procedures; [5] were younger than 18 years; and [6] had incomplete clinical information. Finally, 2,214 eligible patients were included in this study. Electronic medical records were fully reviewed by two independent reviewers according to the inclusion and exclusion criteria. All study personnel had formal training prior to participation in the study. Figure 1: Flow diagram of the study. ## Data collection All data were obtained from the first measurement at admission. Demographic data, comorbidities, blood tests, and echocardiographic results were included for all patients. Comorbidities included hypertension, diabetes mellitus, stroke, atrial fibrillation (AF), and chronic obstructive pulmonary disease (COPD). Patients who reported smoking in the previous 6 months were considered current smokers. Similarly, patients who consumed alcohol in the last half a year were considered current drinkers. Hypertension was defined as patients with at least three resting measurements above $\frac{140}{90}$ mmHg taken from at least two separate healthcare visits or history of hypertension with active treatment, as suggested by the American Heart Association [16]. Diabetes was defined as having a history of diabetes with using hypoglycemic drugs or random intravenous plasma glucose of 200 mg/dL (11.1 mmol/L), or 2-h plasma glucose of 200 mg/dL (11.1 mmol/L) after an oral glucose tolerance test (OGTT), fasting blood glucose (FPG) of 126 mg/dL (7.0 mmol/L), or hemoglobin A1c (HbA1c) of $6.5\%$ [17]. Stoke was diagnosed by magnetic resonance imaging (MRI) combined with clinical neurological dysfunction [18] and a patient known to had stroke prior to this visit was categorized as prior stroke. AF included all types of previously diagnosed AF, including paroxysmal AF, persistent AF, and permanent AF [19]. COPD was defined as a disease characterized by persistent respiratory symptoms and airflow limitation and diagnosed by the Global initiative for chronic Obstructive Lung Disease (GOLD) [20]. Severe renal insufficiency was defined as estimated glomerular filtration rate (eGFR) <30 mL/min [21]. Severe hepatic insufficiency is defined as alanine transaminase (ALT) or aspartate aminotransferase (AST) exceeding the upper limit of normal by a factor of 5, specifically AST >180 U/L and ALT >260 U/L [22]. ## Outcome assessment The primary outcome was unplanned ICU admission, which was defined as transfer to the ICU due to deterioration or developed complications [23]. ## Statistical analysis All statistical analysis were performed using Social Package for the Social Sciences (SPSS) version 26.0 (SPSS Inc., Chicago, IL, United States), and R software version 4.0.3 (https://cran.r-project.org). The R software mainly include package of “glmnet,” “caret,”“rms,” “pROC,” “rmda,” and so on. ## Division of datasets A total of 2,214 DCM patients were randomly divided into two groups, the training group ($$n = 1$$,551) and the validation group ($$n = 663$$), at a theoretical ratio of 7: 3. ## Variable selection The least absolute shrinkage and selection operator (LASSO) regression is an efficient statistical method to filter out the most important features from high-dimensional data. We performed LASSO regression in the training group to screen out the most useful predictor variables for unplanned ICU admissions. The nonzero coefficient characteristic variables corresponding to the maximum λ within 1 standard deviation (SD) of the mean error was the final model predictor variables. In order to select variables that could predict the primary outcome, we included prespecified variables and variables that were statistically different between two groups into the LASSO regression analysis. The prespecified variables were selected based on clinical experience and current literature reports, as well as consensus on DCM prognostic stratification [24, 25]. They were age, LVEDD, LVESD, and LVEF. Finally, we selected six statistically significant variables including emergency admission, previous stroke, NYHA class, heart rate, neutrophil count, and N-terminal pro b-type natriuretic peptide (NT-proBNP)/100 (Figure 2). Figure 2: Significant variables selection using the LASSO. (A) Plot of each variable’s coefficient profile against log(lambda). (B) Ten-fold cross-validation used to validate the optimal lambda in the LASSO model. LASSO, least absolute shrinkage and selection operator. ## Model development and validation The risk prediction model was developed by multivariate logistic regression, where the dependent variable in the model was unplanned ICU admission, while the independent variables included predictors selected from the LASSO regression. To provide clinicians with a quantitative tool to predict the risk of unplanned ICU admissions, we constructed a nomogram based on multivariate logistic regression analysis. Nomogram performance was evaluated by both discriminations, presented as C-index and the area under the receiver operating characteristic curve (AUC), and calibration, expressed as the Hosmer–Lemeshow test and calibration plot. Discrimination and calibration were also accounted for in estimating the validity of the model in the validation group. To assess the clinical validity of the model, the decision curve analysis (DCA) and clinical impact curve (CIC) were constructed, which were mainly quantitative analyses of the net returns under different threshold probabilities. Categorical variables were expressed as frequencies (%) and continuous variables were expressed as mean ± SD or median (interquartile range). The differences in baseline characteristics between the two groups were examined by independent-samples t-test or Mann–Whiney U-test for continuous variables and the Pearson chi-square test (Pearson χ2 test) or Fisher exact test for categorical variables, as appropriate. All tests were two-sided, and a P value <0.05 was considered statistically significant. ## Patient characteristics in the training group A total of 1,551 (male, $73.1\%$) and 663 patients (male, $72.39\%$) with DCM comprised the training and validation groups, respectively. There were 147 ($9.47\%$) and 62 ($9.35\%$) patients who had unplanned ICU admission in the training and validation groups, respectively. As shown in Table 1, compared with the ICU− patients, ICU+ patients, have a higher ratio of emergency admission (EA), β blocker usage, New York Heart Association (NYHA) class, heart rate, white blood cell count, neutrophil count, aspartate aminotransferase, higher N-terminal pro b-type natriuretic peptide, larger right ventricular diameter (RV), and larger right atrial diameter (RA) (all $P \leq 0.05$, respectively); the lymphocyte count, monocyte count, fasting blood glucose, serum albumin, serum sodium, and serum potassium were significantly lower than those in the ICU− group (all $P \leq 0.05$, respectively). Table 1 \| Variable \| Total \| Training group \| Training group.1 \| P value \| Validation group \| Validation group.1 \| P value.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| Total \| ICU− \| ICU+ \| P value \| ICU− \| ICU+ \| P value \| \| Variable \| Total \| (n = 1,404) \| (n = 147) \| P value \| (n = 601) \| (n = 62) \| P value \| \| Age (years) \| 54 (45–63) \| 54 (45–63) \| 53 (43–61.5) \| 0.122 \| 55 (44–62) \| 54 (44–62) \| 0.787 \| \| Male, n (%) \| 1,609 (72.7) \| 1,026 (73.1) \| 103 (70.1) \| 0.435 \| 433 (72.0) \| 47 (75.8) \| 0.528 \| \| Ethnicity, n (%) \| \| \| \| \| \| \| \| \| Han \| 1,133 (51.2) \| 720 (51.3) \| 78 (53.2) \| 0.902 \| 305 (50.7) \| 30 (48.4) \| 0.913 \| \| Uygur \| 706 (31.9) \| 458 (32.6) \| 47 (31.9) \| \| 182 (30.3) \| 19 (30.6) \| \| \| Other races \| 375 (16.9) \| 226 (16.1) \| 22 (14.9) \| \| 114 (19.0) \| 13 (21.0) \| \| \| Admission form, n (%) \| \| \| \| \| \| \| \| \| Emergency \| 620 (28.0) \| 379 (27.0) \| 70 (47.6) \| <0.001 \| 141 (23.5) \| 30 (48.4) \| <0.001 \| \| Referral \| 454 (20.5) \| 285 (20.3) \| 25 (17.0) \| \| 132 (22) \| 12 (19.4) \| \| \| Clinic \| 1,140 (51.5) \| 740 (52.7) \| 52 (35.4) \| \| 328 (54.6) \| 20 (32.2) \| \| \| Smoking, n (%) \| 841 (37.9) \| 547 (39.0) \| 55 (37.4) \| 0.715 \| 217 (36.1) \| 22 (35.5) \| 0.923 \| \| Drinking, n (%) \| 495 (22.4) \| 325 (23.1) \| 33 (22.4) \| 0.848 \| 125 (20.8) \| 12 (19.4) \| 0.789 \| \| NYHA, n (%) \| \| \| \| \| \| \| \| \| Grade II–III \| 1,729 (78.1) \| 1,120 (79.8) \| 84 (57.1) \| <0.001 \| 493 (82.0) \| 32 (51.6) \| <0.001 \| \| Grade IV \| 485 (21.9) \| 284 (20.2) \| 63 (42.9) \| \| 108 (18.0) \| 30 (48.4) \| \| \| Weight (kg) \| 75 (63,85) \| 74.5 (63,84) \| 75 (63,83) \| 0.906 \| 75 (63,86) \| 74.5 (64.2,85) \| 0.619 \| \| SBP (mmHg) \| 120 (105–130) \| 120 (105–130) \| 118 (104.5–130) \| 0.531 \| 120 (105–130) \| 111 (102–123.8) \| 0.043 \| \| DBP (mmHg) \| 76 (67–84) \| 75 (68–83) \| 75 (68.5–85.5) \| 0.369 \| 76 (67–85) \| 75 (65.2–80) \| 0.318 \| \| HR (beats/min) \| 84 (76–99) \| 84 (76–98) \| 96 (78–110) \| <0.001 \| 84 (76–98) \| 90 (78.5–99.8) \| 0.104 \| \| Medical history, n (%) \| \| \| \| \| \| \| \| \| Hypertension \| 666 (30.1) \| 428 (30.5) \| 42 (28.6) \| 0.631 \| 183 (30.4) \| 13 (20.9) \| 0.119 \| \| Diabetes mellitus \| 311 (14.1) \| 196 (14.0) \| 24 (16.3) \| 0.434 \| 79 (13.1) \| 12 (19.4) \| 0.176 \| \| Previous stroke \| 107 (4.8) \| 64 (4.6) \| 20 (13.6) \| <0.001 \| 19 (3.2) \| 4 (6.5) \| 0.26 \| \| AF \| 331 (15.1) \| 213 (15.2) \| 27 (18.4) \| 0.308 \| 80 (13.3) \| 11 (17.7) \| 0.334 \| \| COPD \| 158 (7.1) \| 110 (7.8) \| 9 (6.1) \| 0.458 \| 34 (5.7) \| 5 (8.1) \| 0.398 \| \| Laboratory characteristics \| \| \| \| \| \| \| \| \| WBC (109/L) \| 6.95 (5.68–8.44) \| 6.80 (5.60–8.30) \| 7.60 (6.30–9.40) \| <0.001 \| 7.00 (5.70–8.40) \| 7.70 (6.40–9.90) \| 0.003 \| \| Neut (109/L) \| 4.28 (3.32–5.59) \| 4.20 (3.30–5.50) \| 4.90 (3.90–6.80) \| <0.001 \| 4.30 (3.30–5.50) \| 5.20 (4.10–7.40) \| <0.001 \| \| Lymph (109/L) \| 1.74 (1.34–2.25) \| 1.80 (1.40–2.30) \| 1.60 (1.30–2.20) \| 0.032 \| 1.80 (1.40–2.30) \| 1.40 (1.00–1.90) \| <0.001 \| \| Mono (109/L) \| 0.54 (0.42–0.70) \| 0.50 (0.40–0.70) \| 0.60 (0.50–0.80) \| <0.001 \| 0.50 (0.40–0.70) \| 0.70 (0.50–0.80) \| <0.001 \| \| Hb (g/L) \| 140 (127–152) \| 140 (127–152) \| 140 (126.5–154) \| 0.847 \| 139 (127–152) \| 135 (116.8–147.8) \| 0.082 \| \| Serum creatinine (umol/L) \| 80.00 (66.84–95.83) \| 80.00 (67.00–95.80) \| 83.00 (66.80–101.60) \| 0.238 \| 79.00 (65.10–94.00) \| 90.90 (72.20–105.90) \| 0.006 \| \| Serum urea (mmol/L) \| 6.40 (5.10–8.00) \| 6.40 (5.10–8.00) \| 6.50 (5.20–8.40) \| 0.432 \| 6.20 (5.10–7.90) \| 6.80 (5.50–8.00) \| 0.042 \| \| ALT (U/L) \| 26.50 (17.80–44.32) \| 26.50 (17.40–43.80) \| 26.00 (18.20–49.70) \| 0.428 \| 26.50 (17.90–43.20) \| 28.50 (19.50–53.50) \| 0.118 \| \| AST (U/L) \| 25.41 (18.90–35.60) \| 25.00 (18.70–34.30) \| 29.00 (21.50–40.40) \| <0.001 \| 24.70 (18.60–36.00) \| 28.70 (21.00–48.10) \| 0.024 \| \| Serum albumin (g/L) \| 37.10 (33.40–40.59) \| 37.20 (33.60–40.50) \| 35.3 (31.10–39.70) \| <0.001 \| 37.30 (33.70–40.70) \| 35.10 (31.70–39.20) \| 0.02 \| \| Serum sodium (mmol/L) \| 3.83 (3.54–4.16) \| 3.80 (3.50–4.20) \| 3.80 (3.50–4.20) \| 0.819 \| 3.84 (3.54–4.15) \| 3.78 (3.45–4.28) \| 0.905 \| \| Serum potassium (mmol/L) \| 140.00 (137.08–142.50) \| 140.20 (137.50–142.7) \| 138.40 (135.60–141.00) \| <0.001 \| 140.00 (137.20–142.60) \| 137.70 (134.10–139.80) \| <0.001 \| \| Serum chloride (mmol/L) \| 104.00 (101.00–106.60) \| 104.00 (101.10–106.60) \| 103.90 (100.30–106.60) \| 0.485 \| 104.00 (101.20–106.50) \| 102.80 (98.60–106.10) \| 0.017 \| \| Serum calcium (mmol/L) \| 2.21 (2.13–2.30) \| 2.20 (2.10–2.30) \| 2.20 (2.10–2.30) \| 0.484 \| 2.20 (2.10–2.30) \| 2.20 (2.10–2.30) \| 0.047 \| \| FBG (mmol/L) \| 5.54 (4.74–7.04) \| 5.50 (4.70–6.90) \| 5.90 (5.00–7.30) \| 0.004 \| 5.50 (4.80–7.00) \| 6.20 (5.10–7.90) \| 0.013 \| \| NT-proBNP/100 (ng/mL) \| 23.52 (9.76–50.27) \| 21.60 (8.90–47.60) \| 42.90 (18.30–88.90) \| <0.001 \| 20.70 (9.60–42.40) \| 52.70 (27.40–87.40) \| <0.001 \| \| Echocardiography characteristics \| \| \| \| \| \| \| \| \| LA (mm) \| 44 (40–49) \| 44 (40–49) \| 45 (40–49) \| 0.451 \| 44 (41–49) \| 45.5 (41.2–50.5) \| 0.331 \| \| LVEDD (mm) \| 67 (62–73) \| 67 (62–73) \| 68 (63–74) \| 0.204 \| 67 (62–74) \| 69 (64–75) \| 0.078 \| \| LVESD (mm) \| 55 (50–61.25) \| 55 (50–61) \| 57 (51–64) \| 0.152 \| 55 (50–62) \| 57 (53–64) \| 0.029 \| \| RA (mm) \| 40 (35–46) \| 40 (35–46) \| 42 (37–48) \| 0.003 \| 39 (35–46) \| 40 (35–49) \| 0.306 \| \| RV (mm) \| 21 (19–24.25) \| 21 (19–24) \| 22 (20–25) \| 0.008 \| 21 (19–25) \| 22 (19–25) \| 0.279 \| \| LVEF (%) \| 35.00 (30.36–40.00) \| 35.50 (31.00–40.00) \| 35.00 (29.50–39.00) \| 0.128 \| 35.00 (30.00–40.00) \| 33.00 (30.00–37.80) \| 0.087 \| \| Medication, n (%) \| \| \| \| \| \| \| \| \| ACEI/ARB \| 1,462 (66.0) \| 930 (66.2) \| 93 (63.3) \| 0.469 \| 406 (67.6) \| 33 (53.2) \| 0.023 \| \| β blocks \| 1,614 (72.9) \| 1,043 (74.3) \| 97 (66.0) \| 0.03 \| 433 (72.0) \| 41 (66.1) \| 0.326 \| \| MRA \| 1,729 (78.1) \| 1,110 (79.1) \| 106 (72.1) \| 0.051 \| 468 (77.9) \| 45 (72.6) \| 0.343 \| \| Diuretic \| 1,172 (52.9) \| 745 (53.1) \| 74 (50.3) \| 0.529 \| 320 (53.2) \| 33 (53.2) \| 0.998 \| \| Digoxin \| 849 (38.3) \| 543 (38.7) \| 46 (31.3) \| 0.079 \| 367 (61.1) \| 36 (58.1) \| 0.645 \| \| Instrumentation, n (%) \| \| \| \| \| \| \| \| \| CRT/CRTD \| 90 (4.1) \| 1,349 (96.1) \| 138 (93.9) \| 0.201 \| 23 (3.8) \| 3 (4.8) \| 0.727 \| \| ICD \| 55 (2.5) \| 35 (2.5) \| 6 (4.1) \| 0.253 \| 12 (2.0) \| 2 (3.2) \| 0.383 \| Patients in the ICU+ group were more likely to have larger RA and RV (all $P \leq 0.05$), and the left atrial diameter (LA), LVEDD, left ventricular end-systolic diameter (LVESD), and LVEF had no significant difference between the two groups. ## Model development In order to simplify the model and make it easier to use, based on optimal cutoff values, we converted heart rate (100 beats/min) and neutrophil count (4.385 × 109/L) into classified variables. Then, we used logistic regression analysis to analyze the incidence of unplanned ICU admission of DCM patients in the training group, finding that ED [odds ratio (OR): 2.13; $95\%$ confidence interval (CI): 1.48–3.06, $P \leq 0.001$], previous stroke (OR: 3.12, $95\%$ CI: 1.76–5.55, $P \leq 0.001$), NYHA class IV (OR: 1.81, $95\%$ CI: 1.23–2.65, $$P \leq 0.002$$), heart rate (OR: 2.63, $95\%$ CI: 1.54–3.33, $P \leq 0.001$), neutrophil count (OR: 1.90, $95\%$ CI: 1.31–2.76, $$P \leq 0.001$$), and NT-proBNP/100 (OR: 1.01, $95\%$ CI: 1.01–1.01, $P \leq 0.001$) were independent risk factors for unplanned ICU admission in DCM patients ($P \leq 0.001$) (Table 2). Table 2 \| Variables \| β \| Univariate analysis \| Univariate analysis.1 \| Univariate analysis.2 \| β.1 \| Multivariable analysis \| Multivariable analysis.1 \| Multivariable analysis.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| β \| OR \| 95% CI \| p value \| β \| OR \| 95% CI \| p value \| \| Emergency admission \| 1.06 \| 2.90 \| 2.05–4.09 \| <0.001 \| 0.76 \| 2.13 \| 1.48–3.06 \| <0.001 \| \| Previous stroke \| 1.19 \| 3.30 \| 1.93–5.63 \| <0.001 \| 1.14 \| 3.12 \| 1.76–5.55 \| <0.001 \| \| NYHA class \| 1.08 \| 2.96 \| 2.08–4.20 \| <0.001 \| 0.59 \| 1.81 \| 1.23–2.65 \| 0.002 \| \| HR \| 1.15 \| 3.16 \| 2.20–4.53 \| <0.001 \| 0.82 \| 2.63 \| 1.54–3.33 \| <0.001 \| \| Neut \| 0.87 \| 2.39 \| 1.68–3.41 \| <0.001 \| 0.64 \| 1.90 \| 1.31–2.76 \| 0.001 \| \| NT-proBNP/100 \| 0.01 \| 1.01 \| 1.01–1.02 \| <0.001 \| 0.01 \| 1.01 \| 1.01–1.01 \| <0.001 \| ## Nomogram model display Six independent risk variables were used to build a nomogram for predicting the risk of unplanned ICU admission in patients with DCM. The scores corresponding to each predictor variable in the nomogram were summed, and the resulting probability value corresponding to the total score is the probability of risk of unplanned ICU admission (Figure 3). Figure 3: Nomogram to predict the risk of unplanned ICU admission in DCM inpatients. Points were assigned for each variable by drawing a line upward from the corresponding values to the “points line.” The “total points” was calculated as the sum of the individual score of each of the six variables included in the nomogram. We can estimate the risk of unplanned ICU admission for this patient by the probability corresponding to the “total points.” ICU, intensive care unit; DCM, dilated cardiomyopathy; NYHA, New York Heart Association; NT-proBNP, N-terminal pro b-type natriuretic peptide. ## Nomogram evaluation and validation The discriminatory ability of the nomogram was evaluated by calculating the C-statistic as 0.76 ($95\%$ CI: 0.72–0.80) in the training group. The corrected C-statistic from bootstrap resampling showed good internal validation with a value of 0.75. The model proved to be accurate in predicting unplanned ICU admissions of DCM patients with an AUC of 0.76 ($95\%$ CI: 0.72–0.80, Figure 4A). The Hosmer–Lemeshow test showed that the model has good calibration (χ2 = 14.40, $$P \leq 0.07$$), and the calibration curves similarly showed good calibration between the predicted and actual risk of unplanned ICU admissions for DCM patients (Figure 5A). Figure 4: AUC of the model for predicting unplanned ICU admission of DCM patients. A, Development group. B, Validation group. Red curve shown the receiver operating characteristic curve (ROC) for nomogram. AUC, area under curve. Figure 5: Calibration curve of the nomogram for the development group (A) and the validation group (B). The dotted line represents the ideal prediction, while the red line represents the actual calibration curve of the nomogram. The yellow line meanwhile represents the internal corrected calibration curve of the nomogram. The C-index also reached 0.78 in the validation group and the AUC was 0.77 ($95\%$ CI: 0.70–0.83), as shown in Figure 4B. The calibration curves of the nomogram also suggested a good agreement between the actual and the predicted outcomes (Figure 5B). To estimate the clinical utility of the nomogram, DCA and CIC were used. The results of DCA are presented in Figure 6, showing that the use of this model for making clinical decisions has more benefit than the “no intervention” or “all intervention” scenarios when the unplanned ICU admission threshold probability was between 0 and 0.75 in the training group and was generally between 0.0 and 1 in the validation group. Also, CIC analysis showed the clinical efficiency of the nomogram, when the threshold probability was greater than $65\%$; the prediction model determined that the population at high risk for unplanned ICU admission highly matched the population experiencing unplanned ICU admissions (Figure 7). Figure 6: Decision curve analysis of the nomogram for the development group (A) and the validation group (B). The black line indicates that for extreme cases, the model predicts that all DCM patients have low-ICU+ probability and the clinical net benefit is 0. The gray curve indicates that for extreme cases, the model predicts that all DCM patients have moderate or high ICU+ probability and the clinical net benefit is the negative slope. The red line indicates that the model has clinical net benefit. The red line is higher than the gray and black lines, indicating that patients can benefit from the model. ICU, intensive care unit; DCM, dilated cardiomyopathy. Figure 7: Clinical impact curve analysis of the nomogram for development group (A) and the validation group (B). The red curve indicates the number of people classified as positive (high risk) by the model at each threshold probability, and the blue curve is the number of true positives. ## Performance of the GWTG-HF score of DCM inpatients The data showed that the Get With the Guidelines-Heart Failure (GWTG-HF) score was an independent risk factor for unplanned ICU admission in DCM patients (OR: 1.04; $95\%$ CI: 1.02–1.06; $P \leq 0.001$). In parallel, we evaluated the ability of the GWTG-HF score to assess the risk of unplanned transfer to the ICU for DCM inpatients. However, the diagnostic power of the GWTG-HF score was general, and the AUC of unplanned ICU admission was only 0.58 ($95\%$ CI: 0.53–0.63). The DeLong test suggested a statistically significant difference between the nomogram model and the GWTG-HF score in the ability to differentiate patients with high risk of unplanned ICU admission ($P \leq 0.001$, Figure 8). Figure 8: AUC of nomogram and GWTG-HF. Red and blue curves shown the ROC for nomogram and GWTG-HF. AUC, area under curve; GWTG-HF, Get With the Guidelines-Heart Failure; ROC, receiver operating characteristic curve. ## Discussion Early identification of DCM inpatients at high risk of unplanned ICU admission provides an important opportunity to assess deterioration and make timely changes in the treatment strategy. To overcome this practical need, for the first time we developed and validated a nomogram for DCM inpatients to predict the risk of unplanned ICU admissions. Emergency admission, previous stroke, NYHA class, heart rate, neutrophil count, and NT-proBNP were demonstrated to be predictors of elevated risk for unplanned ICU admissions. Previous studies have reported that, in general internal inpatients, unplanned ICU admissions contribute to $14\%$–$28\%$ of ICU admissions [12, 26]. From our data, the percentage of unplanned ICU admissions occurring is roughly $9.44\%$ ($\frac{209}{2214}$) among DCM inpatients, and most of these inpatients were admitted through ED ($47.85\%$, $\frac{100}{209}$). In our study, the admission pathway was a valuable predictor of deterioration (OR: 2.13, $95\%$ CI: 1.48–3.06, $P \leq 0.001$). Numerous studies have also confirmed that patients transferred from the general unit to ICU for intensive care management have higher in-hospital mortality than those admitted directly from ED [7, 9]. Therefore, we considered that advance risk assessment and management of DCM patients with emergency admissions might be an effectively managed approach. Currently, the Medical Emergency Team (MET) system could be widely applied and extended to assess the risk of an emergency patient. Efficient use of the MET system can reduce the incidence of unplanned ICU admissions and is independently associated with reduced hospital mortality [27, 28]. Of note, some studies have been conducted on the risk assessment of unplanned ICU admissions. At present, the EWS has been used internationally and is widely promoted [29, 30]. The NEWS, established by the Royal College of Physicians of London in 2012, is one of the scoring systems used to assess the severity of acute illnesses [13]. The NEWS has proven to be a very effective tool for assessing the risk of in-hospital adverse events such as unplanned ICU admissions and in-hospital deaths. The predictors included in the NEWS were respiration rate, SpO2, any supplemental oxygen, temperature, SBP, heart rate, and level of consciousness, among which respiration rate, SpO2, and heart rate affect unplanned ICU admissions. Also, heart rate was the most important predictor in our nomogram model, emphasizing the importance of vital signs in assessing the risk of unplanned ICU admissions. Lindgren et al. showed the causal relationship between increased heart rate and myocardial systolic dysfunction, and fast heart rate in adolescents was strongly associated with the development of DCM-related heart failure [31]. The TRED-HF study also found that increased heart rate might be a valid indicator of worsening cardiac function and relapse in patients recovering from DCM [32]. Unfortunately, due to the missing arterial blood gas and temperature data, we were unable to assess the performance of the NEWS in DCM patients. As we know, composite clinical endpoints, including unplanned ICU admission, cardiac arrest, and in-hospital mortality, were the observed outcomes for prior scores (32–35), and most of the study information was based on health records of general inpatients (33–35). Generalizing these scores to risk stratification of cardiovascular disease, therefore, may be somewhat limited. In view of the following, we formulated a new nomogram model to assess the potential risk of unplanned ICU admissions for DCM inpatients based on clinical information and laboratory characteristics. The C-index of our nomogram in the training group and the validation group were 0.76 and 0.78, respectively, indicating that the model had high discriminative power. Moreover, the calibration curve also suggested good agreement between the actual probabilities and the predicted probabilities in the training and validation groups. Our nomogram model still has a number of unique advantages. First, the population enrolled in this study differs from previous studies, which have mostly studied patients in the emergency department. In contrast, our study focused on inpatients with DCM. Second, we used LASSO regression to effectively avoid multivariate multicollinearity and overfitting in the variable selection procedure [36]. It also requires attention that exacerbation of heart failure is an important driver of deterioration in DCM patients. NYHA class and NT-proBNP on admission had previously been confirmed to be associated with an increased risk of major adverse cardiovascular events (MACE) in DCM patients [37, 38]. They are widely used by several models of heart failure to stratify risk and predict prognosis. For this reason, NT-proBNP and NYHA class need to be considered, as we did in our study. In our nomogram model, NYHA class and NT-proBNP were independent predictors of unplanned ICU admissions. In our study, we found that the level of neutrophils was high in DCM inpatients who were unplanned for ICU admission. Previous studies have shown that inflammation plays a very important role in the development of DCM [39], and neutrophils as an index of inflammation was closely associated with the severity of heart failure in DCM patients. Evidence of inflammatory infiltration has also been found in myocardial biopsy samples from DCM patients [40]. Moreover, neutrophils activation may accelerate disease progression in DCM by promoting fibrosis in the myocardium [41]. In this study, we found that elevated neutrophil count was an important marker of deterioration and unplanned ICU admission in DCM inpatients. Similarly, risk stratification in NEWS was improved by including the neutrophil count measured at hospital admission, and these improvements were replicated across several different studies [42]. Redfern et al. recommended routinely collected blood tests combined with vital signs to assess unplanned ICU admissions [43]. Therefore, we included neutrophils in the prediction model we constructed, which was a feature of our model. The GWTG-HF risk score is a risk assessment tool commonly used to predict in-hospital mortality in patients with heart failure [44]. In our patients, the GWTG-HF risk score is also useful as a tool for estimating unplanned ICU admissions in DCM patients (OR: 1.04; $95\%$ CI: 1.02–1.06; $P \leq 0.001$). We compared the diagnostic efficacy of our nomogram and GWTG-HF risk score using ROC curve analysis. Ultimately, our model performs much better (Figure 8). Hence, it is reasonable to believe that our model has the potential to be a useful tool for evaluating unplanned ICU admissions for DCM inpatients. Our study also has some limitations. First, this is a single-center and retrospective study, and some patients with incomplete data were also excluded, leading to selective bias. Therefore, multicenter and prospective studies are still necessary to improve the accuracy and applicability of the model. Second, we lacked data needed to externally validate our risk prediction model, so external validation of other clinical research centers is still needed to verify the predictive effect of our nomogram. Third, the population of our study was restricted to DCM inpatients, which also limited its application. A risk assessment study of unplanned ICU admissions for outpatients with DCM will be our next task. In addition, the course of DCM is a dynamic evolutionary process, and a single cross-sectional analysis cannot comprehensively assess the prognosis of DCM, which requires close observation and long-term follow-up, as well as timely adjustment of the treatment plan. ## Conclusion We developed and validated a new nomogram to predict the risk for unplanned ICU admission in DCM patients, based on six easily accessible independent risk variables. The nomogram may assist physicians in identifying individuals at high unplanned ICU admission risk for DCM inpatients. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committees of the First Affiliated Hospital of Xinjiang Medical University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions Y-TM, AA (Adila Azhati), XL (Xiao-Lei Li), and DA: conception and design of the study, data collecting and analysis, and drafting of article. QZ, AA (Aibibanmu Aizezi), MK, and Y-PL: data collection and data analysis. FL, XM, and XL (Xiao-Mei Li): critical revision of article. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Pinto YM, Elliott PM, Arbustini E, Adler Y, Anastasakis A, Bohm M. Proposal for a revised definition of dilated cardiomyopathy, hypokinetic non-dilated cardiomyopathy, and its implications for clinical practice: a position statement of the ESC working group on myocardial and pericardial diseases. Eur Heart J (2016.0) 37 1850-8. DOI: 10.1093/eurheartj/ehv727 2. Seferovic PM, Polovina M, Bauersachs J, Arad M, Ben Gal T, Lund LH. Heart failure in cardiomyopathies: a position paper from the Heart Failure Association of the European Society of Cardiology. Eur J Heart Fail (2019.0) 21 553-76. DOI: 10.1002/ejhf.1461 3. Kitaoka H, Tsutsui H, Kubo T, Ide T, Chikamori T, Fukuda K. JCS/JHFS 2018 guideline on the diagnosis and treatment of cardiomyopathies. Circ J (2021.0) 85 1590-689. DOI: 10.1253/circj.CJ-20-0910 4. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M. Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation (2016.0) 133 e38-360. DOI: 10.1161/CIR.0000000000000350 5. Halliday BP, Gulati A, Ali A, Newsome S, Lota A, Tayal U. Sex- and age-based differences in the natural history and outcome of dilated cardiomyopathy. Eur J Heart Fail (2018.0) 20 1392-400. DOI: 10.1002/ejhf.1216 6. Chioncel O, Ambrosy AP, Filipescu D, Bubenek S, Vinereanu D, Petris A. Patterns of intensive care unit admissions in patients hospitalized for heart failure: insights from the RO-AHFS registry. J Cardiovasc Med (Hagerstown) (2015.0) 16 331-40. DOI: 10.2459/JCM.0000000000000030 7. Frost SA, Alexandrou E, Bogdanovski T, Salamonson Y, Parr MJ, Hillman KM. Unplanned admission to intensive care after emergency hospitalisation: risk factors and development of a nomogram for individualising risk. Resuscitation (2009.0) 80 224-30. DOI: 10.1016/j.resuscitation.2008.10.030 8. Jennerich AL, Hobler MR, Sharma RK, Engelberg RA, Curtis JR. Unplanned admission to the ICU: a qualitative study examining family member experiences. Chest (2020.0) 158 1482-9. DOI: 10.1016/j.chest.2020.05.554 9. Reese J, Deakyne SJ, Blanchard A, Bajaj L. Rate of preventable early unplanned intensive care unit transfer for direct admissions and emergency department admissions. Hosp Pediatr (2015.0) 5 27-34. DOI: 10.1542/hpeds.2013-0102 10. Essay P, Balkan B, Subbian V. Decompensation in critical care: early prediction of acute heart failure onset. JMIR Med Inform (2020.0) 8 e19892. DOI: 10.2196/19892 11. Grieve R, O'Neill S, Basu A, Keele L, Rowan KM, Harris S. Analysis of benefit of intensive care unit transfer for deteriorating ward patients: a patient-centered approach to clinical evaluation. JAMA Netw Open (2019.0) 2 e187704. DOI: 10.1001/jamanetworkopen.2018.7704 12. Fu LH, Schwartz J, Moy A, Knaplund C, Kang MJ, Schnock KO. Development and validation of early warning score system: a systematic literature review. J Biomed Inform (2020.0) 105 103410. DOI: 10.1016/j.jbi.2020.103410 13. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the national early warning score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation (2013.0) 84 465-70. DOI: 10.1016/j.resuscitation.2012.12.016 14. Blackwell JN, Keim-Malpass J, Clark MT, Kowalski RL, Najjar SN, Bourque JM. Early detection of in-patient deterioration: one prediction model does not fit all. Crit Care Explor (2020.0) 2 e0116. DOI: 10.1097/CCE.0000000000000116 15. Chinese guidelines for the diagnosis and treatment of dilated cardiomyopathy. J Clin Cardiol (2018.0) 34 421-34. DOI: 10.13201/j.issn.1001-1439.2018.05.001 16. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M. 2018 ESC/ESH guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension. J Hypertens (2018.0) 36 1953-2041. DOI: 10.1097/HJH.0000000000001940 17. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V. 2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J (2020.0) 41 255-323. DOI: 10.1093/eurheartj/ehz486 18. Sacco RL, Kasner SE, Broderick JP, Caplan LR, Connors JJ, Culebras A. An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke (2013.0) 44 2064-89. DOI: 10.1161/STR.0b013e318296aeca 19. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomstrom-Lundqvist C. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J (2021.0) 42 373-498. DOI: 10.1093/eurheartj/ehaa612 20. Mirza S, Clay RD, Koslow MA, Scanlon PD. COPD guidelines: a review of the 2018 GOLD report. Mayo Clin Proc (2018.0) 93 1488-502. DOI: 10.1016/j.mayocp.2018.05.026 21. Levey AS, Eckardt KU, Dorman NM, Christiansen SL, Hoorn EJ, Ingelfinger JR. Nomenclature for kidney function and disease: report of a kidney disease: improving global outcomes (KDIGO) consensus conference. Kidney Int (2020.0) 97 1117-29. DOI: 10.1016/j.kint.2020.02.010 22. 22.National Institutes of Health/National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) Version 4.03. 2009. [Accessed June 27, 2014]. Available from: http://www.eortc.be/services/doc/ctc/. 23. Shiloh AL, Ari Eisen L, Savel RH. The unplanned intensive care unit admission. J Crit Care (2015.0) 30 419-20. DOI: 10.1016/j.jcrc.2014.12.010 24. Faggiano A, Avallone C, Gentile D, Provenzale G, Toriello F, Merlo M. Echocardiographic advances in dilated cardiomyopathy. J Clin Med (2021.0) 10 5518. DOI: 10.3390/jcm10235518 25. Marrow BA, Cook SA, Prasad SK, McCann GP. Emerging techniques for risk stratification in nonischemic dilated cardiomyopathy: JACC review topic of the week. J Am Coll Cardiol (2020.0) 75 1196-207. DOI: 10.1016/j.jacc.2019.12.058 26. Bapoje SR, Gaudiani JL, Narayanan V, Albert RK. Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med (2011.0) 6 68-72. DOI: 10.1002/jhm.812 27. Satyavolu R, Ruknuddeen MI, Soar N, Edwards SM. Dosage and clinical outcomes of medical emergency team and conventional referral mediated unplanned intensive care admissions. J Intensive Care Soc (2022.0) 0 1-8. DOI: 10.1177/17511437211060157 28. Lee J, Shin Y, Choi E, Choi S, Son J, Jung YK. Impact of hospitalization duration before medical emergency team activation: a retrospective cohort study. PLoS One (2021.0) 16 e0247066. DOI: 10.1371/journal.pone.0247066 29. Liu VX, Lu Y, Carey KA, Gilbert ER, Afshar M, Akel M. Comparison of early warning scoring systems for hospitalized patients with and without infection at risk for in-hospital mortality and transfer to the intensive care unit. JAMA Netw Open (2020.0) 3 e205191. DOI: 10.1001/jamanetworkopen.2020.5191 30. Kim SH, Choi HS, Jin ES, Choi H, Lee H, Lee SH. Predicting severe outcomes using national early warning score (NEWS) in patients identified by a rapid response system: a retrospective cohort study. Sci Rep (2021.0) 11 18021. DOI: 10.1038/s41598-021-97121-w 31. Lindgren M, Robertson J, Adiels M, Schaufelberger M, Aberg M, Toren K. Elevated resting heart rate in adolescent men and risk of heart failure and cardiomyopathy. ESC Heart Fail (2020.0) 7 1178-85. DOI: 10.1002/ehf2.12726 32. Halliday B, Vazir A, Owen R, Gregson J, Wassall R, Lota AS. Heart rate as a marker of relapse during withdrawal of heart failure therapy in patients with recovered dilated cardiomyopathy: an analysis from TRED-HF. Eur Heart J (2020.0) 41 1057. DOI: 10.1093/eurheartj/ehaa234 33. Watkinson PJ, Pimentel MAF, Clifton DA, Tarassenko L. Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data. Resuscitation (2018.0) 129 55-60. DOI: 10.1016/j.resuscitation.2018.06.003 34. Dziadzko MA, Novotny PJ, Sloan J, Gajic O, Herasevich V, Mirhaji P. Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Crit Care (2018.0) 22 286. DOI: 10.1186/s13054-018-2194-7 35. Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation (2016.0) 102 1-5. DOI: 10.1016/j.resuscitation.2016.02.005 36. Chen S, Chen L, Saguner AM, Chen K, Akdis D, Gasperetti A. Novel risk prediction model to determine adverse heart failure outcomes in arrhythmogenic right ventricular cardiomyopathy. J Am Heart Assoc (2022.0) 11 e024634. DOI: 10.1161/JAHA.121.024634 37. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J (2021.0) 42 3599-726. DOI: 10.1093/eurheartj/ehab368 38. Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol (2022.0) 79 e263-421. DOI: 10.1016/j.jacc.2021.12.012 39. Schultheiss HP, Fairweather D, Caforio ALP, Escher F, Hershberger RE, Lipshultz SE. Dilated cardiomyopathy. Nat Rev Dis Primers (2019.0) 5 32. DOI: 10.1038/s41572-019-0084-1 40. Noutsias M, Rohde M, Goldner K, Block A, Blunert K, Hemaidan L. Expression of functional T-cell markers and T-cell receptor Vbeta repertoire in endomyocardial biopsies from patients presenting with acute myocarditis and dilated cardiomyopathy. Eur J Heart Fail (2011.0) 13 611-8. DOI: 10.1093/eurjhf/hfr014 41. Baci D, Bosi A, Parisi L, Buono G, Mortara L, Ambrosio G. Innate immunity effector cells as inflammatory drivers of cardiac fibrosis. Int J Mol Sci (2020.0) 21 7165. DOI: 10.3390/ijms21197165 42. Carr E, Bendayan R, Bean D, Stammers M, Wang W, Zhang H. Evaluation and improvement of the national early warning score (NEWS2) for COVID-19: a multi-hospital study. BMC Med (2021.0) 19 23. DOI: 10.1186/s12916-020-01893-3 43. Redfern OC, Pimentel MAF, Prytherch D, Meredith P, Clifton DA, Tarassenko L. Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: development and validation of a multivariable model. Resuscitation (2018.0) 133 75-81. DOI: 10.1016/j.resuscitation.2018.09.021 44. Peterson PN, Rumsfeld JS, Liang L, Albert NM, Hernandez AF, Peterson ED. A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program. Circ Cardiovasc Qual Outcomes (2010.0) 3 25-32. DOI: 10.1161/CIRCOUTCOMES.109.854877
--- title: 'Prevalence of depression and its association with quality of life in patients after pacemaker implantation during the COVID-19 pandemic: A network analysis' authors: - Yun Lin - Hong Cai - Hong-Hong Liu - Xue-Jian Su - Chen-Yu Zhou - Jing Li - Yi-Lang Tang - Todd Jackson - Yu-Tao Xiang journal: Frontiers in Psychiatry year: 2023 pmcid: PMC10060541 doi: 10.3389/fpsyt.2023.1084792 license: CC BY 4.0 --- # Prevalence of depression and its association with quality of life in patients after pacemaker implantation during the COVID-19 pandemic: A network analysis ## Abstract ### Background This study was designed to investigate the prevalence and predictors of depression in patients after pacemaker implantation during the COVID-19 pandemic in addition to identifying specific depressive symptoms associated with quality of life (QOL) using network analysis (NA). ### Methods This cross-sectional, observational study was conducted in China between July 1, 2021, and May 17, 2022. Descriptive analysis was used to calculate depression prevalence. Univariate analyses were used to compare differences in demographic and clinical characteristics between depressed and non-depressed patients following pacemaker implantation. Binary logistic regression analysis was used to assess factors independently associated with depression. Network analysis “expected influence,” and flow function indexes were used to identify symptoms central to the depression network of the sample and depressive symptoms that were directly associated with QOL, respectively. Network stability was examined using a case-dropping bootstrap procedure. ### Results In total, 206 patients implanted with a pacemaker met the study entry criteria and completed the assessment. The overall prevalence of depression (PHQ-9 total score ≥ 5) was $39.92\%$ [$95\%$ confidence interval (CI) = 29.37−$42.47\%$]. A binary logistic regression analysis revealed that patients with depression were more likely to report a poor health status ($$p \leq 0.031$$), severe anxiety symptoms ($p \leq 0.001$), and fatigue ($p \leq 0.001$). In the network model for depression, “Sad mood,” “Poor Energy,” and “Guilt” were the most influential symptoms. “ Fatigue” had the strongest negative association with QOL, followed by “Sad mood” and “Appetite”. ### Conclusion Depression is common among patients having undergone pacemaker implantation during the COVID-19 pandemic. Anxiety, central symptoms of depression (i.e., “Sad mood”, “Poor Energy”, and “Guilt”) and depressive symptoms linked to QOL (i.e., “Sad mood”, “Appetite”, and “Fatigue”) identified in this study are promising targets for interventions and preventive measures for depression in patients who have undergone pacemaker implants. ## 1. Introduction Due to lifestyle changes, urbanization, and accelerated population aging, heart diseases, including heart rhythm disorders, have increased in China during the past decade [1]. One common heart rhythm disorder, bradyarrhythmia, refers to an abnormally slow resting heart rate, typically below 60 beats per minute. Currently, there is no oral medication to treat bradyarrhythmias and, in some cases, implanting a pacemaker is the only viable treatment option. Pacemaker implantation involves placing a small device in the chest to help the heart beat at a normal rate and rhythm [2]. Many cardiovascular diseases, including atherosclerotic cardiovascular disease (ASCVD), heart failure, cardiomyopathy and valvular diseases, may lead to bradyarrhythmias during later disease stages. Therefore, the demand for pacemaker implantation has increased with rises in aging populations (3–6). For example, an estimated 600,000 pacemakers are implanted per annum globally while another 3 million people already have pacemakers [7]. Implantation rates have increased by an estimated 10−$15\%$ per year and this upward trend is expected to continue during the next decade [6]. In China, total cardiac pacemaker implantations reached 100,230 cases in 2019, 99,247 cases in 2020, and 111,678 cases in 2021 [8], with elderly patients as the most rapidly growing population segment [6]. While implanted devices are often life-saving for those with life-threatening arrhythmias, implantations can also be life-altering and recipients experience numerous challenges in the psychosocial adaptation process [9]. After pacemaker implantation, patients may confront physical limitations (e.g., limited mobility), financial strain (e.g., medical costs), psychosocial disruptions such as lowered quality of life (QOL), and existential concerns related to their illness and living with a pacemaker [10]. Associations between heart rhythm disorders and depressive symptoms (depression thereafter) are well-documented and assumed to be bi-directional in nature [11, 12]. On one hand, patients with heart rhythm disorders often have a higher risk of developing depression compared to those without heart rhythm disorders. For instance, one study found that the prevalence of depression after pacemaker implantation was $41\%$ among patients in Turkey [13]. In part, biological changes associated with heart rhythm disorders have been linked to an increased risk for depression [12]. Meta-analyses have found increased C-reactive protein (CRP) and interleukin-6 (IL-6) in CVD patients, both of which are associated with higher risk of depression [14]. Conversely, depression may also precipitate or exacerbate heart rhythm disorders as well as associated risk factors (e.g., hypertension, insulin resistance, diabetes, and treatment adherence) [15, 16]. Aside from depression, post-implantation QOL is another important facet of psychosocial adjustment warranting consideration in these patients (17–20). After pacemaker implantation, patients often experience psychological consequences (e.g., depression, anxiety, and fatigue) related to change of lifestyle, limitations in daily activities, and physical discomfort, that lower their QOL [19]. Previous studies have found association between higher overall depression severity and lower QOL [17, 18], but inter-relationships between specific depressive symptoms and QOL have not been well documented in bradyarrhythmia patients after pacemaker implantation. Traditionally, the presence of mental health problems such as depression has been determined on the basis of symptom counts on interviews or total scores and cut-off values from validated questionnaires. However, the assumption that individual symptoms are equally-weighted expressions within a single underlying disorder in traditional statistical approaches to psychopathology assessment has been questioned (21–23). For example, the reliance upon average or total scale scores fails to consider potential causal relations, progressions, and heterogeneity of individual symptoms as well as interrelations between different symptoms [22, 24]. In contrast, network approaches offer novel statistical methods in which mental health problems are viewed as systems of interacting symptoms that may give rise to each other [25]. Network analysis has the potential to map specific relationships among individual symptoms of a disorder/syndrome, pinpoint symptoms that link particular syndromes to other experiences such as QOL, and identify specific symptoms as plausible treatment targets [23]. In network theory, central nodes are the most influential symptoms of a disorder/syndrome that can activate other symptoms. Central symptoms play a major role in causing the onset and/or maintenance of a syndrome. Network analysis may have utility in clarifying features of depression that are more critical to understanding inter-relationships between different symptom clusters in under-studied target populations including patients who have undergone pacemaker implantations. Finally, the COVID-19 pandemic has led to further complications for health service provision in many countries including China. For example, many emergency, intensive, or intermediate care units undertook heavy additional treatment burdens and some wards in regional and tertiary hospitals were converted to COVID-19 isolation units [26]. Consequently, regular medical services have been reduced. Following pacemaker implantation, patients need to attend regular follow-up clinics but reduced medical services and other strict public health measures are potential barriers that interfere with regular assessments [27, 28]; uncertainty in aftercare may contribute to increased risk for depression. Furthermore, compared to rates in the general population, COVID-19 vaccine rates in patients with major medical conditions including heart diseases are typically lower in China (29–31); such trends may contribute to higher infection rates or complications among the medically vulnerable that, in turn, increase depression risk. Based on this overview, this study had three main objectives. First, we assessed the prevalence of depression among patients who had undergone pacemaker implantation during the COVID-19 pandemic. Second, we examined participant characteristics that predicted depressed versus non-depressed status within the research sample. Third, we explored specific depressive symptoms that were most central to depression and QOL among participants. ## 2.1. Sampling and sample size estimation This was a cross-sectional, observational study conducted between July 1, 2021, and May 17, 2022 at the National Clinical Research Center for Cardiovascular Diseases in Beijing, China. Following other studies [32, 33], the WeChat-based “QuestionnaireStar” program was used to collect data. WeChat is a widely used social communication application with more than 1.2 billion active users in China. All patients who had pacemaker implantations and regularly attended their follow-up clinics for maintenance therapy during the study period were consecutively invited by a research physician to participate in this study. Patients needed to present their WeChat-based health code during the pandemic when they entered the clinic and were, presumably, WeChat users. To be eligible, patients met the following selection criteria: [1] aged 18 years or older; [2] received a pacemaker implantation; [3] able to read and understand Chinese. Those with dementia and obvious cognitive problems that interfered with comprehension were excluded. The sample size was calculated using the formula N = Za2P (1–P)/d2, in which $a = 0.05$ and Za = 1.96, and the estimated acceptable margin of error for proportion d was 0.05. The prevalence of depression among older population was estimated to be $35.1\%$ based on a previous study [34]. Assuming that $10\%$ of those invited would refuse participation in this study, a sample size of at least 249 participants would be ideal. ## 2.2. Data collection Patients were invited to scan a Quick Response code (QR Code) linked to the introduction and invitation of this study with their smartphone prior to their clinic appointments. After providing the electronic written informed consent, they were asked to complete the online assessment using their smartphone at an outpatient clinic. Socio-demographic data were collected using a pre-designed data collection sheet and included gender, age, body mass index (BMI, kg/m2), marital status (married/unmarried), education level (high school and below/college education and above), having medical insurance, current smoking, current social drinking, perceived health status and perceived economic status (poor or fair/good). Following a previous study [35], standard (no versus yes) questions related to the pacemaker implantation were asked including the following: [1] “Have you experienced chest discomfort?”; [ 2] “Have you been restricted by chest discomfort during physical activities?”; [ 3] “Have you felt discomfort in the region of the intervention (chest/groin)?”; [ 4] “Have you been restricted in your daily activities by fear of complications?”; [ 5] “Since implantation, have you felt preoccupied with your heart condition and general health?.” ## 2.3. Measurement Severity of depressive symptoms was measured using the validated Chinese version of the nine item-Patient Health Questionnaire (PHQ-9) [36, 37]. PHQ-9 items include [1] “Anhedonia”, [2] “Sad Mood”, [3] “Sleep”, [4] “Energy”, [5] “Appetite”, [6] “Guilt”, [7] “Concentration”, [8] “Motor disturbance”, and [9] “Suicidal ideation”, each of which is rated from 0 (define meaning of “0” anchor here e.g., “not at all”) to 3 (define anchor meaning). Total PHQ-9 scores range from 0 to 27; values of ≥ 5 indicate the presence of depression [36, 37] while values ≥ 10 reflect “having moderate to severe depression.” Severity of anxiety was assessed using the validated General Anxiety Disorder (GAD-7) [38, 39], with total scores ranging from 0 to 21. Severity of fatigue was assessed using a one-item fatigue numeric rating scale with anchors ranging from “0” (no fatigue) to “10” (extreme fatigue) [40]. Finally, global QOL was measured with the first two items of the validated World Health Organization Quality of Life Scale Brief version (WHOQOL-BREF): “How do you assess your quality of life?” and “Are you satisfied with your current health?” [ 41, 42]. Higher scores reflected higher QOL. ## 2.4. Ethical approval The study protocol was approved by the Clinical Research Ethics Committee of Beijing Anzhen Hospital. ## 2.5.1. Univariate and multivariate analyses Data analyses were performed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA). Distributions of continuous variables were checked for normality using P-P plots. Mean PHQ-9 scores were calculated to estimate depression prevalence in the sample. Chi-square tests, independent samples t-tests, and Mann-Whitney U-tests were used to compare sociodemographic and disease-related variables between depression and no depression groups, as appropriate. Binary logistic regression analyses with the “enter” method were performed to examine independent correlates of depression. All variables that had significant group differences in univariate analyses were entered as independent variables, while depression was entered as the dependent variable. Independent associations of depression with QOL were examined using analysis of covariance (ANCOVA) after controlling for variables on which there were significant depression subgroup differences in univariate analyses. Significant statistical differences were set at $P \leq 0.05$ (two-tailed). ## 2.5.2. Network structure The network model was estimated using R software [43]. We computed polychoric correlations of all PHQ-9 items to investigate edges of the network model. We also estimated the Graphical Gaussian Model (GGM), a popular network model, with the graphic least absolute shrinkage and selection operator (LASSO) and Extended Bayesian Information Criterion (EBIC) model using R package “qgraph” [44]. GGM is a pairwise Markov random field (PMRF) model used for interval or ordinal data; edges are interpreted as partial correlation coefficients. The network was visualized using the “qgraph” package, where thicker edges represented stronger relationships between nodes. We used the centrality index, Expected Influence (EI) of nodes, to identify depressive symptoms that were more central (influential) in the network model [45]. To identify particular depressive symptoms that were directly associated with QOL, the “flow” function in R package “qgraph” was used [46]. ## 2.5.3. Network stability Centrality stability was examined using the correlation stability coefficient (CS-coefficient). A CS-coefficient value above 0.25 indicates that observed network model results are stable, though traditionally, CS-coefficient values above 0.5 are preferable. A bootstrapped difference test was conducted to assess the robustness of node EIs and edges. Differences were significant between two nodes or two edges if zero was not included in the 1,000-bootstrap $95\%$ confidence interval (CI). Edge accuracy was estimated with bootstrapped $95\%$ CIs; a narrower CI suggests a more reliable network. These procedures were conducted using the package “bootnet” v1.4.3 [47]. ## 3.1. Participant characteristics Of 210 pacemaker implantation recipients who were invited to participate in the study, 206 met the study entry criteria and completed the assessment, for a participation rate of $98.1\%$. Demographic and clinical characteristics of the sample are shown in Table 1. The mean age of participants was 68.65 years [standardized deviation (SD) = 12.48 years] and $51.5\%$ ($$n = 106$$) were men. TABLE 1 \| Variable \| Total (N = 206) \| Total (N = 206).1 \| No DEP (N = 132) \| No DEP (N = 132).1 \| DEP (N = 74) \| DEP (N = 74).1 \| Univariate analyses \| Univariate analyses.1 \| Univariate analyses.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| N \| % \| N \| % \| N \| % \| χ2 \| Df \| P \| \| Male gender \| 106 \| 51.5 \| 71 \| 53.8 \| 35 \| 47.3 \| 0.800 \| 1 \| 0.387 \| \| College education and above \| 65 \| 31.6 \| 43 \| 32.6 \| 22 \| 29.7 \| 0.178 \| 1 \| 0.755 \| \| Married \| 44 \| 21.4 \| 23 \| 17.4 \| 21 \| 28.4 \| 3.387 \| 1 \| 0.077 \| \| Having medical insurance \| 204 \| 99.0 \| 131 \| 99.2 \| 73 \| 98.6 \| 0.174 \| 1 \| 1.00 \| \| Perceived good economic status \| 23 \| 11.2 \| 18 \| 13.6 \| 5 \| 6.8 \| 2.263 \| 1 \| 0.169 \| \| Perceived good health status \| 45 \| 21.8 \| 43 \| 32.6 \| 2 \| 2.7 \| 24.785 \| 1 \| <0.001 \| \| Nosmoking \| 157 \| 76.2 \| 100 \| 75.8 \| 57 \| 77.0 \| 0.042 \| 1 \| 0.866 \| \| Social drinking \| 45 \| 21.8 \| 30 \| 22.7 \| 15 \| 20.3 \| 0.168 \| 1 \| 0.728 \| \| Having chest discomfort \| 87 \| 42.2 \| 41 \| 31.1 \| 46 \| 62.2 \| 18.801 \| 1 \| <0.001 \| \| Chest discomfort during physical activities \| 124 \| 60.2 \| 71 \| 53.8 \| 53 \| 71.6 \| 6.294 \| 1 \| 0.017 \| \| Discomfort in the region of the intervention (chest/groin) \| 71 \| 34.5 \| 38 \| 28.8 \| 33 \| 44.6 \| 5.245 \| 1 \| 0.032 \| \| Restricted in your daily activities due to fear complications \| 104 \| 50.5 \| 53 \| 40.2 \| 51 \| 68.9 \| 15.698 \| 1 \| <0.001 \| \| Worry about heart condition and general health \| 93 \| 45.1 \| 49 \| 37.1 \| 44 \| 59.5 \| 9.555 \| 1 \| 0.002 \| \| \| Mean \| SD \| Mean \| SD \| Mean \| SD \| t/Z \| Df \| P \| \| Age (years) \| 68.65 \| 12.48 \| 67.70 \| 12.79 \| 70.35 \| 11.79 \| −1.469 \| 204 \| 0.144 \| \| BMI (Kg/m2) \| 25.25 \| 4.84 \| 25.60 \| 4.55 \| 24.62 \| 5.28 \| 1.403 \| 204 \| 0.162 \| \| GAD-7 total \| 2.92 \| 4.38 \| 0.67 \| 1.22 \| 6.95 \| 5.05 \| 657.000 \| —* \| <0.001 \| \| Fatigue total \| 2.96 \| 2.06 \| 2.08 \| 1.59 \| 4.53 \| 1.87 \| 1600.500 \| —* \| <0.001 \| \| Global QOL \| 6.62 \| 1.45 \| 7.23 \| 1.19 \| 5.53 \| 1.22 \| 9.496 \| 204 \| <0.001 \| ## 3.2. Prevalence and correlates of depression The overall prevalence of depression (PHQ-9 total score ≥ 5) was $39.92\%$ [$95\%$ confidence interval (CI) = 29.37−$42.47\%$], while the prevalence of moderate to severe depression (PHQ-9 total score ≥ 10) was $14.98\%$ ($95\%$ CI = 10.11−$19.84\%$). Table 1 summarizes comparisons of demographic and clinical characteristics for depressed versus non-depressed pacemaker implantation patient subgroups. The depressed subgroup reported a poorer self-assessed health status ($p \leq 0.001$), higher mean GAD-7 total score ($p \leq 0.001$), and higher mean fatigue total score ($p \leq 0.001$). The depressed subgroup was also more likely to report having chest discomfort ($p \leq 0.001$), discomfort in the intervention region (chest/groin) ($$p \leq 0.032$$), severe restrictions in daily activities due to fear of complications ($p \leq 0.001$) and worry about their heart condition and general health ($$p \leq 0.002$$). After controlling for other significant depression subgroup differences, the ANCOVA revealed the depressed subgroup had significantly lower QOL scores than the non-depressed subgroup did [F [1, 206] = 47.728, $P \leq 0.001$]. A binary logistic regression analysis indicated depressed patient subgroup members reported a comparatively poorer perceived health status ($$p \leq 0.031$$), more severe anxiety symptoms ($p \leq 0.001$) and higher levels of fatigue ($p \leq 0.001$). No other univariate correlates had statistically significant effects in the multivariate prediction model (Table 2). TABLE 2 \| Variable \| Depression \| Depression.1 \| Depression.2 \| \| --- \| --- \| --- \| --- \| \| \| P \| OR \| 95% CI \| \| Good health status \| 0.031 \| 0.077 \| 0.007–0.795 \| \| Having chest discomfort \| 0.280 \| 2.104 \| 0.546–8.115 \| \| Chest discomfort during physical activities \| 0.781 \| 0.821 \| 0.204–3.310 \| \| Discomfort in the region of the intervention (chest/groin) \| 0.066 \| 0.247 \| 0.055–1.099 \| \| Restricted in your daily activities due to fear of complications \| 1.00 \| 0.322 \| 0.083–1.243 \| \| GAD-7 total \| <0.001 \| 3.165 \| 2.064–4.848 \| \| Fatigue total \| <0.001 \| 2.477 \| 1.664–3.688 \| ## 3.3. Network structure of depressive symptoms Figure 1 presents the network structure of depressive symptoms as measured by PHQ-9 items. The predictability of items is shown as ring-shaped pie charts in Figure 1. The mean predictability was 0.566, indicating that, on average, $56.6\%$ of the variance in each node could be accounted for by neighboring nodes in the model. The connection between nodes PHQ1 (“Anhedonia”) and PHQ2 (“Sad mood”) (average edge weight = 0.435) was the strongest positive edge, followed by edges between nodes PHQ3 (“Sleep”) and PHQ4 (“Energy”) (average edge weight = 0.390), and nodes PHQ7 (“Concentration”) and PHQ8 (“Motor disturbance”) (average edge weight = 0.360). FIGURE 1: Network structure of depression in patients after pacemaker implantation. In terms of EI in the network model, the node PHQ2 (“Sad mood”) had the highest EI centrality, followed by nodes PHQ4 (“Energy”) and PHQ6 (“Guilt”) (Figure 1); together, these were the most influential symptoms for understanding depression in patients who had pacemaker implantations. In addition, PHQ4 (“Fatigue”) had the strongest negative association with QOL (average edge weight = −0.262), followed by PHQ2 (“Sad mood”) (average edge weight = −0.134) and PHQ5 (“Appetite”) (average edge weight = −0.118) (Figure 2). FIGURE 2: Flow network for quality of life (QOL) in study sample. EI centrality of the network model had moderate stability (i.e., CS-coefficient = 0.437; $95\%$ CI: 0.359–0.515). Results of bootstrapped differences tests for edge weights showed that most comparisons between edge weights were statistically significant. As such, the network model had acceptable reliability and stability (Figure 3 and Supplementary Figure 1). FIGURE 3: The stability of centrality index using case-dropping bootstrap. ## 4. Discussion To our knowledge, this is the first study to examine the prevalence and prediction of depression among patients who had undergone pacemaker implantation as well as key symptoms of depression and their associations with QOL using network analysis. In this study the prevalence of depression was $39.92\%$ ($95\%$ CI: 29.37–$42.47\%$), a rate that is similar to that reported in Turkey ($36.9\%$) based on the modified Hamilton Depression Rating Scale [48], but much higher than rates from Iran ($7.1\%$) based on the Beck Depression Inventory [19] and New Zealand ($17.3\%$) as assessed with the Hospital Anxiety and Depression Scale [49]. The higher prevalence of depression in this study than other studies may be due to differences in sampling methods and patient characteristics (e.g., inpatients vs. outpatients), illness stage, and depression measures. Nonetheless, chronic illness patients treated with implantation devices are confronted with considerable uncertainty that may contribute to depression (50–52). For example, after pacemaker implantation, lower physical activity levels, obesity, high stress levels, and hypertension could contribute to depression [53]. Adopting coping strategies that include focusing on activities, maintaining social support from loved ones and having adequate rest, may improve depressive symptoms. Patient support groups also provide opportunities for the exchange of information and ideas, coping options and alternative perspectives about implanted devices that may help to combat depression and/or QOL losses. Additionally, educational interventions designed to highlight the goals, functions, and positive effects of implantation devices may help to curb depression in some cases [19, 54]. Several factors including poor perceived health status, more severe anxiety and heightened fatigue emerged as the most significant, unique correlates of overall depression scores in this sample. These findings dovetail with previous findings documenting higher risk for severe depression among pacemaker implantation recipients due to more highly compromised immune systems [55], poorer health status and limited access to appropriate care during the COVID-19 pandemic [56], anxiety symptoms, increased sleep disturbances [57], fatigue, and lower socioeconomic status [52]. In the depression network model, “Sad mood” was the most central symptom, echoing previous findings among widowed older people in China [58], depressed outpatients [59], and older residents of Hong Kong [60]. In late life, sad mood may result from adverse life events (i.e., severe physical diseases, implementing devices, losses of function, grief from interpersonal losses) and poor social support. Furthermore, fear of device malfunction or over-dependence on health professionals, limitations in daily activities due to pacemaker implantation, physical discomfort, and technical issues including battery depletion may trigger feelings of helplessness or sad mood in patients after pacemaker implantation [17, 51, 52, 61, 62]. Moreover, in the context of the COVID-19 pandemic, restrictive public health measures including mass quarantines, facility closures, and restrictions on public transport (63–65) were adopted in many areas of China. Consequently, increased disruptions to daily life and decreased access to treatment among pacemaker implantation recipients may have contributed to exacerbations in sad mood and fatigue as well as lowered QOL. The node “Energy” was another significant central symptom in the depression network model, consistent with previous findings reported in community-dwelling older adults [66], patients with major depressive disorder [67] and adult Hong Kong residents [60]. Older adults with pacemaker implantation may experience disturbances in sleep and appetite that influence energy levels [68]. Moreover, restricted outdoor exercise due to quarantines from the COVID-19 pandemic could fuel fatigue and energy depletions many patients feel [69]. Guilt also emerged as a central symptom in the depression network model in line with findings from older adult residents of Hong Kong [60]. Participants in this study were typically older adults. Compared to their younger counterparts, older adults are more likely to experience chronic physical illness (e.g., hypertension, heart disease, diabetes, cancer, and stroke) [70, 71], social isolation, a lower socioeconomic status, vision deficits and cognitive impairments [72, 73], loneliness [74] and heavy healthcare burdens [75]; hence, older adults may experience increases in guilt, in part, because they view themselves as a burden for families, the medical system, and society due to functional losses from aging and illness (76–78). Follow-up treatments for pacemaker implantation recipients may increase personal and financial burdens for patients’ families and the healthcare system, hence contributing to more pronounced feelings of guilt in affected patients. Particularly in the context of COVID-19, financial strain may worsen if family members were made redundant or forced to stay at home without income during the pandemic. Fatigue, a common symptom among pacemaker implantation patients, also emerged as a central symptom linked to QOL in the network model. This observation is consistent with previous evidence implicating fatigue as a prevalent, severe symptom in heart disease patients with lower QOL [51, 61, 79]. Due to reduced motivation and/or energy in performing activities of daily living and potential changes in sleep patterns, depressed patients often experience increased fatigue [80]. Once again, prolonged anxiety, fear, and stress responses of pacemaker implantation patients may be even more elevated as a result of living in uncertain and unrelenting COVID-19 pandemic conditions. Over time, such reactions may contribute to high levels of fatigue and lowered QOL. Within the flow network model, sad mood was negatively associated with QOL, consistent with evidence from another study linking negative mood states with poorer QOL among advanced heart failure patients [81]. The salience of both sad mood and QOL for pacemaker implantation patients is highlighted by their status as strong psychosocial predictors for heart disease [82] and independent correlates of physical comorbidities and increased mortality risk [83]. “ Appetite” was another symptom directly associated with QOL in the network analysis. Poor appetite is common symptom among older adults as well as those who have pacemaker implantations [84]. Among the potentially relevant appetite changes, digestive problems, sense perception impairments (e.g., loss of taste, smell and/or appetite) and chewing or swallowing difficulties can affect eating and/or food intake, contribute to weight loss and lead to nutritional deficiencies related to lower QOL [83]. Although strengths of this research included its focus on depression in an understudied population based on both traditional analysis and network analysis approaches, its main limitations should also be noted. First, given the highly specialized nature of the sample, the sample size was relatively small and slightly under-powered; replications are needed in future studies with large sample sizes. Second, due to a cross-sectional design, directions of causality between depression and pacemaker implantation could not be determined. Third, because the study was conducted through the National Clinical Research Center for Cardiovascular Diseases in Beijing, generalizability of findings cannot be made across other regions of China. Fourth, potential confounding influences such as the use of medications and comorbid chronic diseases were not assessed in an effort to maintain reasonable response burdens for unpaid research volunteers in this study. Such factors warrant attention in future extensions. ## 5. Conclusion In conclusion, depression was common among patients who had undergone pacemaker implantation during the COVID-19 pandemic. Reports of a poor perceived health status, more severe anxiety symptoms and heightened fatigue were identified as unique predictors of overall depression scores. Network analysis revealed central symptoms (e.g., “Sad mood”, “Poor Energy”, and “Guilt”) and symptoms linked to QOL (e.g., “Sad mood”, “Appetite”, and “Fatigue”) that are potentially useful targets of interventions designed to prevent or reduce depression among recipients of pacemaker implantation. As suggested by the American Heart Association, clinicians should recognize potentially dynamic illness trajectories among pacemaker recipients, routinely assess patients’ psychological status, and provide timely interventions when high levels of distress are evident. ## Data availability statement The datasets presented in this article are not readily available because the Clinical Research Ethics Committee of Beijing Anzhen Hospital that approved the study prohibits the authors from making publicly available the research dataset of clinical studies. Requests to access the datasets should be directed to Y-TX, xyutly@gmail.com. ## Ethics statement The studies involving human participants were reviewed and approved by Beijing Anzhen Hospital. The patients/participants provided their written informed consent to participate in this study. ## Author contributions YL, HC, and Y-TX: study design. YL, HC, H-HL, X-JS, C-YZ, and JL: data collection, analysis, and interpretation. HC, Y-LT, and Y-TX: drafting of the manuscript. TJ: critical revision of the manuscript. All authors approval of the final version for publication. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1084792/full#supplementary-material ## References 1. Hua W, Ding L-G, Fan X-H, Liu Z-M, Jiang C-L, Qu F-J. Initial experience with multipoint pacing cardiac resynchronization therapy in China.. (2016) 129 1241-3. DOI: 10.4103/0366-6999.181966 2. Mond HG, Proclemer A. The 11th world survey of cardiac pacing and implantablecardioverter-defibrillators: calendar year 2009–a World Society of Arrhythmia’s project.. (2011) 34 1013-27. DOI: 10.1111/j.1540-8159.2011.03150.x 3. Bradshaw PJ, Stobie P, Knuiman MW, Briffa TG, Hobbs MS. Trends in the incidence and prevalence of cardiac pacemaker insertions in an ageing population.. (2014) 1. DOI: 10.1136/openhrt-2014-000177 4. Chen W-W, Gao R-L, Liu L-S, Zhu M-L, Wang W, Wang Y-J. China cardiovascular diseases report 2015: a summary.. (2017) 14 1-10. PMID: 28270835 5. Greenspon AJ, Patel JD, Lau E, Ochoa JA, Frisch DR, Ho RT. Trends in permanent pacemaker implantation in the United States from 1993 to 2009: increasing complexity of patients and procedures.. (2012) 60 1540-5. DOI: 10.1016/S0735-1097(12)60704-9 6. Ma L-Y, Chen W-W, Gao R-L, Liu L-S, Zhu M-L, Wang Y-J. China cardiovascular diseases report 2018: an updated summary.. (2020) 17. DOI: 10.11909/j.issn.1671-5411.2020.01.001 7. De Vries L, Dijk W, Hooijschuur C, Leening M, Stricker B, Van Hemel N. Utilisation of cardiac pacemakers over a 20-year period: results from a nationwide pacemaker registry.. (2017) 25 47-55. DOI: 10.1007/s12471-016-0880-0 8. 8.Chinese Medical Association. 2022 Chinese Medical Association Cardiac Electrophysiological Pacing Branch. Beijing: Chinese Medical Association (2022).. (2022) 9. Humphreys NK, Lowe R, Rance J, Bennett PD. Living with an implantable cardioverter defibrillator: the patients’ experience.. (2016) 45 34-40. DOI: 10.1016/j.hrtlng.2015.10.001 10. Ghojazadeh M, Azami-Aghdash S, Sohrab-Navi Z, Kolahdouzan K. Cardiovascular patients’ experiences of living with pacemaker: qualitative study.. (2015) 11 11. Stapelberg NJ, Hamilton-Craig I, Neumann DL, Shum DH, McConnell H. Mind and heart: heart rate variability in major depressive disorder and coronary heart disease-a review and recommendations.. (2012) 46 946-57. DOI: 10.1177/0004867412444624 12. Taylor CB. Depression, heart rate related variables and cardiovascular disease.. (2010) 78 80-8. DOI: 10.1016/j.ijpsycho.2010.04.006 13. Bilge AK, Ozben B, Demircan S, Cinar M, Yilmaz E, Adalet K. Depression and anxiety status of patients with implantable cardioverter defibrillator and precipitating factors.. (2006) 29 619-26. DOI: 10.1111/j.1540-8159.2006.00409.x 14. Smith KJ, Au B, Ollis L, Schmitz N. The association between C-reactive protein, Interleukin-6 and depression among older adults in the community: a systematic review and meta-analysis.. (2018) 102 109-32. DOI: 10.1016/j.exger.2017.12.005 15. Katon WJ. Epidemiology and treatment of depression in patients with chronic medical illness.. (2022) 13 7-23. DOI: 10.31887/DCNS.2011.13.1/wkaton 16. Taylor WD, Aizenstein HJ, Alexopoulos G. The vascular depression hypothesis: mechanisms linking vascular disease with depression.. (2013) 18 963-74. DOI: 10.1038/mp.2013.20 17. Figueroa C, Alcocer L, Ramos B. Psychological intervention to modify anxiety, depression and quality of life in patients with an implantable pacemaker.. (2016) 7 374-81. DOI: 10.4236/psych.2016.73040 18. Młynarski R, Włodyka A, Kargul W. Changes in the mental and physical components of the quality of life for patients six months after pacemaker implantation.. (2009) 16 250-3. PMID: 19437400 19. Rafsanjani MHAP, Masoudi S, Radmanesh M, Bostani Z. Comparison of depression and anxiety among pacemaker and implantable cardioverter-defibrillator recipients: a cross-sectional study.. (2021) 44 235-9. DOI: 10.1111/pace.14152 20. Thylén I, Dekker RL, Jaarsma T, Strömberg A, Moser DK. Characteristics associated with anxiety, depressive symptoms, and quality-of-life in a large cohort of implantable cardioverter defibrillator recipients.. (2014) 77 122-7. DOI: 10.1016/j.jpsychores.2014.05.007 21. Fried EI, Nesse RM. *Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR D study.. (2015) 172 96-102. DOI: 10.1016/j.jad.2014.10.010 22. Fried EI, Nesse RM. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential.. (2015) 13. DOI: 10.1186/s12916-015-0325-4 23. Fried EI, Nesse RM, Zivin K, Guille C, Sen S. Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors.. (2014) 44 2067-76. DOI: 10.1017/S0033291713002900 24. Schmittmann VD, Cramer AO, Waldorp LJ, Epskamp S, Kievit RA, Borsboom D. Deconstructing the construct: a network perspective on psychological phenomena.. (2013) 31 43-53. DOI: 10.1016/j.newideapsych.2011.02.007 25. Borsboom D. A network theory of mental disorders.. (2017) 16 5-13. DOI: 10.1002/wps.20375 26. Garcia S, Albaghdadi MS, Meraj PM, Schmidt C, Garberich R, Jaffer FA. Reduction in ST-segment elevation cardiac catheterization laboratory activations in the United States during COVID-19 pandemic.. (2020) 75 2871-2. DOI: 10.1016/j.jacc.2020.04.011 27. Kretchy IA, Asiedu-Danso M, Kretchy J-P. Medication management and adherence during the COVID-19 pandemic: perspectives and experiences from low-and middle-income countries.. (2021) 17 2023-6. DOI: 10.1016/j.sapharm.2020.04.007 28. Pfefferbaum B, North CS. Mental health and the Covid-19 pandemic.. (2020) 383 510-2. DOI: 10.1056/NEJMp2008017 29. Wang J, Yuan B, Lu X, Liu X, Li L, Geng S. Willingness to accept COVID-19 vaccine among the elderly and the chronic disease population in China.. (2021) 17 4873-88. DOI: 10.1080/21645515.2021.2009290 30. Wang Y, Duan L, Li M, Wang J, Yang J, Song C. COVID-19 vaccine hesitancy and associated factors among diabetes patients: a cross-sectional survey in Changzhi, Shanxi, China.. (2022) 10. DOI: 10.3390/vaccines10010129 31. Zhang D, Cui Q, Xu J, Cai J, Lu X, Yang Y. Factors related with COVID-19 vaccination willingness among outpatients in China.. (2021) 17 3963-8. DOI: 10.1080/21645515.2021.1954441 32. Sun D, Yang D, Li Y, Zhou J, Wang W, Wang Q. Psychological impact of 2019 novel coronavirus (2019-nCoV) outbreak in health workers in China.. (2020) 148. DOI: 10.1017/S0950268820001090 33. Wu W, Zhang Y, Wang P, Zhang L, Wang G, Lei G. Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy.. (2020) 92 1962-70. DOI: 10.1002/jmv.25914 34. Cai H, Jin Y, Liu R, Zhang Q, Su Z, Ungvari GS. Global prevalence of depression in older adults: a systematic review and meta-analysis of epidemiological surveys.. (2022) 80. DOI: 10.1016/j.ajp.2022.103417 35. Cabanas-Grandío P, Garcia Campo E, Bisbal F, García-Seara J, Pachón M, Juan-Salvadores P. Quality of life of patients undergoing conventional vs leadless pacemaker implantation: a multicenter observational study.. (2020) 31 330-6. DOI: 10.1111/jce.14322 36. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure.. (2001) 16 606-13. DOI: 10.1046/j.1525-1497.2001.016009606.x 37. Zhang YL, Liang W, Chen ZM, Zhang HM, Zhang JH, Weng XQ. Validity and reliability of Patient Health Questionnaire-9 and Patient Health Questionnaire-2 to screen for depression among college students in China.. (2013) 5 268-75. DOI: 10.1111/appy.12103 38. He XY, Li CB, Qian J, Cui HS, Wu WY. Reliability and validity of a generalized anxiety disorder scale in general hospital outpatients (in Chinese).. (2010) 22 200-3 39. Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7.. (2006) 166 1092-7. DOI: 10.1001/archinte.166.10.1092 40. Gladman D, Nash P, Goto H, Birt JA, Lin C-Y, Orbai A-M. Fatigue numeric rating scale validity, discrimination and responder definition in patients with psoriatic arthritis.. (2020) 6. DOI: 10.1136/rmdopen-2019-000928 41. Cheung YB, Yeo KK, Chong KJ, Khoo EYH, Wee HL. Measurement equivalence of the English, Chinese and Malay versions of the World Health Organization quality of life (WHOQOL-BREF) questionnaires.. (2019) 17. DOI: 10.1186/s12955-019-1130-0 42. Skevington SM, Lotfy M, O’Connell KA, Group W. The World Health Organization’s WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group.. (2004) 13 299-310. DOI: 10.1023/B:QURE.0000018486.91360.00 43. 43.R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing (2020).. (2020) 44. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper.. (2018) 50 195-212. DOI: 10.3758/s13428-017-0862-1 45. Beard C, Millner AJ, Forgeard MJ, Fried EI, Hsu KJ, Treadway M. Network analysis of depression and anxiety symptom relationships in a psychiatric sample.. (2016) 46 3359-69. DOI: 10.1017/S0033291716002300 46. Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships in psychometric data.. (2012) 48** 1-18. DOI: 10.18637/jss.v048.i04 47. Epskamp S, Fried EI.. Package ‘Bootnet’. Bootstrap Methods for Various Network Estimation Routines 5, 0.1 (2015) 48. Aydemir Ö, Özmen E, Küey L, Kültur S, Yeşil M, Postaci N. Psychiatric morbidity and depressive symptomatology in patients with permanent pacemakers.. (1997) 20 1628-32. DOI: 10.1111/j.1540-8159.1997.tb03531.x 49. Miranda J, Stiles M, Lever N, Mahadevan H. Prevalence of depression and anxiety among patients with cardiac implantable electronic device (CIED).. (2018) 27 S13-4. DOI: 10.1016/j.hlc.2018.05.129 50. Falk K, Swedberg K, Gaston-Johansson F, Ekman I. Fatigue is a prevalent and severe symptom associated with uncertainty and sense of coherence in patients with chronic heart failure.. (2007) 6 99-104. DOI: 10.1016/j.ejcnurse.2006.05.004 51. Polikandrioti M. Quality of life of patients with cardiac pacemaker: levels, associated characteristics, and the impact of anxiety and depression.. (2022) 64 117-27. DOI: 10.3897/folmed.64.e63234 52. Polikandrioti M, Tzirogiannis K, Zyga S, Koutelekos I, Vasilopoulos G, Theofilou P. Effect of anxiety and depression on the fatigue of patients with a permanent pacemaker.. (2018) 3 8-17. DOI: 10.5114/amsad.2018.73231 53. Dhar AK, Barton DA. Depression and the link with cardiovascular disease.. (2016) 7. DOI: 10.3389/fpsyt.2016.00033 54. Mansouri A, Baraz S, Elahi N, Malehi AS, Saberipour B. The effect of an educational program based on Roy’s adaptation model on the quality of life of patients suffering from heart failure: a clinical trial study.. (2019) 16 459-67. DOI: 10.1111/jjns.12255 55. Edlinger C, Paar V, Kheder SH, Krizanic F, Lalou E, Boxhammer E. Endothelialization and inflammatory reactions after intracardiac device implantation.. (2022) 17 1-22. DOI: 10.1007/5584_2022_712 56. Moreno C, Wykes T, Galderisi S, Nordentoft M, Crossley N, Jones N. How mental health care should change as a consequence of the COVID-19 pandemic.. (2020) 7 813-24. DOI: 10.1016/S2215-0366(20)30307-2 57. Lv G, Zhao X, Xu X, Hou F, Li P. Gender-based differences in the relationships among social support, positive psychological capital, and sleep quality in patients implanted with pacemakers: a moderated mediation model.. (2022) 37 79-85. PMID: 32826482 58. Pan H, Liu Q. Difference of depression between widowed and non-widowed older people in China: a network analysis approach.. (2021) 280 68-76. DOI: 10.1016/j.jad.2020.11.058 59. Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D. What are’good’depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis.. (2016) 189 314-20. DOI: 10.1016/j.jad.2015.09.005 60. Cheung T, Jin Y, Lam S, Su Z, Hall BJ, Xiang Y-T. Network analysis of depressive symptoms in Hong Kong residents during the COVID-19 pandemic.. (2021) 11 1-8. DOI: 10.1038/s41398-021-01543-z 61. Polikandrioti M. Patient perceptions and quality of life in pacemaker recipients.. (2021) 12. DOI: 10.19102/icrm.2021.121103 62. Polikandrioti M, Tzirogiannis K, Zyga S, Gerogianni G, Stefanidou S, Tsami A. Assessment of fatigue in patients with a permanent cardiac pacemaker: prevalence and associated factors.. (2018) 3 166-73. DOI: 10.5114/amsad.2018.81085 63. Kraemer MU, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM. The effect of human mobility and control measures on the COVID-19 epidemic in China.. (2020) 368 493-7. DOI: 10.1126/science.abb4218 64. Tian H, Liu Y, Li Y, Wu C-H, Chen B, Kraemer MU. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China.. (2020) 368 638-42. DOI: 10.1126/science.abb6105 65. Xu W, Wu J, Cao L. COVID-19 pandemic in China: context, experience and lessons.. (2020) 9 639-48. DOI: 10.1016/j.hlpt.2020.08.006 66. Jin Y, Sun H, Lam S, Su Z, Hall B, Cheung T. Depressive symptoms and gender differences in older adults in Hong Kong during the COVID-19 pandemic: a network analysis approach.. (2022) 18 3934-41. DOI: 10.7150/ijbs.69460 67. van Borkulo C, Boschloo L, Borsboom D, Penninx BW, Waldorp LJ, Schoevers RA. Association of symptom network structure with the course of depression.. (2015) 72 1219-26. DOI: 10.1001/jamapsychiatry.2015.2079 68. Kessler K, Pivovarova-Ramich O. Meal timing, aging, and metabolic health.. (2019) 20. DOI: 10.3390/ijms20081911 69. Sepúlveda-Loyola W, Rodríguez-Sánchez I, Pérez-Rodríguez P, Ganz F, Torralba R, Oliveira D. Impact of social isolation due to COVID-19 on health in older people: mental and physical effects and recommendations.. (2020) 24 938-47. DOI: 10.1007/s12603-020-1500-7 70. Tinetti ME, Costello DM, Naik AD, Davenport C, Hernandez-Bigos K, Van Liew JR. Outcome goals and health care preferences of older adults with multiple chronic conditions.. (2021) 4 e211271-211271. DOI: 10.1001/jamanetworkopen.2021.1271 71. Vahia IV, Jeste DV, Reynolds CF. Older adults and the mental health effects of COVID-19.. (2020) 324 2253-4. DOI: 10.1001/jama.2020.21753 72. Burns A, Zaudig M. Mild cognitive impairment in older people.. (2002) 360 1963-5. DOI: 10.1016/S0140-6736(02)11920-9 73. Fricke TR, Jong M, Naidoo KS, Sankaridurg P, Naduvilath TJ, Ho SM. Global prevalence of visual impairment associated with myopic macular degeneration and temporal trends from 2000 through 2050: systematic review, meta-analysis and modelling.. (2018) 102 855-62. DOI: 10.1136/bjophthalmol-2017-311266 74. Luanaigh CÓ, Lawlor BA. Loneliness and the health of older people.. (2008) 23 1213-21. PMID: 18537197 75. Prince MJ, Wu F, Guo Y, Robledo LMG, O’Donnell M, Sullivan R. The burden of disease in older people and implications for health policy and practice.. (2015) 385 549-62. DOI: 10.1016/S0140-6736(14)61347-7 76. Grühn D, Kotter-Grühn D, Röcke C. Discrete affects across the adult lifespan: evidence for multidimensionality and multidirectionality of affective experiences in young, middle-aged and older adults.. (2010) 44 492-500. DOI: 10.1016/j.jrp.2010.06.003 77. Pedroso-Chaparro M.d.S, Márquez-González M, Vara-García C, Cabrera I, Romero-Moreno R, Barrera-Caballero S. Guilt for perceiving oneself as a burden in healthy older adults. Associated factors.. (2021) 25 2330-6. PMID: 32964745 78. Weissberger GH, Mosqueda L, Nguyen AL, Samek A, Boyle PA, Nguyen CP. Physical and mental health correlates of perceived financial exploitation in older adults: preliminary findings from the finance, cognition, and health in elders study (FINCHES).. (2020) 24 740-6. DOI: 10.1080/13607863.2019.1571020 79. Evangelista LS, Moser DK, Westlake C, Pike N, Ter-Galstanyan A, Dracup K. Correlates of fatigue in patients with heart failure.. (2008) 23 12-7. PMID: 18326992 80. Cho JH-J, Olmstead R, Choi H, Carrillo C, Seeman TE, Irwin MR. Associations of objective versus subjective social isolation with sleep disturbance, depression, and fatigue in community-dwelling older adults.. (2019) 23 1130-8. DOI: 10.1080/13607863.2018.1481928 81. Sullivan M, Levy WC, Russo JE, Spertus JA. Depression and health status in patients with advanced heart failure: a prospective study in tertiary care.. (2004) 10 390-6. DOI: 10.1016/j.cardfail.2004.01.011 82. Arnold SV, Spertus JA, Ciechanowski PS, Soine LA, Jordan-Keith K, Caldwell JH. Psychosocial modulators of angina response to myocardial ischemia.. (2009) 120 126-33. DOI: 10.1161/CIRCULATIONAHA.108.806034 83. Mikkola TM, Kautiainen H, von Bonsdorff MB, Salonen MK, Wasenius N, Kajantie E. Body composition and changes in health-related quality of life in older age: a 10-year follow-up of the Helsinki Birth Cohort Study.. (2020) 29 2039-50. DOI: 10.1007/s11136-020-02453-1 84. Cheng C-W, Wang C-H, Chen W-S, Wang C-C, Cherng W-J. Predictors of long-term survival prior to permanent pacemaker implantation in octogenarians or older.. (2019) 31 1001-9. DOI: 10.1007/s40520-018-1044-4
--- title: Metabolome profile variations in common bean (Phaseolus vulgaris L.) resistant and susceptible genotypes incited by rust (Uromyces appendiculatus) authors: - Penny Makhumbila - Molemi E. Rauwane - Hangwani H. Muedi - Ntakadzeni E. Madala - Sandiswa Figlan journal: Frontiers in Genetics year: 2023 pmcid: PMC10060544 doi: 10.3389/fgene.2023.1141201 license: CC BY 4.0 --- # Metabolome profile variations in common bean (Phaseolus vulgaris L.) resistant and susceptible genotypes incited by rust (Uromyces appendiculatus) ## Abstract The causal agent of rust, Uromyces appendiculatus is a major constraint for common bean (Phaseolus vulgaris) production. This pathogen causes substantial yield losses in many common bean production areas worldwide. U. appendiculatus is widely distributed and although there have been numerous breakthroughs in breeding for resistance, its ability to mutate and evolve still poses a major threat to common bean production. An understanding of plant phytochemical properties can aid in accelerating breeding for rust resistance. In this study, metabolome profiles of two common bean genotypes Teebus-RR-1 (resistant) and Golden Gate Wax (susceptible) were investigated for their response to U. appendiculatus races (1 and 3) at 14- and 21-days post-infection (dpi) using liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-qTOF-MS). Non-targeted data analysis revealed 71 known metabolites that were putatively annotated, and a total of 33 were statistically significant. Key metabolites including flavonoids, terpenoids, alkaloids and lipids were found to be incited by rust infections in both genotypes. Resistant genotype as compared to the susceptible genotype differentially enriched metabolites including aconifine, D-sucrose, galangin, rutarin and others as a defence mechanism against the rust pathogen. The results suggest that timely response to pathogen attack by signalling the production of specific metabolites can be used as a strategy to understand plant defence. This is the first study to illustrate the utilization of metabolomics to understand the interaction of common bean with rust. ## 1 Introduction Common bean (Phaseolus vulgaris) is one of the most important human and animal consumable legume worldwide (Vega et al., 2017; Sadohara, 2020). Globally, common bean is cultivated on about 30 million hectares (Mha), while in Africa over 7.5 Mha are cultivated (Mukankusi et al., 2019). Common bean rust, a disease originating from the fungal pathogen Uromyces appendiculatus species affects the production of common bean in many production areas by causing between mild and severe damage to plants upon infection (Delgado et al., 2013). The urediniospores of U. appendiculatus can survive in winter and can be a source of inoculum in summer when the humidity and temperature are favourable (Gross and Venette, 2001; Mukankusi et al., 2019). The severity of the pathogen may occur in cooler environments with temperatures of about 17°C–20°C and high humidity (Liebenberg et al., 2005). It is difficult to predict yield losses from rust infestation, the loss of yields of up to $100\%$ may occur under severe disease pressure (Lindgren et al., 1995; Singh and Schwartz, 2010). Gross and Venette [2001] also reported that even yield losses of about $6\%$ can have reduced financial gain to farmers growing the crop on a larger scale. Currently, climate changes favour rust spreads in common bean producing areas as spores favourably flow in the air from one area to the other (Alleyne et al., 2008; Singh and Schwartz, 2010). Singh and Schwartz [2010] also added that plant debris left over from the previous season can also be a source of the disease. Pathogen-infected plants exhibit signs of whitish raised spots on the underside of the leaf that enlarge overtime (6 days) and form brownish uredenia (Souza et al., 2008). Uredenia of the pathogen spreads rapidly from 10–22 days post-infection (vegetative—flowering stages), depending on the conditions of the environment (Devi et al., 2020). Rust races 1, 3, 5 and others were first characterised in other parts of the world (Brazil and United States) and were later observed in Southern Africa, indicating prevalence of numerous races of U. appendiculatus (Liebenberg et al., 2005; Aruga et al., 2012). South Africa’s major production areas including Mpumalanga, Free State, North West, Gauteng and KwaZulu-Natal are greatly affected by rust races 1, 3 and 11. Although rust race 11 is not highly prevalent, it may cause greater yield losses when compared to other races (Liebenberg and Pretorius, 2004). The overall yield of common bean genotypes can be determined by the development of physiological characteristics (Rosales et al., 2012). Any interference by stressors such as diseases at critical phenological stages such as the vegetative development stages of the plant (V1–V4), pre-flowering (V5) and flowering (V6) among others can result in reduced yield (Singh and Schwartz, 2010; Odogwu et al., 2017). Therefore, it is important to utilise an array of methods to manage the U. appendiculatus pathogen in common bean during these critical stages production areas (Souza et al., 2013). The management of the pathogen relies primarily on the following strategies: i) application of cultural practices, ii) fungicide or chemical application, iii) biological control and iv) host plant resistance (Obongoya et al., 2010). However, due to the aggressiveness of the pathogen and its constant evolution, controlling the pathogen has been problematic (Breiing et al., 2021). Understanding the molecular and biochemical mechanisms involved in plant-host interaction can aid in the development of efficient pathogen management strategies that can improve productivity (Kalavacharia et al., 2000). Progress in legume improvement strategies has been witnessed through the advancement of next-generation sequencing techniques (NGS) and other high throughput genotyping technologies that have been applied to interrogate plant-host interactions (Rubiales et al., 2011; Tayeh et al., 2015; Janila et al., 2016, 2016). This development has led to more omics studies of common bean in response to U. appendiculatus attack (Cooper et al., 2007; Puthoff et al., 2008). For example, methylated and acetylated histones linked to resistance to U. appendiculatus were reported in a genome-wide profiling of histone modifications and gene expression in common bean (Ayyappan et al., 2015). In a similar study, U. appendiculatus resistant genotypes were reported to mediate antioxidant enzymes, phenolic compounds, and other defence genes in response to rust infection (Omara et al., 2022). However, metabolomic changes in common bean after infection with U. appendiculatus have not been addressed yet. Metabolomic techniques have played a vital role in aiding researchers to identify significant metabolites that contribute to legume improvement (Ramalingam et al., 2015). Profiling of metabolites using LC-MS techniques has been widely utilised to evaluate legume performance under disease pressure (Makhumbila et al., 2022). In a recent study, common bean infected with Fusarium solani significantly enriched biosynthesis of amino acids, flavonoid biosynthesis, purine metabolism and other pathways as a pathogen adaptation strategy (Chen et al., 2020). Amino acids and sugars are among metabolite classes that have been found to be up/down regulated in pea infected with Rhizoctonia solani (Turetschek et al., 2017). Similar results have been observed in common bean infected with Fusarium oxysporum where numerous metabolites were highly enriched after pathogen attack (Chen et al., 2019). Genes of U. appendiculatus have been profiled to efficiently understand the genomic characteristics of the pathogen and it was found that the pathogen alters its genes at different growth stages of the plant (Link, 2020). Although the mode of pathogen action has been evaluated, there is a vast knowledge gap on how common bean genotypes respond to U. appendiculatus infection at different growth stages. In order to understand the dynamics of breeding for rust resistance in common bean, metabolomics can play a vital role in selecting parental genotypes for breeding programmes by providing a basis for resistance biomarkers. In this study, the aim was to evaluate metabolomic changes that occur in resistant (Teebus-RR-1) and susceptible (Golden Gate Wax) common bean genotypes when infected with pathogenic U. appendiculatus fungal races (race 1 and 3) at two-time points (14- and 21- dpi). The functions of the identified metabolites expressed were studied, including their significance to U. appendiculatus tolerance at different growth stages. ## 2.1 Plant material and treatments Seeds of common bean genotypes Teebus-RR-1 (resistant/tolerant) and Golden Gate Wax (susceptible) mapped for rust resistant genes (Ur-3 and Ur-6) were obtained from Agricultural Research Council–Grain Crops Institute (ARC-GCI), Potchefstroom, South Africa. Seeds were surface sterilised using $50\%$ bleach solution (Lindsey et al., 2017), rinsed with sterile water, and grown in 9 cm pots with sterile 30 dm3 seedling mix having $50\%$ topsoil and compost, and covered with a vermiculite layer (Figures 1A, B). Inoculation of the respective genotypes was conducted when the leaves were ± 12 − 34 expanded, with five replicates per genotype (Figure 1B). FIGURE 1: A graphical representation of the methodology workflow from seed treatment and planting (A and B), pathogen preparation and inoculation (C), leaf sample harvesting (D) and the storage of harvested material (E). ## 2.2 Fungal material and inoculation Spores of U. appendiculatus races 1 and 3 were provided by ARC-GCI (South Africa) for inoculation purposes. The rust races were previously characterised and collected from isolated common bean infected plants, purified, and were stored in a −80°C ultra-freezer. Purified isolates were then re-hydrated by incubating open cryotubes with the pathogen in a glass beaker with warm water and vermiculite (Figure 1B). The beaker containing the cryotubes with the rust pathogens was sealed with sterile cling wrap plastic and left for 12 h at ±18°C. An aqueous soap suspension of Tween 20 (pure liquid soap) with 5 drops per litre of tap water (rust requires rust Ca2+ and Mg2+ ions) was prepared and the rust in cryotubes mixed thoroughly with the suspension. The concentration of rust spores was adjusted to 2.5 × 104 spores per ml using a hemacytometer (Figure 1C) with spore counts repeated four times to obtain $100\%$ germination of spores (Montejo Dominguez et al., 2022). Plants were spray inoculated on the leaves (the underside leaf targeted) at a low pressure using a compressor attached to a bottle with a spray gun in a disinfected confined booth. The inoculated plants were then left to dry for about 20–30 min and later placed in a dew chamber (Figure 1C) with $95\%$–$100\%$ relative humidity and temperatures of ±18–± 20°C for 48 h. The control experimental plants were mock inoculated with distilled water and subjected to the same treatment as the rust inoculated experimental plants. The inoculated plants were kept in different greenhouse compartments with $\frac{28}{14}$°C day/night temperatures with relative humidity of $75\%$ (Singh and Gupta, 2019). The plants were transferred from the 9 cm pots to 50 L black planting bags (Figure 1D) with sterile oxidic soil. The scoring of rust infection severity based on pustule size (Table 1) was conducted at 14- and 21- dpi as described by Hurtado-Gonzales et al. [ 2017]. TABLE 1 \| Score \| Description \| \| --- \| --- \| \| 1 \| No symptoms \| \| 2 \| Necrotic fleeks or spots without uredenia \| \| 3 \| Uredenia less than 300 µm in diameter \| \| 4 \| Uredenia 300–499 µm in diameter \| \| 5 \| Uredenia 500–799 µm in diameter \| \| 6 \| Uredenia more than 800 µm in diameter \| ## 2.3 Harvesting and metabolite extraction Infected (race 1 and 3) and non-infected (controls) leaf material was harvested from the two genotypes at 14- and 21-dpi, representing flowering and pre-flowering stages, respectively (Dann and Deverall, 1996; Devi et al., 2020). The harvested samples were snap frozen with liquid nitrogen and stored in a −80°C ultra-freezer prior to further analysis (Figure 1E). Leaf samples were then weighed (20 mg) and ground into powder in liquid nitrogen using mortar and pestle and extracted using the methanol extraction method consisting of 1.5 mL (1:75 m/v) of $70\%$ LC/MS grade methanol (Merck, Darmstadt, Germany). The extracted samples were vortexed for 30 s, sonicated for 10 min and centrifuged for 5 min at 5,100 rpm (Thermo Fisher, Johannesburg, South Africa). The supernatant was collected and filtered using nylon filters (0.22 µm) into glass vials containing 500 µL inserts (Agela Technologies, Tianjin, China). Three replicates per sample group were prepared for analysis and extracts were stored at 4°C prior to metabolite profiling. ## 2.4 LC-MS metabolite analysis Infected (race 1 and 3) and non-infected common bean leaf extracts were subjected to analysis on a liquid chromatography-quadrupole time-of flights tandem mass spectrometry instrument (LCMS-9030 qTOF, Shimadzu Corporation, Kyoto, Japan) for quantification of metabolites at different time intervals. A Shim-pack Velox C18 column (100 mm × 2.1 mm with a 2.7 µm particle size) was used for chromatographic separation at 55°C (Shimadzu Corporation, Kyoto, Japan). An injection volume of 3 µL was used for all samples and were run on a binary mobile phase including solvent A: $0.1\%$ formic acid in Milli-Q HPLC grade water (Merck, Darmstadt, Germany) and solvent B: UHPLC grade methanol with $0.1\%$ formic acid (Romil Ltd., Cambridge, United Kingdom). Chromatographic analysis was done using qTOF high-definition mass spectrometer that was set to negative electrospray ionisation for data acquisition. Parameters set included nebulization, interface voltage (4.0 kV), interface temperature (300°C), dry gas flow (3 L/min), detector voltage (1.8 kV), heat block (400°C), DL (280°C) and flight tube (42°C) temperatures. Ion fragmentation was achieved using argon gas for collision with an energy of 30 eV and 5 eV spread (Ramabulana et al., 2021). ## 2.5 Multivariate data analysis Data pre-processing was done using XCMS, with HPLC/UHD-qTOF parameters using the centWave feature detection method, maximum threshold of 15 ppm, a signal to noise ratio of 6, prefilters for intensity and noise at 100 and 3. The retention time correction method was obiwarp with profStep, while the alignment minimum fraction of samples was 0.5 and a 0.015 m/z width. Kruskal–Wallis statistical test was applied to the data that resulted in a feature table with 11,315 characteristics. The feature table was exported to SIMCA version 17.0 software, normalised and pareto scaled prior to model application. Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures—Discriminant Analysis (OPLS-DA) models were applied to the data. ## 2.6 Metabolite annotation, relative quantification and pathway analysis MzMine v2.3 was used for data visualisation, chromatogram deconvolution, MS1/MS2 building, isotope grouping, alignment, filtering and gap filling (Pluskal et al., 2010). The resulting mascot generic format (mfg.) file and metadata for the respective treatments were processed on GNPS online. Libraries used for spectral search included GNPS, ChEBI, HMBD, DRUGNANK, FooDB, and SUPNAT (Wang, 2016). Metabolites were matched to GNPS linked databases and were also putatively annotated or verified through searches in compound databases using their peak mass and isomeric SMILES. Databases including KEGG compound, KNApSAcK, Chemspider, ChEBI, PubChem, and Dictionary of Natural Products. Annotations were further confirmed through literature search of related studies. Metabolite concentrations were used for an overview of metabolomic pathways that were enriched. Overrepresentation with a hypergeometric test and KEGG metabolite pathway for Arabidopsis thaliana (thale cress) was used for pathway analysis in MetaboAnalyst v5.0. ## 3.1 Phenotypic evaluation of common bean post inoculation with U. appendiculatus Plant leaves of Teebus-RR-1 and Golden Gate Wax common bean genotypes were evaluated phenotypically for their response to U. appendiculatus race 1 and 3 throughout the experiment (Figure 2; Table 1). The resistant genotype Teebus-RR-1 exhibited no symptoms of infection by race 1 and 3 at 14 dpi (Figures 2B, C). Similar phenotypic observations were observed at 21 dpi on secondary leaves, after the primary leaves had matured and fallen off (Figures 2E, F). Golden Gate Wax had the highest number of pustules when infected with rust races 1 and 3 at the two time points post infection (Figures 2H, I). Secondary leaves of the susceptible genotypes exhibited a continuous spread of infection with rust race 1 at 21 dpi (Figure 2K) while race 3 had no visible pustules (Figure 2L). Leaf lesions, necrosis and wilting were among symptoms that were prevalent on susceptible genotype Golden Gate Wax at 14 and 21 dpi compared to the control mock inoculated leaves (Figures 2G, J). FIGURE 2: Phenotypic leaf evaluation of Teebus-RR-1 and Golden Gate Wax genotypes in response to U. appendiculatus infection. (A) Teebus-RR-1 control mock inoculated at 14 dpi, (B) Teebus-RR-1 race 1 inoculated leaf at 14 dpi, (C) Teebus-RR-1 race 3 inoculated leaf at 14 dpi, (D) Teebus-RR-1 control mock inoculated at 21 dpi, (E) Teebus-RR-1race 1 inoculated at 21 dpi and (F) Teebus-RR-1 race 3 inoculated at 21 dpi. (G) Golden Gate Wax control mock inoculated at 14 dpi, (H) Golden Gate Wax race 1 inoculated at 14 dpi, (I) Golden Gate Wax race 3 inoculated leaf at 14 dpi, (J) Golden Gate Wax control mock inoculated at 21 dpi, (K) Golden Gate Wax race 1 inoculated at 21 dpi and (L) Golden Gate Wax race 3 inoculated at 21 dpi. ## 3.2 Comprehensive analysis of common bean metabolites Untargeted metabolite profiles of genotypes Teebus-RR-1 and Golden Gate Wax were analysed using LC-MS at 14- and 21- dpi in response to U. appendiculatus. The Principal Component Analysis (PCA) model provided the virtual analysis of the effects of U. appendiculatus treatments on common bean, revealing clustering on genotypes, races and time intervals post infection. The PCA results showed clustering between the two genotypes (Supplementary Figure S1). Although the PCA score plot showed differential sample clustering with a separation of genotypes (indicating differential metabolite profiles), Othorgonal (OPLS-DA) was computed to allow prediction of variations, consequently allowing identification of potential biomarkers (Tugizimana et al., 2013). The OPLS-DA results revealed similar clustering patterns between the two genotypes (Supplementary Figure S2), reflecting the differences in metabolite profiles between the genotypes. A total of 71 known metabolites were identified to be present in both genotypes at varying concentrations for the different treatments and a heatmap with the 33 metabolites were found to be significant (Table 2). Interestingly, excessive metabolite changes were observed between treatments of the susceptible genotype Golden Gate Wax compared to the resistant genotype Teebus-RR-1 that had slight or limited metabolite changes when subjected to U. appendiculatus infection at the different time points. An example, afzelechin-(4alpha->8)-afzelechin, (5-Phenyl-1,2,4-triazol-3-yl) urea, tuberonic acid glucoside, xanthotoxin, chlorflavonin, D-sucrose and linoleate were highly concentrated in Teebus-RR-1 samples infected with rust race 1 at 14 dpi (T114) while there were low concentrations of these metabolites in Golden Gate Wax samples (G114) under similar conditions. These metabolites were further expressed in lower concentrations at 21 dpi race 1 samples (G121) while the resistant genotype kept a moderately high production of these metabolites in samples (T121) at 21 dpi. Kaempferol 3-O-rhamnoside-7-O-glucoside, phylloquinone and sennoside D were produced in moderately high concentrations in the resistant genotype Teebus-RR-1 at both 14 and 21 dpi in race 3 infected samples (T3) while these metabolites were suppressed in the susceptible genotype Golden Gate Wax race 3 infected samples (G3) at both time points (Figure 3). The majority of the differentially expressed metabolites in both these cultivars belong to an array of compound classes including flavonoids, terpenoids, fatty acids and phenols that have been found in numerous plant species including legumes (Table 2; Supplementary Table S1). The resistant genotype Teebus-RR-1 enriched more metabolites in rust infected plants compared to non-infected control samples (Figure 3) for the different treatments. An upregulation of metabolites was prevalent in the resistant genotype Teebus-RR-1 compared to the susceptible genotype that had minimum regulation of metabolites leading to fewer being significant (Figures 5A–C). Metabolites belonging to compound classes such as flavonoids including D-sucrose, 3-O-methylquercetin, chlorflavonin and phenol xanthotoxin (Figure 4A) were enriched in U. appendiculatus race 1 infected samples at 14 dpi; while vitexin, isovitexi-7-O glucoside, saikosaponinBK1 and agrimophol were down regulated (Figures 4A–C). Other metabolites belonging to an array of compound classes such as lipids, anthocyanins and energetic salts were also differentially enriched in the resistant genotype Teebus-RR-1 at 14 dpi when infected with U. appendiculatus race 1 (Figures 4D,F; Table 2). At 21 dpi, most of the differentially expressed metabolites including flavonoids and other compound classes were up regulated and expressed at exponentially higher concentrations, except for phylloquinone (Figures 4E, F). On the other hand, quercetin, galangin and rutin were up regulated, while vitexin was down regulated in the Teebus-RR-1 infected with race 3 U. appendiculatus pathogen at 14 dpi (Figure 4G). Furthermore, terpenoids daphnetoxin, and kansuinine B were differentially expressed at varying concentrations when compared to the control (Figure 4H). In the resistant genotype Teebus-RR-1, flavonoids and terpenoids were expressed at slightly higher concentrations in the U. appendiculatus race 3 infected samples at 21 dpi in comparison to control samples (Figures 4I, J). FIGURE 4: Box plots illustrating concentrations of metabolites differentially enriched in resistant genotype Teebus-RR-1 in response to U. appendiculatus race 1 at 14 dpi (A–D), 21 dpi (E–F), race 3 at 14 dpi (G and H) and 21 dpi (I and J). (A) Flavonoids. (B) Terpenoids. (C) Phenols. (D) Other metabolites belonging to an array of compound classes. (E) Flavonoids. (F) Other metabolites belonging to an array of compound classes. (G) Flavonoids. (H) Terpenoids. (I) Flavonoids. (J) Terpenoids. The red indicates the peak area quantification of metabolites extracted from the control and green indicates the peak area quantification of metabolites extracted from plants infected with U. appendiculatus. In the susceptible genotype Golden Gate Wax, isovetexin-7-glucoside was down regulated at 14 dpi with U. appendiculatus race 1 (Figures 5A,D), while graveoline and tuberonic acid glucoside were also down regulated at 21 dpi (Figures 5B, C, E–G). There were no differentially expressed metabolites at 14 dpi with U. appendiculatus race 3 (Supplementary Figure S3). Meanwhile, tuberonic acid glucoside was differentially down regulated at 21 dpi in U. appendiculatus race 3 infected plants (Figure 5C). FIGURE 5: Volcano and box plots showing metabolite changes and concentrations in common bean susceptible genotype Golden Gate Wax response to U. appendiculatus race 1 at 14 dpi (A&D—flavonoid), 21 dpi [B, (E) alkaloid and (F) lipid], race 3 at 21 dpi (C&G—lipid). The blue indicates significantly down regulated metabolites while grey indicates non-significant metabolites in U. appendiculatus infected plants. The red indicates the peak area quantification of metabolites extracted from the control and green indicates the peak area quantification of metabolites extracted from plants infected with U. appendiculatus. ## 3.3 Infection with U. appendiculatus triggers defence metabolomic pathways The KEGG pathway analysis revealed that numerous metabolites that were putatively annotated were associated with several pathways including alpha-linolenic acid metabolism, biosynthesis of fatty acids, flavonoid biosynthesis and purine metabolism (Figure 6). Alpha-linolenic acid metabolism and biosynthesis of unsaturated fatty acids pathway are known to synthesise a group of fatty acids including linolenate, methyl jasmonate, hexadecenoic acid and linoleate. Linolenate is known as a backbone of metabolite 12(S)-HPOT. Meanwhile, flavonoid biosynthesis pathway resulted in the biosynthesis of quercertin from which 3-O-methylquercetin and quercetin 3-O-glucuronide were derived (Figure 6; Supplementary Table S1). Purine metabolism pathway on the other hand was also present and yielded adenine and deoxyadenosine. FIGURE 6: Schematic summary of pathways in common bean plants that contributed to the production of a wide variety of compounds from different classes on KEGG. Fatty acids (orange), flavonoids (green) and purines (blue) from leaf samples. ## 4.1 Metabolomic analysis of P. vulgaris genotypes in response to U. appendiculatus Metabolites are products that play major roles in plant response to environmental stress conditions. Currently, there is limited knowledge on metabolites involved in resistance against an array of fungal and bacterial pathogens in legumes (Makhumbila et al., 2022). In this study, an untargeted metabolome profiling of two common bean genotypes Teebus-RR-1 and Golden Gate Wax were investigated for their response to race 1 and 3 U. appendiculatus infections at different time-points (14 and 21 days). The analysis demonstrated that under U. appendiculatus infection, different compounds were enriched at varying concentrations in both genotypes at different time points. The resistant genotype showed dominance in metabolite expression in response to U. appendiculatus infections when compared to non-infected controls. For instance, 3-O-methylquercetin was highly enriched at 14 dpi when treated with race 1 in the resistant genotype. An increase in 3-O-methylquercetin has been associated with an increase of hydrogen peroxide (H2O2)) due to biotic stress conditions, which is converted from reactive oxygen species (Quan et al., 2008; Kumar et al., 2015). H2O2 is a signalling molecule that regulates development and stress adaptation in plants. In addition, Singh et al. [ 2021] reported that an increase of 3-O-methylquercetin can also be attributed to an increased accumulation of quercetin in resistant genotypes under biotic stress. Quercetin is a flavonoid known to play a role in the process of protecting plants against stress effects. Moreover, quercetin and rutin were also found to be upregulated in the resistant genotype at 21 dpi. Rutin is a flavonoid that plays a key role in protecting plants against pathogens (Kianersi et al., 2020). The increase in flavonoid levels such as quercetin and its derivatives, and rutin among others, have been reported in pathogen-infected plants (Hadrami et al., 2011; Kianersi et al., 2020). In this study, similar results were observed in the Teebus-RR-1 genotype, suggesting the role of flavonoids in defence in response to U. appendiculatus at different time-points. Terpenoids such as daphnetoxin was induced in the resistant genotype at 14 dpi compared to the non-infected control. Daphnetoxin has been reported for its toxicity (Chen et al., 2022), possibly contributing to the suppression of the pathogen growth in the plant. On the other hand, kansuinine B was suppressed in the same genotype at 14 dpi compared to non-infected control. Reduced concentrations of terpenoids have been reported in Chrysanthemum morifolium inoculated with Alternaria tenuissima (He et al., 2022). The decreased concentration of terpenoids have been shown to cause cell membrane damage as they merge with phosphor-lipid acyl chains (Hammerbacher et al., 2019). In addition, an alkaloid such as aconifine was also moderately enriched in a resistant U. appendiculatus treated genotype (Figure 3). Generally, alkaloids have been reported for their role in herbivory defence in plants (Macel, 2011; Bhambhani et al., 2021), suggesting their possible role in common bean response to U. appendiculatus. On the other hand, the susceptible Golden Gate Wax genotype differentially expressed compounds such as alkaloids and lipids at different time-points post infection with rust races 1 and 3. Metabolites that were downregulated in the susceptible genotype in response to U. appendiculatus races included tuberonic acid glucoside (Figure 5). The downregulation of lipids can be linked to lipid metabolism degradation in plants infected with pathogens, therefore signalling the genotypes’ susceptibility (Robison et al., 2018; Foucher et al., 2020). In addition, U. appendiculatus slows down photosynthetic activity by forming pustules on susceptible genotypes, as it was observed in our study (Figure 2H), consequently causing the cell wall to collapse by lipid peroxidation (Bojtor et al., 2019). An exploration of other metabolites in the susceptible genotype indicated a dramatic decrease in the expression of phenolic compounds (xanthotoxin and chlorogenic acid), which are essential in the plants defence against U. appendiculatus (Omara et al., 2022). The pathogen U. appendiculatus could be promoting the synthesis of proteins for feeding that are synthesised from an array of pathways including lipids (Link, 2020). This group of metabolites were downregulated in the susceptible genotype in our study. It has been widely reported that exposure of plants to pathogen stress increases the production of flavonoids, terpenoids and phenols as a defence strategy (López-Gresa et al., 2010). Flavonoids signal compounds in plant-pathogen interaction that also tend to be highly produced in resistant as compared to susceptible genotypes (Steinkellner et al., 2007; Chin et al., 2018). Terpenoids influence the plant’s ability to inhibit pathogen attack (Toffolatti et al., 2021). Phenols protect plant tissues from the toxic effects of pathogens (Kumar et al., 2015), while alkaloids store nitrogen reserves during pathogen attack (Ali et al., 2019). The results of this study also show that due to U. appendiculatus infection, compounds such as D-sucrose, 3-O-methylquercetin, phenol xanthotoxin and chlorflavonin were highly enriched in the resistant genotype compared to the susceptible genotype. A recent study reported similar findings that resistant genotypes tend to secrete a larger number of metabolites at high concentrations when compared to susceptible genotypes under disease pressure (Robison et al., 2018). Additionally, in barley genotypes infected with stripe rust, similar patterns of metabolite upregulation in resistant genotypes were reported (Singla et al., 2020). ## 4.2 Defence strategies of common bean plants to U. appendiculatus In this study, we found that chlorflavonin, D-sucrose, xanthotoxin, tuberonic acid glucoside, malvin, vitexin, robinobiose, 3-O-methylquercetin, rutarin, aconifine, galangin, rutin, daphnetoxxin, syringin and 24-Methylidenecycloartanol were highly enriched metabolites in response to U. appendiculatus infection with aconifine being the most enriched (Figure 4F). Aconifine is an alkaloid that has been widely linked to Aconitum karakolicum Rapaics which is an antiproliferative that prohibits pathogen cell growth (Tashkhodjaev and Sultankhodjaev, 2009; Thawabteh et al., 2019). On the other hand, rutarin was also expressed in abundance at 21 dpi when the plant was under rust infection. The compound has been reported to have antifeedant/antiparasitic properties in tree plants (Lacroix et al., 2011). Flavonoid D-sucrose was also enriched in common bean at 14 dpi in the resistant genotype. Certain studies have highlighted the importance of this metabolite in plant growth and associated the metabolite with fungal pathogen virulence in maize (Wahl et al., 2010). In another study, it was highlighted that the synthesis of sucrose is beneficial for the plants overall growth and tolerance to stressors (Stein and Granot, 2019). Xanthotoxin, a phenolic compound was also differentially expressed in plants infected with the U. appendiculatus pathogen. This compound has been appraised for its ability to interact with free radicals and biological molecules that might cause lipid peroxidation, protein damage, enzyme inhibition and genetic oxidation that might result in cell death (Bajerová et al., 2014; Dumanović et al., 2021). Another antioxidant characterized compound that was differentially expressed was galangin that has been found to be a beneficial antioxidant in red kidney bean evaluated under water deficiency (Manoj et al., 2022). This is in concurrence with our finding that certain metabolites are highly enriched in common bean leaves as a stress defence strategy. However, it is still unclear how metabolites interact with genes expressed, and for this reason, an integrated omics study to unravel underlying response mechanisms involved in common bean rust interactions will have far reaching impact in common bean breeding. ## 4.3 Metabolomic pathways contribute to the plants ability to respond to U. appendiculatus attack Fatty acids that are synthesized by the plant tend to undergo prokaryotic or eukaryotic pathway. During this occurrence, they are converted to complex lipids for assembly in the eukaryotic pathways. This consequently leads to the production of plant fats that contribute to the plants overall nutrition (Chellamuthu et al., 2022). In the current study, metabolites synthesized through the alpha-linolenic acid metabolism and biosynthesis of unsaturated fatty acids were not significantly enriched. This possibly indicates that despite stress conditions, other metabolomic activities responsible for the overall productivity still occur. Interestingly, Kumaraswamy et al. [ 2011] reported a 4-fold expression of linolenic acid in barley resistant genotypes infected with Fusarium graminearum compared to the susceptible genotypes. In terms of flavonoids, they have been widely reported for their contribution in plant biotic and abiotic stress response (Jan et al., 2022). Our results found that quercetin, rutin and galangin were up regulated in the resistant genotype infected with U. appendiculatus at both time points. Similar findings reported quercetin and kaempferol derivatives to be effective contributors to plants stress response because of their antioxidant properties (Koskimäki et al., 2009). The expression of flavonoids in the susceptible genotype (Golden Gate Wax) was lower when compared to the resistant, suggesting reduced flavonoid biosynthesis. This is supported by the study of Nagamatsu et al. [ 2007] who reported that reduced flavonoid biosynthesis in soybean under viral pathogen stress may be associated with cell wall degradation. Similarly, purine metabolism has also been appraised for its contribution to stress adaptation by contributing to nitrogen metabolism (Watanabe et al., 2014). However, metabolites belonging to the purine metabolism pathway were not significantly enriched in the current study. The biosynthesis of metabolites in plants is related to genes (Raza, 2020) and thus complex, therefore metabolomics and transcriptomics studies should be integrated to gain insight on the integral pathways that play a role in defence. ## 5 Conclusion In the present study, the whole metabolome of common bean infected with U. appendiculatus was characterised. The differences in terms of metabolites between the inoculated and non-inoculated common bean showed that flavonoids, terpenoids, alkaloids and others play an important role in defence response to U. appendiculatus infection. Flavonoid pathway was the main defence response in common bean. Further investigations on early response (24–72 h post-infection) will give a clearer picture of the metabolites involved at the initial stages of infection. Metabolomic expression patterns of genotypes at early and late infection stages can be used to understand plant-pathogen interactions and significantly upregulated metabolites can serve as biomarkers in breeding programmes. Overall, this study provides new insights in understanding common bean interactions with U. appendiculatus. Future research studies should focus on integrating metabolomics with other OMICs to uncover possible underlying defence mechanisms which could represent a helpful tool for developing common bean resistant varieties toward U. appendiculatus. ## Data availability statement Metabolomic data presented in the study are deposited in the MetaboLights repository, accession number MTBLS6972. ## Author contributions SF, MR, and HM conceptualised and designed the experiment. PM conducted the plant growth experiments and applied the treatments. MR, SF, and PM conducted the metabolite extractions. PM, MR, and NM conducted the LC-MS analysis. Data analysis was done by PM, MR, and SF. Revision of the manuscript was by PM, MR, HM, NM, and SF. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1141201/full#supplementary-material ## References 1. Ahmed S. M., Kumar A., Gandhi S. G.. Exogenously applied rohitukine inhibits photosynthetic processes, growth and induces antioxidant defense system in. Antioxidants (2022) 11 1512. DOI: 10.3390/antiox11081512 2. Ali A., Khan A., Sharif R., Mujtaba M., Gao S. J.. Sugarcane omics: An update on the status of research and crop improvement. Plants (2019) 8 334. DOI: 10.3390/plants8090344 3. Alleyne A. T., Steadman J. R., Eskridge K. M.. Monitoring changing virulence patterns of. Eur. J. Plant Path (2008) 122 315-319. DOI: 10.1007/s10658-008-9295-0 4. Amarowicz R., Troszyńska A.. Antioxidant activity of extract of pea and its fractions of low molecular phenolics and tannins. Pol. J. Food Nutr. Sci. (2003) 53 10-15 5. Anding C., Brandt R. D., Ourisson G.. Sterol biosynthesis in. Euglena gracilis z. Eur. J. Biochem. (1971) 24 259-263. DOI: 10.1111/j.1432-1033.1971.tb19679.x 6. Bajerová P., Adam M., Bajer T., Ventura K.. Comparison of various techniques for the extraction and determination of antioxidants in plants. J. Sep. Sci. (2014) 37 835-844. DOI: 10.1002/jssc.201301139 7. Balida L. A. P., Theresa J., Regalado A., Teodosio J. J. R., Dizon K. A. H., Sun Z.. Antibiotic isoflavonoids, anthraquinones, and pterocarpanoids from pigeon pea (. Plant path (2022) 12 279. DOI: 10.3390/metabo12040279 8. Balkema-boomstra A. G., Zijlstra S., Verstappen F. W. A., Inggamer H., Mercke P. E., Jongsma M. A.. Role of cucurbitacin c in resistance to spider mite (. J. Chem. Ecol. (2003) 29 225-235. DOI: 10.1023/a:1021945101308 9. Bhambhani S., Kondhare S. R., Giri A. P.. Diversity in chemical structures and biological properties of plant alkaloids. Molecules (2021) 26 3374. DOI: 10.3390/molecules26113374 10. Bojtor C. S., Győri Z., Sipos P., Radócz L., Tóth B.. Effect of bean rust [. Acta Aliment. (2019) 48 253-259. DOI: 10.1556/066.2019.48.2.13 11. Breiing V., Hillmer J., Schmidt C., Petry M., Behrends B., Steiner U.. Fungicidal efficacy of drying plant oils in green beans against bean rust (. Plants (2021) 10 143. DOI: 10.3390/plants10010143 12. Chellamuthu M., Kumaresan K., Subramanian S.. Increase in alpha-linolenic acid content by simultaneous expression of fatty acid metabolism genes in sesame (. physiology Mol. Biol. plants (2022) 28 559-572. DOI: 10.1007/s12298-022-01152-0 13. Chen H., Singh H., Bhardwaj N., Bhardwaj S. K., Khatri M., Kim K.. An exploration on the toxicity mechanisms of phytotoxins and their potential utilities. Crit. Rev. Environ. Sci. Technol. (2022) 52 395-435. DOI: 10.1080/10643389.2020.1823172 14. Chen L., Wu Q., He T., Lan J., Ding L., Liu T.. Transcriptomic and metabolomic changes triggered by Fusarium solani in common bean (. Genes (2020) 11 177. DOI: 10.3390/genes11020177 15. Chen L., Wu Q., He W., He T., Wu Q., Miao Y.. Combined de novo transcriptome and metabolome analysis of common bean response to. Int. J. Mol. Scie. (2019) 20 6278. DOI: 10.3390/ijms20246278 16. Chen N. W. G., Ruh M., Darrasse A., Foucher J., Briand M., Costa J.. Common bacterial blight of bean: A model of seed transmission and pathological convergence. Mol. Plant Path. (2021) 22 1464-1480. DOI: 10.1111/mpp.13067 17. Chin S., Behm C. A., Mathesius U.. Functions of flavonoids in plant–nematode interactions. Plants (2018) 7 85. DOI: 10.3390/plants7040085 18. Chua T. K., Bujnicki J. M., Tan T., Huynh F., Patel B. K., Sivaraman J.. The structure of sucrose phosphate synthase from halothermothrix orenii reveals its mechanism of action and binding mode. Plant Cell (2008) 20 1059-1072. DOI: 10.1105/tpc.107.051193 19. Chung S. Y., Seki H., Fujisawa Y., Shimoda Y., Hiraga S., Nomura Y.. A cellulose synthase-derived enzyme catalyses 3-o-glucuronosylation in saponin biosynthesis. Nat. Comm. (2020) 11 5664. DOI: 10.1038/s41467-020-19399-0 20. Cooper B., Neelam A., Campbell K., Lee J., Liu G., Garrett W.. Protein accumulation in the germinating. Mol. Plant-Microbe Inter. (2007) 20 857-866. DOI: 10.1094/MPMI-20-7-0857 21. Dann E. K., Deverall B. J.. 2, 6-dichloro-isonicotinic acid (ina) induces resistance in green beans to the rust pathogen,. Australas. Plant Path (1996) 25 199-204. DOI: 10.1071/ap96034 22. Delgado D. Z., de Freitas M. B., Stadnik M. J.. Effectiveness of saccharin and ulvan as resistance inducers against rust and angular leaf spot in bean plants (. Crop Prot. (2013) 47 67-73. DOI: 10.1016/j.cropro.2013.01.003 23. Devi B., Gupta S. K., Singh G., Prasad P.. Efficacy of new generation fungicides against French bean rust caused by. Phytoparasitica (2020) 48 535-543. DOI: 10.1007/s12600-020-00820-9 24. Dias M. C., Pinto D. C. G. A., Silva A. M. S.. Plant flavonoids: Chemical characteristics and biological activity. Molecules (2021) 26 5377. DOI: 10.3390/molecules26175377 25. Dumanović J., Nepovimova E., Kuča M. N. K., Jaćević V., Jacevic V.. The significance of reactive oxygen species and antioxidant defense system in plants: A concise overview. Front. Plant Scie. (2021) 11. DOI: 10.3389/fpls.2020.552969 26. Enciso-roca E. C., Aguilar-felices E. J., Tinco-jayo J. A., Arroyo-acevedo J. L., Calderon O. H.. Biomolecules with antioxidant capacity from the seeds and sprouts of 20 varieties of. Plants (2021) 10 2417. DOI: 10.3390/plants10112417 27. Foucher J., Ruh M., Préveaux A., Carrère S., Pelletier S., Briand M.. Common bean resistance to xanthomonas is associated with upregulation of the salicylic acid pathway and downregulation of photosynthesis. BMC Genomics (2020) 21 566-6. DOI: 10.1186/s12864-020-06972-6 28. Fu K., Zhang J., Wang l., Zhao X., Luo Y.. Xanthotoxin induced photoactivated toxicity, oxidative stress and cellular apoptosis in. Comp. Biochem. Physiol. C. Toxicol. Pharmacol. (2022) 251 109217. DOI: 10.1016/j.cbpc.2021.109217 29. Gross P. L., Venette J. R.. Overwinter survival of bean rust urediniospores in North Dakota. Plant Dis. (2001) 85 226-227. DOI: 10.1094/pdis.2001.85.2.226 30. Hadrami E., Adam A. L., Daayf F.. Biocontrol treatments confer protection against. Mol. Plant-Microbe Interact. (2011) 24 328-335. DOI: 10.1094/MPMI-04-10-0098 31. Hammerbacher A., Coutinho T. A., Gershenzon J.. Roles of plant volatiles in defence against microbial pathogens and microbial exploitation of volatiles. Plant, Cell Environ. (2019) 42 2827-2843. DOI: 10.1111/pce.13602 32. He X., Jiang Y., Chen S., Chen F., Chen F.. Terpenoids and their possible role in defense against a fungal pathogen. J. Plant Growth Reg. (2022) 1-14. DOI: 10.1007/s00344-022-10619-z 33. He X., Yang F., Huang X. A.. Proceedings of chemistry, pharmacology, pharmacokinetics and synthesis of biflavonoids. Molecules (2021) 26 6088. DOI: 10.3390/molecules26196088 34. Hughes R. K., Lawson D. M., Hornostaj A. R., Fairhurst S. A., Casey R.. Mutagenesis and modelling of linoleate-binding to pea seed lipoxygenase. Euro. J. Biochem. (2001) 268 1030-1040. DOI: 10.1046/j.1432-1327.2001.01964.x 35. Hurtado-Gonzales O. P., Valentini G., Gilio T. A., Martins A. M., Song Q., Pastor-Corrales M. A.. Fine mapping of. G3 Genes Genomes Genet. (2017) 7 557-569. DOI: 10.1534/g3.116.036061 36. Ishikawa F. H., Ramalho M. A. P., Souza E. A.. Common bean lines as potential differential cultivars for race 65 of. Colletorichum Lindemuthianum. J. Plant Path. (2011) 5 461-464 37. Jan R., Khan M., Asaf S., LubnaAsifKim S. K. M., Kim K. M.. Bioactivity and therapeutic potential of kaempferol and quercetin: New insights for plant and human health. Plants (2022) 11 2623. DOI: 10.3390/plants11192623 38. Janila P., Variath M. T., Pandey M. K., Desmae H., Motagi B. N., Okori P.. Genomic tools in groundnut breeding program: Status and perspectives. Front. Plant Scie. (2016) 7 289. DOI: 10.3389/fpls.2016.00289 39. Kalavacharia V., Stavely J. R., Myers J. R., McClean P. E.. crg, a gene required for ur-3 -mediated rust resistance in common bean, maps to a resistance gene analog cluster. Mol. Plant Microbe Interact. (2000) 13 1237-1242. DOI: 10.1094/MPMI.2000.13.11.1237 40. Kianersi F., Abdollahi M. R., Mirzaie-Asl A., Dastan D., Rasheed F.. Identification and tissue-specific expression of rutin biosynthetic pathway genes in Capparis spinosa elicited with salicylic acid and methyl jasmonate. Sci. Rep. (2020) 10 8884. DOI: 10.1038/s41598-020-65815-2 41. Koskimäki J. J., Hokkanen J., Jaakola L., Suorsa M., Tolonen A., Mattila S.. Flavonoid biosynthesis and degradation play a role in early defence responses of bilberry (. Eur. J. Plant Pathol. (2009) 125 629-640. DOI: 10.1007/s10658-009-9511-6 42. Krcatović E., Rusak G., Bezić N., Krajačić M.. Inhibition of tobacco mosaic virus infection by quercetin and vitexin. Acta Virol. (2008) 52 119-124. PMID: 18564899 43. Kumar Y., Dholakia B. B., Panigrahi P., Kadoo N. Y., Giri A. P., Gupta V. S.. Metabolic profiling of chickpea-fusarium interaction identifies differential modulation of disease resistance pathways. P (2015) 116 120-129. DOI: 10.1016/j.phytochem.2015.04.001 44. Kumaraswamy K. G., Kushalappa A. C., Choo T. M., Dion Y., Rioux S.. Mass spectrometry based metabolomics to identify potential biomarkers for resistance in barley against fusarium head blight (. J. Chem. Ecol. (2011) 37 846-856. DOI: 10.1007/s10886-011-9989-1 45. Kuo C. L., Agrawal D. C., Chang H. C., Chiu Y. T., Huang C. P., Chen Y. L.. Bot. Stud. (2015) 56 12. DOI: 10.1186/s40529-015-0092-8 46. Lacroix D., Prado S., Kamoga D., Kasenene J., Bodo B.. Structure and. J. Nat. Prod. (2011) 74 2286-2289. DOI: 10.1021/np2004825 47. Le J., Ji H., Zhou X., Wei X., Chen Y., Fu Y.. pharmacology, toxicology, and metabolism of sennoside a, a medicinal plant-derived natural compound. Front. Pharmacol. (2021) 12 714586. DOI: 10.3389/fphar.2021.714586 48. Le Q. U., Joshi R. K., Lay H. L., Wu M. C.. J. Pharmacol. Phytochem. (2018) 7 3202-3211 49. Liebenberg M. M., Liebenberg A. J., Pretorius Z.. Breeding for rust resistance increases dry bean productivity in South Africa. South Afr. J. Plant Soil (2005) 22 129-139. DOI: 10.1080/02571862.2005.10634695 50. Liebenberg M. M., Pretorius Z. A.. Inheritance of resistance to. South Afr. J. Plant Soil (2004) 21 245-250. DOI: 10.1080/02571862.2004.10635057 51. Lindgren D. T., Eskridge K. M., Steadman J. R., Schaaf D. M.. A model for dry bean yield loss due to rust. HortTechnology (1995) 5 35-37. DOI: 10.21273/horttech.5.1.35 52. Lindsey B. E., Rivero L., Calhoun C. S., Grotewold E., Brkljacic J.. Standardized method for high-throughput sterilization of arabidopsis seeds. JoVE (2017) 128 e56587. DOI: 10.3791/56587 53. Link T. I.. Testing reference genes for transcript profiling in. Plos One (2020) 15 e0237273. DOI: 10.1371/journal.pone.0237273 54. Liu C., Guo H., Liu H., Yu J., Li S., Zhu T.. Differential metabolomics reveals pathogenesis of pestalotiopsis kenyana causing leaf spot disease of. J. Fungi (2022) 8 1208. DOI: 10.3390/jof8111208 55. Liu W., Cheng X., Kang R., Wang Y., Guo X., Jing W.. Systematic characterization and identification of saikosaponins in extracts from. Front. Chem. (2021) 9 747987. DOI: 10.3389/fchem.2021.747987 56. López‐Gresa M. P., Maltese F., Bellés J. M., Conejero V., Kim H. K., Choi Y. H.. Metabolic response of tomato leaves upon different plant–pathogen interactions. Phytochem. Anal. Int. J. Plant Chem. Biochem. Tech. (2010) 21 89-94. DOI: 10.1002/pca.1179 57. Macel M.. Attract and deter: A dual role for pyrrolizidine alkaloids in plant–insect interactions. Phytochem. Rev. (2011) 10 75-82. DOI: 10.1007/s11101-010-9181-1 58. Makhumbila P., Rauwane M., Muedi H., Figlan S.. Metabolome profiling: A breeding prediction tool for legume performance under biotic stress conditions. Plants (2022) 11 1756. DOI: 10.3390/plants11131756 59. Manoj B. S., Gupta M., Jeelani M. I., Gupta S.. Metabolic footprints of chitosan primed red kidney bean under restricted irrigation. Macromolecules (2022) 208 367-380. DOI: 10.1016/j.ijbiomac.2022.03.097 60. Moloto M. R., Phan A. D. T., Shai J. L., Sultanbawa Y., Sivakumar D.. Comparison of phenolic compounds, carotenoids, amino acid composition,. Foods (2020) 9 1285. DOI: 10.3390/foods9091285 61. Montejo Domínguez L. D. M. A., McClean P. E., Steadman J., McCoy S., Markell S., Osorno J. M.. Bean rust resistance in the Guatemalan climbing bean germplasm collection. Legume Scie (2022) 4 e149. DOI: 10.1002/leg3.149 62. Mukankusi C., Raatz B., Nkalubo S., Berhanu F., Binagwa P., Kilango M.. Genomics, genetics and breeding of common bean in Africa: A review of tropical legume project. Plant Breed. (2019) 138 401-414. DOI: 10.1111/pbr.12573 63. Munakata K.. Insect antifeeding substances in plant leaves. Pure Appl. Chem. (1975) 42 57-66. DOI: 10.1016/B978-0-408-70708-4.50008-9 64. Nagamatsu A., Masuta C., Senda M., Matsuura H., Kasai A., Hong J. S.. Functional analysis of soybean genes involved in flavonoid biosynthesis by virus‐induced gene silencing. (2007) 5 778-790. DOI: 10.1111/j.1467-7652.2007.00288.x 65. Obongoya B. O., Wagai S. O., Odhiambo G.. Phytotoxic effect of selected crude plant extracts on soil-borne fungi of common bean. Afr. Crop Scie. J. (2010) 18. DOI: 10.4314/acsj.v18i1.54189 66. Odogwu B. A., Nkalubo S. T., Mukankusi C., Odong T., Awale H. E., Rubaihayo P.. Phenotypic and genotypic screening for rust resistance in common bean germplasm in Uganda. Euphytica (2017) 213 49-14. DOI: 10.1007/s10681-016-1795-y 67. Ohiagu F. O., Chikezie P. C., Chikezie C. M.. Toxicological significance of bioactive compounds of plant origin. Pharmacogn. Commun. (2021) 11 67-77. DOI: 10.5530/pc.2021.2.15 68. Omara R. I., Kamel S. M., El-Ganainy S. M., Arafa R. A., Mostafa Y. S., Alamri S. A.. Host resistance to. Plants (2022) 11 628. DOI: 10.3390/plants11050628 69. Pluskal T., Castillo S., Villar-Briones A., Orešič M.. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinforma. (2010) 11 395. DOI: 10.1186/1471-2105-11-395 70. Puthoff D. P., Neelam A., Ehrenfried M. L., Scheffler B. E., Ballard L., Song Q.. Analysis of expressed sequence tags from. Phytopathology (2008) 98 1126-1135. DOI: 10.1094/PHYTO-98-10-1126 71. Quan L. J., Zhang B., Shi W. W., Li H. Y.. Hydrogen peroxide in plants: A versatile molecule of the reactive oxygen species network. J. Integr. Plant Biol. (2008) 50 2-18. DOI: 10.1111/j.1744-7909.2007.00599.x 72. Ramabulana A. T., Petras D., Madala N. E., Tugizimana F.. Metabolomics and molecular networking to characterize the chemical space of four momordica plant species. Metabolites (2021) 11 763. DOI: 10.3390/metabo11110763 73. Ramalingam A., Kudapa H., Pazhamala L. T., Weckwerth W., Varshney R. K.. Proteomics and metabolomics: Two emerging areas for legume improvement. Front. Plant Scie. (2015) 6 1116. DOI: 10.3389/fpls.2015.01116 74. Raza A.. Metabolomics: A systems biology approach for enhancing heat stress tolerance in plants. Plant Cell Rep. (2020) 29 741-763. DOI: 10.1007/s00299-020-02635-8 75. Robison F. M., Turner M. F., Jahn C. E., Schwartz H. F., Prenni J. E., Brick M. A.. Common bean varieties demonstrate differential physiological and metabolic responses to the pathogenic fungus. Plant Cell Environ. (2018) 41 2141-2154. DOI: 10.1111/pce.13176 76. Rosales M. A., Ocampo E., Rodríguez-Valentín R., Olvera-Carrillo Y., Acosta-Gallegos J., Covarrubias A. A.. Physiological analysis of common bean (. Plant Physiol. biochem. (2012) 56 24-34. DOI: 10.1016/j.plaphy.2012.04.007 77. Rubiales D., Castillejo M. A., Madrid E., Barilli E., Rispail N.. Legume breeding for rust resistance: Lessons to learn from the model. Euphytica (2011) 180 89-98. DOI: 10.1007/s10681-011-0367-4 78. Sadohara R.. Quality characteristics of bean paste as a confectionery ingredient and recent breeding efforts of common beans in Japan. J. Scie. Food Agric. (2020) 100 10-15. DOI: 10.1002/jsfa.10013 79. Saleh H. M., Hassan A. A., Mansour E. H., Fahmy H. A., El-Bedawey A. E. F. A.. Melatonin, phenolics content and antioxidant activity of germinated selected legumes and their fractions. J. Saudi Socie. Agric. Scie. (2019) 18 294-301. DOI: 10.1016/j.jssas.2017.09.001 80. Shun-Cheng R.. Proximate composition and flavonoids content and in vitro antioxidant activity of 10 varieties of legume seeds grown in China. J. Med. Plants Res. (2012) 6. DOI: 10.5897/JMPR11.1408 81. Singh G., Gupta S. K.. Role of temperature, relative humidity and rainfall in the development of French bean rust (. Indian Phytopathol. (2019) 72 271-280. DOI: 10.1007/s42360-019-00133-w 82. Singh P., Arif Y., Bajguz A., Hayat S.. The role of quercetin in plants. Plant Physio. Biochem. (2021) 166 10-19. DOI: 10.1016/j.plaphy.2021.05.023 83. Singh S. P., Schwartz H. F.. Breeding common bean for resistance to diseases: A review. Crop Scie (2010) 50 2199-2223. DOI: 10.2135/cropsci2009.03.0163 84. Singla P., Bhardwaj R. D., Kaur S., Kaur J., Grewal S. K.. Metabolic adjustments during compatible interaction between barley genotypes and stripe rust pathogen. Plant physio. biochem. (2020) 147 295-302. DOI: 10.1016/j.plaphy.2019.12.030 85. Souza T. L. P., Faleiro F. G., Dessaune S. N., Paula-Junior T. J. D., Moreira M. A., Barros E. G. D.. Breeding for common bean (. Trop. Plant Path (2013) 38 361-374. DOI: 10.1590/s1982-56762013005000027 86. Souza T. L. P. O., Alzate-Marin A. L., Faleiro F. G., Barros E. G.. Pathosystem common bean-. Pest Technol. (2008) 2 56-69 87. Stein O., Granot D.. An overview of sucrose synthases in plants. Front. Plant Scie. (2019) 10 95. DOI: 10.3389/fpls.2019.00095 88. Steinkellner S., Lendzemo V., Langer I., Schweiger P., Khaosaad T., Toussaint J. P.. Flavonoids and strigolactones in root exudates as signals in symbiotic and pathogenic plant-fungus interactions. Molecules (2007) 12 1290-1306. DOI: 10.3390/12071290 89. Tashkhodjaev B., Sultankhodjaev M. N.. 8β-acetoxy-14α-benzoyloxy- n -ethyl-3α,10β,13β,15α-tetrahydroxy-1α,6α,16β-trimethoxy-4β-(methoxymethylene)aconitane: Aconifine from aconitum karakolicum rapaics. acta crystallogr. sect. struct. rep. online (2009) 65 o1543-o1544. DOI: 10.1107/s1600536809021436 90. Tayeh N., Aubert G., Pilet-Nayel M. L., Lejeune-Hénaut I., Warkentin T. D., Burstin J.. Genomic tools in pea breeding programs: status and perspectives. Front. Plant Scie. (2015) 6. DOI: 10.3389/fpls.2015.01037 91. Tempesta M. S., Kriek G. R., Bates R. B.. Uvaricin, a new antitumor agent from. J. Org. Chem. (1982) 47 3151-3153. DOI: 10.1021/jo00137a024 92. Thawabteh A., Juma S., Bader M., Karaman D., Scrano L., Bufo S. A.. The biological activity of natural alkaloids against herbivores, cancerous cells and pathogens. Toxins (2019) 11 656. DOI: 10.3390/toxins11110656 93. Thottempudi V., Shreeve J. N. M.. Synthesis and promising properties of a new family of high-density energetic salts of 5-nitro-3-trinitromethyl-1h-1,2,4-triazole and 5,5′-bis(trinitromethyl)-3,3′-azo-1h-1,2,4-triazole. J. Am. Chem. Soc. (2011) 133 19982-19992. DOI: 10.1021/ja208990z 94. Toffolatti S. L., Maddalena G., Passera A., Casati P., Bianco P. A., Quaglino F.. Role of terpenes in plant defense to biotic stress. Biocontrol agents and secondary metabolites (2021) 401-417. DOI: 10.1016/b978-0-12-822919-4.00016-8 95. Tugizimana F., Piater L., Dubery I.. Plant metabolomics: A new frontier in phytochemical analysis. South Afr. J. scie. (2013) 109 11. DOI: 10.1590/sajs.2013/20120005 96. Turetschek R., Desalegn G., Epple T., Kaul H. P., Wienkoop S.. Key metabolic traits of pisum sativum maintain cell vitality during didymella pinodes infection: Cultivar resistance and the microsymbionts influence. J. Proteomics (2017) 169 189-201. DOI: 10.1016/j.jprot.2017.03.001 97. Vardavas C. I., Majchrzak D., Wagner K. H., Elmadfa I., Kafatos A.. The antioxidant and phylloquinone content of wildly grown greens in crete. Food Chem. (2006) 99 813-821. DOI: 10.1016/j.foodchem.2005.08.057 98. Vasas A., Hohmann J.. Euphorbia diterpenes: Isolation, structure, biological activity and synthesis (2008 -2012). Chem. Rev. (2014) 114 8579-8612. DOI: 10.1021/cr400541j 99. Vega M. P., Santalla M., Marsolais F.. Prospects: The importance of common bean as a model crop. Common Bean Geno (2017) 289-295. DOI: 10.1007/978-3-319-63526-2_13 100. Wahl R., Wippel K., Goos S., Kämper J., Sauer N.. A novel high-affinity sucrose transporter is required for virulence of the plant pathogen. Plos Biol. (2010) 8 e1000303. DOI: 10.1371/journal.pbio.1000303 101. Wang M., Carver J. J., Phelan V. V., Sanchez L. M., Garg N., Peng Y.. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat. Biotech. (2016) 34 828-837. DOI: 10.1038/nbt.3597 102. Wang Y. Q., Liu Q. S., Du L. X., Hallerman E. M., Li Y. H.. Transcriptomic and metabolomic responses of rice plants to. Insects (2020) 11 705. DOI: 10.3390/insects11100705 103. Watanabe S., Matsumoto M., Hakomori Y., Takagi H., Shimada H., Sakamoto A.. The purine metabolite allantoin enhances abiotic stress tolerance through synergistic activation of abscisic acid metabolism. Plant Cell & Environ (2014) 37 1022-1036. DOI: 10.1111/pce.12218 104. Ye H., Gemperline E., Venkateshwaran M., Chen R., Delaux P., Howes-Podoll M.. MALDI mass spectrometry-assisted molecular imaging of metabolites during nitrogen fixation in the Medicago. Plant J. (2014) 75 130-145. DOI: 10.1111/tpj.12191
--- title: 'Impact of nutritional guidance on various clinical parameters in patients with moderate obesity: A retrospective study' authors: - Kayoko Oda - Takatoshi Anno - Nozomi Ogawa - Yukiko Kimura - Fumiko Kawasaki - Kohei Kaku - Hideaki Kaneto - Mutsuko Takemasa - Miyori Sasano journal: Frontiers in Nutrition year: 2023 pmcid: PMC10060631 doi: 10.3389/fnut.2023.1138685 license: CC BY 4.0 --- # Impact of nutritional guidance on various clinical parameters in patients with moderate obesity: A retrospective study ## Abstract ### Context This study aims to investigate whether there is adequate provision of nutritional guidance through interventions by registered dietitians, especially for patients with moderate obesity. This is particularly important as such interventions may prove to be more effective for Japanese patients. ### Methods In Japan, since there is a system of nutritional guidance with a registered dietitian for patients with a BMI over 30 kg/m2, we recruited 636 patients with obesity who had a BMI over 30 kg/m2 admitted to the Kawasaki Medical School General Medical Center between April 2018 and March 2020 through a review of their medical records. Second, we recruited 153 patients who underwent a blood examination before receiving nutritional guidance and at least one time every 3 to 6 months thereafter after receiving it. We aimed to evaluate whether continued nutritional guidance and follow-up interventions for patients with obesity were effective. We compared the BMI and metabolic markers of the patients who received nutritional guidance from a registered dietitian against those who did not. ### Results A total of 636 patients with obesity who have a BMI over 30 kg/m2 were included in this study. A total of 164 patients with obesity received nutritional guidance from a registered dietitian at least one time, but 472 patients did not. Most interventions on nutritional guidance conducted by a registered dietitian were ordered from internal medicine ($81.1\%$). However, internal medicine was the most common department that did not perform these interventions; however, less than half of the ($49.2\%$) received them. In the second analysis, we compared two groups of patients with obesity. The first group ($$n = 70$$) who underwent blood examinations received nutritional guidance from a registered dietitian, while the second group ($$n = 54$$) did not receive such guidance. We found that there was no significant difference in body weight and BMI between the two groups of patients. We observed a significant decrease in dyslipidemia-associated metabolic markers among the patients who received nutritional guidance compared to those who did not [total cholesterol, −9.7 ± 29.3 vs. 2.3 ± 22.0 mg/dL ($$p \leq 0.0208$$); low-density lipoprotein cholesterol, −10.4 ± 30.5 vs. −2.0 ± 51.0 mg/dL ($$p \leq 0.0147$$), respectively]. Other metabolic markers also tended to decrease, although they did not reach statistical significance. ### Conclusion It is rare for patients with only obesity to receive nutritional guidance. However, when nutritional guidance from a registered dietitian is provided, improvements in BMI and metabolic parameters can be expected. ## 1. Introduction Lifestyles and dietary changes have increased the number of patients with obesity in Japan and in other countries [1]. The Japanese have accumulated visceral rather than subcutaneous fat, and even moderate obesity can cause various lifestyle-related diseases [2]. According to the World Health Organization, overweight and obesity are defined when a body mass index (BMI) ≥ 25 and ≥ 30 kg/m2, respectively (https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). However, in Japan, the threshold for defining obesity is set at a BMI of over 25 kg/m2. This is due to the higher risk of obesity-related complications observed even in patients with a lower BMI compared to the West [3]. Diet therapy with weight loss likely decreases various obesity-related diseases and other lifestyle-related diseases, such as metabolic syndrome [4]. Reducing the amount of visceral fat in the body while improving metabolic syndrome can significantly decrease the number of metabolic risk factors [5]. Obesity with a BMI over 35 kg/m2 is classified as a severe obesity disorder as there are some differences in the pathophysiology between patients with a BMI over 35 and those with a BMI between 25 and 35 kg/m2. Consequently, individuals with a BMI over 35 kg/m2 are treated and cared for differently. Based on such background, in Japan, there is a system in which additional fees can be received if registered dietitians provide nutritional guidance to patients with a BMI over 30 kg/m2. Although registered dietitians are present at each hospital and public health center, there is no established guidance for measuring obesity. While it is advised that individuals reduce overall calorie intake by approximately 500–750 kcal/day or $30\%$ of the calculated energy consumed (6–8), studies have shown the efficacy of a low-carbohydrate diet (9–11). However, initial nutritional guidance often involves an interview to evaluate dietary habits, which ends with a recommendation for an appropriate calorie intake. Therefore, it is not clear that patients with obesity, especially those who visit general hospitals for other diseases, receive proper nutritional guidance and ongoing follow-up. In Japan, patients with moderate obesity are common; however, it is possible that appropriate nutritional guidance or other interventions may not be adequately provided for patients with moderate obesity despite the existence of a system for nutritional guidance by registered dieticians for patients with a BMI over 30 kg/m2. This study aimed to investigate whether nutritional guidance interventions are appropriately provided to patients with obesity. Additionally, we evaluated the effectiveness of nutritional guidance interventions in improving obesity and metabolic parameters over a short period of time. This report outlines the potential effects of nutritional guidance interventions conducted by a registered dietitian on various clinical parameters. ## 2.1. Study population This retrospective study was conducted at the Department of Nutrition of the Kawasaki Medical School General Medical Center, Japan. It involved patients with obesity with a BMI over 30 kg/m2 between April 2018 and March 2020. In Japan, there is a system in place that allows registered dietitians to receive additional compensation for providing nutritional guidance to patients with a BMI over 30 kg/m2. Therefore, we selected patients with a BMI over 30 kg/m2 for this study. In addition, since we provided nutritional guidance directly to each participant, we restricted our study to adults (≥20 years old) and therefore selected all patients with a BMI over 30 kg/m2. This study protocol was approved by the Research Ethics Committee (REC) of Kawasaki Medical School and Hospital (protocol code 3870-00). Since this study was retrospective, we provided public information about it via the hospital homepage instead of obtaining informed consent from each patient. First, we recruited 636 patients with obesity who have a BMI over 30 kg/m2 for this study through a review of medical records. Second, in the patients with obesity (with a BMI over 30 kg/m2), we examined whether they received nutritional guidance from a registered dietitian. In addition, we investigated which department ordered nutritional guidance from doctors for patients with obesity (over 30 kg/m2 of BMI) and what percentage of patients had obesity-related diseases. This indicates that initial nutritional guidance with a registered dietitian involved, at least partially, an assessment of dietary habits through an interview and recommendations for an appropriate calorie intake. Third, we recruited 70 patients who underwent a blood examination right before receiving nutritional guidance, followed by another blood examination at least one time every 3–6 months after receiving nutritional guidance. In addition, we selected 54 patients who underwent a blood examination after being diagnosed with obesity and then another blood examination at least one time every 3–6 months after being diagnosed with obesity. We excluded patients with cancer, secondary obesity, mental disorders, and/or those using steroid drugs. Figure 1 shows the flowchart outlining the selection and exclusion criteria for patients in this study. Figure 1: Flow chart of this study patients. ## 2.2. Statistical analysis The clinical characteristics of the patients used in the analysis were age, gender, body weight and BMI, laboratory findings, and complications of obesity-associated diseases. The Mann–Whitney U and the chi-square tests were performed to examine the possible influence of nutritional guidance on various clinical parameters. A p-value >0.05 was considered statistically significant. Statistical software used was Excel Statistics for Mac version 16.54 (Social Research Information, Tokyo, Japan) and JMP, version 14.0.1 (SAS Institute Inc.). ## 3.1. Names of departments ordering nutritional guidance with a registered dietitian from doctors The names of the departments ordering nutritional guidance with a registered dietitian from doctors are shown in Figure 2. A total of 636 patients with obesity who had a BMI over 30 kg/m2 were included in this study. A total of 164 patients with obesity who had a BMI over 30 kg/m2 received nutritional guidance from a registered dietitian at least one time, but 472 patients with obesity who had a BMI over 30 kg/m2 did not receive such guidance. As shown in Figure 2, most nutritional guidance with a registered dietitian was ordered from internal medicine ($81.1\%$), which was followed by surgery ($6.7\%$), orthopedic surgery ($3.0\%$), otorhinolaryngology ($2.4\%$), dermatology ($1.8\%$), neurosurgery ($1.8\%$), and others ($3.0\%$). The names of the departments that did not order nutritional guidance with a registered dietitian were as follows: internal medicine ($49.2\%$), surgery ($16.7\%$), orthopedic surgery ($14.0\%$), otorhinolaryngology ($7.0\%$), urology ($3.4\%$), dermatology ($3.2\%$), and others ($6.6\%$). Most of the patients who received nutritional guidance from a registered dietitian had comorbidity of lifestyle-related disease [diabetes mellitus, $68.3\%$ ($$n = 112$$); dyslipidemia, $52.4\%$ ($$n = 86$$); hypertension, $47.0\%$ ($$n = 77$$); liver dysfunction, $31.7\%$ ($$n = 52$$); and hyperuricemia, $13.4\%$ ($$n = 22$$)], and we believed that much of the nutritional guidance provided by the registered dietitian was aimed at managing the aforementioned disorders (Supplementary Table 1). In contrast, the complications observed in patients who did not receive nutritional guidance were as follows: diabetes mellitus, $26.7\%$ ($$n = 126$$); dyslipidemia, $23.7\%$ ($$n = 112$$); hypertension, $36.2\%$ ($$n = 171$$); liver dysfunction, $13.3\%$ ($$n = 63$$); and hyperuricemia, $7.6\%$ ($$n = 36$$). Figure 2: Names of department ordering nutritional guidance with registered dietitian from doctors. A total of 636 patients with obesity whose BMI were over 30 kg/m2 were included in this study. (A) A total of 164 patients with obesity whose BMI were over 30 kg/m2 received nutritional guidance from registered dietitian at least one time. The name of the department in which nutritional guidance was requested from doctors most frequently was internal medicine, which was followed by surgery, orthopedic surgery, otorhinolaryngology, dermatology, neurosurgery, and others doctors. (B) A total of 472 patients with obesity whose BMI were over 30 kg/m2 did not receive nutritional guidance from a registered dietitian. The name of the department in which nutritional guidance was not requested from doctors most frequently was also internal medicine, which was followed by surgery, orthopedic surgery, otorhinolaryngology, urology, dermatology, and others doctors. ## 3.2. The frequency of ongoing follow-up is very low in patients with obesity in general hospitals To examine the effectiveness of nutritional guidance with a registered dietitian for patients with obesity, we recruited two groups: those who underwent a blood examination after receiving nutritional guidance from a registered dietitian and those who were diagnosed with but did not receive nutritional guidance. However, most patients who did not receive nutritional guidance did not undergo ongoing follow-up with a blood examination, even those with moderate or severe obesity. After receiving nutritional guidance from a registered dietitian or being diagnosed with obesity, we conducted blood examinations on 70 and 54 patients, respectively, at least once within a period of 3 to 6 months. Therefore, most patients with obesity did not receive ongoing follow-ups. In addition, for most patients, follow-up examinations were ordered by the Department of Internal Medicine ($95.2\%$), including outpatients being treated by other departments. After a 3–6 month period, there was no significant difference in the prevalence of various complications or clinical parameters between patients with obesity who had a BMI over 30 kg/m2, regardless of whether or not they received nutritional guidance from a registered dietitian (Supplementary Table 2). ## 3.3. Characteristics of the study patients who received nutritional guidance from a registered dietitian and from those who did not We recruited 70 patients who received nutritional guidance from a registered dietitian and 54 patients who did not receive it after a diagnosis of obesity. The clinical characteristics of the patients in this study are shown in Table 1. There were no significant differences in the various clinical characteristics (body weight, BMI, total protein, and albumin) between patients who received nutritional guidance from a registered dietitian and those who did not. Lipid metabolism markers such as total and low-density lipoprotein cholesterol levels in patients who received nutritional guidance from a registered dietitian were significantly different from those who did not. However, there was no significant difference in high-density lipoprotein cholesterol and triglycerides. There were no significant differences in glucose metabolism markers, such as plasma glucose and HbA1c levels, between patients who received nutritional guidance from a registered dietitian and from those who did not. The parameters related to liver function and kidney function in the patients who received nutritional guidance from a registered dietitian and from those who did not were not significantly different. Table 1 \| Clinical parameter \| Nutritional guidance (+) n = 70 \| Nutritional guidance (–) n = 54 \| p-value \| \| --- \| --- \| --- \| --- \| \| Age (years) \| 55.8 ± 14.4 \| 57.0 ± 15.0 \| 0.5370 \| \| Men/women \| 43 / 27 \| 29 / 25 \| \| \| Body weight (kg) \| 91.0 ± 14.5 \| 87.0 ± 13.4 \| 0.1364 \| \| BMI (kg/m2) \| 33.4 ± 3.4 \| 32.6 ± 2.9 \| 0.1883 \| \| Total protein (g/dL) \| 7.4 ± 0.5 \| 7.3 ± 0.6 \| 0.7454 \| \| Albumin (g/dL) \| 4.3 ± 0.4 \| 4.3 ± 0.5 \| 0.8879 \| \| Total cholesterol (mg/dL) \| 192.9 ± 37.8 \| 176.2 ± 35.5 \| 0.0253* \| \| HDL cholesterol (mg/dL) \| 50.7 ± 12.6 \| 52.3 ± 12.1 \| 0.4027 \| \| LDL cholesterol (mg/dL) \| 115.9 ± 35.1 \| 99.6 ± 30.4 \| 0.0052* \| \| Triglyceride (mg/dL) \| 147.9 ± 77.7 \| 170.1 ± 92.2 \| 0.1854 \| \| Plasma glucose (mg/dL) \| 150.7 ± 60.7 \| 141.3 ± 52.2 \| 0.4206 \| \| Hemoglobin A1c (%) \| 7.3 ± 1.6 \| 7.3 ± 1.2 \| 0.5770 \| \| AST (U/L) \| 36.0 ± 21.1 \| 34.0 ± 29.2 \| 0.1023 \| \| ALT (U/L) \| 46.7 ± 38.0 \| 39.9 ± 35.1 \| 0.1858 \| \| γ-GTP (U/L) \| 69.0 ± 76.3 \| 59.5 ± 77.0 \| 0.1146 \| \| Uric acid (mg/dL) \| 5.8 ± 1.4 \| 5.6 ± 1.5 \| 0.3650 \| \| Creatinine (mg/dL) \| 0.80 ± 0.30 \| 0.75 ± 0.22 \| 0.2013 \| \| BUN (mg/dL) \| 15.5 ± 5.9 \| 15.0 ± 6.0 \| 0.4163 \| \| Diabetes mellitus \| 52 (74.3%) \| 32 (59.3%) \| \| \| Dyslipidemia \| 42 (60.0%) \| 30 (55.6%) \| \| \| Hypertension \| 32 (45.7%) \| 26 (48.1%) \| \| \| Liver dysfunction \| 27 (38.6%) \| 12 (22.2%) \| \| \| Hyperuricemia \| 11 (15.7%) \| 4 (7.4%) \| \| ## 3.4. Effect of nutritional guidance with a registered dietitian in patients with obesity Previous studies have reported a close association between nutritional guidance and improvements in BMI or metabolic markers in Japanese patients [12]. In this study, we compared the changes in blood examination results 3–6 months after the initial assessment between patients who received nutritional guidance from a registered dietitian and from those who did not (Table 2). The BMI of patients who received nutritional guidance from registered dietitians decreased compared to those who did not receive such guidance. However, dyslipidemia-associated metabolic markers were significantly decreased only among the patients who received nutritional guidance from a registered dietitian as compared to those who did not receive such guidance. Table 2 \| Clinical parameter \| Nutritional guidance (+) n = 70 \| Nutritional guidance (–) n = 54 \| p-value \| \| --- \| --- \| --- \| --- \| \| Body weight (kg) \| –0.8 ± 2.5 \| –0.3 ± 3.2 \| 0.3689 \| \| BMI (kg/m2) \| –0.3 ± 1.0 \| –0.1 ± 1.4 \| 0.3965 \| \| Total protein (g/dL) \| –0.1 ± 1.0 \| –0.1 ± 0.7 \| 0.4567 \| \| Albumin (g/dL) \| –0.0 ± 0.5 \| –0.1 ± 0.6 \| 0.1741 \| \| Total cholesterol (mg/dL) \| –9.7 ± 29.3 \| 2.3 ± 22.0 \| 0.0208* \| \| HDL cholesterol (mg/dL) \| –0.2 ± 7.5 \| –2.1 ± 15.0 \| 0.1386 \| \| LDL cholesterol (mg/dL) \| –10.4 ± 30.5 \| –2.0 ± 26.0 \| 0.0147* \| \| Triglyceride (mg/dL) \| –7.1 ± 72.7 \| 7.0 ± 91.0 \| 0.6807 \| \| Plasma glucose (mg/dL) \| –4.5 ± 60.8 \| –2.0 ± 51.0 \| 0.9249 \| \| Hemoglobin A1c (%) \| –0.3 ± 0.9 \| –0.2 ± 0.8 \| 0.6154 \| \| AST (U/L) \| –1.7 ± 12.1 \| 0.3 ± 15.0 \| 0.7182 \| \| ALT (U/L) \| –4.1 ± 23.9 \| –0.1 ± 22.0 \| 0.3178 \| \| γ-GTP (U/L) \| –0.7 ± 34.3 \| –1.5 ± 20.0 \| 0.5928 \| \| Uric acid (mg/dL) \| 0.0 ± 1.1 \| 0.1 ± 1.2 \| 0.6580 \| \| Creatinine (mg/dL) \| –0.03 ± 0.19 \| 0.02 ± 0.20 \| 0.2028 \| \| BUN (mg/dL) \| 0.0 ± 3.8 \| –0.5 ± 4.3 \| 0.8796 \| ## 4. Discussion The goal of treating patients with obesity is to reduce or prevent the risk of obesity-related diseases and health problems by targeting the reduction of body weight and visceral fat rather than solely focusing on body weight reduction. It has been reported that, in comparison to other populations, the Japanese generally have a relatively low BMI but a high proportion of visceral fat in their adipose tissue [2]. In addition to obesity, an increase in the visceral fat area has also been identified as a risk factor for various obesity-related health disorders [13]. In particular, the accumulation of visceral fat in Japanese individuals with moderate obesity can lead to an increased risk of various lifestyle-related diseases. Even in patients with obesity who have no underlying health disorders, the accumulation of visceral fat can be a predictor of the onset of disease. Therefore, it is recommended to diagnose obesity, measure its severity, and provide appropriate treatment. However, despite the existence of a system for nutritional guidance with a registered dietitian in general hospitals, our study's findings indicate that only a small percentage of patients with moderate obesity receive such guidance. Moreover, this study highlighted the lack of nutritional guidance and treatment during hospitalization, except for the main diseases. In general hospitals that provide acute care treatment, attention may be paid only to the main diseases, with inadequate nutritional guidance provided for any resulting complications, especially for patients with obesity who do not exhibit any symptoms. Additionally, it is difficult to confirm whether chronic disease follow-up continues, especially after being transferred to the next hospital. Considering that patients with mild obesity in Japan are likely to develop various lifestyle-related diseases, it is possible that the absence of nutritional guidance leads to unfavorable results. In addition, another problem is that many patients with obesity are not followed up with after a diagnosis of obesity on whether they make a few and/or short-term visits to the hospital for other diseases. It is believed that the high follow-up rate in internal medicine is due to obesity-related diseases such as diabetes mellitus and dyslipidemia. Even in departments outside of internal medicine, providing nutritional guidance only after a diagnosis of obesity can have a favorable effect on patients with obesity. Therefore, patients with obesity should proactively seek and use nutritional guidance. Another question is whether a few short-term nutritional guidance interventions are beneficial for patients with obesity. Since restricting energy intake is the most effective and established method for weight loss, dietary therapy is the basic treatment for patients with obesity [7]. The results of the Japanese intervention study confirmed that even a $3\%$ weight loss can improve health problems [12]. In the present study, there was no significant difference in BMI between patients who received nutritional guidance 3–6 months later and those who did not. However, dyslipidemia-associated metabolic markers decreased significantly after the patients received nutritional guidance. Moreover, other metabolic markers of diabetes mellitus or liver dysfunction decreased, although the change was not significant. In patients with obese diabetes mellitus, weight loss is known to decrease the risk of outcomes such as cardiovascular events [14, 15]. We believe that even a few short-term nutritional guidance interventions would show favorable effects on patients with obesity, as it would make them understand the importance of nutrition. This study has several limitations. First, this was a single-center, retrospective study. Additionally, the sample size was small, and the study involved only Japanese patients. Therefore, this study's results are not applicable to Caucasians. Second, due to a few short periods, the parameters, such as each disease, were limited to comprehending the usefulness of nutritional guidance. Third, there were no standard programs for nutritional guidance with a registered dietitian, and there was no standard verification of adherence. Fourth, the sample in this study was not homogeneous, and this sample may not necessarily be representative of the whole population. Fifth, it would be important to analyze the relationship between pathology and the number of dietary consultations and/or consider several confounding factors related to this study. We believe that the results of the current study provide an objective evaluation of how nutritional guidance interventions are provided to individuals with obesity who visit general hospitals for the management of other diseases. In addition, we described how visceral fat-associated obesity could lead to various lifestyle-related diseases. It is worth noting that a significant number of Japanese individuals with a BMI of < 25 kg/m2 exhibit visceral fat accumulation, suggesting that BMI may not always correlate with the accumulation of visceral fat [5]. However, BMI is still used as the criteria for specific health checkups and specific health guidance aimed at identifying individuals with preliminary metabolic syndrome. Therefore, we found that a significant proportion of Japanese patients with a BMI of > 30 kg/m2 have accumulated visceral fat. ## 5. Conclusion It is rare for physicians or surgeons to refer patients with obesity to a registered dietitian for nutritional guidance, especially in departments outside of internal medicine. However, diet therapy has been shown to have beneficial effects in these cases. By providing appropriate nutritional guidance, improvements in BMI and metabolic parameters can be expected. Therefore, it is important to incorporate registered dieticians in the nutritional guidance of patients with obesity as it can have a favorable influence on their health outcomes. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement This study protocol was approved by the Research Ethics Committee (REC) of Kawasaki Medical School and Hospital (protocol code 3870-00). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions TA is the guarantor of this work and, as such, has full access to all data in the study and takes responsibility for the integrity of the data and its accuracy. KO and TA researched data and wrote the manuscript. NO, YK, FK, and MS researched data and contributed to the discussion. KK, HK, MT, and MS reviewed the manuscript. ## Conflict of interest HK reports grants and personal fees from Sanofi, Novo Nordisk, Lilly, Boehringer Ingelheim, MSD, Takeda, Ono Pharma, Daiichi Sankyo, Sumitomo Pharm, Mitsubishi Tanabe Pharma, AstraZeneca, Astellas, Novartis, Kowa, Taisho Pharm, and Abbott outside the submitted work. KK reports grants and personal fees from Novo Nordisk, Sanwa Kagaku Kenkyusho, Tadeda, Taisho Pharma, MSD, Kowa, Sumitomo Pharma, Sumitomo Pharm, Novartis, Mitsubishi Tanabe Pharma, AstraZeneca, Boehringer Ingelheim, Chugai, Daiichi Sankyo, and Sanofi outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1138685/full#supplementary-material ## References 1. Afshin A, Reitsma MB, Murray CJL. Health effects of overweight and obesity in 195 countries. N Engl J Med. (2017) 377 1496-7. DOI: 10.1056/NEJMc1710026 2. Nazare JA, Smith JD, Borel AL, Haffner SM, Balkau B, Ross R. Ethnic influences on the relations between abdominal subcutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: the international study of prediction of intra-abdominal adiposity and its relationship with cardiometabolic risk/intra-abdominal adiposity. Am J Clin Nutr. (2012) 96 714-26. DOI: 10.3945/ajcn.112.035758 3. Yoshiike N, Matsumura Y, Zaman MM, Yamaguchi M. Descriptive epidemiology of body mass index in Japanese adults in a representative sample from the National Nutrition Survey 1990-1994. Int J Obes Relat Metab Disord. (1998) 22 684-7. DOI: 10.1038/sj.ijo.0800651 4. Look AHEAD Research Group, Pi-Sunyer X, Blackburn G, Brancati FL, Bray GA, Bright R. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. (2007) 30 1374-83. DOI: 10.2337/dc07-0048 5. Okauchi Y, Nishizawa H, Funahashi T, Ogawa T, Noguchi M, Ryo M. Reduction of visceral fat is associated with a decrease in the number of metabolic risk factors in Japanese men. Diabetes Care. (2007) 30 2392-4. DOI: 10.2337/dc07-0218 6. Das SK, Gilhooly CH, Golden JK, Pittas AG, Fuss PJ, Cheatham RA. Long-term effects of 2 energy-restricted diets differing in glycemic load on dietary adherence, body composition, and metabolism in CALERIE: a 1-y randomized controlled trial. Am J Clin Nutr. (2007) 85 1023-30. DOI: 10.1093/ajcn/85.4.1023 7. Sacks FM, Bray GA, Carey VJ, Smith SR, Ryan DH, Anton SD. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med. (2009) 360 859-73. DOI: 10.1056/NEJMoa0804748 8. Frisch S, Zittermann A, Berthold HK, Götting C, Kuhn J, Kleesiek K. A randomized controlled trial on the efficacy of carbohydrate-reduced or fat-reduced diets in patients attending a telemedically guided weight loss program. Cardiovasc Diabetol. (2009) 8 36. DOI: 10.1186/1475-2840-8-36 9. Shai I, Schwarzfuchs D, Henkin Y, Shahar DR, Witkow S, Greenberg I. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. N Engl J Med. (2008) 359 229-241. DOI: 10.1056/NEJMoa0708681 10. Ajala O, English P, Pinkney J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am J Clin Nutr. (2013) 97 505-16. DOI: 10.3945/ajcn.112.042457 11. Santos FL, Esteves SS, da Costa Pereira A, Yancy WS Jr, Nunes JP. Systematic review and meta-analysis of clinical trials of the effects of low carbohydrate diets on cardiovascular risk factors. Obes Rev. (2012) 13 1048-66. DOI: 10.1111/j.1467-789X.2012.01021.x 12. Muramoto A, Matsushita M, Kato A, Yamamoto N, Koike G, Nakamura M. Three percent weight reduction is the minimum requirement to improve health hazards in obese and overweight people in Japan. Obes Res Clin Pract. (2014) 8 e466-75. DOI: 10.1016/j.orcp.2013.10.003 13. New criteria for ‘obesity disease' in Japan. Circ J. (2002) 66 987-92. DOI: 10.1253/circj.66.987 14. Look AHEAD Research Group, Wing RR, Bolin P, Brancati FL, Bray GA, Clark JM. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. (2013) 369 145-54. DOI: 10.1056/NEJMoa1212914 15. Look AHEAD Research Group, Gregg EW, Jakicic JM, Blackburn G, Bloomquist P, Bray GA. Association of the magnitude of weight loss and changes in physical fitness with long-term cardiovascular disease outcomes in overweight or obese people with type 2 diabetes: a post-hoc analysis of the Look AHEAD randomized clinical trial. Lancet Diabetes Endocrinol. (2016) 4 913-21. DOI: 10.1016/S2213-8587(16)30162-0
--- title: Partial replacement of high-fat diet with n-3 PUFAs enhanced beef tallow attenuates dyslipidemia and endoplasmic reticulum stress in tunicamycin-injected rats authors: - Jiaxiang Zheng - Jisu Lee - Jaemin Byun - Daeung Yu - Jung-Heun Ha journal: Frontiers in Nutrition year: 2023 pmcid: PMC10060633 doi: 10.3389/fnut.2023.1155436 license: CC BY 4.0 --- # Partial replacement of high-fat diet with n-3 PUFAs enhanced beef tallow attenuates dyslipidemia and endoplasmic reticulum stress in tunicamycin-injected rats ## Abstract ### Introduction Metabolic syndrome (MetS) is considered as a complex, intertwined multiple risk factors that directly increase the risk of various metabolic diseases, especially cardiovascular atherosclerotic diseases and diabetes mellitus type 2. While lifestyle changes, including dietary intervention are effective in mitigating or preventing MetS, there are no specific therapies against MetS. Typical western diets comprise of high saturated fatty acid, cholesterol, and simple sugar; consequently their consumption may increase the potential pathological developmental risk of MetS. Partial replacement of dietary fatty acids with polyunsaturated fatty acids (PUFAs) is widely recommended measure to manage MetS-related disorders. ### Methods In the present study, we used rat model to investigate the role of n-3 PUFA enriched beef tallows (BT) on MetS and tunicamycin (TM)-induced endoplasmic reticulum (ER) stress, by partially replacing dietary fat (lard) with equal amounts of two different BTs; regular BT or n-3 PUFA-enriched BT. The experimental rats were randomly assigned to three different dietary groups ($$n = 16$$ per group): [1] high-fat and high-cholesterol diet (HFCD); [2] HFCD partially replaced with regular BT (HFCD + BT1); [3] HFCD partially replaced with n-3 enhanced BT (w/w) (HFCD + BT2). After 10 weeks of dietary intervention, each experimental rodent was intraperitoneally injected with either phosphate-buffered saline or 1 mg/kg body weight of TM. ### Results HFCD + BT2 showed improved dyslipidemia before TM injection, and increased serum high-density lipoprotein cholesterol (HDL-C) levels after TM injection. BT replacement groups had significantly reduced hepatic triglyceride (TG) levels, and decreased total cholesterol (TC) and TG levels in epididymal adipose tissue (EAT). Furthermore, BT replacement remarkably attenuated TM-induced unfolded protein responses (UPRs) in liver, showing reduced ER stress, with BT2 being more effective in the EAT. ### Discussion Therefore, our findings suggest that partially replacing dietary fats with n-3 PUFA to lower the ratio of n-6/n-3 PUFAs is beneficial in preventing pathological features of MetS by alleviating HFCD- and/or TM-induced dyslipidemia and ER stress. ## 1. Introduction Metabolic syndrome (MetS) is considered as a complex, intertwined multiple risk factors that directly increase the prevalence of metabolic diseases, such as atherosclerotic cardiovascular disease and type 2 diabetes mellitus (T2DM) [1, 2]. There are well-recognized potential risk factors for the development of MetS, such as excessive energy intake, lack of physical activity, and concomitant obesity [3]; thus MetS is diagnosed based on waist circumference, blood pressure, fasting blood-glucose, serum triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) levels [4]. Globalization and economic development have spurred changes in dietary patterns globally toward western type [5] which contain more fat (saturated fats and trans fats), cholesterol, simple sugar, and higher amounts of processed foods. Continuous consumption of western style of diet undermines the prevalence of obesity, diabetes and cardiovascular diseases (CVDs) [6, 7]. MetS has become a global health problem, and finding a dietary interventional method may be an effective and preferable strategy for prevention and progression of MetS [8]. For instance, the Mediterranean dietary pattern is widely recommended as against western-style diet as it contains lower saturated fatty acids (SFAs) and higher polyunsaturated fatty acids (PUFAs). The benefits of the Mediterranean diet is largely due to the difference in fatty acids (FAs) compositions compared with western diet [9, 10]. Among the abundant PUFAs in the Mediterranean diet, the n-3 PUFAs may exert their influence in the prevention of systemic inflammation, and the reduction of the incidence of CVDs, together with delaying the progression of metabolic diseases [11, 12]. n-6 PUFAs are also known to regulate renal and pulmonary function, vascular tone and inflammatory response [13, 14], but have distinguishable role in metabolic responses compared to n-3 PUFAs because of their competitive functions. As for FAs consumption, western diet is typified with the lower consumption of n-3 PUFAs, and excessive consumption of n-6 PUFAs results in an improper n-6/n-3 ratio [15]. Moreover, the high n-6/n-3 ratio directly promotes the pathogenesis of multiple diseases, including CVDs, cancer, and autoimmune diseases, while elevated n-3 levels reduce the n-6/n-3 ratio exerting inhibitory effects in these diseases [16, 17]. The recommended range of dietary fatty acid intake as per the American Heart Association (AHA) for adult is approximately 25–35 energy percentage (E%), while that for the PUFAs is approximately 5–10 E% [18]. The endoplasmic reticulum (ER) is an important cellular organelles, where newly synthesized proteins are folded, and subsequently subjected to post-translational modifications [19]. Alterations in the function of the ER can result in accumulation of unfolded or misfolded proteins resulting in disruption of the normal ER homeostasis, the condition referred to as ER stress [20]. ER stress can trigger the activation of the unfolded protein responses (UPRs), the adaptive mechanism that either corrects the misfolded proteins, or induces cellular apoptosis [21]. ER stress-triggered UPRs, and apoptotic responses are closely associated with the pathophysiology of various abnormal MetS or diseases, such as obesity, diabetes, dyslipidemia and atherosclerosis [22]. Moreover, the challenges of translational abnormal homeostasis on lipid metabolism caused by chronic ER stress are key risk factors in the pathogenesis of MetS, including T2DM, obesity and dyslipidemia [23, 24]. Beef tallow (BT) is a by-product of beef processing, and is extensively used in the food industry because of its high thermal and oxidative stability [25]. The unique and desirable flavor of BT after baking, makes it popular as a food supplement in frying, bakery products, and margarine production [26]. Beef is a major component of western diet, and BT can be naturally consumed when eating beef [27]. Beef is a nutrient-dense resource, possessing variety of nutrients, such as essential amino acids, B vitamins, minerals (iron, zinc, and selenium), and various FAs [28]. However, beef consumption may also induce some risks to health, due to innate characteristics to have high cholesterol and SFAs, low PUFAs, and/or inappropriate ratio of n-6/n-3 PUFA [29]. Interestingly, n-3 enriched linseed increases the content of n-3 in cattle muscle and adipose tissue, resulting in a lower ratio of n-6/n-3 [30]. Indeed, reducing the intake of SFAs and increasing the intake of n-3 PUFAs when consuming beef may be beneficial to health [31]. Clinical data also support the beneficial effects of replacing SFAs with PUFAs, in reducing the risk of MetS [32]. Therefore, replacing dietary fats from typical western diet, with n-3 PUFA-enhanced BT can be a good strategy to manage MetS. We hypothesize that partial replacement of dietary fat with PUFAs could be an effective and reasonable strategy to reduce or prevent the incidence of MetS. Diets containing a lower ratio of n-6/n-3 PUFAs appear to be more beneficial in mitigating the development of MetS; this can be applicable by adjusting to a reasonable balance, the intake of n-6 and n-3 [33, 34]. To this end, we partially replaced the fatty acids in high-fat and high-cholesterol diet (HFCD) with either regular BT or a n-3 enhanced BT, to investigate whether a dietary intervention could alleviate the MetS induced by the HFCD diet. Although, BTs contains relatively higher levels of SFAs, we increased the n-3 amount in BT by adding n-3 enriched perilla pomace to cattle feed. Therefore, in this study, we aimed to investigate whether partial replacement of dietary fat with n-3 enhanced BTs could attenuate or prevent the risk factors of HFCD-inducible MetS and tunicamycin (TM)-induced ER stress in Sprague-Dawley (SD) rats. ## 2.1. Animal experiments All animal studies were conducted in accordance with the procedures of the Institutional Animal Care Use Committee of Dankook University (IACUC, No. DKU-21-051). Five-week-old male SD rats were obtained from Doo Yeol Biotech, Inc. (Seoul, Korea). The SD rats were housed under controlled conditions of room temperature 20 ± 2°C and 50–$55\%$ relative humidity with a 12 h light/12 h dark cycle, and ad libitum access to water. The experimental SD rats were randomly assigned to three groups ($$n = 16$$ per group) based on type of dietary intervention: [1] high-fat and high-cholesterol diet (HFCD); [2] lard of HFCD partially replaced with $11\%$ regular beef tallow (w/w) (HFCD + BT1); [3] lard of HFCD partially replaced with $11\%$ beef tallow (w/w) containing a lower n-6/n-3 ratio (HFCD + BT2). The amount of energy obtained from fat was $46.18\%$ in all the diet types. The composition of diets and fatty acid are shown in Tables 1, 2, respectively. BT (Greengrass Bio, Chungju, Korea) was obtained from retroperitoneal adipose tissue (RAT) of either black Angus fed either regular diet (BT1) or perilla pomace (BT2). After 10 weeks of experimental diet feeding, each group was further divided into two subgroups ($$n = 8$$ per group): [1] intraperitoneal injection of phosphate-buffered saline (PBS) or [2] intraperitoneal injection of TM (1 mg/kg body weight). Following the injection of either PBS or TM, the experimental rats were fasted. Twelve hours after PBS or TM injection, the rats were humanely euthanized by thoracotomy after CO2 narcosis. Whole blood was collected by cardiac puncture, and the serum was separated by centrifugation (3,000 × g at 4°C for 15 min). The liver and the white adipose tissues (WATs), including epididymal, mesenteric, retroperitoneal and perirenal tissues, were collected and weighed, subsequently kept at −80°C until analysis. ## 2.2. Dietary fatty acid analysis The fatty acid composition of the experimental diets was measured by the methyl esterification of boron trifluoride (BF3)-methanol following previously published methods (33–36). Gas chromatography (GC) (Agilent Technologies 6890N, Agilent Technologies, CA, USA) was employed for analyzing the FAs, and the results were assessed by comparison with a standard FA reference (Sigma-Aldrich Co., St. Louis, MO, USA). The FA composition of the experimental diets is presented in Table 3. TABLE 3 \| Fatty acid (% total fatty acids) \| HFCD \| HFCD + BT1 \| HFCD + BT2 \| \| --- \| --- \| --- \| --- \| \| C12:0, Lauric acid \| ND \| 0.42 ± 0.02a \| 0.37 ± 0.02b \| \| C14:0, Myristic acid \| 1.49 ± 0.07c \| 2.78 ± 0.14b \| 3.41 ± 0.17a \| \| C15:0, Pentadecanoic acid \| ND \| 0.64 ± 0.03b \| 0.72 ± 0.04a \| \| C16:0, Palmitic acid \| 19.97 ± 1.00a \| 18.95 ± 0.95a \| 20.13 ± 1.01a \| \| C17:0, Heptadecanoic acid \| 3.11 ± 0.16b \| 11.72 ± 0.59a \| 10.62 ± 0.53a \| \| C18:0, Stearic acid \| 12.23 ± 0.61a \| 10.31 ± 0.52b \| 12.46 ± 0.62a \| \| C14:1, Myristoleic acid \| ND \| 1.18 ± 0.06a \| 1.07 ± 0.05a \| \| C16:1, Palmitoleic acid \| 1.81 ± 0.09b \| 3.70 ± 0.19a \| 3.56 ± 0.18a \| \| C18:1, Oleic acid \| 30.79 ± 1.54a \| 24.43 ± 1.22b \| 23.25 ± 1.16b \| \| C20:1, Eicosenoic acid \| 1.21 ± 0.06b \| 1.45 ± 0.07a \| 1.60 ± 0.08a \| \| C18:2, Linoleic acid \| 24.81 ± 1.24a \| 18.33 ± 0.92b \| 16.11 ± 0.81b \| \| C20:2, Eicosadienoic acid \| ND \| 0.42 ± 0.02b \| 0.52 ± 0.03a \| \| C18:3, Linolenic acid \| 4.59 ± 0.23b \| 5.09 ± 0.25b \| 5.88 ± 0.29a \| \| C18:3, γ-Linolenic acid \| ND \| 0.22 ± 0.01a \| ND \| \| C20:3, Eicosatrienoic acid \| ND \| 0.37 ± 0.02a \| 0.31 ± 0.02b \| \| SFAs \| 36.79 ± 1.84b \| 44.82 ± 2.24a \| 47.70 ± 2.39a \| \| MUFAs \| 33.81 ± 1.69a \| 30.76 ± 1.54ab \| 29.49 ± 1.47b \| \| PUFAs \| 29.40 ± 1.47a \| 24.42 ± 1.22b \| 22.81 ± 1.14b \| \| n-6 \| 24.81 ± 1.24a \| 18.55 ± 0.93b \| 16.11 ± 0.81b \| \| n-3 \| 4.59 ± 0.23c \| 5.45 ± 0.27b \| 6.18 ± 0.31a \| \| n-6/n-3 \| 5.41 ± 0.00a \| 3.40 ± 0.00b \| 2.60 ± 0.00c \| \| n-9 \| 30.79 ± 1.54a \| 24.43 ± 1.22b \| 23.25 ± 1.16b \| ## 2.3. Serum lipid and hepatic biological function panel Serum levels of TG, total cholesterol (TC), and HDL-C were measured using commercial kits (TG Assay Kit, TC Assay Kit, and HDL-C Assay Kit, Embiel, Gunpo, Korea). The non-HDL-C level was calculated by subtracting TG and HDL-C values from the TC levels [37]. The cardiac risk factor (CRF) was calculated by using the formula: CRF = TC/HDL-C [38, 39]. Indices for hepatic biological function were determined by measuring serum aspartate aminotransferase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP) using commercial kits (AST Assay Kit, ALT Assay Kit, and ALP Assay Kit, Embiel). The detailed methods have been previously described (33–36). ## 2.4. Serum glucose and insulin levels Fasting glucose (Crystal Chem, Downers Grove, IL, USA), and insulin (Mercodia AB, Uppsala, Sweden) levels at the final sacrifice were measured using standard commercial kits according to the manufacturer’s instructions. The homeostatic index of insulin resistance (HOMA-IR) values was calculated by the following established formula: HOMA-IR = serum insulin (μU/L) × serum glucose (mg/dL)/405 [40]. ## 2.5. Lipid contents in the liver and epididymal adipose tissue Lipids were extracted from the liver and epididymal adipose tissues (EAT) by the previously described method of Bligh and Dyer [41], which has been used in similar experimental settings earlier (33–36). ## 2.6. Analysis of the fatty acid composition of whole blood The methods used for the analysis of the FAs composition of whole blood have been previously described (33–36). A drop of whole blood from each mouse was spiked on a blood spot card (OmegaQuant Analytics, Sioux Falls, SD, USA) pre-coated with an antioxidant mixture. The blood fatty acid composition was analyzed using GC (Agilent Technologies 6890N, Agilent Technologies, CA, USA) as described by Harris and Polreis [42]. The fatty acid composition of the whole blood is expressed as a percentage from the total identified FAs as shown in Table 4. TABLE 4 \| (% Total fatty acids) \| PBS \| PBS.1 \| PBS.2 \| TM \| TM.1 \| TM.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| HFCD \| HFCD + BT1 \| HFCD + BT2 \| HFCD \| HFCD + BT1 \| HFCD + BT2 \| \| n -3 PUFA \| n -3 PUFA \| n -3 PUFA \| n -3 PUFA \| n -3 PUFA \| n -3 PUFA \| n -3 PUFA \| \| α-Linolenic acid \| 0.110 ± 0.072 \| 0.127 ± 0.108 \| 0.127 ± 0.085 \| 0.123 ± 0.075 \| 0.057 ± 0.038 \| 0.063 ± 0.015 \| \| Eicosapentaenoic acid (EPA) \| 0.200 ± 0.066 \| 0.167 ± 0.038 \| 0.303 ± 0.295 \| 0.243 ± 0.137 \| 0.363 ± 0.253 \| 0.157 ± 0.074 \| \| Docosapentaenoic acid (DPA) \| 0.927 ± 0.084 \| 0.920 ± 0.101 \| 0.887 ± 0.178 \| 1.183 ± 0.090 \| 1.243 ± 0.388 \| 1.153 ± 0.159 \| \| Docosahexaenoic acid (DHA) \| 1.170 ± 0.249 \| 1.283 ± 0.407 \| 1.013 ± 0.402 \| 0.947 ± 0.194 \| 1.237 ± 0.348 \| 0.960 ± 0.171 \| \| n -6 PUFA \| n -6 PUFA \| n -6 PUFA \| n -6 PUFA \| n -6 PUFA \| n -6 PUFA \| n -6 PUFA \| \| Linoleic acid \| 13.470 ± 0.493a \| 11.17 ± 1.115b \| 12.57 ± 0.289ab \| 12.10 ± 0.854ab \| 11.77 ± 0.862ab \| 11.50 ± 0.608ab \| \| γ-Linolenic acid \| 0.063 ± 0.023 \| 0.027 ± 0.015 \| 0.030 ± 0.030 \| 0.067 ± 0.040 \| 0.043 ± 0.032 \| 0.020 ± 0.010 \| \| Eicosadienoic acid \| 0.290 ± 0.141 \| 0.273 ± 0.040 \| 0.327 ± 0.035 \| 0.413 ± 0.040 \| 0.213 ± 0.035 \| 0.293 ± 0.101 \| \| Dihomo-γ-linolenic acid \| 1.067 ± 0.067 \| 0.910 ± 0.108 \| 1.110 ± 0.115 \| 0.870 ± 0.171 \| 0.980 ± 0.212 \| 0.903 ± 0.045 \| \| Arachidonic acid \| 21.67 ± 1.159 \| 20.77 ± 0.651 \| 19.97 ± 1.484 \| 22.70 ± 0.400 \| 22.50 ± 1.200 \| 22.20 ± 1.217 \| \| Docosatetraenoic acid \| 1.397 ± 0.261 \| 1.223 ± 0.058 \| 1.233 ± 0.038 \| 1.597 ± 0.072 \| 1.240 ± 0.104 \| 1.407 ± 0.205 \| \| Docosapentaenoic acid \| 0.227 ± 0.049 \| 0.250 ± 0.095 \| 0.293 ± 0.144 \| 0.173 ± 0.075 \| 0.220 ± 0.010 \| 0.217 ± 0.083 \| \| n -9 PUFA \| n -9 PUFA \| n -9 PUFA \| n -9 PUFA \| n -9 PUFA \| n -9 PUFA \| n -9 PUFA \| \| Oleic acid \| 14.10 ± 2.100 \| 18.23 ± 3.066 \| 17.63 ± 2.641 \| 13.47 ± 0.321 \| 14.67 ± 0.929 \| 14.57 ± 0.808 \| \| Eicosenoic acid \| 0.180 ± 0.010ab \| 0.270 ± 0.056a \| 0.210 ± 0.026ab \| 0.203 ± 0.031ab \| 0.093 ± 0.040b \| 0.157 ± 0.127ab \| \| Nervonic acid \| 0.160 ± 0.062 \| 0.063 ± 0.075 \| 0.177 ± 0.021 \| 0.220 ± 0.069 \| 0.247 ± 0.076 \| 0.203 ± 0.127 \| \| SFA \| SFA \| SFA \| SFA \| SFA \| SFA \| SFA \| \| Palmitoleic acid \| 0.677 ± 0.234 \| 1.510 ± 0.553 \| 1.120 ± 0.524 \| 0.603 ± 0.196 \| 0.600 ± 0.130 \| 0.790 ± 0.177 \| \| Myristic acid \| 0.157 ± 0.010 \| 0.377 ± 0.195 \| 0.243 ± 0.038 \| 0.193 ± 0.012 \| 0.203 ± 0.055 \| 0.360 ± 0.120 \| \| Palmitic acid \| 27.07 ± 0.723ab \| 26.43 ± 0.709b \| 26.73 ± 1.079ab \| 27.93 ± 0.551ab \| 27.60 ± 0.100ab \| 28.37 ± 0.351a \| \| Stearic acid \| 16.40 ± 0.781 \| 15.00 ± 1.058 \| 15.23 ± 0.987 \| 16.33 ± 0.321 \| 15.90 ± 0.346 \| 15.67 ± 1.007 \| \| Lignoceric acid \| 0.070 ± 0.017 \| 0.097 ± 0.015 \| 0.103 ± 0.060 \| 0.083 ± 0.006 \| 0.080 ± 0.036 \| 0.110 ± 0.061 \| \| Trans FA \| Trans FA \| Trans FA \| Trans FA \| Trans FA \| Trans FA \| Trans FA \| \| Trans palmitoleic acid \| 0.170 ± 0.026ab \| 0.240 ± 0.053ab \| 0.233 ± 0.071ab \| 0.133 ± 0.047b \| 0.153 ± 0.072b \| 0.310 ± 0.030a \| \| Trans oleic acid \| 0.080 ± 0.052c \| 0.323 ± 0.070a \| 0.197 ± 0.015abc \| 0.097 ± 0.045bc \| 0.270 ± 0.010a \| 0.220 ± 0.070ab \| \| Trans linoleic acid \| 0.010 ± 0.000 \| 0.010 ± 0.000 \| 0.010 ± 0.000 \| 0.010 ± 0.000 \| 0.010 ± 0.000 \| 0.010 ± 0.000 \| \| Trans linoleic acid 2 \| 0.040 ± 0.044 \| 0.037 ± 0.015 \| 0.050 ± 0.010 \| 0.020 ± 0.000 \| 0.023 ± 0.015 \| 0.040 ± 0.000 \| \| Trans linoleic acid 3 \| 0.047 ± 0.046 \| 0.057 ± 0.015 \| 0.033 ± 0.006 \| 0.047 ± 0.021 \| 0.053 ± 0.031 \| 0.063 ± 0.012 \| \| n-3 \| 2.407 ± 0.180 \| 2.497 ± 0.489 \| 2.330 ± 0.095 \| 2.497 ± 0.202 \| 2.900 ± 0.210 \| 2.333 ± 0.167 \| \| n-6 \| 38.18 ± 1.078a \| 34.62 ± 1.697b \| 35.53 ± 1.324ab \| 37.92 ± 0.666a \| 36.96 ± 0.976a \| 36.54 ± 0.872a \| \| n-9 \| 14.44 ± 2.150 \| 18.57 ± 3.082 \| 18.02 ± 2.681 \| 13.89 ± 0.334 \| 15.01 ± 0.977 \| 14.93 ± 0.846 \| \| SFAs \| 43.69 ± 1.345a \| 41.91 ± 1.806a \| 42.31 ± 2.142a \| 44.54 ± 0.575a \| 43.78 ± 0.377ab \| 44.50 ± 0.947ab \| \| Trans FAs \| 0.347 ± 0.119bc \| 0.667 ± 0.057a \| 0.523 ± 0.075ab \| 0.307 ± 0.067c \| 0.510 ± 0.044abc \| 0.643 ± 0.083a \| \| MUFAs \| 15.12 ± 2.381 \| 20.08 ± 3.537 \| 19.14 ± 3.205 \| 14.49 ± 0.526 \| 15.61 ± 1.017 \| 15.72 ± 0.957 \| \| n-6/n-3 \| 15.94 ± 1.518 \| 14.16 ± 2.372 \| 15.25 ± 0.287 \| 15.24 ± 0.988 \| 12.81 ± 1.232 \| 15.71 ± 0.986 \| \| AA/EPA \| 116.3 ± 37.20 \| 128.6 ± 27.30 \| 174.4 ± 92.71 \| 117.6 ± 68.81 \| 84.31 ± 52.61 \| 163.5 ± 73.01 \| ## 2.7. Western blot analysis The proteins from the liver and EAT were extracted using ice-cold radio-immunoprecipitation assay buffer (RIPA) lysis buffer (ATTO, Tokyo, Japan) containing protease inhibitors and phosphatase inhibitors (Thermo Fisher Scientific, Waltham, MA, USA). The proteins were separated using $12\%$ sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and transferred to polyvinylidene fluoride (PVDF) membranes (Bio-Rad Laboratories, Hercules, CA, USA) at 120 volts for 90 min. The membrane was blocked with $5\%$ skim milk (BD Difco™, Franklin Lakes, NJ, USA) for 1 h, and subsequently immunoblotted with relevant primary antibodies at 4°C overnight. The membranes were probed with a secondary antibodies for 1 h, and reacted with SuperSignal Chemiluminescent Substrate (Thermo Fisher Scientific). Chemiluminescence was detected using a Danvich Chemi Fluoro Imager (Danvich-K, Seoul, Korea). Visualization of the detected proteins were analyzed using ImageJ software (v.1.8 National Institutes of Health, Bethesda, MD, USA), with GAPDH as an internal control [34]. Details of antibodies used for western blot analysis is listed in Table 5. TABLE 5 \| Antibody \| Manufacturer \| Catalog number \| Dilution \| \| --- \| --- \| --- \| --- \| \| BiP \| Cell signaling technology (CST, Danvers, MA, USA) \| 3183 \| 1:1000 \| \| XBP-1 \| CST \| 40435 \| 1:1000 \| \| ATF4 \| CST \| 11815 \| 1:1000 \| \| CHOP \| CST \| 2895 \| 1:1000 \| \| GAPDH \| Santa Cruz, Dallas, TX, USA \| sc-47724 \| 1:1000 \| \| Anti-rabbit IgG \| CST \| 7074 \| 1:3000 \| \| Anti-mouse IgG \| CST \| 7076 \| 1:1000 \| ## 2.8. Statistical analysis Data are expressed as mean ± standard deviations (SDs), or as Box-and-whisker plots. Box-and-whisker plots indicated the average of the assigned group with a “+” sign, and depicted the minimum, the lower (25th percentile), the median (50th percentile), the upper (75th percentile), and the maximum ranked samples. One-way analysis of variance (ANOVA) was employed to compare the effects of the dietary intervention. The interaction of diet and TM stimulation was analyzed using two-way ANOVA with Tukey’s post hoc test. Statistical significance was set at $p \leq 0.05.$ Statistical analyses were performed using XLSTAT 2019 (Addinsoft Inc., Paris, France) or SPSS 26.0 (Statistical Package for Social Science, IBM Corp., Armonk, NY, USA), and all figures are depicted using GraphPad prism 5 (GraphPad Software Inc., San Diego, CA, USA). Table 6 presents a summary of the two-way ANOVA for the main effects of dietary intervention and TM-injection, and their interaction. TABLE 6 \| Parameter \| Factor. P-values \| Factor. P-values.1 \| Factor. P-values.2 \| \| --- \| --- \| --- \| --- \| \| \| Tunicamycin \| Diet \| Tunicamycin X diet \| \| Serum glucose, insulin levels \| Serum glucose, insulin levels \| Serum glucose, insulin levels \| Serum glucose, insulin levels \| \| Glucose \| ns p = 0.194 \| ns p = 0.319 \| ns p = 0.791 \| \| Insulin \| p < 0.01 \| ns p = 0.859 \| ns p = 0.950 \| \| HOMA-IR \| ns p = 0.079 \| ns p = 0.292 \| ns p = 0.315 \| \| Relative tissue weights \| Relative tissue weights \| Relative tissue weights \| Relative tissue weights \| \| Liver \| ns p = 0.165 \| ns p = 0.996 \| ns p = 0.965 \| \| EAT \| p < 0.01 \| ns p = 0.219 \| ns p = 0.972 \| \| MAT \| p < 0.05 \| ns p = 0.123 \| ns p = 0.271 \| \| RAT \| p < 0.001 \| ns p = 0.206 \| ns p = 0.340 \| \| PAT \| p < 0.001 \| ns p = 0.912 \| ns p = 0.202 \| \| WAT \| p < 0.001 \| ns p = 0.189 \| ns p = 0.547 \| \| Serum lipid profiles \| Serum lipid profiles \| Serum lipid profiles \| Serum lipid profiles \| \| Total cholesterol \| p < 0.0001 \| p < 0.0001 \| p < 0.001 \| \| HDL-cholesterol \| *p < 0.0001 \| *p < 0.0001 \| p < 0.05 \| \| Non-HDL-cholesterol \| **p < 0.0001 \| p < 0.0001 \| p < 0.001 \| \| Triglyceride \| *p < 0.0001 \| p < 0.0001 \| p < 0.01 \| \| Cardiac risk factor \| **p < 0.0001 \| p < 0.0001 \| p < 0.0001 \| \| Lipid contents in the liver tissue \| Lipid contents in the liver tissue \| Lipid contents in the liver tissue \| Lipid contents in the liver tissue \| \| Hepatic TG \| p < 0.0001 \| p < 0.0001 \| p < 0.01 \| \| Hepatic TC \| ns p = 0.152 \| p < 0.01 \| ns p = 0.257 \| \| Lipid contents in the epididymal adipose tissue \| Lipid contents in the epididymal adipose tissue \| Lipid contents in the epididymal adipose tissue \| Lipid contents in the epididymal adipose tissue \| \| EAT TG \| p < 0.0001 \| *p < 0.0001 \| p < 0.0001 \| \| EAT TC \| p < 0.01 \| **p < 0.0001 \| ns p = 0.193 \| \| Hepatic function parameters \| Hepatic function parameters \| Hepatic function parameters \| Hepatic function parameters \| \| Serum AST \| p < 0.0001 \| p < 0.0001 \| p < 0.001 \| \| Serum ALT \| *p < 0.0001 \| p < 0.0001 \| p < 0.0001 \| \| Serum ALP \| p < 0.0001 \| p < 0.0001 \| ns p = 0.417 \| ## 3.1. Effects of partial replacement of dietary fat with BTs on body weight change, daily food intake, daily energy intake, and food efficiency ratio Experimental SD rats were fed HFCD, HFCD with BT1, or HFCD with BT2 for 10 weeks ($$n = 16$$ per group). After 4 weeks of dietary intervention, the HFCD + BT1 group had significantly higher body weight (BW) than HFCD and HFCD + BT2 groups, but the data for BW in the last 3 weeks of the intervention was not statistically significant (Figure 1A). High-fat and high-cholesterol diets have been reported to increase BW in rats [43]. Though we found no significant difference in daily BW gain between the three groups (Figure 1B). Daily food intake was calculated by dividing the total dietary intake by the experimental period, and there was no significant difference among the three groups (Figure 1C). Daily energy intake, and food efficiency ratio (FER) did not substantially change with dietary intervention (Figures 1D, E). Therefore, in our experimental setting, partial replacement of dietary fat in HFCD with BTs did not significantly affect the daily food intake, daily energy intake and FER. FIGURE 1:** Effects of partial replacement of dietary fat with beef tallows (BTs) on the body weight change, daily food intake, daily energy intake, and food efficiency ratio in Sprague Dawley rats. Five-week-old male Sprague Dawley rats were fed either a high-fat and high-cholesterol (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) diets, respectively for 10 weeks. (A) Body weight changes, (B) daily body weight gain (final body weight – initial body weight), (C) daily food intake, (D) daily energy intake, and (E) food efficiency ratio (FER) was measured. Values are presented as means ± SD; n = 16 per individual group. Data were analyzed using one-way ANOVA followed by Tukey’s multiple comparisons test. and ** denotes a significant main effect of diet at p < 0.05 and p < 0.01, respectively. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower n-6/n-3 ratio.* ## 3.2. Effects of partial replacement of dietary fat with BTs on serum glucose and insulin levels We further investigated whether partial replacement of dietary fat with BT alters the serum glucose, insulin, and HOMA-IR at the end of dietary intervention. IR is characterized by an increased blood glucose, and excessive insulin secretion is necessary due to decreased insulin sensitivity [44], while a reduction in HOMA-IR is often considered to be an evidence for improved insulin sensitivity [45]. TM is considered to reduce insulin-provoked glucose transport and thus cause an increase in the levels of serum glucose [46]. Serum glucose levels in rats fed with HFCD + BT1 and HFCD + BT2 in the presence or absence of TM were lower than those in HFCD group, although the differences were not statistically significant (Figure 2A). TM administration increased serum insulin levels compared to PBS treatment group ($p \leq 0.01$; Figure 2B). Additionally, after TM-injection, both the HFCD + BT1 and HFCD + BT2 groups showed a relatively lower HOMA-IR level of 72.2 and $74.4\%$, respectively, compared to the HFCD group (considered as $100\%$), but there was no statistical differences (Figure 2C). Fasting serum insulin levels, and HOMA-IR in rats fed with HFCD + BT1 in the absence of TM exhibited lower trends than in HFCD and HFCD + BT2 groups, although there was no statistical differences (Figures 2B, C). These results suggest that partial replacement of dietary fat with BT1 may enhance insulin sensitivity. FIGURE 2: Effects of partial replacement of dietary fat with beef tallows (BTs) and tunicamycin (TM) challenge on the serum glucose and insulin levels in Sprague Dawley rats. Five-week-old male Sprague-Dawley rats were fed either a high-fat and high-cholesterol diet (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) for 10 weeks, followed by injection with phosphate-buffered saline (PBS), or TM (1 mg/kg). (A) Serum glucose level, (B) serum insulin level, and (C) homeostasis model assessment of insulin resistance (HOMA-IR) level. Data were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test to determine the interactions or the main effects (diet and TM stimulation). Values are presented as a Box-and-Whisker plots representing 4 rats per individual group. Denotes a significant main effect of TM stimulation at p < 0.01. The mean values are indicated by “+” sign. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower n-6/n-3 ratio; TM, tunicamycin; PBS, phosphate-buffered saline. ## 3.3. Effects of partial replacement of dietary fat with BTs on liver and adipose tissue weights To determine whether partial replacement of dietary fat with BTs could attenuate the lipid accumulation in the liver or WATs, the weights of liver and WATs the sum of the weight of [epididymal (EAT), mesenteric (MAT), retroperitoneal (RAT), and perirenal adipose tissues (PAT)] were weighed. TM is a pharmacological chemical inducer of ER stress that inhibits N-linked glycosylation of nascent proteins, resulting in the activation of UPRs in mammalian cells [47]. No significant difference in the liver weight was observed with presence or absence of TM administration, and dietary intervention (Figure 3A). After TM injection, rats fed HFCD + BT2 had elevated weights of RAT and PAT compared to the PBS-injected group, resulting in elevated weights of total WATs (Figures 3B, E, F). Furthermore, in the TM-injected group, slightly increased MAT compared to the PBS-injected group ($p \leq 0.05$; Figure 3D) was observed. However, dietary intervention did not cause obvious differences in adipose tissue weight among the three groups in the presence or absence of TM administration. These data showed that TM treatment caused accumulation of WAT, while partial replacement of dietary fat with BT2 may be associated with reduced WAT accumulation. FIGURE 3: Effects of partial replacement of dietary fat with beef tallows (BTs) and tunicamycin (TM) challenge on liver and adipose tissue weights in Sprague Dawley rats. Five-week-old male Sprague Dawley rats were fed either a high-fat and high-cholesterol (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) diets, respectively for 10 weeks, and then treated with PBS or TM (1 mg/kg) (n = 8 per group). (A) Liver weight, (B) white adipose tissue (WAT) weight, (C) epididymal adipose tissue (EAT) weight, (D) mesenteric adipose tissue (MAT) weight, (E) retroperitoneal adipose tissue (RAT) weight, (F) perirenal adipose tissue (PAT) weight were measured. Values are presented as Box-and-Whisker plots representing eight rats per group. The mean values are indicated by “+” sign. Data were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test to determine the interactions or the main effects (diet and TM stimulation). Asterisk indicates a significant effect for TM stimulation (p < 0.05). Mean values labeled with different letters indicate statistically significant difference, p < 0.05. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower n-6/n-3 ratio; TM, tunicamycin; PBS, phosphate-buffered saline.* ## 3.4. Effects of partial replacement of dietary fat with BTs on serum lipid profiles and cardiovascular parameters To investigate the effects of partial replacement of dietary fat with BTs on lipid-lowering and CVD risk factors, we measured the CVD-related serum lipid panels, and calculated the CRF. After TM administration, serum TG, TC, HDL-C, and non-HDL-C levels decreased by 72.27, 60.57, 81.30, and $56.04\%$, respectively, whereas CRF levels were significantly increased by $124.44\%$ (Figure 4). Serum TG levels in rats fed HFCD + BT2 were lower than the rats fed HFCD and HFCD + BT1 in the absence of TM (Figure 4A). Conversely, HDL-C levels were increased in the HFCD + BT2 group compared to those in other groups after TM injection (Figure 4C). Interestingly, serum TC (135.1 and $120.4\%$ compared to HFCD and HFCD + BT2, respectively) and non-HDL-C levels (143.8 and $125.8\%$ compared to HFCD and HFCD + BT2, respectively) in rats fed HFCD + BT1 were slightly higher than other groups, which may be caused by HFCD + BT1 group containing higher levels of SFAs than HFCD groups, and a relatively higher ratio of n-6/n-3 than HFCD + BT2 group (Table 4; Figures 4B, D). However, the serum TC and non-HDL-C levels in rats fed HFCD and HFCD + BT2 had no significant differences (Figures 4B, D) despite the fact that dietary n-6/n-3 ratio of BT1 and BT2 is 0.9 and 0.4, respectively. Therefore, BT2 has a higher n-3 content and a lower n-6 content than BT1 (Table 3). Although HFCD + BT2 diet has a higher amount of SFAs than HFCD groups, HFCD + BT2 containing a lower ratio of n-6/n-3 may improve the serum lipid profiles (Table 4). The discrepancies of n-6/n-3 ratio between the diet and whole blood may be due to the innate metabolic competitiveness of n-6 and n-3 FAs. Besides, the dietary intervention did not cause significant changes in HDL-C and CRF in the absence of TM injection (Figures 4C, E). Partial replacement of dietary fat with BT1 and BT2 did not induce any changes in serum TG, TC and non-HDL-C (Figures 4A, B, D), while it decreased the CRF levels in the presence of TM (Figure 4E). These results suggest that partial replacement of dietary fat with BTs had a positive effect on CVD prevention, and BT2 diet has a positive effect on improving the serum lipid profiles. FIGURE 4: Effects of partial replacement of dietary fat with beef tallows (BTs) and tunicamycin (TM) challenge on serum lipid profiles in Sprague Dawley rats. Five-week-old male Sprague Dawley rats were fed either a high-fat and high-cholesterol (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) diets, respectively for 10 weeks, and then treated with PBS or TM (1 mg/kg). (A) Serum triglyceride levels, (B) serum total cholesterol levels, (C) high-density lipoprotein (HDL)-cholesterol levels, (D) non-HDL cholesterol levels, (E) cardiac risk factor (CRF) was measured. Values are presented as Box-and-Whisker plots representing eight rats per group. The mean values are indicated by “+” sign. Data were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test to determine the interactions or the main effects (diet and TM stimulation). Mean values labeled with different letters indicate statistically significant difference, p < 0.05. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower n-6/n-3 ratio; TM, tunicamycin; PBS, phosphate-buffered saline. ## 3.5. Effects of partial replacement of dietary fat with BTs on fat accumulation in the liver and epididymal adipose tissue The serum lipid showed that the n-3-enhanced BTs intervention reduced the contents of serum TG but increased the HDL-C levels (Figures 4A, C), indicating n-3 in BT is beneficial to regulate serum lipid profiles. We also measured the liver and EAT lipid contents to determine the fat accumulation in the liver and EAT. There was no significant difference in hepatic TG contents among the three different dietary interventions in the absence of TM injection. However, hepatic TG contents were substantially increased in the rats fed with HFCD following TM injection, whereas rats given HFCD + BT1 and HFCD + BT2 significantly exhibited lower hepatic TG contents (Figure 5A). In addition, the HFCD + BT1 diet increased hepatic TC contents in rats (Figure 5B), which is consistent with previous observation of serum TC contents (Figure 4B). Moreover, the rats fed with HFCD + BT1 and HFCD + BT2, both had lower EAT TG and TC contents than those in the HFCD groups in the presence or absence of TM intervention (Figures 5C, D). These results showed that partial replacement of dietary fat with BTs could improve the lipid accumulation in the liver and adipose tissues. FIGURE 5: Effects of partial replacement of dietary fat with beef tallows (BTs) and tunicamycin (TM) challenge on lipid contents in the liver and epididymal adipose tissue (EAT). Five-week-old male Sprague Dawley rats were fed either a high-fat and high-cholesterol (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) diets, respectively for 10 weeks, and then treated with PBS or TM (1 mg/kg). (A) Hepatic triglyceride (TG) levels, (B) hepatic total cholesterol (TC) levels, (C) EAT TG levels, and (D) EAT TC levels were measured. Values are presented as a Box-and-Whisker plots representing eight rats per group. The mean values are indicated by “+” sign. Data were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test to determine the interactions or the main effects (diet and TM stimulation). Pound indicates a significant main effect for diet (##p < 0.01). Mean values labeled with different letters indicate statistically significant difference, p < 0.05. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower n-6/n-3 ratio; TM, tunicamycin; PBS, phosphate-buffered saline. ## 3.6. Effects of partial replacement of dietary fat with BTs on hepatic function parameters Since partial replacement of dietary fat with BTs reduced hepatic TG levels after TM injection (Figure 5A), we reasoned that reducing TG levels in liver might alleviate liver function. The accumulation of TG in liver is well known to be detrimental [48]. To this end, the hepatic enzyme function was assessed by examining serum ALT, AST, and ALP activities following the TM challenge. The dietary intervention did not cause any changes in AST and ALT levels, while the HFCD + BT2 diet reduced ALP levels compared to HFCD and HFCD + BT1 groups before TM injection (Figure 6). TM administration significantly increased the AST and ALP levels of HFCD + BT1 and HFCD + BT2 compared to the PBS group (Figures 6A, C). In addition, rats fed HFCD showed no significant changes in ALT levels after TM injection, whereas the HFCD + BT2 groups showed significant increased ALT levels (Figure 6B). The rats fed with HFCD + BT1 showed lower AST levels than HFCD and HFCD + BT2 group after TM injection (Figure 6A). In contrast, the ALP levels in HFCD + BT1 group higher than those in other groups, which probably is related to the higher SFAs content in HFCD + BT1 diet and higher ratio of n-6/n-3 PUFA than HFCD + BT2 diet as previously mentioned (Table 3; Figure 6C). Moreover, TM injection significantly increased ALT levels in the HFCD + BT2 group (Figure 6B). These results showed that partial replacement of lard in HFCD with n-3-enhanced BT may have adverse effects on hepatic function parameters. FIGURE 6: Effects of partial replacement of dietary fat with beef tallows (BTs) and tunicamycin (TM) challenge on hepatic function parameters in serum. Five-week-old male Sprague Dawley rats were fed either a high-fat and high-cholesterol (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) diets, respectively for 10 weeks, and then treated with PBS or TM (1 mg/kg). (A) Aspartate aminotransferase (AST) activity, (B) alanine aminotransferase (ALT) activity, and (C) alkaline phosphate (ALP) activity was measured. Values are presented as a Box-and-Whisker plots representing 8 rats per group. The mean values are indicated by “+” sign. Data were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test to determine the interactions or the main effects (diet and TM stimulation). Mean values labeled with different letters indicate statistically significant difference, p < 0.05. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower ratio of n-6/n-3 PUFA; TM, tunicamycin; PBS, phosphate-buffered saline. ## 3.7. Effects of partial replacement of dietary fat with BTs and TM challenge on protein expression of ER stress in liver and in epididymal adipose tissue To understand the effects of partial replacement of dietary fat with BTs on ER stress in TM-induced SD rats, the expression of UPRs was analyzed. TM is a glycosylation inhibitor that inhibits N-linked glycosylation of nascent proteins, leading to the accumulation of unfolded proteins in the ER, inducing ER stress [47, 49]. In the liver, the protein expression of BiP, ATF4, CHOP, and XBP-1 in rats fed HFCD + BT1 decreased by 0. 05-, 0. 48-, 1. 62-, and 1.26-fold, respectively compared to the HFCD group, respectively: the difference in expression of BiP and ATF4 was not statistically varied (Figures 7A–E). Compared with HFCD group, the expression of BiP, ATF4, CHOP, and XBP-1 were significantly lower in HFCD + BT2 group by, 0. 56-, 2. 08-, 4. 25-, and 3.68-fold, respectively (Figure 7); however, there was also no significant differences in the protein expression of BiP and ATF4 (Figures 7B, C). These results showed that partial replacement of dietary fat with BTs reduced the TM-induced hepatic ER stress protein expression. Therefore, we measured the expression of ER stress-related protein in EAT in TM-injected SD rats. The protein expression of CHOP and XBP-1 in rats fed HFCD + BT1 significantly decreased by 0.71- and 0.51-fold, respectively compared to the HFCD group (Figures 7A, G, H). By contrast, the protein expression of BiP in HFCD + BT1 group was not significantly different from the HFCD group (Figure 7F). Moreover, the ER stress-related proteins BiP, CHOP, and XBP-1 expression in rats fed HFCD + BT2 was, respectively lower than HFCD group by 0. 45-, 0. 87-, and 0.92-fold (Figures 7A, F–H). These results showed that partial replacement of dietary fat with BTs reduced the TM-induced ER stress protein expression in EAT. FIGURE 7: Effects of partial replacement of dietary fat with beef tallows (BTs) and tunicamycin (TM) challenge on protein expression of ER stress in liver and epididymal adipose tissue (EAT). Five-week-old male Sprague Dawley rats were fed either a high-fat and high-cholesterol (HFCD), lard of HFCD partially replaced with regular beef tallow (HFCD + BT1), or lard of HFCD partially replaced with BT containing a lower n-6/n-3 ratio (HFCD + BT2) diets, respectively for 10 weeks, and then treated with PBS or TM (1 mg/kg). (A) Representative western blot images, (B) liver Binding immunoglobulin protein (BiP) levels, (C) liver Activating transcription factor 4 (ATF4) levels, (D) liver C/EBP homologous protein (CHOP) levels, (E) liver X-box binding protein 1 (XBP-1) levels, (F) EAT BiP levels, (G) EAT XBP-1 levels, and (H) EAT CHOP levels were measured. The expression of each protein was normalized to a value for GAPDH, the internal control of protein content. Values are presented as means ± SD; n = 8 per individual group. Data were analyzed using one-way ANOVA followed by Tukey’s multiple comparisons test. Mean values labeled with different letters indicate statistically significant difference, p < 0.05. HFCD, high-fat and high-cholesterol; HFCD + BT1, high-fat and high-cholesterol diet + regular beef tallow; HFCD + BT2, high-fat and high-cholesterol diet + BT containing a lower n-6/n-3 ratio; ER stress, endoplasmic reticulum Stress; TM, tunicamycin; PBS, phosphate-buffered saline. ## 4. Discussion In this study we aimed to investigate partial replacement of lard in HFCD with n-3 enriched-BTs which may attenuate the risk factors of MetS and TM-induced ER stress. To induce a rodent model to mimic clinical MetS, 5-week-old male SD rats were fed HFCD, HFCD + BT1, or HFCD + BT2 diets for 10 weeks, after which they were injected with either PBS or TM. HFCD was fed to the rats as an experimental diet due to a greater negative impact on serum lipid metabolism and liver function than high fat diet (HFD) feeding [50]. The composition of the dietary fats in the BT groups shows higher SFAs content, compared to the HFCD group, while BT fed groups have higher n-3 content and lower ratio of n-6/n-3 (Table 3). Our research question was to elucidate which was the more important factor: the amount of n-3 PUFA consumption or the lower ratio of n-6/n-3 PUFA in the dietary fats, to manage MetS in dietary-induced obese MetS rat model. Our previous study demonstrated that partial replacement of dietary fat with krill oil or coconut oil improved dyslipidemia in lipopolysaccharide (LPS)-injected rats [35]. We also demonstrated that replacing dietary fat with perilla oil or corn oil attenuates LPS-induced hepatic inflammation in rats [36]. These results led us to this follow-up study to investigate the effects of partial replacement of dietary fat with PUFAs on metabolic complications. Increased levels of serum n-6 elevated the risk of MetS, while decreased ratio of n-6/n-3 may be effective in reducing the prevalence of IR and MetS [51], and reducing ratio of n-6/n-3 in the diet attenuates SFAs-induced weight gain in experimental rats [52]. These studies suggest that diets containing high levels of n-6 may have adverse health effects but lowering n-6/n-3 ratio may be an effective and reasonable strategy. Moreover, the above study explored the effects of dietary intervention containing higher n-3, and lower n-6/n-3 ratio in rats being fed diets with high levels of SFAs. In this study, partial replacement of lard in the experimental diet with BTs caused no significant changes in body weight gain, daily food intake, daily energy intake, and FER compared to the HFCD group (Figures 1B–E), which is consistent with results from our previous studies in rats [36]. Interestingly, the HFCD + BT1 group had significantly higher BW than HFCD and HFCD + BT2 groups after 4 weeks of dietary intervention (Figure 1A), which was not sustained by the end of the dietary intervention. HFCD + BT1 group had lower absolute n-3 content and higher ratio of n-6/n-3 than HFCD + BT2 group (Table 3), resulting in body weight changes during the dietary intervention. It has been reported that n-3 consumption could decrease lipid levels and glycemic factors including HOMA-IR in patients [53, 54], which is consistent with our current results (Figure 2), and our previous findings [33, 36]. It has been reported that HFCD may cause the fat accumulation in the liver and adipose tissue [55], thus the weight of liver and adipose tissues were observed and we found no significant effects of dietary intervention with or without TM injection (Figure 3). However, TM injection increased the weight of RAT and PAT in the HFCD + BT2 group, and resulted in elevated weights of total WAT (Figures 3B, E, F). Albeit, the molecular mechanisms underlying the observed anabolic responses should be scrutinized in future studies. Therefore, we logically postulate that partial replacement of dietary fat with BTs may have protective effects against MetS, and further studies are need to observe the serum adiposity levels, to investigate the changes on MetS indicators. It has been shown that n-3 PUFAs consumption reduces the risk of CVDs and decreases the MetS risk factors, by modulating blood lipid levels (56–59). There is a report that the intake of PUFAs with lower ratio of n-6/n-3 ameliorates hepatic steatosis and glucose metabolism [60]. Therefore, the higher contents of SFAs in BT supplement may be a major risk factor for onset or progression of MetS. Decreased HDL-C, together with increased low-density lipoprotein (LDL-C) particles and TG-rich lipoproteins (TRLs) are the main components of dyslipidemia closely associated with the MetS [61]. HFCD + BT2 group showed lower serum TG levels in animals without TM injection, and interestingly, the HFCD + BT1 group showed increased TC and non-HDL-C levels (Figures 4A, B, D) indicating that excessive SFA consumption may lead to dyslipidemia regardless n-3 PUFA contents. Injection of TM resulted in a higher HDL level in the HFCD + BT2 group. Whereas, HFCD + BT1 and HFCD + BT2 groups, respectively showed decreased CRF levels compared to the HFCD group (Figures 4C, E). These results may suggest that partial replacement of dietary fat with BTs may improve dyslipidemia, and consequently reduce CRFs caused by HFCD. Prolonged elevated ER stress is a strong risk factor in the pathogenesis of MetS, T2DM, CVDs and obesity [23]. In this study, the dietary intervention did not significantly influence ER stress related protein expression in the PBS-injected rats (data not shown). We examined the regulation of hepatic and WAT UPRs in TM-injected SD rats. Hepatocytes are ER-rich, and ER stress plays an essential role in mediating various hepatic pathological changes [62]. The results showed that HFCD + BT1 and HFCD + BT2 diets significantly reduced the expression of hepatic UPR-related proteins, such as ATF4, CHOP and XBP-1 (Figures 7A, C, D, E); HFCD + BT2 diet further reduced the expression of the liver ER chaperone protein, BiP (Figures 7A, B). Obesity and some metabolic syndromes are often associated with WAT dysfunction [63], and ER stress in the WAT has a critical pathophysiological role systemically as well as in local tissues [64]. In our study, replacement fatty acids (BT1 and BT2) significantly attenuated the expression of CHOP and XBP-1 proteins in the EAT (Figures 7A, G, H), while HFCD + BT2 diet remarkably reduced BiP expression in EAT (Figures 7A, F). These results support the hypothesis that n-3 consumption mitigates ER stress in the liver [65] and the WAT with reducing oxidative stress [66]. Our previous study also reported that partial replacement of dietary fat in HFD with BTs reduced the expression of ER-related proteins in both liver and EAT [33]. Partial replacement of dietary fat with BTs attenuated ER stress in the liver and EAT, while the HFCD + BT2 group containing a lower n-6/n-3 PUFA ratio seemed to be more effective in attenuating ER stress. Therefore, we logically reason that n-3 consumption and low ratio of n-6/n-3 PUFA may help to regulate metabolic complications, and attenuate ER stress. There is significant debate about whether a high amount of dietary n-3 PUFA consumption is safe, since we observed that replacement with lower n-6/n-3 PUFA (HFCD + BT2) significantly elevated serum AST and ALT levels (Figures 6A, B). n-3 PUFA consumption has numerous beneficial effects; however, due to the structural instability, extra antioxidant supplementation may be necessary [58]. A previous study has reported that α-tocopherol supplementation as an antioxidant ameliorates the DNA damage in human lymphocytes caused by n-3 [67]. The AHA has reported that α-tocopherol has antioxidant and pro-oxidant properties, and that the pro-oxidant properties can be reduced by consumption of ascorbic acid [68]. Moreover, a triple antioxidant combination with ascorbic acid, glutathione, and α-tocopherol has been shown to improve cholesterol levels in diabetic rats [69]. Given the higher AST and ALT levels in the HFCD + BT2 group, we assume that appropriate supplementation with antioxidative reagents concomitant with n-3 consumption may ameliorate the hepatotoxic effects. Partial replacement of lard in HFCD with BTs improved insulin sensitivity and lipid panels, and lowered fat accumulation in the liver and EAT in TM-injected rats. Furthermore, HFCD + BT1 and HFCD + BT2 diets attenuated the expression of TM-induced ER stress proteins in the liver and EAT. We suggest that the increasing dietary n-3 intake and decreasing the ratio of n-6/n-3 may alleviate HFCD-induced dyslipidemia and MetS, although HFCD + BT1 and HFCD + BT2 have shown higher SFAs levels than the HFCD group. The results of this study may serve as a basis for future clinical trials; long-term studies are needed to thoroughly confirm the feasibility of BT with higher amount of n-3, and lower n-6/n-3 ratio as a dietary supplement. Moreover, it would be interesting to investigate the co-intake of n-3 and antioxidants, such as, α-tocopherol, ascorbic acid or glutathione using in vitro and in vivo experiments to find more effective methods of n-3 intake [70]. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by the Institutional Animal Care Use Committee of Dankook University. ## Author contributions JB, DY, and J-HH designed the study. JZ and JL performed data management and data analysis. JZ and JB wrote the first draft of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Grundy S. Metabolic syndrome pandemic.. (2008) 28 629-36. DOI: 10.1161/ATVBAHA.107.151092 2. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies.. (2011) 9. DOI: 10.1186/1741-7015-9-48 3. Grundy S. Metabolic syndrome update.. (2016) 26 364-73. DOI: 10.1016/j.tcm.2015.10.004 4. Alberti K, Eckel R, Grundy S, Zimmet P, Cleeman J, Donato K. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and international association for the study of obesity.. (2009) 120 1640-5. DOI: 10.1161/CIRCULATIONAHA.109.192644 5. da Costa G, da Conceição Nepomuceno G, da Silva Pereira A, Simões B. Worldwide dietary patterns and their association with socioeconomic data: an ecological exploratory study.. (2022) 18. DOI: 10.1186/s12992-022-00820-w 6. Manzel A, Muller D, Hafler D, Erdman S, Linker R, Kleinewietfeld M. Role of “Western diet” in inflammatory autoimmune diseases.. (2014) 14. DOI: 10.1007/s11882-013-0404-6 7. Newman T, Vitolins M, Cook K. From the table to the tumor: the role of Mediterranean and Western dietary patterns in shifting microbial-mediated signaling to impact breast cancer risk.. (2019) 11. DOI: 10.3390/nu11112565 8. Hoyas I, Leon-Sanz M. Nutritional challenges in metabolic syndrome.. (2019) 8. DOI: 10.3390/jcm8091301 9. Serra-Majem L, Román-Viñas B, Sanchez-Villegas A, Guasch-Ferré M, Corella D, La Vecchia C. Benefits of the Mediterranean diet: epidemiological and molecular aspects.. (2019) 67 1-55. DOI: 10.1016/j.mam.2019.06.001 10. Sofi F, Macchi C, Abbate R, Gensini G, Casini A. Mediterranean diet and health.. (2013) 39 335-42. DOI: 10.1002/biof.1096 11. Scoditti E, Capurso C, Capurso A, Massaro M. Vascular effects of the Mediterranean diet-part II: role of omega-3 fatty acids and olive oil polyphenols.. (2014) 63 127-34. DOI: 10.1016/j.vph.2014.07.001 12. Davis C, Bryan J, Hodgson J, Murphy K. Definition of the Mediterranean diet; a literature review.. (2015) 7 9139-53. DOI: 10.3390/nu7115459 13. Mori T, Hodgson J, Caballero B. Fatty acids: health effects of omega-6 polyunsaturated fatty acids.. (2013) 209-14. DOI: 10.1016/B978-0-12-375083-9.00100-8 14. Vafeiadou K, Weech M, Sharma V, Yaqoob P, Todd S, Williams C. A review of the evidence for the effects of total dietary fat, saturated, monounsaturated and n-6 polyunsaturated fatty acids on vascular function, endothelial progenitor cells and microparticles.. (2012) 107 303-24. DOI: 10.1017/S0007114511004764 15. Mika A, Sledzinski T. Alterations of specific lipid groups in serum of obese humans: a review.. (2017) 18 247-72. DOI: 10.1111/obr.12475 16. Marion-Letellier R, Savoye G, Ghosh S. olyunsaturated fatty acids and inflammation.. (2015) 67 659-67. DOI: 10.1002/iub.1428 17. Simopoulos A. The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases.. (2008) 233 674-88. DOI: 10.3181/0711-MR-311 18. Lichtenstein A, Appel L, Brands M, Carnethon M, Daniels S. Diet and lifestyle recommendations revision 2006: a scientific statement from the American Heart Association Nutrition Committee.. (2006) 114 82-96. DOI: 10.1161/CIRCULATIONAHA.106.176158 19. Han J, Kaufman R. The role of ER stress in lipid metabolism and lipotoxicity.. (2016) 57 1329-38. DOI: 10.1194/jlr.R067595 20. Zhang K, Kaufman R. From endoplasmic-reticulum stress to the inflammatory response.. (2008) 454 455-62. DOI: 10.1038/nature07203 21. Xu C, Bailly-Maitre B, Reed J. Endoplasmic reticulum stress: cell life and death decisions.. (2005) 115 2656-64. DOI: 10.1172/JCI26373 22. Lindholm D, Korhonen L, Eriksson O, Kõks S. Recent insights into the role of unfolded protein response in ER stress in health and disease.. (2017) 5. DOI: 10.3389/fcell.2017.00048 23. Mohan S, R P, Brown L, Ayyappan P, G R. Endoplasmic reticulum stress: a master regulator of metabolic syndrome.. (2019) 860. DOI: 10.1016/j.ejphar.2019.172553 24. Lemmer I, Willemsen N, Hilal N, Bartelt A. A guide to understanding endoplasmic reticulum stress in metabolic disorders.. (2021) 47. DOI: 10.1016/j.molmet.2021.101169 25. Meng Z, Liu Y, Shan L, Jin Q, Wang F, Wang X. Specialty fats from beef tallow and canola oil: establishment of reaction conditions, characterization of products, and evaluation of crystal stability.. (2011) 6 115-26. DOI: 10.1007/s11483-010-9186-8 26. Li Y, Zhao J, Xie X, Zhang Z, Zhang N, Wang Y. A low trans margarine fat analog to beef tallow for healthier formulations: optimization of enzymatic interesterification using soybean oil and fully hydrogenated palm oil.. (2018) 255 405-13. DOI: 10.1016/j.foodchem.2018.02.086 27. Agarwal S, Fulgoni IV. Contribution of beef to key nutrient intakes in American adults: an updated analysis with NHANES 2011-2018.. (2022) 105 105-12. DOI: 10.1016/j.nutres.2022.06.009 28. Bourre J. Nutritional value of beef.. (2011) 195 1787-99. DOI: 10.1016/S0001-4079(19)31917-X 29. Pighin D, Pazos A, Chamorro V, Paschetta F, Cunzolo S, Godoy F. A contribution of beef to human health: a review of the role of the animal production systems.. (2016) 2016 1-10. DOI: 10.1155/2016/8681491 30. Scollan N, Hocquette J, Nuernberg K, Dannenberger D, Richardson I, Moloney A. Innovations in beef production systems that enhance the nutritional and health value of beef lipids and their relationship with meat quality.. (2006) 74 17-33. DOI: 10.1016/j.meatsci.2006.05.002 31. Scollan N, Dannenberger D, Nuernberg K, Richardson I, MacKintosh S, Hocquette J. Enhancing the nutritional and health value of beef lipids and their relationship with meat quality.. (2014) 97 384-94. DOI: 10.1016/j.meatsci.2014.02.015 32. Siri-Tarino P, Sun Q, Hu F, Krauss R. Saturated fat, carbohydrate, and cardiovascular disease.. (2010) 91 502-9. DOI: 10.3945/ajcn.2008.26285 33. Lee J, Lee J, Lee J, Park S, Jung S, Lee H. Partial replacement of high-fat diet with beef tallow attenuates dyslipidemia and endoplasmic reticulum Stress in db/db Mice.. (2022) 25 660-74. DOI: 10.1089/jmf.2022.K.0019 34. Park S, Lee J, Lee J, Lee J, Byun J, Kim I. Lowering n-6/n-3 ratio as an important dietary intervention to prevent LPS-Inducible dyslipidemia and Hepatic Abnormalities in ob/ob Mice.. (2022) 23. DOI: 10.3390/ijms23126384 35. Son H, Kim B, Lee J, Park S, Oh C, Jung S. Partial replacement of dietary fat with krill oil or coconut oil alleviates dyslipidemia by partly modulating lipid metabolism in lipopolysaccharide-injected rats on a high-fat diet.. (2022) 19. DOI: 10.3390/ijerph19020843 36. Son H, Xiang H, Park S, Lee J, Lee J, Jung S. Partial replacement of dietary fat with polyunsaturated fatty acids attenuates the lipopolysaccharide-induced hepatic inflammation in Sprague-Dawley rats fed a high-fat diet.. (2021) 18. DOI: 10.3390/ijerph182010986 37. Wt F, Levy R, Fredrickson D. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.. (1972) 18 499-502. PMID: 4337382 38. Kang N, Lee W, Yi B, Lee H, Park M, Park S. Risk of cardiovascular disease is suppressed by dietary supplementation with protamine and chitooligosaccharide in Sprague-Dawley rats.. (2013) 7 127-33. DOI: 10.3892/mmr.2012.1128 39. Park Y, Lee J, Son H, Kim B, Byun J, Ha J. Antiobesity effects of extract from Spergularia marina Griseb in adipocytes and high-fat diet-induced obese rats.. (2020) 12. DOI: 10.3390/nu12020336 40. Motamed N, Miresmail S, Rabiee B, Keyvani H, Farahani B, Maadi M. Optimal cutoff points for HOMA-IR and QUICKI in the diagnosis of metabolic syndrome and non-alcoholic fatty liver disease: a population based study.. (2016) 30 269-74. DOI: 10.1016/j.jdiacomp.2015.11.019 41. Bligh E, Dyer WJ. A rapid method of total lipid extraction and purification.. (1959) 37 911-7. DOI: 10.1139/o59-099 42. Harris W, Polreis J. Measurement of the omega-3 index in dried blood spots.. (2016) 4. DOI: 10.21767/2386-5180.1000137 43. Lee J, Park M, Kim A, Lee M. Effects of ramie leaves on improvement of lipid metabolism and antiobesity effect in rats fed a high fat/high cholesterol diet.. (2011) 43 83-90. DOI: 10.9721/KJFST.2011.43.1.083 44. Phillips D, Clark P, Hales C, Osmond C. Understanding oral glucose tolerance: comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion.. (1994) 11 286-92. DOI: 10.1111/j.1464-5491.1994.tb00273.x 45. Reaven G. What do we learn from measurements of HOMA-IR?. (2013) 56 1867-8. DOI: 10.1007/s00125-013-2948-3 46. Quan X, Wang J, Liang C, Zheng H, Zhang L. Melatonin inhibits tunicamycin-induced endoplasmic reticulum stress and insulin resistance in skeletal muscle cells.. (2015) 463 1102-7. DOI: 10.1016/j.bbrc.2015.06.065 47. Bull V, Thiede B. Proteome analysis of tunicamycin-induced ER stress.. (2012) 33 1814-23. DOI: 10.1002/elps.201100565 48. Van Herpen N, Schrauwen-Hinderling V. Lipid accumulation in non-adipose tissue and lipotoxicity.. (2008) 94 231-41. DOI: 10.1016/j.physbeh.2007.11.049 49. Wang Y, Zhang L, He Z, Deng J, Zhang Z, Liu L. Tunicamycin induces ER stress and inhibits tumorigenesis of head and neck cancer cells by inhibiting N-glycosylation.. (2020) 12 541-50. PMID: 32194902 50. Liang H, Jiang F, Cheng R, Luo Y, Wang J, Luo Z. A high-fat diet and high-fat and high-cholesterol diet may affect glucose and lipid metabolism differentially through gut microbiota in mice.. (2021) 70 73-83. DOI: 10.1538/expanim.20-0094 51. Vanhala M, Saltevo J, Soininen P, Kautiainen H, Kangas A, Ala-Korpela M. Serum omega-6 polyunsaturated fatty acids and the metabolic syndrome: a longitudinal population-based cohort study.. (2012) 176 253-60. DOI: 10.1093/aje/kwr504 52. Liu H, Qiu Y, Mu Y, Zhang X, Liu L, Hou X. A high ratio of dietary n-3/n-6 polyunsaturated fatty acids improves obesity-linked inflammation and insulin resistance through suppressing activation of TLR4 in SD rats.. (2013) 33 849-58. DOI: 10.1016/j.nutres.2013.07.004 53. Kochikuzhyil B, Devi K, Fattepur S. Effect of saturated fatty acid-rich dietary vegetable oils on lipid profile, antioxidant enzymes and glucose tolerance in diabetic rats.. (2010) 42 142-5. DOI: 10.4103/0253-7613.66835 54. Khalili L, Valdes-Ramos R, Harbige L. Effect of n-3 (Omega-3) Polyunsaturated fatty acid supplementation on metabolic and inflammatory biomarkers and body weight in patients with type 2 diabetes mellitus: a systematic review and meta-analysis of RCTs.. (2021) 11. DOI: 10.3390/metabo11110742 55. Jiao J, Han S, Zhang W, Xu J, Tong X, Yin X. Chronic leucine supplementation improves lipid metabolism in C57BL/6J mice fed with a high-fat/cholesterol diet.. (2016) 60. DOI: 10.3402/fnr.v60.31304 56. Poudyal H, Panchal S, Diwan V, Brown L. Omega-3 fatty acids and metabolic syndrome: effects and emerging mechanisms of action.. (2011) 50 372-87. DOI: 10.1016/j.plipres.2011.06.003 57. Jang H, Park K. Omega-3 and omega-6 polyunsaturated fatty acids and metabolic syndrome: a systematic review and meta-analysis.. (2020) 39 765-73. DOI: 10.1016/j.clnu.2019.03.032 58. Leamy A, Egnatchik R, Young J. Molecular mechanisms and the role of saturated fatty acids in the progression of non-alcoholic fatty liver disease.. (2013) 52 165-74. DOI: 10.1016/j.plipres.2012.10.004 59. Calvo M, Martínez M, Torres W, Chávez-Castillo M, Luzardo E, Villasmil N. Omega-3 polyunsaturated fatty acids and cardiovascular health: a molecular view into structure and function.. (2017) 1 116-28. DOI: 10.20517/2574-1209.2017.14 60. Van Name M, Savoye M, Chick J, Galuppo B, Feldstein A, Pierpont B. A Low ω-6 to ω-3 PUFA Ratio (n-6:n-3 PUFA) diet to treat fatty liver disease in obese youth.. (2020) 150 2314-21. DOI: 10.1093/jn/nxaa183 61. Ruotolo G, Howard B. Dyslipidemia of the metabolic syndrome.. (2002) 4 494-500. DOI: 10.1007/s11886-002-0113-6 62. Dara L, Ji C, Kaplowitz N. The contribution of endoplasmic reticulum stress to liver diseases.. (2011) 53 1752-63. DOI: 10.1002/hep.24279 63. Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti G. Adipose tissue dysfunction as determinant of obesity-associated metabolic complications.. (2019) 20. DOI: 10.3390/ijms20092358 64. Kawasaki N, Asada R, Saito A, Kanemoto S, Imaizumi K. Obesity-induced endoplasmic reticulum stress causes chronic inflammation in adipose tissue.. (2012) 2. DOI: 10.1038/srep00799 65. Okada L, Oliveira C, Stefano J, Nogueira M, da Silva I, Cordeiro F. Omega-3 PUFA modulate lipogenesis, ER stress, and mitochondrial dysfunction markers in NASH – Proteomic and lipidomic insight.. (2018) 37 1474-84. DOI: 10.1016/j.clnu.2017.08.031 66. Sakamuri A, Jeyapal S, Kona S, Pothana S, Ibrahim A. Dietary 18:3 and long chain n-3 pufa protected rats from fructose-induced oxidative and endoplasmic reticulum stress in visceral adipose tissue.. (2018) 32 lb173-lb. DOI: 10.1096/fasebj.2018.32.1_supplement.lb173 67. Jenkinson A, Collins A, Duthie S, Wahle K, Duthie G. The effect of increased intakes of polyunsaturated fatty acids and vitamin E on DNA damage in human lymphocytes.. (1999) 13 2138-42. DOI: 10.1096/fasebj.13.15.2138 68. Carr A, Zhu B, Frei B. Potential antiatherogenic mechanisms of ascorbate (vitamin C) and α-tocopherol (vitamin E).. (2000) 87 349-54. DOI: 10.1161/01.RES.87.5.349 69. Yilmaz O, Ersan Y, Dilek Ozsahin A, Ihsan Ozturk A, Ozkan Y. Consequences of the combined α-tocopherol, ascorbic acid and α-lipoic acid on the glutathione, cholesterol and fatty acid composition in muscle and liver of diabetic rats.. (2013) 16 165-72. PMID: 24298385 70. Azzi A. Molecular mechanism of α-tocopherol action.. (2007) 43 16-21. DOI: 10.1016/j.freeradbiomed.2007.03.013
--- title: Pan-cancer analysis identifies PD-L2 as a tumor promotor in the tumor microenvironment authors: - Jingfang Lv - Zheng Jiang - Junhu Yuan - Meng Zhuang - Xu Guan - Hengchang Liu - Yefeng Yin - Yiming Ma - Zheng Liu - Hongying Wang - Xishan Wang journal: Frontiers in Immunology year: 2023 pmcid: PMC10060638 doi: 10.3389/fimmu.2023.1093716 license: CC BY 4.0 --- # Pan-cancer analysis identifies PD-L2 as a tumor promotor in the tumor microenvironment ## Abstract ### Background Programmed cell death protein 1 (PD-1) receptor has two ligands,programmed death-ligand 1 (PD-L1) and PD-L2. When compared with PD-L1, PD-L2 has not received much attention, and its role remains unclear. ### Methods The expression profiles of pdcd1lg2 (PD-L2-encoding gene) mRNA and PD-L2 protein were analyzed using TCGA, ICGC, and HPA databases. Kaplan-Meier and Cox regression analyses were used to assess the prognostic significance of PD-L2. We used GSEA, Spearman’s correlation analysis and PPI network to explore the biological functions of PD-L2. PD-L2-associated immune cell infiltration was evaluated using the ESTIMATE algorithm and TIMER 2.0. The expressions of PD-L2 in tumor-associated macrophages (TAMs) in human colon cancer samples, and in mice in an immunocompetent syngeneic setting were verified using scRNA-seq datasets, multiplex immunofluorescence staining, and flow cytometry. After fluorescence-activated cell sorting, flow cytometry and qRT-PCR and transwell and colony formation assays were used to evaluate the phenotype and functions of PD-L2+TAMs. Immune checkpoint inhibitors (ICIs) therapy prediction analysis was performed using TIDE and TISMO. Last, a series of targeted small-molecule drugs with promising therapeutic effects were predicted using the GSCA platform. ### Results PD-L2 was expressed in all the common human cancer types and deteriorated outcomes in multiple cancers. PPI network and Spearman’s correlation analysis revealed that PD-L2 was closely associated with many immune molecules. Moreover, both GSEA results of KEGG pathways and GSEA results for Reactome analysis indicated that PD-L2 expression played an important role in cancer immune response. Further analysis showed that PD-L2 expression was strongly associated with the infiltration of immune cells in tumor tissue in almost all cancer types, among which macrophages were the most positively associated with PD-L2 in colon cancer. According to the results mentioned above, we verified the expression of PD-L2 in TAMs in colon cancer and found that PD-L2+TAMs population was not static. Additionally, PD-L2+TAMs exhibited protumor M2 phenotype and increased the migration, invasion, and proliferative capacity of colon cancer cells. Furthermore, PD-L2 had a substantial predictive value for ICIs therapy cohorts. ### Conclusion PD-L2 in the TME, especially expressed on TAMs, could be applied as a potential therapeutic target. ## Introduction T cell-based immune systems have evolved to recognize and destroy aberrant cells, such as pathogen-infected and cancer cells. According to the model for T cell activation proposed by Kevin Lafferty et al. [ 1], T cells require two signals to become fully activated. The first signal is provided by the binding of the T cell receptor on T cells to peptide-major histocompatibility complexes on target cells. The second signal, which is delivered to T cells by antigen-presenting cells to promote T cell clonal expansion, cytokine secretion, and effector functions, is an antigen-independent co-stimulatory signal. The discovery of the B7:CD28 family has revealed co-stimulatory pathways that can provide positive and negative second signals to antigen-experienced effector T cells and regulate the quantity and functional activity of antigen-specific T cells [2]. Programmed cell death protein 1 (PD-1) receptor and its ligands, programmed death-ligand 1 (PD-L1) and PD-L2, are the most notable pathways in the B7:CD28 family. PD-L1 encoding gene CD274 and PD-L2 encoding gene pdcd1lg2 are located adjacent to each other on chromosome 9p24.1, and there is a 23-kb non-coding region in mouse and 42-kb in human between these two genes [3]. The amino acid sequence homology between PD-L1 and PD-L2 is approximately 40 percent [4]. Currently, many immune therapies that target the PD-1 axis include monoclonal antibodies against PD-1 and PD-L1. Despite the considerable improvement in patient outcomes has been achieved with anti-PD-1/PD-L1 targeted therapies, durable responses to these therapies are observed in only few patients and intrinsic therapy resistance is common [5, 6]. Therefore, it is crucial to discover a new therapeutic target and identify the biomarkers for immunotherapy. Compared with PD-L1, PD-L2 has received far less research attention and its role in modulating tumor progression remains unclear. Several studies described a T cell inhibitory function for PD-L2. PD-1:PD-L2 interaction resulted in inhibition of proliferation and cytokines production of T cells [7]. Katharina Pfistershammer and colleagues also revealed that PD-L2 inhibited T cell activation and cytokines production in primary as well as in pre-stimulated T cells [8]. In line with these results, blocking of PD-L2 on dendritic cells (DCs) [9] and endothelial cells [10] enhanced their T cell stimulatory capacity. Additionally, it had been found that PD-L2 was also involved in intracellular signaling pathways to promote tumor cell migration, invasion, and induce drug resistance indicating that PD-L2 expression on tumor cells was also involved in evading antitumor immunity [11, 12]. By contrary, Liu X et al. found that the expression of PD-L2 on murine tumor cells could promote CD8+T cell expansion and enhance CD8+T cell mediated rejection of tumor cells [13]. Similarly, PD-L2 expressed by DCs stimulated T cell proliferation and induced a distinct pattern of lymphokine secretion [14]. Evidences obtained from in vitro and in vivo experiments in PD-L2 conventional knockout mice also demonstrated that PD-L2 played a predominantly tuning molecule role in the generation of both T helper 1 and cytotoxic T lymphocyte responses [15]. Intriguingly, studies on PD-1-deficient mice showed that PD-L2 could still interact with and convey costimulatory effects to PD-1-/- T cells, raising the hypothesis of a second, costimulatory receptor in tumor microenvironment (TME) [13]. Collectively, it is currently unclear what are the roles of PD-L2 in modulating tumor progression. Here, we conducted a pan-cancer analysis for the first time and performed in vitro and in vivo studies to illustrate the prevalence, prognostic and predictive values, and biological functions of PD-L2 in cancers to find out which aspects future studies should focus on. ## Materials and methods The study flow chart is presented in Figure 1. Figure 1: The flow chart of the entire study. PD-L2, programmed cell death 1 ligand 2; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of genes and genomes; PPI, protein-protein interaction; scRNA-seq, single-cell RNA sequencing; TAMs, tumor associated macrophages; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; TIDE, Tumor Immune Dysfunction and Exclusion; TISMO, Tumor Immune Syngeneic MOse; GDSC, Genomics of Drug Sensitivity in Cancer; CTRP, Cancer Therapeutics Response Portal. ## Pdcd1lg2 mRNA expression profile analysis The pdcd1lg2 mRNA expression in various types of tumors was analyzed based on The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Since the data from both the databases are publicly available, the present study was exempted from the approval of local ethics committees. Abbreviations for cancer is presented in Supplementary Table 1. Cancer tissue RNA sequencing data from TCGA pan-cancer data were downloaded from UCSC Xena (http://xenabrower.net/) [16]. Normalized gene expression values were converted to transcripts per million (TPM) and log-transformed (log10((normalized_count1e6)+1)). Normalized RNA sequencing data for all available BOCA-FR, BPLL-FR, BRCA-FR, LICA-FR, LIRI-JP, ORCA-IN, OV-AU, PACA-AU, PACA-CA, PRAD-CA, PRAD-FR, and RECA-EU samples were downloaded from the ICGC data portal (http://dcc.icgc.org/). Normalized* gene expression values were log-transformed (log10(normalized_count+1)). ## PD-L2 protein expression profile analysis PD-L2 protein expression levels in 19 types of tumor tissues were verified using immunohistochemistry (IHC) from the Human Protein Atlas (HPA) database (http://www.proteinatlas.org/) [17]. ## Prognostic analysis The pdcd1lg2 expression profiles from TCGA pan-cancer data were used for prognostic analysis. Data on survival was obtained from the UCSC Xena database. Overall survival (OS) is the period from the date of diagnosis until the date of death from any cause. Progression-free survival (PFS) is the period from the date of diagnosis until the date of the first occurrence of a new tumor event, which includes progression of the disease, locoregional recurrence, distant metastasis, new primary tumor, or death with tumor. Bivariate pdcd1lg2 expression levels with the cut-off chosen by the “surv-cutpoint” function of the “survminer” R package were used to perform Kaplan-Meier curve analysis to assess the prognostic role of pdcd1lg2. Moreover, pdcd1lg2 continuous variable expression data were used in the Cox regression analysis, and we calculated the hazard ratios (HR) and $95\%$ confidence intervals (CI). ## Gene set enrichment analysis (GSEA) After ranking pdcd1lg2 mRNA expressions, the data of each cancer from TCGA pan-cancer database were separated into low- (bottom $30\%$) and high-pdcd1lg2 subgroups (top $30\%$). GSEA was used to determine the potential biological and molecular functions of pdcd1lg2 in each cancer and was carried out using GSEA software [18, 19]. The gene expression datasets of low- and high-pdcd1lg2 subgroups were submitted to GSEA software (v.4.2.3). A two-class analysis with 1000 permutations of phenotype and weighted metric was used. After being downloaded from the Molecular Signatures Database (MSigDB, http://www.gseamsigdb.org/gsea/msigdb), the “gmt” files of the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets (c2.cp.kegg.v7.5.1.symbols.gmt) and Reactome gene sets (c2.cp.reactome.v7.5.1.symbols.gmt) were used to calculate the normalized enrichment score (NES) and false discovery rate (FDR) for each biological process. Ggplot2 R package was applied to visualize the results. ## Spearman’s correlation analysis Spearman’s correlation analysis was performed by using the R function “cor.test” to show the associations between the pdcd1lg2 mRNA and immune-related gene expression which were obtained from TCGA pan-cancer data and $p \leq 0.05$ was considered significant. ## Protein-protein interaction (PPI) analysis STRING is an online database used for retrieving interactions among genes/proteins (http://string-db.org/cgi/input.pl) [20]. We performed PPI analysis using the STRING website with high-throughput experimental data, literature, and predictions based on genomic context analysis. A confidence score above 0.7 was set as the cut-off criterion. ## Immune cell infiltration analysis The ESTIMATE algorithm [21] by using the R package “estimate” was used to assess the correlation between pdcd1lg2 expression and stromal cell infiltration (stromal score) and immune cell infiltration (immune score) in the tumor tissues from the TCGA dataset. Tumor IMmune Estimation Resource (TIMER) is a data resource for analyzing immune cell infiltration across distinct cancers using various algorithms [22]. The pdcd1lg2-associated immune cell infiltration correlations of the TCGA pan-cancer project were downloaded from the TIMER 2.0 database (http://timer.cistrome.org/) in the “Gene” function of the “Immune Association” section. We visualized the statistical Spearman’s correlations between pdcd1lg2 mRNA expression and 21 immune cell subsets, including CD8+T cell, CD4+T cell, regulatory T cell (Treg), B cell, neutrophil, monocyte, macrophage, dendritic cell (DC), natural killer (NK) cell, mast cell, cancer-associated fibroblast (CAF), progenitor of lymphoid cell, progenitor of myeloid cell, progenitor of granulocyte-monocyte, endothelial cell (Endo), eosinophil (Eos), hematopoietic stem cell (HSC), T cell follicular helper (Tfh), γ/δ T cell, NK T cell (NKT), and myeloid-derived suppressor cell (MDSC) across cancers in a heatmap. ## Single-cell RNA sequencing analysis Tumor Immune Single-cell Hub (TISCH, http://tisch.comp-genomics.org/home/), including 79 high-quality single-cell datasets, is used to screen for scRNA-seq datasets with detailed cell-type annotation at the single-cell level focusing on tumor microenvironment across different cancers [23]. Based on MAESTRO v.1.1.0 [24], all the collected datasets are uniformly processed with a standardized workflow, including quality control, batch effect removal, cell clustering, differential expression analysis, cell-type annotation, malignant cell classification and GSEA. Briefly, low quality cells are filtered out if the number of total counts per cell is <1000, or the number of detected genes per cell is <500. The entropy-based metric [25, 26] is employed to quantify the mixing of the data across batches. The datasets with a median entropy lower than 0.7 are corrected the batch effect using Seurat v.3.1.2 [27]. The MAESTRO workflow identifies the top 2000 variable features and employs principal component analysis for dimension reduction, K nearest neighbors, and Louvain algorithm for identifying clusters for each dataset [28, 29] and the uniform manifold approximation and projection is used to reduce the dimension further and visualize the results of clusters [30]. For each cluster, TISCH utilizes the Wilcoxon test to identify differentially expressed genes (DEGs) based on the log-transformed fold change (\|logFC\|>=0.25) and FDR (FDR<10-6) and annotates the cell clusters with a marker-based annotation method employed in MAESTRO based on the DEGs. The marker genes of each cell type are collected from the published resources (31–33). Moreover, TISCH also performs manual corrections to all the annotated cell types by combining them with original annotation and malignant cell identification. There are three sources that TISCH combines to identify the clusters of malignant cells, cell-type annotations provided by the original studies, the expression of malignant cell markers from initial research, and the prediction of InferCNV v.1.2.1 [34] based on the predicted copy number variation. After the streamlined processing, TISCH curates the cell-type annotation of all datasets at three levels: malignancy, major-lineage and minor-lineage. In this study, we enrolled GSE166555 and EMTAB8107 to analyze the pdcd1lg2 expression distribution. ## Multiplex immunofluorescence staining The 5 μm sections of the formalin-fixed, paraffin-embedded colon cancer tissue specimens were obtained from patients hospitalized in the department of colorectal surgery of the National Cancer Center after surgery. Informed consent was obtained from all patients enrolled in this study. The medical ethics committee of the National Cancer Center permitted the use of tissues obtained from clinical excision. The included patients, diagnosed with colon adenocarcinoma using histopathological evaluation, did not have a history of autoimmune disease and neoadjuvant chemotherapy or radiotherapy before surgical resection. Multiplex staining and multispectral imaging to identify the co-expression of PD-L2 and macrophage marker CD68 in tumor microenvironment (TME) was performed as previously described [35]. Briefly, the slides were deparaffinized in xylene and rehydrated in ethanol. Antigen retrieval was carried out in citrate buffer (PH 6.0) using microwave heating. The primary antibodies, PD-L2 (Rabbit, 1:100, CST, Danvers, Massachusetts, US) and CD68 (Rabbit, 1:800, Danvers, Massachusetts, US), were sequentially incubated for 1 h in a humidified chamber at room temperature. Detections using the rabbit SuperPicture Polymer Detection HRP kit (Life Technologies, CA), visualizations of each target using fluorescently labeled Tyramide signal amplification (TSA) (1:50, Life technologies, Grand Island, NY), immersing the slide in citrate buffer (PH 6.0) and heating using microwave heating were performed after each incubation of primary antibody followed by incubation with horseradish peroxidase-conjugated secondary antibody incubation and tyramide signal amplification. Nuclei were stained with 4’-6’-diamidino-2-phenylindole (DAPI) (Sigma-Aldich, Saint Louis, Missouri, US). Multispectral images were analyzed, and positive cells were quantified at a single-cell level using the inForm image analysis software (version 2.4, PerkinElmer, Waltham, Massachusetts, US). ## Cell line, mice and animal model The MC38 mouse colon cancer cell line was obtained from Procell Life Science and Technology Co., Ltd. (Wuhan, China). Cells were grown in RPMI 1640 medium supplemented with $10\%$ fetal bovine serum (FBS), and penicillin-streptomycin mix (all from Gibco/Invitrogen Technologies, Waltham, Massachusetts, US) at 37°C in a humidified incubator with $5\%$ CO2. Around 6-8-week-old (18-22g) C57/B6J male mice were housed in the animal care unit of the National Cancer Center. All animal experimental protocols were approved by the ethics committee of the Chinese Academy of Medical Sciences, National Cancer Center. MC38 cells (5 × 105) in 200 μL were subcutaneously injected into the flank of each mouse. Tumors were measured two times per week by caliper, and tumor volumes were calculated using the modified ellipsoid formula $\frac{1}{2}$ × (length × width2). At 7 d, 14 d, 21 d, and 28 d after cell injection, the mice were sacrificed to collect the tumors, which were examined using flow cytometry or fluorescence-activated cell sorting (FACS). ## Flow cytometry In accordance with the manufacturer’s instructions, the tumors were chopped and then digested using enzymes from the tumor dissociation kit (Miltenyi Biotec, Cologne, Germany) at 37°C for 41 min to obtain single-cell suspensions. Viable cells were counted after filtering the digested samples through 70-μm Falcon cell strainers. Loosely attached cells were collected by washing the strainer with 5 mL phosphate-buffered saline (PBS). The cells were collected by centrifuging the cell suspension for 8 min at 300 ×g. Then, the samples were processed into single-cell suspensions and blocked with TruStain FcX (BioLegend, San Diego, California, US). Tumor associated macrophages (TAMs) were incubated with antibodies: FITC anti-mouse CD45 (clone: I$\frac{3}{2.3}$), BV421 anti-mouse CD11b (clone: M$\frac{1}{70}$), PE/Cy7 anti-mouse F$\frac{4}{80}$ (clone: BM8), APC anti-mouse PD-L2 (clone: TY25), and PE anti-mouse CD206 (clone: C068C2) (all from BioLegend, San Diego, California, US), for 15 min in the dark at 4°C. The cells were then washed twice with 4 mL flow buffer, centrifuged (300 ×g, 5 min), and re-suspended in 500 μL flow buffer for analysis. For intracellular staining, the surface antigens-labeled cells were fixed and permeabilized with $4\%$ paraformaldehyde/$1\%$ Triton X-100, and subsequent staining was performed following specific antibody protocols. Flow cytometry was carried out using a FACSCalibur flow cytometer (BD Biosciences, Franklin Lakes, New Jersey, US). Flow cytometry data analysis was performed using the FlowJo software (FlowJo, US). ## FACS Five tumors per group were isolated and digested as described above. Then, 2 × 107 cells in PBS were stained with LIVE/DEAD Fixable Blue for 30 min at room temperature in the dark. After washing the cells with FACS buffer (sterile PBS with $3\%$ BSA), TrueStain FcX in FACS buffer was used to block cells for 20 min at room temperature. Cells were subsequently incubated with FITC anti-mouse CD45, BV421 anti-mouse CD11b, PE/Cy7 anti-mouse F$\frac{4}{80}$, and APC anti-mouse PD-L2 as mentioned above in the dark at 4°C for 20 min, re-suspended in 500 µL FACS buffer, and sorted using the BD Biosciences FACSAria III. CD45+CD11b+F$\frac{4}{80}$+PD-L2+cells were sorted in RPMI 1640 + $10\%$ FBS. The FACS gating strategy for TAMs is shown in Supplementary Figure 1. ## Bone marrow-derived monocytes (BMDMs) isolation, differentiation, and polarization BMDMs were prepared by isolating bone marrow cells from tibias and femurs of C57/B6J mice. In brief, 6-week-old C57BL/6J mice were sacrificed by cervical dislocation and sterilized with $75\%$ ethanol. The skin at the root of hind legs was incised, and muscle tissue was removed from the bones with scissors. The bones were cut from both ends and flushed with DMEM medium using a 1 mL syringe. Bone marrow cells were cultured in DMEM containing $10\%$ FBS, $1\%$ penicillin-streptomycin mix (all from Gibco/Invitrogen Technologies, Waltham, Massachusetts, US) and 50 ng/mL macrophage colony stimulating factor (M-CSF, R&D systems, Minnesota, US) at 37°C in a $5\%$ CO2 atmosphere for 7 d to obtain BMDMs. As shown in Supplementary Figure 2, to identify the purity of cells by flow cytometry, BMDMs were collected with a scraper, blocked with TruStain FcX (BioLegend, San Diego, California, US), and incubated with FITC anti-mouse CD45 (clone: I$\frac{3}{2.3}$), BV421 anti-mouse CD11b (clone: M$\frac{1}{70}$), and PE/Cy7 anti-mouse F$\frac{4}{80}$ (clone: BM8) for 15 min in the dark at 4°C. For M0, only DMEM-$10\%$ FBS was added. To derive M1 and M2 macrophages, BMDMs were treated with LPS (100 ng/mL, Sigma-Aldrich, Taufkirchen, USA) and recombinant IL-4 (20 ng/ml, PeproTech, Lpndon, UK) for 24 h, respectively. After polarization, the cells were collected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. ## qRT-PCR analysis M0, M1, and M2 macrophages, CD45+CD11b+F$\frac{4}{80}$+PD-L2+cells and CD45+CD11b+F$\frac{4}{80}$+PD-L2-cells were washed twice with PBS and total RNA was extracted using the TRIzol reagent (Invitrogen, Waltham, US). RNA quality and quantity were assessed using a Nanodrop ND-1000 Spectrometer (Thermo Scientific, Waltham, US). A total of 1 μg RNA of each sample was transcribed to complementary DNA (cDNA) using RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Waltham, US) according to the manufacturer’s instructions. qRT-PCR reactions were performed using TB Green Premix Ex TaqTM II (Tli RNaseH Plus) (TaKaRa, Waltham, Japan) on a Quant-Applied Biosystems 7500 Fast Real-Time PCR system (Applied Biosystems, CA, USA). Each PCR reaction consisted of 2 μL of the cDNA as template, 0.8 μL (0.4 μM) of forward and reverse primers, 10.4 μL Mastermix, and 6 μL RNAnase-free water. Thermal cycle conditions consisted of denaturation at 95°C for 30 s, PCR at 95°C for 5 s and 60°C for 34 s. After 40 cycles, the reaction was completed with a final extension step at 95°C for 10 s and 55°C for 40 s. qRT-PCR primers sequences is listed in Supplementary Table 2. Each sample was run in triplicate and the levels of mRNA of a target were normalized to GAPDH. Fold induction was calculated using the 2-△△Ct method. ## Cell migration and invasion assays For migration assays, MC38 colon cancer cell suspensions were evenly mixed with serum-free medium (200 μL, 2 × 104 cells/well) and plated in the upper chamber of a 24-well Transwell plate (8 μm; Corning, New York, US). For invasion assays, 30 μL Matrigel matrix (pre-diluted 1:8 with serum-free medium; BD Biosciences, Franklin Lakes, New Jersey, US) was placed in the upper chamber of 24-Transwell plates (8 μm), and MC38 cells (200 μL, 2 × 104 cells/well) were added after 2 h. Subsequently, TAMs were seeded in the lower chambers. After incubation for 24 h (migration) or 48 h (invasion) at 37°C and $5\%$ CO2, tumor cell migration through the membrane was determined by fixing the cells for 10 min in $70\%$ ethanol and staining with 1 × Giemsa (Beyotime Institute of Biotechnology, Shanghai, China) for 45 min. The inserts were washed with tap water after removing non-migrating cells with cotton swabs from the upper side of the filter and dried overnight. At least five random fields were selected for cell counting under a light microscope (Olympus Corp., Tokyo, Japan) at 200 × magnification. ## Colony formation assay For colony formation detection, MC38 cells (2 mL, 1000 cells/well) were uniformly seeded in the lower chamber of 6-well Transwell plate (0.4 μm; Corning, New York, US). TAMs (1 × 105) were suspended in 2 mL of RPMI 1640 containing $10\%$ FBS and added to the upper chamber. The cells were cultured in a humidified incubator at 37°C with $5\%$ CO2 for 7 d. Tumor cells in the lower chamber were fixed with $70\%$ ethanol for 10 min and stained with 1 × Giemsa for 45 min. The colonies were photographed using a high-resolution camera (Leica, MC 170HD) and counted. ## Immunotherapy prediction analysis Tumor Immune Syngeneic MOuse (TISMO) (http://tismo.cistrome.org), a database for investigating and visualizing gene expression, pathway enrichment, and immune cell infiltration levels in syngeneic mouse models across different immune checkpoint blockade (ICB) treatment and response groups [36], was used to identify the relationship between pdcd1lg2 expression and ICB therapy response in mouse cohorts. Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/) is a web platform can prioritize genes in an input gene set for mechanistic follow-up experiments, evaluate the accuracy of biomarkers on many ICB cohorts in comparison with other published biomarkers, and predict whether a patient responds to ICB therapy based on multiple biomarkers [37, 38]. We used TIDE to verify the prediction performance of pdcd1lg2 expression in human ICB therapy cohorts by applying the receiver operating characteristic (ROC) which measures the true-positive rates against the false-positive rates and the area under the ROC curve (AUC) which is an effective measure of accuracy. An AUC of 0.5 represents the performance of random predictor. ## Drug sensitivity prediction Gene Set Cancer Analysis (GSCALite) (http://bioinfo.life.hust.edu.cn/web/GSCALite/) is an online algorithm that integrates genomic and immunogenomic data of 33 cancer types from TCGA, drug responses from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Therapeutics Response Portal (CTRP), and normal tissue data from GTEx [39]. We used the GSCALite server to determine the correlation between pdcd1lg2 mRNA expression and drug sensitivity [$50\%$ inhibitory concentration (IC50)] using the Pearson’s correlation analysis in the GDSC and CTRP databases. ## Statistical analysis All experiments were performed at least in triplicates. Data are presented as the mean ± standard deviation (SD). Statistical significance was determined using a two-tailed Student’s t-test for comparisons between two groups. All statistical analyses were performed using the SPSS software (version 17.0; SPSS Inc., US). Statistical significance was set at $p \leq 0.05.$ ## Multiple human cancers expressed pdcd1lg2 mRNA and PD-L2 protein The mRNA expression of pdcd1lg2 was evaluated in pan-cancer patients of different cohorts according to the RNA-seq data of TCGA, the comprehensive program in cancer genomics that is jointly supported and managed by the National Cancer Institute and the National Human Genome Research Institute of the US National Institutes of Health, and ICGC database which is launched to coordinate large-scale cancer genome studies in tumors across the globe. As shown in Figure 2 and Supplementary Figure 3A, all 33 types of common human cancer types in TCGA database and 12 types of human cancer queues in ICGC database expressed pdcd1lg2, though it exhibited inconsistent mRNA expression. Similarly, by analyzing the IHC images from HPA datasets to assess PD-L2 expression at the protein level in cancer tissues, we found that PD-L2 expression was unbalanced in cancers (Supplementary Figure 3B and Supplementary Table 2). It should be noted that PD-L2 could be expressed in both tumor and stromal cells. Figure 2: Pdcd1lg2 expression in The Cancer Genome Atlas (TCGA) database. ## Pdcd1lg2 expression deteriorated outcomes of patients in multiple cancer types We investigated the prognostic potential of PD-L2 by estimating the association between pdcd1lg2 expression and survival of patients. According to Liu et al. [ 40], which provided recommendations of clinical outcome endpoint usage for 33 cancer types by analyzing the clinicopathologic annotations for over 11000 cancer patients in TCGA, OS and PFS could be derived relatively accurately and all four endpoints (OS, PFS, disease-free survival, and disease-specific survival) could be used in 13 of the 33 cancer types. Considering that OS has been historically considered as the “gold standard” and is the most objective endpoint used in clinical trials, OS would be used as the endpoint if it was applicable. As a result, OS was an appropriate endpoint for ACC, BLCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LAML, LIHC, LUAD, LUSC, MESO, OV, PAAD, SARC, SKCM, STAD, UCEC, UCS, and UVM. PFS was an appropriate endpoint for BRCA, LGG, PRAD, READ, TGCT, THCA, and THYM. In contrast, none of the four outcome endpoints could be recommended for use in the DLBC, KICH, and PCPG cases. As shown in Figures 3A, C and Supplementary Figure 4, Kaplan-Meier survival curves indicated that high pdcd1lg2 expression was significantly associated with the deteriorated outcomes in 10 cancer types by cutting the expression into dichotomous variables, including BLCA, COAD, KIRP, LAML, LGG, MESO, PAAD, THCA, THYM, and UVM. The results shown in forest plot (Figures 3B, C) demonstrated that pdcd1lg2 expression upregulation was closely positively associated with poor prognosis in KIRP (HR=2.168 [$95\%$CI, 1.218-3.858], $$p \leq 0.009$$), LGG (HR=2.231 [$95\%$CI, 1.827-2.725], $p \leq 0.001$), and THYM (HR=1.317 [$95\%$CI, 0.940-1.845], $$p \leq 0.010$$) by taking the expression of pdcd1lg2 as a continuous variable in Cox regression analysis. Figure 3: Prognostic values of pdcd1lg2. (A) The prognostic value of pdcd1lg2 on overall survival (OS) or progression-free survival (PFS) displayed by the Kaplan-Meier method. (B) Cox regression analysis of pdcd1lg2 on OS or PFS in pan-cancer described by the forest plot. (C) Based on the Kaplan-Meier models and Cox regression, summary of the correlation between pdcd1lg2 expression and OS or PFS. ## Pdcd1lg2 played an important role in cancer immune response To determine the biological processes associated with pdcd1lg2 expression in cancers, we performed GSEA across 33 types of common human cancers in TCGA database. GSEA results of KEGG terms revealed that pdcd1lg2 was involved in various pathways, including antigen processing and presentation, apoptosis, autoimmune thyroid disease, cell anhension molecules cams, chemokine signaling pathway, cytokine-cytokine receptor interaction, cytosolic DNA sensing pathway, FC gamma R mediated phagocytosis, JAK-STAT signaling pathway, leukocyte transendothelial migration, natural killer cell mediated cytotoxicity, NOD-like receptor signaling pathway, systemic lupus enythematosus, T cell receptor signaling pathway, TOLL-like receptor signaling pathway, and viral myocarditis signaling pathway (Figure 4). Figure 4: Signaling pathways associated with pdcd1lg2 expression according to Kyoto Encyclopedia of genes and genomes (KEGG) analyzed by the Gene Set Enrichment Analysis (GSEA) in pan-cancer. FDR, false discovery rate; NES, normalized enrichment score. Furthermore, the GSEA results for Reactome analysis also indicated that several immune functional gene sets were enriched in cancers, such as TCR signaling, TNFR2 non-canonical NF-κB pathway, Toll-like receptor cascades, signaling by interleukins, signaling by the B cell receptor BCR, FCERI mediated NF-κB activation, FCgamma receptor (FCGR) dependent phagocytosis, interleukin-12 family signaling, neutrophil degranulation, parasite infection, costimulation by the CD28 family, downstream signaling events of B cell receptor (BCR), activation of IRF3/IRF7 mediated by TBK1/IKK epsilon, and antigen processing cross presentation (Supplementary Figure 5). To confirm our suspicions, we further performed Spearman’s correlation analysis and constructed a PPI network. As shown in Figure 5A, the expressions of nearly all kinds of immune-related genes were significantly related to the expression of pdcd1lg2 in pan-cancer. For example, the expressions of TIGIT, IL-10, IDO1, HAVCR2, CTLA4, CSF1R, CD96, CD274, and CD244 which are immunosuppressive genes, were positively correlated with pdcd1lg2 expression. Some immune activation genes including ULBP1, TNFRSF25, TNFRSF14, TNFRSF13C, RAET1E, PVR, ICOSLG, HHLA2, and CD276 were negatively correlated with pdcd1lg2 expression in some cancers. Moreover, the stimulation of directed migration of immune cells is the most prominent role of the large family of chemokines and their receptors. The results revealed that pdcd1lg2 expression was positively related with the expressions of CXCL13, CXCL11, CXCL10, CCL8, CCL5, CCL4, and CCL3 which are chemokines, and CXCR6, CCR5, CCR4, CCR2 and CCR1 which are chemokine receptor molecules, and was negatively associated with the expressions of other chemokines such as CXCL5, CXCL17, CXCL1, CX3CL1, CCL28, CCL15 and CCL14, and other chemokine receptor such as CXCR4, CXCR2, CXCR1, CX3CR1, CCR10, and CCR9 in some types of cancer. In addition, based on the information from the STRING database, PPI network revealed that PD-L2 was closely associated with CD3G, CD3E, CD3D, LCK, CD247, HLA-DRB1, HLA-DRB5, HLA-DQB2, HLA-DQB1, HLA-DPB1, HLA-DQA1, HAL-DQA2, HLA-DRA, HLA-DPA1, CD86, CD80, CD8A, CD28, ITGAX, TAPBP, HAVCR2, FOXP3, CD274, PDCD1, PTPN11, CD4, CTLA4, HAVCR2, IL-10, CD40, LAG3, IDO1, NEO1, RGMB, and VTCN1(Figure 5B). Figure 5: Relationship between pdcd1lg2/PD-L2 expression and immune-related genesin pan-cancer. (A) The Spearman correlation heatmap between pdcd1lg2 expression levels and immune-related genes. (B) The protein-protein interaction (PPI) network presents the proteins interacting with PD-L2. p<0.05, p<0.01, p<0.001. ## PD-L2 mainly expressed in TAMs in colon cancer To identify the immune aspects of PD-L2 in TME in pan-cancer, we calculated the correlation between pdcd1lg2 levels and the immune scores (represent the infiltration of immune cells in tumor tissue) and stromal scores (capture the presence of stroma in tumor tissue) in 33 types of cancer based on the ESTIMATE algorithm. The results showed that pdcd1lg2 expression was significantly and positively correlated with immune contexture and stromal contexture in almost all cancer types (Supplementary Figure 6). Then, we performed a pan-cancer analysis of relationships between pdcd1lg2 expression and immune cell infiltration levels using TIMER 2.0. As shown in Figure 6, in general, pdcd1lg2 expression was moderate positively and significantly associated with the amount of multiple infiltrating immune cells in various cancers, including CD8+T cell, DC, monocyte, Treg, CAF, and endothelial cell and weak positively and significantly related with the abundance of neutrophil, and γ/δT cell. However, the trends of correlations between pdcd1lg2 expression and the abundance of CD4+T cell, B cell, NK cell, Tfh, and mast cell were different according to different algorithms. Additionally, a strong negative correlation was observed between the expression of pdcd1lg2 and the infiltration of MDSC in all almost kinds of cancers. Notably, the abundance of macrophages was the most positively associated with pdcd1lg2 expression in multiple types of cancer, especially in COAD (Rho=0.917 for macrophage EPIC; Rho=0.496 for macrophage TIMER; Rho=0.818 for macrophage XCELL; Rho=0.913 for macrophage/monocyte MCP-COUNTER). Figure 6: The correlation between the expression of pdcd1lg2 and various immune cells infiltration levels in cancers. DC, dentritic cell; NK cell, natural killer cell; Treg, T cell regulatory; CAF, cancer-associated fibroblast; Endo, endothelial cell; Eos, eosinophil; γ/δ T cell, T cell gamma delta; Tfh, T cell follicular helper; NKT, nature killer T cell; HSC, hematopoietic stem cell; MDSC, myeloid-derived suppressor cell. To further confirm the localization of pdcd1lg2 expression in TAMs, we included and analyzed two scRNA-seq datasets, EMTAB8107 and GSE166555. As shown in Figure 7A, pdcd1lg2 mainly located or bound to monocyte/macrophage in colorectal cancer. Figure 7: The phenotype and functions of PD-L2+ tumor associated macrophages (TAMs). (A) The localization of pdcd1lg2 expression analyzing in two scRNA-seq datasets. (B) The multiplex immunofluorescence images of PD-L2+TAMs in human colon cancer tissues. (C) Representative flow cytometry plots and analysis of the expression of PD-L2 on TAMs in MC38 tumors at different days after engraftment. (D) Flow cytometric analysis of the expression of CD206 on PD-L2+TAMs and PD-L2-TAMs. (E) M1 macrophage and M2 macrophage marker gene expressions in macrophages. (F) Schematic overview of the strategy for identification the functions of PD-L2+TAMs. (G) Tranwell assays and colony formation to detect the role of PD-L2+TAMs in the migration, invasion, and proliferation of MC38 cells. p<0.05, p<0.01, p<0.001. Additionally, we verified the results by analyzing the human and murine specimens from the National Cancer Center. Multiplex immunofluorescence in colon tissue revealed a clear and abundant population of cells that expressed both PD-L2 and CD68, which has been widely recommended as a pan-macrophage marker in human, confirming PD-L2 expression on TAMs (Figure 7B). Furthermore, to assess PD-L2+TAMs in mice in an immunocompetent syngeneic setting, we used the colon cancer mouse cell line, MC38. Flow cytometry analysis of dissociated tumors showed that the PD-L2+TAMs population was not static (Figure 7C and Supplementary Figure 7A); it began to emerge approximately one week after engraftment (117.23 ± 23.26 mm3) and increased subsequently. Two weeks after engraftment into mice (298.03 ± 92.66 mm3), the highest percentage of macrophages in the tumor expressed surface PD-L2, which subsequently decreased. Therefore, PD-L2 expression was correlated with time after engraftment in mice and the tumor size. FACS was used to obtain TAMs from dissociated MC38 tumors 2 weeks after engraftment (Supplementary Figure 7B) for phenotype and function analysis. ## PD-L2+TAMs exhibited protumor phenotype and function In the immune contexture from tumor grafts, we used CD206, a well-established marker of M2 macrophages, to distinguish M2-like protumor TAMs. As shown in Figure 7D, flow cytometry showed that CD206+ subpopulations were more abundant in PD-L2+TAMs than in PD-L2-TAMs, which indicated that PD-L2 was mainly expressed in TAMs with protumor phenotype. Moreover, myeloid-derived macrophages are important innate immune cells that can be induced to differentiate into TAMs. To further demonstrate our hypothesis, we also carried out gene expression analysis of selected marker genes, consisting of M1 macrophage markers (iNOS, IL-6, IL-1β, and TNF-α) and M2 macrophage markers (Arg-1, IL-10, TGF-β, and Ym1), by employing BMDMs-derived macrophage phenotype as control. The results revealed that PD-L2+TAMs showed extremely low M1 polarization-related genes expression compared to LPS-driven M1 macrophages and similar M2 marker genes expression as IL-4-driven M2 macrophages (Figure 7E). Additionally, the relative mRNA expression levels of M1 phenotypic markers (IL-1β and TNF-α) in PD-L2+TAMs were downregulated by more than 5-fold as observed in PD-L2-TAMs, whereas the expression of M2-type markers (Arg-1, IL-10, and TGF-β) in PD-L2+TAMs were upregulated by more than 2-fold as observed in PD-L2-TAMs. Next, we explored the role of PD-L2+TAMs in tumor development. In the study, the impact of PD-L2+TAMs on colon cancer cell migration, invasion, and proliferation was first analyzed (Figure 7F). In migration assays, the migration ability of MC38 cells was greater after incubation with PD-L2+TAMs than with PD-L2-TAMs [Figure 7G (left)]. Similar results were observed in invasion assays [Figure 7G (middle)]. To further verify whether PD-L2+TAMs directly induced the growth of colon tumor cells, we performed colony formation assays by co-culturing MC38 cells with PD-L2+TAMs/PD-L2-TAMs. As shown in Figure 7G (right), PD-L2+TAMs remarkably increased the number of new MC38 cell colonies compared with PD-L2-TAMs. ## Pdcd1lg2 could predict the response to cancer therapy and a series of targeted small-molecule drugs were identified As shown in Figure 8A, pdcd1lg2 could significantly predict the ICB therapy response in 21 murine immunotherapy cohorts, wherein which the responders had elevated pdcd1lg2 expression levels in 19 cohorts. Although pdcd1lg2 expression was higher in non-responders in two cohorts, these two ICB cohorts did not have responders. We also verified the predictive value of pdcd1lg2 in 25 human ICB therapy cohorts by comparing its predictive power with that of other standardized biomarkers. The results revealed that pdcd1lg2 alone had an AUC of more than 0.5 in 15 ICIs therapy cohorts (Figure 8B), suggesting it to be a robust predictive biomarker, while microsatellite instability (MSI) score, tumor mutation burden (TMB), T. Clonality, and B. Clonality gave AUC values above 0.5 in 13, 8, 9, and 7 ICIs therapy cohorts, respectively. However, the predictive significance of pdcd1lg2 was lower than that of CD274, CD8, and IFNG, which had AUC above 0.5 in 21, 18, and 17 ICIs therapy cohorts, respectively. Figure 8: Immunotherapy response prediction, biomarker relevance and sensitive drug prediction of PD-L2. (A) Immunotherapy response of pdcd1lg2 in murine immune checkpoint blockade (ICB) therapy cohorts analyzed by TISMO database. (B) Biomarker relevance of pdcd1lg2 compared to standardized biomarkers with consistent evidence on cancer immune evasion in ICB therapy cohorts. The area under the receiver operating characteristic curve (AUC) is applied to evaluate the prediction performance of the biomarker on ICIs response status. (C) Predictive drugs based on the pdcd1lg2 expression from the GDSC (left) and CTRP (right) databases. Finally, we predicted drug sensitivity based on pdcd1lg2 expression according to data from the GDSC and CTRP datasets (Figure 8C). Based on the GDSC dataset, higher expression levels of pdcd1lg2 were associated with increased sensitivity to BMS-754807 (insulin-like growth factor 1 receptor inhibitor), FR-180204 (extracellular signal-regulated kinase inhibitor), SB52334 (transforming growth factor-β receptor I inhibitor), and VX-11e (extracellular signal-regulated kinase inhibitor). The correlation between pdcd1lg2 levels and drug sensitivity based on the CTRP dataset showed that austocystin D, ML029 (inhibitor of nuclear factor kappa B activation), SCH-79797 (proteinase-activated receptor 1 receptor antagonist), and linsitinib (inhibitor of both type 1 insulin-like growth factor receptor and the insulin receptor) were the top four drugs positively correlated with pdcd1lg2 expression. ## Discussion Over the past few decades, research in cancer therapies focused on exploration of mechanisms of protective tumor immunity, which has provided several therapeutic strategies. Among these, immune checkpoint inhibitors can reverse the negative regulators of T cell function, which revolutionized cancer treatment and became the most dazzling star [5]. Therapeutic antibodies for blocking PD-1 and PD-L1 have been developed and had early success in the clinic. However, since the clinical efficacy of current therapy strategies is limited and clinicians still have very limited tools to distinguish patients who will and will not respond to therapy, identification of new targets and predictive biomarkers are crucial to further improve patients’ survival. Compared to PD-1 and PD-L1, PD-L2 has not received much attention and its role in modulating tumor progression is still being investigated. Our study not only uncovered the expression profile, prognostic value, and predictive potential of PD-L2 in a pan-cancer dataset for the first time, but also identified PD-L2+TAMs as immune effector cells with protumor function in vivo and in vitro. By evaluating the association between pdcd1lg2 and OS or PFS, we found that high pdcd1lg2 expression was closely related to the deteriorated outcomes in BLCA, COAD, KIRP, LAML, LGG, MESO, PAAD, THCA, THYM, and UVM. These results are consistent with previous studies in bladder cancer [41], acute myeloid leukemia [42], and pancreatic ductal adenocarcinoma [43, 44]. However, some studies revealed different conclusions. Qiao et al. found that high expression of PD-L2 was an independent predictor of poor OS in patients with HNSC. [ 45] and Takamori et al. revealed that PD-L2-positive lung adenocarcinoma patients had a significantly shorter OS [46]. The discrepancies among the results may be due to the varying clinical features of the samples analyzed, such as the site and size of cancer, treatments, or different ethnic populations. Besides, PD-L2 is a dynamic marker that can be up- or down-regulated temporarily, verified by our in vivo experiments. Moreover, PD-L2 protein is expressed to varying degrees in stromal, endothelial, and tumor cells. Ariafar et al. [ 47] found that PD-L2 expression on immune cells, especially in draining lymph nodes was valuable for predicting prognosis and survival, while PD-L2 expression on tumor cells was not associated with prognosis. Therefore, it is necessary to explore the function and prognostic value of PD-L2 at a cellular level. TME, which is a complex structure composed of tumor cells, nonmalignant cells, blood vessels, extracellular matrix, and other substances, plays a crucial role in stimulating cancer cells and increasing multidrug resistance, which result in cancer progression and metastasis [48, 49]. Our results revealed an interesting phenomenon that pdcd1lg2 expression was positively correlated with some immunosuppressive and immunostimulatory genes in the same group of patients, which further reflects the complexity of TME. In the TME, the PD-L1 and PD-L2 exhibit distinct patterns of expression. Apart from cancer cells, TAMs, DCs, activated T cells, activated B cells, and CAFs also express PD-L1. In contrast, PD-L2 expression is restricted [3, 50]. In this study, the IHC information from the HPA dataset showed that PD-L2 was expressed in both tumor and stromal cells. There were three pieces of evidence that led us to focus on the immune cells expressed PD-L2. First, the results of GSEA demonstrated that pdcd1lg2 played an important role in cancer immune response. Second, the expression of pdcd1lg2 was related to that of most immune-related genes based on Spearman’s correlation and PPI analysis. Last, pdcd1lg2 expression was significantly positively correlated to the immune score and stromal score in almost all kinds of cancers when analyzed using the ESTIMATE algorithm. In 2002, Dunn et al. proposed the concept of cancer immunoediting as a result of three processes: elimination, equilibrium, and escape, which depended on different immune cells in TME [51]. We performed a pan-cancer analysis using TIMER 2.0 and found that pdcd1lg2 expression was significantly positively associated with multiple infiltrating immune cells in various cancers, especially macrophages in COAD. Macrophages in TME, referred to as TAMs, are a major TME component and the main regulator in response to various microenvironmental signals generated from tumor and stromal cells [52]. An increasing number of studies have shown that the presence of TAMs correlates with tumor progression, poor clinical outcome, and the efficacy of therapeutics in various types of cancers, including colorectal cancer (CRC) (53–55). Since decades, engineering TAMs for cancer immunotherapy and drug delivery has been encouraging clinical applications [56]. Given the crucial roles of TAMs, we validated whether TAMs expressed PD-L2 in the colon cancer microenvironment, and if so, the consequences that PD-L2 expression may have on tumor progression. Using multiplex immunofluorescence and flow cytometry, we found that PD-L2 was expressed in TAMs of both human clinical samples and mice syngeneic tumor models. Further analysis revealed that the PD-L2+ TAMs population was not static and changed over time. Moreover, the in vitro experimental evidence demonstrated the protumor functions of PD-L2+TAMs in colon cancer. CRC exhibits an immunosuppressive TME and the benefits of current immunotherapies in CRC are limited to a few groups of patients with microsatellite instability-high tumors [57, 58]. New therapeutic approaches that do not only benefit a selected group of CRC patients are highly crucial. In additional to the direct promotion of tumor cell growth, migration, and invasion of PD-L2+TAMs identified in our studies, the previous studies also revealed that PD-L1 and PD-L2 had similar protein structures and the affinity of PD-L2 to PD-1 was two to six-fold higher than that of PD-L1. This is because PD-L1 binding to PD-1 requires complex conformational changes in the ligand, whereas PD-L2 directly binds to PD-1 [59, 60], which demonstrates that PD-L2 would outcompete PD-L1 in binding to PD-1 and could be a means by which cancer cells evade the immune system. Therefore, although more evidences of the therapeutic effect of PD-L2 in TAMs are required, our study suggested that PD-L2 signaling in TAMs showed potential as a novel therapeutic target. Apart from TAMs, Treg cells, known as the immunosuppressive class of CD4+T cells suppress anti-cancer immunity [61], and CAFs, which promote tumor growth, angiogenesis, invasion and metastasis and remodel extracellular matrix [62], were also positively associated with pdcd1lg2 expression in this study. Moreover, Tanegashima et al. [ 63] showed that PD-L2 expression in tumor cells also played an important role in evading antitumor immunity. These results can be the primary domain for future studies. However, our results also revealed that pdcd1lg2 expression was moderate positively correlated with CD8+T cells, which are killer cells in the TME, and negatively correlated with MDSCs, which exert immunosuppressive effects by suppressing T cell activity [64]. This finding may partially explain the protective role of PD-L2 in some tumor types such as SKCM. A highly positive correlation between CD274 (PD-L1-encoding gene) and pdcd1lg2 expression in almost all cancers was observed in our results, which might suggest that targeting PD-L1 will not show apparent benefits since PD-L2 is still functional and plays a redundant role. In these cases, combinational therapies against PD-L1 and PD-L2 may further optimize the efficacy. In other words, PD-L2 blockade is necessary for controlling PD-L2-expressing tumors [45, 65]. We also explored several targeted small-molecule drugs with promising therapeutic effects, providing a theoretical basis to develop drugs targeting PD-L2. In addition to studying new immune checkpoints, the prediction of the cancer immunotherapy effect is another requirement for clinical application. Therefore, we also verified the promising predictive value of PD-L2 in murine and human immunotherapy cohorts and found that responders had elevated pdcd1lg2 expression levels in 19 murine immunotherapy cohorts. In 25 human ICB therapy cohorts, pdcd1lg2 exhibited a higher predictive value than MSI score, TMB, T. Clonality, and B. Clonality. Similarly, other studies also showed that expression of PD-L2 had a predictive value for response to pembrolizumab [66, 67], and MPDL3280A treatment [68]. Notably, a predictive value is primarily observed for PD-L2 expression on both immune and tumor cells, thus further studies should also focus on the predictive capacity of PD-L2 expression on immune infiltrating cells, or tumor cells alone. Interestingly, some studies revealed that PD-L2 expression was also correlated with the efficacy of Bacillus-Calmette Guerin vesicle in bladder cancer patients [69] and rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone chemotherapy in DLBC patients [70]. By exploring and incorporating information from several databases and validating them in experiments, there are two implications can be directly applied to further studies. First, the protumor functions of TAMs are executed by expressing cytokines, chemokines, enzymes, and cell surface receptors to activate Treg cells or suppress other effector cells [54, 71]. We have verified that PD-L2+ TAMs promote migration, invasion, and proliferation of cancer cells in colon cancer, and further in-depth mechanistic analysis in vitro or in vivo is required to validate our results. Second, this study has confirmed the potential ability of PD-L2 in predicting ICIs therapy response. Further investigations need to be carried out clinically and mechanistically in individual cancer types. In addition, surface plasmon resonance analysis revealed that the affinity of PD-L2 for PD-1 was 2-fold to 6-fold higher than PD-L1 [59, 60]. Lázár-Molnár E and colleagues even showed that PD-L2 had a 30-fold higher affinity for PD-1 than PD-L1 [72]. This high-affinity binding can be the attractive target for the drug development with small compounds [73]. Moreover, there are three isoforms of PD-L2 identified and both isoforms II and III can be interact with PD-1, while type I form supposedly loses the capacity to bind PD-1 [74, 75]. Xiao Y et al. [ 76] found repulsive guidance molecule b, a co-receptor for bone morphogenetic protein, could also interact with PD-L2 and this interaction inhibited the invasion and metastasis of bladder and breast cancer [77, 78]. The function of PD-L2 is complex, and there is still a long way to go to study it. In conclusion, we comprehensively assessed the expression profiles, prognostic and predictive value and functions of PD-L2 in pan-cancer. We also investigated the phenotype and protumor functions of PD-L2+TAMs. Therapies targeting PD-L2 in the TME, especially TAMs, are promising for improving and prolonging the survival of cancer patients. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by the medical ethics committee of the National Cancer Center. Informed consent was obtained from all patients enrolled in this study. The animal study was reviewed and approved by the ethics committee of the Chinese Academy of Medical Sciences, National Cancer Center. ## Author contributions Writing-Original Draft, Methodology, and Visualization: JFL. Validation and Conceptualization: JFL, ZJ and JHY. Investigation: JFL, MZ and JHY. Methodology: HCL, XG, YFY and YMM. Project Administration: ZL, ZJ and HYW. Supervision, Project Administration and Funding Acquisition: XSW. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1093716/full#supplementary-material ## References 1. Lafferty KJ, Cunningham AJ. A new analysis of allogeneic interactions. Aust J Exp Biol Med Sci (1975) 53 27-42. DOI: 10.1038/icb.1975.3 2. McAdam AJ, Schweitzer AN, Sharpe AH. The role of B7 co-stimulation in activation and differentiation of CD4+ and CD8+ T cells. Immunol Rev (1998) 165. DOI: 10.1111/j.1600-065x.1998.tb01242.x 3. Keir ME, Butte MJ, Freeman GJ, Sharpe AH. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol (2008) 26 677-704. DOI: 10.1146/annurev.immunol.26.021607.090331 4. Okazaki T, Honjo T. PD-1 and PD-1 ligands: From discovery to clinical application. Int Immunol (2007) 19. DOI: 10.1093/intimm/dxm057 5. Bagchi S, Yuan R, Engleman EG. Immune checkpoint inhibitors for the treatment of cancer: Clinical impact and mechanisms of response and resistance. Annu Rev Pathol (2021) 16. DOI: 10.1146/annurev-pathol-042020-042741 6. Xu-Monette ZY, Zhang M, Li J, Young KH. PD-1/PD-L1 blockade: Have we found the key to unleash the antitumor immune response. Front Immunol (2017) 8. DOI: 10.3389/fimmu.2017.01597 7. Carter L, Fouser LA, Jussif J, Fitz L, Deng B, Wood CR. PD-1:PD-L inhibitory pathway affects both CD4(+) and CD8(+) T cells and is overcome by IL-2. Eur J Immunol (2002) 32. DOI: 10.1002/1521-4141(200203)32:3<634::AID-IMMU634>3.0.CO;2-9 8. Pfistershammer K, Klauser C, Pickl WF, Stöckl J, Leitner J, Zlabinger G. No evidence for dualism in function and receptors: PD-L2/B7-DC is an inhibitory regulator of human T cell activation. Eur J Immunol (2006) 36. DOI: 10.1002/eji.200535344 9. Brown JA, Dorfman DM, Ma FR, Sullivan EL, Munoz O, Wood CR. Blockade of programmed death-1 ligands on dendritic cells enhances T cell activation and cytokine production. J Immunol (2003) 170. DOI: 10.4049/jimmunol.170.3.1257 10. Rodig N, Ryan T, Allen JA, Pang H, Grabie N, Chernova T. Endothelial expression of PD-L1 and PD-L2 down-regulates CD8+ T cell activation and cytolysis. Eur J Immunol (2003) 33. DOI: 10.1002/eji.200324270 11. Ren T, Zheng B, Huang Y, Wang S, Bao X, Liu K. Osteosarcoma cell intrinsic PD-L2 signals promote invasion and metastasis. Cell Death Dis (2019) 10 261. DOI: 10.1038/s41419-019-1497-1 12. Sudo S, Kajiya H, Okano S, Sasaki M, Katsumata Y, Ohno J. Cisplatin-induced programmed cell death ligand-2 expression is associated with metastasis ability in oral squamous cell carcinoma. Cancer Sci (2020) 111. DOI: 10.1111/cas.14336 13. Liu X, Gao JX, Wen J, Yin L, Li O, Zuo T. B7DC/PDL2 promotes tumor immunity by a PD-1-independent mechanism. J Exp Med (2003) 197. DOI: 10.1084/jem.20022089 14. Tseng SY, Otsuji M, Gorski K, Huang X, Slansky JE, Pai SI. B7-DC, a new dendritic cell molecule with potent costimulatory properties for T cells. J Exp Med (2001) 193. DOI: 10.1084/jem.193.7.839 15. Shin T, Yoshimura K, Shin T, Crafton EB, Tsuchiya H, Housseau F. J Exp Med (2005) 201. DOI: 10.1084/jem.20050072 16. Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A. Visualizing and interpreting cancer genomics data. Nat Biotechnol (2020) 38. DOI: 10.1038/s41587-020-0546-8 17. Pontén F, Jirström K, Uhlen M. The human protein atlas–a tool for pathology. J Pathol (2008) 216. DOI: 10.1002/path.2440 18. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. (2005) 102. DOI: 10.1073/pnas.0506580102 19. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet (2003) 34. DOI: 10.1038/ng1180 20. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res (2011) 39. DOI: 10.1093/nar/gkq973 21. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun (2013) 4 2612. DOI: 10.1038/ncomms3612 22. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res (2020) 48. DOI: 10.1093/nar/gkaa407 23. Sun D, Wang J, Han Y, Dong X, Ge J, Zheng R. TISCH: A comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res (2021) 49. DOI: 10.1093/nar/gkaa1020 24. Wang C, Sun D, Huang X, Wan C, Li Z, Han Y. Integrative analyses of single-cell transcriptome and regulome using MAESTRO. Genome Biol (2020) 21 198. DOI: 10.1186/s13059-020-02116-x 25. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. (2018) 174 1293-1308.e36. DOI: 10.1016/j.cell.2018.05.060 26. Zhang L, Li Z, Skrzypczynska KM, Fang Q, Zhang W, O'Brien SA. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell. (2020) 181 442-59.e29. DOI: 10.1016/j.cell.2020.03.048 27. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol (2018) 36. DOI: 10.1038/nbt.4096 28. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM. Comprehensive integration of single-cell data. Cell. (2019) 177 1888-1902.e21. DOI: 10.1016/j.cell.2019.05.031 29. Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics. (2015) 31. DOI: 10.1093/bioinformatics/btv088 30. Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG. Dimensionality reduction for visualizing single-cell data using UMAP [published online ahead of print, 2018 Dec 3]. Nat Biotechnol (2018). DOI: 10.1038/nbt.4314 31. Zhang L, Yu X, Zheng L, Zhang Y, Li Y, Fang Q. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. (2018) 564. DOI: 10.1038/s41586-018-0694-x 32. Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med (2019) 25. DOI: 10.1038/s41591-019-0522-3 33. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods (2015) 12. DOI: 10.1038/nmeth.3337 34. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. (2014) 344. DOI: 10.1126/science.1254257 35. Stack EC, Wang C, Roman KA, Hoyt CC. Multiplexed immunohistochemistry, imaging, and quantitation: A review, with an assessment of tyramide signal amplification, multispectral imaging and multiplex analysis. Methods. (2014) 70 46-58. DOI: 10.1016/j.ymeth.2014.08.016 36. Zeng Z, Wong CJ, Yang L, Ouardaoui N, Li D, Zhang W. TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucleic Acids Res (2022) 50. DOI: 10.1093/nar/gkab804 37. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med (2018) 24. DOI: 10.1038/s41591-018-0136-1 38. Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang P. Large-Scale public data reuse to model immunotherapy response and resistance. Genome Med (2020) 12. DOI: 10.1186/s13073-020-0721-z 39. Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: A web server for gene set cancer analysis. Bioinformatics. (2018) 34. DOI: 10.1093/bioinformatics/bty411 40. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. (2018) 173 400-416.e11. DOI: 10.1016/j.cell.2018.02.052 41. Yang Y, Wang X, Bai Y, Feng D, Li A, Tang Y. Programmed death-ligand 2 (PD-L2) expression in bladder cancer. Urol Oncol (2020) 38 603.e9-603.e15. DOI: 10.1016/j.urolonc.2020.01.001 42. Chen C, Liang C, Wang S, Chio CL, Zhang Y, Zeng C. Expression patterns of immune checkpoints in acute myeloid leukemia. J Hematol Oncol (2020) 13 28. DOI: 10.1186/s13045-020-00853-x 43. Zhang Y, Xu J, Hua J, Liu J, Liang C, Meng Q. A PD-L2-based immune marker signature helps to predict survival in resected pancreatic ductal adenocarcinoma. J Immunother Cancer. (2019) 7 233. DOI: 10.1186/s40425-019-0703-0 44. Davidson C, Taggart D, Sims AH, Lonergan DW, Canel M, Serrels A. FAK promotes stromal PD-L2 expression associated with poor survival in pancreatic cancer [published online ahead of print, 2022 sep 22]. Br J Cancer (2022) 127. DOI: 10.1038/s41416-022-01966-5 45. Qiao Y, Liu C, Zhang X, Zhou Q, Li Y, Xu Y. PD-L2 based immune signature confers poor prognosis in HNSCC. Oncoimmunology. (2021) 10. DOI: 10.1080/2162402X.2021.1947569 46. Takamori S, Takada K, Azuma K, Jogo T, Shimokawa M, Toyokawa G. Prognostic impact of programmed death-ligand 2 expression in primary lung adenocarcinoma patients. Ann Surg Oncol (2019) 26. DOI: 10.1245/s10434-019-07231-z 47. Ariafar A, Ghaedi M, Rezaeifard S, Shahriari S, Zeighami S, Ghaderi A. Clinical relevance and prognostic significance of PD-1/PD-Ls in non-metastatic bladder cancer: A role for PD-L2. Mol Immunol (2020) 124 35-41. DOI: 10.1016/j.molimm.2020.05.010 48. Petitprez F, Meylan M, de Reynies A, Sautes-Fridman C, Fridman WH. The tumor microenvironment in the response to immune checkpoint blockade therapies. Front Immunol (2020) 11. DOI: 10.3389/fimmu.2020.00784 49. Baghban R, Roshangar L, Jahanban-Esfahlan R, Seidi K, Ebrahimi-Kalan A, Jaymand M. Tumor microenvironment complexity and therapeutic implications at a glance. Cell Commun Signal (2020) 18 59. DOI: 10.1186/s12964-020-0530-4 50. Jiang X, Wang J, Deng X, Xiong F, Ge J, Xiang B. Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol Cancer. (2019) 18. DOI: 10.1186/s12943-018-0928-4 51. Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol (2002) 3. DOI: 10.1038/ni1102-991 52. Pan Y, Yu Y, Wang X, Zhang T. Tumor-associated macrophages in tumor immunity. Front Immunol (2020) 11. DOI: 10.3389/fimmu.2020.583084 53. Feng Q, Chang W, Mao Y, He G, Zheng P, Tang W. Tumor-associated macrophages as prognostic and predictive biomarkers for postoperative adjuvant chemotherapy in patients with stage II colon cancer. Clin Cancer Res (2019) 25. DOI: 10.1158/1078-0432.CCR-18-2076 54. Zhang H, Li R, Cao Y, Gu Y, Lin C, Liu X. Poor clinical outcomes and immunoevasive contexture in intratumoral IL-10-Producing macrophages enriched gastric cancer patients. Ann Surg (2022) 275. DOI: 10.1097/SLA.0000000000004037 55. Wang N, Liu W, Zheng Y, Wang S, Yang B, Li M. CXCL1 derived from tumor-associated macrophages promotes breast cancer metastasis. Cell Death Dis (2018) 9 880. DOI: 10.1038/s41419-018-0876-3 56. Xia Y, Rao L, Yao H, Wang Z, Ning P, Chen X. Engineering macrophages for cancer immunotherapy and drug delivery. Adv Mater (2020) 32. DOI: 10.1002/adma.202002054 57. Ganesh K, Stadler ZK, Cercek A, Mendelsohn RB, Shia J, Segal NH. Immunotherapy in colorectal cancer: Rationale, challenges and potential. Nat Rev Gastroenterol Hepatol (2019) 16. DOI: 10.1038/s41575-019-0126-x 58. Lin A, Zhang J, Luo P. Crosstalk between the MSI status and tumor microenvironment in colorectal cancer. Front Immunol (2020) 11. DOI: 10.3389/fimmu.2020.02039 59. Rozali EN, Hato SV, Robinson BW, Lake RA, Lesterhuis WJ. Programmed death ligand 2 in cancer-induced immune suppression. Clin Dev Immunol (2012) 2012. DOI: 10.1155/2012/656340 60. Youngnak P, Kozono Y, Kozono H, Iwai H, Otsuki N, Jin H. Differential binding properties of B7-H1 and B7-DC to programmed death-1. Biochem Biophys Res Commun (2003) 307. DOI: 10.1016/s0006-291x(03)01257-9 61. Gonzalez-Navajas JM, Fan DD, Yang S, Yang FM, Lozano-Ruiz B, Shen L. The impact of tregs on the anticancer immunity and the efficacy of immune checkpoint inhibitor therapies. Front Immunol (2021) 12. DOI: 10.3389/fimmu.2021.625783 62. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. (2021) 20 131. DOI: 10.1186/s12943-021-01428-1 63. Tanegashima T, Togashi Y, Azuma K, Kawahara A, Ideguchi K, Sugiyama D. Immune suppression by PD-L2 against spontaneous and treatment-related antitumor immunity. Clin Cancer Res (2019) 25. DOI: 10.1158/1078-0432.CCR-18-3991 64. Groth C, Hu X, Weber R, Fleming V, Altevogt P, Utikal J. Immunosuppression mediated by myeloid-derived suppressor cells (MDSCs) during tumour progression. Br J Cancer. (2019) 120 16-25. DOI: 10.1038/s41416-018-0333-1 65. Okudaira K, Hokari R, Tsuzuki Y, Okada Y, Komoto S, Watanabe C. Blockade of B7-H1 or B7-DC induces an anti-tumor effect in a mouse pancreatic cancer model. Int J Oncol (2009) 35. DOI: 10.3892/ijo_00000387 66. Yearley JH, Gibson C, Yu N, Moon C, Murphy E, Juco J. PD-L2 expression in human tumors: Relevance to anti-PD-1 therapy in cancer. Clin Cancer Res (2017) 23. DOI: 10.1158/1078-0432.CCR-16-1761 67. George S, Papanicolau-Sengos A, Lenzo FL, Conroy JM, Nesline M, Pabla S. PD-L2 amplification and durable disease stabilization in patient with urothelial carcinoma receiving pembrolizumab. Oncoimmunology. (2018) 7. DOI: 10.1080/2162402X.2018.1460298 68. Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. (2014) 515. DOI: 10.1038/nature14011 69. Miyake M, Hori S, Ohnishi S, Owari T, Iida K, Ohnishi K. Clinical impact of the increase in immunosuppressive cell-related gene expression in urine sediment during intravesical bacillus calmette-guerin. Diseases (2019) 7. DOI: 10.3390/diseases7020044 70. Gu Q, Li J, Chen Z, Zhang J, Shen H, Miao X. Expression and prognostic significance of PD-L2 in diffuse Large b-cell lymphoma. Front Oncol (2021) 11. DOI: 10.3389/fonc.2021.664032 71. Noy R, Pollard JW. Tumor-associated macrophages: From mechanisms to therapy. Immunity. (2014) 41 49-61. DOI: 10.1016/j.immuni.2014.06.010 72. Lázár-Molnár E, Scandiuzzi L, Basu I, Quinn T, Sylvestre E, Palmieri E. Structure-guided development of a high-affinity human programmed cell death-1: Implications for tumor immunotherapy. EBioMedicine. (2017) 17 30-44. DOI: 10.1016/j.ebiom.2017.02.004 73. Tang S, Kim PS. A high-affinity human PD-1/PD-L2 complex informs avenues for small-molecule immune checkpoint drug discovery. Proc Natl Acad Sci U S A. (2019) 116. DOI: 10.1073/pnas.1916916116 74. He XH, Liu Y, Xu LH, Zeng YY. Cloning and identification of two novel splice variants of human PD-L2. Acta Biochim Biophys Sin (Shanghai). (2004) 36. DOI: 10.1093/abbs/36.4.284 75. Latchman Y, Wood CR, Chernova T, Chaudhary D, Borde M, Chernova I. PD-L2 is a second ligand for PD-1 and inhibits T cell activation. Nat Immunol (2001) 2. DOI: 10.1038/85330 76. Xiao Y, Yu S, Zhu B, Bedoret D, Bu X, Francisco LM. RGMb is a novel binding partner for PD-L2 and its engagement with PD-L2 promotes respiratory tolerance. J Exp Med (2014) 211. DOI: 10.1084/jem.20130790 77. Li J, Ye L, Kynaston HG, Jiang WG. Repulsive guidance molecules, novel bone morphogenetic protein co-receptors, are key regulators of the growth and aggressiveness of prostate cancer cells. Int J Oncol (2012) 40. DOI: 10.3892/ijo.2011.1251 78. Li J, Ye L, Sanders AJ, Jiang WG. Repulsive guidance molecule b (RGMB) plays negative roles in breast cancer by coordinating BMP signaling. J Cell Biochem (2012) 113. DOI: 10.1002/jcb.24128
--- title: Demand prediction of medical services in home and community-based services for older adults in China using machine learning authors: - Yucheng Huang - Tingke Xu - Qingren Yang - Chengxi Pan - Lu Zhan - Huajian Chen - Xiangyang Zhang - Chun Chen journal: Frontiers in Public Health year: 2023 pmcid: PMC10060662 doi: 10.3389/fpubh.2023.1142794 license: CC BY 4.0 --- # Demand prediction of medical services in home and community-based services for older adults in China using machine learning ## Abstract ### Background Home and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services. ### Methods This was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized $60\%$ of older adults to develop the model, $20\%$ of the samples to examine the performance of models, and the remaining $20\%$ of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model. ### Results Random Forest and XGboost models produced the best results, in which both models were over $80\%$ at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education. ### Conclusion Andersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging. ## 1. Introduction In recent decades, the aging population in China has emerged as a prominent social problem [1]. According to the seventh population census, in 2020, $13.50\%$ of the total population i.e., 190.64 million people living in China were 65 years or older [2]. It is estimated that at this rate China will become a moderately aged society by 2030 [3] leading to considerable health problems, with $75.8\%$ of the aging population suffering from at least one chronic disease [4]. The World Health Organization (WHO) proposes healthy aging as a strategy to deal with aged societies [5]; it thus advises providing older adults with integrated healthcare services. It emphasizes on the concept of bio-psycho-social health i.e., maintaining good physiological, psychological, and social health conditions in older adults [5]. Of the globally available aging care services (6–8), the three mainstream care services are family-based, home- and community-based, and elder care institutions. Due to differing national and cultural conditions, the advantages and limitations of the care services vary. Home and community-based services (HCBS) refer to individual-centered care provided by the community at home. HCBS not only retains the traditional form of caring but also reduces daily care and financial burdens for children [9], along with addressing the psychological [10, 11] and physical needs [11] of older adults. HCBS evolved in Western countries in the 1980s and became widely popular in Europe [12], the USA [13], and Australia [14]. HCBS takes care of people with different needs, such as patients with disability [15], depression [16] and dementia [17]. In China, HCBS gained importance and support from the government in 2008 [18, 19]. Moreover, supply intensity of HCBS among whole nation gradually increased from 2008 to 2018, which supply rates of all services doubled [20]. Over time HCBS became the most appropriate care service for older adults in China [21]. The 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) classified services into the following four types, with each type having two sub-categories: (a). medical service including home visits and healthcare education, (b). daily life care service including personal care and daily shopping, (c). spiritual and cultural service including social and recreational activities and psychological consulting, and (d). mediation service including legal aid and neighborhood relations. Among all four services, medical services were in the highest demand from 2008 to 2018 [22, 23] and provisions of home visit and healthcare education were limited due to strained primary medical resources. Predicting demand for medical services could help managers in better management and targeted delivery of the service. Based on a 2014 national survey of older adults, using a logit model, a study explored the factors that influenced the demand for HCBS [24]. Global research on unmet HCBS demand is scarce, and research predicting HCBS demand is lacking [25, 26]. Former research has adopted classification trees to predict if older adults would use HCBS [27], even though there were deficiencies between demand and supply. Recently, HCBS was in high demand, but the lack of a complete and unified demand assessment system created an inability to convert potential into effective demand [28]. Moreover, community managers lacked comprehensive and accurate supply planning, thus, contributing to a severe mismatch between demand and supply. Thus, suggesting the necessity of exploring methods to assess service demand and provide efficient and cost-effective HCBS [29]. Predicting the demand for HCBS among older adults could help managers provide targeted services and formulate short- and long-term plans to address deficiencies. Traditional regression methods utilized in previous studies require independence of each variable and cannot resolve collinearity between the variables. Extant studies have concentrated on specific populations or certain factors, consequently failing to comprehensively grasp the demands of the whole population and critical characteristics. Machine learning can incorporate variables, produce accurate results with fewer constraints, and explore crucial characteristics. Thus, machine learning has been widely adopted to predict demands of healthcare services. For instance, Light Gradient Boosting Machine was conducted in ambulance demand prediction in Singapore; Long-Short Term Memory, a method based on Recurrent Neural Network, was utilized to predict home hospitalization demand of cancer palliative patients; and Extreme Gradient Boosting (XGboost) was applied in outpatient appointment demand prediction (30–32). During the Covid-19 pandemic, machine learning helped predict demands of ICU, ventilator, and length of hospital stays [33]. Hence, to understand the demand for medical services in HCBS more comprehensively, Andersen's behavioral model of health service could be employed to bridge feature selection and initial feature selection as well as machine learning model fitting. Andersen's behavioral model of health service use was proposed in 1968 and subsequently modified several times. It is widely acknowledged and applied in health-related services, such as medical costs, healthcare utilization, and drug use. It is used to determine the factors that influence health service use at different levels, as well as the variables that could be more logical, diverse, and specific (26, 34–38). Andersen's behavioral model contains multiple domains of an individual: predisposing, enabling, need, and behavior. Each domain is associated with the outcome of demand for healthcare. Predisposing factors generally describe socio-demographic characteristics; enabling factors represent personal healthcare acquirement; need factors manifest self-cognition of a health condition; and behavior factors reflect lifestyle related to their physical, mental, and social health [39]. As medical services in HCBS had the highest demand [21] and a significant positive influence on health and chronic diseases [40, 41], this study aimed to identify the best model to predict demand for medical services in HCBS among older adults in China in 2018 and explore the most critical characteristics of older adults requiring the services. We hope that the findings of this study would help in increasing efficiency in matching the demand and supply of medical services in HCBS, considering the characteristics of older adults, and, thus, contribute to healthy aging. ## 2.1. Data sources and sample This study used the 2018 CLHLS ($$n = 15$$,874) conducted by the Peking University Center for Healthy Aging and Family Studies and the China Mainland Information Group, every 3 years since 1998 [22]. Respondents in CLHLS were sampled randomly from households in half of the counties and cities across 23 provinces in mainland China. Instruments used for data collection were international questionnaires, interviews, basic physical capacity tests, and physical examinations. Former researchers demonstrated that the details of sample design and data quality were excellent [42]. After excluding 3,933, participants younger than 65 years and/or those lacking information about the home and community-based medical services, 15,312 participants were included in the final data analysis. ## 2.2. Outcome variable: Demand for medical services in HCBS Demand for medical services of HCBS was evaluated using two questions: “Do you expect your community to provide home visit services?” and “Do you expect your community to provide healthcare education services?” The expectation of one or more medical services was considered as a demand for HCBS. In case of no services expected, it was considered as no demand for medical services in HCBS. ## 2.3. Predictors and feature selection We included a broad range of candidate predictors. Based on Andersen's behavioral model, the predictors were divided into predisposing, enabling, need, and behavior factors [34, 35]. This model was proposed in 1968 and subsequently modified several times. The model is widely acknowledged and applied in the field of health-related services, such as medical costs, self-medication, and drug use, to determine influencing factors of health service use [36, 43]. Predisposing factors included demographic characteristics that may affect requirements for medical services. Factors included gender (male or female), age (65–79 years or ≥80 years), an education level (literate or illiterate), marital status (married or unmarried), and residence (rural, town, or urban). Enabling factors included individual characteristics that may affect requirements for medical services in HCBS, such as self-rated income level (low or high), pension (yes or no), social insurance (yes or no), living conditions (live with families, live alone, or live in care institution). Need factors included individual health status, such as chronic diseases (yes or no), activities of daily living (ADL) (good or bad), cognitive function (good or bad), and self-rated health (SRH) (good, fair, or poor). Behavioral factors included daily actions and habits that could affect an individual's physiological, mental, and social health, such as smoking (yes or no), alcohol consumption (yes or no), exercising (yes or no), and socializing (yes or no). ## 2.4. Statistical analysis Statistical analyses were performed using the Scikit-Learn package (version 1.1.2) in Python (version 3.9) [44]. Scikit-Learn is a wrapper technique; it was used to apply models to the data, which were randomly split into independent training, testing sets, and validation sets at a ratio of 6:2:2. ## 2.4.1. Processing of missing values To minimize the chance of bias owing to imputation, variables with more than $20\%$ of information were abandoned to acquire reasonable performances. The ultimate variables included were imputed by the “MICE” package in R studio 4.1.2, applying “missForest” multivariate iterative random forest (“RF” method) imputation algorithm with five iterations and 100 estimators to obtain the least variant datasets compared to the original one. ## 2.4.2. Synthetic minority oversampling technique Lack of demand for HCBS medical services was low probability attitude resulting in an imbalanced dataset i.e., adults not requiring medical services while using HCBS were less prevalent than the others. The imbalanced data was a challenge for machine learning, as the sample size of older adults without demand was small. Furthermore, a strong bias toward the majority class is evident while evaluating the classification model, leading to sub-optimal performances. To resolve the issue, we applied Synthetic Minority Oversampling Technique (SMOTE), a statistical technique proposed by Chawla et al. [ 45]. SMOTE generates virtual replicates from the existing minority class, thus expanding the number of minority samples in the datasets [45]. SMOTE algorithm has been widely applied to process imbalance data in medical research and generally performs reasonable results with machine learning (46–48). ## 2.4.3. Machine learning methods We applied five machine learning methods, including single models and ensemble models. These were: logistic regression (LR), LR with lasso regularization, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGboost). The outcome variable in this study was binary, that is, irrespective of whether older adults in China needed medical services in HCBS, all selected five models were widely applied in binary outcome prediction with great performances [46, 49, 50]. We compared their ability to predict demand for medical services in HCBS. ## 2.4.3.1. Logistic regression Logistic regression (LR) is a kind of general linear model. The model has a potential assumption that the outputs or the results conform to the Bernoulli distribution with parameter p. Parameter p is the probability of a positive result (in our case, the probability of demand for medical services in HCBS among older Chinese adults). Moreover, Logistic regression does demands rigorously for number of features and samples, and it could be applied in different populations [51]. Parameters for Logistic regression used in this study are default in the Scikit-Learn package. ## 2.4.3.2. LR with LASSO regularization LASSO regression is a member of the general linear model family. It is an approach to conduct variable selection and regularization while fitting the regression model. By setting parameter α to penalize the original linear model, LASSO regularization deals with the highly correlated variables to minimize the possibilities of over-fit; this automatically drops unnecessary covariates and preserves the most critical variables. Several studies have demonstrated that lasso regression has many ideal properties that can be used to enhance LR model's performance while including more covariates and the ability to predict outcomes in other populations. In this research, we selected the parameter (α = 0.01) to penalize large coefficients that resulted in a maximum correct classification rate and the best model performance [52, 53]. ## 2.4.3.3. Support vector machine Support Vector Machine (SVM) is a manually controlled classification algorithm, by the statistical theory. The working principle of SVM is to create a decision boundary, based on the definition of the hyperplane, that could separate the two categories from each other in an accurate split method. There are four widely adopted kernel functions in SVM: linear, sigmoid, radial basis (RBF), and polynomial. RBF kernel was applied in this study to construct the hyperplane due to the number of features and total samples (54–56). ## 2.4.3.4. Random forest Random Forest (RF) is a typical ensemble algorithm consisting of a series of decision trees as its basic unit using the Bagging method. Each tree randomly selects training samples and sample characteristics from the group and returns them to the original datasets to ensure that the amount of training samples is the same in each model. Due to these two features, the set of constructed decision trees contains abundant information for classification. To analysis the ultimate result, each decision tree is accessed to the final decision for a reliable result. Based on the majority voting on all decision trees, each sample is classified into two classes. We adopted 1,000 estimators with defaults for other parameters to assess the model and explore the features of older adults with/without demand toward medical services in HCBS [57, 58]. ## 2.4.3.5. Extreme gradient boosting (XGboost) XGboost classification algorithm is an ensemble algorithm of a decision tree, adopting boosting sampling method. It is an enhanced Gradient Boosting algorithm that reduces the probability of over-fit by regularizing the loss function and improves algorithm accuracy by approaching the real loss during each gradient process. In addition, XGboost possesses the ability to directly handle the encoded categorical variables. Therefore, we set 1,000 decision trees, with other parameters as defaults, to predict outcomes of demand for HBCS medical services and explore the importance of individual features [59, 60]. ## 2.5. Model assessment To assess the outcomes of each machine learning model, we observed the value of area under the receiver operating curve (ROC; AUC), sensitivity [Eq. [ 1]], specificity [Eq. [ 2]], accuracy [Eq. [ 3]], and balanced accuracy [Eq. [ 4]]. Moreover, to obtain a further understanding of the contribution of each predictor to the machine learning model and to explore the effect of individual characteristics on the demand of HCBS medical services, we calculated the importance of variables in the RF and XGboost models for each result. True positives (TP) and True negatives (TN) indicated older adults who were identified as with and without the demand for HCBS healthcare, respectively; False positives (FP) and false negatives (FN) indicated older adults who were inaccurately identified as with and without the demand for healthcare HCBS, respectively. ## 3. Results As shown in Table 1, 15,312 participants were included in this study, but only 13,244 older adults demanded medical services in HCBSs, thus, the demand rate was $86.48\%$. We also analyzed crude and adjusted odds ratio for older adults who demanded medical services in HCBS using single and multiple variable binary logistic regression. The analysis demonstrates that illiterate older adults had higher possibilities (adjusted OR = 1.21; $95\%$ CI: 1.07–1.36) of requiring medical services in HCBS. Compared to the urban older adults, older adults living in town (adjusted OR = 1.95; $95\%$ CI: 1.70–2.20) and rural (adjusted OR = 1.92; $95\%$ CI: 1.68–2.16) areas had higher demand for the service. Among enabling factors, the older adults not having social insurance (adjusted OR = 1.20; $95\%$ CI: 1.09–1.32), needed more medical services provided by HCBS. Moreover, fair self-rated health status (adjusted OR = 1.18; $95\%$ CI: 1.06–1.31) increased the possibility of demand for services among older adults. The results also indicate that the regular exercising group (adjusted OR = 1.26; $95\%$ CI: 1.13–1.40) and older adults dislike socializing (adjusted OR = 0.85; $95\%$ CI: 0.73–0.99) and had lower demand for medical services in HCBS. Table 1 \| Predictors \| Predictors.1 \| All N (%) \| With demand N (%) \| Without demand N (%) \| Crude OR (95% CI) \| Adjusted OR (95% CI) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Overall \| 15312 \| 13,242 (86.48%) \| 2,070 (13.53%) \| \| \| \| \| Predisposing factors \| Predisposing factors \| Predisposing factors \| Predisposing factors \| Predisposing factors \| Predisposing factors \| Predisposing factors \| \| Gender \| Male \| 6,687 (43.67%) \| 5,788 (43.71%) \| 899 (13.44%) \| Ref \| Ref \| \| \| Female \| 8,625 (56.33%) \| 7,454 (56.29%) \| 1,171 (13.57%) \| 0.99 (0.90, 1.09) \| 0.93 (0.83, 1.05) \| \| Age \| 65–79 \| 5,213 (34.05%) \| 4,549 (34.35%) \| 664 (32.08%) \| Ref \| Ref \| \| \| ≥80 \| 10,099 (65.95%) \| 8,693 (65.65%) \| 1,406 (67.92%) \| 0.90 (0.82, 0.99) * \| 0.91 (0.80, 1.03) \| \| Education \| Non-illiterate \| 7,707 (50.33%) \| 6,759 (51.04%) \| 948 (45.80%) \| Ref \| Ref \| \| \| Illiterate \| 7,605 (49.67%) \| 6,483 (48.96%) \| 1,122 (54.20%) \| 1.23 (1.12, 1.35) * \| 1.21 (1.07, 1.36) *** \| \| Marital status \| Single \| 9,283 (60.63%) \| 7,984 (60.29%) \| 1,299 (62.75%) \| Ref \| Ref \| \| \| Married \| 6,029 (39.37%) \| 5,258 (39.71%) \| 771 (37.25%) \| 0.90 (0.82, 0.99) * \| 0.91 (0.80, 1.03) \| \| Residence \| Urban \| 3,454 (22.56%) \| 2,762 (20.86%) \| 692 (33.43%) \| Ref \| Ref \| \| \| Town \| 5,073 (33.13%) \| 4,488 (33.89%) \| 585 (28.26%) \| 1.92 (1.71, 2.17) * \| 1.95 (1.70, 2.20) * \| \| \| Rural \| 6,785 (44.31%) \| 5,992 (45.25%) \| 793 (38.31%) \| 1.89 (1.69, 2.12) * \| 1.92 (1.68, 2.16) * \| \| Enabling factors \| Enabling factors \| Enabling factors \| Enabling factors \| Enabling factors \| Enabling factors \| Enabling factors \| \| Income level \| Low \| 1,671 (10.91%) \| 1,467 (11.08%) \| 204 (9.86%) \| Ref \| Ref \| \| \| High \| 13,641 (89.09%) \| 11,775 (88.92%) \| 1,866 (90.14%) \| 1.14 (0.98, 1.33) \| 1.01 (0.86, 1.18) \| \| Pension \| Yes \| 9,911 (64.73%) \| 8,568 (64.70%) \| 1,343 (64.88%) \| Ref \| Ref \| \| \| No \| 5,401 (35.27%) \| 4,674 (35.30%) \| 727 (35.12%) \| 0.99 (0.90, 1.09) \| 0.96 (0.87, 1.06) \| \| Social insurance \| Yes \| 8,614 (56.26%) \| 7,481 (56.49%) \| 1,133 (54.73%) \| Ref \| Ref \| \| \| No \| 6,698 (43.74%) \| 5,761 (43.51%) \| 937 (45.27%) \| 1.08 (0.98, 1.18) \| 1.20 (1.09, 1.32) * \| \| Living status \| Family \| 12,315 (80.43%) \| 10,658 (80.49%) \| 1,657 (80.05%) \| Ref \| Ref \| \| \| Alone \| 2,433 (15.895) \| 2,109 (15.93%) \| 324 (15.65%) \| 1.01 (0.89, 1.15) \| 1.02 (0.88, 1.17) \| \| \| Institution \| 564 (3.68%) \| 475 (3.59%) \| 89 (4.30%) \| 0.83 (0.66, 1.05) \| 1.09 (0.86, 1.39) \| \| Need factors \| Need factors \| Need factors \| Need factors \| Need factors \| Need factors \| Need factors \| \| Chronic disease \| Yes \| 2,635 (17.21%) \| 2,271 (17.15%) \| 364 (17.58%) \| Ref \| Ref \| \| \| No \| 12,677 (82.79%) \| 10,971 (82.85%) \| 1,706 (82.42%) \| 0.97 (0.86, 1.10) \| 0.91 (0.81, 1.04) \| \| ADL \| Yes \| 4,305 (28.12%) \| 3,695 (27.90%) \| 610 (29.47%) \| Ref \| Ref \| \| \| No \| 11,007 (71.88%) \| 9,547 (72.10%) \| 1,460 (70.53%) \| 0.93 (0.84, 1.03) \| 1.03 (0.91, 1.17) \| \| Cognitive loss \| Yes \| 5,860 (38.27%) \| 5,047 (38.11%) \| 813 (39.28%) \| Ref \| Ref \| \| \| No \| 9,452 (61.73%) \| 8,195 (61.89%) \| 1,257 (60.72%) \| 0.95 (0.87, 1.05) \| 0.92 (0.81, 1.04) \| \| SRH \| Good \| 7,106 (46.41%) \| 6,067 (45.82%) \| 1,039 (50.19%) \| Ref \| Ref \| \| \| Fair \| 5,987 (39.10%) \| 5,254 (39.68%) \| 733 (35.41%) \| 1.23 (1.11, 1.36) * \| 1.18 (1.06, 1.31) * \| \| \| Bad \| 2,219 (14.49%) \| 1,921 (14.51%) \| 298 (14.40%) \| 1.10 (0.96, 1.27) \| 1.06 (0.92, 1.23) \| \| Behavior factors \| Behavior factors \| Behavior factors \| Behavior factors \| Behavior factors \| Behavior factors \| Behavior factors \| \| Smoking \| Yes \| 2,228 (14.55%) \| 1,933 (14.60%) \| 295 (14.25%) \| Ref \| Ref \| \| \| No \| 13,084 (85.45%) \| 11,309 (85.40%) \| 1,775 (85.75%) \| 0.97 (0.85, 1.11) \| 1.05 (0.91, 1.21) \| \| Alcohol drinking \| Yes \| 2,138 (13.96%) \| 1,848 (13.96%) \| 290 (14.01%) \| Ref \| Ref \| \| \| No \| 13,174 (86.04%) \| 11,394 (86.04%) \| 1,780 (85.99%) \| 1.01 (0.88, 1.15) \| 1.04 (0.90, 1.21) \| \| Exercising \| Yes \| 4,569 (29.84%) \| 3,839 (28.99%) \| 730 (35.27%) \| Ref \| Ref \| \| \| No \| 10,743 (70.16%) \| 9,403 (71.01%) \| 1,340 (64.73%) \| 1.33 (1.21, 1.47) * \| 1.26 (1.13, 1.40) *** \| \| Socializing \| Yes \| 1,987 (12.98%) \| 1,708 (12.90%) \| 279 (13.48%) \| Ref \| Ref \| \| \| No \| 13,325 (87.02%) \| 11,534 (87.10%) \| 1,791 (86.52%) \| 1.05 (0.92, 1.21) \| 0.85 (0.73, 0.99) * \| The confusion metrics and the performance metrics shown in Table 2 illustrate the five machine learning methods in Models I-IV. LR served as the benchmark baseline with the AUC of 0.57, 0.59, 0.63, and 0.66 in Models I–IV, respectively. Lasso had a similar AUC as LR in Models I and IV. SVM had slightly higher AUC of 0.57, 0.60, 0.63, and 0.66, respectively. The AUC of RF (0.57, 0.61, 0.71, and 0.77) and XGboost (0.57, 0.61, 0.70, and 0.76) were higher than the AUC of the other machine learning methods in Models I-IV. Furthermore, RF and XGboost performed best in terms of sensitivity, specificity, accuracy, and balance in Model IV. The addition of need factors to Model II changed it to Model III, and it resulted in a greater change in AUC. This change could predict that need factors may have the greatest impact on the demand for medical services in HCBSs. Table 2 \| Unnamed: 0 \| Classifier \| AUC \| TP/TN/FP/FN \| Sensitivity (%) \| Specificity (%) \| Accuracy (%) \| Balanced accuracy (%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Model I \| LR \| 0.571 (0.555,0.586) \| 2076/891/1768/563 \| 78.67 (77.10,80.23) \| 33.51 (31.71,35.30) \| 56.00 (54.68,57.35) \| 64.04 (62.88,65.21) \| \| Model I \| LASSO \| 0.567 (0.552,0.583) \| 2076/891/1768/563 \| 78.67 (77.10,80.23) \| 33.51 (31.71,35.30) \| 56.00 (54.68,57.35) \| 64.04 (62.88,65.21) \| \| Model I \| SVM \| 0.570 (0.555,0.586) \| 2076/891/1768/563 \| 78.67 (77.10,80.23) \| 33.51 (31.71,35.30) \| 56.00 (54.68,57.35) \| 64.04 (62.88,65.21) \| \| Model I \| RF \| 0.568 (0.553,0.583) \| 1875/1100/1559/764 \| 71.05 (69.32,72.78) \| 41.37 (39.50,43.24) \| 56.15 (54.83,57.50) \| 61.75 (60.53,62.97) \| \| Model I \| XGboost \| 0.568 (0.546,0.577) \| 1891/1082/1577/748 \| 71.66 (69.94,73.38) \| 40.69 (38.82,42.56) \| 56.12 (54.79,57.46) \| 61.93 (60.71,63.15) \| \| Model II \| LR \| 0.594 (0.579,0.609) \| 1679/1361/1298/960 \| 63.62 (61.79,65.46) \| 51.18 (49.29,53.09) \| 57.38 (56.05,58.71) \| 59.79 (58.51,61.08) \| \| Model II \| LASSO \| 0.594 (0.577,0.607) \| 1763/1252/1407/876 \| 66.81 (65.01,68.60) \| 47.09 (45.19,48.98) \| 56.91 (55.58,58.24) \| 60.70 (59.44,61.96) \| \| Model II \| SVM \| 0.602 (0.587,0.618) \| 2076/891/1768/563 \| 78.67 (77.10,80.23) \| 33.51 (31.72,35.30) \| 56.00 (54.68,57.35) \| 64.04 (62.88,65.21) \| \| Model II \| RF \| 0.615 (0.600,0.630) \| 1568/1689/970/1071 \| 59.42 (57.54,61.29) \| 63.52 (61.69,65.35) \| 61.48 (60.18,62.80) \| 60.58 (59.24,61.91) \| \| Model II \| XGboost \| 0.613 (0.597,0.627) \| 1587/1658/1001/1052 \| 60.14 (58.27,62.00) \| 62.35 (60.51,64.20) \| 61.25 (59.95,62.57) \| 60.72 (59.40,62.05) \| \| Model III \| LR \| 0.630 (0.616,0.645) \| 1664/1481/1178/975 \| 63.05 (61.21,64.90) \| 55.70 (53.81,57.59) \| 59.36 (58.04,60.69) \| 60.72 (59.42,62.01) \| \| Model III \| LASSO \| 0.626 (0.611,0.641) \| 1658/1489/1170/981 \| 62.83 (60.98,64.67) \| 56.00 (54.11,57.88) \| 59.40 (58.08,60.72) \| 60.65 (59.36,61.95) \| \| Model III \| SVM \| 0.629 (0.614,0.644) \| 1741/1412/1247/898 \| 65.97 (64.16,67.78) \| 53.10 (51.20,55.00) \| 59.51 (58.20,60.85) \| 61.88 (60.61,63.15) \| \| Model III \| RF \| 0.712 (0.698,0.726) \| 1707/2050/609/932 \| 64.68 (62.86,66.51) \| 77.10 (75.50,78.69) \| 70.91 (69.70,72.15) \| 68.90 (67.61,70.19) \| \| Model III \| XGboost \| 0.697 (0.682.0.710) \| 1748/1940/719/891 \| 66.24 (64.43,68.04) \| 72.96 (71.27,74.65) \| 69.61 (68.39,70.86) \| 68.47 (67.19,69.74) \| \| Model IV \| LR \| 0.656 (0.641,0.671) \| 1681/1540/1119/958 \| 63.70 (61.86,65.53) \| 57.92 (56.04,59.79) \| 60.80 (59.48,62.11) \| 61.81 (60.52,63.10) \| \| Model IV \| LASSO \| 0.652 (0.637,0.667) \| 1659/1557/1102/980 \| 62.86 (61.02,64.71) \| 58.56 (56.68,60.43) \| 60.70 (59.39,62.02) \| 61.44 (60.15,62.74) \| \| Model IV \| SVM \| 0.659 (0.645,0.674) \| 1737/1502/1157/902 \| 65.82 (64.01,67.63) \| 56.49 (54.60,58.37) \| 61.14 (59.84,62.46) \| 62.79 (61.51,64.06) \| \| Model IV \| RF \| 0.773 (0.761,0.786) \| 1881/2191/444/781 \| 70.66 (68.93,72.39) \| 83.15 (81.72,84.58) \| 76.87 (75.74,78.01) \| 75.44 (74.24,76.63) \| \| Model IV \| XGboost \| 0.758 (0.745,0.771) \| 1801/2182/452/862 \| 67.63 (65.85,69.41) \| 82.84 (81.40,84.28) \| 74.16 (72.98,75.33) \| 72.09 (70.83,73.34) \| To evaluate the stability of Model IV, $20\%$ of the total samples were separated, as the validation set, to examine if the models were over-fitted in the RF and XGboost. Figure 1A displays ROCs of Model IV fitted by RF, whose AUC did not show a significant difference between the test set and the validation set. In Figure 1B ROCs were produced by XGboost, which produced robust results in the validation set. Both models fitted by all four factors of Andersen's behavioral model as presented in Table 3 performed steady results to predict the demand for medical services in HCBS compared to the test set of Model IV in Table 2. Figure 1: ROC and AUC performed by (A) RF and (B) XGboost in Model IV for both testing set, and validation set. TABLE_PLACEHOLDER:Table 3 Figure 2 shows the importance of the predictors in the RF and XGboost. In the RF method SRH, exercise, ADL, age, education, and gender were the most important predictors of the demand for medical services in HCBS. Variable importance produced by XGboost demonstrated that SRH, social insurance, education, pension, gender, and exercise were the most critical features. Figure 2: The most important features of the older adults, who demanded for medical services provided by HCBS in CLHLS 2018. ## 4. Discussion To the best of our knowledge, this is the first research to predict the demand for medical services in HCBS among older adults in China using national representative data, CLHLS 2018, and including demographic, social, economic, health, and other parameters. Although the demand proportion for healthcare services was relatively high worldwide [61, 62], our study revealed that it was higher in China. Along with the growing life expectancy, the average age continues to increase in China [18]. As people age, their need for medical services increases [24, 63]. Consequently, the demand for medical services provided by HCBS was high from 2008 to 2018, above $80\%$, with an upward trend. Moreover, with the change in the current family structure and fast-paced social life, the traditional family-based care modes have lost significance in promoting life satisfaction among older adults [64, 65]. Hence, more empty-nest older adults who lived alone failed to get timely treatment [64]. Additionally, a large number of older adults suffered from chronic diseases, such as hypertension, diabetes, and respiratory diseases that required daily medical monitoring to ensure older adults remain in normal living conditions [66]. Some studies successfully adopted traditional regression methods [24]; however, deficiencies in traditional methods, which requires absolute independence among the variables, could lead to information loss during variable selection. Moreover, demand for medical services provided by HCBS had large imbalances, resulting in higher sensitivity and accuracy but lower specificity. Therefore, it was impractical to use, as only ~$15\%$ of the older adults did not need medical services in HCBS. As higher specificity was necessary to predict the group without need, utilizing SMOTE solved this issue; the AUC was higher for specificity ($83.15\%$ in RF and $82.84\%$ in XGboost among Model IV). The performance of SMOTE resulted in better-fit results and produced robust data without missing samples, thus, creating a more practical model to predict older adults with and without need. Machine learning models could include variables with fewer constraints, enabling the models to confront the presence of high dimensions and correlated predictors. Thus, they are a widely acknowledged and adapted method in exploring influencing factors of health-related services. HCBS is an integrated care service, covering the multilevel and diversified demands of older adults; therefore, by using the four factors in Andersen's behavioral model it was possible to explore the critical features above reasonable theoretical basis. The AUC and accuracy of RF and XGboost were increased sharply after including need factors. While all four factors were included in the machine learning models, the AUC of the five models was above 0.60, and RF and XGboost showed good model fit. The AUC of RF was beyond 0.75, demonstrating the feasibility of predicting the demands of older adults for medical services in HCBS, based on Andersen's behavioral model and machine learning methods. With high specificity, the model could filter the people who were more likely to have no demand for medical services in HCBS temporally. This would help decision-makers to provide older adults in urgent demand with targeted care in situations with limited resources. To examine robustness, the performance of the validation set proved the performances of these two models were not over-fitted. Using Andersen's behavioral model, combined with Logistic regression and estimating the contribution of each variable in machine learning models, we further confirmed that self-rated health was the most significant feature to predict if older adults needed medical services in HCBS. The present research illustrated that health conditions had a direct influence on medical services in HCBS, which confirmed the results that SRH had the highest importance in predicting if older adults had demand [24]. Moreover, the aged population with good health had a stronger demand for medical services provided by HCBS [67, 68]. Previous research demonstrated that older adults in bad health went to the hospital and looked for more exhaustive medical services [69] whereas older adults with good health might not have urgent demand. Furthermore, there was strong evidence that confirmed chronic disease was a significant risk factor for poor SRH rate. These results could enable the community to provide medical services preferentially [70, 71]. Furthermore, exercise and education played important roles in demand. Illiterate people aged >65 years had lower health literacy levels [72, 73]. Therefore, they may require healthcare education services more urgently [74]. Participants who rarely exercised were more likely to gain weight and have worse health status. Appropriate exercise could meet the requirement of the bio-psycho-social medical model, by facilitating metabolism in older adults, obtaining a sense of happiness, and getting the chance to meet friends who share the same hobby [75, 76]. Therefore, older adults who do not exercise may need medical services in HCBS more than those who exercise regularly [77]. These findings indicate that the characteristics of older adults should be considered to narrow the gap between supply and demand. Communities could (a) make efforts to focus on older adults with good health, (b) provide health education on conditions like hypertension, diabetes, and stroke, to promote health literacy in the neighborhood, and (c) propose targeted measures to encourage older adults to exercise, based on their abilities, and offer periodical home medical visits to monitor their health condition. Andersen's behavioral model and machine learning could help managers and governments construct a complete and unified demand assessment system, which could also be extrapolated to other types of demands. This would enable HCBS to narrow the supply-demand gap and improve management efficiency and cost-effectiveness. Ultimately, this would promote healthy aging by providing more effective services. ## 5. Limitation This study has some limitations. Firstly, we only adopted data from the 2018 CLHLS to predict demand for medical services provided by HCBS, thus, this cross-sectional data could not explore causality between demand and predictors. Second, the CLHLS provided national representative data. Previous research indicated that the supply situation and intensity of HCBS in China vary significantly temporally and spatially. This regional variance may increase the supply and demand mismatch and affect the information for the use of HCBS among older adults. Simultaneously, including all predictors as factor variables could lead to information loss in estimating the contribution of individual variables. Furthermore, this study included home medical visits and healthcare education as medical services. As interactions between these two services are possible, only extensive characteristic ranges could be determined to identify demand. As, HCBS included four types of services only, hence, to construct an assessment system, further research on demands predictions for other services is required. ## 6. Conclusion This study adapted machine learning to predict the demand for medical services in HCBS using the 2018 CLHLS data based on Andersen's behavioral model. Andersen's behavioral model combined with machine learning successfully constructed a model with reasonable predictors and captured critical characteristics in older adults, who may have higher demand. This method predicting demands could be valuable for the community and decision-makers in arranging limited primary medical resources to promote healthy aging. Future empirical research should examine the models and conduct a longitudinal study to explore the causation between demand and individual characteristics. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/WBO7LK. ## Ethics statement The studies involving human participants were reviewed and approved by Research Ethics Committees of Duke University Research Ethics Committees of Peking University (IRB00001052-13074). The patients/participants provided their written informed consent to participate in this study. ## Author contributions CC, YH, and TX conceived and designed the study. YH and TX participated in acquisition of the data and wrote the original draft. YH and CC contributed to data analysis. YH took charge of the submission. CC, XZ, YH, TX, QY, CP, LZ, and HC substantively revised the manuscript. All authors have read and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Fang EF, Scheibye-Knudsen M, Jahn HJ Li J, Ling L, Guo H. A research agenda for aging in China in the 21st century. Ageing Res Rev. (2015) 24 197-205. DOI: 10.1016/j.arr.2015.08.003 2. 2.General Office of the State Council of the People's Republic of China. Main Data of the Seventh National Population Census. (2020). Available online at: http://www.stats.gov.cn/tjsj/zxfb/202105/t20210510_1817176.html (accessed May 11, 2021).. (2020) 3. 3.General Office of the State Council of the People's Republic of China. Interpretation of the Seventh National Census. (2021). Available online at: http://www.stats.gov.cn/tjsj/sjjd/202105/t20210513_1817394.html (accessed May 13, 2021).. (2021) 4. Wang LM, Chen ZH, Zhang M, Zhao ZP, Huang ZJ, Zhang X. Study of the prevalence and disease burden of chronic disease in the elderly in China. Zhonghua Liu Xing Bing Xue Za Zhi. (2019) 40 277-83. DOI: 10.3760/cma.j.issn.0254-6450.2019.03.005 5. 5.World Health Organization. Developing an Ethical Framework for Healthy Ageing. (2017).. Developing an Ethical Framework for Healthy Ageing (2017) 6. Bai H, Liu L. Research on the New Mode of “Internet+ Combination of Medical Treatment and Endowment” Aged Care Service Industry,. 2018 3rd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2018). (2018). DOI: 10.2991/icesame-18.2018.60 7. Kim H, Jeon B, Frisina Doetter L, Tamiya N, Hashimoto H. Same same but different? Comparing institutional performance in the long-term care systems of Japan and South Korea. Social Policy Administrat. (2022) 56 148-62. DOI: 10.1111/spol.12761 8. Theobald H, Luppi M. Elderly care in changing societies: concurrences in divergent care regimes–a comparison of Germany, Sweden and Italy. Current Sociol. (2018) 66 629-42. DOI: 10.1177/0011392118765232 9. Song J YL, Han M, Wu Y. Study on the mental health of the elderly under different pension models. J Healthc Eng. (2022) 2022 2367406. DOI: 10.1155/2022/2367406 10. Hagedorn A, Chen H, Zhang R. A study on the mental health service demand of empty-nesters under the mode of home-based care for the aged-A case study of nanjing. Adv Psychol (2021) 11 1839-47 11. Muramatsu N, Yin H, Hedeker D. Functional declines, social support, and mental health in the elderly: does living in a state supportive of home and community-based services make a difference?. Soc Sci Med. (2010) 70 1050-8. DOI: 10.1016/j.socscimed.2009.12.005 12. Stuart M, Weinrich M. Home-and community-based long-term care: lessons from Denmark. Gerontologist. (2001) 41 474-80. DOI: 10.1093/geront/41.4.474 13. Birnbaum H, Burke R, Swearingen C, Dunlop B. Implementing community-based long-term care: experience of New York's long term home health care program. Gerontologist. (1984) 24 380-6. DOI: 10.1093/geront/24.4.380 14. Clare J, De Bellis A, Jarrett D. Planning aged care in Australia: a review and critique of the reforms 1975–96. Collegian. (1997) 4 22-9. DOI: 10.1016/S1322-7696(08)60202-4 15. Friedman C, Feldner HA. Physical therapy services for people with intellectual and developmental disabilities: the role of medicaid home- and community-based service waivers. Phys Ther. (2018) 98 844-54. DOI: 10.1093/ptj/pzy082 16. Pepin R, Leggett A, Sonnega A, Assari S. Depressive symptoms in recipients of home-and community-based services in the United States: are older adults receiving the care they need?. Am J Geriatric Psychiatry. (2017) 25 1351-60. DOI: 10.1016/j.jagp.2017.05.021 17. Amjad H, Wong SK, Roth DL, Huang J, Willink A, Black BS. Health services utilization in older adults with dementia receiving care coordination: the MIND at home trial. Health Serv Res. (2018) 53 556-79. DOI: 10.1111/1475-6773.12647 18. 18.China's State Council,. Outline of “Healthy China. (2030). Available online at: http://www.gov.cn/gongbao/content/2016/content_5133024.htm2016 (accessed October 25, 2016).. (2030) 19. Yu Y, Yuan C, Zhang Q, Song C, Cui S, Ye J. Longitudinal association between home and community-based services provision and cognitive function in Chinese older adults: Evidence from the Chinese longitudinal healthy longevity survey. Health Soc Care Community. (2021) 29 e288-e98. DOI: 10.1111/hsc.13353 20. Tingke Xu QZ, Yin C, Yanhong L, Yunyun H, Qianru Z, Tingting Z, Chun C. Researches on the spatial and temporal differences in the provision of home and community-based pension services in China Based on CLHLS (2008-−2018) longitudinal data. Chinese Health Service Manage (2022) 39 170-95 21. Xia C. Community-based elderly care services in China: An analysis based on the 2018 wave of the CLHLS Survey. China Population Develop Stud. (2020) 3 352-67. DOI: 10.1007/s42379-020-00050-w 22. 22.Center for Healthy Aging and Development Studies. The Chinese Longitudinal Healthy Longevity Survey (CLHLS)-Longitudinal Data (1998–2018) Peking University Open Research Data Platform. (2020). 10.18170/DVN/WBO7LK (accessed April 03, 2020).. The Chinese Longitudinal Healthy Longevity Survey (CLHLS)-Longitudinal Data (1998–2018) Peking University Open Research Data Platform (2020). DOI: 10.18170/DVN/WBO7LK 23. Zhang L, Zeng Y, Wang L, Fang Y. Urban–rural differences in long-term care service status and needs among home-based elderly people in China. Int J Environ Res Public Health. (2020). DOI: 10.3390/ijerph17051701 24. Wang Q. Demands and determinants of community home-based care services for urban elderly: based on the 2010 national elderly survey in China. Populat Res. (2016) 40 98-112 25. Mitra M, Bogen K, Long-Bellil LM, Heaphy D. Unmet needs for home and community-based services among persons with disabilities in Massachusetts. Disabil Health J. (2011) 4 219-28. DOI: 10.1016/j.dhjo.2011.08.003 26. Ferris R, Glicksman A, Kleban M. Environmental predictors of unmet home-and community-based service needs of older adults. J Appl Gerontol. (2016) 35 179-208. DOI: 10.1177/0733464814525504 27. Penkunas MJ, Eom KY, Chan AW. Classification trees for identifying non-use of community-based long-term care services among older adults. Health Policy. (2017) 121 1093-9. DOI: 10.1016/j.healthpol.2017.05.008 28. Yang Z, Xia S, Feng S. Construction of a physical and medical care integrated model for the elderly in the community based on artificial intelligence and machine learning. J Healthc Eng. (2022) 2022 3678577. DOI: 10.1155/2022/3678577 29. Qianru Z, Jiahui J, Yunyun H, Tingke X, Tingting Z, Chun C. Match of supply and demand in home and community-based elderly service from the perspective of precise management. Chinese Rural Health Service Administrat (2021) 41 724-55. DOI: 10.19955/j.cnki.1005-5916.2021.10.007 30. Lin AX, Ho AFW, Cheong KH, Li Z, Cai W, Chee ML. Leveraging machine learning techniques and engineering of multi-nature features for national daily regional ambulance demand prediction. Int J Environ Res Public Health (2020) 17 4179. DOI: 10.3390/ijerph17114179 31. Soltani M, Farahmand M, Pourghaderi AR. Machine learning-based demand forecasting in cancer palliative care home hospitalization. J Biomed Inform. (2022) 130 104075. DOI: 10.1016/j.jbi.2022.104075 32. Klute B, Homb A, Chen W, Stelpflug A. Predicting outpatient appointment demand using machine learning and traditional methods. J Med Syst. (2019) 43 288. DOI: 10.1007/s10916-019-1418-y 33. Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: a systematic review. Comput Struct Biotechnol J. (2021) 19 2833-50. DOI: 10.1016/j.csbj.2021.05.010 34. Zhang S, Chen Q, Zhang B. Understanding healthcare utilization in China through the Andersen behavioral model: review of evidence from the China health and nutrition survey. Risk Manag Healthc Policy. (2019) 12 209. DOI: 10.2147/RMHP.S218661 35. Jin SW, Lee HY, Lee J. Analyzing factors of breast cancer screening adherence among Korean American women using Andersen's behavioral model of healthcare services utilization. Ethn Dis. (2019) 29 427. DOI: 10.18865/ed.29.S2.427 36. Yue-e Li SL. A review of the development of the anderson model's theoretical construction and analysis path. Chinese Health Service Manage (2017) 34 324-34 37. Haney JL. Sexual orientation, social determinants of health, and unmet substance use treatment need: findings from a national survey. Subst Use Misuse. (2021) 56 205-13. DOI: 10.1080/10826084.2020.1853775 38. Steinbeisser K, Schwarzkopf L, Grill E, Schwettmann L, Peters A, Seidl H. Gender-linked determinants for utilization of long-term care in community-dwelling adults 65+ in Germany: Results from the population-based KORA-Age study. Exp Gerontol. (2021) 153 111500. DOI: 10.1016/j.exger.2021.111500 39. Diaz JE, Sandh S, Schnall R, Garofalo R, Kuhns LM, Pearson CR. Predictors of past-year health care utilization among young men who have sex with men using andersen's behavioral model of health service use. LGBT Health. (2022) 9 471-8. DOI: 10.1089/lgbt.2021.0488 40. Zhang Y, Chen Y, Gao H, Su D, Tan M, Zhang Y. Community-based health support and self-rated health status of the elderly. Chinese General Practice. (2021) 24 3971 41. Yang L, Wang L, Di X, Dai X. Utilisation of community care services and self-rated health among elderly population in China: a survey-based analysis with propensity score matching method. BMC Public Health. (2021) 21 1-11. DOI: 10.1186/s12889-021-11989-x 42. Gu D. General data quality assessment of the CLHLS. (2008) 20 39-60. DOI: 10.1007/978-1-4020-6752-5_3 43. SoleimanvandiAzar N, Kamal SHM, Sajjadi H, Harouni GG, Karimi SE, Djalalinia S. Determinants of outpatient health service utilization according to Andersen's Behavioral Model: a systematic scoping review. Iran J Med Sci. (2020) 45 405. DOI: 10.30476/ijms.2020.85028.1481 44. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O. Scikit-learn: machine learning in python. J Mach Learn Res. (2011) 12 2825-30 45. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. synthetic minority over-sampling technique. J Artificial Intell Res. (2002) 16 321-57. DOI: 10.1613/jair.953 46. Cooray U, Watt RG, Tsakos G, Heilmann A, Hariyama M, Yamamoto T. Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis. Soc Sci Med. (2021) 291 114486. DOI: 10.1016/j.socscimed.2021.114486 47. van Vuuren CL, van Mens K, de Beurs D, Lokkerbol J, van der Wal MF, Cuijpers P. Comparing machine learning to a rule-based approach for predicting suicidal behavior among adolescents: results from a longitudinal population-based survey. J Affect Disord. (2021) 295 1415-20. DOI: 10.1016/j.jad.2021.09.018 48. Lee YW, Choi JW, Shin EH. Machine learning model for predicting malaria using clinical information. Comput Biol Med. (2021) 129 104151. DOI: 10.1016/j.compbiomed.2020.104151 49. Su D, Zhang X, He K, Chen Y. Use of machine learning approach to predict depression in the elderly in China: a longitudinal study. J Affect Disorders (2021) 282 289. DOI: 10.1016/j.jad.2020.12.160 50. Liu H, Zhang L, Wang W, Huang Y, Li S, Ren Z. Prediction of online psychological help-seeking behavior during the COVID-19 pandemic: an interpretable machine learning method. Front Public Health. (2022) 10 814366. DOI: 10.3389/fpubh.2022.814366 51. Meurer WJ, Tolles J. Logistic regression diagnostics: understanding how well a model predicts outcomes. JAMA. (2017) 317 1068-9. DOI: 10.1001/jama.2016.20441 52. Diebold FX, Shin M. Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives. Int J Forecast. (2019) 35 1679-91. DOI: 10.1016/j.ijforecast.2018.09.006 53. Muthukrishnan R, Rohini R. LASSO: A feature selection technique in predictive modeling for machine learning,. 2016 IEEE international conference on advances in computer applications (ICACA). (2016). DOI: 10.1109/ICACA.2016.7887916 54. Noble WS. What is a support vector machine?. Nat Biotechnol. (2006) 24 1565-7. DOI: 10.1038/nbt1206-1565 55. Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. (1999) 9 293-300. DOI: 10.1023/A:1018628609742 56. Cui S, Wang D, Wang Y, Yu PW, Jin Y. An improved support vector machine-based diabetic readmission prediction. Comput Methods Programs Biomed. (2018) 166 123-35. DOI: 10.1016/j.cmpb.2018.10.012 57. Cutler A, Cutler DR, Stevens JR. Random Forests. Ensemble Machine Learning. (2012) 157-75. DOI: 10.1007/978-1-4419-9326-7_5 58. Biau G. Analysis of a random forests model. J Machine Learn Res. (2012) 13 1063-95 59. Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H. Xgboost: extreme gradient boosting. R Package Vers. (2015) 1 1-4 60. Chen T, Guestrin C. Xgboost: A scalable tree boosting system,. Proceedings of the 22nd acm Sigkdd International Conference on Knowledge discovery and Data Mining. (2016). DOI: 10.1145/2939672.2939785 61. Wren M-A, Keegan C, Walsh B, Bergin A, Eighan J, Brick A. Projections of demand for healthcare in ireland, 2015–2030: first report from the hippocrates model. (2017). DOI: 10.26504/rs67 62. Mhlanga D, Garidzirai R. The influence of racial differences in the demand for healthcare in South Africa: a case of public healthcare. Int J Environ Res Public Health (2020) 17 5043. DOI: 10.3390/ijerph17145043 63. Li L, Du T, Hu Y. The effect of population aging on healthcare expenditure from a healthcare demand perspective among different age groups: evidence from Beijing City in the People's Republic of China. Risk Manag Healthc Policy. (2020) 13 1403. DOI: 10.2147/RMHP.S271289 64. Aboagye E, Agyemang OS, Tjerbo T. Elderly demand for family-based care and support: Evidence from a social intervention strategy. Glob J Health Sci. (2014) 6 94. DOI: 10.5539/gjhs.v6n2p94 65. Shen Y, Yeatts DE. Social support and life satisfaction among older adults in China: family-based support vs. community-based support. Int J Aging Hum Develop. (2013) 77 189-209. DOI: 10.2190/AG.77.3.b 66. von Berenberg P, Dräger D, Zahn T, Neuwirth J, Kuhlmey A, Gellert P. Chronic conditions and use of health care service among German centenarians. Age Ageing. (2017) 46 939-45. DOI: 10.1093/ageing/afx008 67. Subramanian S, Kubzansky L, Berkman L, Fay M, Kawachi I. Neighborhood effects on the self-rated health of elders: uncovering the relative importance of structural and service-related neighborhood environments. J Gerontol Ser B Psychol Sci Soc Sci. (2006) 61 S153-S60. DOI: 10.1093/geronb/61.3.S153 68. Hirdes JP, Forbes WF. Factors associated with the maintenance of good self-rated health. J Aging Health. (1993) 5 101-22. DOI: 10.1177/089826439300500105 69. Tamayo-Fonseca N, Nolasco A, Quesada JA, Pereyra-Zamora P, Melchor I, Moncho J. Self-rated health and hospital services use in the Spanish National Health System: a longitudinal study. BMC Health Serv Res. (2015) 15 1-9. DOI: 10.1186/s12913-015-1158-8 70. Han K-M, Ko Y-H, Yoon H-K, Han C, Ham B-J, Kim Y-K. Relationship of depression, chronic disease, self-rated health, and gender with health care utilization among community-living elderly. J Affect Disord. (2018) 241 402-10. DOI: 10.1016/j.jad.2018.08.044 71. Chan YY, Teh CH, Lim KK, Lim KH, Yeo PS, Kee CC. Lifestyle, chronic diseases and self-rated health among Malaysian adults: results from the 2011 National Health and Morbidity Survey (NHMS). BMC Public Health. (2015) 15 1-12. DOI: 10.1186/s12889-015-2080-z 72. Paasche-Orlow MK, Parker RM, Gazmararian JA, Nielsen-Bohlman LT, Rudd RR. The prevalence of limited health literacy. J Gen Intern Med. (2005) 20 175-84. DOI: 10.1111/j.1525-1497.2005.40245.x 73. Sudhakar S, Aebi ME, Burant CJ, Wilson B, Wenk J, Briggs FB. Health literacy and education level correlates of participation and outcome in a remotely delivered epilepsy self-management program. Epilepsy Behavior. (2020) 107 107026. DOI: 10.1016/j.yebeh.2020.107026 74. Van Der Heide I, Wang J, Droomers M, Spreeuwenberg P, Rademakers J, Uiters E. The relationship between health, education, and health literacy: results from the Dutch Adult Literacy and Life Skills Survey. J Health Commun. (2013) 18 172-84. DOI: 10.1080/10810730.2013.825668 75. Hekmati Pour N, Hojjati H. Effects of exercise on mental health of elderly. J Holistic Nursing And Midwifery. (2016) 26 36-42. PMID: 32546105 76. Park JM, Han SH. The effect of exercise program on health and depression in the elderly. J Korean Acad Nurs. (2003) 33 220-7. DOI: 10.4040/jkan.2003.33.2.220 77. Holland SK, Greenberg J, Tidwell L, Malone J, Mullan J, Newcomer R. Community-based health coaching, exercise, and health service utilization. J Aging Health. (2005) 17 697-716. DOI: 10.1177/0898264305277959
--- title: 'Osteochondrogenesis by TGF-β3, BMP-2 and noggin growth factor combinations in an ex vivo muscle tissue model: Temporal function changes affecting tissue morphogenesis' authors: - Heng Liu - Peter E. Müller - Attila Aszódi - Roland M. Klar journal: Frontiers in Bioengineering and Biotechnology year: 2023 pmcid: PMC10060664 doi: 10.3389/fbioe.2023.1140118 license: CC BY 4.0 --- # Osteochondrogenesis by TGF-β3, BMP-2 and noggin growth factor combinations in an ex vivo muscle tissue model: Temporal function changes affecting tissue morphogenesis ## Abstract In the absence of clear molecular insight, the biological mechanism behind the use of growth factors applied in osteochondral regeneration is still unresolved. The present study aimed to resolve whether multiple growth factors applied to muscle tissue in vitro, such as TGF-β3, BMP-2 and Noggin, can lead to appropriate tissue morphogenesis with a specific osteochondrogenic nature, thereby revealing the underlying molecular interaction mechanisms during the differentiation process. Interestingly, although the results showed the typical modulatory effect of BMP-2 and TGF-β3 on the osteochondral process, and Noggin seemingly downregulated specific signals such as BMP-2 activity, we also discovered a synergistic effect between TGF-β3 and Noggin that positively influenced tissue morphogenesis. Noggin was observed to upregulate BMP-2 and OCN at specific time windows of culture in the presence of TGF-β3, suggesting a temporal time switch causing functional changes in the signaling protein. This implies that signals change their functions throughout the process of new tissue formation, which may depend on the presence or absence of specific singular or multiple signaling cues. If this is the case, the signaling cascade is far more intricate and complex than originally believed, warranting intensive future investigations so that regenerative therapies of a critical clinical nature can function properly. ## 1 Introduction Successful regeneration of cartilage and bone remains an unresolved enigma to be solved clinically (Pittenger et al., 2019; Xiong et al., 2021). Due to intrinsic limitations in the ability of articular cartilage to self-renew and repair, cartilage-related injuries often result in osteoarthritic degeneration and long-term pain (Huang et al., 2019; Huynh et al., 2019). Among numerous restoration techniques, osteochondral grafts hold a more favorable prognosis than cartilage grafts alone because the bone-to-bone interface is more likely to integrate than the cartilage-to-chondral interface (Schaefer et al., 2002; Sheehy et al., 2013). An engineered osteochondral construct with cartilage and bone phenotypes seems to be a potential strategy for the treatment of chondral and osteochondral defects (Schaefer et al., 2002; Alhadlaq and Mao, 2005). During the past decade, although some great successes have been achieved to engineer ideal biomimetic osteochondral tissue, numerous challenges still need to be cleared to realize its final clinical application (Chen et al., 2011; Rodrigues et al., 2012; Zhang et al., 2019). Therefore, alternative models and improved osteochondral tissue engineering (TE) technologies should be explored. According to previous studies, the growth factors-loaded, muscle tissue-based, biomaterial induction system is a promising novel technology for TE (Betz et al., 2015; Betz et al., 2018; Ren et al., 2018; Xiong et al., 2020). Muscle is a relatively easily obtained tissue with a firm and durable self-repair capability; thus, harvesting muscle tissue does not cause severe morbidity in the donor area (Betz et al., 2009). It is well known that muscle tissue is an attractive cell source for TE since it contains abundant stem cells, which possess the potential to differentiate into an osteogenic lineage (Bosch et al., 2000; Ren et al., 2019). Compared to traditional cell culture-based TE approaches, the tissue culture system does not require the extraction and proliferation of autologous-derived osteoprogenitor cells, thus making it easier to operate and much cheaper (Betz et al., 2008; Virk et al., 2011). Additionally, the muscle tissue fragment is a one hundred percent biocompatible scaffold with a complex three-dimensional (3D) structure (Betz et al., 2008; Ren et al., 2019). Its intrinsic extracellular matrix (ECM) contains the necessary amino acids and the essential signaling molecules, providing an in vivo-like culturing milieu that supports cell growth and activity (Brand, 1997; Albert, 2005; Blair et al., 2017). Moreover, as a natural soft tissue scaffold, its easy deformability facilitates its matching to osteochondral defect sites. Furthermore, muscle tissue typically contains tiny blood vessels and numerous capillaries critical for nutrient flow and anabolic activities (Betz et al., 2013; Perniconi and Coletti, 2014; He et al., 2020). Members of the transforming growth factor-beta (TGF-β) superfamily perform various pleiotropic functions during both antenatal and postnatal development (Alliston et al., 2008). Among them, TGF-β3 and bone morphogenetic protein-2 (BMP-2) play crucial roles in processes of skeletogenesis, including the regulation of mesenchymal stem cell condensation, chondrocyte and osteoblast differentiation, and growth plate expansion (Ripamonti et al., 2016; Wu et al., 2016). TGF-β3 has a bi-functional impact on the maintenance of cartilage metabolic homeostasis, as it favors early-stage chondrocyte proliferation but arrests downstream chondrocyte hypertrophy, which is crucial to preserving hyaline cartilage integrity (Kato et al., 1988; Wu et al., 2016). However, TGF-β signaling is also known to induce osteogenesis and accelerate osteoarthritis through a Smad$\frac{2}{3}$ independent pathway (van der Kraan et al., 2012; van der Kraan, 2014). The osteogenic potential of TGF-β3 has been demonstrated in many different models. For instance, Ripamonti et al. [ 2015] identified in vivo experiments that TGF-β3 functions as the crucial signaling in regulating osteogenic relative gene expression and thus inducing ectopic bone formation in baboons. BMP-2 is a prerogative molecule during bone formation, as it plays a role in nearly the entire endochondral bone formation process (Gazzerro and Canalis, 2006; Ripamonti, 2006). Evidence has shown that BMP-2 is one of the most potent inducers for osteogenic differentiation (Huang et al., 2010), in which Noel et al. [ 2004] certified that even a short duration of BMP-2 expression is sufficient to induce irreversible endochondral bone. Moreover, amongst its other tissue-inductive capabilities, BMP-2 can also promote chondrogenesis (Keller et al., 2011; Chen et al., 2020). The first evidence of this ability was given by Urist [1965], who discovered that BMP-2 could induce both ectopic cartilage and bone formation within the rectus abdominis muscle of adult rabbits. As a classical extracellular antagonist of BMP-2, Noggin performs pleiotropic roles in various physiological and pathological developmental processes, such as the induction of neural and skeletal muscle tissue in early embryogenesis (Smith and Harland, 1992), and it is also crucial for chondrogenic and osteogenic differentiation (Bayramov et al., 2011; Krause et al., 2011). In mice overexpressing Noggin in the skeleton, osteoblast differentiation and bone formation were impaired, resulting in decreased bone mineral density and weakened osteoblastic function (Devlin et al., 2003; Wu et al., 2003). Nevertheless, the downregulation of Noggin in cells in the bone environment increases the expression of osteogenic differentiation markers and thus enhances the regeneration of bone defects (Gazzerro et al., 2003; Wan et al., 2007). Furthermore, proximal symphalangism and multiple synostoses syndrome in humans can also be attributed to Noggin mutations (Gong et al., 1999). Previous experimental studies have reported that a combination of morphogens acting synergistically or in modulatory roles could result in superior morphogenesis (Cicione et al., 2015; Huang et al., 2020). For example, Xiong et al. [ 2020] demonstrated that the combined treatment of TGF-β3, BMP-2, and BMP-7 could promote chondrogenesis in muscle tissue more efficiently than either morphogen applied on its own or in various duplicate combinations. Similar synergistic effects have also been investigated by other scientists, in which co-administration of BMP-2 and TGF-β3 resulted in an improved bone formation response (Haschtmann et al., 2012; Wang et al., 2016; He et al., 2019). However, the antagonistic effect between different TGF-βs and BMPs has also been discussed by other researchers (Mehlhorn et al., 2007; Wakefield and Hill, 2013; Xiong, 2020). In addition, the mutual impact between BMPs and Noggin has been intensively explored in the last decades (Re’em-Kalma et al., 1995; Zakin and De Robertis, 2010; Wang et al., 2013). Recent studies have also shown the association between TGF-β3 and Noggin during the process of endochondral bone formation within muscle tissue (Klar et al., 2014; Ripamonti et al., 2015). Nevertheless, the detailed complex interaction mechanisms among these three growth factors and their temporal and spatial behavior have yet to be thoroughly explained. Therefore, the present study attempted to detect what the osteochondrogenic effects, if any, would be under a temporal signaling cascade of these three growth factors, which are applied to this specialized muscle tissue model platform in seven different patterns. The differentiated cultured muscle tissue was analyzed at 7, 14, and 30 days using three methods (Pittenger et al., 2019): quantitative reverse transcription-polymerase chain reaction (RT-qPCR) (Xiong et al., 2021), immunohistochemistry (IHC), and (Huang et al., 2019) histology. The objectives of this study were (Pittenger et al., 2019): to assess the osteochondrogenic induction potential of the muscle tissue after 1 month of continuous application of BMP-2 and/or TGF-β3 and/or Noggin and (Xiong et al., 2021) to investigate the so far unclear interaction mechanisms between the three growth factors during the endochondral bone induction process and if there are unique interactions in respect to tissue morphogenesis between the various growth factor combinations. ## 2.1 Chondrogenesis The chondrogenesis was evaluated at the following levels: gene expression (Figure 1), Alcian Blue (Figure 2) and IHC-ACAN staining (Figure 3, Table 1). FIGURE 1: The relative gene expression of (A) Col2a1, (B) Sox9, (C) Acan, (D) Six1 and (E) Abi3bp at 7, 14, and 30 days, which were shown as CNRQ. The asterisks indicate that the stimulated group is statistically significant compared to the control group. The baseline number 0 indicates non-cultured fresh tissue was used as the normalization parameter. ($$n = 6$$; $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$). FIGURE 2: The staining results of Alcian Blue in each group. (A) Staining results on day 30; the positive staining color was blue (marked by black arrows). (B) Histomorphometrical assessment; the result was shown as Mean IOD/Area. Control group vs. stimulated groups at 7, 14, and 30 days; the asterisks indicate that the stimulated group is statistically significant compared to the control group. (Magnification: ×40; $$n = 6$$; $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$). FIGURE 3: The staining results of ACAN antigen in IHC in each group. (A) Staining results on day 30; the positive staining color was green (marked by black arrows). (B) Histomorphometrical assessment; the result was shown as Mean IOD/Area. Control group vs. stimulated groups at 7, 14, and 30 days; the asterisks indicate that the stimulated group is statistically significant compared to the control group. (Magnification: ×40; $$n = 6$$; $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$). TABLE_PLACEHOLDER:TABLE 1 In order to evaluate chondrogenic gene expression in response to single or combined exposure of the modulating factors TGF-β3 (T), BMP-2 (B) and Noggin (N) in the muscle tissue model, temporal gene expression of cartilage-specific marker genes were analyzed by RT-qPCR. For the fibrillar collagen marker gene Col2a1, the control group showed similar, moderately upregulated expression on each day compared to the non-cultured, fresh muscle tissue (Figure 1A). The B group had the highest relative Col2a1 expression on day 7, which was significantly upregulated compared to the control, similar to T and T + B + N groups. The combination of T + B increased Col2a1 expression significantly only on days 14 and 30. The T + B and T + B + N groups showed the highest relative gene expression on days 14 and 30, respectively. Except for day 30 of the T + N and T + B + N groups and day 7 of the T + B + N group, Col2a1 gene expression did not change significantly in all other N-treated groups compared to the control (Figure 1A, Supplementary Table S1). The expression of the chondrogenic master transcription factor Sox9 peaked in groups B, T + B, and T + B + N on days 7, 14, and 30, respectively, and all three groups showed significant differences compared to the control. The T group exhibited the highest Sox9 expression on day 14 and showed significant upregulation on days 14 and 30. Among N-treated groups, significant Sox9 upregulation was observed in the N group on day 14 and in the T + N and T + B + N groups on days 14 and 30, respectively (Figure 1B, Supplementary Table S1). For the major proteoglycan marker Acan, significantly upregulated gene expression was found in the B-, T-, T + B-, and T + N-stimulated groups on day 7 compared to the control. On day 14, only the T and T + B groups showed significant upregulation. On day 30, all groups except N and B + N presented significant Acan upregulation compared to the control (Figure 1C, Supplementary Table S1). It has been previously shown that transcripts of Six1 and Abi3bp are enriched in articular chondrocytes compared to growth plate chondrocytes; therefore, these genes have been proposed as markers for articular cartilage (Lee et al., 2021). In our muscle tissue model, we found that on day 30, Six1 gene expression was upregulated in all treated groups compared to the control, except N (Figure 1, Supplementary Table S1), and Abi3bp was upregulated in all treated groups, except N and N + B (Figure 1, Supplementary Table S2). In the case of Six1, there was no significant difference in gene expression relative to control in either group on day 7, while on day 14, only the T and T + B groups displayed significant Six1 upregulation (Figure 1D, Supplementary Table S1). Significantly upregulated expression of Abi3bp was found in the B, B + T, and T + N groups on day 7 and in the B, B + T, T + N, and T + B + N groups on day 14. Moreover, Abi3bp gene expression in the N and B + N groups showed no significant difference compared with control at all three time points (Figure 1E, Supplementary Table S2). Alcian Blue staining was used to assess chondrogenesis in the cultured muscle tissue samples. Increased staining areas in blue were observed near the fascia or in the intercellular region of the muscle when stimulated by T, B, T + B, and T + B + N at all detection time points compared to the control (Figure 2A). Histomorphometric comparisons with the control showed that the B and T + B groups presented a significantly increased positive reaction area at all three-time points, while the T- and T + B + N-stimulated groups displayed significant positive reactions on days 7 and 14. On the other hand, all groups showed the strongest positive Alcian Blue staining results at 14 days, while the N and B + N groups consistently showed no significant differences compared with the control (Figure 2B, Supplementary Table S4). IHC-ACAN staining was carried out to show the presence of ACAN antigens. A positive antigen–antibody interaction would be stained in a green color, which could be observed in close proximity to the fascia or in the intercellular region of the muscle when stimulated by B, T, T + B, T + N, and T + B + N at all detection time points (Figure 3A). The histomorphometrical assessments of IHC-ACAN staining showed that the B and T + B groups presented a positive reaction during all three-time points, while the T and T + B + N groups displayed a positive reaction on days 14 and 30. In addition, the T + N group also exhibited a significant difference on day 30. Additionally, no B + N group showed a significant difference (Figure 3B, Supplementary Table S4). ## 2.2 Osteogenesis The osteogenesis was evaluated at the following levels: gene expression (Figure 4), Alizarin Red S (Figure 5) and IHC-OCN staining (Figure 6). FIGURE 4: The relative gene expression of (A) Alp, (B) Runx2, (C) Bmp-2, (D) Ocn, and (E) Col1a1 at 7, 14, and 30 days, which were shown as CNRQ. The asterisks indicate that the stimulated group is statistically significant compared to the control group. The baseline number 0 indicates non-cultured fresh tissue was used as the normalization parameter. (n = 6; p < 0.05; p < 0.01; p < 0.001). FIGURE 5: The staining results of Alizarin Red S in each group. (A) Staining results on day 30; the positive staining color was red (marked by black arrows). (B) Histomorphometrical assessment; the result was shown as Mean IOD/Area. Control group vs. stimulated groups at 7, 14, and 30 days; the asterisks indicate that the stimulated group is statistically significant compared to the control group. (Magnification: ×40; n = 6; p < 0.05; p < 0.01; p < 0.001). FIGURE 6: The staining results of the OCN antigen in the IHC in each group. (A) Staining results on day 30; the positive staining color was green (marked by black arrows). (B) Histomorphometrical assessment; the result was shown as Mean IOD/Area. Control group vs. stimulated groups at 7, 14, and 30 days; the asterisks indicate that the stimulated group is statistically significant compared to the control group. (Magnification: ×40; n = 6; p < 0.05; p < 0.01; p < 0.001). For Alp, the B group showed the highest relative gene expression on day 7, which was significantly upregulated, along with the T and T + B + N groups. Additionally, the T-induced group was the only one that presented a significant Alp expression on day 14, and only the T + N and T + B + N groups demonstrated a significant upregulation of Alp expression. On the other hand, Alp expression in all N-involved groups showed no significant difference (Figure 4A, Supplementary Table S2). For the relative Runx2 gene expression, the T + N group became the only group that showed a significant difference at 7 days, while at 14 days, the significantly upregulated Runx2 gene expression was found in B, T, and T + B groups. By 30 days, the B, T, and T + B + N groups showed high and significant gene expression. In addition, except for the 7-day T + N and 30-day T + B + N groups, Runx2 gene expression in all other Noggin-involved groups was not significant (Figure 4B, Supplementary Table S2). For the relative Bmp-2 gene expression, the T + N group showed a significant difference across all three-time points; in addition, T, N, and T + B groups presented significantly upregulated Bmp-2 gene expression at both day 14 and 30. Moreover, the T + B + N group showed the highest and most significant gene expression on day 30. In addition, all B + N groups showed non-significant Bmp-2 gene expression (Figure 4C; Supplementary Table S3). For the relative Ocn gene expression, the B, T, T + B, and T + B + N groups all presented significant upregulation among the three-time points. Additionally, the T + N group also showed significant Ocn gene expression on days 7 and 30, but a non-significant difference was found on day 14. Furthermore, the N and B + N groups exhibited non-significant Ocn gene expression all the time (Figure 4D, Supplementary Table S3). For Col1a1, no treatment group showed upregulated relative gene expression at 7 days, while the T and T + B groups were significantly upregulated at 14 days. By 30 days, although most stimulated groups showed upregulation of Col1a1 gene expression, only the T group exhibited a significant difference (Figure 4E, Supplementary Table S3). Alizarin Red S staining was applied to show the depositions of calcium ions in tissues as a measure of osteogenesis. Under B, T, T + B, T + N, and T + B + N stimulation, areas of positive staining in red were observed in close proximity to the fascia or intercellular regions of the muscle at all detection time points (Figure 5A). Histomorphometric evaluation of Alizarin Red S staining showed that the B group presented a significant positive reaction on days 7 and 14, while the T group only displayed a significant positive reaction on day 14 compared to the control. In addition, the T + B group displayed a positive reaction on days 7 and 30. Moreover, the T + B + N group showed significant stimulation of osteogenesis from day 14 until day 30. The N and B + N groups consistently showed no significant difference compared to the control (Figure 5B, Supplementary Table S4). IHC-OCN staining was carried out to show the presence of the OCN antigen. Under the stimulation of B, T, T + B, T + N, and T + B + N, areas of positive staining were observed in close proximity to the fascia or intercellular regions of the muscle with green color at all detection time points (Figure 6A). The histomorphometrical assessment of IHC-OCN staining showed that, although there was a generally high positive reaction on day 7, the B group was the only one that had a significant difference. In addition, the T + B + N group became the only significant positive stimulation group at 14 days, while the B, T, T + B, and T + B + N groups all showed significant differences by day 30. Additionally, no B + N group showed a significant difference (Figure 6B, Supplementary Table S4). ## 2.3 Heat map analysis The heat map analysis of gene expression and histomorphometrical data are represented in Figure 7 and Figure 8, respectively. The heat map is a summary of the results (Table 1) indicating where significant differences exist in the gene expression and tissue development. FIGURE 7: Heat map of gene expression. All relative gene expression at 7 (A), 14 (B) and 30 days (C). Acan = Aggrecan, Col2a1= Collagen type II alpha 1, Sox9 = Sex determining region Y (SRY)-box transcription factor 9, Six1= Six homeobox 1, Abi3bp = Abi family member 3 binding protein, Runx2 = Runx family transcription factor 2, Alp= Alkaline phosphatase, Bmp-2 = Bone morphogenetic protein-2, Ocn = Osteocalcin, Col1a1 = Collagen type I alpha 1 chain; (n = 6). FIGURE 8: Heat map of histomorphometrical analyses. (A) Alcian Blue staining. (B) Alizarin Red S staining. (C) IHC-ACAN staining. (D) IHC-OCN staining. IHC = Immunohistochemistry, ACAN = Aggrecan, OCN = Osteocalcin; (n = 6). The heat map of gene expression showed that the B- and T + N-stimulated groups promoted relatively high gene expression at 7 days (Figure 7A); the T- and T + B-stimulated groups displayed relatively high gene expression at 14 days (Figure 7B); while the stimulation of T, T + B, T + N, and T + B + N exhibited high gene expression at 30 days (Figure 7C). Compared to 7 and 30 days, stimulation by T + N resulted in less gene expression at 14 days. Additionally, the N and B + N groups did not show high gene expression at all time periods (Figure 7). As seen in all the histomorphometrical analyses of the heat map, the B and T + B groups presented the most robust positive response results compared to the other participating groups. However, the single B group performed better in the early phase (7 and 14 days), while the combined T + B group was more dominant in the late phase (30 days). In addition, stimulation by T alone also displayed positive results, although slightly weaker than the T + B combination. Furthermore, the T + B + N group resulted in relatively higher positive reactions in all staining at late stages (at 14 and 30 days), except for the 30-day Alcian Blue staining (Figure 8). All results were summarized in Table 1. ## 3 Discussion The TGF-β/BMP signaling pathway is an important thread essential for osteochondrogenic tissue formation (Zhou et al., 2005; Bami et al., 2016; Izadpanahi et al., 2018). Endochondral bone development and articular chondrogenesis are closely regulated by diverse growth factors (Chung et al., 2001; Liao et al., 2014). Generally, the results of this study showed that both chondrogenic and osteogenic-related genes underwent significant changes over the 30 days of in vitro culturing with TGF-β3 and/or BMP-2 groups and the TGF-β3+BMP-2+Noggin sets. Combined with the histomorphometrical results, the findings suggest that our muscle tissue may be undergoing an osteochondrogenic process, favoring an articular to endochondral bone transdifferentiation activity. The positive IHC-ACAN and the strong Alcian Blue staining in conjunction with the significant upregulation of Sox9, Acan, and Col2α1 genes, in addition to the upregulation of articular cartilage genes Abi3bp and Six1, suggest that a form of articular chondrogenesis was being induced (Xiong et al., 2020). Whilst it remains unclear if this is proper articular cartilage or a specialized undiscovered form of the process, its detection corroborates the principle that the process of endochondral bone formation is always accompanied by the appearance of hyaline cartilage (Blumer et al., 2005; Grässel and Aszódi, 2016). On the other hand, the positive results of IHC-OCN and Alizarin Red S staining showed the abundant presence of OCN and calcium deposition, inferring that a bone-related ECM was either also being formed or a transition was underway from the chondrogenic tissue to that of a bone-like tissue (McLeod, 1980; Ding et al., 2019). We believed this to be the case, as the increases in Runx2, Alp, Ocn, Bmp-2, and Col1α1 gene expressions over the culturing periods were indicative of a trend towards osteogenic morphogenesis (Karsenty et al., 2009; Scott et al., 2012). Though this seemed to be a general trend among the various growth factor groups analyzed, marked differences were also recorded. The present research experiment verified that both TGF-β3 and BMP-2 alone could initiate osteochondrogenesis. Especially Sox9 and Runx2, master transcription factors for chondrogenesis and osteogenesis, respectively (Eames et al., 2004; Zhang et al., 2013), showed overlapping and significantly increased expressional regulation. On day 7, Sox9 was positively expressed in the single BMP-2 group, while no significant result was detected for Runx2 gene expression. This result was consistent with many previous studies that Sox9 and Runx2 play a reciprocal inhibitory role during osteo-chondrogenesis to influence mesenchymal cell fate (Yamashita et al., 2009; Cheng and Genever, 2010). During the early chondrogenic differentiation stage, BMP-2-induced Runx2 expression was suppressed by Sox9 to inhibit the subsequent endochondral ossification process and maintain the hyaline cartilage phenotype (Zhou et al., 2006; Liao et al., 2014). However, Sox9 also contributed to BMP-2-induced osteogenic differentiation since Sox9 silencing causes reduced osteogenesis in bone-marrow-derived mesenchymal stem cells (BMSCs) (Zhao, 2008; Fang et al., 2019). Alternatively, the groups treated with TGF-β3 only showed the positive upregulation of Bmp-2 and Runx2 gene expressions on days 14 and 30, confirming previous claims by Klar et al. [ 2014] and other studies that TGF-β3 seems to be able to regulate osteogenesis by modulating endogenous Bmp-2 levels, followed by increased Runx2 expression (Wang et al., 2016). For the TGF-β3+BMP-2 groups, both synergistic and antagonistic activities were discovered that occurred at specific temporal culturing stages of our in vitro model. From day 0 to day 7 and 14, the addition of TGF-β3 blocked most of the BMP-2 gene and protein upregulation that normally would occur if TGF-β3 were absent. In relation to the inhibitory effects, it is known that both TGF-β3 and BMP-2 have similar receptor binding mechanisms, inferring that competitive inhibition of the TGF-βs and BMPs receptors is possible (Keller et al., 2011). Alternatively, TGF-βs could be blocking BMP signaling transduction by forming mix-linked Smad$\frac{1}{5}$-Smad3 inhibitory complexes (Daly et al., 2008; van der Kraan et al., 2012), or it could be that TGF-β3-induced inhibitory Smad6 or Smad7 are also interfering with the BMP signaling pathway (Keller et al., 2011). This has been well-described by various scientists. For instance, Ehnert et al. [ 2010]; Ehnert et al. [ 2012] showed that Smad$\frac{1}{5}$/8-mediated BMP-2 and -7 signaling could be blocked entirely by adding recombinant human TGF-β in primary human osteoblasts. Similarly, Mehlhorn et al. [ 2007] presented that BMP-2 induced chondrogenesis and osteogenesis in adipocyte-derived stem cells could be prevented by simultaneously applying any of the three TGF-β isoforms. However, the synergistic activities between TGF-β and BMP signaling were also found in the same tissue model system, but only during the later 30-day stages of culture. From 14 to 30 days, most detected genes and proteins were significantly higher upregulated in the TGF-β3+BMP-2 group than either the TGF-β3 or BMP-2 groups (Figure 7B). The possible underlying mechanisms of the synergistic effect, and those at specific time points, could be that TGF-βs switch function over time. This would suggest that TGF-β3 can alternate between being a competitive inhibitor of the BMPs pathways to being an activator of cellular stimulation, at specific time points, either due to changes in concentration or intrinsic cellular alteration. Apart from binding ALK5 to stimulate the canonical Smad$\frac{2}{3}$ signaling pathway, TGF-βs can also exert functions via activating the BMP signaling pathway by associating with ALK1 and ALK2 directly and then triggering Smad$\frac{1}{5}$/8 for signal transmission (Wrighton et al., 2009; Keller et al., 2011). The synergistic effect between TGF-βs and BMPs is well known (Wu et al., 2016). However, if growth factors change function with time, switching roles based on cellular activity or differentiation/transformation changes, this needs to be further analyzed in future studies. This is especially critical given that our Noggin results showed a similar function switching from inhibitor to stimulator. The ectopic application of Noggin in our experiment confirms that one of the roles of Noggin is to antagonize BMP-2-induced osteochondrogenic differentiation. Nearly all applications of Noggin alone and BMP-2+Noggin combined presented insignificant expressional changes, both at the gene and protein levels and at all culturing time points. As a key natural BMPs antagonist, Noggin can specifically bind BMP-2, -4, -5, -6, and -7 with several degrees of affinity, including GDF-5/-6, yet provides little to no binding affinity to the other TGF family members (Smith and Harland, 1992; Song et al., 2010). However, our experiment counteracts this assumption as Noggin seemed to actively inhibit exogenously applied TGF-β3 growth factor functioning, since Noggin prevented the upregulation of all genes that TGF-β3 normally activated on day 14 (Figure 7B). Indeed, the inhibitory effect of Noggin on TGF-β3 has been discovered and reported by many scholars. Nakayama et al. [ 2003] showed that Noggin could block TGF-β3 induced chondrogenesis, suggesting a BMP-associated pathway was involved. In addition, Bayramov et al. [ 2011] put forward a novel inhibitory function of Noggin by demonstrating that, in addition to BMPs, several non-BMP ligands, such as Activin B, Xnr2, and Xnr4, can also be antagonized by Noggin, albeit less efficiently. Interestingly, these blocked non-BMP ligands regulate specific genes’ transcription through cytoplasmic Smad$\frac{2}{3.}$ This point may indicate another link between TGF-β3 and Noggin regarding non-BMP ligands and downstream effectors Smad$\frac{2}{3.}$ From this and in conjunction with our results, we deduce that the application of Noggin can, at specific time points, inhibit the differentiation function of both BMP-2 and TGF-β3 signaling. However, the inhibition effect of Noggin + TGF-β3 was not observed at day 7 nor day 30. Instead, at these time points, our results showed that most of the gene and protein expression markers increased significantly, promoting the idea that signals, whether they be growth factors or antagonists such as Noggin, possess various roles that are not limited to a single function but are temporally dependent. Indeed, our results suggest a positive function of Noggin in osteo-chondrogenesis at specific temporal stages. Interestingly, a similarly positive result could also be observed with our TGF-β3+BMP-2+Noggin groups at day 30 (Figures 7A, C; Figures 8B–D). While this interpretation does go against the traditional concept that Noggin should inhibit osteo-chondrogenesis, Noggin’s positive stimulatory functions have been reported. For instance, Chen et al. [ 2012] proposed that Noggin can stimulate human MSCs osteogenesis, as the suppression of significantly reduced BMP-2-induced ALP activity. Rifas [2007] made a similar observation showing that Noggin could induce ALP action and upregulate Bmp-2 and Ocn gene expression. Unusually, other than these ordinary osteogenic markers, they also found increased ActRII expression (Rifas, 2007). Furthermore, Hashimi [2019] found that exogenous Noggin treatment could induce osteogenesis by binding to and stimulating the BMP-2 receptor (Hashimi, 2019). Taken together with our discoveries, this would suggest that Noggin may perform a stimulatory role during specific temporal stages of osteo-chondrogenesis development, especially when it is in the presence of TGF-β3. Future research needs to investigate this more clearly, as there is a definitive lack of knowledge regarding the temporal behavior of growth factors and inhibitors over time and at which time points signals change their function. Over the course of nearly 3 decades, research into the possible mechanisms for the formation and regenerating of bone or articular cartilage tissue, have yielded few clinically relevant solutions (Wei and Dai, 2021). Whilst a large spectrum of regenerative scientists and tissue engineers are trying to find new alternatives, Klar [2018] possibly provided one of the most prudent solutions to solving this dilemma, being that “all of the relevant signals and their interactions had not been fully established”. This inferred that gaps in the knowledge exist in how ligands are activated and how their effect changes over time when affecting tissue development. Indeed, the current work not only establishes that our knowledge on signals and their behavior over the course of time changes drastically between stimulation, antagonism, and regulation, but that with the correct combination of signals any tissue could be indirectly (in vitro) or directly (in vivo) transformed into whatever tissue/organ we desire. The clinical implications of such information would be invaluable for future therapies as whole organs or even limbs could be fully grown from excess damaged tissue areas or excess tissue types be biological recycled to form new tissues/organs (Betz et al., 2009; Xiong et al., 2020). Whilst our results did show some critical new discoveries and possible avenues of research, the study also had certain limitations. A critical limitation was that we did not consider the effect of the dose gradient of the applied growth factors on the experimental results. Whilst we tried to choose a dose that would elicit a response without causing inhibition, some studies have reported that the TGF-β superfamily factors serve as a double-edged sword in DNA synthesis (Chen et al., 1991; Harris et al., 1994). For example, a low concentration of TGF-β can promote osteogenic differentiation but inhibit it at a high concentration (Karst et al., 2004; Crane et al., 2016). In addition, low doses of active TGF-β have also been shown in chondrocytes to preferentially signal through the Smad$\frac{2}{3}$ pathway, while the Smad$\frac{1}{5}$ pathway becomes predominant at high doses (Finnson et al., 2008; Blaney Davidson et al., 2009). In addition, that BMP-2 controls bone formation in a concentration-dependent manner has also been demonstrated in bone TE studies (Meinel et al., 2006; Shi et al., 2012; Dang et al., 2016). Thus, an appropriate molecular concentration may play a vital role in a differentiation system as time progresses. Subsequently, another limitation was that we did not apply the growth factors in a truly temporal manner, i.e., first BMP-2 for 2 days then TGF-β3 for 2 days, etc., nor adjust the application order. Iwakura et al. [ 2013] established that morphogen treatment order could produce varying effects. Applying growth factors, such as BMP-7 followed by TGF-β1, resulted in more effective chondrogenesis than TGF-β1 following BMP-7. On the other hand, although numerous types of cells give the muscle tissue the possibility of multiple differentiation, it also increases the uncertainty of its differentiation direction. It is challenging to match various differentiated phenotypes with corresponding cell types in such a complex 3D cellular assembly. As such, a comparison between the muscle tissue explant and a specific single cell type, such as satellite cells or myoblasts, may be necessary to be conducted, especially in a 3D pellet culture condition, to confirm the superiority of this muscle tissue induction model. Moreover, the increasing trend of gene expression in the control group, although not significantly different compared to the 0-day sample (baseline), might suggest that the induced phenotypes were not absolutely derived from the exogenous molecules. One of the explanations may be that the FBS in the normal medium provided some supplementary signals for differentiation. The other reason may be attributed to the injury from tissue excision since the trauma-induced various BMPs expression and followed heterotopic ossification have been verified by many investigators (Li, 2020; Strong et al., 2021). Finally, to achieve a more realistic in vitro physiological simulation system, mechanical and even electrochemical stimulation, as directed by nerves, should also be considered as a complement to biochemical cues in this muscle-tissue-based model because they can also play essential and unique roles as temporal biophysical signals to participate in cellular activities (Boonen et al., 2010; Maleiner et al., 2018; Urdeitx and Doweidar, 2020). ## 4.1 Collection of muscle tissue samples The rectus abdominis muscle tissue was collected from two Fischer-344 adult Rattus norvegicus (Charles River Wiga, Sulzbach, Germany). The animals were sacrificed with an excess of isoflurane (Abbot, Chicago, United States) and disinfected with $10\%$ povidone-iodine (Betadine, Bonn, Germany) and $75\%$ alcohol (Apotheke Großhadern, Munich, Germany). Under a sterile environment, the harvested muscle was incubated in graded concentrations of penicillin and streptomycin ($2\%$ and $1\%$) (A2213; P/S, Biochrom GmbH, Berlin, Germany) in Alpha-Medium (Biochrom GmbH, Berlin, Germany) for 20min, respectively. Then, 288 fragments of the tissue 4 mm in diameter were obtained with a specific biopsy punch (PFM medical, Cologne, Germany). The rules and regulations of the Animal Protection Laboratory Animal Regulations [2013] of the European Directive $\frac{2010}{63}$/EU Act were strictly complied with during the above procedures. The experiments were also approved by the Animal ethics research committee of the Ludwig Maximillian University of Munich (LMU), Bavaria, Germany Tierschutzgesetz §1/§4/§17 (https://www.gesetze-im-internet.de/tierschg/TierSchG.pdf) with regard to animal usage for pure tissue or organ harvesting only. ## 4.2 Muscle tissue culture The muscle tissue biopsies ($$n = 288$$) were cultured in 96-well plates (Thermo Fisher Scientific, Waltham, MA, United States) in normal culture medium (containing Alpha-Medium, $1\%$ P/S, 0.02 mM/mL L-glutamine (Biochrom GmbH, Berlin, Germany) and $15\%$ fetal bovine serum (FBS; Biochrom GmbH, Berlin, Germany)) for 48 h in a humidified incubator with $5\%$ CO2 at 37°C to allow for the cells in the tissue to recover. The muscle tissue fragments were then divided into eight independent treatment groups: (Pittenger et al., 2019) Control (Con) group, containing the normal culture medium (Xiong et al., 2021); Rat BMP-2 (B) group, containing the normal culture medium and 50 ng/mL BMP-2 (CUSABIO, United States) (Huang et al., 2019); Rat TGF-β3 (T) group, containing the normal culture medium and 50 ng/mL TGF-β3 (Cloud-Clone Corp, United States) (Huynh et al., 2019); Rat Noggin (N) group, containing the normal culture medium and 50 ng/mL Noggin (Cloud-Clone Corp, United States) (Sheehy et al., 2013); TGF-β3+BMP-2 (T + B) group, containing the normal culture medium and 50 ng/mL TGFb3+50 ng/mL BMP-2 (Schaefer et al., 2002); TGF-β3+Noggin (T + N) group, containing the normal culture medium and 50 ng/mL TGF-β3+50 ng/mL Noggin (Alhadlaq and Mao, 2005); BMP-2+Noggin (B + N) group, containing the normal culture medium and 50 ng/mL BMP-2+50 ng/mL Noggin (Zhang et al., 2019); TGF-β3+BMP-2+Noggin (T + B + N) group, containing the normal culture medium and 50 ng/mL TGF-β3+50 ng/mL BMP-2+50 ng/mL Noggin. Each modality had 36 samples that were divided up into quantitative genes ($$n = 6$$) as well as histological ($$n = 6$$) assessment groups and further into subsequent culture period lengths of 7, 14, and 30 days. In the end, for each treatment modality, there were always 6 muscle fragments for a given culture length and assessment method. ## 4.3 RT–qPCR The minimum information for publication of quantitative real-time PCR experiments (MIQE) principles was strictly applied to guide the entire RT–qPCR procedure (Bustin and Wittwer, 2017). After flash freezing in liquid nitrogen, the harvested muscle tissue samples were homogenized by a mortar and pestle under an RNase-free work hood. The RNeasy® Fibrous Tissue Mini Kit (Qiagen, Hilden, Germany) was used to extract and purify the total RNA. The obtained RNA samples had an A260/A280 ratio of 1.86–2.07 and a concentration of 76.7–123.7 ng/μL, which were measured by a NanoDropTMLite (Thermo Fisher Scientific, Waltham, MA, United States). Finally, a QuantiTect complementary DNA (cDNA) Synthesis Kit (Qiagen, Hilden, Germany) was applied according to their specialized protocol to conduct reverse transcription. The resulting cDNA was deposited at −20°C for subsequent qPCR analysis. The qPCR process was performed on a LightCycler® 96 Instrument (Roche, Switzerland), utilizing the FastStart Essential DNA Green Master and SYBR Green I Kit (Roche, Switzerland). The thermal cycling parameters were set in 3 min initial denaturation steps at 95°C; 40 cycles, including a denaturation step at 95°C for 10 s, an annealing step at 60°C for 15 s, and an extension step at 72°C for 30 s, respectively; and a final extension at 72°C for 5 min. The final reaction volume was 10 μL, consisting 2 μL cDNA (5 ng/μL), 1.8 μL RNase-free water, 5 μL Green Master, 0.6 μL forward primer, and 0.6 μL reverse primer. The primers of eight reference genes and ten target genes (Table 2) were designed and analyzed on the IDT website (https://eu.idtdna.com/site). TABLE 2 \| Unnamed: 0 \| Gene name \| Accession nr \| Fwd. (5′-3′) \| Rev. (5′-3′) \| \| --- \| --- \| --- \| --- \| --- \| \| \| Actb \| NM_031144.3 \| AGCTATGAGCTGCCTGA \| GGCAGTAATCTCCTTCTGC \| \| \| Rplp0 \| BC001834.2 \| CAACCCAGCTCTGGAGA \| CAGCTGGCACCTTATTGG \| \| Reference genes \| Gapdh \| BC083511.1 \| CATGGGTGTGAACCATGA \| TGTCATGGATGACCTTGG \| \| \| Polr2e \| BC158787.1 \| GACCATCAAGGTGTACTGC \| CAGCTCCTGCTGTAGAAAC \| \| \| Sdha \| NM_130428.1 \| GCGGTATGAGACCAGTTATT \| CCTGGCAAGGTAAACCAG \| \| \| Acan \| NM_022190.1 \| CAAGTGGAGCCGTGTTT \| TTTAGGTCTTGGAAGCGAG \| \| \| Col2a1 \| NM_012929.1 \| ATCCAGGGCTCCAATGA \| TCTTCTGGAGTGCGGAA \| \| \| Sox9 \| NM_080403.1 \| CCAGAGAACGCACATCAAG \| ATACTGATGTGGCTGGTGG \| \| \| Six1 \| NM_053759.1 \| CAGGTTCTTGTGGTCGTT \| TTTGGGATGGTTGTGAGG \| \| Target genes \| Abi3bp \| XM_017598145.1 \| ACGGGACATTCCTCTCATA \| GGTGCCTGAGTTGTCTTT \| \| \| Runx2 \| NM_001278484.2 \| CCCAAGTGGCCACTTAC \| CTGAGGCGGTCAGAGA \| \| \| Alp \| NM_013059.2 \| CGACAGCAAGCCCAAG \| AGACGCCCATACCATCT \| \| \| Bmp-2 \| NM_017178.1 \| GGAAGTGGCCCACTTAGA \| TCACTAGCAGTGGTCTTACC \| \| \| Ocn \| NM_013414.2 \| GCGACTCTGAGTCTGACA \| GGCAACACATGCCCTAAA \| \| \| Col1a1 \| NM_053304.1 \| GGTGACAGAGGCATAAAGG \| AGACCGTTGAGTCCATCT \| The GeNorm (http://medgen.ugent.be/wjvdesomp/genorm/) was applied to assess and select Glyceraldehyde-3-phosphate dehydrogenase (Gapdh); Succinate dehydrogenase complex flavoprotein subunit A (Sdha); Ribosomal protein lateral stalk subunit P0 (Rplp0); RNA polymerase II, I and III subunit E (Polr2e); and Actin beta (Actb) as the final reference genes (Table 1) for the subsequent gene expression calibration process. Targets included the chondrogenesis-associated genes collagen type II (Col2a1), SRY-box transcription factor 9 (Sox9), aggrecan (Acan), SIX homeobox 1 (Six1) and ABI family member 3 binding protein (Abi3bp), and the osteogenesis-associated genes Alkaline phosphatase (Alp), RUNX family transcription factor 2 (Runx2), Bone morphogenetic protein 2 (Bmp-2), osteocalcin (Ocn) and Collagen type I alpha 1 chain (Col1a1). The relative gene expression levels were characterized in calibrated normalized relative quantities (CNRQs), which were obtained by normalization with the pre-determined reference genes in the qBase + software (https://www.qbaseplus.com/), including the relevant endogenous control (fresh muscle tissue 0-day). ## 4.4 Histological and immunohistochemical (IHC) staining Harvested samples for histological analysis were first placed in $4\%$ paraformaldehyde (Microcos GmbH, Garching, Germany) for overnight fixation, followed by dehydration in Spin Tissue Processor-120 (Especialidades Médicas Myr, S.L., Tarragona, Spain), then embedded in paraffin blocks. Afterwards, 3 μm-thick sections were cut for subsequent staining. Alcian Blue staining was used to evaluate the effectiveness of chondrogenesis in this study. Deparaffinized and hydrated sections were stained in $1\%$ Alcian Blue solution (pH 2.5, Morphisto, Frankfurt, Germany) and counterstained in $0.1\%$ Nuclear Fast Red solution (Morphisto, Frankfurt, Germany) and were then dehydrated and covered with EUKITT mounting media (O. Kindler GmbH, Bobingen, Germany). Alizarin Red S staining was used to identify the efficiency of osteogenesis in this study. Sections were stained in Alizarin Red S solution (pH 9, Morphisto, Frankfurt, Germany), re-stained in Alizarin Red S solution (pH 7, Morphisto, Frankfurt, Germany), then dehydrated and mounted in synthetic resin (O. Kindler GmbH, Bobingen, Germany). To observe the chondrogenic or osteogenic response within the muscle tissue samples, Rabbit polyclonal anti-ACAN (1:150, orb213537) and anti-OCN (1:100, orb259644) antibodies (Biorbyt, Eching, Germany) were utilized for IHC staining. Rabbit-on-Rodent HRP-Polymer (ZYTOMED SYS-TEMS GmbH) was applied as a secondary antibody. Negative control was also set up using Antibody Diluent (ZYTOMED SYSTEMS GmbH, Berlin, Germany) instead of the primary antibodies. Finally, a Vina-Green™ chromogenic kit (Biocare-Medical, Concord, CA, United States) was used to show positive interactions between antigen and antibody. ## 4.5 Histomorphometric analysis Histological and IHC stainings were captured using the PreciPoint M8 Digital Microscope & Scanner (PreciPoint GmbH, Freising, Germany). The images were histomorphometrically analyzed by the Image-Pro plus 6.0 software (Media Cybernetics, Inc. Silver spring, MD United States). The ratio of the positive-range optical density value (IOD) to the whole range of the sample was the raw staining result. ## 4.6 Statistical analysis GraphPad Prism software 8 (La Jolla, CA, United States, http://www.graphpad.com) was used for statistical assessment. Quantile-quantile (q-q) plot was used to test the normality of the data distribution (Supplementary Figure S1–S3). The comparison between different experimental and corresponding control groups was performed by one-way analysis of variance (ANOVA) with Dunnet’s multiple comparisons test. The comparison between each group at different time periods was performed by one-way ANOVA with Tukey’s multiple comparisons test. A significance level of $p \leq 0.05$ was considered statistically significant. The results are shown as box plots showing the mean and the upper and lower interquartile range with whiskers encompassing the minimum and the maximum value of each group. Rstudio (R-Studio, Boston, MA, United States; http://www.rstudio.com) was utilized to create the final heat maps. Depending on the culture conditions, the heat map was grouped into 8 clusters. The materials and methods were summarized as a graphical abstract in Figure 9. FIGURE 9: A graphical abstract of the whole experiment. ## 5 Conclusion Tissue morphogenesis is a tightly modulated temporal and spatial combination of various signaling cues that are improperly elucidated, causing clinical TE processes to fail. Continuing our systematic studies that attempt to understand how the interactions of multiple growth factors regulate osteo-chondrogenesis of muscle tissue over a specific time frame, we have observed clear differences. The combination of BMP-2+TGF-β3, while able to synergize with each other to stimulate osteo-chondrogenesis, also showed that they could antagonize each other in a time-dependent manner. However, the Noggin results were most intriguing. Not only does Noggin appear to be able to antagonize TGF-β3, albeit only at specific temporal intervals, but Noggin appears to be able to synergize with TGF-β3 to promote osteo-chondrogenesis in a temporal manner. This study thus demonstrated a clear need to reconsider the temporal function of growth factors and their inhibitors during the differentiation process in order to achieve more effective TE approaches in clinical applications. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. ## Ethics statement The animal study was reviewed and approved by Animal ethics research committee of the Ludwig Maximillian University of Munich (LMU), Bavaria, Germany Tierschutzgesetz §1/§4/§17 (https://www.gesetze-im-internet.de/tierschg/TierSchG.pdf). ## Author contributions Conceptualization, RK; methodology, RK and HL; software, RK and HL; validation, RK and HL; formal analysis, RK and HL; investigation, HL; resources, RK; data curation, RK, AA, and HL writing—original draft preparation, HL; Writing—review and editing, RK, PM, and AA; visualization, HL; supervision, RK and PM; project administration, RK and AA; funding acquisition, PM and RK. All authors have read and agreed to the published version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1140118/full#supplementary-material ## References 1. Albert R.. Scale-free networks in cell biology. J. Cell Sci. (2005) 118 4947-4957. DOI: 10.1242/jcs.02714 2. Alhadlaq A., Mao J. J.. Tissue-engineered osteochondral constructs in the shape of an articular condyle. J. bone Jt. Surg. Am. volume (2005) 87 936-944. DOI: 10.2106/jbjs.d.02104 3. Alliston T., Piek E., Derynck R., Miyazono K., Derynck R.. TGF-Beta family signaling in skeletal development, maintenance, and disease. (2008) 667 4. Bami M., Mavrogenis A. F., Angelini A., Milonaki M., Mitsiokapa E., Stamoulis D.. Bone morphogenetic protein signaling in musculoskeletal cancer. J. Cancer Res. Clin. Oncol. (2016) 142 2061-2072. DOI: 10.1007/s00432-016-2149-9 5. Bayramov A. V., Eroshkin F. M., Martynova N. Y., Ermakova G. V., Solovieva E. A., Zaraisky A. G.. Novel functions of noggin proteins: Inhibition of activin/nodal and wnt signaling. Development (2011) 138 5345-5356. DOI: 10.1242/dev.068908 6. Betz O. B., Betz V. M., Abdulazim A., Penzkofer R., Schmitt B., Schroder C.. Healing of large segmental bone defects induced by expedited bone morphogenetic protein-2 gene-activated, syngeneic muscle grafts. Hum. Gene Ther. (2009) 20 1589-1596. DOI: 10.1089/hum.2009.037 7. Betz O. B., Betz V. M., Schroder C., Penzkofer R., Gottlinger M., Mayer-Wagner S.. Repair of large segmental bone defects: BMP-2 gene activated muscle grafts vs. autologous bone grafting. BMC Biotechnol. (2013) 13 65. DOI: 10.1186/1472-6750-13-65 8. Betz V. M., Betz O. B., Harris M. B., Vrahas M. S., Evans C. H.. Bone tissue engineering and repair by gene therapy. Front. Biosci. (2008) 13 833-841. DOI: 10.2741/2724 9. Betz V. M., Betz O. B., Rosin T., Keller A., Thirion C., Salomon M.. The effect of BMP-7 gene activated muscle tissue implants on the repair of large segmental bone defects. Injury (2015) 46 2351-2358. DOI: 10.1016/j.injury.2015.09.016 10. Betz V. M., Ren B., Messmer C., Jansson V., Betz O. B., Muller P. E.. Bone morphogenetic protein-2 is a stronger inducer of osteogenesis within muscle tissue than heterodimeric bone morphogenetic protein-2/6 and -2/7: Implications for expedited gene-enhanced bone repair. J. Gene Med. (2018) 20 e3042. DOI: 10.1002/jgm.3042 11. Blair H. C., Larrouture Q. C., Li Y., Lin H., Beer-Stoltz D., Liu L.. Osteoblast differentiation and bone matrix formation. Tissue Eng. Part B Rev. (2017) 23 268-280. DOI: 10.1089/ten.teb.2016.0454 12. Blaney Davidson E. N., Remst D. F., Vitters E. L., van Beuningen H. M., Blom A. B., Goumans M. J.. Increase in ALK1/ALK5 ratio as a cause for elevated MMP-13 expression in osteoarthritis in humans and mice. J. Immunol. Baltim. Md : 1950) (2009) 182 7937-7945. DOI: 10.4049/jimmunol.0803991 13. Blumer M. J., Longato S., Richter E., Perez M. T., Konakci K. Z., Fritsch H.. The role of cartilage canals in endochondral and perichondral bone formation: Are there similarities between these two processes?. J. Anat. (2005) 206 359-372. DOI: 10.1111/j.1469-7580.2005.00404.x 14. Boonen K. J., Langelaan M. L., Polak R. B., van der Schaft D. W., Baaijens F. P., Post M. J.. Effects of a combined mechanical stimulation protocol: Value for skeletal muscle tissue engineering. J. Biomech. (2010) 43 1514-1521. DOI: 10.1016/j.jbiomech.2010.01.039 15. Bosch P., Musgrave D. S., Lee J. Y., Cummins J., Shuler T., Ghivizzani T. C.. Osteoprogenitor cells within skeletal muscle. J. Orthop. Res. (2000) 18 933-944. DOI: 10.1002/jor.1100180613 16. Brand M. D.. Regulation analysis of energy metabolism. J. Exp. Biol. (1997) 200 193-202. DOI: 10.1242/jeb.200.2.193 17. Bustin S. A., Wittwer C. T.. Miqe: A step toward more robust and reproducible quantitative PCR. Clin. Chem. (2017) 63 1537-1538. DOI: 10.1373/clinchem.2016.268953 18. Chen C., Uludag H., Wang Z., Jiang H.. Noggin suppression decreases BMP-2-induced osteogenesis of human bone marrow-derived mesenchymal stem cells. J. Cell Biochem (2012) 113 3672-3680. DOI: 10.1002/jcb.24240 19. Chen J., Chen H., Li P., Diao H., Zhu S., Dong L.. Simultaneous regeneration of articular cartilage and subchondral bone. Biomaterials (2011) 32 4793-4805. DOI: 10.1016/j.biomaterials.2011.03.041 20. Chen L., Liu J., Guan M., Zhou T., Duan X., Xiang Z.. <p>Growth factor and its polymer scaffold-based delivery system for cartilage tissue engineering</p>. Int. J. Nanomedicine (2020) 15 6097-6111. DOI: 10.2147/ijn.s249829 21. Chen T. L., Bates R. L., Dudley A., Hammonds R. G., Amento E. P.. Bone morphogenetic protein-2b stimulation of growth and osteogenic phenotypes in rat osteoblast-like cells: Comparison with TGF-beta 1. J. Bone Min. Res. (1991) 6 1387-1393. DOI: 10.1002/jbmr.5650061216 22. Cheng A., Genever P. G.. SOX9 determines RUNX2 transactivity by directing intracellular degradation. J. Bone Min. Res. (2010) 25 2680-2689. DOI: 10.1002/jbmr.174 23. Chung U. I., Schipani E., McMahon A. P., Kronenberg H. M.. Indian hedgehog couples chondrogenesis to osteogenesis in endochondral bone development. J. Clin. Invest (2001) 107 295-304. DOI: 10.1172/jci11706 24. Cicione C., Muinos-Lopez E., Hermida-Gomez T., Fuentes-Boquete I., Diaz-Prado S., Blanco F. J.. Alternative protocols to induce chondrogenic differentiation: Transforming growth factor-beta superfamily. Cell Tissue Bank. (2015) 16 195-207. DOI: 10.1007/s10561-014-9472-7 25. Crane J. L., Xian L., Cao X.. Role of TGF-β signaling in coupling bone remodeling. methods Mol. Biol. (2016) 1344 287-300. DOI: 10.1007/978-1-4939-2966-5_18 26. Daly A. C., Randall R. A., Hill C. S.. Transforming growth factor beta-induced Smad1/5 phosphorylation in epithelial cells is mediated by novel receptor complexes and is essential for anchorage-independent growth. Mol. Cell Biol. (2008) 28 6889-6902. DOI: 10.1128/mcb.01192-08 27. Dang P. N., Dwivedi N., Yu X., Phillips L., Bowerman C., Murphy W. L.. Guiding chondrogenesis and osteogenesis with mineral-coated hydroxyapatite and BMP-2 incorporated within high-density hMSC aggregates for bone regeneration. ACS Biomater. Sci. Eng. (2016) 2 30-42. DOI: 10.1021/acsbiomaterials.5b00277 28. Devlin R. D., Du Z., Pereira R. C., Kimble R. B., Economides A. N., Jorgetti V.. Skeletal overexpression of noggin results in osteopenia and reduced bone formation. Endocrinology (2003) 144 1972-1978. DOI: 10.1210/en.2002-220918 29. Ding Y., Jiang H., Meng B., Zhu B., Yu X., Xiang G.. Sweroside-mediated mTORC1 hyperactivation in bone marrow mesenchymal stem cells promotes osteogenic differentiation. J. Cell Biochem (2019) 120 16025-16036. DOI: 10.1002/jcb.28882 30. Eames B. F., Sharpe P. T., Helms J. A.. Hierarchy revealed in the specification of three skeletal fates by Sox9 and Runx2. Dev. Biol. (2004) 274 188-200. DOI: 10.1016/j.ydbio.2004.07.006 31. Ehnert S., Baur J., Schmitt A., Neumaier M., Lucke M., Dooley S.. TGF-β1 as possible link between loss of bone mineral density and chronic inflammation. PLoS One (2010) 5 e14073. DOI: 10.1371/journal.pone.0014073 32. Ehnert S., Zhao J., Pscherer S., Freude T., Dooley S., Kolk A.. Transforming growth factor beta1 inhibits bone morphogenic protein (BMP)-2 and BMP-7 signaling via upregulation of ski-related novel protein N (SnoN): Possible mechanism for the failure of BMP therapy?. BMC Med. (2012) 10 101. DOI: 10.1186/1741-7015-10-101 33. Fang S., Li Y., Chen P.. Osteogenic effect of bone marrow mesenchymal stem cell-derived exosomes on steroid-induced osteonecrosis of the femoral head. Drug Des. Devel Ther. (2019) 13 45-55. DOI: 10.2147/dddt.s178698 34. Finnson K. W., Parker W. L., ten Dijke P., Thorikay M., Philip A.. ALK1 opposes ALK5/Smad3 signaling and expression of extracellular matrix components in human chondrocytes. J. Bone Min. Res. (2008) 23 896-906. DOI: 10.1359/jbmr.080209 35. Gazzerro E., Canalis E.. Bone morphogenetic proteins and their antagonists. Rev. Endocr. Metab. Disord. (2006) 7 51-65. DOI: 10.1007/s11154-006-9000-6 36. Gazzerro E., Du Z., Devlin R. D., Rydziel S., Priest L., Economides A. N.. Noggin arrests stromal cell differentiation. Bone (2003) 32 111-119. DOI: 10.1016/s8756-3282(02)00948-1 37. Gong Y., Krakow D., Marcelino J., Wilkin D., Chitayat D., Babul-Hirji R.. Heterozygous mutations in the gene encoding noggin affect human joint morphogenesis. Nat. Genet. (1999) 21 302-304. DOI: 10.1038/6821 38. Grässel S., Aszódi A.. Cartilage (2016) 39. Harris S. E., Bonewald L. F., Harris M. A., Sabatini M., Dallas S., Feng J. Q.. Effects of transforming growth factor beta on bone nodule formation and expression of bone morphogenetic protein 2, osteocalcin, osteopontin, alkaline phosphatase, and type I collagen mRNA in long-term cultures of fetal rat calvarial osteoblasts. J. Bone Min. Res. (1994) 9 855-863. DOI: 10.1002/jbmr.5650090611 40. Haschtmann D., Ferguson S. J., Stoyanov J. V.. BMP-2 and TGF-β3 do not prevent spontaneous degeneration in rabbit disc explants but induce ossification of the annulus fibrosus. Eur. Spine J. (2012) 21 1724-1733. DOI: 10.1007/s00586-012-2371-3 41. Hashimi S. M.. Exogenous noggin binds the BMP-2 receptor and induces alkaline phosphatase activity in osteoblasts. J. Cell Biochem (2019) 120 13237-13242. DOI: 10.1002/jcb.28597 42. He T., Hausdorf J., Chevalier Y., Klar R. M.. Trauma induced tissue survival. BMC Biotechnol. (2020) 20 8. DOI: 10.1186/s12896-020-0602-y 43. He W., Chen L., Huang Y., Xu Z., Xu W., Ding N.. Synergistic effects of recombinant Lentiviral-mediated BMP2 and TGF-beta3 on the osteogenic differentiation of rat bone marrow mesenchymal stem cells. Cytokine (2019) 120 1-8. DOI: 10.1016/j.cyto.2019.03.020 44. Huang Y., Seitz D., Chevalier Y., Muller P. E., Jansson V., Klar R. M.. Synergistic interaction of hTGF-β3 with hBMP-6 promotes articular cartilage formation in chitosan scaffolds with hADSCs: Implications for regenerative medicine. BMC Biotechnol. (2020) 20 48. DOI: 10.1186/s12896-020-00641-y 45. Huang Y., Seitz D., Konig F., Muller P. E., Jansson V., Klar R. M.. Induction of articular chondrogenesis by chitosan/hyaluronic-acid-based biomimetic matrices using human adipose-derived stem cells. Int. J. Mol. Sci. (2019) 20 4487. DOI: 10.3390/ijms20184487 46. Huang Z., Ren P. G., Ma T., Smith R. L., Goodman S. B.. Modulating osteogenesis of mesenchymal stem cells by modifying growth factor availability. Cytokine (2010) 51 305-310. DOI: 10.1016/j.cyto.2010.06.002 47. Huynh N. P. T., Zhang B., Guilak F.. High-depth transcriptomic profiling reveals the temporal gene signature of human mesenchymal stem cells during chondrogenesis. FASEB J. (2019) 33 358-372. DOI: 10.1096/fj.201800534r 48. Iwakura T., Sakata R., Reddi A. H.. Induction of chondrogenesis and expression of superficial zone protein in synovial explants with TGF-β1 and BMP-7. Tissue Eng. Part A (2013) 19 2638-2644. DOI: 10.1089/ten.tea.2013.0047 49. Izadpanahi M., Seyedjafari E., Arefian E., Hamta A., Hosseinzadeh S., Kehtari M.. Nanotopographical cues of electrospun PLLA efficiently modulate non-coding RNA network to osteogenic differentiation of mesenchymal stem cells during BMP signaling pathway. Mater Sci. Eng. C Mater Biol. Appl. (2018) 93 686-703. DOI: 10.1016/j.msec.2018.08.023 50. Karsenty G., Kronenberg H. M., Settembre C.. Genetic control of bone formation. Annu Rev. Cell Dev. Biol. (2009) 25 629-648. DOI: 10.1146/annurev.cellbio.042308.113308 51. Karst M., Gorny G., Galvin R. J., Oursler M. J.. Roles of stromal cell RANKL, OPG, and M-CSF expression in biphasic TGF-beta regulation of osteoclast differentiation. J. Cell Physiol. (2004) 200 99-106. DOI: 10.1002/jcp.20036 52. Kato Y., Iwamoto M., Koike T., Suzuki F., Takano Y.. Terminal differentiation and calcification in rabbit chondrocyte cultures grown in centrifuge tubes: Regulation by transforming growth factor beta and serum factors. Proc. Natl. Acad. Sci. U S A (1988) 85 9552-9556. DOI: 10.1073/pnas.85.24.9552 53. Keller B., Yang T., Chen Y., Munivez E., Bertin T., Zabel B.. Interaction of TGFβ and BMP signaling pathways during chondrogenesis. PLoS One (2011) 6 e16421. DOI: 10.1371/journal.pone.0016421 54. Klar R. M., Duarte R., Dix-Peek T., Ripamonti U.. The induction of bone formation by the recombinant human transforming growth factor-β3. Biomaterials (2014) 35 2773-2788. DOI: 10.1016/j.biomaterials.2013.12.062 55. Klar R. M.. The induction of bone formation: The translation enigma. Front. Bioeng. Biotechnol. (2018) 6 74. DOI: 10.3389/fbioe.2018.00074 56. Krause C., Guzman A., Knaus P.. Noggin. Int. J. Biochem Cell Biol. (2011) 43 478-481. DOI: 10.1016/j.biocel.2011.01.007 57. Lee M. S., Stebbins M. J., Jiao H., Huang H. C., Leiferman E. M., Walczak B. E.. Comparative evaluation of isogenic mesodermal and ectomesodermal chondrocytes from human iPSCs for cartilage regeneration. Sci. Adv. (2021) 7 eabf0907. DOI: 10.1126/sciadv.abf0907 58. Li Tuan R. S.. Mechanism of traumatic heterotopic ossification: In search of injury-induced osteogenic factors. J. Cell Mol. Med. (2020) 24 11046-11055. DOI: 10.1111/jcmm.15735 59. Liao J., Hu N., Zhou N., Lin L., Zhao C., Yi S.. Sox9 potentiates BMP2-induced chondrogenic differentiation and inhibits BMP2-induced osteogenic differentiation. PLoS One (2014) 9 e89025. DOI: 10.1371/journal.pone.0089025 60. Maleiner B., Tomasch J., Heher P., Spadiut O., Runzler D., Fuchs C.. The importance of biophysical and biochemical stimuli in dynamic skeletal muscle models. Front. Physiol. (2018) 9 1130. DOI: 10.3389/fphys.2018.01130 61. McLeod M. J.. Differential staining of cartilage and bone in whole mouse fetuses by alcian blue and alizarin red S. Teratology (1980) 22 299-301. DOI: 10.1002/tera.1420220306 62. Mehlhorn A. T., Niemeyer P., Kaschte K., Muller L., Finkenzeller G., Hartl D.. Differential effects of BMP-2 and TGF-β1 on chondrogenic differentiation of adipose derived stem cells. Cell Prolif. (2007) 40 809-823. DOI: 10.1111/j.1365-2184.2007.00473.x 63. Meinel L., Hofmann S., Betz O., Fajardo R., Merkle H. P., Langer R.. Osteogenesis by human mesenchymal stem cells cultured on silk biomaterials: Comparison of adenovirus mediated gene transfer and protein delivery of BMP-2. Biomaterials (2006) 27 4993-5002. DOI: 10.1016/j.biomaterials.2006.05.021 64. Nakayama N., Duryea D., Manoukian R., Chow G., Han C. Y.. Macroscopic cartilage formation with embryonic stem-cell-derived mesodermal progenitor cells. J. Cell Sci. (2003) 116 2015-2028. DOI: 10.1242/jcs.00417 65. Noel D., Gazit D., Bouquet C., Apparailly F., Bony C., Plence P.. Short-term BMP-2 expression is sufficient for. Stem Cells (2004) 22 74-85. DOI: 10.1634/stemcells.22-1-74 66. Perniconi B., Coletti D.. Skeletal muscle tissue engineering: Best bet or black beast?. Front. Physiol. (2014) 5 255. DOI: 10.3389/fphys.2014.00255 67. Pittenger M. F., Discher D. E., Péault B. M., Phinney D. G., Hare J. M., Caplan A. I.. Mesenchymal stem cell perspective: Cell biology to clinical progress. NPJ Regen. Med. (2019) 4 22. DOI: 10.1038/s41536-019-0083-6 68. Re'em-Kalma Y., Lamb T., Frank D.. Competition between noggin and bone morphogenetic protein 4 activities may regulate dorsalization during Xenopus development. Proc. Natl. Acad. Sci. U S A (1995) 92 12141-12145. DOI: 10.1073/pnas.92.26.12141 69. Ren B., Betz V. M., Thirion C., Salomon M., Jansson V., Muller P. E.. Gene-activated tissue grafts for sustained bone morphogenetic protein-2 delivery and bone engineering: Is muscle with fascia superior to muscle and fat?. J. Tissue Eng. Regen. Med. (2018) 12 1002-1011. DOI: 10.1002/term.2575 70. Ren B., Betz V. M., Thirion C., Salomon M., Jansson V., Muller P. E.. Osteoinduction within BMP-2 transduced muscle tissue fragments with and without a fascia layer: Implications for bone tissue engineering. Gene Ther. (2019) 26 16-28. DOI: 10.1038/s41434-018-0047-2 71. Rifas L.. The role of noggin in human mesenchymal stem cell differentiation. J. Cell Biochem (2007) 100 824-834. DOI: 10.1002/jcb.21132 72. Ripamonti U., Dix-Peek T., Parak R., Milner B., Duarte R.. Profiling bone morphogenetic proteins and transforming growth factor-βs by hTGF-β3 pre-treated coral-derived macroporous bioreactors: The power of one. Biomaterials (2015) 49 90-102. DOI: 10.1016/j.biomaterials.2015.01.058 73. Ripamonti U., Parak R., Klar R. M., Dickens C., Dix-Peek T., Duarte R.. The synergistic induction of bone formation by the osteogenic proteins of the TGF-beta supergene family. Biomaterials (2016) 104 279-296. DOI: 10.1016/j.biomaterials.2016.07.018 74. Ripamonti U.. Soluble osteogenic molecular signals and the induction of bone formation. Biomaterials (2006) 27 807-822. DOI: 10.1016/j.biomaterials.2005.09.021 75. Rodrigues M. T., Lee S. J., Gomes M. E., Reis R. L., Atala A., Yoo J. J.. Bilayered constructs aimed at osteochondral strategies: The influence of medium supplements in the osteogenic and chondrogenic differentiation of amniotic fluid-derived stem cells. Acta Biomater. (2012) 8 2795-2806. DOI: 10.1016/j.actbio.2012.04.013 76. Schaefer D., Martin I., Jundt G., Seidel J., Heberer M., Grodzinsky A.. Tissue-engineered composites for the repair of large osteochondral defects. Arthritis Rheum. (2002) 46 2524-2534. DOI: 10.1002/art.10493 77. Scott M. A., Levi B., Askarinam A., Nguyen A., Rackohn T., Ting K.. Brief review of models of ectopic bone formation. Stem Cells Dev. (2012) 21 655-667. DOI: 10.1089/scd.2011.0517 78. Sheehy E. J., Vinardell T., Buckley C. T., Kelly D. J.. Engineering osteochondral constructs through spatial regulation of endochondral ossification. Acta Biomater. (2013) 9 5484-5492. DOI: 10.1016/j.actbio.2012.11.008 79. Shi Q., Li Y., Sun J., Zhang H., Chen L., Chen B.. The osteogenesis of bacterial cellulose scaffold loaded with bone morphogenetic protein-2. Biomaterials (2012) 33 6644-6649. DOI: 10.1016/j.biomaterials.2012.05.071 80. Smith W. C., Harland R. M.. Expression cloning of noggin, a new dorsalizing factor localized to the Spemann organizer in Xenopus embryos. Cell (1992) 70 829-840. DOI: 10.1016/0092-8674(92)90316-5 81. Song K., Krause C., Shi S., Patterson M., Suto R., Grgurevic L.. Identification of a key residue mediating bone morphogenetic protein (BMP)-6 resistance to noggin inhibition allows for engineered BMPs with superior agonist activity. J. Biol. Chem. (2010) 285 12169-12180. DOI: 10.1074/jbc.m109.087197 82. Strong A. L., Spreadborough P. J., Dey D., Yang P., Li S., Lee A.. BMP ligand trap ALK3-fc attenuates osteogenesis and heterotopic ossification in blast-related lower extremity trauma. Stem Cells Dev. (2021) 30 91-105. DOI: 10.1089/scd.2020.0162 83. Urdeitx P., Doweidar M. H.. Mechanical stimulation of cell microenvironment for cardiac muscle tissue regeneration: A 3D. Comput. Mech. (2020) 66 1003-1023. DOI: 10.1007/s00466-020-01882-6 84. Urist M. R.. Bone: Formation by autoinduction. Science (1965) 150 893-899. DOI: 10.1126/science.150.3698.893 85. van der Kraan P. M.. Age-related alterations in TGF beta signaling as a causal factor of cartilage degeneration in osteoarthritis. Bio-medical Mater. Eng. (2014) 24 75-80. DOI: 10.3233/bme-140976 86. van der Kraan P. M., Goumans M. J., Blaney Davidson E., ten Dijke P.. Age-dependent alteration of TGF-beta signalling in osteoarthritis. Cell Tissue Res. (2012) 347 257-265. DOI: 10.1007/s00441-011-1194-6 87. Virk M. S., Sugiyama O., Park S. H., Gambhir S. S., Adams D. J., Drissi H.. Same day". Mol. Ther. : J. Am. Soc. Gene Ther. (2011) 19 960-968. DOI: 10.1038/mt.2011.2 88. Wakefield L. M., Hill C. S.. Beyond TGFβ: Roles of other TGFβ superfamily members in cancer. Nat. Rev. Cancer (2013) 13 328-341. DOI: 10.1038/nrc3500 89. Wan D. C., Pomerantz J. H., Brunet L. J., Kim J. B., Chou Y. F., Wu B. M.. Noggin suppression enhances. J. Biol. Chem. (2007) 282 26450-26459. DOI: 10.1074/jbc.m703282200 90. Wang Y., He T., Liu J., Liu H., Zhou L., Hao W.. Synergistic effects of overexpression of BMP-2 and TGF-β3 on osteogenic differentiation of bone marrow mesenchymal stem cells. Mol. Med. Rep. (2016) 14 5514-5520. DOI: 10.3892/mmr.2016.5961 91. Wang Y., Hong S., Li M., Zhang J., Bi Y., He Y.. Noggin resistance contributes to the potent osteogenic capability of BMP9 in mesenchymal stem cells. J. Orthop. Res. (2013) 31 1796-1803. DOI: 10.1002/jor.22427 92. Wei W., Dai H.. Articular cartilage and osteochondral tissue engineering techniques: Recent advances and challenges. Bioact. Mater (2021) 6 4830-4855. DOI: 10.1016/j.bioactmat.2021.05.011 93. Wrighton K. H., Lin X., Yu P. B., Feng X. H.. Transforming growth factor β can stimulate Smad1 phosphorylation independently of bone morphogenic protein receptors. J. Biol. Chem. (2009) 284 9755-9763. DOI: 10.1074/jbc.m809223200 94. Wu M., Chen G., Li Y. P.. TGF-beta and BMP signaling in osteoblast, skeletal development, and bone formation, homeostasis and disease. Bone Res. (2016) 4 16009. DOI: 10.1038/boneres.2016.9 95. Wu X. B., Li Y., Schneider A., Yu W., Rajendren G., Iqbal J.. Impaired osteoblastic differentiation, reduced bone formation, and severe osteoporosis in noggin-overexpressing mice. J. Clin. Invest (2003) 112 924-934. DOI: 10.1172/jci15543 96. Xiong F., Cheng X., Zhang C., Klar R. M., He T.. Optimizations for identifying reference genes in bone and cartilage bioengineering. BMC Biotechnol. (2021) 21 25. DOI: 10.1186/s12896-021-00685-8 97. Xiong F., Hausdorf J., Niethammer T. R., Jansson V. A., Klar R. M.. Temporal TGF-beta supergene family signalling cues modulating tissue morphogenesis: Chondrogenesis within a muscle tissue model?. Int. J. Mol. Sci. (2020) 21 4863. DOI: 10.3390/ijms21144863 98. Xiong F.. Induction of chondrogenic morphogenesis in tissue culture using different combinations of transforming growth factor-beta superfamily proteins (2020) 99. Yamashita S., Andoh M., Ueno-Kudoh H., Sato T., Miyaki S., Asahara H.. Sox9 directly promotes Bapx1 gene expression to repress Runx2 in chondrocytes. Exp. Cell Res. (2009) 315 2231-2240. DOI: 10.1016/j.yexcr.2009.03.008 100. Zakin L., De Robertis E. M.. Extracellular regulation of BMP signaling. Curr. Biol. (2010) 20 R89-R92. DOI: 10.1016/j.cub.2009.11.021 101. Zhang H., Zhao X., Zhang Z., Chen W., Zhang X.. An immunohistochemistry study of Sox9, Runx2, and Osterix expression in the mandibular cartilages of newborn mouse. Biomed. Res. Int. (2013) 2013 1-11. DOI: 10.1155/2013/265380 102. Zhang Y., Liu X., Zeng L., Zhang J., Zuo J., Zou J.. Tissue engineering: Polymer fiber scaffolds for bone and cartilage tissue engineering (adv. Funct. Mater. 36/2019). Adv. Funct. Mater. (2019) 29 1970246. DOI: 10.1002/adfm.201970246 103. Zhao L.. The role of Sox9 in osteogenesis (2008) 104. Zhou G., Zheng Q., Engin F., Munivez E., Chen Y., Sebald E.. Dominance of SOX9 function over RUNX2 during skeletogenesis. Proc. Natl. Acad. Sci. U S A (2006) 103 19004-19009. DOI: 10.1073/pnas.0605170103 105. Zhou S., Yates K. E., Eid K., Glowacki J.. Demineralized bone promotes chondrocyte or osteoblast differentiation of human marrow stromal cells cultured in collagen sponges. Cell Tissue Bank. (2005) 6 33-44. DOI: 10.1007/s10561-005-4253-y
--- title: High frequency of autoimmune thyroiditis in euthyroid girls with premature adrenarche authors: - Eleni Magdalini Kyritsi - Ioannis-Anargyros Vasilakis - Ioanna Kosteria - Aimilia Mantzou - Alexandros Gryparis - Eva Kassi - Gregory Kaltsas - Christina Kanaka-Gantenbein journal: Frontiers in Pediatrics year: 2023 pmcid: PMC10060666 doi: 10.3389/fped.2023.1064177 license: CC BY 4.0 --- # High frequency of autoimmune thyroiditis in euthyroid girls with premature adrenarche ## Abstract ### Objective The purpose of this study was to investigate the frequency of autoimmune thyroiditis (AT) among euthyroid prepubertal girls presenting with premature adrenarche (PA). We also aimed to identify the clinical, metabolic, and endocrine profile of girls with AT and concurrent PA and compare them to girls with AT without PA, PA alone and healthy controls. ### Methods Ninety-one prepubertal girls aged 5–10 years, who attended our department for AT, PA and normal variants of growth and puberty were recruited for the study: 73 girls had PA, 6 AT without PA and 12 were referred for investigation of growth. All girls underwent clinical examination, detailed biochemical and hormonal screen. Standard dose Synachten stimulation test (SDSST) and oral glucose tolerance test (OGTT) were performed in all girls with PA. The whole study population was divided in 4 groups: Group PA−/AT+ included 6 girls with AT without PA; Group PA+/AT− PA subjects without AT; Group PA+/AT+ girls with PA and concomitant AT; Group PA-/AT- twelve healthy girls without PA nor AT (controls). ### Results Among 73 girls presenting with PA 19 had AT ($26\%$). BMI, systolic blood pressure (SBP) and the presence of goiter significantly differed between the four groups ($$p \leq 0.016$$, $$p \leq 0.022$$ and $p \leq 0.001$, respectively). When comparing hormonal parameters among the four groups significant differences were found in leptin ($$p \leq 0.007$$), TSH ($$p \leq 0.044$$), anti-TPO ($$p \leq 0.002$$), anti-TG ($$p \leq 0.044$$), IGF-BP1 ($$p \leq 0.006$$), Δ4-Α ($$p \leq 0.01$$), DHEA-S (p = <0.001), IGF-1 ($$p \leq 0.012$$) and IGF-BP3 ($$p \leq 0.049$$) levels. TSH levels were significantly higher in Group PA+/AT+ compared to PA+/AT− and PA−/AT− ($$p \leq 0.043$$ and $$p \leq 0.016$$, respectively). Moreover, girls with AT (Groups PA−/AT+ and PA+/AT+) had higher TSH levels than those in Group PA+/AT- ($$p \leq 0.025$$). Girls in Group PA+/AT + showed higher cortisol response at 60 min post-SDSST than girls in Group PA+/AT− ($$p \leq 0.035$$). During the OGTT, insulin concentrations at 60 min were significantly higher in Group PA+/AT + compared to Group PA+/AT− ($$p \leq 0.042$$). ### Conclusion A high frequency of AT among euthyroid prepubertal girls with PA was observed. The combination of PA with AT even in euthyroid state may be associated with a greater degree of insulin resistance, than PA alone. ## Introduction Autoimmune thyroiditis (AT) is the most common organ-specific autoimmune disease, affecting $2\%$–$5\%$ of the general population with a strong female preponderance (i.e., women $5\%$–$15\%$ and men $1\%$–$5\%$) [1]. The pathogenesis of AT involves a complex interplay between environmental factors and genetic background, with up to $50\%$ of cases having a first-degree relative with positive antithyroid antibodies (2–6). The prevalence of AT in children and adolescents ranges between $1.2\%$–$9.6\%$ (depending on the diagnostic criteria, age, sex, pubertal stage, ethnicity, and iodine status of the population studied), occurring rarely in children younger than 3 years and reaching a peak in early to mid-puberty. A female predominance of up to 6:1 has been observed [6, 7]. Accumulating evidence suggests an association between thyroid autoimmunity, obesity and cardiometabolic risk factors (8–10), whereas, elevated thyroid autoantibodies have been observed in an average of $22.3\%$ of patients with PCOS compared with an average of $8.5\%$ in healthy women [11]. PCOS is a heterogeneous endocrine disorder affecting $5\%$–$20\%$ of women of reproductive age worldwide [12]. It is characterized mainly by clinical and/or biochemical hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology and is associated with multiple metabolic aberrations, including insulin resistance and hyperinsulinemia, an increased risk of glucose intolerance and type 2 diabetes mellitus, dyslipidemia, hypertension, and endothelial dysfunction, independent of body mass index (BMI) [12, 13]. It has been speculated that the combination of PCOS with AT, even in the euthyroid state, may be associated with more pronounced metabolic derangements than either of these conditions alone, although an underlying mechanism has not been defined (11, 14–16). Results from previous studies underline the early developmental origin of PCOS, suggesting that premature adrenarche (PA) may represent a forerunner of this condition [17]. PA refers to the appearance of clinical signs of androgen action before the age of 8 years in girls or 9 years in boys associated with adrenal androgen precursors concentrations high for the prepubertal chronological age, but appropriate for Tanner pubertal stage II–III, in the absence of central puberty, congenital adrenal hyperplasia due to steroidogenic enzyme defects, androgen producing tumors and exogenous source of androgens [18, 19]. Traditionally, PA has been considered to represent a benign variant of pubertal development, however, several studies have revealed an association between PA and intrauterine growth retardation, being born small for gestational age (SGA), insulin resistance and components of the metabolic syndrome, as well as higher incidence of functional ovarian hyperandrogenism, that may precede the development of PCOS in these girls in adolescence or later in young adulthood (19–24). The aim of the present study was to investigate the frequency of AT among euthyroid prepubertal girls presenting with PA in the outpatient setting. We also aimed to identify clinical, metabolic, and endocrine characteristics of euthyroid girls with AT and concurrent PA and compare them to those of prepubertal euthyroid girls with AT but no PA, PA alone and healthy controls. ## Subjects Ninety-one prepubertal girls 5–10 years of age, who attended the Division of Endocrinology, Metabolism and Diabetes of the First Department of Pediatrics, National and Kapodistrian University of Athens Medical School at the Aghia Sophia Children's Hospital for AT, PA and normal variants of growth and puberty (i.e., isolated premature thelarche) between 2020 and 2022 were recruited for the study: 73 girls presented with or had a history of PA, 6 had AT without PA and 12 were referred to our Department for investigation of growth. The latter showed a normal prepubertal growth rate, growing along the lower height percentiles within or slightly below their genetic potential, with a predicted final height falling within 2 standard deviations (∼8.5 cm) above and below their mid-parental height, based on the bone age. Baseline biochemical and hormonal investigations were within normal range in all cases, Taken together, these 12 girls were considered as having a variant of normal growth. PA was defined as the appearance of any clinical sign(s) of adrenarche (pubic/axillary hair, oily hair/skin, adult-type body odor) before 8 years of age together with elevated concentrations of adrenal androgens in the absence of central puberty or other causes of androgen excess. Congenital adrenal hyperplasia was ruled out in all PA cases based on normal responses of cortisol and 17-hydroxyprogesterone (17-OHP) following a standard dose Synachten stimulation test (SDSST). AT diagnosis was made when at least two of the following criteria were met: i) presence of subclinical hypothyroidism defined by modestly elevated thyroid stimulating hormone (TSH) levels (5–10 IU/ml) with normal concentrations of free thyroxine FT4 or overt hypothyroidism, as assessed by a an elevated TSH with a low FT4 [6]; ii) positive serum anti-thyroid peroxidase (anti-TPO) and/or anti-thyroglobulin (anti-TG) antibodies; and iii) typical sonographic features of AT (diffuse or irregular hypoechogenicity of the thyroid parenchyma) [25, 26]. Out of 73 girls with PA: 2 had transient congenital hypothyroidism and had stopped levothyroxine (LT4) replacement therapy more than one year before study entry, showed negative thyroid autoantibodies and normal ultrasonographic findings of the thyroid (thus, no evidence of AT). Another 6 had subclinical hypothyroidism without the concomitant features of autoimmune thyroid disease and were euthyroid under levothyroxine treatment. Two out of 6 girls with AT but no PA were also receiving levothyroxine substitution therapy. Girls who had abnormal TSH and/or FT4 levels were excluded from the study. Birth weight, gestational age, pregnancy, perinatal, personal, and detailed family history for thyroid disorders, diabetes, PCOS, obesity, dyslipidemia, and cardiovascular disease was obtained from all participants. ## Clinical evaluation The weight and height of each subject were measured to the nearest 0.1 cm and 0.1 kg, respectively, and pubertal status was assessed according to standard Tanner's staging [27]. During the initial assessment 85 girls were prepubertal. Six PA girls presented with Tanner stage II breast development. Central precocious puberty was excluded in all these cases by prepubertal responses to the Luteinizing Hormone Releasing Hormone (LHRH) stimulation test, low serum estradiol concentrations and prepuberal sonographic findings on pelvic ultrasound, indicating isolated premature thelarche. Body mass index (BMI) was calculated by dividing body weight in kilograms by height in meters squared. Waist circumference (WC) and hip circumference (HC) were assessed in standing position by using a non-stretched and flexible tape. WC was measured midway between the lowest border of rib cage and the upper border of iliac crest, at the end of normal expiration. HC was measured at the widest part of the hip at the level of the greater trochanter [28]. Waist-to-hip ratio (WHR) was calculated by diving waist to hip circumference and waist-to-height ratio (WHtR) by dividing waist circumference to height. Blood pressure was measured using an automated sphygmomanometer after the girls had rested for 5 min in sitting position. Neck and thyroid examination and all clinical assessments were performed by the same physician (EMK). ## Study protocol-endocrine and biochemical assessment-assays Baseline blood samples were obtained between 8:00 and 9:00 AM by venipuncture after 12 h overnight fasting in supine position. All samples were immediately centrifuged, and serum and plasma were separated and frozen at −80°C until assayed. Basal levels of the following biochemical and hormonal parameters were measured: (i) glucose, hemoglobin A1c (HbA1c), total cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides (ii) serum high-sensitivity C-reactive protein (hs-CRP) was quantitatively measured using a latex-enhanced immunoturbidimetric assay on a Roche Cobas 6,000 clinical chemistry analyzer. The detectable limit was 0.01 mg/dl, and the interassay and intraassay coefficients of variation were <$2\%$ (iii) FT4, TSH, anti-TPO, anti-TG, IGF-1 (insulin-like growth factor 1), IGF binding protein 3 (IGFBP-3), adrenocorticotropic hormone (ACTH), DHEAS (dehydroepiandrosterone sulfate), Δ4-androstenedione (Δ4-Α), testosterone, sex hormone-binding globulin (SHBG), estradiol, luteinizing hormone (LH), follicle stimulating hormone (FSH) and prolactin were measured using validated chemiluminescence immunoassays (CLIA) on the Immulite 2,000 automated analyser (Siemens Llanberis, Gwynedd LL55 4EL, United Kingdom). Quantification of cortisol, insulin and total 25-hydroxyvitamin D [25 (OH)D] was performed based on electrochemiluminescence immunoassay principle (ECLIA) of Cobas e411 automated analyser (Roche Diagnostcs GmbH, Mannheim, Germany). Serum 17-OHP concentrations were measured using ELISA (DIAsource Immunoassays, Ottignies-Louvain-la-Neuve, Belgium). Concentration of IGF binding protein 1 (IGFBP-1) was measured using validated ELISA (Mediagnost, Reutlingen, Germany) with a reported sensitivity of 0.055 ng/ml and total precision CV < $6.5\%$ (assay range 0.1–8 ng/ml). Circulating levels of leptin and adiponectin were detected by validated ELISA kits (BioVendor, Brno, Czech Republic). Analytical sensitivity for leptin is 0.2 ng/ml (assay range 1–50 ng/ml) and for adiponectin 26 ng/ml (assay range 0.1-10 μg/ml). Total precision CV is <$8.0\%$ for both ELISA kits. Concentrations of tumor necrosis factor-alpha (TNF-α) were determined using the high-sensitivity enzyme-linked immunosorbent assay (ELISA, Invitrogen, Bender MedSystems GmbH, Vienna, Austria), with a lower detection limit of 0.13 pg/ml and total precision coefficient of variation (CV) < 9,$8\%$, (assay range 0.31-20 pg/ml). All girls with PA underwent a standard dose Synachten Stimulation test (SDSST) as well as an oral glucose tolerance test (OGTT), on two different days, between 8:00 and 9:00 AM after 12 hr overnight fasting. The SDSST was carried out first. 250 μg of cosyntropin (synthetic ACTH) were administered intravenously and cortisol and 17-OHP were measured at baseline, 30 and 60 min after administering cosyntropin. An OGTT was performed by administering 1.75 g glucose/kg body weight (max 75 g) to each participant and blood samples for glucose and insulin measurements were drawn at baseline, 30 min, 60 min, 90 min and 120 min. Homeostasis model assessment of insulin resistance (HOMA-IR) and the quantitative insulin-sensitivity check index (QUICKI) were calculated as follows: HOMA-IR = [fasting glucose (mmol/l)×fasting insulin (μU/ml)/22.5] and QUICKI = 1/Log (fasting Insulin, µU/ml) + Log (fasting glucose, mg/dl) [29]. An x-ray of the left hand and wrist was obtained in all PA subjects and bone age (BA) was determined using the method of Greulich and Pyle [30]. A thyroid ultrasound was performed in case of goiter, hypothyroidism, and in all subjects with AT, by the same pediatric radiologist familiar with thyroid disease applying standard diagnostic criteria for AT and using the same device. ## Statistical analysis Categorical variables are presented as absolute and relative (%) frequencies. Quantitative variables are presented as median (min, max). In terms of graphical representation, quantitative variables are presented with boxplots, when needed. To compare quantitative variables between groups Kruskal-Wallis test was employed, while for pairwise comparisons Mann-Whitney test was used. Linear regression was used to investigate whether hormonal levels differed among the four groups when adjusting for the effect of BMI. Finally, Spearman's correlation coefficient (ρ) was used to investigate linear associations between quantitative variables. A 2-tailed p-value < 0.05 was considered statistically significant. Statistical analysis was implemented using ΙΒΜ SPSS v. 26 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.). ## Ethics The study protocol was approved by the Ethics Committee of the “Aghia Sophia” Children's Hospital, Athens. Written informed consent was obtained from children's parents, and assent was obtained from the children before children have been enrolled in the study. ## Results Among 73 girls with PA, 19 had AT ($26\%$). Of these 19 girls with PA and concomitant AT, 6 were positive for both thyroid autoantibodies, 6 for anti-TPO only, and 3 for anti-TG only, whereas 4 were negative for both antibodies. Those 4 girls had already been diagnosed with subclinical hypothyroidism, were euthyroid under levothyroxine treatment and showed ultrasonographic findings reminiscent of AT when evaluated in our Department. Overall, the thyroid ultrasound showed typical sonographic features of AT in $\frac{13}{19}$ girls. Extrathyroidal autoimmune diseases were present in $\frac{2}{19}$ subjects: 1 had psoriasis and 1 celiac disease. Based on the presence of AT and/or PA our whole study population was divided in 4 groups: Group PA-/AT+ consisted of those 6 girls who had AT without PA; Group PA+/AT− included 54 (out of 73) PA subjects without AT, among whom: 2 with a history of transient congenital hypothyroidism and 2 with subclinical hypothyroidism under levothyroxine substitution treatment; the above mentioned 19 (of 73) girls with PA and concomitant AT formed Group PA+/AT+ including those 4 girls with subclinical hypothyroidism, under levothyroxine replacement therapy and sonographic features of AT; and 12 healthy girls referred for investigation of growth with neither AT nor PA formed Group PA−/AT− of our study (control group). ## Clinical and biochemical parameters The participants of the four groups did not differ in age, birthweight, gestational age, mode of conception or delivery, family history for thyroid disorders, obesity, diabetes, PCOS, dyslipidemia, or cardiovascular disease. In terms of anthropometric and clinical variables, BMI was significantly different between the four groups ($$p \leq 0.016$$) (Figure 1A). Furthermore, a significant difference was observed in the frequency of goiter ($p \leq 0.001$), i.e., $33.3\%$ in group PA-/AT+ ($\frac{2}{6}$), $1.85\%$ in PA+/AT− ($\frac{1}{54}$) and $42.1\%$ in PA+/AT+ ($\frac{8}{19}$), whereas none of the control girls had a goiter ($\frac{0}{12}$). No difference was observed with regards to WHR or WHtR. Figure 1: (A) body mass index (BMI) (B) systolic blood pressure (SBP) (C) leptin and (D) insulin-like growth factor-binding protein-1 (IGFBP-1) values of girls with autoimmune thyroiditis (AT) but no premature adrenarche (PA) (group PA−/AT+); girls with PA but no AT (group PA+/AT−); girls with PA and AT (group PA+/AT+); and girls without PA or AT [group PA−/AT− (controls)]. Considering cardiovascular parameters systolic blood pressure was significantly different between groups ($$p \leq 0.022$$) (Figure 1B). No statistical important differences were detected in glucose, HbA1c, lipid profile and hsCRP levels. The clinical and laboratory characteristics of all participants are shown in Tables 1, 2, respectively. ## Hormonal features The endocrine features of all study groups are summarized in Table 2. When comparing hormonal parameters among the four groups of our study significant differences were found in leptin ($$p \leq 0.007$$), TSH ($$p \leq 0.044$$), anti-TPO ($$p \leq 0.002$$), anti-TG ($$p \leq 0.044$$), IGF-BP1 ($$p \leq 0.006$$), Δ4-Α ($$p \leq 0.01$$), DHEA-S (p = <0.001), IGF-1 ($$p \leq 0.012$$) and IGF-BP3 ($$p \leq 0.049$$) levels (Figures 1C, D]. In particular, TSH levels were significantly higher in Group PA+/AT+ compared to Groups PA+/AT− and PA−/AT− ($$p \leq 0.043$$ and $$p \leq 0.016$$, respectively). Moreover, girls with AT (Groups PA−/AT+ and PA+/AT+) had higher TSH levels than girls in Group PA+/AT− ($$p \leq 0.025$$). In addition, after adjustment for BMI, leptin concentrations were significantly higher in Group PA-/AT+ compared to Group PA-/AT− ($$p \leq 0.048$$) (Table 3). No differences were noted in TNF-α or adiponectin levels between groups. There were no differences with respect to insulin resistance indices, i.e., HOMA-IR and QUICKI between the four groups. Table 3 \| Variable \| Group PA+/AT + vs. Group PA−/AT− \| Group PA+/AT + vs. Group PA−/AT+ \| Group PA+/AT + vs. Group PA+/AT− \| \| --- \| --- \| --- \| --- \| \| Variable \| P value \| P value \| P value \| \| BMI \| 0.012 \| 0.279 \| 0.261 \| \| Leptin \| 0.026* \| 0.788 \| 0.542 \| \| TSH \| 0.016 \| 0.955 \| 0.043 \| \| FT4 \| 0.056 \| 0.529 \| 0.082 \| Girls in Group PA+/AT + showed higher cortisol response at 60 min post-SDSST than girls in Group PA+/AT- ($$p \leq 0.035$$) (Table 4, Figure 2A). During OGTT, insulin concentrations at 60 min after glucose load were significantly higher in Group PA+/AT+ compared to Group PA+/AT− ($$p \leq 0.042$$) (Table 4, Figure 2B). The difference between the bone age and the chronological age was similar in Groups PA+/AT− and PA+/AT +. Figure 2: (A) cortisol response at 60 min post-standard dose synacthen stimulation test (SDSST) and (B) insulin concentrations at 60 min during oral glucose tolerance test (OGTT) in girls with premature adrenarche (PA) but no autoimmune thyroiditis (AT) (group PA+/AT−); girls with PA and AT (group PA+/AT+).Small circles denote outlier values; asterisks denote extreme outlier values. TABLE_PLACEHOLDER:Table 4 Finally, in the entire cohort ($$n = 91$$) the following significant associations were found: BMI correlated positively to TSH (ρ = 0.243, $$p \leq 0.021$$), DHEA-S (ρ = 0.320, $$p \leq 0.003$$) and 17-OHP (ρ = 0.242, $$p \leq 0.022$$); TSH positively correlated to Δ4-Α (ρ = 0.227, $$p \leq 0.04$$) and 17-OHP (ρ = 0.443, $p \leq 0.001$); insulin positively correlated to testosterone (ρ = 0.277, $$p \leq 0.027$$) and 17-OHP (ρ = 0.251, $$p \leq 0.044$$); leptin positively correlated to 17-OHP (ρ = 0.329, $$p \leq 0.038$$); IGF-BP3 to Δ4-A (ρ = 0.326, $$p \leq 0.031$$); and systolic blood pressure to 17-OHP (ρ = 0.241, $$p \leq 0.037$$). On the other hand, FT4 inversely correlated to testosterone (ρ = −0.364, $$p \leq 0.001$$) and SHBG inversely correlated to DHEA-S (ρ = −0.334, $$p \leq 0.029$$) and Δ4-Α (ρ = −0.407, $$p \leq 0.007$$). ## Discussion The association between AT and PCOS was first described more than a decade ago by Janssen et al., who identified AT in $26.9\%$ of PCOS patients compared to $8.4\%$ of controls [31]. This almost threefold higher prevalence of AT in PCOS patients was subsequently confirmed by other investigators (11, 15, 16, 32–35). On the other hand, a higher prevalence of PCOS ($46.8\%$) compared to controls ($4.3\%$) was observed among 13–18-year-old euthyroid girls with AT [14], suggesting a relationship between these two conditions [36]. The clinical features, hormonal and metabolic derangements of PCOS may emerge during childhood, first expressed as PA and evolving later to PCOS [17]. Previous studies have shown an increased frequency of insulin resistance, cardiometabolic aberrations and ovarian hyperandrogenism in girls with a history of PA (21–23, 37–39), indicating that PA may be a harbinger of PCOS rather than a normal variant. In this regard, the risk of developing PCOS in patients with premature pubarche seems to be $15\%$–$20\%$ [17]. The present study revealed a high frequency of AT (i.e $26\%$) among euthyroid prepubertal girls with PA that is significantly higher than the one reported in healthy schoolgirls in our country, being $2.2\%$ in prepubertal and $8.2\%$ in pubertal girls, respectively [40]. In concordance with studies from other countries, the prevalence of AT in the pediatric age has been reported to range between $1.2\%$–$9.6\%$, reaching a peak in early to mid-adolescence [7]. As expected [40, 41], the presence of goiter in our study was more frequent in girls with positive thyroid autoantibodies (with or without PA). To the best of our knowledge, this is the first study to investigate an association between PA and thyroid autoimmunity in euthyroid prepubertal girls. In relation to our findings, three possible mechanisms linking PA and AT could be speculated: i) obesity ii) insulin resistance iii) increased leptin levels and associated immune dysregulation [36]. With respect to the role of obesity, our analysis demonstrated significant differences in BMI values among the four groups of our study. Of interest, there was a trend -although not statistically significant- towards higher BMI in girls with PA and concomitant AT. The association between increased BMI and both PA-PCOS is well documented [13, 19]. Moreover, an intriguing link between adiposity and AT has been increasingly reported. Overall, the frequency of AT in obese subjects ranges between $5.6\%$–$12.4\%$ in children and $10\%$–$23.6\%$ in adults (10, 42–45). In the United Kingdom Medical Research Council 1946 British Birth Cohort study, levothyroxine use and positive anti-TPO antibodies in women at age 60–64 years were positively associated with body weight or overweight in childhood and adult BMI. Furthermore, women who were overweight or obese at age 14 years had a higher risk of developing positive anti-TPO antibodies later in life [46]. Of interest, patients with both PCOS and AT were more obese by 2 kg/m² BMI on average than PCOS subjects without AT [16]. Similarly, euthyroid girls with AT and PCOS had higher BMI, fasting glucose, cholesterol and HOMA-IR compared to girls with AT alone or controls [14]. The pathophysiological link between obesity and AT remains largely unclear, however growing evidence points to reduced immunological tolerance secondary to an altered balance of adipokines (e.g., leptin and adiponectin) and cytokines [e.g., interleukin 6 and TNF-α) produced by the white adipose tissue and resultant abnormalities of the immune response [47]. In addition, numerous studies have demonstrated higher TSH values in overweight or obese compared to normal-weight subjects [48, 49]. BMI was positively correlated with TSH and negatively with serum fT4 levels in both adults and children [50, 51], suggesting that mild variations in thyroid function even within the reference range may influence the regulation of body weight or, inversely, may be influenced by the body weight [51]. On the other hand, increased TSH levels were more frequently observed in children with high anti-TPO titers. A positive correlation between TSH and anti-TPO levels has been previously reported, indicating that AT could be linked to an increased prevalence of subclinical hypothyroidism [10, 40, 41, 49, 52]. Our findings, i.e., the significant positive correlation between TSH and BMI in the whole study population and the higher TSH levels in AT girls with or without PA are in agreement with these findings, suggesting a possible link between thyroid hormonal status, obesity and thyroid autoimmunity. Hyperthyrotropinaemia and a moderate increase in thyroid hormone concentrations have been observed in obese children, but these are mostly considered as an adaptation process to weight gain, being the consequence of overweight rather than the cause of it. Nevertheless, aberrations in thyroid function usually normalize after moderate weight loss, indicating that they may be reversible [44, 53]. The pathophysiology underlying these alterations remains unclear, however, several mechanisms have been proposed, including: 1) an impaired negative feedback mechanism, due to a reduced number of T3 receptors in the pituitary in obese patients (pituitary resistance) 2) peripheral resistance to thyroid hormones associated with reduced expression of thyroid genes, particularly TSHR, in subcutaneous and visceral fat in obese subjects 3) obesity-induced chronic low-grade inflammation, characterized by release of inflammatory cytokines from the adipose tissue, such as TNF-α, interleukin-1 and interleukin-6 which were shown to have an inhibitory effect on sodium/iodide symporter (NIS) mRNA expression, thus possibly contributing to compensatory TSH elevation 4) abnormal activity of the deiodinase enzymes D1 and D2 in adiposity resulting in a higher FT3 to FT4 ratio 5) development of thyroid autoimmunity 6) increased leptin levels promoting TRH secretion from the hypothalamus (42, 44–46, 53). With regards to this latter mechanism, and in concordance with previous studies we found higher leptin levels in girls with AT compared to controls [43, 54]. Leptin is a 16-kD adipokine released from the white adipose tissue in proportion to body fat mass, with rising levels as BMI increases [55, 56]. Leptin has pleiotropic effects, regulating appetite and energy expenditure but also neuroendocrine function, metabolism, and immune responses [56, 57]. Leptin activates pro-thyrotropin releasing hormone (pro-TRH) expression, which results in TRH release and increased TSH secretion [53]. Moreover, leptin receptor is expressed in normal CD4+, CD8+ T cells, NK cells and B cells, mediating the immunomodulatory actions of leptin, which has been shown to enhance the Th1 response (43, 57–59). Among obese subjects, those with AT had higher leptin levels. Of note, leptin levels were associated with AT, independent of fat mass and BMI [43]. On the other hand, a subsequent study demonstrated a positive correlation between increased leptin levels and thyroid autoantibodies in nonobese males [56]. Finally, insulin resistance has been increasingly recognized as contributing factor to the development of thyroid autoimmunity. Higher fasting glucose, insulin and HOMA-IR values were observed in euthyroid AT individuals [8, 60]. TSH levels were found to positively correlate with insulin and HOMA-IR levels [8, 61]. Moreover, anti-TPO titers were positively associated to HOMA-IR and hsCRP levels, independently of thyroid function in non-obese subjects, indicating that mild changes of thyroid function even in the euthyroid state, chronic inflammation, and insulin resistance may be implicated in the development of AT [8, 9]. On top of the above, patients with PCOS and concomitant AT showed higher total cholesterol, TSH, HOMA-IR and insulin levels at both 30 and 60 min post-OGTT, lower FT4 and FT4/TSH ratio compared to those without AT [62]. Furthermore, as previously reported in the study of Ganie et al., euthyroid girls with HT and PCOS had higher fasting glucose, cholesterol and HOMA-IR compared to girls with HT alone or controls. In addition, there was a significant inverse correlation between serum FT4 quartiles and various components of PCOS [14]. The above findings imply that the concurrence of AT and PCOS may exacerbate the metabolic aberrations observed in PCOS or AT alone. In line with these observations, we found higher insulin concentrations at 60 min during OGTT in girls with PA and AT compared to those with PA only. Hyperinsulinemia and insulin resistance associated with variable components of the metabolic syndrome are well known features in girls with PA (21–23, 37, 63, 64). Taking the above data into account it could be postulated that the combined occurrence of AT and PA may be associated with more pronounced metabolic abnormalities than observed in PA alone. Another interesting finding of the present study was the higher cortisol response at 60 min post-SDSST in girls with both PA and AT than in those with PA only. Elevated levels of salivary and plasma cortisol have been previously observed in PA girls and attributed to hyperactivation of the hypothalamus-pituitary-adrenal (HPA) axis. It has been hypothesized, that overactivation of the HPA axis (e.g., related to obesity or prenatal stress exposure) and the resultant chronic cortisol hypersecretion in susceptible individuals may lead to visceral fat accumulation, insulin resistance, release of pro-inflammatory cytokines in adipose tissue and activation of signaling pathways implicated in the pathogenesis of PCOS and cardiovascular disease later in life [20, 65, 66]. The positive correlation between 17-OHP levels and systolic blood pressure in our study might indirectly reflect an association between chronic dysregulation of the HPA axis, resultant hypercortisolism and the development of components of the metabolic syndrome. In addition, the higher cortisol together with higher insulin levels observed in PA girls with AT, points towards a worse metabolic and endocrine profile, which -through mechanisms already discussed- might lead to immune alterations and thus render these individuals more prone to the development of thyroid autoimmunity and/or PCOS later in life. Given that most girls included in our study are girls with PA, the correlations observed between clinical and endocrine parameters reflect mainly abnormalities frequently encountered in PA subjects [21, 23, 37, 63, 64]. Interestingly, girls with PA and concomitant AT tended to have lower IGF-BP1 levels compared to the other groups. As the hepatic production of both SHBG and IGF-BP1 is downregulated by insulin, their concentrations have been suggested as useful markers of insulin resistance [37]. The above finding could indirectly reflect a higher degree of insulin resistance in these subjects and is in parallel with the greater response of insulin during OGTT noted in the same group. Reduced IGF-BP1 levels would result in higher unbound (free) IGF-1 concentrations and subsequently an increase in both adrenal and ovarian androgen secretion [66]. Indeed, both insulin and IGF-1 were shown to enhance the ACTH-driven adrenal steroidogenesis, thus contributing to increased androgen secretion during adrenarche [18, 37]. The positive correlation between BMI and both DHEAS and 17-OHP observed in our study population is in alignment with previous observations, suggesting an association between obesity and androgen excess that may further aggravate the metabolic dysfunction of affected individuals [13]. Overall, the associations found in this study are in concordance with previous reports, pointing towards a link between weight gain, elevation of leptin levels, hyperinsulinemia and consequently increased adrenal androgen production. As previously mentioned, not only, BMI but also TSH has been linked to insulin resistance indices [67]. It is noteworthy, that, among women with PCOS, the association between thyroid function and IR was shown to be independent of age and BMI [68], underlining the important role of thyroid hormones on regulation of insulin sensitivity. A negative association between FT4 and insulin resistance indices has been previously reported [61]. In addition, FT4 quartiles were negatively correlated with various PCOS components in euthyroid girls with AT [14]. Our findings, i.e., the positive correlation between TSH and androgen levels, and negative between FT4 and testosterone, are in concordance with previous observations, implying a possible association between insulin resistance and thyroid function in our study population. The main strength of the present study is that we included unselected and well-characterized girls with PA, AT, and controls, who were evaluated applying the same protocol. To our knowledge, this is the first study aiming to investigate the clinical and endocrine features of girls with PA and concurrent AT, although there are some limitations too that need to be addressed. Firstly, the number of girls with AT and controls is small. With regards to the group with AT, the small sample size reflects the rarity of this condition among prepubertal children. Concerning the control group, the study was conducted during the COVID-19 pandemic and the number of children referred for evaluation in the outpatient setting especially for normal variants of growth and puberty was significantly reduced. Secondly, our study population consisted only of girls, so that our results cannot be extrapolated to boys. Moreover, before study entry all participants were prepubertal with normal TSH and FT4 levels. Taking into consideration that the role of estrogens in the development of autoimmune diseases is well established, puberty is a state of relative “normal” insulin resistance, and hypothyroidism is also associated with insulin resistance we stratified only euthyroid prepubertal girls to reduce the impact of confounding factors on our results, such as subclinical or overt thyroid dysfunction and hormonal changes occurring during different pubertal stages [37, 40, 60]. On the other hand, considering the existing data supporting a possible positive effect of levothyroxine treatment on thyroid autoimmune activity, we acknowledge that our results might have been affected by administration of levothyroxine therapy in a minority of our participants [60]. Furthermore, hormonal measurements in our study were carried our using immunoassay-based techniques, which are widely used in clinical and research settings, albeit known to have reduced specificity, when compared with the mass spectrometry methods. In conclusion, the present study revealed a high frequency of AT among euthyroid prepubertal girls with PA, and while waiting for larger studies to confirm our findings, screening PA girls for autoimmune thyroid disease would be justifiable. Girls with PA and concomitant AT had higher TSH levels, cortisol response post-SDSST and insulin levels during OGTT compared to those with PA alone. Our findings indicate that a combination of PA with AT even in the euthyroid state may be associated with a greater degree of insulin resistance, which might aggravate the metabolic abnormalities observed in PA alone. The importance of achieving and maintaining a normal weight should by stressed and lifestyle changes and regular exercise should be encouraged in all overweight and obese PA children with or without AT. Further studies are required to better elucidate the underlying pathophysiological mechanisms linking PA and AT. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. ## Ethics statement Our study involving human participants was reviewed and approved by the Ethics Committee of the “Aghia Sophia” Children's Hospital, Athens. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin, and assent was obtained from the children before children have been enrolled in the study. ## Author contributions CKG contributed to the conception and design of the study and critically revised the manuscript. G.K and E.K. helped supervise the project. EMK and CKG interpreted the data and drafted the manuscript. EMK and IAV contributed to clinical and biochemical assessment of girls who participated in the study. EMK, IAV and IK were involved in the recruitment and selection process and data collection. AM carried out the hormonal assessments and aided in interpretation of laboratory results. AG conducted the statistical analysis and contributed to the interpretation of data. All authors contributed substantially to the manuscript, revised the manuscript and approved the final version. CKG supervised the study. We acknowledge Ioannis Papassotiriou invaluable support in providing technical assistance. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Franco JS, Amaya-Amaya J, Anaya JM, Anaya JM, Shoenfeld Y, Rojas-Villarraga A. Thyroid disease and autoimmune diseases. Autoimmunity: From bench to bedside (2013) 2. Gopalakrishnan S, Marwaha RK. Juvenile autoimmune thyroiditis. Journal of Pediatric Endocrinology and Metabolism (2007) 20 961-70. DOI: 10.1515/JPEM.2007.20.9.961 3. Hanley P, Lord K, Bauer AJ. Thyroid disorders in children and adolescents: a review. JAMA Pediatr (2016) 170 1008-19. DOI: 10.1001/jamapediatrics.2016.0486 4. Kyritsi EM, Kanaka-Gantenbein C. Autoimmune thyroid disease in specific genetic syndromes in childhood and adolescence. Front Endocrinol (Lausanne) (2020) 11 543. DOI: 10.3389/fendo.2020.00543 5. Kakourou T, Kanaka-Gantenbein C, Papadopoulou A, Kaloumenou E, Chrousos GP. Increased prevalence of chronic autoimmune (Hashimoto's) thyroiditis in children and adolescents with vitiligo. J Am Acad Dermatol (2005) 53 220-3. DOI: 10.1016/j.jaad.2005.03.032 6. Cappa M, Bizzarri C, Crea F. Autoimmune thyroid diseases in children. J Thyroid Res (2010) 2011 675703. DOI: 10.4061/2011/675703 7. Segni M, Feingold KR, Anawalt B, Boyce A. Disorders of the thyroid gland in infancy, childhood and adolescence. Endotext (2000) 8. Varim C, Kaya T, Varim P, Nalbant A, Vatan MB, Yaylaci S. Insulin resistance in the patients with euthyroid hashimoto thyroiditis. Biomed Res (2017) 28 1543-7 9. Liu J, Duan Y, Fu J, Wang G. Association between thyroid hormones, thyroid antibodies, and cardiometabolic factors in non-obese individuals with normal thyroid function. Front Endocrinol (Lausanne) (2018) 9 130. DOI: 10.3389/fendo.2018.00130 10. García-García E, Vázquez-López MA, García-Fuentes E, Galera-Martínez R, Gutiérrez-Repiso C, García-Escobar I. Thyroid function and thyroid autoimmunity in relation to weight Status and cardiovascular risk factors in children and adolescents: a population-based study. J Clin Res Pediatr Endocrinol (2016) 8 157-62. DOI: 10.4274/jcrpe.2687 11. Zeber-Lubecka N, Hennig EE. Genetic susceptibility to joint occurrence of polycystic ovary syndrome and Hashimoto's Thyroiditis: how far is our understanding?. Front Immunol (2021) 12 606620. DOI: 10.3389/fimmu.2021.606620 12. Azziz R, Carmina E, Chen Z, Dunaif A, Laven JS, Legro RS. Polycystic ovary syndrome. (2016) 2 16057. DOI: 10.1038/nrdp.2016.57 13. Randeva HS, Tan BK, Weickert MO, Lois K, Nestler JE, Sattar N. Cardiometabolic aspects of the polycystic ovary syndrome. Endocr Rev (2012) 33 812-41. DOI: 10.1210/er.2012-1003 14. Ganie MA, Marwaha RK, Aggarwal R, Singh S. High prevalence of polycystic ovary syndrome characteristics in girls with euthyroid chronic lymphocytic thyroiditis: a case-control study. Eur J Endocrinol (2010) 162 1117-22. DOI: 10.1530/EJE-09-1012 15. Gaberšček S, Zaletel K, Schwetz V, Pieber T, Obermayer-Pietsch B, Lerchbaum E. Mechanisms in endocrinology: thyroid and polycystic ovary syndrome. Eur J Endocrinol (2015) 172 R9-21. DOI: 10.1530/EJE-14-0295 16. Ulrich J, Goerges J, Keck C, Müller-Wieland D, Diederich S, Janssen OE. Impact of autoimmune thyroiditis on reproductive and metabolic parameters in patients with polycystic ovary syndrome. Exp Clin Endocrinol Diabetes (2018) 126 198-204. DOI: 10.1055/s-0043-110480 17. Rosenfield RL. Clinical review: identifying children at risk for polycystic ovary syndrome. J Clin Endocrinol Metab (2007) 92 787-96. DOI: 10.1210/jc.2006-2012 18. Idkowiak J, Lavery GG, Dhir V, Barrett TG, Stewart PM, Krone N. Premature adrenarche: novel lessons from early onset androgen excess. Eur J Endocrinol (2011) 165 189-207. DOI: 10.1530/EJE-11-0223 19. Voutilainen R, Jääskeläinen J. Premature adrenarche: etiology, clinical findings, and consequences. J Steroid Biochem Mol Biol (2015) 145 226-36. DOI: 10.1016/j.jsbmb.2014.06.004 20. Kanaka-Gantenbein C, Mastorakos G, Chrousos GP. Endocrine-related causes and consequences of intrauterine growth retardation. Ann N Y Acad Sci (2003) 997 150-7. DOI: 10.1196/annals.1290.017 21. Utriainen P, Jääskeläinen J, Romppanen J, Voutilainen R. Childhood metabolic syndrome and its components in premature adrenarche. J Clin Endocrinol Metab (2007) 92 4282-5. DOI: 10.1210/jc.2006-2412 22. Livadas S, Dracopoulou M, Vasileiadi K, Lazaropoulou C, Magiakou MA, Xekouki P. Elevated coagulation and inflammatory markers in adolescents with a history of premature adrenarche. Metab Clin Exp (2009) 58 576-81. DOI: 10.1016/j.metabol.2008.12.002 23. Utriainen P, Laakso S, Liimatta J, Jääskeläinen J, Voutilainen R. Premature adrenarche–a common condition with variable presentation. Horm Res Paediatr (2015) 83 221-31. DOI: 10.1159/000369458 24. Neville KA, Walker JL. Precocious pubarche is associated with SGA, prematurity, weight gain, and obesity. Arch Dis Child (2005) 90 258-61. DOI: 10.1136/adc.2004.053959 25. Dayan CM, Daniels GH. Chronic autoimmune thyroiditis. N Engl J Med (1996) 335 99-107. DOI: 10.1056/NEJM199607113350206 26. Rotondi M, de Martinis L, Coperchini F, Pignatti P, Pirali B, Ghilotti S. Serum negative autoimmune thyroiditis displays a milder clinical picture compared with classic Hashimoto's Thyroiditis. Eur J Endocrinol (2014) 171 31-6. DOI: 10.1530/EJE-14-0147 27. Marshall WA, Tanner JM. Variations in pattern of pubertal changes in girls. Arch Dis Child (1969) 44 291-303. DOI: 10.1136/adc.44.235.291 28. Bacopoulou F, Efthymiou V, Landis G, Rentoumis A, Chrousos GP. Waist circumference, waist-to-hip ratio and waist-to-height ratio reference percentiles for abdominal obesity among Greek adolescents. BMC Pediatr (2015) 15 50. DOI: 10.1186/s12887-015-0366-z 29. Cutfield WS, Jefferies CA, Jackson WE, Robinson EM, Hofman PL. Evaluation of HOMA and QUICKI as measures of insulin sensitivity in prepubertal children. Pediatr Diabetes (2003) 4 119-25. DOI: 10.1034/j.1399-5448.2003.t01-1-00022.x 30. Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist (1959) 31. Janssen OE, Mehlmauer N, Hahn S, Offner AH, Gärtner R. High prevalence of autoimmune thyroiditis in patients with polycystic ovary syndrome. Eur J Endocrinol (2004) 150 363-9. DOI: 10.1530/eje.0.1500363 32. Garelli S, Masiero S, Plebani M, Chen S, Furmaniak J, Armanini D. High prevalence of chronic thyroiditis in patients with polycystic ovary syndrome. Eur J Obstet Gynecol Reprod Biol (2013) 169 248-51. DOI: 10.1016/j.ejogrb.2013.03.003 33. Arora S, Sinha K, Kolte S, Mandal A. Endocrinal and autoimmune linkage: evidences from a controlled study of subjects with polycystic ovarian syndrome. J Hum Reprod Sci (2016) 9 18-22. DOI: 10.4103/0974-1208.178636 34. Al-Saab R, Haddad S. Detection of thyroid autoimmunity markers in euthyroid women with polycystic ovary syndrome: a case-control study from Syria. Int J Endocrinol Metab (2014) 12 e17954. DOI: 10.5812/ijem.17954 35. Karaköse M, Hepsen S, Çakal E, Saykı Arslan M, Tutal E, Akın Ş. Frequency of nodular goiter and autoimmune thyroid disease and association of these disorders with insulin resistance in polycystic ovary syndrome. J Turk Ger Gynecol Assoc (2017) 18 85-9. DOI: 10.4274/jtgga.2016.0217 36. Singla R, Gupta Y, Khemani M, Aggarwal S. Thyroid disorders and polycystic ovary syndrome: an emerging relationship. Indian J Endocrinol Metab (2015) 19 25-9. DOI: 10.4103/2230-8210.146860 37. Ibáñez L, Potau N, Zampolli M, Riqué S, Saenger P, Carrascosa A. Hyperinsulinemia and decreased insulin-like growth factor-binding protein-1 are common features in prepubertal and pubertal girls with a history of premature pubarche. J Clin Endocrinol Metab (1997) 82 2283-8. DOI: 10.1210/jcem.82.7.4084 38. Ibañez L, Potau N, Virdis R, Zampolli M, Terzi C, Gussinyé M. Postpubertal outcome in girls diagnosed of premature pubarche during childhood: increased frequency of functional ovarian hyperandrogenism. J Clin Endocrinol Metab (1993) 76 1599-603. DOI: 10.1210/jcem.76.6.8501168 39. Güven A, Cinaz P, Bideci A. Is premature adrenarche a risk factor for atherogenesis?. Pediatr Int (2005) 47 20-5. DOI: 10.1111/j.1442-200x.2004.02006.x 40. Kaloumenou I, Mastorakos G, Alevizaki M, Duntas LH, Mantzou E, Ladopoulos C. Thyroid autoimmunity in schoolchildren in an area with long-standing iodine sufficiency: correlation with gender, pubertal stage, and maternal thyroid autoimmunity. Thyroid (2008) 18 747-54. DOI: 10.1089/thy.2007.0370 41. Kabelitz M, Liesenkötter KP, Stach B, Willgerodt H, Stäblein W, Singendonk W. The prevalence of anti-thyroid peroxidase antibodies and autoimmune thyroiditis in children and adolescents in an iodine replete area. Eur J Endocrinol (2003) 148 301-7. DOI: 10.1530/eje.0.1480301 42. Stichel H, l'Allemand D, Grüters A. Thyroid function and obesity in children and adolescents. Horm Res (2000) 54 14-9. DOI: 10.1159/000063431 43. Marzullo P, Minocci A, Tagliaferri MA, Guzzaloni G, Di Blasio A, De Medici C. Investigations of thyroid hormones and antibodies in obesity: leptin levels are associated with thyroid autoimmunity independent of bioanthropometric, hormonal, and weight-related determinants. J Clin Endocrinol Metab (2010) 95 3965-72. DOI: 10.1210/jc.2009-2798 44. Radetti G, Kleon W, Buzi F, Crivellaro C, Pappalardo L, di Iorgi N. Thyroid function and structure are affected in childhood obesity. J Clin Endocrinol Metab (2008) 93 4749-54. DOI: 10.1210/jc.2008-0823 45. Ruszała A, Wójcik M, Starzyk JB. The impact of thyroid function on the occurrence of metabolic syndrome in obese children and adolescents. Pediatr Endocrinol Diabetes Metab (2019) 25 1-5. DOI: 10.5114/pedm.2019.84705 46. Ong KK, Kuh D, Pierce M, Franklyn JA. Medical research council national survey of health and development scientific and data collection teams. Childhood weight gain and thyroid autoimmunity at age 60-64 years: the 1946 British birth cohort study. J Clin Endocrinol Metab (2013) 98 1435-42. DOI: 10.1210/jc.2012-3761 47. Hersoug LG, Linneberg A. The link between the epidemics of obesity and allergic diseases: does obesity induce decreased immune tolerance?. Allergy (2007) 62 1205-13. DOI: 10.1111/j.1398-9995.2007.01506.x 48. Asvold BO, Bjøro T, Vatten LJ. Association of serum TSH with high body mass differs between smokers and never-smokers. J Clin Endocrinol Metab (2009) 94 5023-7. DOI: 10.1210/jc.2009-1180 49. Bhowmick SK, Dasari G, Levens KL, Rettig KR. The prevalence of elevated serum thyroid-stimulating hormone in childhood/adolescent obesity and of autoimmune thyroid diseases in a subgroup. J Natl Med Assoc (2007) 99 773-6. PMID: 17668643 50. Jin HY. Prevalence of subclinical hypothyroidism in obese children or adolescents and association between thyroid hormone and the components of metabolic syndrome. J Paediatr Child Health (2018) 54 975-80. DOI: 10.1111/jpc.13926 51. Knudsen N, Laurberg P, Rasmussen LB, Bülow I, Perrild H, Ovesen L. Small differences in thyroid function may be important for body mass index and the occurrence of obesity in the population. J Clin Endocrinol Metab (2005) 90 4019-24. DOI: 10.1210/jc.2004-2225 52. Loviselli A, Velluzzi F, Mossa P, Cambosu MA, Secci G, Atzeni F. ; sardinian schoolchildren study group. The sardinian autoimmunity study: 3. Studies on circulating antithyroid antibodies in sardinian schoolchildren: relationship to goiter prevalence and thyroid function. Thyroid (2001) 11 849-57. DOI: 10.1089/105072501316973109 53. Witkowska-Sędek E, Kucharska A, Rumińska M, Pyrżak B. Thyroid dysfunction in obese and overweight children. Endokrynol Pol (2017) 68 54-60. DOI: 10.5603/EP.2017.0007 54. Drobniak A, Kanecki K, Grymowicz M, Radowicki S. Serum leptin concentration in women of reproductive age with euthyroid autoimmune thyroiditis. Gynecol Endocrinol (2016) 32 128-31. DOI: 10.3109/09513590.2015.1092512 55. Kennedy A, Gettys TW, Watson P, Wallace P, Ganaway E, Pan Q. The metabolic significance of leptin in humans: gender-based differences in relationship to adiposity, insulin sensitivity, and energy expenditure. J Clin Endocrinol Metab (1997) 82 1293-300. DOI: 10.1210/jcem.82.4.3859 56. MacIver NJ, Thomas SM, Green CL, Worley G. Increased leptin levels correlate with thyroid autoantibodies in nonobese males. Clin Endocrinol (Oxf) (2016) 85 116-21. DOI: 10.1111/cen.12963 57. Duntas LH, Biondi B. The interconnections between obesity, thyroid function, and autoimmunity: the multifold role of leptin. Thyroid (2013) 23 646-53. DOI: 10.1089/thy.2011.0499 58. Kiernan K, MacIver NJ. The role of the adipokine leptin in immune cell function in health and disease. Front Immunol (2021) 11 622468. DOI: 10.3389/fimmu.2020.622468 59. Wang S, Baidoo SE, Liu Y, Zhu C, Tian J, Ma J. T cell-derived leptin contributes to increased frequency of T helper type 17 cells in female patients with Hashimoto's Thyroiditis. Clin Exp Immunol (2013) 171 63-8. DOI: 10.1111/j.1365-2249.2012.04670.x 60. Mazaheri T, Sharifi F, Kamali K. Insulin resistance in hypothyroid patients under levothyroxine therapy: a comparison between those with and without thyroid autoimmunity. J Diabetes Metab Disord (2014) 13 103. DOI: 10.1186/s40200-014-0103-4 61. Cunha CA, Neves C, Neves JS, Oliveira SC, Sokhatska O, Dias C. Cardiovascular risk factors in patients with autoimmune thyroiditis. Rev Port Endocrinol Diabetes Metab (2017) 12 133-41. DOI: 10.1530/endoabs.49.GP199 62. Zhao H, Zhang Y, Ye J, Wei H, Huang Z, Ning X. A comparative study on insulin secretion, insulin resistance and thyroid function in patients with polycystic ovary syndrome with and without Hashimoto's Thyroiditis. Diabetes Metab Syndr Obes (2021) 14 1817-21. DOI: 10.2147/DMSO.S300015 63. Ibáñez L, Potau N, Chacon P, Pascual C, Carrascosa A. Hyperinsulinaemia, dyslipaemia and cardiovascular risk in girls with a history of premature pubarche. Diabetologia (1998) 41 1057-63. DOI: 10.1007/s001250051030 64. Sopher AB, Jean AM, Zwany SK, Winston DM, Pomeranz CB, Bell JJ. Bone age advancement in prepubertal children with obesity and premature adrenarche: possible potentiating factors. Obesity (Silver Spring) (2011) 19 1259-64. DOI: 10.1038/oby.2010.305 65. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med (2011) 9 48. DOI: 10.1186/1741-7015-9-48 66. Cizza G, Dorn LD, Lotsikas A, Sereika S, Rotenstein D, Chrousos GP. Circulating plasma leptin and IGF-1 levels in girls with premature adrenarche: potential implications of a preliminary study. Horm Metab Res (2001) 33 138-43. DOI: 10.1055/s-2001-14927 67. Dittrich R, Kajaia N, Cupisti S, Hoffmann I, Beckmann MW, Mueller A. Association of thyroid-stimulating hormone with insulin resistance and androgen parameters in women with PCOS. Reprod Biomed Online (2009) 19 319-25. DOI: 10.1016/s1472-6483(10)60165-4 68. Mueller A, Schöfl C, Dittrich R, Cupisti S, Oppelt PG, Schild RL. Thyroid-stimulating hormone is associated with insulin resistance independently of body mass index and age in women with polycystic ovary syndrome. Hum Reprod (2009) 24 2924-30. DOI: 10.1093/humrep/dep285
--- title: Do peripheral neuropathies differ among immune checkpoint inhibitors? Reports from the European post-marketing surveillance database in the past 10 years authors: - Rosanna Ruggiero - Nunzia Balzano - Raffaella Di Napoli - Federica Fraenza - Ciro Pentella - Consiglia Riccardi - Maria Donniacuo - Marina Tesorone - Romano Danesi - Marzia Del Re - Francesco Rossi - Annalisa Capuano journal: Frontiers in Immunology year: 2023 pmcid: PMC10060793 doi: 10.3389/fimmu.2023.1134436 license: CC BY 4.0 --- # Do peripheral neuropathies differ among immune checkpoint inhibitors? Reports from the European post-marketing surveillance database in the past 10 years ## Abstract Although the immunotherapy advent has revolutionized cancer treatment, it, unfortunately, does not spare cancer patients from possible immune-related adverse events (irAEs), which can also involve the peripheral nervous system. Immune checkpoint inhibitors (ICIs), blocking cytotoxic T-lymphocyteassociated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), or programmed cell death ligand 1 (PD-L1), can induce an immune imbalance and cause different peripheral neuropathies (PNs). Considering the wide range of PNs and their high impact on the safety and quality of life for cancer patients and the availability of large post-marketing surveillance databases, we chose to analyze the characteristics of ICI-related PNs reported as suspected drug reactions from 2010 to 2020 in the European real-world context. We analyzed data collected in the European pharmacovigilance database, Eudravigilance, and conducted a systematic and disproportionality analysis. In our study, we found 735 reports describing 766 PNs occurred in patients treated with ICIs. These PNs included Guillain-Barré syndrome, Miller-Fisher syndrome, neuritis, and chronic inflammatory demyelinating polyradiculoneuropathy. These ADRs were often serious, resulting in patient disability or hospitalization. Moreover, our disproportionality analysis revealed an increased reporting frequency of PNs with tezolizumab compared to other ICIs. Guillain-Barré syndrome is a notable potential PN related to ICIs, as it is associated with a significant impact on patient safety and has had unfavorable outcomes, including a fatal one. Continued monitoring of the safety profile of ICIs in real-life settings is necessary, especially considering the increased frequency of PNs associated with atezolizumab compared with other ICIs. ## Introduction Peripheral neuropathies (PN) include several pathologic conditions that affect the peripheral nervous system and result from damage to peripheral nerves. Nerve injury can cause a wide range of disorders, mainly characterized by various degrees of alterations in sensitivity, pain, muscle strength and endurance, osteotendinous reflexes, and/or fine motor skills [1]. The symptoms and signs of PNs depend on the type of damaged nerves. Even if the hands and the feet are most frequently involved in PNs, the gastrointestinal, cardiovascular, and urogenital systems can also be affected [2]. In particular, bowel, bladder, or digestive problems, in addition to drops in blood pressure causing dizziness, are other possible signs and symptoms of PN related to damage to autonomic nerves. So, these conditions can have a significant impact on the quality of life of patients. PNs causes can be different, like inherited and hereditary aspects, traumatic injuries, infections, metabolic problems, nutritional deficiencies, or toxic exposure, but also drugs or specific diseases can induce them (2–8). Sensorimotor polyneuropathies are often experienced by cancer patients [9], and these can be related to their oncologic condition, due to tumor invasion or compression exercised on nerves, or can be associated with a paraneoplastic effect [10]. Furthermore, PNs can often also be iatrogenic, being associated with treatments [11]. PNs are well-known as adverse events of classic chemotherapy or radiotherapy, which can damage healthy nerve tissue [12]. Uncommonly, neuropathies can also be immune-mediated. The different possible causes make a correct differential diagnosis difficult [13]. Among immune-mediated PNs, those induced by immunotherapies have recently emerged. Although the advent of oncological immunotherapy has revolutionized cancer treatment, leading to long-lasting tumor responses, it, unfortunately, does not spare cancer patients from possible adverse events, like PNs. Indeed, this new type of immune-driven neuropathy is added to the toxic neuropathies associated with exposure to traditional chemotherapies (13–15). Immune checkpoint inhibitors (ICIs), which block cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), or programmed cell death ligand 1 (PD-L1), can also induce immune imbalance and cause different immune-related adverse events [16], also in the peripheral nervous system (Figure 1) (17–22). From our previous study, PNs emerged as the second nervous subclass of ICI-related neurological complications more frequently described in the European pharmacovigilance database [23]. Therefore, given the wide range of PNs and their impact on patients’ quality of life and the availability of large pharmacovigilance databases [24], we decided to analyze in more detail the characteristics of ICI related PNs emerging from 10 years of ICI-use in a real-world context. Figure 1: The mechanism of action of ICIs and possible associated neuropathies. ## Data sources Large and international pharmacovigilance databases, such as the European one, Eudravigilance (EV), are important sources of clinical data, even the rarest ones. The analysis of ADR cases collected in these databases allows for the extraction of important information useful for clinicians’ prompt differential diagnosis and management. Analysis of these databases allows for constant drug safety surveillance in order to identify any new important safety signal coming from the real-world context. Therefore, for our analysis, we retrieved from the EV database all safety cases reporting PN complications that occurred in patients treated with at least one ICI and were collected in EV from $\frac{01}{01}$/2010 until $\frac{07}{02}$/2020. The ICIs included in our analysis were the ones authorized by the European Medicines Agency (EMA) until January 2020: ipilimumab, nivolumab, pembrolizumab, cemiplimab, atezolizumab, durvalumab, and avelumab, in mono- or combination therapies. The EV is the European pharmacovigilance database, managed by the EMA. It collects all individual case safety reports (ICSRs) describing cases of suspected ADR related to drugs or vaccines. In fact, there are different levels of access to the data via the EMA website (www.adrreports.eu), the most comprehensive of which requires specific authorization from the agency. According to recent pharmacovigilance legislation, ICSR present in the EV database can be reported both by a healthcare professional and a non-healthcare professional (e.g., a citizen or other professional figures). The reported adverse events are categorized according to the Medical Dictionary for Regulatory Activities (MedDRA). According to a hierarchical structure, this dictionary is characterized by five different levels, from the lowest one (the lowest level term, LLT), up to the highest one (the System Organ Class, SOC). Thus, it is possible to select specific cases in EV by searching for SOCs. In light of this, we selected all cases reporting at least one ICI as a suspected drug and an ADR belonging to the SOC “Neurological disorders”, focusing our analysis on those cases that described at least one neurological complication belonging to the MedDRA “Peripheral neuropathies” High-Level Group Terms (HLGT). ## Descriptive analysis ADR reports were analyzed for patient characteristics, describing age group, gender, and therapies, and differentiating suspected ICI (ipilimumab, nivolumab, pembrolizumab, cemiplimab, atezolizumab, durvalumab, avelumab, or combination ICI treatments) from other suspected or concomitant drugs (Level II ATC). Moreover, reports were categorized by source, including reporter type (Healthcare Professional, HCP, or Non-Healthcare Professional, N-HCP), and the country for regulatory purposes, distinguished into the European Economic Area and the Non-European Economic Area. Our aim was to compare which type of PN was more frequently reported as a suspected ADR for each ICI treatment. PNs were described in terms of action taken to manage them, outcome, and severity, with severity criteria specified in accordance with the International Council on Harmonization E2D guidelines. In particular, an ADR was considered serious when it resulted in death, hospitalization or its prolongation, severe or permanent disability, or congenital anomalies/birth defects, or if it was a life-threatening or clinically relevant event. The outcome of neurological complications was classified as “recovered/resolved”, “recovering/resolving”, “recovered/resolved with sequelae”, “not recovered/not resolved”, “fatal”, and “unknown”. Moreover, PNs were categorized according to the MedDRA High-Level Terms (HLT), and the different neurological diagnoses (p-term) included in each reference HLT. Overall, we found 735 ICSRs that reported at least one ICI as a suspected drug and described PNs as ADRs in EV. Safety reports were mainly related to nivolumab, followed by pembrolizumab and ipilimumab. Specifically, 264 ($35.9\%$) ICSRs were related to nivolumab, 193 ($26.3\%$) to pembrolizumab, and 109 ($14.8\%$) to ipilimumab. The nivolumab/ipilimumab association was reported as a suspected drug in 89 ($12.1\%$) ICSRs. Other ICIs were reported less frequently (<$10\%$), of which cemiplimab was the least reported, resulting in only 1 ($0.1\%$) ICSR (Table 1). The majority of reports were in elderly patients ($$n = 340$$; $46.3\%$) and reported by HCPs ($$n = 682$$; $92.8\%$). Among the more frequently reported ICIs (> 100 ICSRs), elderly patients were particularly represented in nivolumab-related ICSRs ($$n = 137$$; $51.9\%$) (Table 1). Table 1 \| Variable \| Level \| AllICSRs(N=735; 100%) \| ICSRswithnivolumab(N=264; 35.9%) \| ICSRswithpembrolizumab(N=193; 26.3%) \| ICSRswithipilimumab(N=109; 14.8%) \| ICSRswithdurvalumab(N=17; 2.3%) \| ICSRswithatezolizumab(N=51; 6.9%) \| ICSRswithnivolumab and ipilimumab(N=89;12.1%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age Group \| Adult \| 269 (36.6) \| 95 (36.0) \| 65 (33.7) \| 39 (35.8) \| 7 (41.2) \| 26 (51.0) \| 34 (38.2) \| \| \| Elderly \| 340 (46.3) \| 137 (51.9) \| 92 (47.7) \| 50 (45.9) \| 7 (41.2) \| 16 (31.4) \| 32 (36.0) \| \| \| Not specified \| 126 (17.1) \| 32 (12.1) \| 36 (18.6) \| 20 (18.3) \| 3 (17.6) \| 9 (17.6) \| 23 (25.8) \| \| Gender \| W (%) \| 235 (32.0) \| 76 (28.8) \| 68 (35.2) \| 27 (24.8) \| 7 (41.2) \| 20 (39.2) \| 31 (34.8) \| \| Gender \| M (%) \| 458 (62.3) \| 175 (66.3) \| 108 (56.0) \| 75 (68.8) \| 9 (52.9) \| 30 (58.8) \| 55 (61.8) \| \| Gender \| Missing (%) \| 42 (5.7) \| 13 (4.9) \| 17 (8.8) \| 7 (6.4) \| 1 (5.9) \| 1 (2.0) \| 3 (3.4) \| \| Primary Source \| Healthcare Professional \| 682 (92.8) \| 243 (92.0) \| 187 (97.0) \| 93 (85.3) \| 16 (94.1) \| 46 (90.2) \| 85 (95.5) \| \| Primary Source \| Non-Healthcare Professional \| 52 (7.1) \| 20 (7.6) \| 6 (3.0) \| 16 (14.7) \| 1 (5.9) \| 5 (9.8) \| 4 (4.5) \| \| \| Not available \| 1 (0.1) \| 1 (0.4) \| – \| – \| – \| – \| – \| \| Primary Source Country for Regulatory Purposes \| European Economic Area \| 271 (36.9) \| 111 (42.0) \| 55 (28.5) \| 43 (39.4) \| 7 (41.2) \| 14 (27.5) \| 37 (41.6) \| \| Primary Source Country for Regulatory Purposes \| Non-European Economic Area \| 463 (63.0) \| 152 (57.6) \| 138 (71.5) \| 66 (60.6) \| 10 (58.8) \| 37 (72.5) \| 52 (58.4) \| \| \| Not available \| 1 (0.1) \| 1 (0.4) \| – \| – \| – \| – \| – \| \| Suspected drug(s) other than ICIs \| 0 \| 604 (82.2) \| 224 (85.0) \| 151 (78.2) \| 98 (90.0) \| 11 (64.7) \| 29 (56.9) \| 81 (91.0) \| \| Suspected drug(s) other than ICIs \| 1 \| 51 (6.9) \| 17 (6.4) \| 16 (8.5) \| 5 (4.6) \| 2 (11.8) \| 8 (15.7) \| 3 (3.4) \| \| Suspected drug(s) other than ICIs \| 2 \| 49 (6.7) \| 15 (5.7) \| 21 (10.9) \| 5 (4.6) \| 2 (11.8) \| 3 (5.8) \| 2 (2.2) \| \| Suspected drug(s) other than ICIs \| 3 \| 20 (2.7) \| 4 (1.5) \| 2 (1.0) \| 1 (0.8) \| – \| 11 (21.6) \| 1 (1.1) \| \| Suspected drug(s) other than ICIs \| 4 \| 4 (0.5) \| 1 (0.4) \| 1 (0.5) \| – \| 2 (11.8) \| – \| – \| \| Suspected drug(s) other than ICIs \| ≥ 5 \| 7 (1.0) \| 3 (1.0) \| 2 (1.0) \| – \| – \| – \| 2 (2.3) \| \| Concomitant drug(s) \| 0 \| 521 (70.9) \| 197 (74.7) \| 128 (66.3) \| 72 (66.0) \| 9 (52.9) \| 38 (74.5) \| 72 (80.9) \| \| \| 1 \| 42 (5.7) \| 12 (4.5) \| 18 (9.3) \| 5 (4.6) \| – \| 3 (5.9) \| 3 (3.4) \| \| \| 2 \| 39 (5.3) \| 9 (3.4) \| 10 (5.2) \| 6 (5.5) \| 3 (17.6) \| 4 (7.8) \| 5 (5.6) \| \| \| 3 \| 26 (3.5) \| 6 (2.3) \| 9 (4.6) \| 6 (5.5) \| – \| 1 (2.0) \| 3 (3.4) \| \| \| 4 \| 14 (1.9) \| 3 (1.1) \| 3 (1.6) \| 3 (2.8) \| 1 (6.0) \| 1 (2.0) \| 2 (2.2) \| \| \| ≥ 5 \| 93 (12.7) \| 37 (14.0) \| 25 (13.0) \| 17 (15.6) \| 4 (23.5) \| 4 (7.8) \| 4 (4.5) \| The reporter distribution remained the same for each ICI, for which more than $80\%$ of ICSRs were reported by HCPs. Ipilimumab-related ICSRs showed a slightly higher percentage of N-HCPs ($14.7\%$) as the reported source compared to the other ICIs (Table 1). Looking at all ICSRs, the male gender was more frequently represented ($$n = 458$$; $62.3\%$). Similarly, when our dataset was also stratified for each suspected drug ICI, the male gender was reported in more than $50\%$ of ICSRs, particularly for those related to ipilimumab ($68.8\%$) or nivolumab ($66.3\%$). Only in four ICSRs related to the association of ipilimumab/pembrolizumab, the female gender was represented in $75.0\%$ of ICSRs (Table 1). In terms of the primary country of origin for regulatory purposes, the Non-European Economic Area was the most representative one for all ICSRs ($$n = 463$$; $63.0\%$), especially for those related to atezolizumab (37 out of 51; $72.5\%$) and pembrolizumab (138 out of 193; $71.5\%$) (Table 1). As more than one ADR can be reported in each ICSR, we overall observed a total of 766 neurological irADRs categorized as PN according to MedDRA (Table 2). Among these, the adverse events were more frequently reported with a generic term, such as “peripheral neuropathy” ($$n = 335$$; $43.7\%$) and “polyneuropathy” ($$n = 69$$; $9.0\%$) p-terms. Specific syndromes were also reported, such as Guillain-Barré syndrome (GBS) ($$n = 154$$; $20.1\%$), carpal tunnel syndrome ($$n = 13$$; $1.7\%$), and Miller-Fisher syndrome ($$n = 4$$; $0.5\%$) (Table 2). Regarding the gender distribution of the reported PNs, men continued to be more commonly involved in the reported of GBS ($M = 477$; $62.0\%$ vs $F = 247$; $32.2\%$). In contrast, carpal tunnel syndrome ($F = 9$; $1.2\%$ vs $M = 4$; $0.5\%$) and Miller-Fisher syndrome ($F = 2$; $1.2\%$ vs $M = 1$; $0.5\%$) were more frequently reported in women. Table 2* \| High-Level Terms \| TOT(N=766) \| Ipilimumab (N=114) \| Nivolumab (N=272) \| Pembrolizumab (N=203) \| Atezolizumab (N=52) \| Durvalumab (N=17) \| Ipilimumab/nivolumab(N=94) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Peripheral neuropathies (NEC) \| 496 (64.8) \| 70 (61.4) \| 182 (66.9) \| 133 (65.7) \| 38 (73.1) \| 14 (82.4) \| 51 (54.2) \| \| Peripheral neuropathy \| 335 (43.7) \| 49 (43.0) \| 120 (44.1) \| 90 (44.3) \| 33 (63.5) \| 8 (47.1) \| 30 (31.9) \| \| Polyneuropathy \| 69 (9.0) \| 10 (8.8) \| 24 (8.8) \| 16 (7.9) \| 4 (7.7) \| 2 (11.8) \| 11 (11.7) \| \| Peripheral sensory neuropathy \| 42 (5.5) \| 5 (4.4) \| 17 (6.3) \| 15 (7.4) \| 1 (1.9) \| 3 (17.6) \| 1 (1.1) \| \| Autoimmune neuropathy \| 18 (2.3) \| 2 (1.8) \| 6 (2.2) \| 4 (2.0) \| - \| 1 (5.9) \| 5 (5.3) \| \| Peripheral motor neuropathy \| 10 (1.3) \| \| 5 (1.8) \| 4 (2.0) \| - \| - \| 1 (1.1) \| \| Axonal neuropathy \| 4 (0.5) \| – \| – \| 3 (1.5) \| – \| – \| - \| \| Peripheral sensorimotor neuropathy \| 7 (0.9) \| 3 (2.6) \| 2 (0.7) \| – \| – \| – \| 2 (2.1) \| \| Brachial plexopathy \| 3 (0.4) \| – \| 2 (0.7) \| – \| – \| – \| 1 (1.1) \| \| Immune-mediated neuropathy \| 3 (0.4) \| – \| 2 (0.7) \| 1 (0.5) \| – \| – \| – \| \| Toxic neuropathy \| 3 (0.4) \| 1 (0.9) \| 2 (0.7) \| – \| – \| – \| – \| \| Neuralgic amyotrophy \| 2 (0.3) \| - \| 2 (0.7) \| – \| – \| – \| – \| \| Acute polyneuropathies \| 165 (21.5) \| 31 (27.2) \| 49 (18.0) \| 45 (22.0) \| 7 (13.6) \| 2 (11.8) \| 26 (27.6) \| \| Guillain- Barré syndrome \| 154 (20.1) \| 29 (25.4) \| 46 (16.9) \| 41 (20.2) \| 7 (13.5) \| 2 (11.8) \| 25 (26.5) \| \| Acute polyneuropathy \| 7 (0.8) \| 1 (0.9) \| 2 (0.7) \| 2 (1.0) \| – \| \| 1 (1.1) \| \| Acute motor axonal neuropathy \| 2 (0.3) \| - \| 1 (0.4 \| 1 (0.5) \| – \| – \| – \| \| Acute motor-sensory axonal neuropathy \| 2 (0.3) \| 1 (0.9) \| – \| 1 (0.5) \| – \| – \| – \| \| Chronic polyneuropathies \| 45 (5.9) \| 8 (7.0) \| 17 (6.2) \| 9 (4.4) \| 2 (3.8) \| - \| 8 (8.5) \| \| Demyelinating polyneuropathy \| 29 (3.8) \| 6 (5.3) \| 11 (4.0) \| 5 (2.5) \| 2 (3.8) \| – \| 5 (5.3) \| \| Chronic inflammatory demyelinating polyradiculoneuropathy \| 12 (1.6) \| 2 (1.8) \| 4 (1.5) \| 3 (1.5) \| – \| – \| 2 (2.1) \| \| Diabetic neuropathy \| 2 (0.3) \| - \| 1 (0.4) \| 1 (0.5) \| – \| – \| \| \| Multifocal motor neuropathy \| 1 (0.1) \| – \| – \| – \| – \| – \| 1 (1.1) \| \| Polyneuropathy in malignant diseases \| 1 (0.1) \| - \| 1 (0.4) \| – \| – \| – \| – \| \| Mononeuropathies \| 37 (4.8) \| 4 (3.5) \| 15 (5.5) \| 11 (5.5) \| 3 (5.7) \| - \| 4 (4.3) \| \| Carpal tunnel syndrome \| 13 (1.7) \| 1 (0.9) \| 7 (2.6) \| 5 (2.5) \| – \| – \| – \| \| Mononeuropathy multiplex \| 6 (0.8) \| - \| 5 (1.8) \| 1 (0.5) \| – \| – \| – \| \| Peroneal nerve palsy \| 5 (0.7) \| 1 (0.9) \| – \| 1 (0.5) \| 1 (1.9) \| – \| 2 (2.1) \| \| Phrenic nerve paralysis \| 5 (0.7) \| 2 (1.8) \| – \| 1 (0.5) \| 1 (1.9) \| – \| 1 (1.1) \| \| Mononeuritis \| 3 (0.4) \| – \| – \| 2 (1.0) \| \| – \| 1 (1.1) \| \| Mononeuropathy \| 1 (0.1) \| – \| – \| - \| 1 (1.9) \| – \| – \| \| Peripheral nerve lesion \| 1 (0.1) \| – \| – \| 1 (0.5) \| – \| – \| – \| \| Peripheral nerve palsy \| 1 (0.1) \| – \| 1 (0.4) \| – \| – \| – \| – \| \| Radial nerve palsy \| 1 (0.1) \| – \| 1 (0.4) \| – \| – \| – \| – \| \| Sciatic nerve neuropathy \| 1 (0.1) \| – \| 1 (0.4) \| – \| – \| – \| – \| \| Peripheral neuropathies \| 19 (2.5) \| 1 (0.9) \| 9 (3.3) \| 4 (1.9) \| 1 (1.9) \| - \| 4 (4.3) \| \| Neuritis \| 19 (2.5) \| 1 (0.9) \| 9 (3.3) \| 4 (1.9) \| 1 (1.9) \| – \| 4 (4.3) \| \| Acute peripheral neuropathies \| 4 (0.5) \| – \| – \| 1 (0.5) \| 1 (1.9) \| 1 (5.8) \| 1 (1.1) \| \| Miller-Fisher syndrome \| 4 (0.5) \| – \| – \| 1 (0.5) \| 1 (1.9) \| 1 (5.8) \| 1 (1.1) \| We categorized all reported PNs, such as ICI-related irADRs, according to the MedDRA HLTs in Table 2. “ Peripheral neuropathies NEC” ($$n = 496$$; $64.8\%$) were the most common HLT. Overall, we considered 32 different disorders or clinical diagnoses grouped into six different HLTs, such as the categories of acute ($$n = 165$$; $21.5\%$) or chronic polyneuropathies ($$n = 45$$; $5.9\%$), and mononeuropathies ($$n = 37$$; $4.8\%$). GBS was the most frequently reported PN among acute polyneuropathies ($$n = 154$$; $20.1\%$). In particular, GBS was mainly reported in ICSRs related to nivolumab ($$n = 46$$) and pembrolizumab ($$n = 41$$). Among the chronic polyneuropathies, CIDP was described in 12 cases, mainly related to nivolumab ($$n = 4$$) and pembrolizumab ($$n = 3$$), while only one case of MMN was reported, as ADRs occurred in patients treated with the ipilimumab/nivolumab combination. Moreover, 29 cases of demyelinating polyneuropathy ($3.8\%$), mainly related to nivolumab ($$n = 11$$), and two cases of diabetic neuropathy ($0.3\%$) were also described. The latter were reported as suspected ADRs of nivolumab ($$n = 1$$) and pembrolizumab ($$n = 1$$). As reported in Table 2, the majority of events were related to anti-PD1 agents, nivolumab ($$n = 272$$; $35.5\%$) and pembrolizumab ($$n = 203$$; $26.5\%$), followed by the anti-CTLA4 drug ipilimumab ($$n = 114$$; $14.9\%$), the combination therapy ipilimumab/nivolumab ($$n = 94$$; $12.3\%$), and atezolizumab ($$n = 52$$; $7.0\%$). Approximately $2.2\%$ of PNs were attributed to durvalumab ($$n = 17$$). We found only one case related to cemiplimab. This latter described a case of polyneuropathy occurred in an elderly male patient treated with cemiplimab in off-label use. Nevertheless, the therapeutic indication for cemiplimab was expressed generically as malignant neoplasm. In the majority of ICSRs, the reported PNs resulted in serious ADRs ($95.2\%$), most of which were medically significant ($$n = 335$$; $43.7\%$), or caused or prolonged hospitalization ($$n = 228$$; $29.8\%$). Moreover, PNs resulted in death in 81 cases ($10.6\%$) or disability in 43 cases ($5.6\%$) (Table 3). In particular, the higher percentages of disabling PNs were associated with pembrolizumab ($$n = 15$$; $7.4\%$) and ipilimumab ($$n = 8$$; $7.0\%$). In the same table, we reported the distribution of PNs by outcome. The outcome was unknown in $45.5\%$ of cases, especially for PNs related to ipilimumab therapy ($55.3\%$). Overall, $22.2\%$ of ICI-related PNs had unfavorable outcomes, defined as “not resolved” or “resolved with sequelae” at the time of reporting ($20.9\%$ and $1.3\%$, respectively). Positive outcomes were reported in $28.5\%$ of adverse events. In particular, $9.7\%$ of PNs ($$n = 74$$) were completely resolved, while $18.8\%$ ($$n = 144$$) improved. In contrast, a fatal outcome was observed in $3.8\%$ ($$n = 29$$) and Guillain-Barré syndrome was the most common PN ($$n = 12$$; $41.4\%$) among those with fatal outcomes. Table 3* \| Unnamed: 0 \| Number of neurological eventsTOTAL(n=766) \| Number of eventswithnivolumab(n=272) \| Number of eventswithpembrolizumab(n=203) \| Number of eventswithipilimumab(n=114) \| Number of eventswithdurvalumab(n=17) \| Number of eventswithatezolizumab(n=52) \| Number of eventswithavelumab(n=3) \| Number of eventswithcemiplimab(n=1) \| Number of eventswithtwo or more ICIs(n=104) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Severity \| Severity \| Severity \| Severity \| Severity \| Severity \| Severity \| Severity \| Severity \| Severity \| \| Not available \| 37 (4.8) \| 15 (5.5) \| 7 (3.4) \| 5 (4.4) \| 3 (17.6) \| 4 (7.7) \| – \| – \| 3 (2.9) \| \| Severe \| 729 (95.2) \| 257 (94.5) \| 196 (96.6) \| 109 (95.6) \| 14 (82.4) \| 48 (92.3) \| – \| – \| 1 (97.1) \| \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| Severity Criteria \| \| Other medically important conditions \| 335 (43.7) \| 127 (46.7) \| 79 (38.9) \| 53 (46.5) \| 5 (29.4) \| 20 (38.5) \| 1 (33.3) \| 1 (100) \| 49 (47.1) \| \| Results in death \| 81 (10.6) \| 39 (14.3) \| 20 (9.9) \| 15 (13.2) \| – \| 3 (5.8) \| – \| – \| 4 (3.8) \| \| Caused/prolonged hospitalization \| 228 (29.8) \| 69 (25.4) \| 67 (33.0) \| 29 (25.4) \| 7 (41.2) \| 21 (40.4) \| – \| – \| 35 (33.7) \| \| Life-threatening \| 42 (5.5) \| 8 (3.0) \| 15 (7.4) \| 4 (3.5) \| 1 (5.9) \| 3 (5.8) \| 2 (66.7) \| – \| 9 (8.7) \| \| Disability \| 43 (5.6) \| 14 (5.1) \| 15 (7.4) \| 8 (7.0) \| 1 (5.9) \| 1 (1.8) \| – \| – \| 4 (3.8) \| \| Outcome \| Outcome \| Outcome \| Outcome \| Outcome \| Outcome \| Outcome \| Outcome \| Outcome \| Outcome \| \| Recovered/resolved \| 74 (9.7) \| 29 (10.7) \| 17 (8.4) \| 11 (9.6) \| 3 (17.6) \| 4 (7.7) \| – \| – \| 10 (9.6) \| \| Recovering/resolving \| 144 (18.8) \| 59 (21.7) \| 35 (17.2) \| 14 (12.3) \| 2 (11.8) \| 9 (17.3) \| – \| – \| 25 (24.0) \| \| Recovered/resolved with sequelae \| 10 (1.3) \| 4 (1.5) \| 3 (1.5) \| 2 (1.7) \| – \| – \| 1 (33.3) \| – \| – \| \| Not recovered/Not resolved \| 160 (20.9) \| 47 (17.3) \| 62 (30.5) \| 18 (15.8) \| 4 (23.5) \| 14 (26.9) \| – \| – \| 15 (14.4) \| \| Fatal \| 29 (3.8) \| 17 (6.2) \| 5 (2.5) \| 6 (5.3) \| – \| – \| – \| – \| 1 (1.0) \| \| Unknown \| 349 (45.5) \| 116 (42.6) \| 81 (39.9) \| 63 (55.3) \| 8 (47.1) \| 25 (48.1) \| 2 (6.7) \| 1 (100) \| 53 (51.0) \| In 131 ICSRs, suspected drugs other than ICIs were reported (Table 1); in the majority of cases, only one ($$n = 51$$) or two ($$n = 49$$) suspected drugs other than ICIs were reported. Looking at each ICI, suspect drugs were reported in 40 of 264 nivolumab-related ICSRs ($15.2\%$), 42 of 193 pembrolizumab-related ICSRs ($21.8\%$), 11 out of 109 ipilimumab-related ICSRs ($10.1\%$), 22 of 51 atezolizumab-related ICSRs ($43.1\%$), six of 17 durvalumab- related ICSRs ($35.3\%$) and one of 2 avelumab-related ICSRs ($50.0\%$) (Table 1). The most frequently reported other suspect drugs were other antineoplastic agents ($$n = 206$$; $73.3\%$), in particular taxanes ($$n = 56$$) and platinum compounds ($$n = 51$$). The distribution of other suspected drugs for Level II ATC is reported in Table 4. Table 4 \| ATC CODE \| ATC NAME \| N \| % \| \| --- \| --- \| --- \| --- \| \| L01 \| Antineoplastic agents \| 206 \| 73.3% \| \| A07 \| Antidiarrheals, intestinal anti-inflammatory/anti-infective agents \| 6 \| 2.1% \| \| D07 \| Corticosteroids, dermatological preparations \| 6 \| 2.1% \| \| J05 \| Antivirals for systemic use \| 4 \| 1.4% \| \| N02 \| Analgesics \| 4 \| 1.4% \| \| B01 \| Antithrombotic Agents \| 3 \| 1.1% \| \| J07 \| Vaccines \| 3 \| 1.1% \| \| L04 \| Immunosuppressants \| 3 \| 1.1% \| \| N01 \| Anesthetics \| 3 \| 1.1% \| \| N05 \| Psycholeptics \| 3 \| 1.1% \| \| A01 \| Stomatological Preparations \| 2 \| 0.7% \| \| A02 \| Antacids \| 2 \| 0.7% \| \| A03 \| Combinations of psycholeptics and antispasmodics \| 2 \| 0.7% \| \| A06 \| Drugs for Constipation \| 2 \| 0.7% \| \| A10 \| Drugs used in diabetes \| 2 \| 0.7% \| \| A11 \| Vitamins \| 2 \| 0.7% \| \| B02 \| Antihemorrhagics \| 2 \| 0.7% \| \| C01 \| Cardiac therapy \| 2 \| 0.7% \| \| C10 \| Lipid-modifying agents \| 2 \| 0.7% \| \| H01 \| Pituitary and hypothalamic hormones and analogs \| 2 \| 0.7% \| \| J01 \| Antibacterials for systemic use \| 2 \| 0.7% \| \| L03 \| Immunostimulants \| 2 \| 0.7% \| \| M02 \| Topical products for joint and muscular pain \| 2 \| 0.7% \| \| M05 \| Drugs for the treatment of bone diseases \| 2 \| 0.7% \| \| R03 \| Drugs for obstructive airway diseases \| 2 \| 0.7% \| \| V03 \| All other therapeutic products \| 2 \| 0.7% \| \| B03 \| Anti-anemic preparations \| 1 \| 0.4% \| \| B05 \| Blood substitutes and perfusion solutions \| 1 \| 0.4% \| \| C07 \| Beta blocking agents \| 1 \| 0.4% \| \| J06 \| Immune sera and immunoglobulins \| 1 \| 0.4% \| \| N03 \| Antiepileptic drugs \| 1 \| 0.4% \| \| R01 \| Nasal preparations \| 1 \| 0.4% \| \| R05 \| Cough and cold preparations \| 1 \| 0.4% \| \| S02 \| Otologicals \| 1 \| 0.4% \| In the majority of ICSRs ($$n = 521$$; $70.9\%$), there were no other concomitant medications, which were only reported in 214 ICSRs. More than five concomitant drugs were reported in 93 ICSRs, mainly related to nivolumab ($$n = 37$$), followed by pembrolizumab ($$n = 25$$). Generally, the most frequently reported concomitant drug classes were other antineoplastic agents ($$n = 126$$; $10.9\%$), followed by analgesics ($$n = 99$$; $8.5\%$) and drugs for acid-related disorders ($$n = 83$$; $7.2\%$). The distribution of concomitant drugs for Level II ATC is reported in Figure 2. Figure 2: Drugs reported as concomitants in the Individual Case Safety Reports (ICSRs) collected in Eudravigilance, categorized by therapeutic reference class (Level II ATC). A02, Antacids; A03, Combinations of psycholeptics and antispasmodics; A04, Antiemetics and antinausea; A05, Biliary and hepatic therapy; A06, Drugs for constipation; A07, Antidiarrheals, anti-inflammatory/anti-infective agents; A10, Drugs used in diabetes; A11, Vitamins; A12, Mineral supplements; B01, Antithrombotic agents; B02, Antihemorrhagics; B03, Antianemic preparations; B05, Blood substitutes and perfusion solutions; C01, Cardiac therapy; C02, Antihypertensives; C03, Diuretics; C05, Vasoprotectives; C07, Beta blocking agents; C08, Calcium channel blockers; C09, Agents acting on the renin–angiotensin; C10, Lipid modifying agents; D07, Corticosteroids. dermatological preparations; D10, Anti-acne preparations; D11, Other dermatological preparations; G01, Gynecological anti-infectives and antiseptics; G02, Other gynecologicals; G03, Sex hormones and modulators of the genital system; G04, Urologicals; H02, Corticosteroids for systemic use; H03, Thyroid therapy; H04, Pancreatic hormones; J01, Antibacterials for systemic use; J02, Antimycotics for systemic use; J05, Antivirals for systemic use; J06, Immune sera and immunoglobulins; L01, Antineoplastic agents; L02, Endocrine therapy; L03, Immunostimulants; L04, Immunosuppressants; M01, Anti-inflammatory and antirheumatic products; M02, Topical products for joint and muscular pain; M03, Muscle relaxants; M04, Antigout preparations; M05, Drugs for treatment of bone diseases; N01, Anesthetics; N02, Analgesics; N03, Antiepileptics; N04, Anti-Parkinson drugs; N05, Psycholeptics; N06, Psychoanaleptics; R01, Nasal preparations; R02, Throat preparations; R03, Drugs for obstructive airway diseases; R05, Cough and cold preparations; R06, Antihistamines for systemic use; S01, Ophthalmologicals; V01, Allergens; V03, All other therapeutic products; V04, Diagnostic agents. Six drugs (0.5%) reported as concomitant were officinal drugs. Finally, when considering the reported actions taken to manage the adverse events, drug withdrawal was reported in $53.6\%$ of cases ($$n = 394$$), while the suspected ICI dose remained unchanged in $7.2\%$ of cases ($$n = 53$$). Only one report described a peripheral neuropathy managed with the ICI dosage reduction. This case was referred to an adult male patient (age group 18-64 years) affected by Hodgkin's disease in treatment with nivolumab (3 mg/kg/iv). ## Disproportionality analysis In order to compare the frequency of PNs reporting between each ICI or ICI class, we performed] disproportionality analysis applying the Reporting Odds Ratio (ROR) of $95\%$ CI and considered a statistically significant signal if the lower limit of the $95\%$ CI of a ROR exceeded 1.0. For our analysis, we considered the first and most frequent PNs belonging to each different HLT. Thus, RORs were calculated by comparing each ICI treatment (ipilimumab, nivolumab, pembrolizumab, atezolizumab, durvalumab, ipilimumab/nivolumab) with each other. We also classified ICIs based on their mechanism of action into anti-CTLA-4 (ipilimumab), anti-PD-1 (nivolumab and pembrolizumab), and anti-PDL-1 (atezolizumab and durvalumab). Therefore, ROR was also performed comparing ICI classes using other classes as comparators (anti-CTLA-4 vs anti-PD-1 or anti-PDL-1 and anti-PD-1 vs anti-PDL-1). The Rstudio software was used to perform the disproportionality analysis. The most common PNs belonging to the “Peripheral neuropathies NEC”, “Acute polyneuropathies” and “Chronic polyneuropathies” HLTs were “peripheral neuropathy”, “Guillain-Barré syndrome” and “demyelinating polyneuropathy”, respectively (Table 2). We, therefore, applied ROR to these adverse events by comparing different ICIs or ICI classes between them. As reported in section A of Figure 3, ipilimumab, pembrolizumab, and nivolumab were associated with a lower reporting probability of “peripheral neuropathy” (p-term) compared to atezolizumab (ROR 0.436; $95\%$ CI 0.208-0.897; $$p \leq 0.019$$; ROR 0.46; $95\%$ CI 0.231-0.896; $$p \leq 0.019$$; ROR 0.456; $95\%$ CI 0.232-0.872; $$p \leq 0.015$$, respectively). The ipilimumab/nivolumab association also showed a lower probability of reporting peripheral neuropathy compared to atezolizumab (ROR 0.3, $95\%$ CI 0.1-0.6; $p \leq 0.001$). Moreover, when comparing ICI classes, anti-CTLA-4 and anti-PD-1 classes were associated with a lower likelihood of reporting peripheral neuropathy compared to anti-PDL-1 (ROR 0.510; $95\%$ CI 0.267-0.966; $$p \leq 0.03$$; ROR 0.533; $95\%$ CI 0.31-0.907; $$p \leq 0.016$$, respectively). No significant statistical difference was observed when comparing ICI or ICIs classes for the reporting probability of Guillain-Barré syndrome (Figure 3B), nor for demyelinating polyneuropathy, except for a higher reporting probability of Guillain-Barré syndrome for anti-CTLA4 compared to PD-L1 (ROR 2.378; $95\%$ CI 1.007-6.127;$$p \leq 0.04$$). Figure 3: Reporting odds ratio (ROR) for disproportionality analysis of the most frequently reported peripheral neuropathies as suspected ADRs associated with ICIs. ROR was performed for the following p-term “neuropathy peripheral” (A), “Guillain-Barre syndrome” (B) and “demyelinating polyneuropathy” (C). ## Discussion ICI-related neuropathies represent one of the most frequently reported neurological complications in the European database [23]. Given how in our study the PNs described in the majority of retrieved ICSRs were categorized as serious, these adverse events represent an important safety concern for the therapeutic use of ICIs. Severe motor or sensory neuropathies are among the complications of ICIs that may require permanent discontinuation of ICI therapy. Moreover, even if ICI withdrawal may not be necessary, the occurrence of PNs can have a critical impact on the patient’s quality of life and lead to negative outcomes. Immunotherapy-related neuropathies represent a small subset of autoimmune PNs due to an aberrant immune response against components of the peripheral nervous system. This abnormal immune response induced by immunotherapeutic agents can lead to various adverse manifestations, characterized by possible demyelination processes or axonal damages [25, 26]. In our dataset, both types of injuries emerged, although demyelinating PNs were more frequently reported than axonal injuries associated with ICIs. Among the demyelinating disorders, we found several cases of Guillain-Barré syndrome, demyelinating polyneuropathy, and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). We also found some cases of ICI-related Miller-Fisher syndrome, a rare variant of Guillain-Barré syndrome characterized by a triad of symptoms: ophthalmoplegia, ataxia, and areflexia [26]. Fortunately, these neurologically adverse events are rare. According to a systematic review conducted by Yan Li et al., from 1990 up to 2021, only 33 ICI-related case reports of Guillain-Barré syndrome or its subtypes, such as Miller-Fisher syndrome (MFS), acute inflammatory demyelinating polyneuropathy (AIDP), and acute motor axonal neuropathy (AMAN), were described in the literature [27]. In our analysis, a few cases of AMAN and motor-sensory axonal neuropathy emerged, which were mainly related to pembrolizumab. Although rare, these adverse events require special attention from clinicians given their potentially serious consequences. In addition to the characteristic damage type, PNs can also be categorized based on the time of onset and duration. According to our results, the acute forms were more frequently reported as ICI-related ADRs than the chronic ones. Among the latter, our dataset described some cases of chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), an immune-mediated progressive neuropathy characterized by electrodiagnostic evidence of peripheral nerve demyelination, and only one case of multifocal motor neuropathy (MMN). According to our results, these chronic forms were mainly related to nivolumab treatment, either as monotherapy or in association with ipilimumab. According to Okada et al., polyradiculoneuropathy induced by ICIs presents specific clinical features, such as severe motor weakness affecting both legs symmetrically and resulting in gait disturbance, and an effective response to steroid treatment for its management. In addition, the authors suggested its early detection by electrodiagnostic evaluation of demyelination and cerebrospinal fluid, typically characterized by elevated lymphocytes [28]. Other case reports have been described in the literature describing acute sensorimotor neuropathy and polyneuropathy occurring in patients treated with ipilimumab [25, 29]. The possible relationship between the occurrence of neuropathy events and the administration of ICIs is supported by a close temporal association between symptomatic onset and a positive response to corticosteroids or immunomodulatory therapy [30]. Among the acute PNs emerging from our analysis, GBS is noteworthy. In particular, it was reported in $20\%$ of ICSRs. Moreover, Guillain-Barré syndrome was associated with a significant impact on patient safety, as it was the most prevalent PN with adverse outcomes, including fatalities. As reported in the literature, there is an increased incidence of fatal Guillain-Barré syndrome in patients treated with immune checkpoint inhibitors [31]. Overall, fatal cases in our dataset were more frequently related to nivolumab and ipilimumab. Moreover, the majority of disabling PNs were also related to anti-PD-1 agents, nivolumab, and pembrolizumab. Overall, the majority of all ICSRs were related to these three ICIs, which are the older approved ICIs and therefore the most widely used and longest used in clinical practice. In the same way, the higher incidence of tumor pathologies like lung cancer, melanoma, and renal cell cancer in elderly male patients should be considered when looking at the gender and age distribution of the reported PNs. In fact, these tumors are more common in adult or elderly men. However, the data in the literature regarding the greater susceptibility of men to ICI-related irADRs, including PNs, are contradictory and still controversial, requiring further investigation [32]. Concerning the biological plausibility of ICI-related PNs, results from experimental and clinical studies confirm the possible immune-mediated pathogenesis of these disorders. In particular, inhibition of T-reg cell activity may be one potential mechanism for breaking immune tolerance. It has been shown that T-reg cells expressing PD-1 and CTLA-4 receptors on their surfaces are involved in self-tolerance processes. AntiPD-1 and antiCTLA-4 inhibitors block immune checkpoints that break physiological immune tolerance, with a consequent hyperproliferation and hyperactivation of immune effector cells, which can lead to the development of neurological adverse events, such as peripheral neuropathies [33]. Humoral and/or cellular immune mechanisms against Schwann cell/myelin antigens may underlie or participate in the pathogenesis of ICI-related PNs [34, 35]. This pathogenetic mechanism should be linked to the phenomenon of molecular mimicry in terms of cross-reactivity between the tumor antigens and similar epitopes on healthy peripheral nerve cells. In addition, epitope spreading [36, 37] is one of the suggested mechanisms. PNs can be stimulated to increase the production of inflammatory cytokines, such as TNF-alpha and IL-17, by activated effector T-cells and their consequent effects on the structures of the peripheral nervous system [29]. As suggested by Xi Chen et al., another possible hypothesis may be related to a pre-existing neuropathy, which may be induced by previous or concurrent chemotherapy or due to comorbidities such as diabetes. A pre-existing condition could increase the patient’s susceptibility to immune-mediated ICIs complications, which could worsen after ICIs treatments [30]. In our dataset, 107 ICSRs reported at least one anticancer agent as other suspect drugs, which is considered a predisposing factor or alternative cause of PNs. Si Zhihua et al. described a higher risk of developing peripheral neuropathy when PD-1/PD-L1 inhibitors were used in combination with chemotherapy [38]. Similarly, Yuan Tian et al. also showed an increased incidence trend of neurological toxicities, especially grade 3–5 peripheral neuropathy, with anti-PD-1 and anti-PD-L1 plus chemotherapy [39]. Finally, our disproportionality analysis surprisingly revealed an increased frequency of reports of peripheral neuropathy associated with atezolizumab compared to other ICIs, both in monotherapy and in combination therapy with ipilimumab/nivolumab. Although some case reports of neuropathy following atezolizumab treatments were reported in the literature (40–42), these differences in neurological adverse events reporting with anti-PD-L1 agents, in particular atezolizumab, compared to other ICIs have not been previously documented in the literature. Thus, the cause of this higher frequency of reporting remains largely unknown, requiring further investigation. ## Study strengths and limitations Our study has inherent limitations due to the post-marketing surveillance system, as it was based on data from the European pharmacovigilance reporting system. For example, considering that safety reports are mainly sent in a spontaneous way by patients and physicians, they may be affected by the so-called underreporting phenomenon [43]. In addition, the data reported in spontaneous ICSRs may often be incomplete [44]. Moreover, information about the patients’ previous predisposing conditions is not available. These can only be inferred from the reported concomitant and suspected medications. As we did not have full-level access to EV, we were unable to analyze the time to onset or time to resolution, or the median age of the patients, which was only reported as an age group. Despite these limitations, pharmacovigilance databases are useful tools for monitoring the safety of medicines and represent a significant source of information [44]. Spontaneous reporting systems allow for a better characterization of the safety profile of drugs and overcome the inherent limitations of clinical trials [45, 46]. In recent years, the efforts of regulatory agencies and the scientific community have greatly improved the quantity, quality, and timeliness of ADRs reporting. These advances have increased the value of spontaneous reporting systems for post-marketing pharmacovigilance purposes. It is worth emphasizing that drug and vaccine safety signals require further ad hoc confirmatory investigations based on different study designs in order to validate them and evaluate the hypothetically necessary regulatory actions required [47, 48]. ## Conclusion PNs are one of the most frequent ICIs-induced neurological ADRs. We analyzed data from the European pharmacovigilance database on the association between ICIs and PNs. In our study, we found a total of 766 PNs in 735 patients treated with immunotherapy. These included GBS, Miller-Fisher syndrome, carpal tunnel syndrome, neuritis, and demyelinating polyneuropathy. These ADRs were often serious, resulting in disability or hospitalization for the patient. Given the negative outcomes associated with the reported PNs, preventing their occurrence and obtaining earlier treatment would certainly improve the quality of life of patients with a better chance of a complete recovery and also reduce the associated healthcare costs. Moreover, our disproportionality analysis revealed an increased frequency of reported PNs after treatment with atezolizumab compared to other ICIs. This result requires further investigation to better characterize this potential risk. In this context, continuous monitoring of the safety profile of ICIs in real-life settings and conducting pharmacovigilance studies serve as essential instruments to identify specific safety signals. Tools to evaluate and monitor the safety of these new therapeutic approaches in clinical use are crucial to improving public health and reducing costs to healthcare systems. Pharmacovigilance remains a key component of effective public health programs aimed at ensuring the safer use of medical interventions for patients. In this context, the description and analysis of irADRs collected in pharmacovigilance datasets can increase the knowledge of possible ICI-related ADRs, making them more easily identifiable and better managed by clinicians and oncologists. Finally, advances in immunotherapy have greatly and positively changed the way cancer is managed and controlled. However, despite all their benefits, scientific evidence demonstrates that irADRs related to ICIs are a common cause of disability in oncology patients and require continuous monitoring. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: www.adrreports.eu. ## Author contributions Conceptualization, AC and RD; AC and FR designed the study and managed the project; RR, FF, RDN, and NB drafted the manuscript; methodology, AC and RR; data curation, MT, NB, FF, RDN, and RR; writing—original draft preparation, RR and FF; RDN, and NB prepared the figures; writing—review and editing, CP and CR; supervision, MR and FR; project administration, MR and RD; funding acquisition, RD. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1134436/full#supplementary-material ## References 1. Brown TJ, Sedhom R, Gupta A. Chemotherapy-induced peripheral neuropathy. JAMA Oncol (2019) 5 750. DOI: 10.1001/jamaoncol.2018.6771 2. Patel K, Horak H, Tiryaki E. Diabetic neuropathies. Muscle Nerve (2021) 63 22-30. DOI: 10.1002/MUS.27014 3. Freeman R. Diabetic autonomic neuropathy. Handb Clin Neurol (2014) 126 63-79. DOI: 10.1016/B978-0-444-53480-4.00006-0 4. Høyer H, Busk ØL, Holla ØL, Strand L, Russell MB, Skjelbred CF. Hereditary peripheral neuropathies diagnosed by next-generation sequencing. Tidsskrift den Norske Laegeforening (2015) 135. DOI: 10.4045/TIDSSKR.14.1002 5. Eggermann K, Gess B, Häusler M, Weis J, Hahn A, Kurth I. Hereditary neuropathies. Dtsch Arztebl Int (2018) 115. DOI: 10.3238/ARZTEBL.2018.0091 6. Freeman R. Autonomic peripheral neuropathy. CONTINUUM Lifelong Learn Neurol (2020) 26 58-71. DOI: 10.1212/CON.0000000000000825 7. Gwathmey K, Grogan J. Nutritional neuropathies. Muscle Nerve (2020) 62 13-29. DOI: 10.1002/MUS.26783 8. Luo JJ, Dun NJ. Neuropathies and vitamin d deficiency. Muscle Nerve (2020) 62. DOI: 10.1002/MUS.27069 9. Krøigård T, Svendsen TK, Wirenfeldt M, Schrøder HD, Qvortrup C, Pfeiffer P. Oxaliplatin neuropathy: Predictive values of skin biopsy, QST and nerve conduction. J Neuromuscul Dis (2021) 8 679-88. DOI: 10.3233/JND-210630 10. Amato AA, Collins MP. Neuropathies associated with malignancy. Semin Neurol (1998) 18. DOI: 10.1055/s-2008-1040868 11. Forman A. Peripheral neuropathy in cancer patients: clinical types, etiology, and presentation. part 2. Oncol (Williston Park N.Y.) (1990) 4 12. Seretny M, Currie GL, Sena ES, Ramnarine S, Grant R, Macleod MR. Incidence, prevalence, and predictors of chemotherapy-induced peripheral neuropathy: A systematic review and meta-analysis. Pain (2014) 155. DOI: 10.1016/j.pain.2014.09.020 13. Gwathmey K, Smith A. Immune-mediated neuropathies. Neurol Clin (2020) 38. DOI: 10.1016/J.NCL.2020.03.008 14. Sederholm BH. Treatment of acute immune-mediated neuropathies: Guillain-Barré syndrome and clinical variants. Semin Neurol (2010) 30. DOI: 10.1055/S-0030-1267280 15. Hersalis Eldar A, Chapman J. Guillain Barré Syndrome and other immune mediated neuropathies: Diagnosis and classification. Autoimmun Rev (2014) 13. DOI: 10.1016/J.AUTREV.2014.01.033 16. Mascolo A, Scavone C, Ferrajolo C, Rafaniello C, Danesi R, del Re M. Immune checkpoint inhibitors and cardiotoxicity: An analysis of spontaneous reports in eudravigilance. Drug Saf (2021) 44. DOI: 10.1007/S40264-021-01086-8 17. Psimaras D, Velasco R, Birzu C, Tamburin S, Lustberg M, Bruna J. Immune checkpoint inhibitors-induced neuromuscular toxicity: From pathogenesis to treatment. J Peripheral Nervous System (2019) 24. DOI: 10.1111/JNS.12339 18. Reynolds K, Guidon A. Diagnosis and management of immune checkpoint inhibitor-associated neurologic toxicity: Illustrative case and review of the literature. Oncologist (2019) 24. DOI: 10.1634/THEONCOLOGIST.2018-0359 19. Oliveira MCB, de Brito MH, Simabukuro MM. Central nervous system demyelination associated with immune checkpoint inhibitors: Review of the literature. Front Neurol (2020) 11. DOI: 10.3389/FNEUR.2020.538695 20. Velasco R, Villagrán M, Jové M, Simó M, Vilariño N, Alemany M. Encephalitis induced by immune checkpoint inhibitors: A systematic review. JAMA Neurol (2021) 78. DOI: 10.1001/JAMANEUROL.2021.0249 21. Vogrig A, Muñiz-Castrillo S, Joubert B, Picard G, Rogemond V, Skowron F. Cranial nerve disorders associated with immune checkpoint inhibitors. Neurology (2021) 96. DOI: 10.1212/WNL.0000000000011340 22. Dinoto A, Mantovani E, Ferrari S, Mariotto S, Tamburin S. Cerebellar involvement associated with immune checkpoint inhibitors: A systematic review. Eur J Neurol (2022) 30 774–81. DOI: 10.1111/ENE.15624 23. Ruggiero R, Stelitano B, Fraenza F, di Mauro G, Scavone C, Sportiello L. Neurological manifestations related to immune checkpoint inhibitors: Reverse translational research by using the European real-world safety data. Front Oncol (2022) 12. DOI: 10.3389/FONC.2022.824511/FULL 24. Mascolo A, di Mauro G, Fraenza F, Gaio M, Zinzi A, Pentella C. Maternal, fetal and neonatal outcomes among pregnant women receiving COVID-19 vaccination: The preg-co-vax study. Front Immunol (2022) 13. DOI: 10.3389/FIMMU.2022.965171/FULL 25. Gu Y, Menzies AM, Long GV, Fernando SL, Herkes G. Immune mediated neuropathy following checkpoint immunotherapy. J Clin Neurosci (2017) 45. DOI: 10.1016/j.jocn.2017.07.014 26. Rocha Cabrero F, Morrison EH. Miller-Fisher syndrome (2022) 27. Li Y, Zhang X, Zhao C. Guillain-Barré Syndrome-like polyneuropathy associated with immune checkpoint inhibitors: A systematic review of 33 cases. BioMed Res Int (2021) 2021. DOI: 10.1155/2021/9800488 28. Okada K, Seki M, Yaguchi H, Sakuta K, Mukai T, Yamada S. Polyradiculoneuropathy induced by immune checkpoint inhibitors: a case series and review of the literature. J Neurol (2021) 268. DOI: 10.1007/S00415-020-10213-X 29. Thaipisuttikul I, Chapman P, Avila EK. Peripheral neuropathy associated with ipilimumab: A report of 2 cases. J Immunother (2015) 38. DOI: 10.1097/CJI.0000000000000070 30. Chen X, Haggiagi A, Tzatha E, DeAngelis LM, Santomasso B. Electrophysiological findings in immune checkpoint inhibitor-related peripheral neuropathy. Clin Neurophysiol (2019) 130. DOI: 10.1016/j.clinph.2019.03.035 31. Fan Q, Hu Y, Wang X, Zhao B. Guillain–Barré Syndrome in patients treated with immune checkpoint inhibitors. J Neurol (2021) 268. DOI: 10.1007/S00415-021-10404-0/TABLES/2 32. Ruggiero R, Fraenza F, Scavone C, di Mauro G, Piscitelli R, Mascolo A. Immune checkpoint inhibitors and immune-related adverse drug reactions: Data from Italian pharmacovigilance database. Front Pharmacol (2020) 11. DOI: 10.3389/FPHAR.2020.00830 33. Vilariño N, Bruna J, Kalofonou F, Anastopoulou GG, Argyriou AA. Immune-driven pathogenesis of neurotoxicity after exposure of cancer patients to immune checkpoint inhibitors. Int J Mol Sci (2020) 21 1-12. DOI: 10.3390/IJMS21165774 34. Mathey EK, Park SB, Hughes RAC, Pollard JD, Armati PJ, Barnett MH. Chronic inflammatory demyelinating polyradiculoneuropathy: From pathology to phenotype. J Neurol Neurosurg Psychiatry (2015) 86. DOI: 10.1136/jnnp-2014-309697 35. Kuwabara S, Misawa S. Chronic inflammatory demyelinating polyneuropathy. Adv Exp Med Biol (2019) 1190. DOI: 10.1007/978-981-32-9636-7_21/COVER 36. Léger JM, Guimarães-Costa R, Muntean C. Immunotherapy in peripheral neuropathies. Neurotherapeutics (2016) 13 96-107. DOI: 10.1007/s13311-015-0401-7 37. Albarrán V, Chamorro J, Rosero DI, Saavedra C, Soria A, Carrato A. Neurologic toxicity of immune checkpoint inhibitors: A review of literature. Front Pharmacol (2022) 13. DOI: 10.3389/FPHAR.2022.774170/BIBTEX 38. Si Z, Zhang S, Yang X, Ding N, Xiang M, Zhu Q. The association between the incidence risk of peripheral neuropathy and PD-1/PD-L1 inhibitors in the treatment for solid tumor patients: A systematic review and meta-analysis. Front Oncol (2019) 9. DOI: 10.3389/fonc.2019.00866 39. Tian Y, Gao A, Wen Q, Wang S, Zhang S, Yang X. Immune-related neurological toxicities of PD-1/PD-L1 inhibitors in cancer patients: A systematic review and meta-analysis. Front Immunol (2020) 11. DOI: 10.3389/FIMMU.2020.595655 40. Eltobgy M, Oweira H, Petrausch U, Helbling D, Schmidt J, Mehrabi A. Immune-related neurological toxicities among solid tumor patients treated with immune checkpoint inhibitors: A systematic review. Expert Rev Neurother (2017) 17. DOI: 10.1080/14737175.2017.1336088 41. Morimoto T, Orihashi T, Yamasaki K, Tahara M, Kato K, Yatera K. Paraneoplastic sensory polyneuropathy related to anti-PD-L1-including anticancer treatment in a patient with lung cancer. Intern Med (2021) 60. DOI: 10.2169/INTERNALMEDICINE.5629-20 42. Yamanaka N, Oishi M, Shimizu F, Koga M, Kanda T. [Atezolizumab-induced Guillain-Barré syndrome-like acute demyelinating polyneuropathy responsive to steroid therapy: a case report]. Rinsho Shinkeigaku (2021) 61. DOI: 10.5692/CLINICALNEUROL.CN-001562 43. Dittrich ATM, Smeets NJL, de Jong EFM, Kämink JL, Kroeze Y, Draaisma JMT. Quality of active versus spontaneous reporting of adverse drug reactions in pediatric patients: Relevance for pharmacovigilance and knowledge in pediatric medical care. Pharm (Basel) (2022) 15 1148. DOI: 10.3390/PH15091148 44. Bihan K, Lebrun-Vignes B, Funck-Brentano C, Salem JE. Uses of pharmacovigilance databases: An overview. Therapie (2020) 75 591–8. DOI: 10.1016/J.THERAP.2020.02.022 45. Oliveira JL, Lopes P, Nunes T, Campos D, Boyer S, Ahlberg E. The EU-ADR web platform: delivering advanced pharmacovigilance tools. Pharmacoepidemiol Drug Saf (2013) 22. DOI: 10.1002/PDS.3375 46. Patadia VK, Coloma P, Schuemie MJ, Herings R, Gini R, Mazzaglia G. Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project. Expert Rev Clin Pharmacol (2015) 8 95-102. DOI: 10.1586/17512433.2015.992878 47. Scavone C, Carnovale C, Ruggiero R, Radice S, Scatigna M, Racagni G. On the policy of the Italian government in the discovery, development, and access to medicines. Clin Ther (2018) 40. DOI: 10.1016/J.CLINTHERA.2018.09.015 48. Sultana J, Crisafulli S, Almas M, Antonazzo IC, Baan E, Bartolini C. Overview of the European post-authorisation study register post-authorization studies performed in Europe from September 2010 to December 2018. Pharmacoepidemiol Drug Saf (2022) 31 689-705. DOI: 10.1002/PDS.5413
--- title: 'Associations of macular microvascular parameters with cerebral small vessel disease in rural older adults: A population-based OCT angiography study' authors: - Zhe Xu - Yi Dong - Yongxiang Wang - Lin Song - Sijie Niu - Shanshan Wang - Mingqing Zhao - Jiafeng Wang - Lin Cong - Xiaojuan Han - Tingting Hou - Shi Tang - Qinghua Zhang - Yifeng Du - Chengxuan Qiu journal: Frontiers in Neurology year: 2023 pmcid: PMC10060796 doi: 10.3389/fneur.2023.1133819 license: CC BY 4.0 --- # Associations of macular microvascular parameters with cerebral small vessel disease in rural older adults: A population-based OCT angiography study ## Abstract ### Objective To explore the associations of macular microvascular parameters with cerebral small vessel disease (CSVD) in rural-dwelling older adults in China. ### Methods This population-based cross-sectional study included 195 participants (age ≥ 60 years; $57.4\%$ women) in the optical coherence tomographic angiography (OCTA) sub-study within the Multimodal Interventions to delay Dementia and disability in rural China (MIND-China). Macular microvascular parameters were measured using the OCTA. We automatically estimated volumes of gray matter, white matter, and white matter hyperintensity (WMH), and manually assessed numbers of enlarged perivascular spaces (EPVS) and lacunes on brain magnetic resonance imaging. Data were analyzed with the general linear models. ### Results Adjusting for multiple confounders, lower vessel skeleton density (VSD) and higher vessel diameter index (VDI) were significantly associated with larger WMH volume ($P \leq 0.05$). Lower VSD and foveal density-300 (FD-300) of left eye were significantly associated with lower brain parenchymal volume ($P \leq 0.05$). In addition, lower areas of foveal avascular zone (FAZ) and FD-300 of left eye were significantly associated with more EPVS ($P \leq 0.05$). The associations of abnormal macular microvascular parameters with WMH volume were evident mainly among females. Macular microvascular parameters were not associated with lacunes. ### Conclusion Macular microvascular signs are associated with WMH, brain parenchymal volume, and EPVS in older adults. The OCTA-assessed macular microvascular parameters can be valuable markers for microvascular lesions in the brain. ## Introduction Cerebral small vessel disease (CSVD) refers to a group of pathological processes with various etiologies affecting the small arteries, venules, and capillaries of the brain [1]. Previous studies have shown that CSVD is associated with clinical stroke and cognitive impairment [2]. Therefore, it is important to identify simple, non-invasive, and inexpensive markers for CSVD. Microvasculatures in the brain and retina are similar in embryologic origin, anatomic features, metabolic activities, patterns of vascularization, and extracellular deposits [3]. Thus, the retinal microvasculature could be a potential vehicle for studying changes in the cerebral vasculature. Some population-based studies have examined the associations of alterations in retinal microvasculature and imaging markers of CSVD, in which retinal microvascular signs (e.g., retinal focal arteriolar narrowing and arteriovenous nicking) were assessed using fundus photography [4, 5]. However, due to limited resolution and sensitivity, traditional fundus imaging techniques can only qualitatively measure signs of microangiopathy (e.g., micro-hemorrhages and micro-aneurysms) or quantitatively measure arteriolar and venular parameters, but cannot directly quantify parameters of the capillary system (e.g., vessel density and vessel diameter index) [6]. As a non-invasive and label-free technique, optical coherence tomography angiography (OCTA) can quantitatively detect the movement of red blood cells at capillary-level resolution and offer imaging markers for retinal microvascular signs, such as retinal vessel density (VD), foveal avascular zone (FAZ), and vessel diameter index (VDI) [6]. Previous studies showed that VD was related to the CSVD burden or global white matter hyperintensity (WMH) [7, 8]. However, most of these studies have been conducted in the clinical settings of patients with mild cognitive impairment or Alzheimer's disease (AD), and data from the general population settings are sparse. In addition, cerebral WMH is typically located in paraventricular and deep white matter regions, and etiopathological mechanisms of WMH differ depending on their location [9]. However, the potential differential associations of macular microvascular parameters with paraventricular and deep WMH have rarely been explored. Furthermore, the sex differences in the associations of retinal microvascular signs with CSVD in older adults remain to be clarified. This is important because sex differences in CSVD and retinal capillary plexus have been frequently reported [10, 11] and there are substantial sex differences in the prevalence of lifestyle risk factors (e.g., smoking and alcohol consumption), especially among rural older adults in China [12, 13]. Thus, in this population-based study, we aimed to investigate the associations of macular microvascular signs with markers of CSVD among dementia-free older adults who were living in the rural communities in China. We hypothesize that macular microvascular signs are associated with CSVD markers and that the associations may vary by sex. ## Study design and participants This population-based cross-sectional study used data from the baseline survey of the Multimodal Interventions to delay Dementia and disability in rural China (MIND-China), which is part of the World-Wide FINGERS Network, as previously reported [14]. In brief, baseline assessments of MIND-China targeted people who were aged ≥ 60 years by the end of 2017 and living in the 52 villages of Yanlou Town, Yanggu County, western Shandong Province, China. In March–September 2018, 5,765 participants ($57.2\%$ female) were examined during the baseline survey of MIND-China [15]. From June 2019 to November 2020, 284 participants accomplished the brain MRI and OCTA examination in Southwest Lu Hospital. The cluster (village)-randomized sampling approach was used in the selection of participants for the MRI and OCTA substudies in MIND-China. Of these, 47 were excluded due to missing data on OCTA images of left eye ($$n = 30$$) or right eye ($$n = 17$$). We further excluded 42 participants due to prevalent dementia ($$n = 2$$), and suboptimal quality of OCTA images ($$n = 31$$) and brain MRI images ($$n = 9$$). Therefore, the final analytical sample included 195 dementia-free participants. Compared to the MIND-China participants who were not included in this study ($$n = 5$$,570), those in the analytical sample ($$n = 195$$) were slightly younger (mean age 67.95 vs. 70.99 years, $P \leq 0.001$) and more educated (mean years of schooling education 3.66 vs. 3.16 years, $$P \leq 0.041$$), but the two groups did not differ significantly in the distribution of sex (female 57.44 vs. $57.18\%$, $$P \leq 0.944$$). The MIND-China study was approved by the Ethics Committee at Shandong Provincial Hospital. All participants were informed of the study protocol in detail and signed the informed consent form. Research within MIND-China was conducted in accordance with the ethical principles expressed in the Declaration of Helsinki. MIND-China was registered in the Chinese Clinical Trial Registry (Registration No. ChiCTR1800017758). ## Data collection and assessments In March-September 2018, the trained research staff collected data through face-to-face interviews, clinical examinations, neuropsychological tests, and laboratory tests, as part of the baseline examinations of MIND-China [15]. The data included demographic features (e.g., age, sex, and education), lifestyles (e.g., smoking and alcohol consumption), health history (e.g., hypertension, diabetes, and dyslipidemia). Smoking and alcohol intake were classified as never and ever smoking or consuming alcohol. The data collection and assessments were previously described in detail [15]. ## MRI acquisition and assessment protocols Participants were scanned on the Philips Ingenia 3.0T MR System in the Southwestern Lu Hospital. The MRI protocols (acquisition, sequences, processing, and quantification) were previously described in detail [15, 16]. The protocol included a sagittal three-dimensional T1-weighted fast field echo sequence, an axial T2-weighted fast field echo sequence, and a sagittal three-dimensional fluid attenuated inversion recovery (FLAIR) sequence. We used AccuBrain® (BrainNow Medical Technology Ltd., Shenzhen, Guangdong, China) to assess WMH volume, brain parenchymal volume, and total intracranial volume (ICV), as previously reported [17]. AccuBrain® segmented the T1-weighted images and quantified the volumes of gray matter, white matter, and cerebrospinal fluid based on the principle of similarity measures. The ICV was estimated as the sum of gray matter, white matter, and cerebrospinal fluid volume. The brain parenchymal volume was estimated as the sum of gray matter and white matter volume. To acquire WMH volume, AccuBrain® used T2- FLAIR images to calculate the signal contrast between normal brain tissue and WMH, and set the signal threshold to recognize WMH. Based on predefined threshold, WMH was recognized and extracted using T2-FLAIR images. Finally, AccuBrain® refined and localized WMH using the transformed T1-weighted brain structure mask extracted from our study sample. Perivascular spaces (PVS) were microscopic fluid-filled structures that surround the small penetrating blood vessels in the brain. The number of enlarged perivascular spaces (EPVS) was counted on T2-weighted images according to a validated protocol [18]. The EPVS appear linear when imaged parallel to the course of the vessel, and round or ovoid (diameters <3 mm) when imaged perpendicular to the course of the vessel. EPVS of the basal ganglia (BG) and centrum semiovale (CSO) were visually counted by the trained rater (M.Z.) who was unaware of the clinical information, under the supervision of an experienced clinical neurologist (L.S). The rater first reviewed all MRI slices showing BG and CSO regions and then counted the number of EPVS on the slices with the highest number of EPVS [18]. Three months after the initial assessment, EPVS were reassessed on MRI images from 30 randomly selected subjects, and the intra-evaluator correlation coefficients for BG-EPVS and CSO-EPVS were 0.88 and 0.83, respectively. The total number of global EPVS (i.e., BG-EPVS plus CSO-EPVS) was used in our analysis. Lacune was defined as a region of abnormal signal intensity in a vascular distribution, 3–15 mm in size with a cerebrospinal fluid density on the subtraction image [19]. Lacunes were counted on FLAIR images by the trained rater (J.W.) who was blind to clinical information, under the supervision of an experienced clinical neurologist (L.S). Three months after the initial assessment, lacunes were reassessed on MRI images of 200 randomly selected subjects from all participants in MIND-China who undertook brain MRI scans, and the intra-rater correlation coefficients for lacunes was 0.838. ## Optical coherence tomography angiography acquisition and assessment The OCTA examination was conducted by ophthalmologists in Southwestern Lu Hospital using Spectralis HRA+OCT (Software Version 1.10.2.0; Heidelberg Engineering, Inc., Heidelberg, Germany). Macular OCTA images of 6 × 6 mm areas (10° × 10° scan angle that included 512 A-scans × 512 B-scans, no pre-determined automatic real time) centered at the fovea were acquired in a dark room without pupil dilation. We scanned the right eye first and then the left eye. We excluded images of suboptimal quality due to eye movement, decentration, defocus, shadow, Z offset, and segmentation error [20]. OCTA images of both eyes were included and analyzed in this study. Because OCTA signals in the deeper layers carry the shadows of blood vessels from the superficial plexus, our analysis only focused on the superficial vascular plexus (extending from the inner limiting membrane to 17 mm above the inner plexiform layer) [21]. All macular OCTA images were processed into a binary image in MATLAB (R2019a, MathWorks, Inc.) using a global threshold, hessian filter, and adaptive threshold. Macular microvascular parameters, including vascular density (e.g., VD, VSD, and FD-300) and vascular morphology (e.g., FAZ and VDI), were calculated. FAZ area was defined as the area of the avascular region in the center of the blood flow image [22]. VD was calculated as the ratio of the total image area occupied by the blood flow signal to the total image area in the binary vessel maps (subtract FAZ area) [22]. VSD was calculated as the ratio of the length occupied by the blood flow signal to the total area in the skeletonized vessel map (subtract FAZ area) [22]. VDI, defined as the average diameter of blood vessels, was calculated as the ratio of the area covered by blood flow signal to the length of vessel skeletonization [23]. FD-300 was defined as the foveal vessel density in a 300-μm wide zone around the FAZ [24]. ## Statistical analysis We reported frequencies (%) for categorical variables and mean (SD) for continuous variables. Characteristics of study participants by sex were compared using non-parametric test for continuous variables, and chi-square test for categorical variables. General linear models were used to examine the associations of macular microvascular parameters with WMH volume, number of lacunes, brain parenchymal volume, and EPVS count. WMH volume and numbers of lacunes and EPVS were log-transformed due to the skewed distribution. By simultaneously entering the independent variables and their cross-product term into the same model, we tested the statistical interactions of retinal microvascular parameters with sex on CSVD and when statistically significant interaction was detected, we further performed stratified analyses by sex. We reported the main results from two models: model 1 was adjusted for age, sex, and education, if applicable, for ICV; and model 2 was additionally adjusted for alcohol consumption, smoking status, hypertension, diabetes, and dyslipidemia. IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA) was used for all the analyses. A two-tailed $P \leq 0.05$ was considered to be statistically significant. ## Characteristics of the study participants The mean age of the 195 participants in the analytical sample was 67.95 (SD = 4.01) years, and $57.4\%$ were females. Compared with females, male participants were more educated, and more likely to smoke and drink alcohol, but less likely to have dyslipidemia ($P \leq 0.05$). In addition, male participants had a smaller FAZ area on both eyes and a smaller brain parenchymal volume but a larger ICV volume ($P \leq 0.05$; Table 1). The two groups did not differ significantly in mean age, FD-300, VD, VSD, VDI (on both eyes), global WMH volume, periventricular WMH (PWMH) volume, deep WMH (DWMH) volume, numbers of EPVS and lacunes, and in the prevalence of diabetes and hypertension ($P \leq 0.05$). Table 1 \| Characteristics \| Total (n = 195) \| Female (n = 112) \| Male (n = 83) \| P-valuea \| \| --- \| --- \| --- \| --- \| --- \| \| Age (years) \| 67.95 (4.01) \| 68.03 (4.09) \| 67.86 (3.91) \| 0.696 \| \| Education (years) \| 3.66 (3.66) \| 1.91 (2.68) \| 6.02 (3.49) \| <0.001 \| \| Ever smoking, n (%) \| 63 (32.30) \| 1 (0.90) \| 62 (74.70) \| <0.001 \| \| Ever alcohol drinking, n (%) \| 81 (41.50) \| 9 (8.00) \| 72 (86.70) \| <0.001 \| \| Diabetes, n (%) \| 17 (8.70) \| 13 (11.60) \| 4 (4.80) \| 0.097 \| \| Dyslipidemia, n (%) \| 45 (23.10) \| 32 (28.60) \| 13 (15.70) \| 0.034 \| \| Hypertension, n (%) \| 130 (66.70) \| 75 (67.00) \| 55 (66.30) \| 0.918 \| \| CSVD \| CSVD \| CSVD \| CSVD \| CSVD \| \| ICV (ml) \| 1,431.03 (128.19) \| 1,369.03 (110.93) \| 1,514.70 (99.41) \| <0.001 \| \| Global WMH volume (ml) \| 7.68 (10.46) \| 7.21 (11.05) \| 8.31 (9.63) \| 0.247 \| \| Periventricular WMH volume (ml) \| 6.38 (9.80) \| 6.00 (10.50) \| 6.89 (8.80) \| 0.163 \| \| Deep WMH volume (ml) \| 1.30 (1.46) \| 1.21 (1.36) \| 1.41 (1.58) \| 0.595 \| \| Brain parenchymal volume (L) \| 1.09 (0.10) \| 1.04 (0.09) \| 1.14 (0.08) \| <0.001 \| \| EPVS \| 61.48 (29.93) \| 58.05 (28.27) \| 66.10 (31.63) \| 0.088 \| \| Lacunesb \| 0.76 (1.93) \| 0.56 (1.52) \| 1.03 (2.35) \| 0.313 \| \| Macular microvascular index \| Macular microvascular index \| Macular microvascular index \| Macular microvascular index \| Macular microvascular index \| \| FAZ area (mm2), OS \| 0.59 (0.29) \| 0.64 (0.33) \| 0.53 (0.20) \| 0.030 \| \| FAZ area (mm2), OD \| 0.58 (0.25) \| 0.62 (0.25) \| 0.53 (0.24) \| 0.004 \| \| FD-300 (%), OS \| 0.40 (0.04) \| 0.40 (0.04) \| 0.40 (0.04) \| 0.650 \| \| FD-300 (%), OD \| 0.40 (0.04) \| 0.40 (0.04) \| 0.39 (0.05) \| 0.281 \| \| VD (%), OS \| 0.43 (0.03) \| 0.43 (0.03) \| 0.43 (0.03) \| 0.411 \| \| VD (%), OD \| 0.43 (0.03) \| 0.43 (0.03) \| 0.43 (0.03) \| 0.536 \| \| VSD (%), OS \| 0.15 (0.01) \| 0.15 (0.01) \| 0.15 (0.01) \| 0.142 \| \| VSD (%), OD \| 0.15 (0.01) \| 0.15 (0.01) \| 0.15 (0.01) \| 0.558 \| \| VDI (μm), OS \| 33.10 (2.51) \| 33.45 (2.78) \| 32.62 (2.02) \| 0.056 \| \| VDI (μm), OD \| 32.96 (2.55) \| 33.02 (2.74) \| 32.89 (2.29) \| 0.920 \| ## Associations of macular microvascular parameters with global cerebral small vessel disease Adjusting for age, sex, education, and if applicable, for ICV, lower VSD but higher VDI were significantly associated with higher global WMH volume ($P \leq 0.05$); Lower FD-300 and VSD of the left eye were significantly associated with lower brain parenchymal volume ($P \leq 0.05$); Lower FD-300 and FAZ areas of the left eye were significantly associated with more EPVS ($P \leq 0.05$); these associations remained statistically significant even after further controlling for dyslipidemia, diabetes, hypertension, smoking, and alcohol consumption ($P \leq 0.05$; Table 2). The number of lacunes was not significantly associated with any of the examined macular microvascular parameters ($P \leq 0.05$; Table 2). Table 2 \| Macular microvascular index \| Global WMH volume \| Global WMH volume.1 \| Brain parenchymal volume \| Brain parenchymal volume.1 \| EPVS \| EPVS.1 \| Lacunes b \| Lacunes b.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Macular microvascular index \| \| \| \| \| \| \| \| \| \| \| Model 1 a \| Model 2 a \| Model 1 a \| Model 2 a \| Model 1 a \| Model 2 a \| Model 1 a \| Model 2 a \| \| OS \| OS \| OS \| OS \| OS \| OS \| OS \| OS \| OS \| \| FAZ area (mm2) \| 0.01 (−0.19, 0.20) \| 0.03 (−0.17, 0.22) \| 0.00 (−0.01, 0.02) \| 0.01 (−0.01, 0.02) \| –0.14 (–0.25, –0.03)* \| –0.14 (–0.25, –0.03)* \| 0.00 (−0.13, 0.14) \| 0.01 (−0.12, 0.15) \| \| FD-300 (%) \| −0.55 (−1.84, 0.73) \| −0.47 (−1.75, 0.82) \| 0.09 (0.01, 0.17)* \| 0.08 (0.00, 0.16)* \| –0.82 (–1.57, –0.07)* \| –0.76 (–1.51, 0.00)* \| −0.38 (−8.42, 4.92) \| −0.28 (−1.20, 0.63) \| \| VD (%) \| −0.04 (−2.09, 2.00) \| 0.10 (−1.93, 2.13) \| 0.12 (−0.01, 0.24) \| 0.11 (−0.02, 0.23) \| −0.60 (−1.81, 0.61) \| −0.53 (−1.73, 0.67) \| 0.08 (−1.38, 1.53) \| 0.23 (−1.22, 1.68) \| \| VSD (%) \| –4.97 (–9.43, –0.50)* \| –4.79 (–9.24, –0.35)* \| 0.33 (0.06, 0.60)* \| 0.31 (0.04, 0.59)* \| −0.92 (−3.59, 1.75) \| −0.56 (−3.22, 2.10) \| 1.00 (−2.13, 4.13) \| 1.34 (−1.81, 4.49) \| \| VDI (μm) \| 0.03 (0.01, 0.05)* \| 0.03 (0.01, 0.05)* \| 0.00 (0.00, 0.00) \| 0.00 (0.00, 0.00) \| 0.00 (−0.01, 0.01) \| 0.00 (−0.02, 0.01) \| 0.00 (−0.02, 0.01) \| 0.00 (−0.02, 0.01) \| \| OD \| OD \| OD \| OD \| OD \| OD \| OD \| OD \| OD \| \| FAZ area (mm2) \| 0.15 (−0.07, 0.38) \| 0.16 (−0.06, 0.38) \| 0.01 (−0.01, 0.02) \| 0.01 (−0.01, 0.02) \| 0.01 (−0.12, 0.15) \| 0.02 (−0.11, 0.15) \| −0.02 (−0.18, 0.14) \| −0.02 (−0.18, 0.14) \| \| FD-300 (%) \| −0.69 (−2.02, 0.64) \| −0.76 (−2.07, 0.55) \| 0.01 (−0.07, 0.09) \| 0.01 (−0.07, 0.09) \| −0.03 (−0.82, 0.76) \| −0.05 (−0.82, 0.73) \| 0.95 (0.00, 1.90)* \| 0.90 (−0.05, 1.84) \| \| VD (%) \| −0.66 (−2.67, 1.36) \| −0.80 (−2.79, 1.18) \| 0.02 (−0.11, 0.14) \| 0.02 (−0.10, 0.14) \| 0.12 (−1.08, 1.31) \| 0.07 (−1.11, 1.25) \| 1.31 (−0.12, 2.75) \| 1.21 (−0.22, 2.64) \| \| VSD (%) \| −3.89 (−8.65, 0.86) \| −4.69 (−9.40, 0.03) \| 0.08 (−0.21, 0.37) \| 0.09 (−0.21, 0.38) \| 0.60 (−2.23, 3.43) \| 0.50 (−2.32, 3.31) \| 1.35 (−1.99, 4.68) \| 0.93 (−2.42, 4.28) \| \| VDI (μm) \| 0.01 (−0.01, 0.03) \| 0.01 (−0.01, 0.03) \| 0.00 (0.00, 0.00) \| 0.00 (0.00, 0.00) \| 0.00 (−0.01, 0.01) \| 0.00 (−0.01, 0.01) \| 0.01 (−0.01, 0.02) \| 0.01 (−0.01, 0.02) \| ## Associations of macular microvascular parameters with regional WMH volume We further examined the associations of macular microvascular parameters with volumes of periventricular and deep WMH. Controlling for age, sex, education, and ICV, lower VSD and higher VDI of the left eye were significantly associated with larger PWMH volume ($P \leq 0.05$); the associations remained statistically significant even after further controlling for dyslipidemia, diabetes, hypertension, smoking, and alcohol consumption ($P \leq 0.05$; Table 3). In addition, lower VSD of the right eye was significantly associated with larger PWMH volume, even in the fully-adjusted model ($P \leq 0.05$; Table 3). There was no significant association between macular microvascular parameters and DWMH volume ($P \leq 0.05$; Table 3). Table 3 \| Macular microvascular index \| Periventricular WMH volume (ml) \| Periventricular WMH volume (ml).1 \| Deep WMH volume (ml) \| Deep WMH volume (ml).1 \| \| --- \| --- \| --- \| --- \| --- \| \| Macular microvascular index \| Model 1a \| Model 2a \| Model 1a \| Model 2a \| \| OS \| OS \| OS \| OS \| OS \| \| FAZ area (mm2) \| 0.02 (−0.17, 0.21) \| 0.04 (−0.16, 0.23) \| −0.03 (−0.15, 0.08) \| −0.02 (−0.13, 0.09) \| \| FD-300 (%) \| −0.52 (−1.79, 0.75) \| −0.45 (−1.73, 0.83) \| −0.46 (−1.21, 0.29) \| −0.39 (−1.14, 0.36) \| \| VD (%) \| 0.07 (−1.96, 2.10) \| 0.20 (−1.82, 2.21) \| −0.51 (−1.71, 0.68) \| −0.39 (−1.57, 0.78) \| \| VSD (%) \| –4.79 (–9.22, –0.37)* \| –4.65 (–9.08, –0.22)* \| −1.62 (−4.26, 1.02) \| −1.52 (−4.13, 1.08) \| \| VDI (um) \| 0.03 (0.01, 0.05)* \| 0.03 (0.01, 0.05)** \| 0.00 (−0.01, 0.02) \| 0.01 (−0.01, 0.02) \| \| OD \| OD \| OD \| OD \| OD \| \| FAZ area (mm2) \| 0.18 (−0.04, 0.40) \| 0.19 (−0.03, 0.41) \| −0.02 (−0.15, 0.12) \| −0.01 (−0.14, 0.12) \| \| FD-300 (%) \| −0.49 (−1.81, 0.83) \| −0.55 (−1.86, 0.75) \| −0.61 (−1.38, 0.17) \| −0.65 (−1.41, 0.11) \| \| VD (%) \| −0.36 (−2.35, 1.64) \| −0.49 (−2.46, 1.49) \| −0.82 (−2.00, 0.36) \| −0.91 (−2.06, 0.24) \| \| VSD (%) \| −4.05 (−8.75, 0.65) \| –4.76 (–9.45, –0.07)* \| −0.28 (−3.09, 2.52) \| −0.85 (−3.61, 1.92) \| \| VDI (μm) \| 0.01 (−0.02, 0.07) \| 0.02 (−0.01, 0.04) \| −0.01 (−0.02, 0.01) \| −0.01 (−0.02, 0.01) \| ## Interactions of macular microvascular parameters with sex on cerebral small vessel disease We found statistical interactions of macular microvascular parameters (e.g., VSD and FD-300) with sex on the global WMH and PWMH volumes (P-interactions < 0.05). Further analyses stratified by sex showed that macular microvascular parameters (e.g., VSD and FD-300) were significantly associated with higher global WMH and PWMH volumes only among females not in males ($P \leq 0.05$; Figure 1). Figure 1: Associations of macular microvascular parameters with (A) global white matter hyperintensity volume and (B) periventricular white matter hyperintensity volume by sex (n = 195). CI, confidence interval; OD, right eye; OS, left eye; VSD, vessel skeleton density; FD-300, foveal vessel density in the 300 μm ring; PWMH, periventricular white matter hyperintensity. ## Discussion In this population-based cross-sectional study of rural-dwelling Chinese older adults, we found that macular microvascular parameters (e.g., FAZ, FD-300, VSD, and VDI), especially in the left eye, were associated with signs of CSVD (e.g., WMH volume, EPVS count, and brain parenchymal volume). Furthermore, macular microvascular parameters (e.g., VSD and VDI) were associated with larger PWMH volume but not DWMH volume. Our study also revealed that the associations of macular vascular parameters (e.g., VSD and VDI) with volumes of global WMH and PWMH were only evident among females. The associations between macular microvascular parameters and signs of CSVD have been reported in several case-control studies, but the findings were inconsistent [7, 25]. The lower vascular density is indicative of reduced retinal blood flow. Our study indicated that lower vascular density (e.g., VSD and FD-300) was associated with higher global WMH volume and more EPVS, which were in line with the previous clinical-based studies [26, 27]. Furthermore, we revealed that lower VSD and FD-300 were strongly associated with lower brain parenchymal volume, which has rarely been reported in previous studies. The FAZ area is most sensitive to ischemia and enlargement of FAZ area indicates macular ischemia [28]. Our findings suggested that lower FAZ area was associated with more EPVS. However, a clinical-based case-control study in Shanghai found no association between FAZ area and EPVS count, which may be partially due to the fact that the case-control study included young adults (age ≥ 18 years) [26]. VDI, which reflects vascular dilation, represents the average vessel caliber regardless of the vessel length [22]. Our data suggested that higher VDI was associated with higher global WMH volume. This may hypothetically be due to the loss of smaller capillaries or compensatory vasodilation of the perfused capillaries secondary to a more hypoxic environment and increased local inflammatory molecules [29, 30]. Previously, a population-based cohort study has linked the larger venular diameters with the progression of WMH [5]. By contrast, the small-scale case-control study ($$n = 64$$) in Wuhan, China found no association between VDI and WMH (e.g., PWMH and DWMH), partly due to limited statistical power [31]. Taken together, the findings from our population-based study and previous case-control studies suggest that macular microvascular parameters can be valuable markers for microvascular lesions in the brain. PWMH and DWMH have distinct etiopathogenic mechanisms. PWMH is more likely to be determined by chronic hemodynamic insufficiency, whereas DWMH may be more attributed to small vessel disease [32]. In line with this view, two hospital-based case-control studies suggested that lower vessel density was associated with severity of DWMH but not PWMH [26, 31]. However, our study indicated that lower VSD and higher VDI were associated with higher PWMH volume, but not with DWMH volume. The discrepancy across studies may be partly attributed to differences in the study settings (e.g., clinical vs. the general population setting) and characteristics of study populations (e.g., young adults vs. older adults). Thus, further longitudinal studies with larger sample sizes are needed to clarify the potential causal relationship between macular microvascular parameters and WMH by anatomic locations. In this study, we further examined the interactions of macular microvascular parameters with sex on CSVD markers and found that the associations of macular vascular parameters (e.g., VSD and VDI) with volumes of global WMH and PWMH were only evident among females. This was consistent with the previous community-based study, which showed that associations between WMH severity and arteriovenous nicking were stronger in women than in men [33]. It has been hypothesized that estrogens can increase tissue perfusion of the retina and brain probably by reducing vascular resistance, protecting against oxidative stress, and stimulating synaptogenesis in premenopausal women [34, 35]. However, estrogen levels decline markedly after the menopause, the vascular protection of estrogen disappears and women become more susceptible to vascular changes and diseases in the brain [34, 36]. Another interesting observation from our study is that the associations between macular microvascular parameters and CSVD were evident mainly in the left eye. This is in agreement with a hospital-based study of adults (age ≥ 18 years) in Beijing, which showed that lower VD of the left eye, but not right eye, was associated with higher burdens of WMH and EPVS [37]. This may partially be attributed to structural and functional brain asymmetry occurred during normal human brain development [32, 38]. Furthermore, previous studies also showed that macular microvascular signs of left eye reflect function and connectivity of the right hemisphere [39] and the small vascular lesions of the right hemisphere occur earlier and are much more severer than those of the left hemisphere [40]. Finally, there was evidence showing that the left eye appeared to be more sensitive to damage due to hypoxia compared with the right eye [41]. The pathophysiologic mechanisms underlying the associations of macular microvascular parameters with CSVD are not fully understood but can be speculated. First, anatomically, the retina is regarded as an extension of the diencephalon and has a similar pattern of angiogenesis. Physiologically, similar to the brain, the retina has a highly isolated and protected vascular system [3, 42]. Second, retinal and brain microvascular lesions share common cardiovascular risk factors [43]. Third, dysfunction of the blood-retina barrier from retina hypoxia owing to long-term exposures to cardiovascular risk factors, which is analogous to and associated with the blood-brain barrier dysfunction, might play a role in the pathogenesis of both retinal microvascular changes and CSVD [42, 44]. Finally, inflammation and endothelial dysfunction may also be involved in the process of retinal and cerebral microangiopathy [29]. Given the homology of microvasculature in the brain and the retina, the link of macular microvascular parameters with signs of CSVD may reflect underlying pathological processes common in both the brain and the retina [25, 27]. The major strengths of our study include the population-based design and comprehensive assessments of macular microvascular and CSVD indicators. Our study also has limitations. First, the cross-sectional study cannot determine a causal relationship. Second, the study sample was relatively small and the statistical power might not be big enough to detect weak-to-moderately strong associations between retinal parameters and markers of CSVD. Third, data on cerebral microbleeds and microinfarcts were not available, which might underestimate the associations of macular microvascular parameters with CSVD. Finally, the study participants were derived from just one rural area and they were also slightly younger and more educated than the MIND-China total sample, which might not be representative of rural population in China. These should be borne in mind when extrapolating the findings to other rural populations. ## Conclusions In summary, our population-based study of rural-dwelling older adults showed that macular microvascular parameters were independently associated with CSVD markers, and that the observed associations with WMH volume were evident mainly among females. This suggests that quantitative macular microvascular parameters could be useful markers for CSVD. These findings may have potential implications for clinical management of macular microvascular signs in older adults. For instance, ophthalmologists should be aware of the potential link of macular microvascular signs with brain lesions. Structural brain MRI examination may be considered when abnormal macular microvascular signs were detected, especially among older women. Future large-scale prospective cohort studies are warranted to clarify the potential causal relationships of macular microvascular signs with CSVD markers as well as the mechanisms underlying their associations. In addition, the functional consequences (e.g., cognitive and physical dysfunction) of macular microvascular signs among older adults deserve further investigation. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Committee at Shandong Provincial Hospital. The patients/participants provided their written informed consent to participate in this study. ## Author contributions ZX, QZ, YDu, and CQ: study concept and design. ZX, YW, LS, SN, SW, MZ, JW, YDo, TH, LC, ST, and XH: data collection and assessments. ZX: data analysis and writing the first draft of the manuscript. YDu and CQ: study supervision. All authors have critically revised the manuscript for important intellectual content and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol. (2010) 9 689-701. DOI: 10.1016/S1474-4422(10)70104-6 2. Debette S, Beiser A, DeCarli C, Au R, Himali JJ, Kelly-Hayes M. Association of mri markers of vascular brain injury with incident stroke, mild cognitive impairment, dementia, and mortality: the framingham offspring study. Stroke. (2010) 41 600-6. DOI: 10.1161/STROKEAHA.109.570044 3. London A, Benhar I, Schwartz M. The retina as a window to the brain-from eye research to Cns disorders. Nat Rev Neurol. (2013) 9 44-53. DOI: 10.1038/nrneurol.2012.227 4. Qiu C, Cotch MF, Sigurdsson S, Klein R, Jonasson F, Klein BEK. Microvascular lesions in the brain and retina: the age, gene/environment susceptibility-reykjavik study. Ann Neurol. (2009) 65 569-76. DOI: 10.1002/ana.21614 5. Ikram MK, De Jong FJ, Van Dijk EJ, Prins ND, Hofman A, Breteler MMB. Retinal vessel diameters and cerebral small vessel disease: the Rotterdam scan study. Brain. (2006) 129 182-8. DOI: 10.1093/brain/awh688 6. Spaide RF, Fujimoto JG, Waheed NK, Sadda SR, Staurenghi G. Optical coherence tomography angiography. Prog Retin Eye Res. (2018) 64 3. DOI: 10.1016/j.preteyeres.2017.11.003 7. Lee J-Y, Kim JP, Jang H, Kim J, Kang SH, Kim JS. Optical coherence tomography angiography as a potential screening tool for cerebral small vessel diseases. Alzheimers Res Ther. (2020) 12 73. DOI: 10.1186/s13195-020-00638-x 8. Peng C, Kwapong WR, Xu S, Muse FM, Yan J, Qu M. Structural and microvascular changes in the macular are associated with severity of white matter lesions. Front Neurol. (2020) 11 521. DOI: 10.3389/fneur.2020.00521 9. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. (2013) 12 822-38. DOI: 10.1016/S1474-4422(13)70124-8 10. Jiménez-Sánchez L, Hamilton OKL, Clancy U, Backhouse EV, Stewart CR, Stringer MS. Sex differences in cerebral small vessel disease: a systematic review and meta-analysis. Front Neurol. (2021) 12 756887. DOI: 10.3389/fneur.2021.756887 11. Su B, Zhu X, Yang K, Xiao Y, Li C, Shi K. Age- and sex-related differences in the retinal capillary plexus in healthy Chinese adults. Eye Vis. (2022) 9 38. DOI: 10.1186/s40662-022-00307-0 12. Song A, Liang Y, Yan Z, Sun B, Cai C, Jiang H. Highly prevalent and poorly controlled cardiovascular risk factors among chinese elderly people living in the rural community. Eur J Prev Cardiol. (2014) 21 1267-74. DOI: 10.1177/2047487313487621 13. Han X, Jiang Z, Li Y, Wang Y, Liang Y, Dong Y. Sex disparities in cardiovascular health metrics among rural-dwelling older adults in China: a population-based study. BMC Geriatr. (2021) 21 158. DOI: 10.1186/s12877-021-02116-x 14. Kivipelto M, Mangialasche F, Snyder HM, Allegri R, Andrieu S, Arai H. World-wide fingers network: a global approach to risk reduction and prevention of dementia. Alzheimers Dement. (2020) 16 1078-94. DOI: 10.1002/alz.12123 15. Wang Y, Han X, Zhang X, Zhang Z, Cong L, Tang S. Health status and risk profiles for brain aging of rural-dwelling older adults: data from the interdisciplinary baseline assessments in mind-China. Alzheimers Dement (N Y). (2022) 8 e12254. DOI: 10.1002/trc2.12254 16. Wang M, Li Y, Cong L, Hou T, Luo Y, Shi L. High-density lipoprotein cholesterol and brain aging amongst rural-dwelling older adults: a population-based magnetic resonance imaging study. Eur J Neurol. (2021) 28 2882-92. DOI: 10.1111/ene.14939 17. Abrigo J, Shi L, Luo Y, Chen Q, Chu WCW, Mok VCT. Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer's disease imaging biomarker. Acta Radiol. (2019) 60 769-76. DOI: 10.1177/0284185118795327 18. Potter GM, Chappell FM, Morris Z, Wardlaw JM. Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability. Cerebrovasc Dis. (2015) 39 224-31. DOI: 10.1159/000375153 19. Zhang J, Han F, Liang X, Li M, Zhang D, Zhai F. Lacune and large perivascular space: two kinds of cavities are of different risk factors and stroke risk. Cerebrovasc Dis. (2020) 49 522-30. DOI: 10.1159/000508732 20. Kamalipour A, Moghimi S, Hou H, Penteado RC, Oh WH, Proudfoot JA. Oct angiography artifacts in glaucoma. Ophthalmology. (2021) 128 1426-37. DOI: 10.1016/j.ophtha.2021.03.036 21. Sampson DM, Dubis AM, Chen FK, Zawadzki RJ, Sampson DD. Towards standardizing retinal optical coherence tomography angiography: a review. Light Sci Appl. (2022) 11 63. DOI: 10.1038/s41377-022-00740-9 22. Kalra G, Zarranz-Ventura J, Chahal R, Bernal-Morales C, Lupidi M, Chhablani J. Optical coherence tomography (Oct) angiolytics: a review of oct angiography quantitative biomarkers. Surv Ophthalmol. (2022) 67 1118-34. DOI: 10.1016/j.survophthal.2021.11.002 23. Xu Q, Sun H, Huang X, Qu Y. Retinal microvascular metrics in untreated essential hypertensives using optical coherence tomography angiography. Graefes Arch Clin Exp Ophthalmol. (2021) 259 395-403. DOI: 10.1007/s00417-020-04714-8 24. Inanc M, Tekin K, Kiziltoprak H, Ozalkak S, Doguizi S, Aycan Z. Changes in retinal microcirculation precede the clinical onset of diabetic retinopathy in children with type 1 diabetes mellitus. Am J Ophthalmol. (2019) 207 37-44. DOI: 10.1016/j.ajo.2019.04.011 25. Tao W, Kwapong WR, Xie J, Wang Z, Guo X, Liu J. Retinal microvasculature and imaging markers of brain frailty in normal aging adults. Front Aging Neurosci. (2022) 14 945964. DOI: 10.3389/fnagi.2022.945964 26. Fu W, Zhou X, Wang M, Li P, Hou J, Gao P. Fundus changes evaluated by octa in patients with cerebral small vessel disease and their correlations: a cross-sectional study. Front Neurol. (2022) 13 843198. DOI: 10.3389/fneur.2022.843198 27. Wang X, Wei Q, Wu X, Cao S, Chen C, Zhang J. The vessel density of the superficial retinal capillary plexus as a new biomarker in cerebral small vessel disease: an optical coherence tomography angiography study. Neurol Sci. (2021) 42 3615-24. DOI: 10.1007/s10072-021-05038-z 28. Bulut M, Kurtuluş F, Gözkaya O, Erol MK, Cengiz A, Akidan M. Evaluation of optical coherence tomography angiographic findings in Alzheimer's type dementia. Br J Ophthalmol. (2018) 102 233-7. DOI: 10.1136/bjophthalmol-2017-310476 29. Lenin R, Thomas SM, Gangaraju R. Endothelial activation and oxidative stress in neurovascular defects of the retina. Curr Pharm Des. (2018) 24 4742-54. DOI: 10.2174/1381612825666190115122622 30. Wong TY, Islam FMA, Klein R, Klein BEK, Cotch MF, Castro C. Retinal vascular caliber, cardiovascular risk factors, and inflammation: the multi-ethnic study of atherosclerosis (Mesa). Invest Ophthalmol Vis Sci. (2006) 47 2341-50. DOI: 10.1167/iovs.05-1539 31. Zhou X, Li T, Qu W, Pan D, Qiu Q, Wu L. Abnormalities of retinal structure and microvasculature are associated with cerebral white matter hyperintensities. Eur J Neurol. (2022) 29 2289-98. DOI: 10.1111/ene.15378 32. Kim KW, MacFall JR, Payne ME. Classification of white matter lesions on magnetic resonance imaging in elderly persons. Biol Psychiatry. (2008) 64 273-80. DOI: 10.1016/j.biopsych.2008.03.024 33. Longstreth W, Larsen EKM, Klein R, Wong TY, Sharrett AR, Lefkowitz D. Associations between findings on cranial magnetic resonance imaging and retinal photography in the elderly: the cardiovascular health study. Am J Epidemiol. (2007) 165 78-84. DOI: 10.1093/aje/kwj350 34. Nuzzi R, Scalabrin S, Becco A, Panzica G. Gonadal hormones and retinal disorders: a review. Front Endocrinol. (2018) 9 66. DOI: 10.3389/fendo.2018.00066 35. Krause DN, Duckles SP, Pelligrino DA. Influence of sex steroid hormones on cerebrovascular function. J Appl Physiol. (2006) 101 1252-61. DOI: 10.1152/japplphysiol.01095.2005 36. Lohner V, Pehlivan G, Sanroma G, Miloschewski A, Schirmer MD, Stöcker T. Relation between sex, menopause, and white matter hyperintensities: the rhineland study. Neurology. (2022) 99 e935-e43. DOI: 10.1212/WNL.0000000000200782 37. Ma L, Wang M, Chen H, Qu Y, Yang L, Wang Y. Association between retinal vessel density and neuroimaging features and cognitive impairment in cerebral small vessel disease. Clin Neurol Neurosurg. (2022) 221 107407. DOI: 10.1016/j.clineuro.2022.107407 38. Saltoun K, Adolphs R, Paul LK, Sharma V, Diedrichsen J, Yeo BTT. Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles. Nat Hum Behav. (2022) 7 251-68. DOI: 10.1038/s41562-022-01461-0 39. Wang C, Burtis DB, Ding M, Mo J, Williamson JB, Heilman KM. The effects of left and right monocular viewing on hemispheric activation. J Clin Exp Neuropsychol. (2018) 40 198-204. DOI: 10.1080/13803395.2017.1332169 40. Zhou H, Tang Y, Yuan Z. White matter asymmetries in patients with cerebral small vessel disease. J Integr Neurosci. (2018) 17 159-67. DOI: 10.31083/JIN-170037 41. Garcia-Martin E, Ruiz-de Gopegui E, León-Latre M, Otin S, Altemir I, Polo V. Influence of cardiovascular condition on retinal and retinal nerve fiber layer measurements. PLoS ONE. (2017) 12 e0189929. DOI: 10.1371/journal.pone.0189929 42. Patton N, Aslam T, Macgillivray T, Pattie A, Deary IJ, Dhillon B. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat. (2005) 206 319-48. DOI: 10.1111/j.1469-7580.2005.00395.x 43. Cheung CY-l, Ikram MK, Sabanayagam C, Wong TY. Retinal microvasculature as a model to study the manifestations of hypertension. Hypertension. (2012) 60 1094-103. DOI: 10.1161/HYPERTENSIONAHA.111.189142 44. Wong SM, Jansen JFA, Zhang CE, Hoff EI, Staals J, van Oostenbrugge RJ. Blood-brain barrier impairment and hypoperfusion are linked in cerebral small vessel disease. Neurology. (2019) 92 e1669-e77. DOI: 10.1212/WNL.0000000000007263
--- title: Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms authors: - Linfeng Tao - Yue Zhu - Jun Liu journal: Frontiers in Genetics year: 2023 pmcid: PMC10060809 doi: 10.3389/fgene.2023.1129476 license: CC BY 4.0 --- # Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms ## Abstract Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that Sepsis may be associated with MetS in several studies. Therefore, this study investigated diagnostic genes and metabolic pathways associated with both diseases. In addition to microarray data for Sepsis, PBMC single cell RNA sequencing data for Sepsis and microarray data for MetS were downloaded from the GEO database. Limma differential analysis identified 122 upregulated genes and 90 downregulated genes in Sepsis and MetS. WGCNA identified brown co-expression modules as Sepsis and MetS core modules. Two machine learning algorithms, RF and LASSO, were used to screen seven candidate genes, namely, STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR and UROD, all with an AUC greater than 0.9. XGBoost assessed the co-diagnostic efficacy of Hub genes in Sepsis and MetS. The immune infiltration results show that Hub genes were expressed at high levels in all immune cells. After performing Seurat analysis on PBMC from normal and Sepsis patients, six immune subpopulations were identified. The metabolic pathways of each cell were scored and visualized using ssGSEA, and the results show that CFLAR plays an important role in the glycolytic pathway. Our study identified seven Hub genes that serve as co-diagnostic markers for Sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolic pathway. ## Introduction Sepsis is caused by dysregulation of the immune system’s response to infection (Font et al., 2020). In spite of a growing number of innovative treatments, Sepsis still ranks as a leading cause of death in hospitals. As a result of the diversity of clinical symptoms of Sepsis, diagnosing, treating, and managing patients with Sepsis remains challenging (Huang et al., 2019). This is why it is urgent to understand the pathophysiology of Sepsis in order to identify new biomarkers that may improve the diagnosis, treatment, and prognosis of the disease. The prevalence of Metabolic syndrome (MetS) has also been rising dramatically in adults in contrast to the prevalence of Sepsis (Grundy et al., 2004). MetS is an umbrella term for various cardiovascular disease risk factors such as diabetes, obesity, and hypertension, the mechanisms of which are not yet fully understood (Pugazhenthi, 2017). There has been some evidence that MetS increases the risk of adverse outcomes, including coronary artery disease, which can result in a higher mortality rate in patients with Sepsis (Grundy et al., 2004; Wilson et al., 2005). Sepsis is a primarily acute inflammatory condition with mortality peaking within days, whereas MetS-related complications manifest as chronic inflammation that results in mortality over several years (Meydan et al., 2018). It has been shown that both Sepsis and MetS are inflammation-related diseases. A deeper understanding of the emerging long non-coding RNAs (lncRNAs) has revealed the influence of inflammation-related molecular agents and cytokines in both Sepsis and MetS (Meydan et al., 2018). Linking non-coding RNA regulators to Sepsis and MetS may lead to the identification of new high-value biomarkers as well as targets for clinical intervention (Meydan et al., 2018). It has been reported that Sepsis and MetS are regulated by the same upstream regulators, such as microRNAs (miRNAs) and lncRNAs (Meydan et al., 2018). In MetS, Lethe lncRNAs are known to inhibit the binding of NF-κB’s p65 subunits to DNA, thus exerting anti-inflammatory effects by inhibiting NF-κB’s DNA binding, which could have a beneficial effect on Sepsis-induced immune disorders (Zgheib et al., 2017). HOTAIR is another lncRNA involved in pathogen inflammation through the NF-κB pathway (Wu et al., 2016). HOTAIR lncRNA-associated transcripts are overexpressed in adipose tissue, where they play an important role in the Metabolic syndrome (Wu et al., 2016). In an analysis based on human peripheral RNA sequencing data, 1,152 acutely ill patients were recruited for this study and divided into systemic inflammatory response syndrome (SIRS) and four Sepsis groups of increasing severity, with HOTAIR-related genes elevated 2-3-fold in patients with severe Sepsis compared to SIRS (Meydan et al., 2018). These studies suggest that HOTAIR plays a significantly interactive role in Sepsis and MetS. Recent findings have demonstrated that MetS improved survival in septic mice, attenuated the increase in plasma nitric oxide (NO) in septic mice, and lower NO production may help reduce hypotensive events in the MetS animal group (Nakama et al., 2021). These findings provide new insights into the association between MetS and Sepsis in mice. Although the relationship between the two diseases is supported by some evidence, the molecular mechanisms shared by Sepsis and MetS are still being explored. More recently, bioinformatics has been widely applied to oncological and non-oncological diseases, including sepsis (Lai et al., 2020; Li Z. et al., 2021; Wu Z. et al., 2021). Previous studies have focused on differential genes in the blood of Sepsis patients and revealed the molecular pathways of differential genes (Chen et al., 2021). However, the common diagnostic genes of Sepsis and MetS and the shared metabolic pathways remain unclear. Therefore, in this study, WGCNA was utilized to identify the common pivotal genes in the plasma of Sepsis patients and MetS. In addition, the CIBERSORT algorithm was used to identify immune infiltrating cells and to investigate the potential mechanism of the Hub gene in immune cells, which will provide some guidance for the identification of common biomarkers for Sepsis and MetS in the future and provide a theoretical basis for new diagnosis and treatment. ## Identification and analysis of differential genes We obtained a sepsis RNA microarray dataset and a sepsis RNA-seq dataset [GSE28750, GSE154918], respectively (Sutherland et al., 2011; Herwanto et al., 2021). Among them, there were 30 samples (20 normal and 10 sepsis) in the GSE28750 dataset. In the GSE154918 dataset, 60 samples (40 normal and 20 sepsis) were selected. Two samples of peripheral blood mononuclear cells (PBMCs) with sepsis and two samples of normal PBMCs were selected in the GSE167363 dataset (Qiu et al., 2021) from GEO database (Barrett et al., 2013); we also obtained the MetS dataset GSE98895 (D'Amore et al., 2018). Forty samples (20 normal and 20 MetS) were selected in the GSE98895 dataset. In order to analyze the data, the software R was employed. Principal component analysis (PCA) was used before and after correcting batch effects and visualizing the distribution of these datasets. The datasets were corrected for background, transformed by log2, and normalized. We also merged the datasets GSE28750 and GSE154918, and used the Combat method in the “sva” package to batch-correct the merged data (Leek et al., 2012). Then, the merged result was viewed by PCA dimensionality reduction algorithm. Following the identification of differentially expressed genes (DEGs) by the limma analysis, the following filtering criteria was applied to screen for significantly differentiating genes: $p \leq 0.05$ and \|log2 Fold change (FC) \| > 1.0 (Ritchie et al., 2015). Finally, Venn plots were used to illustrate common genes which are up- and downregulated in Sepsis and MetS, respectively. Meanwhile, Gene Ontology (GO) enrichment analysis of the common DEGs were conducted utilizing the “clusterProfiler” package of R software (Wu T. et al., 2021). The pathways with $p \leq 0.05$ were considered significant. ## Analysis of weighted gene Co-expression networks The “WGCNA” R package was employed to identify genes associated with Sepsis and MetS using a weighted gene co-expression network (Langfelder and Horvath, 2008). We used genes with expression >0 for further analysis to exclude outlier data. For the construction of the co-expression network, co-expression analysis was utilized. Flash clust was used for cluster analysis in our study. Clustering each sample from the beginning ensured the reliability of the network. As a result of calculating Pearson product moment correlation coefficients between gene pairs, we group genes with similar expression patterns into modules, thus creating a correlation matrix. Soft threshold functions are also used to transform the correlation matrix into a weighted adjacency matrix. To identify the most relevant Sepsis and MetS modules, we set the optimal soft threshold and identified the multi-co-expressed module genes simultaneously. ## Screening candidate genes with machine learning In order to further filter candidate genes for Sepsis and MetS diagnosis, two machine learning algorithms have been applied: random forest (RF) (Garge et al., 2013) and least absolute shrinkage selection (LASSO) (Alhamzawi and Ali, 2018). The search for important genes was carried out using the “random forest” R package. Based on decision tree theory, the RF algorithm was classified according to its ability to handle high-dimensional data and select highly informative gene clusters. The RF algorithm was used to screen diagnostic genes whose importance score exceeded 0.5. LASSO regression can be used for high-dimensional data to enhance the effectiveness of the analysis. For further reducing the dimensions of the obtained genes, the LASSO algorithm was applied to obtain the final diagnostic genes. The genes deemed most significant were selected as the core genes for further research. ## Assessing the diagnostic value of candidate genes In supervised integrated learning, eXtreme Gradient Boosting (XGBoost) is one of the most commonly used algorithms due to its scalability and convenience (Parente, 2021). For the model, hyperparameters were tuned using an optimisation method based on a Bayesian sequence model. Optimisation was performed on the training set, using K-fold cross-validation ($K = 10$) for continuous iteration. Candidate gene models were constructed using XGBoost on the training data set (GSE154918) and evaluated on the validation data set (GSE28750). Following that, the diagnostic efficacy of the model was evaluated using receiver operating characteristic curves (ROC), precision-recall curves (PR), and areas under the curve (AUC). This was verified in MetS patients. ## Investigating the infiltration of immune cells Assess the presence of immune cells in each sample using the CIBERSORT algorithm (Hu et al., 2022). Based on linear support vector regression, the CIBERSORT deconvolution algorithm calculates the percentage of 22 immune cells in tissues or cells using machine learning. These 22 cell types included dendritic cells, CD4+ and CD8+ T Cells, B cells, macrophages M1 and M2, monocytes, neutrophils, natural killer cells, and natural killer cells. The proportion of immune cells in peripheral blood mononuclear cells (PBMCs) was compared between the disease and control groups. Meanwhile, the relationship between the Hub gene and immune cells in Sepsis and MetS was explored. ## Single-cell data analysis To analyze the dataset of single cells GSE167363, we used the “Seurat” R package to run PCA and t-distributed stochastic neighbor embedding (t-SNE) (Butler et al., 2018). Those cells with more than 4,000 features, mitochondrial genes over $25\%$, or less than 200 features were excluded from the analysis. After scaling the level of gene expression, a technique called “LogNormalize” was utilized to normalize the data. After normalizing the data, 3,000 highly variable genes (HVGs) within each sample were identified using the “vst” method. Principal component analysis (PCA) was then performed and the significant principal components (PCs) were identified using the elbow method. In the end, t-SNE analysis was performed using 20 PCs that were chosen. We used FindClusters function to cluster cells into 21 clusters. In order to locate differentially expressed genes (DEGs) for each cluster, the logfc. threshold parameter was set to 0.25 using the FindeMarker function. The “singleR” package comes with seven reference datasets, of which 5 are human and 2 are mouse, and we have selected “HumanPrimaryCellAtlasData” as the reference dataset (Aran et al., 2019). An automated annotation using “SingleR”package in conjunction with DEGs in each cluster to identify cell types, and then identifying the cell types in each cluster separately (Li et al., 2020). The Hub gene expression was also visualized by violin diagrams in different immune cells. ## Correlation of single-cell metabolic pathways with core genes Molecular Signature Database (MSigDB) (Li J. et al., 2021) was used to download the hallmark gene set, and single sample gene set enrichment analysis (ssGSEA) was done to analyze metabolic pathways associated with the Hub genes. Lastly, we analyzed the correlation between immune cells and metabolism in Sepsis and MetS. Use the Pearson correlation method in the “stats” package of R language to calculate its correlation. Differences in metabolic pathway scores between single cell subpopulations were demonstrated by violin plots, where significant differences were determined by Wilcoxon tests. ## Statistical analysis All statistical tests were performed using R version 4.1.2. The Wilcoxon or Student’s t-test was used to analyse the difference between the two groups. Correlations between variables were determined using Pearson’s or Spearman’s correlation test. Statistical significance was set at a two-tailed $p \leq 0.05.$ ## Screening of common differential genes As shown in Figure 1, the study flow chart explains how it was conducted. The PCA was performed on three datasets (GSE28750, GSE154918 and GSE98895) before corrections and normalizations (Supplementary Figures S1A, B). The datasets were normalized, and 3,902 DEGs (1930 upregulated and 1972 downregulated) were found in Sepsis, while 2,639 DEGs (1,354 upregulated and 1,285 downregulated) were found in MetS. By identifying common DEGs between Sepsis and MetS, 122 common upregulated DEGs and 90 common downregulated DEGs were found (Figures 2A, B) (Supplementary Tables S1, S2). GO enrichment analysis of the identified common DEGs was performed to investigate their biological functions and pathways. According to GO analysis, the commonly upregulated DEGs are mainly involved in cell activation and leukocyte activation involved in immune response and regulation of regulated secretory pathway, while the common downregulated DEGs were enriched in epithelial tube morphogenesis, actin cytoskeleton, mitochondrial matrix, SMAD protein signal transduction (Figures 2C, D) (Supplementary Tables S3,S4). FIGURE 1: Research technology flow chart. FIGURE 2: Differential analysis and KEGG enrichment analysis of Sepsis and MetS patients (A) Intersection of DEGs upregulated by sepsis and DEGs upregulated by MetS (B) The intersection of MetS downregulated DEGs and Sepsis downregulated DEGs (C) GO enrichment analysis for common genes upregulated (D) Analysis of downregulated common genes based on GO enrichment. ## Analysis of Co-expressed gene modules in WGCNA With a threshold of 80, 2 outlier samples were detected and removed, and 98 samples were retained (Supplementary Figures S1C, D). The “pick Soft Threshold” function of the “WGCNA” package is used to filter out power parameters from 1 to 30. As a soft threshold, a power of 6 was selected for ensuring the scale-free network (Figure 3A). A total of 14 modules containing genes with similar co-expression traits were obtained using the “cutree” dynamic and module eigengenes functions (Figure 3B). The heatmap displayed the correlation between each module and the diseases (Figure 3C). “ Brown” modules indicate that Sepsis and MetS are highly linked (Sepsis: $r = 0.46$, $$p \leq 0.009$$; MetS: $r = 0.26$, $$p \leq 0.003$$). Sepsis and MetS have positively linked genes in the brown module (Sepsis: cor = 0.38, $$p \leq 2.8$$e-18; MetS: cor = 0.37, $$p \leq 2.4$$e-17) (Figures 3D,E). For this brown module gene, a GO analysis was performed. The results show that it was mainly enriched in histone modification, peptidyl−lysine modification, regulation of response to DNA damage stimulus in biological process (BP), Mitochondrial matrix, mitochondrial inner membrane and Mitochondria containing protein complexes in cellular component (CC) and transcription coregulator activity and structural constituent of ribosome in molecular function (MF) (Figure 3F). FIGURE 3: Co-expression modules and enrichment analysis in patients with Sepsis and MetS (A) Analysis of the network topology of soft threshold power (B) Cluster dendrogram identifying co-expressed genes in Sepsis and MetS (C) The module–trait relationships in Sepsis and MetS. Correlations and p-values are provided for each module (D) Correlation of brown modules with Sepsis (E) Correlation between brown modules and MetS (F) Analysis of GO enrichment for brown module genes. ## Identification of candidate central genes using machine learning We used the RF algorithm in combination with LASSO regression to finally obtain seven diagnostic genes, including STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR, UROD (Figures 4A–D). Afterwards, we evaluated these genes’ diagnostic value. The AUC values of ROC curves were 0.995 of STOM (Supplementary Figure S2A), 0.996 of BATF (Supplementary Figure S2B),0.995 of CASP4 (Supplementary Figure S2C), 0.995 of MAP3K14 (Supplementary Figure S2D), 0.968 of MT1F (Supplementary Figure S2E), 0.934 of CFLAR (Supplementary Figure S2E), 0.973 of UROD (Supplementary Figure S2E). All seven gene features had high accuracy with AUC >0.9, demonstrating their predictive power. Based on the training set GSE154918, we constructed a candidate gene model (STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR) and evaluated it on the validation set GSE28750. As displayed in Figure 4E, in GSE154918, the AUC of ROC value was 0.997 and the PR value was 0.995. The ROC and PR values for GSE28750 are 0.965 and 0.951, respectively (Figure 4F), demonstrating the model’s diagnostic accuracy. It has also been validated in MetS, indicating that the model is applicable and effective in MetS, with a ROC of 0.97 and a PR of 0.971 (Figure 4G). FIGURE 4: Co-diagnostic gene screening and model construction using machine learning (A) Relationship between the number of decision trees and the error rate. The yellow node represents the root node, the black node represents the non-leaf node, and the red leaf node represents the classification result (B) The top 40 candidate genes screened by random forest. Gene importance coefficients are indicated by the horizontal coordinates. The vertical coordinates indicate the names of the genes (C) Spectrum of Lasso coefficients for candidate genes (D) Evaluation of the optimal tuning parameters log(Lambda) in LASSO regression with cross-validation (E) A training set for XGBost modeling has been created for Sepsis (F) Demonstration of validity using the Sepsis Validation Set (G) MetS dataset validation. ## Infiltration of immune cells in sepsis and MetS patients Sepsis and MetS patients with immune infiltration were studied. In addition, heat maps show the differential expression of seven key genes in immune cells (Figures 5B, D). Normal tissues contained fewer neutrophils and monocytes than Sepsis tissues ($p \leq 0.05$). A comparison of Sepsis tissues and normal tissues revealed that Sepsis tissues contained significantly fewer naïve B cells, memory naïve B cells, CD8 naïve T Cells, and CD4 naïve T Cells (Figure 5A). The expression of STOM, BATF, CASP4, MT1F, CFLAR, and UROD was negatively correlated with infiltration levels of resting NK cells, CD4 naïve T Cells, CD8 T Cells, and CD4 resting T Cells. And MAP3K14 expression was negatively associated with neutrophils, activated mast cells, monocytes, macrophage M0, and NK activated cells (Figure 5B). We also calculated the immune cell content in patients with MetS, and monocyte proportions were higher in patients with MetS than in controls (Figure 5C). The expression levels of key genes differed significantly in patients with MetS, with six genes (STOM, BATF, CASP4, MT1F, CFLAR, UROD) being expressed at lower levels in M0, M1 and M2 macrophages than in MAP3K14. However, six genes (STOM, BATF, CASP4, MT1F, CFLAR, UROD) were expressed at higher levels in monocytes, B memory cells and T cell regulation (Tregs), but all higher than MAP3K14 ($p \leq 0.05$) (Figure 5D). To explore the potential metabolic pathways involved in hub genes, correlations between hub genes and classical metabolic pathways were analysed. In sepsis samples, significant positive correlations were found between six hub genes (STOM, BATF, CASP4, MT1F, CFLAR, UROD) and the pathways of glycolysis, bile acid metabolism, adipogenesis, cholesterol homeostasis and xenobiotic metabolism, while the pathways of glycolysis, bile acid metabolism, adipogenesis, cholesterol homeostasis and xenobiotic metabolism showed negative correlations with MAP3K14 (Figure 5E). Similarly, MetS samples expressing six central genes (STOM, BATF, CASP4, MT1F, CFLAR, UROD) showed positive correlations in glycolysis, oxidative phosphorylation and fatty acid metabolism pathways (Figure 5F), while MAP3K14 expression was negatively correlated with glycolysis, oxidative phosphorylation and fatty acid metabolism (Figure 5F). FIGURE 5: Immune cells and metabolic pathways in patients with Sepsis and MetS (A) Infiltration of immune cells between Sepsis and healthy samples (B) Immune infiltration analysis of seven candidate genes in Sepsis (C) Comparison of immune cell infiltration between samples from the MetS group and the normal group (D) Analysis of seven MetS candidate genes’ immune infiltration (E) Correlation between the expression levels of seven hub genes and the ssGSEA enrichment scores of the classical metabolic pathways in the sepsis data (F) Correlation between the expression levels of seven hub genes and the ssGSEA enrichment scores of the classical metabolic pathways in the MetS data. p < 0.05, p < 0.01, *p < 0.001. ## Single-cell sequencing analysis in sepsis and normal patients In order to check the quality of the single-cell dataset GSE167363, a preliminary quality check was performed. The correlation between nFeature RNA, nCount RNA, and precent. mt was examined to make sure the cell samples used in the study were of high quality. Figure 6A exhibited a positive correlation between nCount RNA and nFeature RNA representing unique molecular identifiers, with a correlation coefficient of 0.94. We excluded some cells and the result was diaplayed in Figures 6B,C. In the scRNA-seq dataset, a total of 3,000 genes with high levels of variation were identified, and ten of the most significant markers were tagged. A PCA analysis of the top 20 PCs was performed (Figure 6D). The t-SNE algorithm was used to cluster cells, obtaining 21 clusters (Figure 7A). We showed the top ten marker genes for the 21 clusters (Supplementary Table S5). In the Sepsis group, monocyte clusters, T Cells, and NK cells were decreased, and the B cell subpopulation increased (Figure 7B). We extracted mainly monocytes, NK cells, T Cells and B cells from the sepsis single cell dataset. GO enrichment analysis was performed to obtain the pathways of the differential genes (Supplementary Table S6-9) (Supplementary Figure S3-6). We show differential genes by plotting volcanoes, where red dots represent upregulated genes, blue dots represent downregulated genes, and yellow dots represent the seven core genes (Figure 7C). We found the most significant differences in CFLAR, STOM and BATF. Similarly, we compared the expression levels of the seven core genes in normal subjects and sepsis patients. In both groups, STOM, CASP4 and CFLAR were expressed at higher levels, while the remaining four genes were expressed at lower levels (Figure 7D). FIGURE 6: Sepsis single cell data quality control process (A) Analysis of the correlation between gene expression and cell counts and mitochondrial content in each sample. (B) Precent. mt, nFeatureRNA, and nCountRNA for each sample before filtering (C) nCount RNA, nFeature RNA, and precent. mt for each sample after filtration (D) Principal component analysis (PCA) plot, where each dot in the plot, represents a cell. Elbow plot, a method used to determine the number of PCs. FIGURE 7: Single-cell subpopulation identification and expression levels of diagnostic genes in Sepsis and normal groups (A) TSNE display plot of cell subpopulations in Sepsis patients (B) Comparison of immune cell composition in patients with Sepsis and normal (C) Volcano diagram showing differential genes, with red dots representing upregulated genes, blue dots representing downregulated genes and yellow dots representing hub genes (D) An expression plot showing the levels of diagnostic genes in Sepsis patients and normal individuals. A heatmap showing the proportion of core genes expressed in immune cells is then displayed. A high expression level of STOM, BATF, CASP4, and CLFAR was found in all samples in all 6 cell types (Figure 8A), and CLFAR and STOM were expressed at a high level at the gene expression level as well. STOM was highly expressed in platelet subpopulations in the normal and Sepsis groups (Figure 8B), while CLFAR expression was higher in the monocyte subpopulation, neutrophil subpopulation, and NK cell subpopulations in the Sepsis group compared to the normal group. We found that these results were generally consistent with what we found in Figure 6D in our previous analysis. Moreover, performing ssGSEA metabolic pathway analysis, we found that the glucose metabolism scores of monocytes and NK cells were different in normal and Sepsis (Figures 8C, D). Sepsis patients had higher glucose metabolism scores on monocytes and NK cells than normal patients, and CLFAR appears to be involved in this pathway (Figure 8E). FIGURE 8: Co-localization and differential expression of co-diagnostic genes in immune cells of Sepsis patients (A) Co-diagnostic gene expression ratios in Sepsis and normal immune cells (B) Expression of co-diagnostic genes in Sepsis and normal individuals in each immune cell (C) A violin plot showing the difference between normal and Sepsis monocyte glucose metabolism (D) A violin plot depicting the differences in glucose metabolism in normal and Sepsis NK cells (E) Co-localization of glucose metabolic pathway and CFLAR in patients with Sepsis and healthy individuals. ** (p < 0.001).* ## Discussion Bioinformatic tools and software have advanced rapidly in recent years, making public databases an excellent resource for understanding the pathophysiology of Sepsis. Sepsis mainly consists of two cross-developing pathophysiological phases, starting with immune activation and ending with chronic immunosuppression, which eventually leads to immune cell death (Nedeva, 2021). As a result, there is a tremendous amount of pro- and anti-inflammatory mediators produced, which can both lead to a severe imbalance in the immune system, as well as metabolic disorders (Hirasawa et al., 2009; van der Poll et al., 2021). The metabolic changes that cause hyperglycemia include muscle glycolysis and lipolysis, followed by hepatic glycogenesis and glycolysis (Hirasawa et al., 2009; Ferreira et al., 2022). Among patients with Sepsis, variability in blood glucose levels is associated with higher mortality rates (Ali et al., 2008; Lu et al., 2022). In addition to insulin resistance, metabolic dysfunction is also correlated with the MetS (Marette et al., 2014). The combination of WGCNA and machine learning enabled us to identify key genes common to both Sepsis and MetS, thus making it easier to identify patients at an early stage of the disease. ssGSEA was also used to assess patients’ glucose metabolism levels in order to identify metabolic disorders as early as possible. There are a number of studies examining the relationship between Sepsis and MetS, but few have examined the diagnostic genes and metabolic pathways and immune cells that are associated with both diseases. This core brown module was developed using WGCNA, and enriched for analysis in mitochondrial matrix, endosomes, and protein pathways. The mitochondria played a key role in the production of ATP, the release of reactive oxygen species, and the regulation of cell death (Stanzani et al., 2019). Several studies have suggested mitochondrial dysfunction plays a crucial role in Sepsis-induced organ failure (Stanzani et al., 2019). Several studies suggest mitochondrial nitric oxide synthase (NOS) plays an important role in Sepsis progression, but their exact role remains unclear (Mantzarlis et al., 2017). However, mitochondrial respiratory impairment is a key factor in multi-organ failure and death in Sepsis patients (Mantzarlis et al., 2017; Stanzani et al., 2019). These studies are consistent with our findings. The study also found that Sepsis and MetS share common diagnostic genes. Based on RF and LASSO machine learning methods, seven common diagnoses were identified, including STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR, and UROD. By analyzing their ROC curves, their predictive ability was demonstrated. XGBoost machine learning model was used to validate seven genes in Sepsis and MetS. Based on immune infiltration and metabolic pathway analysis, the seven genes were highly expressed at different levels of immune cell subpopulations and metabolic pathways. According to Sepsis single cell data, two genes, CFLAR and CASP4, were highly expressed in all immune cell subpopulations. CFLAR, also known as cFlip, includes CASP8 and FADD-like apoptosis regulators (Xiao et al., 2012; Faiz et al., 2018). CFLAR is an integral component of the body’s natural immune defense system (Schattenberg et al., 2011). CFLAR also plays a key role in inflammation and apoptosis in the body (Xiaohong et al., 2019). There is evidence that reduced levels of CFLAR contribute to inflammation after myocardial infarction (Xiao et al., 2012). The CFLAR was primarily found on neutrophils in immune infiltration analysis. Single-cell RNA sequencing has also revealed CFLAR expression on neutrophils in Sepsis patients’ blood. Cystein recruitment domains (CARDs) at the N terminus of CASP4 distinguish it from other cysteine-aspartate proteases (Papoff et al., 2018). In the innate immune response, CASP4 promotes phagosome-lysosome fusion, as well as maturation and secretion of pro-inflammatory molecules (Papoff et al., 2018). Apoptosis induced by endoplasmic reticulum stress (ER) is also mediated by CASP4 (Songane et al., 2018). In Sepsis and MetS, metabolic correlation analysis demonstrated the relevance of core genes in glucose metabolic pathways. As a result of our findings, we believe the common diagnostic genes we obtained contribute to the onset and progression of Sepsis and MetS. As a result of the limited number of studies that have been conducted on these two genes in Sepsis and MetS, we can only use our analysis as a preliminary reference, and further tests are necessary to confirm our findings. Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, with impaired glucose metabolism being a common problem leading to increased mortality in sepsis patients (Ferreira et al., 2022). Immune cells in patients with sepsis initially exhibit a hyperinflammatory state, which may be followed by a state of immune tolerance (Arts et al., 2017). The glycolytic pathway has been shown to be upregulated in hyperinflammatory cells, whereas the glycolytic pathway is usually downregulated in immune-tolerant cells (Arts et al., 2017). A large body of evidence suggests that changes in cellular metabolism during the inflammatory and suppressive phases of disease may influence the immune response to sepsis. We have used metabolic correlation analysis to demonstrate the association of hub genes in the glucose pathway in sepsis and MetS. Next, we will continue to investigate the mechanisms of glucose metabolism in sepsis and MetS in animal models. Further screening by multiple machine learning algorithms yielded seven diagnostic genes common to both Sepsis and MetS, and assessment of diagnostic gene expression levels in immune cell subpopulations and metabolic pathways, all of which contribute to early diagnosis of patients. However, this study still has limitations. In order to better understand the potential of key genes in the diagnosis of Sepsis, we plan to conduct a prospective cohort study. While we have improved the diagnostic efficacy of core genes through WGCNA combined with machine learning algorithms and validated their differential expression in a single-cell dataset, we will also investigate the potential of signature genes in the treatment of Sepsis. Several genes will also be knocked out in rat models for further study. ## Conclusion The effector genes involved in Sepsis and MetS are identified using a combination of single cell analysis and WGCNA as well as machine learning techniques. Additionally, it was found that disease diagnostic genes are associated with multiple immune cells and metabolic pathways. It is possible that glucose metabolism-related pathways are common to both Sepsis and MetS, and in Sepsis patients, glucose metabolism may work through monocytes and NK cells. We found that the CFLAR gene is likely to play a key role in glucose metabolism in Sepsis patients. This study may provide a new approach to diagnosing and treating Sepsis. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. ## Author contributions LT and JL conceived and designed the study. LT and YZ collected data. LT and YZ wrote the paper. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1129476/full#supplementary-material ## References 1. Alhamzawi R., Ali H. T. M.. The Bayesian adaptive lasso regression. Math. Biosci. (2018) 303 75-82. DOI: 10.1016/j.mbs.2018.06.004 2. Ali N. A., O'Brien J. M., Dungan K., Phillips G., Marsh C. B., Lemeshow S.. Glucose variability and mortality in patients with sepsis. Crit. Care Med. (2008) 36 2316-2321. DOI: 10.1097/CCM.0b013e3181810378 3. Aran D., Looney A. P., Liu L., Wu E., Fong V., Hsu A.. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. (2019) 20 163-172. DOI: 10.1038/s41590-018-0276-y 4. Arts R. J., Gresnigt M. S., Joosten L. A., Netea M. G.. Cellular metabolism of myeloid cells in sepsis. J. Leukoc. Biol. (2017) 101 151-164. DOI: 10.1189/jlb.4MR0216-066R 5. Barrett T., Wilhite S. E., Ledoux P., Evangelista C., Kim I. F., Tomashevsky M.. NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. (2013) 41 D991-D995. DOI: 10.1093/nar/gks1193 6. Butler A., Hoffman P., Smibert P., Papalexi E., Satija R.. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. (2018) 36 411-420. DOI: 10.1038/nbt.4096 7. Chen C., Li L., Zhao C., Zhen J., Yan J.. Analysis of sepsis-related genes through weighted gene co-expression network. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue (2021) 33 659-664. DOI: 10.3760/cma.j.cn121430-20210127-00135 8. D'Amore S., Hardfeldt J., Cariello M., Graziano G., Copetti M., Di Tullio G.. Identification of miR-9-5p as direct regulator of ABCA1 and HDL-driven reverse cholesterol transport in circulating CD14+ cells of patients with metabolic syndrome. Cardiovasc Res. (2018) 114 1154-1164. DOI: 10.1093/cvr/cvy077 9. Faiz A., Heijink I. H., Vermeulen C. J., Guryev V., van den Berge M., Nawijn M. C.. Cigarette smoke exposure decreases CFLAR expression in the bronchial epithelium, augmenting susceptibility for lung epithelial cell death and DAMP release. Sci. Rep. (2018) 8 12426. DOI: 10.1038/s41598-018-30602-7 10. Ferreira B. L., Sousa M. B., Leite G. G. F., Brunialti M. K. C., Nishiduka E. S., Tashima A. K.. Glucose metabolism is upregulated in the mononuclear cell proteome during sepsis and supports endotoxin-tolerant cell function. Front. Immunol. (2022) 13 1051514. DOI: 10.3389/fimmu.2022.1051514 11. Font M. D., Thyagarajan B., Khanna A. K.. Sepsis and Septic Shock - basics of diagnosis, pathophysiology and clinical decision making. Med. Clin. North Am. (2020) 104 573-585. DOI: 10.1016/j.mcna.2020.02.011 12. Garge N. R., Bobashev G., Eggleston B.. Random forest methodology for model-based recursive partitioning: The mobForest package for R. BMC Bioinforma. (2013) 14 125. DOI: 10.1186/1471-2105-14-125 13. Grundy S. M., Brewer H. B., Cleeman J. I., Smith S. C., Lenfant C., American Heart A.. Definition of metabolic syndrome: Report of the national heart, lung, and blood institute/American heart association conference on scientific issues related to definition. Circulation (2004) 109 433-438. DOI: 10.1161/01.CIR.0000111245.75752.C6 14. Herwanto V., Tang B., Wang Y., Shojaei M., Nalos M., Shetty A.. Blood transcriptome analysis of patients with uncomplicated bacterial infection and sepsis. BMC Res. Notes (2021) 14 76. DOI: 10.1186/s13104-021-05488-w 15. Hirasawa H., Oda S., Nakamura M.. Blood glucose control in patients with severe sepsis and septic shock. World J. Gastroenterol. (2009) 15 4132-4136. DOI: 10.3748/wjg.15.4132 16. Hu X., Ni S., Zhao K., Qian J., Duan Y.. Bioinformatics-led discovery of osteoarthritis biomarkers and inflammatory infiltrates. Front. Immunol. (2022) 13 871008. DOI: 10.3389/fimmu.2022.871008 17. Huang M., Cai S., Su J.. The pathogenesis of sepsis and potential therapeutic targets. Int. J. Mol. Sci. (2019) 20 5376. DOI: 10.3390/ijms20215376 18. Lai C., Wu Z., Shi J., Li K., Zhu J., Chen Z.. Autophagy-related long noncoding RNAs can predict prognosis in patients with bladder cancer. Aging (Albany NY) (2020) 12 21582-21596. DOI: 10.18632/aging.103947 19. Langfelder P., Horvath S.. Wgcna: an R package for weighted correlation network analysis. BMC Bioinforma. (2008) 9 559. DOI: 10.1186/1471-2105-9-559 20. Leek J. T., Johnson W. E., Parker H. S., Jaffe A. E., Storey J. D.. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics (2012) 28 882-883. DOI: 10.1093/bioinformatics/bts034 21. Li C., Luo J., Xu X., Zhou Z., Ying S., Liao X.. Single cell sequencing revealed the underlying pathogenesis of the development of osteoarthritis. Gene (2020) 757 144939. DOI: 10.1016/j.gene.2020.144939 22. Li J., Du J., Wang Y., Jia H.. A coagulation-related gene-based prognostic model for invasive ductal carcinoma. Front. Genet. (2021a) 12 722992. DOI: 10.3389/fgene.2021.722992 23. Li Z., Huang B., Yi W., Wang F., Wei S., Yan H.. Identification of potential early diagnostic biomarkers of sepsis. J. Inflamm. Res. (2021b) 14 621-631. DOI: 10.2147/JIR.S298604 24. Lu Z., Tao G., Sun X., Zhang Y., Jiang M., Liu Y.. Association of blood glucose level and glycemic variability with mortality in sepsis patients during ICU hospitalization. Front. Public Health (2022) 10 857368. DOI: 10.3389/fpubh.2022.857368 25. Mantzarlis K., Tsolaki V., Zakynthinos E.. Role of oxidative stress and mitochondrial dysfunction in sepsis and potential therapies. Oxid. Med. Cell Longev. (2017) 2017 5985209. DOI: 10.1155/2017/5985209 26. Marette A., Liu Y., Sweeney G.. Skeletal muscle glucose metabolism and inflammation in the development of the metabolic syndrome. Rev. Endocr. Metab. Disord. (2014) 15 299-305. DOI: 10.1007/s11154-014-9296-6 27. Meydan C., Bekenstein U., Soreq H.. Molecular regulatory pathways link sepsis with metabolic syndrome: Non-coding RNA elements underlying the sepsis/metabolic cross-talk. Front. Mol. Neurosci. (2018) 11 189. DOI: 10.3389/fnmol.2018.00189 28. Nakama R. P., Malvezi A. D., Lovo-Martins M. I., Dos Santos L. F., Canizares Cardoso A. P., Scacco G.. Metabolic syndrome improves cardiovascular dysfunction and survival during cecal ligation and puncture-induced mild sepsis in mice. Life Sci. (2021) 286 120033. DOI: 10.1016/j.lfs.2021.120033 29. Nedeva C.. Inflammation and cell death of the innate and adaptive immune system during sepsis. Biomolecules (2021) 11 1011. DOI: 10.3390/biom11071011 30. Papoff G., Presutti D., Lalli C., Bolasco G., Santini S., Manelfi C.. CASP4 gene silencing in epithelial cancer cells leads to impairment of cell migration, cell-matrix adhesion and tissue invasion. Sci. Rep. (2018) 8 17705. DOI: 10.1038/s41598-018-35792-8 31. Parente D. J.. PolyBoost: An enhanced genomic variant classifier using extreme gradient boosting. Proteomics Clin. Appl. (2021) 15 e1900124. DOI: 10.1002/prca.201900124 32. Pugazhenthi S.. Metabolic syndrome and the cellular phase of alzheimer's disease. Prog. Mol. Biol. Transl. Sci. (2017) 146 243-258. DOI: 10.1016/bs.pmbts.2016.12.016 33. Qiu X., Li J., Bonenfant J., Jaroszewski L., Mittal A., Klein W.. Dynamic changes in human single-cell transcriptional signatures during fatal sepsis. J. Leukoc. Biol. (2021) 110 1253-1268. DOI: 10.1002/JLB.5MA0721-825R 34. Ritchie M. E., Phipson B., Wu D., Hu Y., Law C. W., Shi W.. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. (2015) 43 e47. DOI: 10.1093/nar/gkv007 35. Schattenberg J. M., Zimmermann T., Worns M., Sprinzl M. F., Kreft A., Kohl T.. Ablation of c-FLIP in hepatocytes enhances death-receptor mediated apoptosis and toxic liver injury. J. Hepatol. (2011) 55 1272-1280. DOI: 10.1016/j.jhep.2011.03.008 36. Songane M., Khair M., Saleh M.. An updated view on the functions of caspases in inflammation and immunity. Semin. Cell Dev. Biol. (2018) 82 137-149. DOI: 10.1016/j.semcdb.2018.01.001 37. Stanzani G., Duchen M. R., Singer M.. The role of mitochondria in sepsis-induced cardiomyopathy. Biochim. Biophys. Acta Mol. Basis Dis. (2019) 1865 759-773. DOI: 10.1016/j.bbadis.2018.10.011 38. Sutherland A., Thomas M., Brandon R. A., Brandon R. B., Lipman J., Tang B.. Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis. Crit. Care (2011) 15 R149. DOI: 10.1186/cc10274 39. van der Poll T., Shankar-Hari M., Wiersinga W. J.. The immunology of sepsis. Immunity (2021) 54 2450-2464. DOI: 10.1016/j.immuni.2021.10.012 40. Wilson P. W., D'Agostino R. B., Parise H., Sullivan L., Meigs J. B.. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation (2005) 112 3066-3072. DOI: 10.1161/CIRCULATIONAHA.105.539528 41. Wu H., Liu J., Li W., Liu G., Li Z.. LncRNA-HOTAIR promotes TNF-α production in cardiomyocytes of LPS-induced sepsis mice by activating NF-κB pathway. Biochem. Biophys. Res. Commun. (2016) 471 240-246. DOI: 10.1016/j.bbrc.2016.01.117 42. Wu T., Hu E., Xu S., Chen M., Guo P., Dai Z.. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. (Camb) (2021a) 2 100141. DOI: 10.1016/j.xinn.2021.100141 43. Wu Z., Shi J., Lai C., Li K., Li K., Li Z.. Clinicopathological significance and prognostic value of cancer-associated fibroblasts in prostate cancer patients. Urol. Oncol. (2021b) 39 433.e17-433.433.e23. DOI: 10.1016/j.urolonc.2021.05.004 44. Xiao J., Moon M., Yan L., Nian M., Zhang Y., Liu C.. Cellular FLICE-inhibitory protein protects against cardiac remodelling after myocardial infarction. Basic Res. Cardiol. (2012) 107 239. DOI: 10.1007/s00395-011-0239-z 45. Xiaohong W., Jun Z., Hongmei G., Fan Q.. CFLAR is a critical regulator of cerebral ischaemia-reperfusion injury through regulating inflammation and endoplasmic reticulum (ER) stress. Biomed. Pharmacother. (2019) 117 109155. DOI: 10.1016/j.biopha.2019.109155 46. Zgheib C., Hodges M. M., Hu J., Liechty K. W., Xu J.. Long non-coding RNA Lethe regulates hyperglycemia-induced reactive oxygen species production in macrophages. PLoS One (2017) 12 e0177453. DOI: 10.1371/journal.pone.0177453

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.